Intelligent blending and user interface

A food processor system includes a user interface including a plurality of indicators, the user interface configured to receive a user input. A monitoring device is configured to detect at least one property associated with processing one or more food items and generating at least one detection signal. A controller is configured to control operations of a controllable component based on receiving the at least one detection signal, identifying one or more types of food items based on the received at least one detection signal, activating of a first indicator of the plurality of indicators on the user interface, and determining of one or more food processing actions based at least in part on the identified one or more types of food items.

TECHNICAL FIELD

This specification describes example implementations related to blenders and, more particularly, and implementations including providing dynamic indicators regarding the controlling of food item textures being processed by blenders.

BACKGROUND

As used herein, a “food processor” is not limited to being a specific type of small appliance commonly referred to as a food processor, but rather, is a kitchen and/or laboratory appliance used to blend, mix, crush, purée, chop, grind, cut, comminute and/or emulsify food, beverages and other substances, for example, during one or more cycles, and may include, but is not limited to, a blender, a mixer, a juicer, a grinder, a frother, a micro puree machine, other types of devices for processing food, and any suitable combination of the foregoing. A food processor may include a container with a rotating blade powered by a motor. Current blenders may include a microprocessor configured to control operations of the blender related to processing food items to create blended food items such as smoothies, ice creams, or whipped cream. Existing blenders may include computer-controlled programs or recipes that implement particular operational sequences of the motor and mixing blades that are specific to particular food items. Unfortunately, such operational sequences are typically fixed and do not account for different conditions or consistencies of food item ingredients being processed, leading to variable and inconsistent outcomes in the characteristics of processed food items. Further, the processing of these food ingredients is a fragile procedure, and often, suffers from a consistent means of processing. Accordingly, there is a need for more adaptable processing of food items to ensure more consistent and accurate food item outcome conditions, such as an expected texture of the food item being processed, and a need to more readily provide users with dynamic indicators for optimizing processing based on detected textures.

SUMMARY

The application, in various implementations, addresses deficiencies associated with more accurately and consistently blending food items.

This application describes illustrative systems, methods, and devices that enable a blender to detect the values of physical properties (e.g., sense conditions) associated with processing a food item, analyze the values, activate a first indicator, and determine how to further process the food item based on the analyzed values and/or a user input via a user interface. For example, various blending ingredients to create a smoothie (e.g., the blended ingredients constituting a food item) may be added to a blender container. One or more indicators on the user interface may be activated to articulate the underlying detecting and/or identifying of the food item(s) by a controller, MC, processor, and/or MP. The blender may receive an input from a user via a user interface to process the ingredients, which may include executing a predefined processing sequence for a smoothie. The controller may then control execution of the processing, including executing computer program instructions, to automatically process the ingredients, for example, according to the predefined processing sequence. When processing the blending ingredients, i.e., mixing the ingredients, the controller may receive at least one detection signals based on power consumption of the motor sensed by one or more sensors, analyze the detection signals and, based on this analysis, adjust processing of the blending ingredients, e.g., to realize a desired and/or expected condition for the blended ingredients, e.g., a desired and/or expected texture for a smoothie. The controller may utilize machine learning (ML) and/or artificial intelligence (AI) techniques to more adaptively and accurately analyze the detected values and control production of a desired and/or expected condition for the blended ingredients. The processor may analyze other electronic signals such as, without limitation, temperature of a mixing vessel or current signals associated with a heating element and based on one or more of those signals and/or the motor signals, adjust processing of the blending ingredients.

In one aspect, a food processor system is disclosed. The food processor system may include a user interface including a plurality of indicators and configured to receive a user input. The food processor system may include other processing components including one or more heating elements. One or more processing components of the food processor system may be controllable by a controller of the food processor, for example, a motor or heating element, and may be referred to herein as controllable components. While several implementations are described herein using the example of a motor as the controllable component, it should be appreciated that the invention is not so limited, and other controllable components may be used in certain implementations, in addition to or as an alternative to a motor.

The food processor system may further include a monitoring device configured to detect at least one property associated with processing one or more food items and generating at least one detection signal. The food processor system may further include a controller configured to control operations of the controllable component. Several implementations are described herein using the example of a microprocessor and/or food analyzer as the controller, but the disclosure is not so limited. Other types of controllers can be used.

In some implementations, a controller of the food processor system may receive the at least one detection signal and identify one or more types of food items based on the received at least one detection signal. The controller may further activate a first indicator of the plurality of indicators on the user interface, the first indicator indicative of at least one of the detecting and the identifying being in progress. The controller may then determine one or more food processing actions based at least in part on the identified one or more types of food items. The controller then may control operation of the controllable component based at least in part on the one or more determined food processing actions.

The food processor system may further include a memory configured to: i) store a plurality of food item vectors, each of the plurality of food item vectors being associated with a type of food item, and ii) store a plurality of indicator configuration instructions associated with the plurality of indicators, a first indicator configuration instruction associated with detecting the at least one property and a second indicator configuration instruction associated with activating a controllable component. In some implementations, the first indicator configuration instruction includes activating the first indicator of the plurality of indicators on the user interface. In some implementations, the second indicator configuration instruction includes activating a second indicator of the plurality of indicators on the user interface.

Controlling operation of the controllable component may be based at least in part on the one or more determined food processing actions includes first receiving the user input via the user interface based on an activation of the one or more of the plurality of indicators. The user interface may include one or more static prompts. The user interface may include a rotatable dial. The rotatable dial may be configured to display the first and second indicators of the plurality of indicators. The controller may receive the user input via the user interface based on an activation of the one or more of the plurality of indicators when the user rotates the rotatable dial.

The monitoring device may activate a sensing indicator while the monitoring device detects the at least one property. A blending indicator of the plurality of indicators may be activated when the controllable component is activated. Displaying the first and second indicators of the plurality of indicators may include illuminating one or more LEDs. A blending indicator of the plurality of indicators may be deactivated when the controllable component is deactivated. The plurality of indicators may include functionality and ring indicators. The plurality of indicators may communicate controllable component processing modes, functions, intensities, lengths, and/or speeds.

Detecting at least one property associated with the processing of the one or more food items may include determining a type and/or size of the one or more components, wherein activating the plurality of indicators may be based on the determined type and/or size of the one or more components.

The controller may be further configured to identify the one or more types of food items associated with a detection vector by determining which of the plurality of food item vectors is closest to the detection vector in the multi-dimensional feature space, wherein the controller may be further configured to identify the one or more types of food items associated with the detection vector by determining the position of the detection vector in the multi-dimensional feature space with respect to positions of two or more of the plurality of food item vectors in the multi-dimensional feature space. The controller may be configured to control the operation based on applying a weight factor to each of the two or more of the first plurality of food item vectors, the weight factor being based on at least one of a distance of a food item vector from the detection vector, a frequency of determining a type of food item, and a type of container used during food processing, wherein the controller may activate the plurality of indicators based at least in part on the applied weight factor.

Another aspect includes a method for processing food items via a controllable component configured to process one or more food items including: operating the controllable component via one or more user interfaces configured to receive a user input and operably coupled to a base, wherein the one or more user interfaces include a plurality of indicators; detecting, via a monitoring device, at least one property associated with processing one or more food items and generating at least one detection signal; receiving the at least one detection signal; identifying one or more types of food items based on the received at least one detection signal; activating a first indicator of the plurality of indicators on the user interface, the first indicator indicative of at least one of the detecting and the identifying being in progress; determining one or more food processing actions based at least in part on the identified one or more types of food items; and controlling operation of the controllable component based at least in part on the one or more determined food processing actions.

In a further aspect, a non-transitory computer-readable storage medium storing instructions including a plurality of food processing instructions associated with a food processing sequence which when executed by a computer cause the computer to perform a method for processing food items using a food processor via a controllable component configured to process one or more food items, where the method includes: operating the controllable component via one or more user interfaces configured to receive a user input and operably coupled to a base, wherein the one or more user interfaces include a plurality of indicators; detecting, via a monitoring device, at least one property associated with processing one or more food items and generating at least one detection signal; receiving the at least one detection signal; identifying one or more types of food items based on the received at least one detection signal; activating a first indicator of the plurality of indicators on the user interface, the first indicator indicative of at least one of the detecting and the identifying being in progress; determining one or more food processing actions based at least in part on the identified one or more types of food items; and controlling operation of the controllable component based at least in part on the one or more determined food processing actions.

Any two or more of the features described in this specification, including in this summary section, may be combined to form implementations not specifically described in this specification.

At least part of the systems and processes described in this specification may be configured or controlled by executing, on one or more processing devices, instructions that are stored on one or more non-transitory machine-readable storage media. Examples of non-transitory machine-readable storage media include read-only memory, an optical disk drive, memory disk drive, and random access memory. At least part of the test systems and processes described in this specification may be configured or controlled using a computing system comprised of one or more processing devices and memory storing instructions that are executable by the one or more processing devices to perform various control operations.

The details of one or more implementations are set forth in the accompanying drawings and the following description. Other features and advantages will be apparent from the description and drawings, and from the claims.

DETAILED DESCRIPTION

The application, in various implementations, addresses deficiencies associated with blending one or more food items. The application includes illustrative devices, systems, and methods that enable efficient and reliable sensory features regarding the state of a food processor, such as a blender.

This application describes illustrative systems, methods, and devices that enable a blender to sense conditions associated with processing a food item and determine when the food item satisfies expected characteristics of a processed outcome of the food item. These example methods, devices, and systems may have advantages in dynamically sensing cavitation and solidification of blending ingredients in areas of the blending jar remote from the blade configuration at the bottom of the blending jar. For example, some implementations can work by sensing features from a blender within the first 15 seconds, and then identifying which data points are closest in distance. Based on which points are closest, example processes can then calculate a weighted average of the times that can be used for the program time based on what is being blended.

