Smart stirring and user interface

Abstract

A food processor system includes a user interface including a plurality of indicators, the user interface configured to receive a user input. The monitoring device is configured to detect at least one characteristic associated with processing one or more food products and generate at least one detection signal. The controller is configured to, based on receiving the at least one detection signal, identify one or more food types based on the received at least one detection signal, activate a first indicator of the plurality of indicators on the user interface, and determining one or more food processing actions based at least in part on the identified one or more food types to control operation of the controllable component.

Description

Technical Field

[0001] This specification describes example embodiments related to blenders, and more particularly, describes embodiments that include providing dynamic indicators for controlling the texture of food being processed by the blender. Background Art

[0002] As used herein, "food processor" is not limited to a small appliance of a specific type commonly referred to as a food processor, but a kitchen and / or laboratory appliance for stirring, mixing, crushing, pounding, chopping, grinding, cutting, crushing and / or emulsifying food, beverages and other substances, such as 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-purée, other types of devices for processing food, and any suitable combination of the above. A food processor may include a container with a rotating blade powered by a motor. Current blenders may include a microprocessor configured to control the operation of the blender related to processing food to produce a blended food, such as a smoothie, ice cream or whipped cream. Existing blenders may include a computer-controlled program or recipe that implements a specific sequence of operations of a motor and a mixing blade specific to a specific food. Unfortunately, this sequence of operations is usually fixed and does not take into account the different conditions or consistencies of the food ingredients being processed, resulting in variable and inconsistent results in the characteristics of the processed food. In addition, the processing of these food ingredients is a fragile process and often lacks consistent processing means. Therefore, there is a need for more adaptive processing of food products to ensure more consistent and accurate food product outcome conditions, such as the expected texture of the food product being processed, and to more easily provide users with dynamic indicators for optimizing processing based on the detected texture. Summary of the invention

[0003] In various embodiments, the present application addresses deficiencies associated with more accurately and consistently blending food products.

[0004] The present application describes illustrative systems, methods, and devices that enable a blender to detect physical property values ​​associated with processing food (e.g., sensed conditions), analyze these values, activate a first indicator, and determine how to further process the food based on the analyzed values ​​and / or user input via a user interface. For example, various blending ingredients (e.g., blending ingredients constituting food) for making a smoothie can be added to a blender container. One or more indicators on the user interface can be activated to illustrate basic detection and / or identification of food by a controller, MC, processor, and / or MP. The blender can receive input from a user via a user interface to process ingredients, which can include executing a predefined processing sequence for a smoothie. Then, the controller can control the execution of the processing (including executing computer program instructions) to automatically process ingredients, for example, according to a predefined processing sequence. When processing blending ingredients (i.e., mixing ingredients), the controller can receive at least one detection signal based on the power consumption of a motor sensed by one or more sensors, analyze the detection signal, and based on the analysis, adjust the processing of the blending ingredients, for example, to achieve the desired and / or expected conditions of the blending ingredients, for example, the desired and / or expected texture of the 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 the generation of desired and / or expected conditions for blending ingredients. The processor may analyze other electronic signals, such as but not limited to the temperature of the mixing container or a current signal associated with a heating element, and adjust the processing of the blended ingredients based on one or more of these signals and / or motor signals.

[0005] 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 user input. The food processor system may include other processing components including one or more heating elements. The one or more processing components of the food processor system may be controlled by a controller (e.g., a motor or a heating element) of the food processor and may be referred to herein as controllable components. Although several embodiments are described herein using motors as examples of controllable components, it should be understood that the present invention is not limited thereto, and in addition to or as an alternative to motors, other controllable components may be used in certain embodiments.

[0006] The food processor system may also include a monitoring device configured to detect at least one characteristic associated with processing one or more foods and generate at least one detection signal. The food processor system may also include a controller configured to control the operation of the controllable components. Several embodiments are described herein using a microprocessor and / or a food analyzer as examples of controllers, but the disclosure is not limited thereto. Other types of controllers may be used.

[0007] In some embodiments, a controller of a food processor system may receive at least one detection signal and identify one or more food types based on the at least one detection signal received. The controller may further activate a first indicator of a plurality of indicators on a user interface, the first indicator indicating that at least one of detection and identification is in progress. The controller may then determine one or more food processing actions based at least in part on the identified one or more food types. The controller may then control the operation of the controllable component based at least in part on the determined one or more food processing actions.

[0008] The food processor system may also include a memory configured to: i) store a plurality of food vectors, each of the plurality of food vectors being associated with a food type, 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 characteristic, and a second indicator configuration instruction associated with activating a controllable component. In some embodiments, the first indicator configuration instruction includes activating a first indicator of the plurality of indicators on a user interface. In some embodiments, the second indicator configuration instruction includes activating a second indicator of the plurality of indicators on the user interface.

[0009] Controlling the operation of the controllable component based at least in part on the determined one or more food processing actions includes first receiving user input via a user interface based on activation of one or more of the multiple 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 a first indicator and a second indicator of the multiple indicators. When the user rotates the rotatable dial, the controller may receive user input via the user interface based on activation of one or more of the multiple indicators.

[0010] The monitoring device may activate the sensing indicator when the monitoring device detects at least one characteristic. When the controllable component is activated, a stirring indicator among the plurality of indicators may be activated. Displaying a first indicator and a second indicator among the plurality of indicators may include illuminating one or more LEDs. When the controllable component is deactivated, a stirring indicator among the plurality of indicators may be deactivated. The plurality of indicators may include function and ring indicators. The plurality of indicators may convey the controllable component processing mode, function, intensity, length and / or speed.

[0011] Detecting at least one characteristic associated with processing of the one or more food products 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.

