Patent Publication Number: US-11640636-B2

Title: Sensors and executable instructions to compute consumable usage to automate replenishment or service of consumables via an adaptive distribution platform

Description:
CROSS-REFERENCE TO APPLICATIONS 
     This application is continuation-in-part (“CIP”) application of U.S. patent application Ser. No. 15/801,002 filed on Nov. 1, 2017, and titled “Consumable Usage Sensors and Applications to Facilitate Automated Replenishment of Consumables via an Adaptive Distribution Platform,” which claims the benefit of U.S. Provisional Patent Application No. 62/579,871, filed on Oct. 31, 2017, and titled “Consumable Usage Sensors and Applications to Facilitate Automated Replenishment of Consumables via an Adaptive Distribution Platform,” and the benefit of U.S. Provisional Patent Application No. 62/579,872, filed on Oct. 31, 2017, and titled “Consumable Usage Sensors and Applications to Facilitate Automated Replenishment of Consumables via an Adaptive Distribution Platform,” U.S. patent application Ser. No. 15/801,002 is a CIP application of U.S. patent application Ser. No. 15/479,230, filed on Apr. 4, 2017, and titled “Electronic Messaging to Distribute Items Based on Adaptive Scheduling,” and U.S. patent application Ser. No. 15/801,002 claims the benefit of U.S. Provisional Patent Application No. 62/425,191, filed on Nov. 22, 2016; This application is also a CIP of U.S. patent application Ser. No. 15/801,172 filed on Nov. 1, 2017, and titled “Consumable Usage Sensors and Applications to Facilitate Automated Replenishment of Consumables via an Adaptive Distribution Platform,” which claims the benefit of U.S. Provisional Patent Application No. 62/579,871, filed on Oct. 31, 2017, and titled “Consumable Usage Sensors and Applications to Facilitate Automated Replenishment of Consumables via an Adaptive Distribution Platform,” and the benefit of U.S. Provisional Patent Application No. 62/579,872, filed on Oct. 31, 2017, and titled “Consumable Usage Sensors and Applications to Facilitate Automated Replenishment of Consumables via an Adaptive Distribution Platform,” U.S. patent application Ser. No. 15/801,172 is a CIP application of U.S. patent application Ser. No. 15/479,230, filed on Apr. 4, 2017, and titled “Electronic Messaging to Distribute Items Based on Adaptive Scheduling,” and U.S. patent application Ser. No. 15/801,172 claims the benefit of U.S. Provisional Patent Application No. 62/425,191, filed on Nov. 22, 2016; and This application is a CIP application of U.S. patent application Ser. No. 15/821,362 filed on Nov. 22, 2017, and titled “Adaptive Scheduling to Facilitate Optimized Distribution of Subscribed Items,” which claims the benefit of U.S. Provisional Patent Application No. 62/425,191, filed on Nov. 22, 2016, all of which are herein incorporated by reference in their entirety for all purposes. 
    
    
     FIELD 
     Various embodiments relate generally to data science and data analysis, computer software and systems, and control systems to provide a platform to facilitate implementation of an interface and one or more sensors, and, more specifically, to one or more sensors and/or computing algorithms that implement specialized logic to facilitate in-situ monitoring and characterization of resource usage and/or device usage to determine usage of one or more consumables to updates inventories of consumables for automated replenishment of a consumable. In at least one example, one or more sensors and/or computing algorithms facilitate formation of an automated home inventory replenishment network. 
     BACKGROUND 
     Advances in computing hardware and software, as well as computing networks and network services, have bolstered growth of Internet-based product and service procurement and delivery. For example, online shopping, in turn, has fostered the use of “subscription”-based delivery computing services with an aim to provide convenience to consumers. In particular, a user becomes a subscriber when associated with a subscriber account, which is typically implemented on a remote server for a particular seller. In exchange for electronic payment, which is typically performed automatically, a seller ships a specific product (or provides access to a certain service) at periodic times, such as every three (3) months, every two (2) weeks, etc., or any other repeated periodic time intervals. With conventional online subscription-based ordering, consumers need not plan to reorder to replenish supplies of a product. 
     But conventional approaches to provide subscription-based order fulfillment, while functional, suffer a number of other drawbacks. For example, traditional subscription-based ordering relies on computing architectures that predominantly generate digital “shopping cart” interfaces with which to order and reorder products and services. Traditional subscription-based ordering via shopping cart interfaces generally rely on a user to manually determine a quantity and a time period between replenishing shipments, after which the quantity is shipped after each time period elapses. Essentially, subscribers receive products and services on “auto-pilot.” 
     Unfortunately, conventional approaches to reordering or procuring subsequent product and services deliveries are plagued by various degrees of rigidity that interject sufficient friction into reordering that cause some users to either delay or skip making such purchases. Such friction causes some users to supplement the periodic deliveries manually if an item is discovered to be running low more quickly than otherwise might be the case (e.g., depleting coffee, toothpaste, detergent, wine, or any other product more quickly than normal). 
     Examples of such friction include “mental friction” that may induce stress and frustration in such processes. Typically, a user may be required to rely on one&#39;s own memory to supplement depletion of a product and services prior to a next delivery (e.g., remembering to buy coffee before running out) or time of next service. Examples of such friction include “physical friction,” such as weighing expending time and effort to either physically confront a gauntlet of lengthy check-out and shopping cart processes, or to make an unscheduled stop at a physical store. 
     Further, conventional applications and computing devices used to reorder or procure product and services deliveries are not well-suited to assist users in identifying rates of consumption (e.g., at granular levels of consumption) with which to determine when to replenish inventories of items, including consumable or depletable items, such as food, drink, or other perishable items. A predominant number traditional computing devices and applications are directed to form inventories of durable goods for which users rarely reorder such goods, whereby an inventory of durable goods is maintained for insurance or loss purposes. 
     Thus, what is needed is a solution to facilitate techniques of determining usage of a consumable and monitoring and determining resource usage and/or device usage to predict consumption of one or more consumables to adjust an inventory of the consumable for purposes of replenishment, without the limitations of conventional techniques. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       Various embodiments or examples (“examples”) of the invention are disclosed in the following detailed description and the accompanying drawings: 
         FIG.  1    is a diagram depicting one or more usage sensors configured to interact with an adaptive distribution platform, according to some embodiments; 
         FIG.  2    is a diagram depicting an example of a sensor device configured to detect usage of an electric-powered device to generate data for monitoring inventories of consumables, according to various examples; 
         FIG.  3    is flow diagram depicting an example of adjusting inventories of consumable items based on sensor data, according to various examples; 
         FIG.  4    is a diagram depicting an example of configuring a sensor device to facilitate inventory monitoring of a consumable, according to various examples; 
         FIG.  5    is a flow diagram depicting application of sensor data to update an amount of inventory for automated replenishment, according to some examples; 
         FIG.  6    is a diagram depicting an example of a sensor device configured to detect usage of consumables to generate data for monitoring inventories of consumables, according to various examples 
         FIGS.  7 A and  7 B  are diagrams depicting examples of weight monitoring device implementations, according to some examples; 
         FIG.  8    is a flow diagram depicting an example of monitoring inventory of a consumable using a weight monitoring device to determine a time at which to replenish an inventory, according to some examples; 
         FIG.  9    is a diagram depicting a home inventory monitoring network including a variety of sensors coupled to one or more computing devices to monitor inventories of consumables and to facilitate replenishment of consumables, according to various examples; 
         FIG.  10    illustrates examples of various computing platforms configured to provide various functionalities to monitor an inventory of a consumable to facilitate automated distribution and replenishment of an item, according to various embodiments; 
         FIGS.  11 A to  11 E  are diagrams each depicting an example of a sub-flow that may be interrelated to other sub-flows to illustrate a composite flow, according to some examples; 
         FIG.  12    is a diagram depicting one or more usage sensors configured to determine consumption characteristics of one or more consumables, according to some embodiments; 
         FIG.  13    is a diagram depicting examples of audio sensors implemented to determine consumption characteristics of one or more consumables, according to some embodiments; 
         FIG.  14    is a diagram depicting examples of detecting usage of resources or devices based on monitoring resources at a macro-level, according to some examples; 
         FIG.  15    depicts an example of a modified sensor device of  FIG.  2   , according to some examples; 
         FIG.  16    is a flow diagram depicting an example of application of sensor data to update an amount of inventory for automated replenishment, according to some examples; 
         FIG.  17    is an example of implementing a variety of sensors to determine consumption of consumables in a work space context, according to some examples; and 
         FIG.  18    is an example of implementing a variety of sensors to determine consumption of consumables in a kitchen space context, according to some examples. 
     
    
    
     DETAILED DESCRIPTION 
     Various embodiments or examples may be implemented in numerous ways, including as a system, a process, an apparatus, a user interface, or a series of program instructions on a computer readable medium such as a computer readable storage medium or a computer network where the program instructions are sent over optical, electronic, or wireless communication links. In general, operations of disclosed processes may be performed in an arbitrary order, unless otherwise provided in the claims. 
     A detailed description of one or more examples is provided below along with accompanying figures. The detailed description is provided in connection with such examples, but is not limited to any particular example. The scope is limited only by the claims, and numerous alternatives, modifications, and equivalents thereof. Numerous specific details are set forth in the following description in order to provide a thorough understanding. These details are provided for the purpose of example and the described techniques may be practiced according to the claims without some or all of these specific details. For clarity, technical material that is known in the technical fields related to the examples has not been described in detail to avoid unnecessarily obscuring the description. 
       FIG.  1    is a diagram depicting one or more usage sensors configured to interact with an adaptive distribution platform, according to some embodiments. Sensors, such as sensors  160  and  180 , may be implemented to monitor usage in-situ of, for example, electric-powered appliances, whereby usage of an electric-powered appliance or device may correlate to a consumption rate of a consumable, such as coffee, toasted bagels, dish detergent, air filters, etc. Examples of electric-powered devices include a coffee maker, a toaster, a dishwasher, a washer machine, a dryer, an air conditioner, and a hot water kettle, among any other type of electric-powered device. A sensor may also monitor usage of a consumable by detecting, for example, a change in weight, displacement, motion (e.g., including vibrations and intensity), orientation, or the like. In one example, a weight monitoring device may include a sensor to determine changes in weight of a consumable due to usage of a product. Usage of electrical-powered appliances and changes in weight may correlate to an amount of a consumable that may be used or consumed at a point in time (e.g., during operation of a device). Based on a correlated amount of product consumed, a computing device may execute instructions to determine inventories of products, and, when an amount of inventory reaches a specific amount, the computing device may generate notifications to assist in replenishment. For example, a notification (e.g., visually or audio) may be generated to alert a consumer to a state of an inventory, such as whether an inventory is low or whether a consumable ought to be automatically reordered. Or, a computing device may be configured to automatically generate a request to reorder a consumable product. Therefore, a variety of structures and/or functionalities, as described herein, may facilitate automated reordering or replenishment of consumable goods with reduced or negligible efforts by users or consumers to otherwise manually determine inventory quantities of a product or manually reorder such a product. 
     Diagram  100  depicts an example of adaptive distribution platform  110  that may be configured to facilitate automatic distribution of items in accordance with, for example, an adaptive schedule (e.g., an adaptive shipment schedule). The term “distribution” of an item, which may be any good or service, may include distributing or shipping items based on orders or reorders of the items, such as online orders or a predicted depletion of an item (e.g., a predicted consumption of a product). Thus, adaptive distribution platform  110  may be configured to determine a “predicted distribution event” to replenish a consumable item (e.g., a depletable product) based on a usage rate of the item (e.g., a calculated usage rate, or sensed usage rate determined by sensors  160  and  180 ). Further, sensor data from sensors  160  and  180  may enhance accuracies of determining a usage rate to more accurately predict or determine a date or time at which to ship a consumable to replenish an inventory amount that, for example, may be nearing exhaustion or depletion. A “usage rate” may be a rate at which a product or service is distributed (e.g., ordered or reordered), consumed, or depleted. Sensors  160  and  180  may facilitate in determining a usage rate (e.g., rate of consumption) for a particular product in which a sensor  160  or a sensor  180  is being used. 
     In various examples, adaptive distribution platform  110  may perform a variety of computations to determine a usage rate so as to predict ordering and delivery of a product when a consumer most likely needs a product. In some examples, adaptive distribution platform  110 , as well as techniques to determine a usage rate, may be implemented as set forth in U.S. patent application Ser. No. 15/479,230, filed on Apr. 4, 2017, and titled “Electronic Messaging to Distribute Items Based on Adaptive Scheduling,” which is herein incorporated by reference. In at least one example, adaptive distribution platform  110  may be implemented as a platform provided by OrderGroove, Inc., of New York, N.Y., U.S.A. 
     According to various embodiments, accuracy of a usage rate may be enhanced based on sensor data (from one or more sensors  160  and  180 ) that may correlate to usage of a consumable item. To illustrate, consider that diagram  100  also depicts a location, such as a residence or building  150 , that includes a number of sensors  160  and  180  associated with a user account  144  to determine one or more usage rates for a variety of consumables. In accordance with various embodiments, any number of products that may be ordered online, for example, may be associated with user account  144 , and, thus, a geographic location associated with residential building  150 . 
     Sensors  180  may be configured to determine an amount of power consumed via device or appliance that may be correlated to an amount of a consumable that is consumed during operation of a device. As shown within inset  155 , sensor  180  may be coupled to a power outlet  154 . For example, sensor  180   a  may detect an amount of power consumed by a coffee maker  182   a  for determining an amount of coffee consumed, and, for determining a remaining inventory of coffee. Sensor  180   b  may detect an amount of power consumed by a dishwasher  182   b  for determining (e.g., via correlation) an amount of dish detergent consumed, and a remaining inventory of dish detergent. Sensors  180   c  and  180   d  may detect amounts of power consumed by a washer machine  182   c  and a dryer  182   d , respectively, to determine relative amounts of laundry detergent and fabric softener sheets consumed. As another example, sensor  180   e  may detect amounts of power consumed by an air conditioner  182   e  to determine a consumption rate of one or more air filters. 
     Sensors  160  may detect a characteristic of a consumable (e.g., a consumable characteristic), such as a weight of the consumable, to determine or enhance a usage rate of consumable. As shown within inset  151 , a weight monitoring sensor  160  may be integrated with a container  152  to form an inventoriable container  153 . For example, inventoriable container  153   a  may be configured to determine a weight of its contents, and thus, an amount of coffee or any other solid or liquid consumable. Inventoriable container  153   b  may be configured to determine a weight of an amount of cereal, whereas inventoriable container  153   c  may be configured to determine a weight of an amount of flour. In some implementations, weight monitoring sensor  180  may be implement without container  152  for use, for example, in a refrigerator to monitor a consumption rate of milk by monitoring a weight of a container of milk. In some implementations, a weight monitoring sensor, examples of which are described in  FIG.  6   , among other passages, may be formed as a surface or “pad”-like scale upon which one or more items may be placed for monitoring weight and changes in weight. Such weight monitoring sensors may be implemented on, or integrated within, a shelf. Further, the weight sensor may be enhanced by using NFC or other communication technology to indicate what amount has been place on scale. 