FIG.1is an example of a blender, a type of food processor,100. While implementations are described herein using the example of a blender, it should be appreciated that the invention is not so limited, and applies to other types of food processors. In some implementations, the blender100includes a motorized base104and a blending container and/or jar108. In use, the blending jar or container108can fit into a recess (not shown) formed in the base104. The blending jar108includes a removable lid110that fits into an open top112of the blending jar108. The blending jar108can thereby be filled with one or more food items, such as fruit ingredients106. One or more food items, as described herein, can include and/or refer to any organic substance containing nutrients, such as carbohydrates, proteins, and fats, that can be ingested by a living organism and metabolized into energy and body tissue. The base104includes an electrical motor, e.g., motor214ofFIG.2, for providing rotary power to a blade assembly102disposed within the blending jar108. In some implementations, the motor is coupled to a drive shaft116and configured to rotate the drive shaft116. The blade assembly102may also be coupled to the drive shaft116, and can be configured to process food ingredients, such as fruit ingredients106, while being rotated by the drive shaft116. The blade assembly102is a type of blade assembly that may be referred to as a stacked blade. Other types of blade assemblies, for example, a more traditional bottom blade assembly, which may rotate at higher speeds than a stacked blade assembly (e.g., a high-speed bottom blade (HSBB) blade assembly), may be used.

The blender100may be considered a traditional type of blender, which has a removable lid110at the top end of the blender jar108into which ingredients may be added to the blender jar108, where the blender jar108is coupled at its bottom end to the motorized base104. However, other types of blenders may be used, for example, a single-serve blender, which has a smaller capacity than a traditional blender, and may have a lid or cap including a blade assembly at an end of the blender jar (i.e., container or cup) through which ingredients are introduced into the blender jar before coupling the cap to it, where the blending assembly including the blender jar coupled with the cap including the blade assembly then may be flipped to couple the cap to the blending base.

Electronic controls, such as user interface212ofFIG.2, can control electrical power to the motor214which in an implementation may include one or more switches for controlling the motor214at various speeds including “off”, “low”, “medium” and “high”. In some implementations ofFIG.1, the electronic controls of food processor100may include a controller and/or microprocessor, such as controller202ofFIG.2, with memory storing pre-programmed and/or dynamic routines for controlling the motor214. This controller202may be configured to receive the first series of motor signals, determine a power consumption time-series pattern of the motor214over the first time period, identify a plurality of time-series pattern features associated with the time-series pattern, calculate a detection vector based on the plurality of time-series pattern features, compare a position of the detection vector with the positions of the plurality of known food item vectors in the multi-dimensional feature space, and/or identify a food item (e.g., nut butter or a smoothie drink) associated with the detection vector by determining which one of the plurality of known food item vectors is closest to the detection vector in the multi-dimensional feature space.

FIG.1may also include a monitoring device, such as sensor(s)206ofFIG.2, as part of food processor100, which can be configured to detect one or more physical properties associated with processing food items, for example, detect at least one of a current and voltage associated with operation of the motor, i.e., controllable component, over a first time period and output a first series of motor signals, i.e., detection signals, over the first time period. For example, the first series of motor signals may correspond to at least one property of the food item being processed. Additionally, food processor100may include a memory, such as memory (RAM)204ofFIG.2, configured to store a plurality of known food item vectors in a multi-dimensional feature space, each of the plurality of known food item vectors being associated with a type of food item. The blade assembly102can be inserted into an opening on the bottom end114of the blending jar108and secured therein. For example, blade assembly102can be secured by internal threads that engage complementary threads around the bottom end114of the blending jar108.

In some implementations ofFIG.1, the controller202, based on the identified food item derived from mixing ingredients106, continues to rotate, i.e., operate, the motor for a second period of time. In some implementations, the second period of time is between 0 seconds and 30 seconds. More specifically, the second period of time in which the controller202, based on the identified food item, continues to rotate the motor for a second period of time, can be 15 seconds. The first period of time in which the controller202determines a power consumption time-series pattern of the motor204can also be 15 seconds. In some implementations ofFIG.1, the comparison and identifying of the food item can be based on a K-NN classification. The first and/or second periods of time may be of other durations.

In some implementations ofFIG.1, the monitoring device206includes at least one of a current sensor and voltage sensor. In some implementations, the type of food item derived from ingredients106includes one of a smoothie, extract, sauce, ice cream, pudding, nut butter, whip cream, a frozen drink, another type of food item, or any suitable combination of the foregoing. It should be noted that the values of one or more physical properties associated with the processing of a food item, for example, physical properties of food item derived from ingredients106and/or blending jar108within blender100may be detected over time, and represented as time series data and/or a time series pattern based on the physical property values. In some instances, the value of a static physical property associated with the processing of a food item, for example, the capacity of the blending jar108, is also detected, and such value can be used as part of further processing as described elsewhere herein.

In some implementations, values for a plurality of features can be generated based on the time series data, and these feature values can be represented as a detection vector. As described in more detail elsewhere herein, such features may include: a standard deviation of the time series data (which as described herein represents a detected property value over a period of time) or a subset of the time series data (i.e., for values detected during a subset of the time period); an average value of the time series data or subset thereof; a value at a particular point in time during the period of time represented by the time series; a difference between a value at a first point in time and a value at a second point in time during the period of time represented by the time series data; a momentum of the data represented by the time series data data or a subset thereof; a gradient of a curve representing the time series data data or a subset thereof; other features; or any suitable combination of the foregoing.

In some implementations, a detected food item is initially classified as a class of food item based on the time series data, for example, based on one or more feature values determined therefrom; and the subsequent processing of the time series data and/or feature values is based on this initial classification, as described in more detail elsewhere herein. For example, the controller202can initially classify a food item as being a type of nut butter, or a type of dough, in which case subsequent processing is handled in certain way; whereas if the food item is initially classified as a food item that is not a type of nut butter or type of dough, subsequent processing is handled in a different way. In some implementations, controller202classifies a first subset of food item vectors as a first category of food items and controls the controllable component, e.g., motor, based at least in part on determining that the position of the detection vector in the multi-dimensional feature space is within a first area of the multi-dimensional feature space associated with the first category of food items. For example, various types of nut butter may be members of the first subset of food item vectors and, therefore, have their food item vectors in the first area of the multi-dimensional feature space, while non-nut butter food items and/or frozen drink food items may be members of a second subset of food item vectors and, therefore, have their food item vectors in a second area of the multi-dimensional feature space. The classification of a detected food item and the subsequent processing may employ any of a variety of known or later developed techniques, and may employ one or more known or later developed technologies to implement such techniques, for example, using any of a variety of ML and/or neural networks.

The controller202can be further configured to determine one or more closest food types to the detected food item based on determined feature values. For example, this determination can include the selection of a subset of multi-dimensional feature vectors based on a determined capacity of the blending jar108(e.g., a data sets for 28-ounce or 64-ounce capacity) and comparing the detection vector against the subset of multi-dimensional feature vectors. In some instances, each such feature vector represents and/or is otherwise associated with a food type, such as a margarita, type of smoothie and/or another type of food item, and such vectors may be referred to herein as “food item vectors.” Such comparison may include determining which one or more food item vectors are closest in the multi-dimensional feature space to the detection vector, e.g., who are the nearest neighbors. This determination may use any of a variety of known or later developed techniques, for example, a K-Nearest Neighbors Algorithm (KNN), and may employ one or more known or later developed technologies to implement such techniques, for example, any of a variety of neural networks. For example, controller202can identify the food type associated with the detection vector as being a a particular type of beverage, juice, frozen beverage, smoothie, butter, shake, cream, sauce, soup, frosting, whipped topping, other type of food, or any suitable combination of the foregoing.

Controller202can then determine additional controller202actions based on determine one or more closest food types, for example, add additional blending time for the detected food time. For example, an additional blending time may be associated with each food food item vector, and the additional time for the detected food item may be determined by calculating a combined (e.g., weighted average) of the additional blending times associated with the determined one or more closest food items. For example, for each of the one or more closest food items, the weight of its additional time may be proportion to the determined proximity of its food item vector to the detected vector in the multi-dimensional feature Controller202then can control an action being taken, for example, by sending one or more signals to the motor214(e.g., via a switch connected to motor214) to control the continuing of blending for the additional blending time, or stopping the motor, for example, if the additional blending time=0 seconds.

FIG.2is a block diagram of an electronic control system200of a food processor according to various implementations of the present disclosure. Control system200could represent an electronic control and/or processing system within a device such as, for example, a micro puree machine, a blender, an ice cream maker, an immersion blender, a stand mixer, or an attachment to any of such devices. Control system200may include a microcontroller, a processor, a system-on-a-chip (SoC), a client device, and/or a physical computing device and may include hardware and/or virtual processor(s). In some implementations, control system200and its elements as shown inFIG.2each relate to physical hardware and in some implementations one, more, or all of the elements could be implemented using emulators or virtual machines. Regardless, electronic control system200may be implemented on physical hardware, such as food processor100.

As also shown inFIG.2, control system200may include a user interface212, having, for example, a keyboard, keypad, touchpad, or sensor readout (e.g., biometric scanner) and one or more output devices, such as displays, speakers for audio, LED indicators, and/or light indicators. Control system200may also include communications interfaces210, such as a network communication unit that could include a wired communication component and/or a wireless communications component, which may be communicatively coupled to controller and/or processor202. The network communication unit may utilize any of a variety of proprietary or standardized network protocols, such as Ethernet, TCP/IP, to name a few of many protocols, to effect communications between processor202and another device, network, or system. Network communication units may also comprise one or more transceivers that utilize the Ethernet, power line communication (PLC), Wi-Fi, cellular, and/or other communication methods.

Control system200may include a processing element, such as controller and/or processor202, that contains one or more hardware processors, where each hardware processor may have a single or multiple processor cores. In one implementation, the processor202includes at least one shared cache that stores data (e.g., computing instructions) that are utilized by one or more other components of processor202. For example, the shared cache may be a locally cached data stored in a memory for faster access by components of the processing elements that make up processor202. Examples of processors include but are not limited to a central processing unit (CPU) and/or microprocessor. Controller and/or processor202may utilize a computer architecture base on, without limitation, the Intel® 8051 architecture, Motorola® 68HCX, Intel® 80×86, and the like. The processor202may include, without limitation, an 8-bit, 12-bit, 16-bit, 32-bit, or 64-bit architecture. Although not illustrated inFIG.2, the processing elements that make up processor202may also include one or more other types of hardware processing components, such as graphics processing units (GPUs), application specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), and/or digital signal processors (DSPs).