[0012] The controller may be further configured to identify one or more food types associated with the detection vector by determining which of a plurality of food vectors in the multidimensional feature space is closest to the detection vector, wherein the controller may be further configured to identify the one or more food types associated with the detection vector by determining a position of the detection vector in the multidimensional feature space relative to a position of two or more of the plurality of food vectors in the multidimensional feature space. The controller may be configured to control operation based on applying a weighting factor to each of two or more of the first plurality of food vectors, the weighting factor being based on at least one of a distance of the food vector from the detection vector, a frequency of determining the food type, and a type of container used during food processing, wherein the controller may activate a plurality of indicators based at least in part on the applied weighting factor.

[0013] On the other hand, a method for processing food via a controllable component configured to process one or more food items comprises: operating the controllable component via one or more user interfaces configured to receive user input and operably coupled to a base, wherein the one or more user interfaces include a plurality of indicators; detecting at least one characteristic associated with processing the one or more food items via a monitoring device and generating at least one detection signal; receiving at least one detection signal; identifying one or more food types based on the at least one detection signal received; activating a first indicator of a plurality of indicators on the user interface, the first indicator indicating that at least one of detection and identification is in progress; determining one or more food processing actions based at least in part on the identified one or more food types; and controlling the operation of the controllable component based at least in part on the determined one or more food processing actions.

[0014] In another aspect, a non-transitory computer-readable storage medium storing instructions, the instructions including a plurality of food processing instructions associated with a food processing sequence, the plurality of food processing instructions, when executed by a computer, causing the computer to perform a method for processing food using a food processor via controllable components configured to process one or more foods, wherein the method comprises: operating the controllable components via one or more user interfaces configured to receive user input and operably coupled to a base, wherein the one or more user interfaces include a plurality of indicators; detecting at least one characteristic associated with processing one or more foods via a monitoring device and generating at least one detection signal; receiving at least one detection signal; identifying one or more food types based on the at least one detection signal received; activating a first indicator of a plurality of indicators on the user interface, the first indicator indicating that at least one of detection and identification is in progress; determining one or more food processing actions based at least in part on the identified one or more food types; and controlling the operation of the controllable components based at least in part on the determined one or more food processing actions.

[0015] Any two or more of the features described in this specification (including this summary) may be combined to form embodiments not specifically described in this specification.

[0016] At least a portion of the systems and processes described in this specification may be configured or controlled by executing instructions stored on one or more non-transitory machine-readable storage media on one or more processing devices. Examples of non-transitory machine-readable storage media include read-only memory, optical drives, memory disk drives, and random access memory. At least a portion 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 a memory storing instructions executable by the one or more processing devices to perform various control operations.

[0017] The details of one or more embodiments are set forth in the accompanying drawings and the description below. Other features and advantages will be apparent from the description and drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 is a perspective view of an example food processor according to some embodiments;

[0019] Figure 2 According to some embodiments Figure 1 A block diagram of an example of a system architecture within a food processor;

[0020] Figure 3 is a diagram showing that according to some embodiments Figure 1 A diagram of an example of a process workflow within a food processor;

[0021] Figure 4 is a diagram illustrating an example of a multi-dimensional feature space of a food product according to some embodiments;

[0022] Figure 5 is a diagram illustrating a method for monitoring, analyzing, and executing Figure 1 A flowchart of an example of a process of one or more actions within a food processor;

[0023] Figure 6 is a flow chart illustrating food processing via controllable components configured to process one or more food products according to some embodiments; and

[0024] Figure 7 is a graph showing an increasing trend of a power load detected to confirm whether a food being processed is a specific food classification according to an example of some embodiments;

[0025] Figure 8is a perspective view of an example food processor base according to some embodiments;

[0026] Fig.9A and Fig. 9B According to some embodiments Figure 8 perspective view of an example user interface (UI) within a food processor;

[0027] Fig.10 is a diagram showing a user according to some embodiments Fig.9A and Fig. 9B A flowchart of an example process of initiating one or more actions by an example user interface within a food processor;

[0028] Fig.11A and Fig. 11B According to some embodiments of the present invention Fig.10 A perspective animated view of an example dial UI for the smart blending ready state within a food processor;

[0029] Fig. 11C is a diagram showing that according to some embodiments Fig.10 A flowchart of an example process for a food processor to sense, analyze, and output one or more food processing suggestions;

[0030] Fig.11D According to some embodiments, Fig.10 An example dial UI for a food processor illustrating a perspective animated view of a corresponding food processing mode;

[0031] Fig.11E including according to some embodiments Fig.11D corresponding color / mode indicators related to the food processing mode;

[0032] FIG. 12A to FIG. 12D According to some embodiments of the present invention Fig.10 A perspective animated view of an example dial UI for sensing food data within a food processor;

[0033] FIG. 13A to FIG. 13C According to some embodiments of the present invention Fig.10 A perspective animated view of an example dial UI for the smart blending operation state in a food processor;

[0034] FIG. 14A to FIG. 14C According to some embodiments of the present invention Fig.10 A perspective animated view of the sample dial UI for the ready and running states of the mode within the food processor;

[0035] FIG. 15A to FIG. 15C According to some embodiments of the present invention Fig.10A perspective animated view of the sample dial UI for the manual ready state within a food processor;

[0036] Fig.16A According to some embodiments, Fig.10 Example table of number and intensity of ring indicators in a food processor related to timer speed data;

[0037] Fig. 16B Including according to some embodiments Fig.10 An example of a food processor dial UI illustrated within Fig.16A A perspective animation view of the corresponding food processing speed;

[0038] 17A to 17C According to some embodiments of the present invention Fig.10 a perspective animated view of an exemplary dial UI for a manual operation state within a food processor; and

[0039] Fig.18 is shown in accordance with some embodiments via a Fig.10 Flow chart of food processing of one or more food products in a food processor with controllable components

[0040] The same reference numbers in different drawings denote the same elements. DETAILED DESCRIPTION

[0041] In various embodiments, the present application addresses deficiencies associated with blending one or more food products. The application includes illustrative devices, systems, and methods that enable efficient and reliable sensory characterization of the status of a food processor, such as a blender.