     Sensors  160  and  180 , as well as any other sensor, may be used to transmit via a network endpoint  162  (e.g., a router) data representing either a state of inventory for a specific consumable or a request to replenish the consumable. An example of a state of an inventory is a value representing a weight of a consumable at a point in time. As shown, raw data  122   a , such as raw sensor data, may be transmitted to sensor manager  190  to determine a state of inventory of an item. Raw data  122   a , at least in some examples, may include raw sensor data, such as one or more values representing electrical energy used per any unit time, such as in units of watts or kilowatts (“kWs”). Sensor manager  190  may also receive updated data  122   b  that describes a state or change of unit of consumption or a weight, among other things. Thus, updated data  122   b  may include data representing a weight of a consumable, which sensor manager  190  may monitor to determine whether to replenish the inventory at location  150 . Otherwise, adaptive distribution platform  110  may receive reorder data  122   c  to invoke replenishment of an item, such as a bag of coffee beans. Examples of other sensors include sensors configured to sense or measure resource usage, such as electricity, water, natural gas, etc., or further configured to sense or measure operation characteristics, such as device or appliance vibration, motion, acoustic emissions, electromagnetic reflections/emissions, such a visible or IR light, magnetic field changes, etc.). 
     In view of the foregoing, the structures and/or functionalities depicted in  FIG.  1    may illustrate an example of usage rate determination to automatically facilitate in-situ inventory monitoring of consumables and/or automated replenishment and distribution of items (e.g., shipping an item) that may be ordered or reordered in accordance with various embodiments. According to some embodiments, adaptive distribution platform  110  may be configured to facilitate online ordering and shipment of a product responsive to sensor data retrieved from sensors  160 ,  180 , or any other sensor. Real-time (or near real-time) consumption amounts or rate may be determined for items being monitored by sensors  160 ,  180 , and the like, thereby improving accuracy in determining shipment quantities and timing, among other things, according to various examples. Thus, consumption of resources and time for both users and merchant, as well as associated computing systems, may be reduced such that “friction” of replenishment may be reduced or negated (e.g., based on sensor data from sensors  160  and  180 ), at least in some cases. In some examples, adaptive distribution platform  110  may provide replenishment services for multiple entities (e.g., for multiple merchant computing systems  130 ), thereby reducing resources that otherwise may be needed to perform replenishment services individually at each merchant computing system  130   a ,  130   b , and  130   n . In some cases, in-situ inventory monitoring may obviate a need to perform a step of monitoring that may otherwise encumber usage of a consumable. 
     In the example shown, adaptive distribution platform  110  may include a distribution predictor  114 , among other components. Distribution predictor  114  may be configured to predict a point in time (or a range of time) at which an item may be exhausted. Based on the prediction, adaptive distribution platform  110  may be further configured to determine a zone of time or a time interval (not shown) in which depletion and near exhaustion of an item may be predicted. In at least one example, sensor-based data  122  received from any number of sensors  160  and  180  may determine a point in time at which to replenish an inventory. In some examples, sensor-based data  122  may enhance accuracy of predicting or calculating a point in time at which an inventory may be depleted. 
     As shown, adaptive distribution platform  110  may be configured to facilitate “adaptive” scheduling services via a computing system platform for multiple online or Internet-based retailers and service providers, both of which may be referred to as merchants. In some cases, scheduling of consumable shipments to replenish inventories may be “adapted” based on sensor data and corresponding measured usage rates (e.g., a coffee maker may be idle while a user spends a month traveling overseas, whereby sensed usage may essentially be zero during that time). Further to the example shown in diagram  100 , a merchant may be associated with a corresponding one of merchant computing systems  130   a ,  130   b , or  130   n  that includes one or more computing devices (e.g., processors, servers, etc.), one or more memory storage devices (e.g., databases, data stores, etc.), and one or more applications (e.g., executable instructions for performing specialized algorithms to implement adaptive subscription services, etc.). Examples of merchant computing systems  130   a ,  130   b , or  130   n  may be implemented by any other online merchant. Accordingly, adaptive distribution platform  110  can be configured to distribute items in accordance with predicted distribution events (e.g., a predicted time of distribution), any of which may be adaptively derived to optimize delivery of items based on sensor data from sensors  160 ,  180 , and the like. According to some examples, one or more of merchant computing systems  130   a ,  130   b , or  130   n  may implement an inventory management controller  131  to manage an amount of inventory for purposes of enhancing the efficacy of fulfilling and replenishing items over an aggregate number of consumers in, for example, an automated manner In some cases, a merchant entity (e.g., a warehouse from which products are shipped) associated with merchant computing system  130   a  may fulfill inventory replenishment by shipping a consumable in shipment container  124 . 
     As shown, distribution predictor  114  may include a distribution calculator  116 , among other components. Distribution calculator  116  may be configured to calculate one or more predicted distribution events or replenishment-related data to form an adaptive schedule (e.g., an adaptive shipping schedule) based on sensor data  122   a ,  122   b , and  122   c  communicated via network  120   a . Distribution calculator  116  may be configured to receive data representing item characteristics data  102 , according to some embodiments, and may be configured further to determine (e.g., identify, calculate, derive, etc.) one or more distribution events based on one or more item characteristics  102 , or combinations thereof (e.g., based on derived item characteristics). 
     For example, distribution calculator  116  may compute a projected date of depletion for a particular product, such as a coffee product, based on usage patterns and/or ordering patterns associated with a specific user account  144 , as well sensor data from sensor  180   a . In at least one example, distribution calculator  116  may be configured to operate on data representing an item characteristic  102 , which may be derived or calculated based on one or more other item characteristics  102 . Examples of item characteristics data  102  may include, but are not limited to, data representing one or more characteristics describing a product, such as a product classification (e.g., generic product name, such as paper towels), a product type (e.g., a brand name, whether derived from text or a code, such as a SKU, UPC, etc.), a product cost per unit, item data representing a Universal Product Code (“UPC”), item data representing a stock keeping unit (“SKU”), etc., for the same or similar items, or complementary and different items. Item characteristics  102  may also include product descriptions associated with either a SKU or UPC. Based on a UPC for paper towels, for example, item characteristics  102  may include a UPC code number, a manufacture name, a product super-category (e.g., paper towels listed under super-category “Home &amp; Outdoor”), product description (e.g., “paper towels,” “two-ply,” “large size,” etc.), a unit amount (e.g.,  12  rolls), etc. Item characteristics  102  also may include any other product characteristic, and may also apply to a service, as well as a service type or any other service characteristic. 
     In some examples, the various structures and/or functions described herein may facilitate in-situ inventory monitoring and/or automated replenishment of non-consumable items or services. In other examples, any item, material, resource, or product, finished or unfinished, could be replenished using the techniques described herein. 
       FIG.  2    is a diagram depicting an example of a sensor device configured to detect usage of an electric-powered device to generate data for monitoring inventories of consumables, according to various examples. Diagram  200  depicts a sensor device  201  including a housing  202 , a subset  203  of conductors configured as a socket-outlet to receive a plug of an electric-powered device (not shown) configured to process a consumable, and a subset  205  of conductors configured to plug into a socket-outlet from which electric power may be accessed. Sensor device  201  optionally may include a data port  292  with which to exchange data, such as sensor data, usage data, consumption data, or the like, via a cable, such as a USB cable (not shown), with a computing device or mobile computing device (e.g., a mobile phone  290   b ). Sensor device  201  may also include a radio to facilitate radio frequency (“RF”)-based communications via a wireless data link  294 . 
     Diagram  200  further depicts one or more components that may be implemented in sensor device  201  including, but not limited to, a sensor  214 , a characterizer  220 , a correlator  240 , a memory  232 , a radio  234 , and an inventory manager  248 . In at least one example, sensor  214  may include a power sensor coupled to subsets  203  and  205  of conductors to detect instantaneous or continuous amounts of electrical energy used by an electric-powered device plugged into subset  203  of conductors. Sensor  214  may receive electrical signals  212  based on either AC or DC power, and may be configured to detect instantaneous or continuous amounts of voltage usage, current usage, or any other electrical-related parameter to monitor power used in the processing of a consumable (e.g., energy used to brew a cup of coffee). Further, sensor  214  may be configured to generate raw sensor data  280  that may exhibit a specific pattern  226  or profile of electric energy usage per unit time. As shown, an electric-powered device may use electrical power at magnitudes (“P”) at points of time (“t”) during an interval in which a consumable is processed (e.g., a duration in which a washer machine consumes 3 ounces of laundry detergent for a “heavy” load). Note that arrangements of subsets  203  and  205  of conductors are intended to be illustrative and not limiting, and, as such, subsets  203  and  205  of conductors may be arranged in any configuration to adapt to any plug or socket (e.g., European power outlets, DC-powered sockets, etc.). Note, too, that sensor device  201  may be implemented using electro-magnetic phenomena (e.g., as a current-measuring probe). 
     Characterizer  220  may be configured to characterize usage of a device by characterizing amounts of electrical energy consumed or used per unit time to generate characterize values of electrical energy consumed or used per unit time. As shown, characterizer  220  may include an analyzer  222  and a pattern detector  224 . Analyzer  222  may be configured to determine characteristics of pattern  226  to identify a magnitude of power at a particular point in time. Time, t, may be expressed in any unit of time, such as milliseconds, seconds, minutes, etc., and magnitudes of power, P, may be expressed in watts, kilowatts, kilowatt-hours, joules, or any other units of power. 
     Pattern detector  224  may be configured to form data representing pattern  226  based on amounts of power used by an electrical-powered device, whereby pattern  226  may be used to identify whether an electric-powered device is in use (i.e., whether powered off or powered on to process a consumable), whether the electric-powered device is idle, and the like. In at least one example, pattern  226  may be determined by way of one or more “training modes” to establish a baseline pattern of power usage to identify future states or modes of operation for a particular device. Pattern  226  may also be used to determine whether the electric-powered device is drawing different amounts of power during different times of the day. Different amounts of power may be due to using electric power at different magnitudes and/or different lengths of usage. For example, a coffee maker may consume more power to brew 10 cups of coffee than the power used to brew 2 cups of coffee. Thus, pattern  226  of power usage for 10 cups (in one mode of operation) may extend for a longer time, “t,” than another pattern of power for 2 cups of coffee (in another mode of operation). As another example, a longer wash cycle of a washer machine, and increased power consumption, may indicate a “heavy” load of laundry (in one mode of operation) that may process a greater quantity of laundry detergent than a “light” load of laundry (in another mode of operation). Thus, pattern  226  of power usage for a heavier load of laundry may extend for a longer time, “t.” In various examples, different patterns  226  of power usage may relate to different modes of operation, which, in turn, may indicate different subsets of consumables (or characteristics thereof) may be consumed for specific modes of operation. 
     Characterizer  220  may receive device attribute data  215  and/or usage signature data  217 , according to some examples. Device attribute data  215  may include data describing a type of electric-powered device for which sensor device  201  may be configured to monitor. For example, data  215  may include data describing an electric-powered device as a coffee maker, a dishwasher, a toaster, a dryer, a vacuum cleaner, an air conditioner, a furnace, a rice maker, an electric tea kettle, or any other device with which a product may be consumed in associated with the usage of a device. Usage signature data  217  may include data describing any number of patterns  226  for specific types of electric-powered devices, and, optionally, specific power consumption patterns  226  based on unique models or manufacturers of the electric-powered device under one or more different modes of operations (e.g., brewing 2, 4, 6, 8, or 10 cups of coffee may be viewed as different operations of a coffee machine, or different modes of operations thereof). In one example, coffee makers or espresso machines made by different manufacturers likely have different patterns  226  of power usage, and, thus, unique power usage signatures. In some examples, one or more patterns in usage signature data  217  may also be associated with a corresponding amount of consumable (e.g., a product) that may be associated with a specific usage signature (i.e., a predetermined pattern). 
     In operation, characterizer  220  may use usage signature data  217  to predict a type of electric-powered device or tool that may be coupled to sensor device  201  in the absence of device attribute data  215  or any indication of the type of device for which power may be monitored. For example, pattern detector  224  may also be configured to detect whether pattern  226 , as monitored by sensor  214 , matches any known power patterns included in usage signature data  217 , which, in turn, may include data describing an associated electric-powered device. Therefore, pattern detector  224  may compare pattern  226  to any number of power usage patterns for coffee makers, espresso machines, washer machines, dishwashers, etc., that may be included in usage signature data  217  to identify a device type coupled to sensor device  201 . Also, characterizer  220  may use device attribute data  215  (e.g., indicating an electric-powered device is an espresso machine) to identify a subset of usage signatures in usage signature data  217  (i.e., patterns of power usage for a variety of espresso machines). In some examples, device attribute data  215  may include data describing a specific type or model of a device, machine, or tool, relevant resources (e.g., electric power, water, natural gas, etc.) being used in connection with a specific device, as well as associated types and quantities of consumable that may be processed by an associated device, machine, or tool. 
     Characterizer  220  may generate characterized data (e.g., characterized data  204   a ,  204   b ) that describes, summarizes, or encapsulates one or more of the following: characterized values of power usage (e.g., one or more magnitudes of power per unit time), an indication of a type and/or model of an electric-powered device, a duration of power usage, a time of day of the power usage, etc., whereby the characterized data may be transmitted as characterized data  204   a  to correlator  240  and, optionally, as characterized data  204   b  to memory  232  and radio  234 . Memory  232  may store one or more cycles or instances of power usage captured by sensor device  201  for further processing or certain times at which the data may be transmitted to, for example, a mobile computing device  290   b  or any other computing device, including an adaptive distribution platform. Radio  234  may be configured to transmit characterized data  204   b  via wireless datalink  294  to mobile computing device  290   b  or any other computing device. Examples of radio  234  include RF transceivers to implement WiFi® protocols, BlueTooth® protocols (including BlueTooth Low Energy), and the like. Sensor device  201  may be associated with an identifier, such as an IP address. Further, radio  234  may be used to include sensor device  201  in a mesh network, and may exchange data via power lines coupled to subset  204  of conductors. 
     Correlator  240  may be configured to correlate a portion of a product consumed to a characterized value of power usage, whereby one or more units of consumable processed by a device can be determined. For example, correlator  240  may correlate an “X” amount of watts used by a coffee maker (e.g., for 4 minutes) to an amount of coffee used. Units of ground coffee consumed may be expressed volumetrically (e.g., 2 scoops or tablespoons), by unit (e.g., 1 pod of coffee or other pre-package unit of coffee or tea), by weight (e.g., 15 mg), or by any other parameter. Next, consider that a coffee machine may use about 300 to 600 watts to brew 2 cups of coffee and about 1000 to 1500 watts to brew 8 cups of coffee. A first pattern of power usage may be associated with using 300 to 600 watts, whereas a second pattern of power usage may be associated with using 1000 to 1500 watts. These patterns may be included in usage signature data  217 . Alternatively, such patterns may be generated from previous usages with sensor device  201  and stored in memory  232 . As such, correlator  240  need not rely on usage signature data  217  and may use patterns  226  generated internally within sensor device  201 , according to some examples. 