FIG.2also illustrates that memory204may be operatively and communicatively coupled to controller202. Memory204may be a non-transitory medium configured to store various types of data. For example, memory204may include one or more storage devices208that include a non-volatile storage device and/or volatile memory. Volatile memory, such as random-access memory (RAM), can be any suitable non-permanent storage device. The non-volatile storage devices208may include one or more disk drives, optical drives, solid-state drives (SSDs), tape drives, flash memory, read-only memory (ROM), and/or any other type memory designed to maintain data for a duration time after a power loss or shut down operation. In certain configurations, the non-volatile storage devices208may be used to store overflow data if allocated RAM is not large enough to hold all working data. The non-volatile storage devices208may also be used to store programs that are loaded into the RAM when such programs are selected for execution. Data store and/or storage devices208may be arranged to store a plurality of food processing instruction programs associated with a plurality of food processing sequences, i.e., recipes. Such food processing instruction programs may include instruction for controller and/or processor202to: start or stop one or motors214(e.g., such as the electrical motor214in base104of food processor100, as shown inFIG.1), operate the one or more motors214at certain periods during a particular food processing sequence, issue one or more cue instructions to user interface212that are output to a user to illicit a response, action, and/or input from the user.

Persons of ordinary skill in the art are aware that software programs may be developed, encoded, and compiled in a variety of computing languages for a variety of software platforms and/or operating systems and subsequently loaded and executed by processor202. In one implementation, the compiling process of the software program may transform program code written in a programming language to another computer language such that the processor202is able to execute the programming code. For example, the compiling process of the software program may generate an executable program that provides encoded instructions (e.g., machine code instructions) for processor202to accomplish specific, non-generic, particular computing functions.

After the compiling process, the encoded instructions may be loaded as computer executable instructions or process steps to processor202from storage208, from memory204, and/or embedded within processor202(e.g., via a cache or on-board ROM). Processor202may be configured to execute the stored instructions or process steps in order to perform instructions or process steps to transform the electronic control system200into a non-generic, particular, specially programmed machine or apparatus. Stored data, e.g., data stored by a data store and/or storage device208, may be accessed by processor202during the execution of computer executable instructions or process steps to instruct one or more components within control system200and/or other components or devices external to system200.

User interface212can include a display, positional input device (such as a mouse, touchpad, touchscreen, or the like), keyboard, keypad, one or more buttons, or other forms of user input and output devices. The user interface components may be communicatively coupled to processor202. When the user interface output device is or includes a display, the display can be implemented in various ways, including by a liquid crystal display (LCD) or a cathode-ray tube (CRT) or light emitting diode (LED) display, such as an OLED display.

Sensor(s)206may include one or more sensors that detect and/or monitor at least one property associated with the processing of one or more food items by system100and/or physical properties (i.e., environmental conditions) within or surrounding system100and/or200, such as within or surrounding, for example, blending container or jar108ofFIG.1. A property associated with the processing of one or more food items and/or environmental conditions may include, without limitation, rotation, speed of rotation, and/or movement of a device or component (e.g., a motor), rate of such movement, frequency of such movement, direction of such movements, temperature, pressure, motor current, position of a device or component (e.g., whether a trap door or lid is open or closed), and/or the presence of a device or component (e.g., whether a lid is connected to, for example, blending jar108ofFIG.1). Types of sensors may include, for example, electrical metering chips, electrical current and/or voltage sensors, Hall sensors, inertial measurement units (IMUs), accelerometers, gyroscopes, pressure sensors, temperature sensors, cameras, other types of sensors, or any suitable combination of the foregoing.

Sensors206may also include one or more safety and/or interlock switches that prevent or enable operation of certain components, e.g., a motor, when certain conditions are met (e.g., enabling activation of motor214when lid110is attached to container108). Persons of ordinary skill in the art are aware that electronic control system200may include other components well known in the art, such as power sources and/or analog-to-digital converters, not explicitly shown inFIG.2.

In some implementations, control system200and/or processor202includes an SoC having multiple hardware components, including but not limited to:a microcontroller, microprocessor or digital signal processor (DSP) core and/or multiprocessor SoCs (MPSoC) having more than one processor cores;memory blocks including a selection of read-only memory (ROM), random access memory (RAM), electronically erasable programmable read-only memory (EEPROM) and flash memory;timing sources including oscillators and phase-docked loops;peripherals including counter-timers, real-time timers and power-on reset generators;external interfaces, including industry standards such as universal serial bus (USB), FireWire, Ethernet, universal synchronous/asynchronous receiver/transmitter (USART), serial peripheral interface (SPI);analog interfaces including analog-to-digital converters (ADCs) and digital-to-analog converters (DACs); andvoltage regulators and power management circuits.

A SoC includes both the hardware, described above, and software controlling the microcontroller, microprocessor and/or DSP cores, peripherals and interfaces. Most SoCs are developed from pre-qualified hardware blocks for the hardware elements (e.g., referred to as modules or components which represent an IP core or IP block), together with software drivers that control their operation. The above listing of hardware elements is not exhaustive. A SoC may include protocol stacks that drive industry-standard interfaces like a universal serial bus (USB).

Once the overall architecture of the SoC has been defined, individual hardware elements may be described in an abstract language called RTL which stands for register-transfer level. RTL is used to define the circuit behavior. Hardware elements are connected together in the same RTL language to create the full SoC design. In digital circuit design, RTL is a design abstraction which models a synchronous digital circuit in terms of the flow of digital signals (data) between hardware registers, and the logical operations performed on those signals. RTL abstraction is used in hardware description languages (HDLs) like Verilog and VHDL to create high-level representations of a circuit, from which lower-level representations and ultimately actual wiring can be derived. Design at the RTL level is typical practice in modern digital design. Verilog is standardized as Institute of Electrical and Electronic Engineers (IEEE) 1364 and is an HDL used to model electronic systems. Verilog is most commonly used in the design and verification of digital circuits at the RTL level of abstraction. Verilog may also be used in the verification of analog circuits and mixed-signal circuits, as well as in the design of genetic circuits. In some implementations, various components of control system200are implemented on a printed circuit board (PCB).

FIG.3is a diagram showing an example of a workflow300of processes implemented by controller and/or processor202within the food processor100ofFIG.1. Workflow300involves processing detected values of physical property values associated with processing of a food item such as, for example, current and voltage of motor214, i.e., detection signals, over a period of time during processing of a food item, and taking one or more actions as a result of such processing. In some implementations, one or more actions include adding processing time and/or adjusting the power supplied to the controllable component, e.g., motor. The controller202can monitor the power consumption of motor214; e.g., detect current and/or voltage, during a blend cycle over an initial period (e.g., an initial 15-second period) via sensor(s)206, and generate time series data (e.g. power values), from these detected values, which can be visually represented as illustrated by graph302. In graphs302,304and306, the horizontal axis represents time and the vertical axis represents power consumption.

The time series power values form a curve303. A time=0 is the motor is initially energized—i.e., electrical current is provided to the motor. As illustrated, the power initially spikes from 0 watts to over 600 watts. This initial spike is a manifestation of a phenomena called “inrush current”, also referred to as “locked rotor current.” Inrush current is the excessive current flow experienced within a motor and its conductors during the first few moments following the energizing (switching on) of the motor. The peak value of this spike, the time it takes to reach the peak value, and the rate at which the power consumption reaches and recedes from this peak value all may be impacted by the load the food item imposes on the motor. As such, the peak value of this spike, which is also the peak value of the entire curve303, the time it takes to reach the peak value, and the rate at which the power consumption reaches and recedes from this peak value, may be indicative of the type of food being processed. Graph304illustrates a subset of the curve303of time series property values, from 0-5 seconds. Graph302illustrates a subset of the curve303of time series property values, from around 5 seconds through about 15 seconds, in which only a subset of the vertical axis is shown, representing a sub-range of the power consumption values shown in graph302and304.

As illustrated in graphs304and306ofFIG.3, controller202can generate feature values from the time series power values illustrated by curve303, including, but not limited to: a peak power consumption value (“Peak”) in the time series power values of graph302; a difference (“Drop”) between the Peak and the power consumption at a particular point in time (e.g., at which the power consumption appears to have recovered from the initial inrush current-induced spike); a standard deviation (“Stdev”) of the power consumption for a sub-period of the time period (e.g., 5 seconds-15 seconds) covered by the time series power values; power consumption (“Wattage) at a particular time within the time period (e.g., 15 seconds) represented by the time series power values; momentum of the power consumption (e.g., slope of the curve303) over a sub-period of the time period represented by the time series power values, a gradient of the curve of the time series power values over a sub-period of the the time period; and/or other feature values.

The time period of detection, features for which values are determined, and the particular times and sub-periods for which these feature values are determined, may vary, and are not limited to those illustrated and described herein. In some implementations, these parameters and their values are selected based on testing and empirical data from which the parameters that are optimal for generating feature values to distinguish between food items can be determined.

In some implementations, controller202and/or food analyzer308can classify the processed food item based on one or more of the feature values determined from the time series data, e.g., the time series property values, for example if one or more food items derived from ingredients106includes a nut-butter. Food analyzer308may be implemented as a software program or function, hardware function, firmware functions, or a combination thereof. In some implementations, controller202implements food analyzer308as computer-implemented program, function, and/or routine. This classification can occur via one or more neural networks, such as a multi-layer perception (MLP) classifier. In some implementations, this classification can include classifying the one or more food items derived from ingredients106as nut-butter or another thicker food type. Determining certain properties/features of one or more food items being processed, as illustrated in graphs302,304, and306, can further include controller and/or processor202first sensing, for example, the food processor container type, size, or other related attributes. This data can provide classification and/or categorization information, which can aid controller202and/or processor in assigning one or more actions to accommodate the relevant component. For example, controller202may instruct motor214to perform differently depending upon the size of the blending container108. For example, these actions and/or performances may include controller202directing more or less current and/or power to motor214, directing more or less current and/or power to a heating element, directing different drive shaft rotation speeds, and/or adjusting an amount of time or periods when the motor is rotating.