[0042] The present application describes illustrative systems, methods, and devices that enable a blender to sense conditions associated with processing food and determine when the food meets the expected characteristics of the processing results of the food. These example methods, devices, and systems may have advantages in dynamically sensing the cavitation and solidification of blended ingredients in a blending tank in an area away from the blade configuration at the bottom of the blending tank. For example, some embodiments may work by sensing features from the blender within the first 15 seconds and then identifying which data points are closest in distance. Based on which points are closest, the example process may then calculate a weighted average of the time available for program time based on the blending content.

[0043] Figure 1is an example of a blender 100 (hereinafter also referred to as a food processor 100) as a type of food processor. Although the example of a blender is used herein to describe the embodiments, it should be understood that the present invention is not limited thereto and is applicable to other types of food processors. In some embodiments, the blender 100 includes a motorized base 104 and a blending container and / or blending jar 108. When in use, the blending jar or blending container 108 can be fitted into a recess (not shown) formed in the base 104. The blending jar 108 includes a removable lid 110 that fits into an open top 112 of the blending jar 108. Thus, the blending jar 108 can be filled with one or more food products such as fruit ingredients 106. As described herein, the one or more food products may include and / or refer to any organic substance containing nutrients such as carbohydrates, proteins, and fats that can be ingested by living organisms and metabolized into energy and body tissue. The base 104 includes a motor, such as Figure 2 The motor 214 is used to provide rotational power to the blade assembly 102 disposed in the blending jar 108. In some embodiments, the motor is coupled to the drive shaft 116 and is configured to rotate the drive shaft 116. The blade assembly 102 may also be coupled to the drive shaft 116 and may be configured to process ingredients, such as fruit ingredients 106, while being rotated by the drive shaft 116. The blade assembly 102 is a type of blade assembly that may be referred to as a stacked blade. Other types of blade assemblies may be used, such as a more conventional bottom blade assembly (e.g., a high-speed bottom blade (HSBB) blade assembly) that can rotate at a higher speed than a stacked blade assembly.

[0044] The blender 100 may be considered a conventional type of blender having a removable lid 110 at a top end of a blending jar 108 into which ingredients may be added, wherein the blending jar 108 is coupled at its bottom end to a motorized base 104. However, other types of blenders may be used, such as single-serving blenders that have a smaller capacity than conventional blenders and may have a lid or cover including a blade assembly at the end of a blending jar (i.e., a container or cup) through which ingredients are introduced into the blending jar and then the cover is coupled to the blending jar, wherein the blending assembly including the blending jar coupled to the cover including the blade assembly may then be flipped over to couple the cover to a blending base.

[0045] Electronic controls, such as Figure 2 The user interface 212 can control power to the motor 214, which in an embodiment can include one or more switches for controlling the motor 214 at various speeds including "off", "low", "medium" and "high". Figure 1 In some embodiments, the electronic controls of the food processor 100 may include a controller and / or a microprocessor, such as Figure 2A controller (or processor) 202, wherein a memory stores preprogrammed and / or dynamic routines for controlling a motor 214. This controller 202 may be configured to: receive a first series of motor signals, determine a time series pattern of power consumption of the motor 214 over a 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 location of the detection vector with locations of the plurality of known food vectors in the multidimensional feature space, and / or identify a food (e.g., a nut butter or a smoothie drink) associated with a detection vector by determining which food vector of the plurality of known food vectors is closest to the detection vector in the multidimensional feature space.

[0046] Figure 1 Monitoring devices may also be included, e.g. Figure 2 A sensor 206 as part of the food processor 100 can be configured to detect one or more physical characteristics associated with processing food, for example, to detect at least one of a current and a voltage associated with operation of a motor, i.e., a controllable component, during a first time period, and to output a first series of motor signals, i.e., detection signals, during the first time period. For example, the first series of motor signals can correspond to at least one characteristic of the food being processed. In addition, the food processor 100 can include a memory, such as Figure 2 A memory (RAM) 204 is configured to store a plurality of known food vectors in a multi-dimensional feature space, each of the plurality of known food vectors being associated with a food type. The blade assembly 102 can be inserted into an opening on the bottom end 114 of the blending jar 108 and secured therein. For example, the blade assembly 102 can be secured by internal threads that engage complementary threads around the bottom end 114 of the blending jar 108.

[0047] exist Figure 1 In some embodiments, based on the identified food from the mixed food 106, the controller 202 continues to rotate, i.e., operate the motor, for a second time period. In some embodiments, the second time period is between 0 seconds and 30 seconds. More specifically, the second time period in which the controller 202 continues to rotate the motor for a second time period based on the identified food can be 15 seconds. The first time period in which the controller 202 determines the power consumption time series pattern of the motor 214 can also be 15 seconds. Figure 1 In some embodiments, the comparison and identification of the food products may be based on K-NN classification. The first and / or second time period may have other durations.

[0048] exist Figure 1In some embodiments, the monitoring device 206 includes at least one of a current sensor and a voltage sensor. In some embodiments, the type of food derived from the ingredient 106 includes one of a smoothie, an extract, a sauce, an ice cream, a pudding, a nut butter, a whipped cream, a frozen drink, another type of food, 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 the food, for example, the values ​​of the physical properties of the food derived from the ingredient 106 and / or the blending tank 108 within the blender 100, can be detected over time and represented as time series data and / or a time series pattern based on the values ​​of the physical properties. In some cases, the values ​​of static physical properties associated with the processing of the food, such as the capacity of the blending tank 108, are also detected, and such values ​​can be used as part of further processing described elsewhere herein.

[0049] In some embodiments, values ​​of multiple features may be generated based on the time series data, and these feature values ​​may be represented as detection vectors. As described in more detail elsewhere herein, such features may include: standard deviation of the time series data (which, as described herein, represents characteristic values ​​detected over a certain time period) or a subset of the time series data (i.e., for values ​​detected during the subset of the time period); mean value of the time series data or its subset; value at a particular time point during the time period represented by the time series data; difference between a value at a first time point and a value at a second time point during the time period represented by the time series data; momentum of the data represented by the time series data or its subset; gradient of a curve representing the time series data or its subset; other features; or any suitable combination of the above.