     Correlator  240  is shown to include a matcher  242  and a predictor  246 , according to some examples. Matcher  242  may identify a pattern of values of electric energy used per unit time, and may further match the pattern against data representing units of consumable processed by a device in association with the pattern. Further to the above example, matcher  242  may match a first pattern of 300 to 600 watts to 2 tablespoons of ground coffee or match a second pattern of 1000 to 1500 watts to 8 tablespoons of ground coffee. If pattern  226 , which is sensed by sensor  214 , matches the second pattern of 1000 to 1500 watts, then matcher  242  may determine 8 tablespoons of ground coffee was used. Thus, matcher  242  may match a sensed pattern of values of electric energy per unit time to data representing a usage signature associated with a device, or to any other associated data that specifies a corresponding one or more units of consumable was processed by the device. Further, matcher  242  may transmit data representing 8 tablespoons of ground coffee as consumed unit(s) data  206   a  to an inventory manager  248 . 
     In some examples, usage signature data  217  may include a subset of usage signature patterns that represent power usage profiles or patterns that may be associated with different modes of operations. A mode of operation may be associated with one or more common or different consumables as other modes of operation for a particular device. To illustrate, consider that a combined coffee and espresso machine may have different modes of operation to make coffee (e.g., different amounts or cups of coffee based on, for example, an 8-ounce pod of coffee) and different modes of operation to make espresso-based drinks (e.g., based on 1.2-ounce pod of espresso coffee), including a “frothing wand” to make latte drinks with steamed or foamed milk. 
     Thus, correlator  240  may be configured to correlate subsets of patterns of power usage to corresponding various modes of operation, whereby a first subset of patterns (and first subset of modes of operation) may relate to consumption of a first type of consumable (e.g., coffee pods). A second subset of patterns (and second subset of modes of operation) may relate to consumption of a second type of consumable (e.g., espresso pods). In other examples, modes of operation that includes brewing coffee may be associated with usage of “coffee filters” as a consumable, whereas making espresso coffee may not use a coffee filter. So, based on detected patterns of usage (e.g., power usage), specific types and quantities of consumables may be predicted to be consumed to, for example, enhance accuracy 
     Predictor  246  may be configured to predict a value representing one or more units of consumable processed by a device based on a sensed pattern of electric energy per unit time. In some examples, predictor  246  may predict an amount of a product consumed based on previously-processed amounts. For example, consider that sensor device  201  has previously sensed a number of patterns  226  associated with brewing 2 cups of coffee (e.g., average of 450 Watts). At a subsequent point in time, a coffee maker is used to brew 6 cups of coffee. Predictor  246  may be configured to approximate an amount of ground coffee used to brew 6 cups of coffee, which may be extrapolated from 450 Watts (for 2 cups) to 800 Watts sensed by sensor  214 . Hence, a pattern of 800 watts of usage may predictively correlate to 6 cups of coffee. Probabilistic techniques may be used (with or without extrapolation) to statistically compute probabilities to predict computed amounts of consumable based on empirically-derived values, at least in some examples. In some cases, predictor  246  may generate feedback request  279  (e.g., transmitted to mobile computing device  290   b ) to calibrate whether 6 tablespoons of ground coffee were used in the brewing process. As such, feedback data  219  may be used to confirm the predicted amounts of product consumed, or to recalibrate the predicted amount for subsequent uses. In at least one example, data representing a consumption rate  221  may be provided to correlator  240  to confirm one or more rates of consumption that may be used for a particular electric-powered device. Continuing with the coffee maker example, consider that a user desires stronger coffee and prefers a greater amount of ground coffee to be used in a coffee maker. Thus, consumption rate  221  may be used to adjust amounts of units of consumable upward to correlate with specific patterns of power usage. Further, predictor  246  may transmit data representing a predicted number of tablespoons of ground coffee as consumed unit(s) data  206   a  to an inventory manager  248 . Predictor  246  may be configured to predict which type of consumable is being consumed, as well as a corresponding quantity of consumable per unit of resource used (e.g., per unit of power, water, or natural gas consumed). 
     Inventory manager  248  may be configured to receive data  206   a  representing a number of consumed units to monitor an amount of inventory of a consumable. Inventory manager  248  may be configured to adjust an amount or value representing an inventory of the consumable responsive to usage of product. For example, inventory manager  248  may adjust an amount of inventory by determining one or more units of a consumable associated with a pattern of electric energy per unit time (e.g., via data  206   a ), and then reducing the amount of the inventory via response data  208   a  by the determined units of the consumable to form an adjusted amount of inventory. For example, an inventory of 20 pods of coffee may be reduced to 19 pods of coffee responsive to usage of a coffee machine. Response data  208   a  may include data representing usage of 1 pod of coffee to cause an amount of inventory of coffee to adjust accordingly. 
     Further, inventory manager  248  may be configured to monitor the amount of inventory against one or more threshold values or one or more ranges of inventory values to determine an action, such as generating a notification or a request for replenishment  210   a  (e.g., reordering a product). The notification may be one or more of an audio message (e.g., via a smart speaker computing device) or an electronic message that describes a state of inventory for presentation on a display of mobile computing device  290   b . As an example, a threshold value may include 105 mg of ground coffee (e.g., 7 days of coffee if 15 mg are brewed per day) or a threshold range may include values from 90 to 130 mg of ground coffee. If inventory manager  248  detects that an adjusted amount of inventory complies (e.g., meets) a threshold value for the inventory, inventory manager  248  may be configured to generate data representing a request  210   a  to replenish the inventory of the consumable, which may be included in response data  208   a  of a transmitted electronic message. In one example, response data  208   a  may be transmitted as an electronic message via a network to a computing system implementing an adaptive distribution platform (not shown). According to various examples, inventory manager  248  may be configured to automatically reorder a consumable to replenish the amount of the inventory. 
     Note that inventory manager  248  may be programmed or configured to include any number of threshold values. For example, a threshold value to reorder (e.g., automatically reorder) ground coffee may be set to 75 mg (e.g., 5 days of coffee if 15 mg are brewed per day), whereby 75 mg may be described as “a bridging amount,” which is an amount that may be a predicted amount to maintain a number of units of the consumable during a time interval (e.g., 5 days) in which the amount of inventory is replenished. In some cases, the bridging amount may be based on nominal durations of time to receive an item via delivery services (e.g., FedEx, etc.) after an order is placed online. Another threshold value may be set to 30 mg (e.g., 2 days of coffee) to alert a user to obtain via “same day” delivery services or inform a user of low inventory so the user may obtain coffee or any other item at a physical location of a retail store. 
     In some examples, more or fewer components shown in  FIG.  2    may be implemented in sensor device  201 . For example, sensor device  201  may include sensor  214  for transmitting raw data  280  to mobile computing device  290   a , which may include an application  250  (e.g., executable instructions) to implement a correlator  240   a  and an inventory manager  248   a , either of which may have similar structures and/or functionalities as correlator  240  and inventory manager  248  of sensor device  201 . Reorder manager  249   a  includes instructions to cause data  210   a  representing a replenishment request to be transmitted to, for example, an adaptive distribution platform for purposes of reordering a product near or at depletion. In some examples, sensor device  201  may include characterizer  220  that may transmit characterized data  204   b  to application  250  stored within mobile computing device  290   b . As such, sensor device  201  may omit one or more of characterizer  220 , memory  232 , radio  234 , correlator  240 , and inventory manager  248 . Any of components of sensor device  201  and mobile computing device  290   b  may be interchanged or distributed between sensor device  201  and mobile computing device  290 , as well as distributed among other devices not shown, including an adaptive distribution platform. For example, reorder manager  249   a  may be implemented within sensor device  201 , and characterizer  220  may be implemented within application  250 . One or more components of sensor device  201  may be implemented in logic as either hardware or software, or a combination thereof. 
       FIG.  3    is flow diagram depicting an example of adjusting inventories of consumable items based on sensor data, according to various examples. At  302  of flow diagram  300 , sensor data representing usage may be received. In some examples, sensor data may indicate usage of a device (e.g., an electric-powered device, a mechanical-powered device, a chemical energy-powered device, such as a natural gas-based device (e.g., a gas stove, furnace or the like), and other resource-powered or resource consumption device or appliance, etc.) configured to process or use a consumable in, for instance, operation of the device. In other examples, sensor data may represent a state of a consumable, such as a weight of an item. Based on a weight of an item, a change in weight may be determined or otherwise calculated to determine a rate or amount of consumption. 
     In at least one example, other sensor data may describe other states or characteristics of a consumable item, such as a position, an orientation, motion, etc., especially relative to one or more points in time, whereby a change in state indicates usage. A change back to an initial state may indicate usage has stopped. If an estimated rate of consumption is associated with each usage, then an amount of product consumed may be calculated for automatic reordering. Examples of positional or orientation sensor data may include data generating by a “mercury switch,” a “tilt switch,” a “rolling ball sensor,” and the like. In some examples, a wireless position switch coupled to a water faucet (to monitor hand soap usage) or mechanical parts of a toilet (to monitor toilet paper usage) may detect a change in orientation. To illustrate, at time, T 1 , a faucet handle may change orientation (i.e., water is on), whereas the faucet handle may change orientation back to an initial position at time, T 2  (i.e., water is off). The usage of water may be used to predict consumption of hand soap. In one example, a sensor may indirectly sense consumption by detecting and characterizing, for example, an attribute or characteristic of operation of a device, such as the magnitudes and duration of vibrations generated by an electric toothbrush, whereby consumption of toothpaste may be quantified. As such, any sensor that may be configured to measure and track phenomena associated with operation of a machine, appliance or device may be suitable to predict consumption of an associated consumable. 
     In some examples, sensor data may originate from any source. For example, sensor data representing power consumption or energy expenditure may originate from a “smart appliance,” such as a refrigerator, coffee machine, etc. that may have one or more structures and/or functions similar to sensor device  201  of  FIG.  2   . Therefore, sensor data or other data representing usage or consumption may originate at sensors other than sensor device  201  and may be received wirelessly into logic that may perform one or more function set forth in flow  300  or as described herein. 
     At  304 , usage of a device or a consumable may be characterized to form characterized values that may represent, for example, values of power usage or a weight (or change in weight), according to some examples. At  306 , flow  300  may facilitate identification of a pattern of values of usage, such as a pattern of power usage. At  308 , data representing one or more units of a consumable (e.g., processed by a device) may be correlated to a pattern of values, such as a pattern of electrical energy consumed by an appliance at a rate per unit time. 
     At  310 , data representing consumption of a portion of a product, such as one or more units of a consumable (e.g., one or more scoops or tablespoons of coffee), may be generated. Based on the use of one or more units of a consumable, an amount of inventory of a consumable may be adjusted (e.g., reduced) at  312 . At  314 , data representing an amount of inventory may be compared against one or more threshold range of values to detect whether a threshold is met. In response to detecting a threshold value, data representing a request to replenish inventory of the consumable may be generated (e.g., automatically) at  316 . Note that one or more of elements  302  to  316  may be optional. 
       FIG.  4    is a diagram depicting an example of configuring a sensor device to facilitate inventory monitoring of a consumable, according to various examples. Diagram  400  depicts a sensor device  402  configured to monitor power usage, a sensor device (“weight monitor device”)  460  to monitor changes in weight of a consumable, which may be optional in this example, and a mobile computing device  410  including an application having executable instructions configured to facilitate inventory monitoring. One or more wireless communication data links  405   a ,  405   b , and  405   c  may be implemented to exchange data among mobile computing device  410  and sensor devices  402  and  404 . A portion  415  of a user interface for mobile computing device  410  may depict a communications link  405   b  is established with “socket  21 ,” which identifies sensor device  402 . 
     In the example shown, an application executing in mobile computing device  410  may capture an image (or picture) of a bar code  407  (or any other coded symbol, such as a product label or code (e.g., UPC) or SKU number). The application may use barcode  407  to transmit an electronic message  411  to request the data, such as an initial weight, quantity, amount, etc., of a purchased product  404  prior to consumption. Further, response data  412  may include a type of product or consumable  404  associated with barcode  407 , whereby the consumable type may be displayed in interface portion  416  as “coffee.” Therefore, sensor device  402  may be configured to monitor consumption of coffee, and, thus, may predict that power usage is by a coffee or espresso machine. As such, sensed patterns of power usage may be compared against previously-determined or used power usage patterns, such as included in usage signature data. In some cases, sensor device  402  may include logic (e.g., appliance predictor  472 ) that may be configured to predict a type of appliance or electric-powered device to which sensor device  402  is coupled based on a type of consumable being monitored. 
     An application executing in association with mobile computing device  410  may also be configured to detect or receive information describing a type or model of an electric-powered device, such as “Brand X” displayed in user input field  417 . Optionally, the application may be configured to receive data via input field  418  to describe an initial state of inventory. For example, inventory monitoring and management may begin for a half-used container of coffee by weighing consumable  404  to determine an amount of coffee, which can be set at an initial value of 50% (not shown) in user input field  418 . Note that a weight monitor  460  may be used to facilitate inventory management either independent of, or in collaboration with, sensor device  402 . In some cases, usage of an electric-powered device or a consumable may be detected based on a signal (e.g., a single signal) indicating a device is “on” or “in use,” or whether a jug of milk is picked up. When a jug of milk is displaced, weight monitor  460  may detect a weight of “zero” or negative momentarily, or during the pouring of milk. Based on the signal, a rate of consumption may be set within user input field  419  to predict rates of reducing an inventory per detected use. Alternatively, user input field  419  may be used to provide feedback to the application, sensor device  402 , or weight monitor device  460  to recalibrate calculations that determine usage or consumption of a product. 
     According to some examples, elements depicted in diagram  400  of  FIG.  4    may include structures and/or functions as similarly-named or similarly-numbered elements depicted in other drawings. For example, while logic implementing appliance predictor  472 , a characterizer  420 , and a correlator  440  may be depicted as being within sensor device  402 , and logic implementing an inventory manager  448  and a reorder manager  449  may be depicted as being within mobile computing device  410 , any of the aforementioned elements or components may be implemented within either in sensor device  402  or in mobile computing device  410 , or may be distributed in any permutation thereof. 