As shown inFIG.3, a calculation of various (e.g., 1, 2, 3, 4, 5, or more) features can be performed based on the detected time-series patterns illustrated in graphs302,304, and/or306to provide signals comprising a set of feature values. This can allow for the subsequent performance of a nearest neighbor analysis via, for example, a KNN analysis. Each of these time-series pattern conditions/properties/features may include a peak in a plot of the pattern, a drop in a plot of the pattern, a standard deviation of a plot of the pattern, and/or a steady state power consumption in a plot of the pattern, as detailed above. Again, these conditions, properties, and/or features which can allow for the example processes, can include one or more: mean(s) and/or averages, including the mean wattage to be taken over a specific time period; standard(s), including the standard deviation of the wattage to be taken over a specific time period (shown in graph306); momentum(s), including the slope of the wattage to be taken over a specific time period (shown in graph306); max(es), including the maximum wattage of the data recorded (shown in graph306); and drop(s), including percent drop from the max to steady wattage (shown in graph304). These time periods, and the definition of steady wattage, can be informed by and/or formatted to apply to several contexts, such as United State engineering standards.

This set of feature values, which can be determined via the detected time series data and/or patterns shown in graphs302,304, and306ofFIG.3can subsequently serve as inputs to food type analyzer308as one or more detection vectors. Additionally, certain properties/features of one or more food items being processed in a multi-dimensional feature space, as detailed in graphs302,304, and306, can further include controller and/or processor202first determining, for example, one or more food item details, such as associated features, vectors, and/or other related attributes. For example, a multi-dimensional feature space can include two-dimensional, as shown inFIG.4, three-dimensional, and/or four-dimensional parameters, or higher dimensional parameters. The feature values/detection vector can provide food type information based on a determination of the closest food type in the multi-dimensional feature space. For example, controller202and/or food type analyzer308can select one or more data sets of multi-dimensional feature vectors stored in memory204based on the determined capacity of the blending jar108. In other instances, the selected one or more data sets of multi-dimensional feature vectors stored in memory204may be based on other factors and/or properties relating to food processor100and/or blending jar108. This data can provide classification and/or categorization information, which can aid controller and/or processor202in assigning one or more data sets and/or subsequent actions to accommodate the associated one or more food items in, for example, blending container108.

As shown inFIG.3, controller202and/or food type analyzer308can compare the detection vector generated from the time series data against the selected data set of food item vectors in the multi-dimensional feature space. In some instances, each food item vector represents and/or is associated with a food type. For example, controller202may instruct motor214to perform differently depending upon information from the predetermined food item vector, including data set, relating to blending performance within blending container108. This may include, for example, optimizing blending time and/or speed to best blend one or more different food items according to instructions provided by the predetermined food item vector. For example, a determination of a margarita vector may result in an addition of X amount of extra blending time, whereas a determination of a tropical smoothie vector may result in an addition of Y amount of extra blending time. Food type analyzer308may include software, hardware, or a combination therefore that may implement one or more routines and/or algorithms to analyze the time series data shown in graphs302,304, and305, and detect and/or identify a food item. Food type analyzer308may be implemented as part of control system200and/or processor202. For example, the previously provided detection vector/feature values from the time series data can be represented as variables a, b, c, d, e, e.g., as coordinates for each data point, with each row representing a data point. The number of columns can be equal to the number of detection vector/feature values from the time series pattern being measured, and the number of rows can be equal to the number of data points:

[a1⋯e1⋮⋱⋮an⋯en]
In some implementations, the following array holds the program time (ti) associated with each data point (e.g., time to be added), with the same number of rows as the above data array:

According toFIG.3, once food type analyzer308of example workflow300analyzes the detection vector/feature values from the time series pattern and determines what the input(s) to the food type analyzer308are going to be, it may need to scale the data for both accuracy of the food type analyzer308and efficiency of its functions and/or code. For example, with a goal to store data points, the input can be represented as int8_t data types to save memory. In some implementations, in order for a standard scaler to best perform machine learning, the data may need to be scaled between −128 and 127. This scaling can be done with the following equation according to United State engineering standards:

xscaled=x-μs
where x is the raw detection vector data, μ is the mean value of the detection vector data, e.g., mean value of current or wattage, S is the standard deviation of the detection vector data, and Xscaledis the scaled detection vector data.
In some implementations, scaling to use int8_t may not have a dramatic effect on the results of the food type analyzer308; therefore, it may be important to ensure a new data point remains within the target range.

Additionally, inFIG.3, once the example food processor has determined the input points following sensing the data of the detection vector/feature values from the time series data and/or pattern, a determination of the distance from the detection vector and each food item vector may need to be calculated. For example, a KNN analysis may be initiated to provide a determination of one or more positions of one or more nearest neighbors in the multi-dimensional feature space in comparison with that of the position of the detection vector. This can involve multiple distance metrics; for example, based on the initial analysis, and may use either Mikowski distance or Bray-Curtis distance, as presented below:

dM=∑j=1m❘"\[LeftBracketingBar]"xj-yJ❘"\[RightBracketingBar]"pp
where j=1, m=number of iterations, xj=Object A vector point first coordinate value, yj=Object B vector point first coordinate value, p=order (via integer value) between two points, dBC=Bray-Curtis distance, and dM=Mikoswki distance. For Bray-Curtis, which can measure the distance between points A and B, if all coordinates are positive, its value is between 0 and 1. If both Objects are in the 0 coordinates, such as (0,0), however, the Bray-Curtis distance may be undefined. The normalization can be done using the absolute difference divided by the summation. For p≥1, the Minkowski distance is a metric as a result of the Minkowski inequality. When p<1, the distance between (0,0) and (1,1) is 21/p>2, but the point (0,1) is at a distance 1 from both of these points. Since this violates the triangle inequality, for p<1, it is not a metric. However, a metric can be obtained for these values by simply removing the exponent of 1/p. The resulting metric is also an F-norm.

According to the example implementation ofFIG.3, once the k closest neighbors have been determined, a program and/or routine of food type analyzer308determines its output as an action. For each determined one or more closest food types, as previously introduced, a determination of extra time for blending may be dictated. This extra time can be specified in the feature vector for the food type based on the stored multi-dimensional feature vector data. As mentioned, each datapoint may have a time associated with it, necessitating a determination of what time it will output based on, for example, a weighted average combining each of the extra times to produce the total time to be added to the blending process. For example, inFIG.3, this includes a weighted average determination310of 8 additional seconds of time to be added to the blending process. Like the distance measurement, there are several different weight functions that can be used, such as:

w=1dZ
where d=distance between two Objects/vector points, z=number of distance calculations between two respective Objects/vector points, and w=weight.
At this time, the output from food type analyzer308is returned and represented in the easternmost region of graph302of workflow300, illustrated as “Added Time.”.

In some implementations, a special sensor chip can sample, detect, and/or monitor power by sampling the voltage on a terminal/lead of the motor. A controller, such as controller and/or processor202ofFIG.2, and/or food type analyzer308can thereby acquire this sampled voltage value from sensor (2)206, e.g., a sensor chip, analyze the data, and based on this analysis, act by sending a control signal to and/or signaling a TRIAC switch that controls power input to blender motor214. The output signaling of the TRAIC switch (or another type of switch or control mechanism) can stop, start, increase or decrease the motor speed based on the control signal and/or signaling from processor202and/or food type analyzer308. That is, the analysis described herein can also include identifying one or more recipes associated with individual or aggregated food items within the blending container108, and when the nearest one or more recipes are identified, adding blend time per the determined one or more nearest recipes, as shown in control step310ofFIG.3.

FIG.4shows an illustrative multi-dimensional feature space400, containing vector points representing food item vectors such as vector points410,412,426, and428, for example, determined by food type analyzer308ofFIG.3. Specifically, for illustrative purposes, the feature space400is a two-dimensional feature space, where the horizontal axis430represents a value of a first feature and the vertical axis432represents a value of a second feature. In some implementations, some of the vector points in feature space400represent detection vectors determined by workflow300ofFIG.3via controller202and/or food type analyzer308. For example, feature space400can include detection vectors402,404,406and408, represented as points in the feature space400.

A nearest neighbor analysis, which may be similar or different to the KNN analysis discussed inFIG.3, can be performed based on the closest one or more food items (e.g., two types of smoothie) to the currently detected food item. In some implementations, controller202classifies a first subset of food item vectors as a first category of food items, e.g., nut butters, and controls the controllable component, e.g., motor214, based at least in part on determining that the position of the detection vector, e.g. detection vector point408, is within a first area416of the multi-dimensional feature space400associated with the first category of food items, e.g., nut butters. For example, various types of nut butter may be members of the first subset of food item vectors and, therefore, have their food item vectors, such as vector point428, in the first area416of the multi-dimensional feature space400, while non-nut butter food items may be members of a second subset of food items and, therefore, have their food item vectors (e.g., vector points410,426, and412) in a second area418of the multi-dimensional feature space400. The first area416and second area418may be separated by a boundary414. In some implementations, the multi-dimensional feature space400may include three or more areas associated with three or more categories of food items.

FIG.4illustrates the two-dimensional spatial relationship among food item vectors and detection vectors. For example, detection vector402is spaced away from food item vector410by distance420, spaced away from food item vector426by distance422, and spaced away from food item vector428by distance424. If, for example, food item vector410is associated with a margarita drink, then detection vector402may be identified as a vector associated with a margarita drink based on distance420being the shortest distance, i.e., based on food item vector410being the closest food item vector to detection vector402. But in some implementations, controller202and/or analyzer308may use distances from detection vector402to multiple food item vectors to identify a food item associated with detection vector402. Controller202may identify the food item associated with detection vector402based on the two closest food item vectors, based on the three closest food item vectors, or more food item vectors. Where two distances are similar, e.g., distances422and424, controller202may consider a third distance420additionally or alternatively to identify the food item associated with the detection vector such as detection vector402.