[0050] In some embodiments, the detected food is initially classified as a type of food based on the time series data, for example, based on one or more feature values ​​determined therefrom; and 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 controller 202 may initially classify the food as a nut butter type or a paste type, in which case subsequent processing is performed in a certain manner; whereas if the food is initially classified as a food that is not a nut butter type or a paste type, subsequent processing is performed in a different manner. In some embodiments, the controller 202 classifies a first subset of food vectors into a first food category, and controls the controllable component, such as a motor, based at least in part on determining that the position of the detection vector in the multidimensional feature space is within a first region associated with the first food category in the multidimensional feature space. For example, various types of nut butters may be members of a first subset of food vectors, and therefore have their food vectors in a first region of the multidimensional feature space, while non-nut butter foods and / or frozen drink foods may be members of a second subset of food vectors, and therefore have their food vectors in a second region of the multidimensional feature space. Classification and subsequent processing of detected food products may employ any of a variety of known or later developed techniques, and such techniques may be implemented using one or more known or later developed techniques, for example, using any of a variety of ML and / or neural networks.

[0051] The controller 202 may be further configured to determine one or more food types that are closest to the detected food based on the determined feature values. For example, this determination may include selecting a subset of the multidimensional feature vectors based on the determined capacity of the blending jar 108 (e.g., a data set of 28 ounces or 64 ounces of capacity), and comparing the detection vector with the subset of the multidimensional feature vectors. In some cases, each such feature vector represents a food type and / or is otherwise associated with a food type, such as a margarita, a smoothie type, and / or another food type, and such vectors may be referred to herein as "food vectors". Such comparisons may include determining which one or more food vectors are closest to the detection vector in the multidimensional feature space, such as determining who is the nearest neighbor. This determination may use any of various known or later developed techniques, such as the K-nearest neighbor algorithm (KNN), and one or more known or later developed techniques may be used to implement such techniques, such as any of various neural networks. For example, the controller 202 may identify the food type associated with the detection vector as a particular type of beverage, juice, frozen drink, smoothie, butter, milkshake, cream, sauce, soup, frosting, whipped cream, other types of food, or any suitable combination of the foregoing.

[0052] The controller 202 may then determine additional controller 202 actions based on the determined one or more closest food types, such as adding additional stirring time to the detected food time. For example, additional stirring time may be associated with each food vector, and the additional time for the detected food may be determined by calculating a combination (e.g., a weighted average) of the additional stirring times associated with the determined one or more closest foods. For example, for each of the one or more closest foods, the weight of its additional time may be proportional to the determined proximity of its food vector to the detected vector in the multidimensional feature. The controller 202 may then control the action being taken, such as by sending one or more signals to the motor 214 (e.g., via a switch connected to the motor 214), to control continued stirring during the additional stirring time, or, for example, if the additional stirring time = 0 seconds, stop the motor.

[0053] Figure 2 is a block diagram of an electronic control system 200 for a food processor according to various embodiments of the present disclosure. The control system 200 may represent an electronic control and / or processing system within a device, such as a micro-purée, blender, ice cream maker, immersion blender, stand mixer, or any accessory for such a device. The control system 200 may 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 processors. In some embodiments, such as Figure 2 As shown in , the control system 200 and its elements each involve physical hardware, and in some embodiments, one, more or all elements can be implemented using an emulator or a virtual machine. Regardless, the electronic control system 200 can be implemented on physical hardware, such as the food processor 100.

[0054] Likewise Figure 2 As shown in , the control system 200 may include a user interface 212, which has, for example, a keyboard, keypad, touch pad or sensor readout (e.g., a biometric scanner) and one or more output devices, such as a display, a speaker for audio, an LED indicator, and / or a light indicator. The control system 200 may also include a communication interface 210, which may include a network communication unit of wired communication components and / or wireless communication components, which may be communicatively coupled to the controller and / or processor 202. The network communication unit may utilize any of a variety of proprietary or standardized network protocols, such as Ethernet, TCP / IP (to name some of the many protocols) to enable communication between the processor 202 and another device, network, or system. The network communication unit may also include one or more transceivers utilizing Ethernet, power line communication (PLC), Wi-Fi, cellular, and / or other communication methods.

[0055] The control system 200 may include a processing element, such as a controller and / or processor 202, which includes one or more hardware processors, each of which may have a single or multiple processor cores. In one embodiment, the processor 202 includes at least one shared cache that stores data (e.g., computing instructions) utilized by one or more other components of the processor 202. For example, a shared cache may be a local cache of data stored in a memory for faster access by components that make up the processing element of the processor 202. Examples of processors include, but are not limited to, a central processing unit (CPU) and / or a microprocessor. The controller and / or processor 202 may utilize a processor based on, but not limited to, 8051 architecture, 68HCX, 80X86 or other computer architectures. Processor 202 may include, but is not limited to, 8-bit, 12-bit, 16-bit, 32-bit, or 64-bit architectures. Figure 2 Although not shown, the processing elements making up processor 202 may also include one or more other types of hardware processing components, such as a graphics processing unit (GPU), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), and / or a digital signal processor (DSP).