       FIG.  5    is a flow diagram depicting application of sensor data to update an amount of inventory for automated replenishment, according to some examples. Flow  500  includes  501 , at which an amount of inventory (e.g., weight, liquid volume, quantity, etc.) may be initialized to indicate an initial inventory prior to usage. At  502 , a determination may be made to identify whether usage data is streamed, for example, via a wireless communication link. If not, usage data may be stored in a memory at  504 . Otherwise, flow  500  continues to  506 , at which sensor data may be accessed by, for example, a processor in a sensor device or a mobile computing device. At  508 , a determination is made whether a consumable is processed, for example, by an electric-powered device. If not, flow  500  moves to  510  at which an amount of a consumable that is used may be determined based on data from a usage sensor. In one example, a usage sensor may include a weight monitoring device. Flow  500  then may proceed to  518 . 
     If a consumable is processed (e.g., by an espresso machine), then flow  500  moves to  512  at which a determination is made whether access to usage signatures is available to access a set of power usage patterns. If yes, then flow  500  moves to  516  at which a usage signature may be accessed to compare against a sensed pattern of power usage by a corresponding electrical appliance. A usage signature may be associated with rate of consumption. Thus, at  517 , a pattern of power usage may be correlated to predict a consumed amount of product (e.g., an amount of ground coffee used). Flow  500  then moves to  518 . 
     But if, at  512 , a determination is made that a usage signature is not available (e.g., either no pattern may be stored for comparison purposes, or an associated consumption rate or amount corresponding to a pattern may be absent). Flow  500  then may move to  514  at which an amount of consumption may be associated with (e.g., assigned to) to a pattern of power usage. For example, a user may enter an amount of ground coffee used (e.g., 1 pod) to link with a sensed pattern of power usage. Therefore, a correlation between power usage and an amount of product consumed may be “learned” or “predicted” over multiple training cycles, or by implementing machine learning or other artificial intelligence techniques. In some examples, flow  500  may implement a “self-learning” algorithm to, for example, run one or more cycles to train logic to match usage (e.g., watt consumption) to product depletion for an unknown device. Flow  500  then moves to  20 . 
     At  518 , feedback regarding a predicted amount of consumption may be received to calibrate subsequent correlations between power usage patterns and amounts of product used. At  520 , a rate or amount of consumption per use (e.g., per pattern detection) may be updated to generated updated consumption rate data to enhance accuracy of an amount of consumable used per unit of processing or operation by a device. According to some examples, a consumption or usage rate may be updated or modified based on characteristics of a consumable, which may reduce inventoried amounts of a consumable due to factors other than usage. A characteristic of a consumable may describe a decay rate or degradation rate with which a consumable may rendered “degraded” (whether in terms of quantity or quality). For example, a quantity or volume of a consumable, such as ink for jet ink printers, may evaporate at a certain rate. Consider that printer ink may evaporate at a rate of 3 mL per unit time. Accuracy in identifying a quantity of ink in inventory, therefore, may be enhanced by compensating for evaporation during idle durations. Similarly, a consumption or usage rate may be updated or modified based on expected expiration of a consumable, such as a perishable good (e.g., vegetables, milk, meat, eggs, etc.). Or, a consumption or usage rate may be updated or modified based on procurement of a consumable external, for example, to a subscription or known rate of acquisition. For example, if a user has a subscription to receive  40  coffee pods every month, the inventoried amount may be altered if the user purchases extra coffee pods during (e.g., frequent) trips to a grocery store. Hence, a rate of consumption of coffee obtained by subscription may be reduced to reflect additional amounts of coffee that are purchases outside the subscription. Thus, over-stocking of inventoried coffee may be reduced. 
     At  522 , an amount of inventory may be updated (e.g., reduced) by an amount consumed, as determined at  520 . At  524 , a determination is made whether an amount of inventory may be compliant with a range of threshold values. If yes, then inventory is available for consumption at  526 . In some examples, as flow  500  passes through loop  599  over multiple occasions, a sensor device may be “trained,” through repeated use, to more accurately correlate, and thus predict, power usage to an amount of consumption. If, at  524 , a determination is made that an amount of inventory is not compliant with a range of threshold values, flow  500  proceeds to  528 , at which a replenishment of a consumable may be automatically reordered. At  530 , an electronic message including a request to reorder a consumable may be transmitted to an adaptive distribution platform, according to some examples, whereby the adaptive distribution platform may facilitate fulfillment of a request for replenishment. In some examples, one or more (e.g., all) of the portions of flow  500  may be performed at or with computing devices implementing an adaptive distribution platform. 
       FIG.  6    is a diagram depicting an example of a sensor device configured to detect usage of consumables to generate data for monitoring inventories of consumables, according to various examples. Diagram  600  depicts a sensor device  660  implemented as a weight monitoring device  660 , which may include a surface  661  having a surface area configured to receive a consumable, and a weight sensor  614  coupled to surface  661  to detect a weight of a consumable placed thereon. Weight monitoring device  660  may also include a radio to facilitate radio frequency (“RF”)-based communications via a wireless data link  694 . Diagram  600  further depicts one or more components that may be implemented in weight monitoring device  660  including, but not limited to, a sensor  614 , a characterizer  620 , a correlator  640 , memory  632 , a radio  634  and an inventory manager  648 . In at least one example, sensor  614  may include a weight sensor implemented as a “load cell” or “transducer.” In some examples, a load cell may be configured translate pressure (e.g., one or more forces of compression or tension) into an electrical signal  612 , whereby characteristics of electrical signal  612  can be characterized to detect a weight. 
     According to some examples, characterizer  620  may be configured to generate characterized weight data, such as characterized weight data  604   a  and characteristic data  604   b . Characterizer  620  may translate electrical signal  612  into a value representative of a weight of a consumable set upon surface  661 . Hence, characterizer  620  may be configured to characterize usage of a consumable to form a characterized value representing a weight of the consumable (or change in weight). For example, characterizer  620  may be configured to determine a weight of ground coffee is 500 mg as characterized weight data  604   a . In at least one example, characterizer  620  may be configured to receive configuration attribute data  612  that may include, for example, a weight of a container that may be implemented to form an inventoriable container (not shown). Thus, configuration attribute data  612  may be used to exclude (e.g., zero-out) the weight of the container from weight monitoring of a consumable, such as coffee. 
     Correlator  640  may receive character as we data  604   a  to correlate with an amount of product consumed. For example, correlator  640  may reduce a previously-measured weight generated by weight monitoring device  660  (e.g., a weight of coffee when last placed on surface  661  prior to removal for next consumption). Hence, the difference or change in weight of a consumable between a first point of time and a second point may be determined at either characterizer  620  or at correlator  640 . Correlator  640  then may be configured to correlate a change in weight to an amount of consumable used, such as an amount of ground coffee used as indicated in data  606   a  representing one or more units of consumption. 
     Inventory manager  648  may be configured to adjust a valued representing an amount of an inventory of the consumable to update the inventory. Further, inventory manager  648  may be configured to detect an adjusted amount of the inventory that is associated with a range of threshold values. Upon detecting of a threshold value, data representing a request to replenish the inventory of the consumable may be generated as response data  608   a.    
     Mobile computing device  690   a  which may include an application  650  (e.g., executable instructions) to implement a correlator  640   a  and an inventory manager  648   a , either of which may have similar structures and/or functionalities as correlator  640  and inventory manager  648  of weight monitoring device  660 . Reorder manager  649   a  may include instructions to cause data  610   a  representing a replenishment request to be transmitted to, for example, an adaptive distribution platform for purposes of reordering a product near or at depletion. In some examples, weight monitoring device  660  may include characterizer  620  that may transmit characterized weight data  604   b  to application  650  stored within mobile computing device  690   b . As such, weight monitoring device  660  may omit one or more of characterizer  620 , memory  632 , radio  634 , correlator  640 , and inventory manager  648 . Any of components of weight monitoring device  660  and mobile computing device  690   b  may be interchanged or distributed between weight monitoring device  660  and mobile computing device  690 , as well as distributed among other devices not shown, including an adaptive distribution platform. According to some examples, elements depicted in diagram  600  of  FIG.  6    may include structures and/or functions as similarly-named or similarly-numbered elements depicted in other drawings. 
       FIGS.  7 A and  7 B  are diagrams depicting examples of weight monitoring device implementations, according to some examples. Diagram of  FIG.  7 A  depicts a weight monitoring device  760   a  configured to receive, for example, a milk container  702  upon surface  761   a , whereby a weight of milk, or changes in a weight in milk, may be transmitted via message data  704   a . Weight monitoring device  760   a  may be configured to operate in relatively cold temperatures within refrigerators. In some examples, logic within weight monitoring device  760   a  may periodically (or aperiodically) measure a weight of milk (and milk container  702 ). For example, if detects a “zero” or negative weight, message data  704   a  may be transmitted to indicate “in use,” which may or may not be associated with an approximate consumption rate. In another example, subsequent to weight monitoring device  760   a  detecting milk container  702  being returned to surface  761   a  after usage, then weight monitoring device  760   a  may determine a change in weight correlatable to an amount of milk consumed. This change in weight may be transmitted via electronic message data  704   a . According to some examples, weight monitoring device  760   a  may monitor changes in milk weight against a threshold value to determine a time at which to reorder or replenish milk. Weight manager device  760   a  may be used to monitor weight of any solid or liquid item. 
     Diagram of  FIG.  7 B  depicts a weight monitoring device  760   b  attached to (e.g., using adhesive) or otherwise integrated with a container  752  to form an inventoriable container  751 . In operation, container  752  may be filled with liquids or solids, such as cereal. In some examples, logic within weight monitoring device  760   b  may determine a change in weight correlatable to an amount of cereal consumed. This change in weight may be transmitted via electronic message data  704   b . According to some examples, weight monitoring device  760   b  may monitor changes in cereal weight against a threshold value to determine a time at which to reorder or replenish cereal. Weight manager device  760   b  of inventoriable container  751  may be used to monitor weight of any solid or liquid item. In some examples, inventoriable container  751  may include an orientation sensor (not shown) that detects when container  752  is “tipped” to allow contents to pour out, whereby the change in orientation may be associated with usage. According to some examples, elements depicted in diagrams  700  and  750  of respective  FIGS.  7 A and  7 B  may include structures and/or functions as similarly-named or similarly-numbered elements depicted in other drawings. 
       FIG.  8    is a flow diagram depicting an example of monitoring inventory of a consumable using a weight monitoring device to determine a time at which to replenish an inventory, according to some examples. Flow  800  begins at  802 , at which sensor data (e.g., load cell-derived data) in a weight monitoring device may be received. The sensor data may represent a usage of a consumable. At  804 , usage of a consumable may be characterized to form a characterized value. For example, electric signals from a load cell (or any other sensor) may be characterized so as to determine a weight of a consumable. At  806 , data representing one or more units of a consumable (associated with the characterized value) may be correlated to a usage, whereby the usage may be described as a value representing a differential or a change in weight. At  808 , an amount representing an inventory of a consumable may be adjusted (e.g., reduced) to update an amount of inventory. At  810 , data representing an amount of the inventory that is associated with one or more threshold values, or a range of threshold values, may be detected. Subsequent to the detection, electronic message may be generated at  812  to include data representing a request to replenish the inventory of the consumable. 
       FIG.  9    is a diagram depicting a home inventory monitoring network including a variety of sensors coupled to one or more computing devices to monitor inventories of consumables and to facilitate replenishment of consumables, according to various examples. Diagram  900  depicts a set of sensor devices  902  each of which may be independently or individually linked via a home network including data links  909 . In this example, sensor data  902  each may be configured or adapted to sense power usage by a corresponding electric-powered device. Further, diagram  900  depicts sets of weight monitoring devices  960   a  and inventoriable containers  952  that include weight monitoring devices  960   b . Weight monitoring devices  960   a  and  960   b  may each be independently or individually linked via a home network that includes data links  909 . 
     Diagram  900  also shows sensor devices  902  linked to weight monitoring devices  960   a  and  960   b , as well as to one or more computing devices, such as a mobile computing device  901  and a voice-controlled speaker device  950 . Voice-controlled speaker device  950 , or “smart” speaker computing device, may include logic, such as an appliance predictor  972 , a characterizer  920 , a correlator  990 , and inventory manager  948 , and a reorder manager  949 . According to some examples, elements depicted in diagram  900   FIG.  9    may include structures and/or functions as similarly-named or similarly-numbered elements depicted in other drawings. 
     Accordingly, voice-controlled speaker device  950  may monitor whether any one of a number of consumable inventories reach a threshold value (e.g., a notification limit). If a consumable is detected to have a quantity or an amount of inventory that matches a threshold value, voice-controlled speaker device  950  may generate an audio notification: “your inventory of coffee is low. You are projected to have 4 more days&#39; worth. Would you like to reorder?” Should a user reply with “yes,” then voice-controlled speaker device  950  may be configured to perform other actions. For example, voice-controlled speaker device  950  may request whether to automatically reorder a consumable in the future. As such, voice-controlled speaker device  950  may generate an audio request: “Thank you. Coffee is reordered. Would you like automatic replenishment in the future?” Should the user reply verbally, such as “yes,” then reorder manager  949  may include logic to cause automatic reordering of a consumable upon an inventory amount reaching a certain threshold value. 
     According to various examples, voice-controlled speaker device  950  may include any other logic to facilitate monitoring of consumables at a remote location. In some examples, mobile computing device  901  and voice-controlled speaker device  950  may exchange electronic messages via network  921  to coordinate reordering of consumables through one or more computing devices implementing adaptive distribution platform  910 . In one example, voice-controlled speaker device  950  may incorporate specialized logic into, for example, an Amazon Echo™ speaker device of Amazon.com, Inc., of Seattle, Wash., U.S.A. 
       FIG.  10    illustrates examples of various computing platforms configured to provide various functionalities to monitor an inventory of a consumable to facilitate automated distribution and replenishment of an item, according to various embodiments. In some examples, computing platform  1000  may be used to implement computer programs, applications, methods, processes, algorithms, or other software, as well as any hardware implementation thereof, to perform the above-described techniques. 
     In some cases, computing platform  1000  or any portion (e.g., any structural or functional portion) can be disposed in any device, such as a computing device  1090   a , mobile computing device  1090   b , a voice-controlled speaker device  1090   c , and/or a processing circuit in association with implementing any of the various examples described herein. 
     Computing platform  1000  includes a bus  1002  or other communication mechanism for communicating information, which interconnects subsystems and devices, such as processor  1004 , system memory  1006  (e.g., RAM, etc.), storage device  1008  (e.g., ROM, etc.), an in-memory cache (which may be implemented in RAM  1006  or other portions of computing platform  1000 ), a communication interface  1013  (e.g., an Ethernet or wireless controller, a Bluetooth controller, NFC logic, etc.) to facilitate communications via a port on communication link  1021  to communicate, for example, with a computing device, including mobile computing and/or communication devices with processors, including database devices (e.g., storage devices configured to store any types of data, etc.). Processor  1004  can be implemented as one or more graphics processing units (“GPUs”), as one or more central processing units (“CPUs”), such as those manufactured by Intel® Corporation, or as one or more virtual processors, as well as any combination of CPUs and virtual processors. Computing platform  1000  exchanges data representing inputs and outputs via input-and-output devices  1001 , including, but not limited to, keyboards, mice, audio inputs (e.g., speech-to-text driven devices), user interfaces, displays, monitors, cursors, touch-sensitive displays, LCD or LED displays, and other I/O-related devices. 