In some implementations, controller202identifies one or more types of food items associated with a detection vector by determining a position of the detection vector, e.g., detection vector402, in the multi-dimensional feature space400relative to positions of some or all of food item vectors (e.g., food item vectors410,412,426, and428), respectively, in the multi-dimensional feature space400. Controller202may determine one or more actions based at least in part on the identified one or more types of food items. Controller202may control an operation of a controllable component, e.g., motor214, based at least in part on the determined one or more actions. In some implementations, controller202determines one or more actions based at least in part on the area, e.g., area416or area418, where a detection vector is located in the feature space400. For example, detection vector406is located in area416which may be associated with a nut butter group or subset of food items, while detection vector402is located in area418which may be associated with a non-nut butter and/or drink group or subset of food items. Controller202may control an operation of a controllable component, e.g., motor214, based at least in part on the determined one or more actions associated with a group or subset of food items.

In one implementation, a microcontroller and/or microprocessor, such as controller and/or processor202, receives a series of signals from motor214from one or more sensors, such as sensor206. Processor202, via food type analyzer308, determines a power consumption timeseries pattern of the motor214over the first time period. Processor202identifies a plurality of timeseries pattern features associated with the timeseries pattern and then calculates a detection vector, e.g., detection vector402, based on the plurality of time-series pattern features. Depending upon the underlying feature values from the time series which result in detection vector402, an initial classification of food type, such as nut butter in area416or drink in area418, can include an MLP classification resulting in a KNN or non-KNN analysis. These classification events can aggregate over time to more effectively and efficiently inform additional classifications. Controller202and/or food type analyzer308compares a position of the detection vector402with the positions of some or all of the plurality of food item vectors in the multi-dimensional feature space400. Controller202and/or food type analyzer308identifies the food item associated with detection vector402by determining which one of the plurality of food item vectors is closest to detection vector402in the multi-dimensional feature space400, such as food item vector410at distance420from detection vector402. If food item vector410is associated with a smoothie, controller202and/or food type analyzer308determines that food item being processed is a smoothie. Controller202may then determine how much longer motor214and mixing blades should rotate, e.g., a second period of time. In one implementations, controller202determines the second time period based on one or more of the closest food item vectors such as, for example, food item vectors410,412,424, and/or426. In some implementations, controller202determines the second time period based on a combined weighted average of extra time depending upon one or more determinations associated with each of the food item vectors being used to identify the detection vector (e.g., food item vectors410,412,426, and428), until motor214is stopped to realize a more accurate and/or consistent smoothie.

In another instance, controller202and/or food type analyzer308receives a series of motor214signals from one or more sensors, such as sensor206. Controller202, via food type analyzer308, determines a power consumption time-series pattern and/or data set of the motor214over the first time period. Controller202identifies a plurality of time-series pattern features associated with the time-series pattern and then calculates a detection vector, e.g., detection vector404, based on the plurality of time-series pattern features. In some implementations, calculating a detection vector includes determining a time series pattern from the detected signals, with the time series pattern including a gradient of power curve, e.g., curve303. Controller202and/or food type analyzer308compares a position of the detection vector404with the positions of the plurality of known food item vectors in the multi-dimensional feature space400. Controller202and/or food type analyzer308identifies the type of food item associated with detection vector404by determining which one of the first plurality of food item vectors is closest to detection vector404in the multi-dimensional feature space400. In this instance, the closest know food item vector is vector412. If known food item vector412is associated with whip cream, controller202and/or food type analyzer308determines that food item being processed is whip cream. The processor202may then determine how much longer motor214and mixing blades should rotate, e.g., a second period of time, until motor214is stopped to realize a more accurate and/or consistent whip cream.

In some implementations, an additional series of motor signals corresponding to processing a food item can be detected to more accurately identify and/or confirm the type of food item being processed. For example, after controller202classifies a type and/or first subset of food item vectors, as a nut butter, one or more sensors, such as sensor206, may continue sensing for an additional period of time, e.g., 15 seconds, and provide an additional series of motor214signals to controller202and/or food type analyzer308. Based on analyzing this additional series of motor signals, controller202may operate motor214to rotate the mixing blades of blade assembly102for an additional period of time. These additional series of motor214signals may include a power consumption and/or motor current trend over multiple increments or periods of time, such as over multiple 100 ms time segments, that are output from sensor206and analyzed by controller202and/or food type analyzer308. Based on its analysis, controller202and/or food type analyzer308may determine and/or confirm the identity and/or classification of a food item and, thereby, determine that additional processing of the food item is necessary. This determination may be based on, for example, if the power consumption trend of motor214as detected every 100 ms trends in an increasing or decreasing direction or is greater than or equal to a threshold rate of increase or decrease, or is greater than or equal to a threshold increase from a minimum recorded value.

FIG.7show a graph700that illustrates detected increasing trends of power load detected with respect to motor214via, for example, sensor206, that controller202can analyze to confirm that a food item being processed is or is not a particular classification of food item, e.g., a type of nut butter. Such additional sensing of motor power and/or current reduces the possibility of a misclassification of a type of food item, such as for example, a nut butter. When the power consumption and/or current trend of motor214over a period of time is no longer greater than or equal to a set threshold, controller202and/or food type analyzer308may determine processing of the food item is no longer required.

In some instances, inFIG.4, when controller202detects at least one property associated with the processing of the one or more food items, such as a food item derived from ingredients106, controller202also determines a type and/or size of the one or more components, such as blending jar108. In this way, controller202may more efficiently or readily identify a food item based on understanding that certain types of food items are most often or only processed using certain types of components and/or containers. In some implementations, controller202is configured to classify the one or more food items, such as a food item in nut butter area416, based on the detection vector, such as detection vector406or408and by detecting the type of jar108being used to create the food item associated with detection vector406or408. In some implementations, controller202can be configured to control motor214based solely on the type and/or size of one of the components, such as blending jar108, based on a recognition that certain types of food items use certain types of components and/or containers when being processed.

In some implementations, controller202is configured to identify one or more types of food items, such as a type of nut butter associated with detection vector408and a type of frozen drink associated with detection vector402, based on applying a weight factor to some or all of the food item vectors in feature space400, such as food item vectors410and412. In some implementations the weight factor is based on at least one of: a distance of a food item vector from the detection vector, a type of food item associated with a food item vector, a frequency of determining a type of food item, and a type of container used during food processing, within multi-dimensional feature space400. For example, a weight factor can be measured and/or assigned on a scale of 0.0-1.0, or any other reasonable weighted scaled metric, that may be used to adjust a value of one or more features of a food item vector and/or shift the position of a food item vector in the multi-dimensional feature space400, to effect identification of the type of food item by controller202. In some implementations, each of the food item vectors can be associated with a known type of food item such as food item vector410which may be associated with a margarita drink. Further, some or all of the food item vectors may be used by controller202to identify a food item associated with a detection vector. As previously discussed, a first plurality of food items vectors can be based on retrieving data related to the one or more food items (e.g., food item vectors410,426, and428), that, based on the one or more components, can be used to identify a food item associated with a particular detection vector (e.g., detection vector402) as being associated with a margarita drink) in order to determine blending conditions, such as time period of blending by operating motor214, speed of motor214at certain time periods, temperature of a food item at certain times and/or periods, pressure in a blending and/or mixing chamber such as jar108, and so on. Each food item vector can define values for multiple features.

FIG.5is flowchart showing an example process500for monitoring, analyzing, and performing one or more actions within the food processor ofFIG.1. Process500can include processing one or more food items, involving rotating, via a motor, one or more components (Step502). For example, this can include rotating, via a motor, a draft shaft coupled to a blade assembly. The motor, drive shaft, and blade assembly can be similar to those introduced inFIG.1, such as motor214, drive shaft116, and blade assembly102, respectively. Process500can also include processing one or more food items, such as a food item derived from ingredients106, in blending container and/or jar108while the blade assembly102is being rotated by the drive shaft116(Step504). Process500can further include detecting at least one property associated with the processing of the one or more food items during a first period of time (Step506). A first series of detection signals can be generated from the at least one property detected over the first period of time. For example, a first series of detection signals can include at least one of a current and voltage, via sensor(s)208, associated with operation of motor214over a first time period. Process500can also include storing, in a memory, a first plurality of food item vectors, such as food item vectors410,412,426, and428ofFIG.4(Step508). Each food item vector can define values for a plurality of features in a multi-dimensional feature space such as feature space400. Accordingly, each of a first plurality of food item vectors, e.g., food item vectors410and420, can be associated with a type of food item. For example, food item vector410may be associated with a margarita drink, while food item vector412may be associated with whipped cream. A first series of motor signals corresponding to at least one property of a food item can be processed to generate a detection vector such as detection vector402. A plurality of known food item vectors (e.g.,410,412,426, and428) can then be stored in a memory204and storage208of system200(Step508). Controller202may classify a first subset of the food item vectors as a first category of food items (e.g., nut butter or drink) and control the controllable component, e.g., motor214, based on determining that the position of the detection vector, e.g., detection vector402, is within area418of the multi-dimensional feature space400which is associated with particular subset, group, and/or category of food items, e.g., food drink items. Any of steps502-510may be performed by a microcontroller and/or microprocessor, such as controller202ofFIG.2.

FIG.6shows a process600for identifying a type of food item and controlling operations of a controllable component based on the identification of the particular food item during processing of the food item. Process600includes operating a controllable component, by a controller202, for a first period of time (Step602). The controllable component may include a motor such as motor214that is arranged to rotate a drive shaft102and blade assembly116to mix ingredients106. The controllable component may include a heater or heating element within or adjacent to jar108that is arranged to heat a food item being processed. The controllable component may include a pump and/or valve arranged to adjust a pressure within jar108when a food item is being processed. The controllable component may include any device or component configured to affect a physical property of a food item during processing.