[0056] Figure 2 It is also shown that the memory 204 can be coupled to the controller 202 in an operational manner and in a communication manner. The memory 204 can be a non-transitory medium configured to store various types of data. For example, the memory 204 may include one or more storage devices 208, which include non-volatile storage devices and / or volatile memory. Volatile memory such as random access memory (RAM) can be any suitable non-permanent storage device. The non-volatile storage device 208 may include one or more disk drives, optical drives, solid-state drives (SSDs), tape drives, flash memories, read-only memories (ROMs) and / or any other type of memory designed to maintain data for a certain duration after a power-off or shutdown operation. In some configurations, if the allocated RAM is not enough to accommodate all working data, the non-volatile storage device 208 can be used to store overflow data. The non-volatile storage device 208 can also be used to store programs that are loaded into the RAM when such programs are selected for execution. The data storage area and / or storage device 208 can be arranged to store multiple food processing instruction programs associated with multiple food processing sequences, i.e., recipes. Such food processing instruction programs may include instructions for causing the controller and / or processor 202 to: start or stop one or more motors 214 (e.g., Figure 1, a motor 214 in the base 104 of the food processor 100); operating one or more motors 214 for a period of time during a particular food processing sequence; and issuing one or more prompt instructions to the user interface 212, which are output to the user to prohibit a response, action and / or input from the user.

[0057] As will be appreciated by those of ordinary skill in the art, software programs may be developed, coded, and compiled in a variety of computing languages ​​for a variety of software platforms and / or operating systems, and subsequently loaded and executed by processor 202. In one embodiment, the compilation process of a software program may convert program code written in one programming language into another computer language so that the programming code can be executed by processor 202. For example, the compilation process of a software program may generate an executable program that provides coded instructions (e.g., machine code instructions) to processor 202 to implement a specific, non-general purpose, specific computing function.

[0058] After the compilation process, the coded instructions may be loaded into the processor 202 from the storage device 208, from the memory 204, and / or embedded within the processor 202 (e.g., via a cache or onboard ROM) as computer-executable instructions or process steps. The processor 202 may be configured to execute the stored instructions or process steps so that the instructions or process steps are executed to convert the electronic control system 200 into a non-general purpose, specific, specially programmed machine or device. Stored data, such as data stored by the data storage area and / or storage device 208, may be accessed by the processor 202 during execution of the computer-executable instructions or process steps to instruct one or more components within the control system 200 and / or other components or devices external to the system 200.

[0059] The user interface 212 may include a display, a position input device (e.g., a mouse, a touch pad, a touch screen, etc.), a keyboard, a keypad, one or more buttons, or other forms of user input and output devices. The user interface components may be communicatively coupled to the processor 202. When the user interface output device is or includes a display, the display may be implemented in various ways, including by a liquid crystal display (LCD) or a cathode ray tube (CRT) or a light emitting diode (LED) display, such as an OLED display.

[0060] Sensor 206 may include one or more sensors that detect and / or monitor at least one characteristic associated with processing one or more food products by food processor 100 and / or within or around food processor 100 and / or control system 200—e.g., Figure 1The physical characteristics (i.e., environmental conditions) of the interior or surroundings of the mixing container or tank 108. Characteristics and / or environmental conditions associated with processing one or more food products may include, but are not limited to, rotation, rotational speed and / or movement of a device or component (e.g., a motor), rate of such movement, frequency of such movement, direction of such movement, temperature, pressure, motor current, position of the device or component (e.g., whether a flap or lid is open or closed), and / or presence of the device or component (e.g., whether a lid is attached to, for example, Figure 1 The stirring tank 108). The types of sensors may include, for example, electrical metering chips, 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.

[0061] The sensor 206 may also include one or more safety and / or interlock switches that prevent or enable the operation of certain components, such as motors, when certain conditions are met (e.g., enabling activation of the motor 214 when the lid 110 is attached to the mixing container (mixing tank 108)). A person of ordinary skill in the art will appreciate that the electronic control system 200 may include other components well known in the art, such as Figure 2 Power supplies and / or analog / digital converters not explicitly shown.

[0062] In some embodiments, the control system 200 and / or processor 202 includes a SoC having a number of hardware components, including but not limited to:

[0063] Microcontrollers, microprocessors or digital signal processor (DSP) cores and / or multi-processor SoCs (MPSoCs) having more than one processor core;

[0064] storage areas, including a choice of read-only memory (ROM), random access memory (RAM), electrically erasable programmable read-only memory (EEPROM), and flash memory;

[0065] Timing sources, including oscillators and phase-locked loops;

[0066] Peripherals including counter timers, real-time timers, and power-on reset generators;

[0067] External interfaces, which include industry standards such as Universal Serial Bus (USB), FireWire, Ethernet, Universal Synchronous / Asynchronous Receiver / Transmitter (USART), Serial Peripheral Interface (SPI);

[0068] analog interfaces, including analog-to-digital converters (ADCs) and digital-to-analog converters (DACs); and

[0069] Voltage regulators and power management circuits.

[0070] SoC includes the hardware described above, as well as software that controls microcontrollers, microprocessors and / or DSP cores, peripherals and interfaces. Most SoCs are developed from pre-certified hardware blocks of hardware elements (e.g., called modules or components, which represent IP cores or IP blocks) and software drivers that control their operation. The above list of hardware elements is not exhaustive. SoCs may include protocol stacks that drive industry standard interfaces, such as Universal Serial Bus (USB).

[0071] Once the overall architecture of the SoC has been defined, individual hardware elements can be described in an abstract language called RTL, which represents the register transfer level. RTL is used to define circuit behavior. Hardware elements are connected together in the same RTL language to produce a complete SoC design. In digital circuit design, RTL is a design abstraction that models synchronous digital circuits based on the digital signal (data) flow between hardware registers and the logical operations performed on those signals. RTL abstraction is used to create a high-level representation of the circuit in hardware description languages ​​(HDLs) such as Verilog and VHDL, from which lower-level representations can be derived and actual wiring can be ultimately derived. RTL-level design is a typical practice in modern digital design. Verilog is standardized as the Institute of Electrical and Electronics Engineers (IEEE) 1364 and is an HDL for modeling electronic systems. Verilog is most commonly used for the design and verification of digital circuits at the RTL abstraction level. Verilog can also be used to verify analog circuits and mixed-signal circuits, as well as for designing genetic circuits. In some embodiments, the various components of the control system 200 are implemented on a printed circuit board (PCB).