     Note that in some examples, input-and-output devices  1001  may be implemented as, or otherwise substituted with, a user interface or a voice-controlled interface in a computing device in accordance with the various examples described herein. 
     According to some examples, computing platform  1000  performs specific operations by processor  1004  executing one or more sequences of one or more instructions stored in system memory  1006 , and computing platform  1000  can be implemented in a client-server arrangement, peer-to-peer arrangement, or as any mobile computing device, including smart phones and the like. Such instructions or data may be read into system memory  1006  from another computer readable medium, such as storage device  1008 . In some examples, hard-wired circuitry may be used in place of or in combination with software instructions for implementation. Instructions may be embedded in software or firmware. The term “computer readable medium” refers to any tangible medium that participates in providing instructions to processor  1004  for execution. Such a medium may take many forms, including but not limited to, non-volatile media and volatile media. Non-volatile media includes, for example, optical or magnetic disks and the like. Volatile media includes dynamic memory, such as system memory  1006 . 
     Known forms of computer readable media includes, for example, floppy disk, flexible disk, hard disk, magnetic tape, any other magnetic medium, CD-ROM, any other optical medium, punch cards, paper tape, any other physical medium with patterns of holes, RAM, PROM, EPROM, FLASH-EPROM, any other memory chip or cartridge, or any other medium from which a computer can access data. Instructions may further be transmitted or received using a transmission medium. The term “transmission medium” may include any tangible or intangible medium that is capable of storing, encoding or carrying instructions for execution by the machine, and includes digital or analog communications signals or other intangible medium to facilitate communication of such instructions. Transmission media includes coaxial cables, copper wire, and fiber optics, including wires that comprise bus  1002  for transmitting a computer data signal. 
     In some examples, execution of the sequences of instructions may be performed by computing platform  1000 . According to some examples, computing platform  1000  can be coupled by communication link  1021  (e.g., a wired network, such as LAN, PSTN, or any wireless network, including WiFi of various standards and protocols, Bluetooth®, NFC, Zig-Bee, etc.) to any other processor to perform the sequence of instructions in coordination with (or asynchronous to) one another. Computing platform  1000  may transmit and receive messages, data, and instructions, including program code (e.g., application code) through communication link  1021  and communication interface  1013 . Received program code may be executed by processor  1004  as it is received, and/or stored in memory  1006  or other non-volatile storage for later execution. 
     In the example shown, system memory  1006  can include various modules that include executable instructions to implement functionalities described herein. System memory  1006  may include an operating system (“O/S”)  1032 , as well as an application  1036  and/or logic module(s)  1059 . One or more logic modules  1059  may each be configured to perform at least one function as described herein. 
     The structures and/or functions of any of the above-described features can be implemented in software, hardware, firmware, circuitry, or a combination thereof. Note that the structures and constituent elements above, as well as their functionality, may be aggregated with one or more other structures or elements. Alternatively, the elements and their functionality may be subdivided into constituent sub-elements, if any. As software, the above-described techniques may be implemented using various types of programming or formatting languages, frameworks, syntax, applications, protocols, objects, or techniques. As hardware and/or firmware, the above-described techniques may be implemented using various types of programming or integrated circuit design languages, including hardware description languages, such as any register transfer language (“RTL”) configured to design field-programmable gate arrays (“FPGAs”), application-specific integrated circuits (“ASICs”), or any other type of integrated circuit. According to some embodiments, the term “module” can refer, for example, to an algorithm or a portion thereof, and/or logic implemented in either hardware circuitry or software, or a combination thereof. These can be varied and are not limited to the examples or descriptions provided. 
     In some embodiments, modules  1059  of  FIG.  10   , or one or more of their components, or any process or device described herein, can be in communication (e.g., wired or wirelessly) with a mobile device, such as a mobile phone or computing device, or can be disposed therein. 
     In some cases, a mobile device, or any networked computing device (not shown) in communication with one or more modules  1059  or one or more of its/their components (or any process or device described herein), can provide at least some of the structures and/or functions of any of the features described herein. As depicted in the above-described figures, the structures and/or functions of any of the above-described features can be implemented in software, hardware, firmware, circuitry, or any combination thereof. Note that the structures and constituent elements above, as well as their functionality, may be aggregated or combined with one or more other structures or elements. Alternatively, the elements and their functionality may be subdivided into constituent sub-elements, if any. As software, at least some of the above-described techniques may be implemented using various types of programming or formatting languages, frameworks, syntax, applications, protocols, objects, or techniques. For example, at least one of the elements depicted in any of the figures can represent one or more algorithms. Or, at least one of the elements can represent a portion of logic including a portion of hardware configured to provide constituent structures and/or functionalities. 
     For example, modules  1059  of  FIG.  10    or one or more of its/their components, or any process or device described herein, can be implemented in one or more computing devices (i.e., any mobile computing device, such as a wearable device, such as a hat or headband, or mobile phone, whether worn or carried) that include one or more processors configured to execute one or more algorithms in memory. Thus, at least some of the elements in the above-described figures can represent one or more algorithms. Or, at least one of the elements can represent a portion of logic including a portion of hardware configured to provide constituent structures and/or functionalities. These can be varied and are not limited to the examples or descriptions provided. 
     As hardware and/or firmware, the above-described structures and techniques can be implemented using various types of programming or integrated circuit design languages, including hardware description languages, such as any register transfer language (“RTL”) configured to design field-programmable gate arrays (“FPGAs”), application-specific integrated circuits (“ASICs”), multi-chip modules, or any other type of integrated circuit. 
     For example, modules  1059  of  FIG.  10   , or one or more of its/their components, or any process or device described herein, can be implemented in one or more computing devices that include one or more circuits. Thus, at least one of the elements in the above-described figures can represent one or more components of hardware. Or, at least one of the elements can represent a portion of logic including a portion of a circuit configured to provide constituent structures and/or functionalities. 
     According to some embodiments, the term “circuit” can refer, for example, to any system including a number of components through which current flows to perform one or more functions, the components including discrete and complex components. Examples of discrete components include transistors, resistors, capacitors, inductors, diodes, and the like, and examples of complex components include memory, processors, analog circuits, digital circuits, and the like, including field-programmable gate arrays (“FPGAs”), application-specific integrated circuits (“ASICs”). Therefore, a circuit can include a system of electronic components and logic components (e.g., logic configured to execute instructions, such that a group of executable instructions of an algorithm, for example, and, thus, is a component of a circuit). According to some embodiments, the term “module” can refer, for example, to an algorithm or a portion thereof, and/or logic implemented in either hardware circuitry or software, or a combination thereof (i.e., a module can be implemented as a circuit). In some embodiments, algorithms and/or the memory in which the algorithms are stored are “components” of a circuit. Thus, the term “circuit” can also refer, for example, to a system of components, including algorithms. These can be varied and are not limited to the examples or descriptions provided. 
       FIGS.  11 A to  11 E  are diagrams each depicting an example of a sub-flow that may be interrelated to other sub-flows to illustrate a composite flow, according to some examples. Sub-flow  1100  begins at  1102   a  at which a device may be coupled to a usage sensing device, such as a sensor device configured to sense power usage, a weight monitoring device, or other in-situ sensing devices. At  1104   a , a user (e.g., consumer) may provide shipping and payment information, both of which may be stored in a memory within a data arrangement constituting a user account. At  1106   a , a computing device may be configured to prompt a user to select a device, for example, from a list of existing or relevant devices (e.g., stored in a database). At  1108   a , a determination is made as to whether an inventory monitoring system includes an indicator that data associated with a device exists in the system (e.g., within a database in the system). Note that portions of the system may be distributed in an adaptive distribution platform or within any other computing device. If no, then sub-flow  1100  moves to  1110   a  at which data representing attributes of a device may be added. At  1112   a , a computing device may prompt a user or consumer to run a device through each operation that may consume a consumable. From here, sub-flow  1100  moves to a sub-flow  1101  at “A” of  FIG.  11 B . 
     Referring to  FIG.  11 B , sub-flow  1101  begins at  1102   b , whereby elements of sub-flow  1101  may be repeated for each operation of interest for a device. An operation may relate to a specific processing of a consumable by a device, according to some examples. At  1104   b , an operation of a device may be added, whereby a consumption pattern model (e.g. a new consumption pattern mode) may be added for the device at  1106   b . At  1108   b , a computing device may be configured to execute instructions to prompt (e.g., repeatedly prompt) a user to identify one or more consumables of an operation. At  1110   b , a determination is made as to whether a consumable exists within a database or other data arrangement of the system. If not, data identifying and describing a consumable may be added at  1112   b . Otherwise, sub-flow  1101  moves to  1114   b , at which a quantity of a consumable may be stored for an operation performed or tested. At  1116   b , a determination is made as to whether a specific operation may use other consumables. If yes, then sub-flow  1101  proceeds back to  1108   b  until a determination is made subsequently at  1116   b  that there are no more consumables. In this case, sub-flow  1101  moves to  1118   b , at which a determination is made whether there are any more operations in which consumables may be identified. If yes, then sub-flow  1101  moves back to  1102   b . Otherwise, sub-flow  1101  proceeds to sub-flow  1102  at “D” of  FIG.  11 C . 
     Referring to  FIG.  11 C , sub-flow  1102  begins from “D” of  FIG.  11 B  at  1104   c , at which a metric, characteristic, attribute, or parameter that correlates to consumption may be monitored. Sub-flow  1102  flows to  1106   c , at which a correlation may be performed locally (e.g., at a sensor device, a mobile computing device, or any other computing device) or at a remote computing system platform (e.g., in the “cloud”), such an adaptive distribution platform and/or a platform provided by OrderGroove, Inc., of New York, N.Y., U.S.A. At  1108   c , a pattern is checked or matched against known operation consumption patterns for a device. Examples of known operation consumption patterns may include usage signature data, according to some implementations. At  1110   c , a determination is made to detect whether a known consumption pattern for a device matches a sensed pattern. If yes, sub-flow  1102  may proceed to sub-flow  1104  of  FIG.  11 E  at “F.” 
     Referring to  FIG.  11 E , sub-flow  1104  begins from “F” of  FIG.  11 C  at  1102   e , at which a consumption quantity associated with a pattern may be sent or otherwise identified. At  1104   e , for each consumable associated with a device, an inventory amount for a consumable may be adjusted. At  1106   e , inventory for a consumable may be decremented by a consumed amount. At  1108   e , a determination is made as to whether a consumption threshold is met to replenish inventory for a specific consumable. If yes, then an order may be placed for a consumable at  1110   e . In some cases, the order is automatically placed. Otherwise, sub-flow  1104  moves to  1112   e , at which a determination is made as to whether inventories of other consumables may be adjusted. If yes, sub-flow  1104  moves based to  1104   e , which may be implemented for each consumable. Otherwise, sub-flow  1104  may proceed back to sub-flow  1102  of  FIG.  11 C  at “E.” 
     Referring back to  FIG.  11 C , sub-flow  1102  begins from “E” of  FIG.  11 E  at  1104   c , whereby sub-flow  1102  may proceed as previously discussed up through to  1110   c . In this flow, however, consider that at  1110   c , a determination may be made that there is no known consumption patterns for a device that matches, for example, a sensed pattern. Thus, sub-flow  1102  proceeds to  1112   c , at which a determination is made to identify whether a probability or confidence indicates that a sensed pattern represents consumption. If confidence or a probability is low, then nothing need be performed at  1114   c . Otherwise, if confidence or a probability is relatively high, then an electronic message may be generated for transmission to a user to request feedback at  1116   c  to confirm whether a consumable had been consumed. At  1118   c , a determination may be made whether something had been consumed responsive to request for feedback. If nothing had been consumed, then nothing need be performed at  1114   c . Otherwise, sub-flow  1102  progresses to “G” of sub-flow  1103  of  FIG.  11 D . 
     Referring to  FIG.  11 D , sub-flow  1103  begins from “G” of  FIG.  11 C  at  1102   d , at which executable instructions performed on a computing device may prompt a user for an operation that was run in relation to a device, such as a coffee maker. At  1104   d , a determination is made whether an operation and associate descriptive data are stored (e.g., exists) in a database for a device. If no, an operation is added at  1106   d  and a pattern matching model may be created. If yes, an existing pattern matching model may be updated for an operation at  1108   d . At  1110   d , executable instructions performed on a computing device may prompt a user to provide a quantity that is used for each consumable of an operation. At  1112   d , a determination may be made as to whether a consumable already exists or is stored for device consumable for an operation may be created at  1114   d  along with an associated to a quantity of product consumed in the operation. Otherwise, an existing consumable and quantity may be associated at  1116   d  with an operation of a device. In some cases, sub-flow  1103  may proceed from  1116   d  to “H” of sub-flow  1104  of  FIG.  11 E . 
     Referring back to sub-flow  1103 , a determination may be made at  1118   d  as to whether any more consumables for an operation (e.g., processing of a consumable) may be considered. If yes, sub-flow  1103  may return to  1110   d , otherwise sub-flow  1103  may move to  1120   d . At  1120   d , a determination is made as to whether any new consumables may be added. If no, then nothing need be performed at  1122   d . If yes, sub-flow  1103  may proceed to “C” of sub-flow  1100  in  FIG.  11 A . 
     Referring to  FIG.  11 A , sub-flow  1100  begins from “C” of  FIG.  11 D  at  1116   a , at which executable instructions performed on a computing device may prompt a user to choose a consumable and fulfillment option (e.g., one or more ways to select a product and select a way to facilitate that a consumable may be reordered). Note that,  1116   a  may be obtained if a determination at  1108   a  indicates “yes” that a device (and associated descriptive data) under consideration exists in a system database. At  1114   a , executable instructions performed at  1116   a  on a computing device for each operation of a device that consumes a consumable. From  1116   a , sub-flow  1100  may proceed to “B” of sub-flow  1102  in  FIG.  11 C . Referring back to  FIG.  11 C , sub-flow  1102  begins from “B” of  FIG.  11 A  at  1102   c , at which executable instructions performed on a computing device may prompt a user for an existing inventory (e.g., amount) of a consumable as an initial amount. Thereafter, sub-flow  1102  may be performed as previously described above, thereby continuing a flow through sub-flows in  FIGS.  11 A to  11 E , according to some examples. 