Process600also includes detecting, via a monitoring device such as sensor(s)206, at least one property associated with the processing of one or more food items during a first period of time, where a first series of detection signals are generated from the at least one property detected over the first period of time (Step604). Process600includes storing, in a memory such as memory204and/or data storage208, a plurality of food item vectors (e.g., food item vectors410,412,426, and428), where each food item vector defines values for a plurality of features in a multi-dimensional feature space400, such that each of the plurality of food item vectors is associated with a type of food item (Step606). Then, calculating, by controller202and/or food item analyzer308, a detection vector, e.g., detection vector402, based on the series of detection signals, where the detection vector defines feature values for a plurality of features in the multi-dimensional feature space400(Step608). Controller202and/or food item analyzer308identifies one or more types of food items associated with the detection vector, e.g., detection vector402, by determining a position of the detection vector in the multi-dimensional feature space relative to positions of one or more of a plurality of food item vectors (e.g., food item vectors410,426, and428), respectively, in the multi-dimensional feature space (400) (Step610). For example, food item vector410may be associated with a margarita drink. Food item vectors426may be associated with another type of frozen drink, while food item vector428may be associated with a peanut butter.

In one implementation, controller202may determine that detection vector402is associated with a margarita drink based on food item vector410being closest to detection vector402. Controller202may identify the type of food item associated with detection vector402based on the position of detection vector402in relation to one or more of the known food item vectors in feature space400. Controller202and/or food item analyzer308may then determine one or more actions based at least in part on the identified one or more types of food items (Step612). Controller202may control operations of the controllable component, e.g., motor214, based at least in part on the determined one or more actions. For example, the one or more actions may include controller202continuing to operate the controllable component for a second period of time based on the identified one or more types of food items. Controller202and/or analyzer308may identify a food item based at least in part on performing a K-NN analysis. Controller202may determine a how much longer motor214and one or more components, such as mixing blades, should rotate, e.g., a second period of time, until motor214is stopped to realize a more accurate and/or consistent smoothie.

In some implementations, the second period of time is between 0 seconds and 30 seconds. In some implementations, the second period of time is 15 seconds. In some implementations, the first period of time is 15 seconds. Further, identifying the food item can be based, at least in part, on a K-NN classification. Further, calculating a detection vector can include determining a time series pattern from the detection signals, where the time series pattern includes a gradient of power curve. In some implementations, the type of food item identified via processes500and600includes one of a apple-peanut-butter smoothie, beat-ginger-smoothie, chocolate-peanut-butter-oat, maple-almond-butter, cinnamon-coffee-smoothie, citrus smoothie, essentially green smoothie, triple-green smoothie, tropical smoothie, smoothie of any type, extract, sauce, ice cream, pudding, nut butter, whip cream, margarita, pomegranate-cashew berry, strawberry-banana, strawberry-limeade, and a frozen drink.

FIG.8includes an example800of a food processor base,804, which can be included in a blender, such as blender100. While implementations are described previously and now herein using the example of a blender, it should be appreciated that the invention is not so limited, and applies to other types of food processors. In some implementations, the base804is motorized, and includes base housing806. Base804can also include dial808and one or more static interfaces814, also previously referred to as user interfaces212, which can both influence the behavior and functionality of blender100. As shown inFIG.8, static interfaces814can be embedded in UI cap816, which itself can be molded around base804above base housing806. Dial808can include UI screen810and can be referred to collectively as an interface and/or user interface (UI). In some implementations, UI screen810can include one or more/plurality of indicators820, which can be represented across screen rim812or dial face818in different configurations. One or more indicators820, which includes LEDs in the present example, can also include LCDs, OLEDs, and/or other like methods of illuminating indicators. Food item data, including that previously discussed, can be stored in memory204, and can include a database and/or a lookup table. In some implementations, this database and/or lookup table can include a list of indicator configuration instructions and associated sensed property or actions via which UI screen810can utilize different configurations of one or more indicators820to inform user inputs during usage of food processor100. In this way, UI screen810can utilize the different configurations of one or more indicators820to provide accessibility, information, and/or feedback to a user of food processor base804based on food item data processed by controller202and/or food analyzer308during operation of blender100.

FIGS.9A and9Binclude examples900and950, respectively, of one or more static interfaces814and UI screen810of dial808. In example900,FIG.9Ashows one or more static interfaces814, which include prompts for a user of a blender, such as blender100. These prompts, which can be initiated by a user via one or more static interfaces814, can include power prompt902, manual prompt904, Smart Blend prompt906, mode prompt908, and pulse prompt910. In some implementations, these prompts may include buttons and/or other like elements. In some implementations, these static interfaces may include LED indicators on the prompts, such as static LED indicators912,914, and916, to illustrate an on/off state of engagement for the prompts and/or other indicators (such as ring indicators1102and functionality indicators1104) relating to food processor100. During use of food processor100, Manual prompt904, Smart Blend prompt906, mode prompt908, and pulse prompt910may thereby be engaged by a user to initiate Smart Blend Ready State1018, Manual Ready State1020, Mode Ready State1022, and Pulse Run State1028.

In example950,FIG.9Bshows one or more indicators820within UI screen810, including ring indicators1102and functionality indicators1104. As previously discussed, these indicators820can be configured as a ring around the edge of UI screen810and can include one or more LEDs. These indicators, when illuminated and/or otherwise activated in different configurations on UI screen810, can provide accessibility, information, and/or feedback to a user of food processor base804based on food item data processed by controller202and/or food analyzer308during operation of blender100. These different configurations of one or more indicators820can be further influenced by illuminated dial, Smart Blend, and iQ functions and/or be used to further represent timer/speed data on UI screen810. For reference, the term, “function” may be used interchangeably with “mode” throughout this disclosure. In some implementations, functionality indicators1104dial functions can include start/stop function952, blending function954, add liquid function956, undetectable and/or install function958, and sensing function960. In some implementations, Smart Blend functions can include disc, mince, small chop, and large chop functions962based on food item data processed by controller202and/or food analyzer308during operation of blender100. In some implementations, iQ functions can include crush, max-crush, thick mode, and dough function964, which also can be based on food item data processed by controller202and/or food analyzer308during operation of blender100. In some implementations, timer/speed data can include low, medium, and high speeds966and/or timer data968. These functions and/or data on UI screen810, which can also be referred to as prompts, may include buttons and/or other like elements. As will be discussed, these ring indicators1102and functionality indicators1104may be illuminated at various intensities, patterns, and/or color to indicate food processor100analysis, processes, and/or to provide functionality selection during usage.

FIG.10illustrates via process1000how the functions, prompts, and/or data observed and/or interacted with by a user via UI screen810can also be influenced by the one more static interface814prompts. For example, a user can turn on a food processor, such as food processor100, by pressing power at step1010, which can be initiated via power prompt902of one or more static interfaces814. At this time, for example, food processor100may be in a plugged-in off state, as shown at step1012, and illuminate one or more indicators820. In some implementations, the vessel108, also referred to as blending container and/or jar108, may not be engaged with the recess (not shown) formed in the base104. In that event, the power LED/power prompt902of one or more static interfaces814may blink 0.5 seconds on, and 0.5 seconds off, with all other LEDs and/or prompts offline. However, when vessel108is engaged with the recess (not shown) formed in the base104, the power LED/power prompt902of one or more static interfaces814may be illuminated/on, with all other LEDs and/or prompts offline.

At this time, process1000considers if one or more interlocks is engaged at step1014. In some implementations, the interlock may be positioned in the removable lid110that fits into an open top112of the blending jar108. In some implementations, the interlock can be a safety interlock, which may prevent the motor214of the blender100from being operated unless the vessel108is properly engaged with the interlock on lid110. If the interlock is not properly engaged, and the food processor100is in an uninstalled state, in some implementations, step1016provides that the timer data968LEDs will blink 0.5 seconds on, and 0.5 seconds off, with all other LEDs and/or prompts offline, except for the subsequent blinking of the install function958, which may blink 0.5 seconds on, and 0.5 seconds off. At this time, the process1000may restart, and require a user to initiate power at step1010once again. Similarly, if a user presses power at step1010and/or removes vessel108during usage of food processor100, steps1012and/or1016may initiate.

If interlock is and/or remains engaged at step1014during process1000, then food processor100may proceed with enabling a user to initiate a variety of functionalities according to a number of “ready” and “run” states, including those influenced by food item data processed by controller202and/or food analyzer308, which can also be referred to as “Smart Blend” data. This enables Smart Blend Ready State1018, which, so long the interlock remains engaged and the vessel is not removed, permits blender100to perform several run states. In some implementations, the ready states can include Smart Blend Ready State1018, Manual Ready State1020, and Mode Ready State1022. The run states can include Smart Blend Run State1024, Manual Run State1026, Pulse Run State1028, and Mode Run State1030. Mode Ready State1022and Smart Blend Ready State1018may proceed to initiate Smart Blend Run State1024, Manual Run State1026, Pulse Run State1028, and Mode Run State1030. The user, via one or more static interfaces814and dial808, can then utilize the different configurations of one or more indicators820to provide accessibility, information, and/or feedback to a user of food processor base804based on food item data processed by controller202and/or food analyzer308during operation of food processor100. At this time, one or more indicators820within UI screen810of dial808may be illuminated and/or ready for user engagement. These one or more indicators820may articulate one or more states of food processor100functionality, depending on, for example, previous activity of food processor100and/or food item data processed by controller202and/or food analyzer308during operation of food processor100.FIG.10details the various relationships between the ready and run states, and how various user inputs (engaging the start/stop functionality indicators952/1104, press/release pulse prompt910) can allow for a user to cycle between various ready and run states before a program is completed. However, in some implementations, Manual Ready State1020may only commence Manual Run State1026. Operation of food processor100may timeout if unit inactivity exceeds 180 seconds, and food processor100may turn off.