[0072] Figure 3 It is shown by Figure 1300 is a diagram of an example of a workflow 300 of a process implemented by a controller and / or processor 202 within a food processor 100. The workflow 300 involves processing detected values ​​of physical property values ​​associated with the processing of food, such as current and voltage of a motor 214, i.e., a detection signal, over a certain period of time during the processing of the food, and taking one or more actions as a result of such processing. In some embodiments, the one or more actions include adding processing time and / or adjusting the power supplied to a controllable component such as a motor. The controller 202 may monitor the power consumption of the motor 214; for example, during a stirring cycle, the current and / or voltage is detected via the sensor 206 over an initial period of time (e.g., an initial 15 second period of time), and time series data (e.g., power values) is generated based on these detected values, which can be intuitively represented by a graph 302. In the graphs 302, 304, and 306, the horizontal axis represents time, and the vertical axis represents power consumption.

[0073] The time series power values ​​form a curve 303. Time = 0 refers to when the motor is initially powered on, i.e., current is supplied to the motor. As shown, the power initially surges from 0 watts to over 600 watts. This initial spike is a manifestation of a phenomenon known as "inrush current", also known as "locked rotor current". Inrush current is the excess current experienced in the motor and its conductors during the first few moments after the motor is powered on (switched on). The peak value of this spike, the time required to reach the peak value, and the rate at which the power consumption reaches this peak value and falls back from this peak value may all be affected by the load imposed on the motor by the food. Thus, the peak value of this spike, i.e., the peak value of the entire curve 303, the time required to reach the peak value, and the rate at which the power consumption reaches this peak value and falls back from this peak value, may indicate the type of food being processed. Graph 304 shows a subset of the curve 303 of the time series characteristic values ​​from 0 to 5 seconds. Graph 302 shows a subset of a curve 303 of time series characteristic values ​​from about 5 seconds to about 15 seconds, in which only a subset of the vertical axis is shown, representing a sub-range of power consumption values ​​shown in graphs 302 and 304 .

[0074] like Figure 3As shown in graphs 304 and 306 of , the controller 202 can generate characteristic values ​​from the time series power values ​​shown in curve 303, including but not limited to: the peak power consumption value ("peak") in the time series power values ​​of graph 302; the difference ("drop") between the peak and the power consumption at a specific time point (e.g., at this time point, the power consumption appears to have recovered from the spike caused by the initial inrush current); the standard deviation ("Stdev") of the power consumption in the sub-time periods of the time period covered by the time series power values ​​(e.g., 5 seconds to 15 seconds); the power consumption ("wattage") at a specific time within the time period represented by the time series power values ​​(e.g., 15 seconds); the momentum of the power consumption in the sub-time period of the time period represented by the time series power values ​​(e.g., the slope of curve 303); the gradient of the curve of the time series power values ​​in the sub-time period of the time period; and / or other characteristic values.

[0075] The time periods for detection, the characteristics for determining the values, and the specific times and sub-time periods for determining the characteristic values ​​may vary and are not limited to those shown and described herein. In some embodiments, these parameters and their values ​​are selected based on testing and empirical data, from which the parameters that are optimal for generating characteristic values ​​to distinguish between foods can be determined.

[0076] In some embodiments, the controller 202 and / or the food analyzer (or food type analyzer) 308 may classify the processed food based on one or more of the feature values ​​determined from the time series data (e.g., time series feature values), for example, if one or more foods derived from the ingredients 106 include nut butter. The food analyzer 308 may be implemented as a software program or function, a hardware function, a firmware function, or a combination thereof. In some embodiments, the controller 202 implements the food analyzer 308 as a computer-implemented program, function, and / or routine. This classification may be performed via one or more neural networks, such as a multi-layer perceptron (MLP) classifier. In some embodiments, this classification may include classifying one or more foods derived from the ingredients 106 as nut butter or another thicker food type. Determining certain characteristics / features of one or more foods being processed, as shown in graphs 302, 304, and 306, may further include the controller and / or processor 202 first sensing, for example, a food processor container type, size, or other relevant attributes. This data may provide classification and / or classification information, which may help the controller 202 and / or processor assign one or more actions to accommodate the associated components. For example, the controller 202 may instruct the motor 214 to operate differently depending on the size of the mixing container 108. For example, these actions and / or operations may include the controller 202 directing more or less current and / or power to the motor 214, directing more or less current and / or power to the heating element, directing a different drive shaft speed, and / or adjusting the amount of time or period when the motor rotates.

[0077] like Figure 3 As shown in , calculations of various (e.g., 1, 2, 3, 4, 5 or more) features may be performed based on the detected time series patterns shown in graphs 302, 304, and / or 306 to provide a signal including a set of feature values. This may allow for subsequent performance of a nearest neighbor analysis via, for example, a KNN analysis. Each of these time series pattern conditions / characteristics / features may include a peak in a graph of the pattern, a dip in a graph of the pattern, a standard deviation of a graph of the pattern, and / or a steady-state power consumption in a graph of the pattern, as described in detail above. Likewise, these conditions, characteristics, and / or features that may implement the example process may include one or more of the following: mean and / or average, including the average wattage to be employed over a particular time period; standard, including the standard deviation of the wattage to be employed over a particular time period (shown in graph 306); momentum, including the slope of the wattage to be employed over a particular time period (shown in graph 306); maximum, including the maximum wattage of the recorded data (shown in graph 306); and decline, including the percentage decline from the maximum value to the stable wattage (shown in graph 304). These time periods and definitions of stable wattage may be informed and / or formatted by several scenarios to apply to several scenarios, such as U.S. engineering standards.