       FIG.  12    is a diagram depicting one or more usage sensors configured to determine consumption characteristics of one or more consumables, according to some embodiments. Diagram  1200  depicts examples of one or more sensors (e.g., usage sensors) that may be implemented individually or collectively (e.g., with others in  FIG.  12   , or with power-based usage sensors, weight-based usage sensors, etc.) to determine or predict consumption of a consumable, as well as characteristics thereof. Examples of various sensors may be configured to generate sensor data indicative of usage (e.g., coincidental usage) of a device or a tool implemented or associated with consumption of one or more consumables, and/or indicative of usage of a consumable (e.g., either directly or indirectly indicative of consumption). 
     A usage sensor may be configured to determine usage of a resource, such as electric power, natural gas, cold water (e.g., unheated water), hot water (e.g., heated water correlatable to either gas or electric heating) and other resources that may be used in association with consumption of the consumable. An example of an electric power-based sensor is depicted in  FIG.  2   . An example of a water-based usage sensor is a flow meter (not shown) for detecting water usage (e.g., direct water usage) of, for example, a toilet, a shower, a sink (e.g., a kitchen or bathroom sink), a clothes washing machine, a lawn sprinkler, etc. An example of a gas-based usage sensor is a gas flow meter (not shown) for detecting natural gas usage (e.g., directly) of, for example, one or more stove burners, a gas oven, a water heater, a furnace, etc. 
     In some embodiments, sensors may be configured to generate sensor data indicative of usage of a device or a tool based on, for example, operational characteristics of a device or tool. For example, sensors  1214   a ,  1214   b , and  1214   c  may be configured to determine sensor data indicative of either a state of operation of a device that processes a consumable or usage of a resource, or both. Vibration sensor  1214   a  may be disposed in, on, or in association with electric toothbrush  1218   a  or a washer machine  1281   b , and may be configured to receive or detect any magnitude of vibratory energy  1212   a  associated with oscillatory displacement (e.g., in 1D, 2D or 3D) about a point in space, whereby one or more patterns of vibrations may uniquely identify a device (e.g., washer machine  1281   b ) or a mode of operation thereof (e.g., a spin cycle and duration, an agitation cycle and duration, etc.). Examples of vibration sensor  1214   a  include accelerometers (e.g., 2D or 3D), piezo-based vibration sensors (e.g., a cantilever-type vibration sensor), and the like. Sensed vibrations data  1280   a  may be transmitted as raw data  1280  to an application  1250  in mobile computing device  1290   b , or as data  1280  to a communications module  1299   a  and/or a processor module  1299   b , either or both of which may be disposed as part of sensor  1214   a , mobile computing device  1290   b , or an adaptive distribution platform (not shown). Vibration sensor  1214   a  may be disposed in, on, or in association with any other device or tool that may produce vibrations incidental to usage, such as a vacuum cleaner, a blender, a coffee maker, a hand-held mixer, a dishwasher, etc. 
     Temperature sensor  1214   b  may be disposed in, on, or in association with a hot water hose  1283   b  of a washer machine  1281   b  or a cold water hose  1283   c  of a toilet  1282   c , and may be configured to receive or detect any magnitude of thermal energy  1212   a  (or changes thereof) associated with a component that may provide changes in temperature due to one or more modes of operation of a washer machine  1281   b , of toilet  1282   c , or of any other device in which temperature may be indicative of a state of operation or mode of operation. Sensed temperature data  1280   b  may be transmitted as raw data  1280  similar to sensed vibration data  1280   a . Temperature sensor  1214   b  may be disposed in, on, or in association with any other device or tool that may produce or modify thermal energy of a component or a device, such as a coffee maker (e.g., associated with boiling water), a dishwasher (e.g., associated with a heated drying element and/or use of hot water), a heating cycle of a clothes dryer, etc. Examples of temperature sensor  1214   b  include thermocouples, thermistors (e.g., thermally-sensitive resistors), temperature-sensitive integrated circuits (e.g., measurable temperature-variations of voltage generation by diodes), and the like. 
     Position sensor  1214   c  may be disposed in, on, under, or otherwise in association with a mechanism, such as a hot or cold water faucet or shower handle, a toilet flush lever  1281   c , or any other water-flow or gas-flow activation component (e.g., mechanical or electro-mechanical). A change in frequency in positions (or orientations) or a duration between changes in positions (or orientations) may be indicative to usage rates of, for example, water, and hence associated consumables, such as soap, shaving cream, shampoo, conditioner, body wash, etc. In some cases, position sensor  1214   c  may be coupled to a movable or translatable object, such as a door. For example, position sensor  1214   c  may be implemented in or on a refrigerator door to detect instances when a door is in either open or closed, or implemented in or on an expresso machine frothing wand (e.g., to determine when steamed milk is being used), or implemented in or on any other component of any other device or tool. Positional changes may be based on data signals  1212   c  (e.g., electrical signals) generated responsive to changes in position or orientation. Sensed position (or orientation) data  1280   c  may be transmitted as raw data  1280  similar to sensed vibration data  1280   a . Examples of position and/or orientation sensors  1214   c  include accelerometers, mercury switches, tilt switches, rolling ball switches, laser beam-based motion sensor, photodiodes, gravity sensors (e.g., for orientation relative to gravitational force), or any other sensor configured to determine motion or displacement regardless of whether the displacement is linear or rotational in one to three dimensions. 
     In some embodiments, sensors may be configured to generate sensor data indicative of changes in state, characteristics, or qualities of a consumable, whereby the changes for a consumable need not be associated with usage of a device or tool. Examples of consumable characteristics may include values of volume (e.g., of liquids or solids), weight, size(s), color, age (e.g., dates until expiry or degradation), etc., including any item characteristic or attribute. Examples of consumable characteristics also may include sounds or audio (not shown) associated with use (e.g., sounds of boiling water being indicative of the usage of coffee or tea, sounds of pouring liquid into a glass with carbonated “fizzing” sounds being indicative of usage of a soda beverage, and the like). Other examples of consumable characteristics may include image characteristics that may be detectable via, for example, computer vision recognition or other computer-based recognition algorithms implementing pattern recognition for digital imagery, and may include machine learning, deep-learning, or the like. For example, an image sensor (not shown) disposed in a kitchen area, for instance, may be used to detect colors of bananas and tomatoes being placed on a kitchen counter (out from a grocery bag or delivery shipment box) prior to storage. If the bananas and tomatoes are detected as “green,” or in an unripen state, then an inventory manager may adjust an expiry date (e.g., extend date of freshness to include a duration for the fruit or vegetable to ripen). 
     In the example shown, a level sensor  1214   d  may be configured to receive data  1212   d  representing surface level  1283   d  of a liquid (e.g., milk, juice, soup, etc.) or a solid or solid particulate (e.g., coffee beans, cereal, sugar, etc.). As such, level sensor  1214   d  may identify a state of liquid or solid within container  1281   d , as well as changes in the state of liquid or solid indicative of the usage of a consumable. A sensed state or consumable characteristic data  1280   d  may be transmitted as raw data  1280  similar to sensed vibration data  1280   a . Examples of level sensor  1214   d  include ultrasonic sensor  1282   d  (e.g., implementing ultrasonic pulse-echoing techniques to detect a distance between surface level  1283   d  and sensor  1282   d , thereby providing for a computed volume (or change in volume)), a fluid level sensor, a capacitance-based volume sensor (e.g., based on changes in capacitance of liquid), a laser light-based surface sensor (e.g., based on reflectivity of surface level  1283   d ), and any other fluid or solid level or volume sensor devices. 
     Raw data  1280 , which may be representative of sensor data from one or more sensors  1214   a  to  1214   d , or any other sensor (e.g., a moisture or humidity sensor, which is not shown) may be transmitted into a communications module  1299   a  and/or a processor module  1299   b . Communications module  1299   a  may be configured to store (e.g., temporary storage) sensor data for subsequent transmission. Memory  1232  may store one or more cycles or instances of sensor data captured by sensor devices  1214   a  to  1214   d  for further processing or certain times at which the data may be transmitted to, for example, a mobile computing device  1290   b  or any other computing device, including an adaptive distribution platform (not shown). Radio  1234  may be configured to transmit raw data  1280  or characterized data  1204   b  via a wireless datalink to mobile computing device  1290   b  or any other computing device. According to some examples, elements depicted in diagram  1200  of  FIG.  12    may include structures and/or functions as similarly-named or similarly-numbered elements depicted in other drawings. 
     Processor module  1299   b  may include a characterizer  1220  including an analyzer  1222  and a pattern detector  1224 , a correlator  1240  including a matcher  1242  and a predictor  1246 , and an inventory manager  1248 , any of which may be implement in hardware or software, or a combination thereof. According to some examples, elements depicted in diagram  1200  of  FIG.  12    may include structures and/or functions as similarly-named or similarly-numbered elements depicted in other drawings. 
     Characterizer  1220  may receive sensor data  1280  from one or more sensors to generate characterized data (e.g., characterized data  204   a ,  204   b ) that describes, summarizes, or encapsulates one or more of the following: characterized sensor values indicative of resource usage (e.g., one or more magnitudes of resources used per unit time, such as power, water, gas or other processing media used per unit time), characterized sensor values indicative of device usage (e.g., a type and mode of operation of a device or tool per unit time), an indication of a type and/or model of a device used (e.g., an electric tooth brush, a toilet, a washer machine, a dishwasher, etc.), a duration of resource usage, a time of day of the resource usage, etc., as well as complementary or other associated devices that may be used coincidentally with a particular device, or the like. Analyzer  1222  may be configured to determine characteristics of patterns of sensor data indicative of usage for identifying a magnitude of a sensed data value at, for example, a particular point in time. Time, t, may be expressed in any unit of time, such as milliseconds, seconds, minutes, etc., and magnitudes of sensed data may be expressed in relevant units. Pattern detector  1224  may be configured to form data representing a pattern of sensed data based on, for example, amounts of resources used by a device, whereby the pattern may be used to identify whether a device is use and/or operating in accordance with a particular mode of operation, or whether the device is idle. 
     Correlator  1240  may be configured to correlate a portion of a product consumed (e.g., a consumable) to a characterized value of resource or device usage, whereby one or more units of consumable processed by a device or in association with a resource can be correlated. Matcher  1242  may be configured to identify a pattern of sensed values of resource usage or device usage per unit time, and may further match a pattern against data representing units of consumable processed by a device or otherwise consumed in association with the pattern. Predictor  1246  may be configured to predict a value representing one or more units of consumable processed by a device based a sensed pattern of data values indicative of resource or device usage per unit time. In some cases, predictor  1246  may generate a feedback request  1279  (e.g., transmitted to mobile computing device  1290   b ) to ask a user to calibrate, for example, a consumption rate, type, or amount. 
     To illustrate operation of processor module  1299   b  in a first example, consider that sensed vibration data values  1280   a  are received into characterizer  1220 . Analyzer  1222  may be configured to detect various amplitudes of displacement per unit time about one or more reference points (e.g., in units of vibrations per second, or Hertz (“Hz”)). Such vibrations may be generated in association with operation of either electric toothbrush  1281   a  or washer machine  1281   b . Sensed vibration data may describe oscillatory displacement in one or more planes (e.g., an XY plane, an YZ plane, and/or an XZ plane). Further, electric toothbrush  1281   a  and washer machine  1281   b  each may have various vibration patterns for different modes of operation (e.g., agitation, spin cycles and rinse cycles may have different vibration patterns and rates of vibration for washer machine  1281   b , etc.). In a “training mode” or “learning mode” of operation, vibration data  1280   a  from each of electric toothbrush  1281   a  and washer machine  1281   b  may be used to generate vibration patterns that may be used subsequently to identify a particular device and mode of operation, and, in turn, identify one or more processed consumables. To confirm association of a vibration pattern to usage of a resource or a device, device attribute data  1215  may include data describing a type of resource or device for which sensor  1214   a  may be configured to monitor. For example, data  1215  may include data describing electric toothbrush  1281   a  and washer machine  1281   b , as well as associated products that may be consumed in associated with usage of a device. For example, device attribute data  1215  may describe a replaceable brush head  1282   a  as a consumable, an associated amount of toothpaste consumed as a consumable, and a lifetime charge for an internal, replaceable battery as a consumable. Usage signature data  1217  may include data describing any number of patterns for specific types, manufacturers, models, and modes of operation for electric toothbrush  1281   a  and washer machine  1281   b . Such patterns may be used to forego training or to supplement learned vibration patterns. Characterizer  1220  may be configured to transmit characterized data  1204   a  to correlator  1240  (and/or characterized data  1204   b  to communications module  1299   a ). 
     Further to the first example, correlator  1240  may be configured to correlate a type and/or a portion of a product consumed (e.g., a consumable) to a characterized data values  1204   a  of resource or device usage, whereby one or more units of consumable processed by electric toothbrush  1281   a  or washer machine  1281   b  can be correlated. Matcher  1242  may be configured to identify a pattern of sensed values of resource usage or device usage per unit time for electric toothbrush  1281   a  and washer machine  1281   b , and may further match a pattern against data representing units of consumable processed by a device or otherwise consumed in association with the pattern. For example, different vibration patterns having longer lengths and larger amplitudes for electric toothbrush  1281   a  may correspond to longer brushing times (e.g., with an increased amount of wear, and, thus, a shorter life for replaceable brush head  1282   a ). Predictor  1246  may be configured to predict a value representing one or more units of consumable processed by electric toothbrush  1281   a  (e.g., amounts of toothpaste) and washer machine  1281   b  (e.g., amounts of detergent or fabric softener) based on sensed patterns of data values. In some examples, feedback data  1219  may be used to confirm the predicted amounts of product consumed, or to recalibrate the predicted amount for subsequent uses. In at least one example, data representing a consumption rate  1221  may be provided to correlator  1240  to confirm one or more rates of consumption that may be used for particular resource usage or device usage (e.g., usage of electric toothbrush  1281   a  and washer machine  1281   b ). 
     In a second example, consider that that sensed temperature data values  1280   b  are received into characterizer  1220 . Analyzer  1222  may be configured to detect various temperatures and/or temperatures changes per unit time in association with operation of a hot water intake hose  1283   b  of washer machine  1281   b  or a cold water intake hose  1283   c  of toilet  1282   c . Further, washer machine  1281   b  and toilet  1282   c  each may have various temperature patterns for different modes of operation (e.g., a washer machine  1281   b  may have a “hot” wash cycle in which an amount of bleach is used as a consumable to enhance cleaning of bleachable fabrics, etc.). Temperature data  1280   b  from washer machine  1281   b  and toilet  1282   c  may be used to generate temperature patterns (during a training mode) that may be used subsequently to identify a particular device and/or mode of operation, and, in turn, one or more consumables processed. To confirm association of a temperature pattern with usage of a resource or a device, device attribute data  1215  may include data describing a type of resource or device for which sensor  1214   b  may be configured to monitor. Usage signature data  1217  may include data describing any number of temperature patterns for specific types, manufacturers, models, and modes of operation for washer machine  1281   b  and toilet  1282   c . Such patterns may be used to forego training or to supplement learned temperature patterns. Characterizer  1220  is configured to transmit characterized data  1204   a  to correlator  1240 . 