FIGS.11A and11Bshow example ready state animations1100and1125, as depicted on dial808of food processor100. In the illustrative implementations, showing Smart Blend Ready State1018, both1100and1125articulate UI flow animations via UI screen810by way of one or more indicators820. For example, animation1100depicts the illumination of one or more LEDs comprising one or more indicators820, which as discussed, manifest as perceived by a user/viewer of dial808/food processor100as an illuminated ring around the edge of UI screen810, as shown in animation1125. For clarity, one or more indicators820, when referring to those comprising the illuminated ring, can be referred to as one or more ring indicators1102. In this way, one or more indicators820, when referring to those represented within one or more ring indicators1102as different configurations of one or more indicators820, can be referred to as one or more functionality indicators1104. In example animation1125, blue ring indicators1102and start/stop functionality indicators1104are illuminated at 100%, whereas the other functionality indicators1104, in purple, are not illuminated, as the LEDs are off. As previously introduced, and shown in animation1125ofFIG.11B, these one or more functionality indicators1104can be influenced by illuminated dial, Smart Blend, and iQ functions and/or be used to further represent timer/speed data on UI screen810.

FIG.11Cshows an example sensing process1150as controller202and/or food analyzer308process food item data. When fruit ingredients106, or any organic material a user wishes to process via food processor100are present in vessel108, a user can select the Smart Blend prompt906on one or more static interfaces814to prompt the detection and collection of food item data by controller202and/or food analyzer308during step1110and according to the present disclosure. The user can then select the pulse prompt910and initiate food data processing at pulsing step1112. In some implementations, this prompts the recognition by controller202and/or food analyzer308to relate weighted average data in one or more detection vectors within vessel108collected during step1110to an appropriate time series for food processing during operation of food processor100. This can also be illustrated in Pulse Run State step1028in some implementations, as start/stop functionality indicators1104are off, timer data968counts up, and the Pulse animation begins, with all other LEDs off. When pulsing step1112concludes, one or more indicators820may become illuminated at step1114as Smart Blend Ready State1018or Manual Ready State1020are activated, depending on prior food data processed and/or user inputs, and as shown inFIG.10. More specifically, the data processing via controller202and/or food analyzer308at pulsing step1112can then illuminate one or more one or more functionality indicators1104, recommending a user to initiate blending1116, crushing1118or max crush1120, and/or thick mode1122sequences. This dynamic can optimize motor214output, and thereby, initial and/or continued processing of food ingredients in vessel108by rotation of the mixing blades of blade assembly102for the established period of time and/or intensity.

FIG.11Dshows an example ready state animation1175illuminating one or more one or more ring indicators1102, which can also occur at step1114as introduced inFIG.11C. In animation1175, the illuminated portions of the one or more ring indicators1102can change based upon automatic cycling and/or the user's selection of the processing modes via one or more functionality indicators1104. For example, when a user chooses between Smart Blend functions such as disc, mince, small chop, and large chop functions962based on food item data processed by controller202and/or food analyzer308during operation of blender100, the sections of the ring indicators1102correspond to these larger or smaller outputs. In some implementations, different colors, patterns, and/or intensity of the LEDs of the ring indicators1102can indicate the mode of the food processor100. For example, during step1114, animation1175may cycle through various colors at 0.75 second intervals, comprising 4.5 seconds/loop. These colors and/or patterns of ring indicators1102correspond with the respective title and/or color of functionality indicators1104on UI screen810, along with those discussed throughout this disclosure with like names.FIG.11Eshows that these colors and/or patterns may articulate processing modes introduced in example950, including green for Blend1152, teal for Crush1154, blue for Max-crush1156, purple for thick-mode1158, orange for dough1160, yellow for disc1162, and pink for mince, small chop, and large chop functions1164, according to some implementations.

Other ring indicators1102may be displayed, such as white for variable speed/PULSE1166, and red for error/ADD LIQUID1168, along with other errors indicating a failure of some kind and similarly resulting in red ring indicators1102. For example, if controller202and/or food analyzer308determine that more liquid is needed to be added to the contents of vessel108, motor214will cease functioning, and ADD LIQUID” indicator1168will become 100% illuminated as a functionality indicator1104on UI screen810, in addition to the LED ring indicators similarly 100% illuminated in red. All other LEDs may be off and/or display segments reading “AL”, referring to adding liquid. In some implementations, Smart Blend Ready State1018will be returned to as the default when interlock is re-engaged following the adding of more liquid to vessel108. As mentioned previously, the user must disengage interlock and re-engage within 180 seconds, or food processor100may return to off state plugged in1012. Additional errors and/or notification indicators via1168can include “ERROR 1: Runtime Protection Mode”, which may occur when food processing runtime via blade assembly102exceeds 180 seconds. In some implementations, if controller202and/or food analyzer308determine that the runtime exceeds this length, motor214will cease functioning, and static interface814and dial808become disabled. More specifically, all static interface814LEDs may blink alongside timer data968blinking “E1” at 0.25 seconds on, 0.25 seconds off, in unison, until food processor100is unplugged. In some implementations, food processor100must be unplugged to reset and cancel Runtime Protection Mode, as disengaging the interlocks and idle timeout may not reset the food processor100. However, this error may only apply to single serve interlocks.

FIGS.12A-12Dshow animation views1210,1220,1230, and1240of the Smart Blend sensing animation of step1110. Animation1210ofFIG.12Aillustrates that in some implementations, during Smart Blend sensing of vessel108's contents, animations on UI screen810include ring indicators1102as four illuminated LEDs for rotating clockwise, which can be seen in animations1220and1240. In example animations1220-1240, blue ring indicators1102and start/stop functionality indicators1104are illuminated at 100%, yellow timer data968at 10%, functionality indicator1104sensing per1110pulsing in orange at 0.5 seconds on, 0.5 seconds off, and purple functionality indicators1104not illuminated.FIG.12Cillustrates via animation1230the clockwise rotation of timer data968during sensing.FIG.12Dillustrates via animation1240that, in some implementations, during sensing, colors may fade to transition at 0.5 second intervals to next color, from dark blue, magenta, to orange, and teal, comprising 2 seconds per sequence. In some implementations, Smart Blend Ready State1018has commenced, as Smart Blend prompt906is illuminated and ring indicators1102appear as those introduced inFIGS.11A-11C. In some implementations, only the start/stop functionality indicator1104will be available to a user to commence steps1024and1112. Turning dial808may be unavailable, with all other LEDs alongside the timer data968off.

FIGS.13A-13Cshow animation views1310,1320, and1330of the Smart Blend running animation of steps1024and1112. Animation1310ofFIG.13Aillustrates that in some implementations, following sensing in step1110, food item data is processed via Smart Blend by controller202and/or food analyzer308during operation of food processor100and as discussed in steps1024and1112. Animations on UI screen810can include ring indicators1102as illuminated LEDs which turn off in a clockwise rotation, which can be seen in animations1310's three views showcasing from left to right the start of the Smart Blend program, the Smart Blend program at 50% completion, and the end of the Smart Blend program, respectively. Animations1320and1330further showcase this example implementation of the Smart Blend program. Again, in example animations1320and1330, blue ring indicators1102and start/stop functionality indicators1104are illuminated at 100%, functionality indicator1104sensing per1110pulsing in orange at 0.5 seconds on, 0.5 seconds off, and purple functionality indicators1104not illuminated. As introduced in step1024ofFIG.10, an example process according toFIGS.13A-13Cbegins with motor214operational and start/stop functionality indicators1104illuminated. In some implementations, sensing animation begins as functionality indicators1104for sensing function960pulse in orange at 0.5 seconds on, 0.5 seconds off. Once controller202and/or food analyzer308complete sensing1110, Smart Blend processing at step1112initiates. This processing includes illuminating functionality indicators1104for iQ functions including crush, max-crush, thick mode, and dough functions964may be illuminated at 100%, unless no program is detected, and standard blending is thereby initiated. In this situation, and as seen inFIG.13B, blending function954may be illuminated at 100%. Animation1330inFIG.13Cprovides that, in some implementations, the LEDs for ring indicators1102may be green to showcase the Blend1116and/or1152mode, as introduced inFIGS.11C and11E.

FIGS.14A-14Cshow animation views1410,1420, and1430of when a user, during operation of dial808of food processor100, selects and engage Mode Ready State1022and Mode Run State1030. In some implementations, if a user engages Mode prompt908to initiate Mode Ready State1022, manual and Smart Blend prompts906and910, respectively, may be off. Start/stop functionality indicators1104may be illuminated at 100%, whereas LEDs for timer data968may be off. In some situations, if Smart Blend processing determines a particular program is appropriate, such as disc, mince, small chop, and large chop functions962based on food item data processed by controller202and/or food analyzer308during operation of blender100, processing animation begins with blending function954illuminated at 100%, as shown inFIG.13B. In some implementations, these functionality indicators1104for blending function954may then pulse 0.5 seconds on, 0.5 seconds off. Animations on UI screen810can include ring indicators1102as illuminated LEDs in various clusters of static LEDs to articulate and correspond with respective functionality indicators1104and program initiation following sensing at step1110and Smart Blend processing step1112. For example, animations1410includes four views of functionality indicators1104showcasing, from left to right, Smart Blend programs for mince, small chop, large chop, and disc functions962, respectively.

FIG.14Bshows via animation1420that food processor100and/or the user may cycle between functionality indicators1104as relating to a particular function, such as mince, small chop, large chop, and disc functions962. In this way, the illuminated LED clusters via indicators1102and1104can change based upon the user's selection of the processing modes as a user may turn dial808counterclockwise to cycle from large chop to small chop to mince to disc functions, and clockwise from disc to mince to small chop to large chop, respectively. In some implementations, no continuous scrolling is available. For example, when a user chooses between, mince, large chop and small chop functions962, the ring indicators1102correspond to these larger or smaller outputs. In example animations1420inFIG.14B, blue ring indicators1102, start/stop functionality indicators1104, and currently selected Smart Blend program are illuminated at 100%, whereas functionality indicator1104for available but not-currently selected Smart Blend program are shown in green and illuminated at 20%. Again, animation1430inFIG.14Cprovides that, in some implementations, LED ring indicators1102can match the animation for corresponding default function, such as pink to showcase the LG chop/SM chop/mince mode1164, as introduced inFIG.11E. During this time, in some implementations, timer data968may be illuminated and decrease to “count down” via UI screen810to articulate the default function/mode time for each program. Alternatively, the color of ring indicators1102as shown inFIG.11Ecan be changed via turning of dial808to the corresponding iQ function color. In some implementations, if pulse prompt910of static indicators814is pressed during operation, food processor may return to a ready state, such as Smart Blend Ready State1018, Manual Ready State1020, and Mode Ready State1022. However, in some implementations, if variable speed1166is engaged while the Smart Blend program is running, the food processor may return to variable speed ready state.