[0078] Available through Figure 3 The set of feature values ​​determined by the detected time series data and / or patterns shown in the graphs 302, 304, and 306 in the figure can then serve as input to the food type analyzer 308 as one or more detection vectors. In addition, as detailed in the graphs 302, 304, and 306, certain characteristics / features of one or more foods processed in the multidimensional feature space can additionally include: the controller and / or processor 202 first determines, for example, one or more food details, such as associated features, vectors, and / or other related attributes. For example, the multidimensional feature space may include, for example, Figure 4 , or higher dimensional parameters as shown in . The feature value / detection vector may provide food type information based on the determination of the closest food type in the multidimensional feature space. For example, the controller 202 and / or the food type analyzer 308 may select one or more data sets of multidimensional feature vectors stored in the memory 204 based on the determined capacity of the mixing tank 108. In other cases, the selected one or more data sets of multidimensional feature vectors stored in the memory 204 may be based on other factors and / or characteristics related to the food processor 100 and / or the mixing tank 108. This data may provide classification and / or classification information, which may help the controller and / or processor 202 assign one or more data sets and / or subsequent actions to adapt to the associated one or more foods in, for example, the mixing container 108.

[0079] like Figure 3 As shown in , the controller 202 and / or the food type analyzer 308 may compare the detection vector generated from the time series data with a selected data set of food vectors in the multidimensional feature space. In some cases, each food vector represents and / or is associated with a food type. For example, the controller 202 may instruct the motor 214 to operate in different ways according to information from a predetermined food vector, the information including a data set related to the stirring execution in the stirring container 108. This may include, for example, optimizing the stirring time and / or speed to optimally stir one or more different foods according to the instructions provided by the predetermined food vector. For example, the determination of the Margarita vector may cause the addition of an additional stirring time of an X amount, while the determination of the tropical smoothie vector may cause the addition of an additional stirring time of an Y amount. The food type analyzer 308 may include software, hardware, or a combination, so that one or more routines and / or algorithms may be implemented to analyze the time series data shown in the graphs 302, 304, and 305 and detect and / or identify food. The food type analyzer 308 may be implemented as part of the control system 200 and / or the processor 202. For example, the previously provided detection vectors / eigenvalues ​​from time series data may be represented as variables a, b, c, d, for example as coordinates of each data point, where each row represents a data point. The number of columns may be equal to the number of detection vectors / eigenvalues ​​from the measured time series pattern, and the number of rows may be equal to the number of data points:

[0080]

[0081] In some embodiments, the following array maintains the program time (t i ) (e.g., the time to be added), with the same number of rows as the data array above:

[0082]

[0083] according to Figure 3 Once the food type analyzer 308 of the example workflow 300 analyzes the detection vectors / feature values ​​from the time series pattern and determines what the input to the food type analyzer 308 will be, it may be necessary to scale the data for the accuracy of the food type analyzer 308 and the efficiency of its functionality and / or code. For example, to store the data points, the input may be represented as an int8_t data type to save memory. In some embodiments, in order for a standard scaler to optimally perform machine learning, it may be necessary to scale the data between -128 and 127. This scaling may be done according to the U.S. engineering standard using the following equation:

[0084]

[0085] Where x is the raw detection vector data, μ is the mean of the detection vector data, such as the mean of current or wattage, S is the standard deviation of the detection vector data, and X 缩放 is the scaled detection vector data.

[0086] In some embodiments, scaling to use int8_t may not have a significant impact on the results of the food type analyzer 308; therefore, it may be important to ensure that new data points remain within the target range.

[0087] In addition, Figure 3 In, once the example food processor determines the input point after sensing the data of the detection vector / feature value from the time series data and / or pattern, it may be necessary to calculate the determination of the distance to the detection vector and each food vector. For example, a KNN analysis may be initiated to provide a determination of one or more positions of one or more nearest neighbors in a multidimensional feature space compared to the position of the detection vector. This may involve multiple distance metrics; for example, based on the initial analysis, the Mikowski distance or the Bray-Curtis distance may be used, as shown below:

[0088] Bray-Curtis:

[0089]

[0090] Mikowski:

[0091]

[0092] Where j = 1, m = number of iterations, x j = the first coordinate value of the vector point of object A, y j = the first coordinate value of the vector point of object B, p = the order between the two points (by integer value), d BC = Bray-Curtis distance, and d M = Minkowski distance. For Bray-Curtis, which measures the distance between point A and point B, its value is between 0 and 1 if all coordinates are positive. However, if both objects are at 0 coordinate, such as (0,0), then the Bray-Curtis distance is not defined. It can be normalized by dividing the absolute difference by the sum. For p≥1, the Minkowski distance is a metric that arises from the Minkowski inequality. When p<1, the distance between (0,0) and (1,1) is 2 1 / p >2, but the point (0,1) is at distance 1 from both of them. Since this violates the triangle inequality, it is not a metric for p < 1. However, we can obtain a metric for these values ​​by simply dropping the exponent 1 / p. The resulting metric is also the F-norm.

[0093] according to Figure 3 In an example embodiment of the present invention, once the k nearest neighbors are determined, the program and / or routine of the food type analyzer 308 determines its output as an action. For each of the determined one or more nearest food types, as previously described, a determination of additional time for blending may be specified. This additional time may be specified in the feature vector for the food type based on the stored multi-dimensional feature vector data. As mentioned, each data point may have a time associated with it, thereby requiring a determination of the time at which the data point will be output based on, for example, combining a weighted average of each additional time to produce a total time to be added to the blending process. For example, in Figure 3 This includes a weighted average determination 310 of the additional 8 seconds of time to be added to the stirring process. As with the distance measurement, several different weighting functions may be used, for example:

[0094]

[0095] Where d = the distance between two objects / vector points, z = the number of distance calculations between two corresponding objects / vector points, and w = the weight.

[0096] At this point, the output of the food type analyzer 308 is returned and represented in the easternmost portion of the graph 302 of the workflow 300, shown as "Time Added".