     Further to the second example, correlator  1240  may be configured to correlate a type and/or a portion of a consumable to characterized data values  1204   a  of resource or device usage, whereby one or more units of consumable processed by washer machine  1281   b  and toilet  1282   c  can be correlated. Matcher  1242  may be configured to identify a pattern of sensed values of resource usage or device usage per unit time for washer machine  1281   b  or toilet  1282   c , and may further match a pattern against data representing units of consumable processed by a device or otherwise consumed in association with the pattern. For example, consider cold water intake hose  1283   c  of toilet  1282   c  initially is at ambient or room temperature after a sufficient amount of time after a last flush so that a temperature of intake hose  1283   c  equalizes to room temperature. At a subsequent point in time, during one or more flushes, colder temperature flows through intake hose  1283   c , thereby dropping the temperature detected by sensor  1214   b . A greater drop in temperature may correlate to multiple flushes (and increased flows of colder water), which, in turn, may correlate to disposition of greater amounts of toilet paper or toilet tank automatic cleaner tablets. As another example, a length of time that hot water flows through hot water intake hose  1283   b  of washer machine  1281   b  may differentiate between hot, warm, and cold wash modes of operation. Predictor  1246  may be configured to predict a value representing one or more units of consumable processed by washer machine  1281   b  (e.g., amounts of bleach used in a hot water cycle) and by toilet  1282   c  (e.g., amounts of toilet paper or toilet tank automatic cleaner tablets used during one or more flushes) based on sensed patterns of temperature values. Feedback data  1219  and data representing a consumption rate  1221  may be used in relation to temperature data and usage of washer machine  1281   b  and toilet  1282   c.    
     In a third example, consider that that sensed position data values  1280   c  may be received into characterizer  1220 . Analyzer  1222  may be configured to detect various positions, orientations, and/or position changes per unit time in association with operation of toilet  1282   c . Further, a flush handle  1281   c  of toilet  1282   c  may have various position patterns for different modes of operation (e.g., a half-way flush, a single flush, multiple flushes, etc.). Position data  1280   c  associated with flush handle  1281   c  of toilet  1282   c  may be used to generate position patterns (during a training mode) that may be used subsequently to identify a particular device, such as toilet  1292   c , and/or modes of operation of the toilet, and, in turn, one or more consumables that are processed may be predicted. To confirm association of a position pattern to usage of a resource or a device, device attribute data  1215  may include data describing a type of resource or device for which sensor  1214   c  may be configured to monitor. Also, usage signature data  1217  may include data describing any number of position or orientation patterns for specific types, manufacturers, models, and modes of operation for toilet  1282   c . Such patterns may be used to forego training or to supplement learned position patterns. Characterizer  1220  may be configured to transmit characterized data  1204   a  to correlator  1240 . 
     Further to the third example, correlator  1240  may be configured to correlate a type and/or a portion of a consumable to characterized data values  1204   a  of resource or device usage, whereby one or more units of consumable processed by toilet  1282   c  can be correlated. Matcher  1242  may be configured to identify a pattern of sensed values of resource usage or device usage per unit time for toilet  1282   c , and may further match a pattern against data representing units of consumable processed by a device or otherwise consumed in association with the pattern. For example, consider that a position sensor can detect rotations of flush handle  1281   c  of toilet  1282   c  to detect a mode of operation. Predictor  1246  may be configured to predict a value representing one or more units of consumable processed by toilet  1282   c  (e.g., amounts of toilet paper or toilet tank automatic cleaner tablets used during one or more flushes) based on sensed patterns of position change values. Feedback data  1219  and data representing a consumption rate  1221  may be used in relation to position data and usage of toilet  1282   c.    
     In a fourth example, consider that that sensed level data values  1280   d  may be received into characterizer  1220 . Analyzer  1222  may be configured to detect various levels of liquids or solids, and/or changes in depth of surface level  1283   d  per unit time in association with use of container  1281   d . To confirm association of a level pattern per unit time to usage of a resource or a device, device attribute data  1215  may include data describing a type of resource or device for which sensor  1214   d  may be configured to monitor. Also, usage signature data  1217  may include data describing any specific type, manufacturer, model, and modes of operation for container  1281   d . Such patterns may be used to forego training or to supplement learned patterns of level changes per unit time (e.g., daily use of pouring out milk or cereal out from container  1281   d ). Characterizer  1220  may be configured to transmit characterized data  1204   a  to correlator  1240 . 
     Further to the fourth example, correlator  1240  may be configured to correlate a type and/or a portion of a consumable to characterized data values  1204   a  for a container, whereby one or more units of consumable associated with container  1281   d  can be correlated. Matcher  1242  may be configured to identify a pattern of sensed level values of consumables per unit time for container  1281   d . For example, consider that ultrasonic sensor  1282   d  can detect changes in surface level  1283   d  for a consumable in container  1281   d . Predictor  1246  may be configured to predict a value representing one or more units of consumable associated with container  1281   d  based on sensed patterns of surface level change values. Feedback data  1219  and data representing a consumption rate  1221  may be used in relation to surface level  1283   d  data and usage of container  1281   d.    
     Inventory manager  1248  may be configured to receive data  1206   a  representing a number of consumed units of a consumable (e.g., toothpaste, toilet paper, bleach, replacement brush heads  1282   a , etc.) to monitor an amount of inventory of a consumable. Inventory manager  1248  also may be configured to adjust an amount or value representing an inventory and may generate response data  1208   a  to generate a request to reorder an amount and type of a consumable. 
     According to some examples, processor module  1299   b  may be associated with a single device, such as toilet  1282   c  or washer machine  1281   b , and may be configured to receive sensor data from multiple sensors. For example, washer machine  1281   b  may receive sensor data from power-based sensor  1282   b , temperature sensor  1214   b , and vibration sensor  1214   a , whereby each may be correlated with another to confirm and detect modes of operation of washer machine  1281   b . As another example, toilet  1282   c  may receive sensor data from temperature sensor  1214   b  and position sensor  1214   c , whereby each may be correlated with another to confirm and detect modes of operation of toilet  1282   c . In other examples, processor module  1299   b  may be configured to receive multiple subsets of sensor data from any number of sensors, such as moisture sensors, magnetic-based sensors, infrared sensors, light-based sensors including lasers, and any other suitable sensor. 
     In some examples, a device may include an electric-powered device, a mechanical-powered device, a chemical energy-powered device, such as a natural gas-based device (e.g., a gas stove, a gas-based clothes dryer, a furnace or the like), and other resource-powered or resource consumption device or appliance, etc.) that may be configured to process or use a consumable in, for instance, operation of the device. In some examples, a tool is an instrument that may be used or applied to a consumable independent of the usage of resources or devices. For example, a tool may include a knife, a hand-powered can opener, a cork screw, a hammer, a hacksaw, etc. In some examples, the term “consumption characteristics” may refer to one or more of a type, amount, a mode of operation, associated consumables (e.g., an associated coffee filter and milk, if a frothing wand used, in relation to coffee as a principal consumable), etc. 
     In some examples, more or fewer components shown in  FIG.  12    may be implemented to monitor and manage inventory amounts by way of computing and predicting consumable consumption. In the example shown, an application  1250  (e.g., executable instructions) may be configured to implement a correlator  1240   a  and an inventory manager  1248   a , either of which may have similar structures and/or functionalities as correlator  1240  and inventory manager  1248 . Reorder manager  1249   a  includes instructions to cause data  1210   a  representing a replenishment request to be transmitted to, for example, an adaptive distribution platform for purposes of reordering a product near or at depletion. In some examples, processor module  1299   a  may include characterizer  1220  that may transmit characterized data  1204   b  to application  1250  stored within mobile computing device  1290   b . As such, communications module  1299   a  and processor module  1299   c  (or any other element of  FIG.  12   ) may omit one or more of characterizer  1220 , memory  1232 , radio  1234 , correlator  1240 , and inventory manager  1248 . Any of components in diagram  1200  and mobile computing device  1290   b  may be interchanged or distributed between sensor device  1201  and mobile computing device  1290 , as well as distributed among other devices not shown, including an adaptive distribution platform. For example, reorder manager  1249   a  may be implemented within processor module  1299   c , and characterizer  1220  may be implemented within application  1250 . One or more components of diagram  1200  may be implemented in logic as either hardware or software, or a combination thereof. 
       FIG.  13    is a diagram depicting examples of audio sensors implemented to determine consumption characteristics of one or more consumables, according to some embodiments. Diagram  1300  includes a mobile computing device  1301  and voice-controlled speaker device  1350 , each being configured to communicate via wireless links  1309  with each other and remote computing devices, such as at least a portion of an adaptive distribution platform (not shown). One or both of mobile computing device  1301  and voice-controlled speaker device  1350  may include communications module  1299   a  of  FIG.  12    and a process module  1399   b , which may include a characterizer  1320  including an analyzer  1322 , a pattern detector  1324 , and an audio formulator  1325 , a correlator  1340  including a matcher  1342  and a predictor  1346 , and an inventory manager  1348 , any of which may be implement in hardware or software, or a combination thereof. According to some examples, elements depicted in diagram  1300  of  FIG.  13    may include structures and/or functions as similarly-named or similarly-numbered elements depicted in other drawings, such as  FIG.  9   . 
     According to some examples, processor module  1399   b  may be configured to receive sensed audio data, such as captured audio  1303 , via microphones and sound/audio logic within mobile computing device  1301  and voice-controlled speaker device  1350 . Also, processor module  1399   b  may be configured to detect audio correlatable with usage of a consumable (as well as with a resource or a device), and may be further configured to correlate audio characteristics of captured audio  1303  to determine or approximate a type or amount of consumable consumed coincident with detected audio. In the example shown, an analyzer  1322  may be configured to analyze audio to detect audio patterns. Analyzer  1322  is shown to include an audio isolator  1321  that may be configured to isolate audio (e.g., filter out noise or other non-relevant audio) that may be used to determine a pattern of audio, which, in turn, may be correlated to usage of a consumable and consumable characteristics thereof. In some cases, analyzer  1322  may determine audio characteristics, such as amplitude, volume, voltage, dB, or pressure (e.g., along a Y-axis) relative to time (e.g., along an X-axis) of captured audio  1303 . 
     Pattern detector  1324  may form audio patterns based on audio patterns  1303   a ,  1303   b ,  1303   c  and  1303   d , and the like. In this example, each of audio patterns  1303   a  and  1303   b  may relate to sounds of “ice cubes dropping into a glass.” Audio pattern  1303   c  may relate to a sound of a “liquid being poured” into a glass. And, audio pattern  1303   d  may relate to a “fizzing” sound associated with a carbonated drink subsequent to being poured. Each of audio patterns  1303   a ,  1303   b ,  1303   c  and  1303   d , when considered individually, may or may not suggest an event with which to associate with a consumption rate. In some instances, audio formulator  1325  may be configured to formulate an event by integrating audio patterns  1303   a ,  1303   b ,  1303   c  and  1303   d  as a sequence of sounds that accompany an event in which soda pop may be poured into a glass with ice. In some cases, a length of time of audio pattern  1303   c  may be correlatable to an amount of soda used (e.g., as an amount of consumable). In at least some examples, audio patterns  1303   a ,  1303   b ,  1303   c  and  1303   d  may be captured along with verbal instructions  1351  by a user that an action of “pouring a diet soda with ice” is coincident with captured audio  1303 . Characterizer  1320  may use verbal instructions  1351  (e.g., by using voice recognition technologies) to identify key words that may relate to one or more audio patterns  1303   a ,  1303   b ,  1303   c  and  1303   d . Thus, verbal instructions  1351  may assist in determining or confirming an event for captured audio  1303 . 
     To confirm association of an audio pattern to usage of a consumable, or, in some cases, a resource or a device, audio attribute data  1315  may include data describing a subset of audio patterns that may correlate to an event (or portion thereof) in which a product may be consumed. Usage signature data  1317  may include data describing any number of audio patterns that may be used to detect specific sounds associated with consumption of a consumable. For example, “popping” sounds may accompany heating of popcorn in a microwave, a “chopping” sound may accompany the use of a knife chopping a vegetable, a “boiling or bubbling” sound may accompany brewing of coffee, etc. As another example, audio for different modes of operation of various devices may be provided via usage signature data  1317  to determine subsequent uses of specific types, manufacturers, models, and modes of operation of devices, such as a blender, a hand mixer, and the like. Such patterns may be used to forego training or to supplement learned audio patterns. Characterizer  1320  may be configured to transmit characterized data  1304   a  to correlator  1340  or characterized data  1304   b  to communications module  1399   a.    
     Correlator  1340  may be configured to correlate a type and/or a portion of a consumable to characterized audio data values  1304   a  of one or more audio patterns  1303   a  to  1303   d , whereby one or more units of consumable that are consumed may be associated with detected audio  1303 . Matcher  1342  may be configured to identify a pattern of sensed audio values that matches a type or amount of consumables per unit time consumed. For example, consider that a length of time of audio pattern  1303   c  may be correlatable to an amount of carbonated beverage used (e.g., an amount of consumable). A shorter duration may be correlated to pouring a 12 ounce can of soda, whereas a longer duration may be correlated to pouring a 32 ounce cup of soda (e.g., from a 2 liter bottle). Predictor  1346  may be configured to predict a value representing one or more units of consumable associated with captured audio  1303 . Feedback data  1319  and data representing a consumption rate  1321  may be used in relation to a subset of audio data and usage of a consumable. In some cases, predictor  1346  may generate a feedback request  1379  (e.g., transmitted to mobile computing device  1301 ) to ask a user to calibrate, for example, a consumption rate, type, or amount based on an amount of sensed audio data. 
     Inventory manager  1348  may be configured to receive data  1306   a  representing a number of consumed units of a consumable (e.g., an amount of soda) to monitor an amount of inventory of the consumable. Inventory manager  1348  may be configured to adjust an amount or value representing an inventory, and may generate response data  1308   a  to generate a request to reorder an amount and type of a consumable responsive to captured audio  1303 . 
     According to some examples, captured audio  1303  may include audio patterns associated with operation of a device, such as a juicer. In some cases, processor module  1399   b  may be configured to detect whether operation of the juicer is operating nominally. That is, audio patterns associated with the operation of the juicer may be indicative of normal operation. However, usage signature data  1317  may also include audio patterns associated with improperly functioning devices. For example, a diagnostic audio pattern may include a noise associated with a bad bearing in a juicer. Correlator  1340  may compare a diagnostic audio pattern against captured audio  1303  from a juicer in operation, and, if a match exists, an indication that the juicer may have defect may be generated. For example, a user may receive a visual notification in mobile computing device  1301  or an audio notification in voice-controlled speaker device  1350 . Note that any type or quantity of diagnostic audio patterns may be accessible via mobile computing device  1301  and voice-controlled speaker device  1350  to diagnose proper operation, for example, power tools (e.g., jig saws, circular saws, lathes, etc.), automobile operations, and any other device or machine-related operation. In some cases, processor module  1399   b  may automatically order a part to replace a defective part (e.g., defective ball bearing) or to schedule servicing of a defective device. 