FIGS.15A-15Cshow via animations1510,1520, and1530the Manual Ready State1020and subsequent animations. In example animations1520-1530inFIGS.15B-15C, blue ring indicators1102, start/stop functionality indicators1104, and currently selected Smart Blend program are illuminated at 100%, whereas functionality indicator1104for available but not-currently selected Smart Blend program are shown in green and illuminated at 20%.FIG.15Aillustrates via animations1510on UI screen810that ring indicators1102may be illuminated in a “bottom-up” pattern increasing upward via both sides to articulate and correspond with user and/or food processor100selected speed for blade assembly102operation based on communication between motor214and controller202and/or food analyzer308. For example, animations1510show 1.5 LEDs illuminated to articulate a speed of 1 (low), 11.5 LEDs illuminated for a speed of 5 (medium), and 24 LEDs illuminated for a speed of 10 (high), respectively. Exact example speed implementations can be seen via table1600inFIG.16A.FIG.15Billustrates via animations1520that functionality indicators1104for timer data968may reflect and/or be configured by a user via dial808to reflect these various speed modes, with example speeds of 1, 5, and 10, respectively. In some implementations, this may apply to when vessel108is a 72-ounce jar.FIG.15Cshows via animations1530that a user may more conveniently alternate between these modes via functionality indicators1104for low, medium, and high speed966modes. In some implementations, this may apply to when vessel108is a 24-ounce jar.

For example, if manual prompt904of static interface814is engaged by a user, blue ring indicators1102, start/stop functionality indicators1104, and manual prompt904are illuminated at 100%. As shown in animations1520ofFIG.15B, when vessel/jar108is installed, timer data968may read, “01”, and the user may turn dial808clockwise and/or counterclockwise to increase and/or decrease speed up to and down from “10”. In some implementations, ring indicators1102correspondingly grow in illumination clusters from the bottom center from 1 to grow incrementally as 10 is approached, whereas full ring indicator1102illumination occurs. If the installed vessel/jar108is 24-ounces, timer data968may reads “--” and default to “low” mode966. In this example, these LEDs may be 100% illuminated, and all other (medium and high) modes 20% illuminated. As mentioned, and shown in animations1530ofFIG.15C, the user may turn dial808clockwise to cycle from low to high, and counterclockwise to cycle from high to low. In some implementations, no continuous scrolling is available, and if a user engages manual prompt904again, nothing occurs, and all other LEDs turn off.

As discussed, table1600ofFIG.16Ashows exact example speed implementations of food processor100. Correspondingly,FIG.16Bshows via animations1650example illumination clusters and/or intensities of ring indicators1102, illustrating half and full illumination trends to satisfy the metrics articulated in table1600.

FIGS.17A-17Cshow via animations1710,1720, and1730the Manual Run State1026and subsequent animations. Animation1710ofFIG.17Aillustrates that in some implementations, during Manual Run State1026, animations on UI screen810include ring indicators1102as four illuminated LEDs for rotating clockwise, which can be seen in animations1720and1730. As shown animation1720inFIG.17B, blue ring indicators1102, timer data968, and start/stop functionality indicators1104are illuminated at 100% and purple functionality indicators1104not illuminated.FIG.17Cillustrates via animation1730the clockwise rotation of ring indicators1102during Manual Run State1026. Once a user has initiated the example sequences of Manual Ready State1024, and has decided for themselves simply to non-specifically process/blend the contents of vessel108, they may select their preferred speed animation per timer data968, which proceeds manually as described inFIGS.15A-16B. Again, when vessel/jar108is installed, timer data968may read, “01”, and the user may turn dial808clockwise and/or counterclockwise to increase and/or decrease speed up to and down from “10”. In some implementations, ring indicators1102correspondingly grow in illumination clusters from the bottom center from 1 to grow incrementally as 10 is approached, whereas full ring indicator1102illumination occurs. If the installed vessel/jar108is 24-ounces, timer data968may reads “--” and default to “low” mode966. In this example, these LEDs may be 100% illuminated, and all other (medium and high) modes 20% illuminated. The user may turn dial808clockwise to cycle from low to high, and counterclockwise to cycle from high to low. They may then select the start/stop functionality indicator to begin food processing, as shown in animation1720ofFIG.17B. As shown in animations1730ofFIG.17C, the countdown of timer data968may be articulated in clockwise rotating ring indicators1104, rotating every 0.5 seconds for a 2 second full cycle around the face of UI screen810. In some implementations, no continuous scrolling is available, and if a user engages Smart Blend prompt906and/or pulse prompt910while running, food processor100via UI screen810returns once more to at least one of the aforementioned Ready States, such as Smart Blend Ready State1018, Manual Ready State1020, and Mode Ready State1022. Elements of a computer include one or more processors for executing instructions and one or more storage area devices for storing instructions and data. Generally, a computer will also include, or be operatively coupled to receive data from, or transfer data to, or both, one or more machine-readable storage media, such as mass storage devices for storing data, such as magnetic, magneto-optical disks, or optical disks. Non-transitory machine-readable storage media suitable for embodying computer program instructions and data include all forms of non-volatile storage area, including by way of example, semiconductor storage area devices, such as EPROM (erasable programmable read-only memory), EEPROM (electrically erasable programmable read-only memory), and flash storage area devices; magnetic disks, such as internal hard disks or removable disks; magneto-optical disks; and CD-ROM (compact disc read-only memory) and DVD-ROM (digital versatile disc read-only memory).

FIG.18is flowchart showing an example process1800for monitoring, identifying, determining, and performing one or more actions within the food processor ofFIG.10. Process1800can include operating the controllable component via one or more user interfaces configured to receive a user input and operably coupled to a base (Step1802). For example, the one or more user interfaces can include a plurality of indicators, such as static indicators814and/or plurality of indicators820. The base, controllable component, and user interfaces can be similar to those introduced inFIGS.1and/or8, such as base804, motor214, drive shaft116, blade assembly102, user interfaces212, dial808, and one or more static interfaces814, respectively.

Process1800can also include detecting, via a monitoring device, at least one property associated with processing one or more food items and generating at least one detection signal (Step1804). For example, the monitoring device can include sensor(s)206ofFIG.2. Detecting at least one property associated with the processing of the one or more food items may include controller202and/or food analyzer308monitoring, sensing, and processing food item data derived from ingredients106, with controller202and/or food analyzer308also determining a type and/or size of the one or more components, such as blending jar108.

As introduced previously, at least one property associated with processing one or more food items may involve controller202and/or food analyzer308communicating with memory204, which may store a plurality of food item vectors, such as food item vectors410,412,426, and428ofFIG.4. Each food item vector can define values for a plurality of features in a multi-dimensional feature space, such as feature space400. Accordingly, each of plurality of food item vectors, e.g., food item vectors410and420, can be associated with a type of food item. For example, food item vector410may be associated with a margarita drink, while food item vector412may be associated with whipped cream. A series of motor signals corresponding to at least one property of a food item can be processed to generate a detection vector such as detection vector402. A plurality of known food item vectors (e.g.,410,412,426, and428) can then be stored in a memory204and storage208of system200. Controller202and/or food analyzer308may classify a first subset of the food item vectors as a first category of food items (e.g., nut butter or drink) and control the controllable component, e.g., motor214, based on determining that the position of the detection vector, e.g., detection vector402, is within area418of the multi-dimensional feature space400which is associated with particular subset, group, and/or category of food items, e.g., food drink items. At least one detection signal can include at least one of a current and voltage, via sensor(s)208, associated with operation of motor214, and can be based on the at least one property associated with processing one or more food items. Controller202and/or food analyzer308can then receive the at least one detection signal (Step1806).

Controller202and/or food analyzer308can then identify a type of food item and controlling operations of a controllable component based on received at least on detection signal (Step1808). The controllable component may include a motor such as motor214that is arranged to rotate a drive shaft102and blade assembly116to mix ingredients106. The controllable component may include a heater or heating element within or adjacent to jar108that is arranged to heat a food item being processed. The controllable component may include a pump and/or valve arranged to adjust a pressure within jar108when a food item is being processed. The controllable component may include any device or component configured to affect a physical property of a food item during processing.

Process1800also includes activating a first indicator of the plurality of indicators on the user interface, the first indicator indicative of at least one of the detecting and the identifying being in progress (Step1810). In some implementations, the first indicator includes the illumination of static LED indicators912,914,916, ring indicators1102, and/or functionality indicators1104. Controller202and/or food item analyzer308may then one or more food processing actions based at least in part on the identified one or more types of food items (Step1812). Controller202and/or food analyzer308may control operations of the controllable component, e.g., motor214, based at least in part on the determined one or more actions (Step1814). For example, the one or more actions may include controller202operating the controllable component by way of a particular processing mode, such as Blend1152, for a period of time, and/or speed based on the identified one or more types of food items. In some implementations, this may involve the activation of a second indicator of the plurality of indicators on the user interface. In some implementations, controller202and/or food analyzer308may continue monitoring and detecting at least one property associated with processing the one or more food items, generating subsequent detection signals, and determining subsequent food processing actions, such as mixing blades, should rotate, e.g., a second period of time, until motor214is stopped to realize a more accurate and/or consistent smoothie. Any of steps1802-1814may be performed by a microcontroller and/or microprocessor, such as controller202and/or food analyzer308ofFIGS.2and3, respectively.

Elements of different implementations described may be combined to form other implementations not specifically set forth previously. Elements may be left out of the systems described previously without adversely affecting their operation or the operation of the system in general. Furthermore, various separate elements may be combined into one or more individual elements to perform the functions described in this specification.

Other implementations not specifically described in this specification are also within the scope of the following claims.