       FIG.  14    is a diagram depicting examples of detecting usage of resources or devices based on monitoring resources at a macro-level, according to some examples. Diagram  1400  depicts a macro-power usage profile  1401  for power consumption for an aggregate number of circuits at a residence, a business building, or any other macro-level consumption of power. In some cases, a power monitoring device or wattmeter may have sufficient resolution for use to determine instantaneous (or nearly instantaneous) power consumption of specific devices over a duration of time (e.g., a 24 hour period of time). As shown, a refrigerator compressor consumes power  1419  overnight from 10 pm to 6 am (and throughout the day). At about 8 am, a coffee maker is shown to consume power  1421  and a waffle iron may consume power  1422 . Between approximately 10 am to 2 pm, a washer machine may consume power in association with power pattern  1424 , whereas between approximately 2 pm and 6 pm an electric stove consumes power in accordance with pattern  1426 . A tea kettle may be used to brew tea near bed time at 7 pm and after 8 pm. Based on the aforementioned detected power patterns or signatures, corresponding amounts and types of consumables may be identified as being consumed in relation to, for example, waffle iron and its consumed power  1422 . For example, an amount of eggs, flour, etc. may be correlated with power pattern  1422  used to make waffles. Further, an amount of syrup may be predicted to be used in relation to consumed waffles. 
     In at least some examples, a macro-water usage profile  1403  may depict patterns of water usage throughout the day and a macro-natural gas usage profile  1405  may depict patterns of natural gas usage, whereby usages in profiles  1403  and  1405  may be used to predict consumption of a product (e.g., consumption of shampoo and conditioner during a shower in which a gas-powered water heater may be activated responsive to a user taking a hot shower). 
     Usage data from profiles  1401 ,  1403 , and  1405  may be used by one or more of a device predictor  1472 , a characterizer  1420 , a correlator  1490 , an inventory manager  1448 , and a reorder manager  1449  to generate replenishment request data  1410   a  to replenish consumption of products used in association with profiles  1401 ,  1403 , and  1405 . According to some examples, elements depicted in diagram  1400  of  FIG.  14    may include structures and/or functions as similarly-named or similarly-numbered elements depicted in other drawings, such as  FIG.  9   , among others. 
       FIG.  15    depicts an example of a modified sensor device of  FIG.  2   , according to some examples. Diagram  1500  depicts a sensor device  1501  configured to receive electric power for monitoring usage of an electric-powered device, whereby usage of an electric-powered device may be correlatable to an amount of product consumed to facilitate automated inventory replenishment, among other things. According to some examples, elements depicted in diagram  1500  of  FIG.  15    may include structures and/or functions as similarly-named or similarly-numbered elements depicted in other drawings, such as  FIG.  2   , among others. 
     In the example shown, sensor device  1501  includes an audio input  1506 , such as a microphone, in or through a housing  1502 . Sensor device  1501  includes a power sensor  1514   a  for detecting an amount of power used by a device, such as a coffee maker, and an audio sensor  1514   b , which is configured to detect audio generated by a device (e.g., sounds of boiling water) that may coincidentally be drawing power via sensor  1501 . Sensor device  1501  also may include a power characterizer  1520   a , an audio characterizer  1520   b , and a correlator  1540 . Responsive to detecting audio emitted from a device, such as a vacuum cleaner, audio characterizer  1520   b  may compare one or more stored audio patterns to match the audio received via audio input  1506 . A match with an audio pattern indicative of operation of a vacuum cleaner may cause generation of a signal to power characterizer  1520   a , whereby the signal indicates that audio of a vacuum cleaner coincides with power consumption. 
     Thus, power characterizer  1520   a  may select one or more power profiles to determine usage of a vacuum cleaner, and may transmit power characterization data to correlator  1540 . After a number of operational hours, correlator  1540  may detect that a vacuum clean bag ought to be replaced. Thus, replenishment request data  1510   a  may be transmitted via wireless datalink  1594  to reorder vacuum cleaner bags, should the inventory level warrant reordering. Note that the above-described example is not intended to be limiting, and any other sensor may be integrated into sensor device  1501  or variants thereof. 
       FIG.  16    is a flow diagram depicting an example of application of sensor data to update an amount of inventory for automated replenishment, according to some examples. Flow  1600  includes  1602 , at which an amount of inventory (e.g., weight, liquid volume, quantity, etc.) and type of consumable inventory (e.g., coffee, tea, espresso pods, coffee filters, each of which may be associated with a device) may be initialized to indicate an initial inventory prior to usage. At  1604 , consumption of one or more consumables may be detected or analyzed. At  1606 , one or more sensors configured to determine consumption and related consumption characteristics (e.g., amounts, types, etc.) may be determined. At  1610  a determination is made whether activation of a device is detected, such as activation of a device by identifying a power-on button is “pressed.” Note that in some examples, activation need not be identified by detection of a button being pressed or other mechanical switching mechanisms. As such, activation may be detected based on amounts and/or types of resource usage (e.g., electric power, water, etc.). Based on activation of a device, such as an activated espresso machine, may be identified at  1608 . Sensor data originating from one or more sensors may be received at  1612 . At  1616 , a determination may be made as to whether a consumable is processed, for example, in association with a resource (e.g., electric power, water, natural gas, etc.) or a device or appliance, such as an electric-powered device. If so, flow  1600  moves to  1614 , at which sensor data associated with one or more sensors may be accessed. At  1620 , a determination is made as to whether multiple modes of operation (e.g., of a device) may be detectable. If so, a type and/or amount of one or more consumables may be correlated with at least one mode of operation (e.g., a mode of operation of a combined espresso and coffee machine in which espresso is made rather than coffee, and the “type” of consumable is “espresso” and an “amount” may be a “1.25 oz.” espresso pod). If not, a consumed amount for specific type may be correlated to at least one subset of sensor data. 
     If a consumable is not processed at  1616 , flow  1600  moves to  1618  at which an amount of a consumable that is used may be determined based on data from, for example, a usage sensor that may determine a state of a consumable. In one example, a usage sensor may include a weight monitoring device. In another example, a usage sensor may include a surface level sensor for determining a volume of liquid or solid (e.g., particulate) in a container. In some examples, usage of a consumable independent of a resource-powered or related device may be detectable by using an audio sensor or an image sensor. An audio sensor may identify usage of a consumable (e.g., audio of a knife slicing an apple into 6 pieces on a cutting board), which may be correlatable to consumption of 1 apple. Or, an image sensor may identify an activity with an optically-recognized knife and an optically-recognized apple, whereby the apple is reduced into pieces during image sensing by the image sensor. Flow  1600  then may proceed via  1622  to  1625 , at which a determination is made as to whether other sensors may provide consumption-related data. If so, a next sensor may be analyzed at  1623  to confirm or identify types or amounts of consumption. If there are no other sensors at  1625 , flow  1600  moves to update consumption rate data at  1626 , if applicable. For example, if rate of decay or degradation of a particular consumable is such that an amount of consumable may be reduced by factors other than use (e.g., evaporation or spoilage), than an amount of inventory may be reduced due to an enhanced consumption or degradation rate. 
     At  1628 , an amount of inventory for one or more consumables may be updated (e.g., reduced) by an amount and type consumed. At  1632 , a determination is made whether an amount of inventory may be compliant with a range of threshold values. If yes, then inventory is available for consumption at  1630 . In some examples, as flow  1600  passes through loop  1699  over multiple occasions, a sensor device may be “trained,” through repeated use, to more accurately correlate, and thus predict, a usage to an amount of consumption. If, at  1632 , a determination is made that an amount of inventory is not compliant with a range of threshold values, flow  1600  proceeds to  1634 , at which a replenishment of a consumable may be automatically reordered. At  1636 , an electronic message including a request to reorder a consumable may be transmitted to an adaptive distribution platform, according to some examples, whereby an adaptive distribution platform may facilitate fulfillment of a request for replenishment. In some examples, one or more (e.g., all) of the portions of flow  1600  may be performed at or with computing devices implementing an adaptive distribution platform. 
       FIG.  17    is an example of implementing a variety of sensors to determine consumption of consumables in a work space context, according to some examples. Diagram  1700  includes a mobile computing device  1791   b  and a voice-controlled speaker device  1791   c  associated with an environment including a work bench  1713 , each device being configured to communicate via wireless links with each other and remote computing devices via a network access point  1762  and/or a network  1721 . The remote computing devices may include at least a portion of an adaptive distribution platform  1710 . One or both of mobile computing device  1791   b  and voice-controlled speaker device  1791   c  may include an appliance predictor  1772 , a characterizer  1720 , a correlator  1740 , an inventory manager  1748 , and a reorder manager  1749 , any of which may be implement in hardware or software, or a combination thereof. According to some examples, elements depicted in diagram  1700  of  FIG.  17    may include structures and/or functions as similarly-named or similarly-numbered elements depicted in other drawings, such as  FIG.  9   . 
     A power-based sensor device  1702  may be configured to monitor power usage of various power tools and equipment, such as a shop vac  1722  (via cord  1728 ), a power sander  1732  (via cord  1738 ), and a mitre saw  1752  (via cord  1758 ). Power-related data may be transmitted to one or more of appliance predictor  1772 , characterizer  1720 , correlator  1740 , inventory manager  1748 , and reorder manager  1749  via link  1709   b . Usage of shop vac  1722 , power sander  1732 , and mitre saw  1752  may be determined via vibration sensors  1726 ,  1736 , and  1756 , respectively, or any other sensors that may communicate via links  1709   c ,  1709   d , and  1709   e  to one or more of appliance predictor  1772 , characterizer  1720 , correlator  1740 , inventory manager  1748 , and reorder manager  1749 . In some cases, sensor  1756  may be a position sensor configured to detect rotation or angular displacement  1755  associated with operation of mitre saw  1752 . Further, one or both of mobile computing device  1791   b  and voice-controlled speaker device  1791   c  may be configured to receive audio signals  1724 ,  1734 , and  1754  from shop vac  1722 , power sander  1732 , and mitre saw  1752 , respectively, to detect one or more modes of operation. 
     In some examples, inventory manager  1748  and reorder manager  1749  may be configured to monitor vacuum clean bag consumption and air filter consumption for shop vac  1722 , sand paper consumption for power sander  1732 , and blade sharping or replacement for mitre saw  1752 . One or both of characterizer  1720  and correlator  1740  may be configured to compare diagnostic audio patterns against audio of shop vac  1722 , power sander  1732 , and mitre saw  1752  during operation to determine whether operation is nominal or whether a power tool that may need service or replacement parts. 
     According to some examples, one or more image sensors  1711  may be used to establish an “optic zone”  1712  in which consumables may be placed therein for recordation into, for example, an inventory of work bench-related consumables, such as paint  1714  and sand paper delivered in shipping box  1715 . Image characteristics (e.g., text) may be detectable via, for example, computer vision recognition or other computer-based recognition algorithms implementing pattern recognition for digital imagery, and may include machine learning, deep-learning, or the like. In some cases, machine-readable symbols (e.g., bar codes  1716  and  1717 ) may be used to identify consumables placed in optic zone  1712 . Image data captured by image capture devices  1711  may be transmitted via wireless link  1709   a  to, for example, an adaptive distribution platform  1710  or to one or both of mobile computing device  1791   b  and voice-controlled speaker device  1791   c.    
       FIG.  18    is an example of implementing a variety of sensors to determine consumption of consumables in a kitchen space context, according to some examples. Diagram  1800  includes a mobile computing device  1891   b  and a voice-controlled speaker device  1891   c  associated with an environment including a kitchen counter  1813 , each device being configured to communicate via wireless links with each other and remote computing devices via a network access point  1862  and/or a network  1821 . The remote computing devices may include at least a portion of an adaptive distribution platform  1810 . One or both of mobile computing device  1891   b  and voice-controlled speaker device  1891   c  may include an appliance predictor  1872 , a characterizer  1820 , a correlator  1840 , an inventory manager  1848 , and a reorder manager  1849 , any of which may be implement in hardware or software, or a combination thereof. According to some examples, elements depicted in diagram  1800  of  FIG.  18    may include structures and/or functions as similarly-named or similarly-numbered elements depicted in other drawings, such as  FIGS.  9 ,  14 , and  17   . 
     A power-based sensor device  1802  may be configured to monitor power usage various kitchen appliances and devices, such as a coffee maker  1822  (via cord  1828 ), and the like. Power-related data may be transmitted to one or more of appliance predictor  1872 , characterizer  1820 , correlator  1840 , inventory manager  1848 , and reorder manager  1849  via link  1809   b . Usage of coffee maker  1822  may be determined via vibration sensors  1826  or any other sensors that may communicate via link  1809   c  to one or more of appliance predictor  1872 , characterizer  1820 , correlator  1840 , inventory manager  1848 , and reorder manager  1849 . Further, one or both of mobile computing device  1891   b  and voice-controlled speaker device  1891   c  may be configured to receive audio signals  1824 ,  1834 , and  1854  from coffee maker  1822 , a can of soda  1832  being poured into a glass, and a knife used to chop or cut through an apple  1855  to hit a cutting board  1853 . 
     In some examples, inventory manager  1848  and reorder manager  1849  may be configured to monitor ground coffee amounts and types and coffee filter consumption for coffee maker  1822 , soda consumption based on units of canned soda  1832  consumed, and apple consumption based on units of apples  1855  (i.e., 1 apple) that is shown sliced-up for consumption. One or both of characterizer  1820  and correlator  1840  may be configured to compare diagnostic audio patterns against audio of coffee maker  1822  or any other kitchen appliance during operation to determine whether operation is nominal or whether a kitchen appliance may need service or replacement parts. 
     According to some examples, one or more image sensors  1811  may be used to establish an “optic zone”  1812  on kitchen counter  1813  in which consumables may be placed therein for recordation into, for example, an inventory of kitchen-related consumables, such as pasta  1814  and tomatoes  1815 . Image characteristics (e.g., text) may be detectable via, for example, computer vision recognition or other computer-based recognition algorithms implementing pattern recognition for digital imagery. In some cases, machine-readable symbols (e.g., bar code  1816 ) may be used to identify consumables placed in optic zone  1812 . Or, image recognition may detect that objects  1815  are tomatoes. Image data captured by image capture devices  1811  may be transmitted via wireless link  1809   a  to, for example, an adaptive distribution platform  1810  or to one or both of mobile computing device  1891   b  and voice-controlled speaker device  1891   c.    
     Although the foregoing examples have been described in some detail for purposes of clarity of understanding, the above-described inventive techniques are not limited to the details provided. There are many alternative ways of implementing the above-described invention techniques. The disclosed examples are illustrative and not restrictive.