[0097] In some embodiments, a specific sensor chip can sample, detect and / or monitor the power by sampling the voltage on the terminals / leads of the motor. A controller, such as Figure 2 The controller and / or processor 202, and / or the food type analyzer 308, may thereby obtain this sampled voltage value from the sensor (2) 206, such as a sensor chip, analyze the data, and take action based on this analysis by sending a control signal and / or signaling to the TRIAC switch that controls the power input of the blender motor 214. Based on the control signal and / or signaling from the processor 202 and / or the food type analyzer 308, the output signal of the TRIAC switch (or another type of switch or control mechanism) may stop, start, increase or decrease the motor speed. That is, the analysis described herein may also include identifying one or more recipes associated with a single or aggregated food within the blending container 108, and when the closest one or more recipes are identified, adding blending time according to the determined one or more closest recipes, such as Figure 3 As shown in control step 310.

[0098] Figure 4 An illustrative multidimensional feature space 400 is shown, which contains representations such as those given by Figure 3The food vectors determined by the food type analyzer 308 are vector points, such as vector points 410, 412, 426, and 428. Specifically, for illustrative purposes, the feature space 400 is a two-dimensional feature space, where the horizontal axis 430 represents the value of the first feature and the vertical axis 432 represents the value of the second feature. In some embodiments, some vector points in the feature space 400 represent the values ​​of the first feature and the second feature. Figure 3 The workflow 300 may include detection vectors determined via the controller 202 and / or the food type analyzer 308. For example, the feature space 400 may include detection vectors 402, 404, 406, and 408, which are represented as points in the feature space 400.

[0099] Can be used with Figure 3 A nearest neighbor analysis similar to or different from the KNN analysis discussed in the description of the present invention may be performed based on one or more foods (e.g., two types of smoothies) that are closest to the currently detected food. In some embodiments, the controller 202 classifies the first subset of food vectors into a first food category, such as nut butter, and controls a controllable component, such as the motor 214, based at least in part on determining that the position of the detection vector, such as the detection vector point 408, is within a first region 416 in the multidimensional feature space 400 associated with the first food category, such as nut butter. For example, various types of nut butters may be members of the first subset of food vectors and therefore have their food vectors, such as the vector point 428, in the first region 416 of the multidimensional feature space 400, while non-nut butter foods may be members of the second subset of food vectors and therefore have their food vectors (e.g., the vector points 410, 426, and 412) in the second region 418 of the multidimensional feature space 400. The first region 416 and the second region 418 may be separated by a boundary 414. In some embodiments, the multidimensional feature space 400 may include three or more regions associated with three or more food categories.

[0100] Figure 44 shows a two-dimensional spatial relationship between a food vector and a detection vector. For example, detection vector 402 is spaced apart from food vector 410 by distance 420, spaced apart from food vector 426 by distance 422, and spaced apart from food vector 428 by distance 424. For example, if food vector 410 is associated with a margarita drink, detection vector 402 may be identified as a vector associated with a margarita drink based on distance 420 being the shortest distance, i.e., based on food vector 410 being the closest food vector to detection vector 402. However, in some embodiments, controller 202 and / or analyzer 308 may use distances from detection vector 402 to multiple food vectors to identify a food associated with detection vector 402. Controller 202 may identify a food associated with detection vector 402 based on two closest food vectors, based on three closest food vectors, or more food vectors. In cases where two distances are similar, such as distances 422 and 424 , controller 202 may additionally or alternatively consider third distance 420 to identify a food product associated with a detection vector, such as detection vector 402 .

[0101] In some embodiments, the controller 202 identifies one or more food types associated with the detection vector by determining the position of the detection vector, such as the detection vector 402, in the multidimensional feature space 400, respectively, relative to the position of some or all food vectors (e.g., food vectors 410, 412, 426, and 428) in the multidimensional feature space 400. The controller 202 may determine one or more actions based at least in part on the identified one or more food types. The controller 202 may control the operation of a controllable component, such as the motor 214, based at least in part on the determined one or more actions. In some embodiments, the controller 202 determines one or more actions based at least in part on the region in which the detection vector is located in the feature space 400, such as region 416 or region 418. For example, the detection vector 406 is located in region 416 that may be associated with a nut butter food group or subset, while the detection vector 402 is located in region 418 that may be associated with a non-nut butter and / or beverage food group or subset. Controller 202 may control the operation of a controllable component, such as motor 214, based at least in part on the determined one or more actions associated with the food group or subset.

[0102] In one embodiment, a microcontroller and / or microprocessor, such as controller and / or processor 202, receives a series of signals from motor 214 from one or more sensors, such as sensor 206. Processor 202 determines a time series pattern of power consumption of motor 214 over a first time period via food type analyzer 308. Processor 202 identifies a plurality of time series pattern features associated with the time series pattern and then calculates a detection vector, such as detection vector 402, based on the plurality of time series pattern features. Depending on the basic feature values ​​of the time series that produced detection vector 402, an initial classification of food types, such as nut butter in region 416 or beverages in region 418, may include MLP classifications that result in KNN or non-KNN analysis. These classification events may be aggregated over time to more effectively and efficiently inform additional classifications. Controller 202 and / or food type analyzer 308 compares the position of detection vector 402 in multidimensional feature space 400 with the positions of some or all of the plurality of food vectors. The controller 202 and / or the food type analyzer 308 identifies the food associated with the detection vector 402 by determining which of the plurality of food vectors is closest to the detection vector 402 in the multidimensional feature space 400, such as the food vector 410 at a distance 420 from the detection vector 402. If the food vector 410 is associated with a smoothie, the controller 202 and / or the food type analyzer 308 determines that the food being processed is a smoothie. The controller 202 may then determine how long the motor 214 and the mixing blade should rotate, such as a second time period. In one embodiment, the controller 202 determines the second time period based on one or more of the closest food vectors, such as the food vectors 410, 412, 424, and / or 426. In some embodiments, the controller 202 determines the second time period based on a combined weighted average of additional time dependent upon one or more determinations associated with each food vector (e.g., food vectors 410, 412, 426, and 428) used to identify the detection vector until the motor 214 is stopped to achieve a more accurate and / or consistent smoothie.

[0103] In another case, the controller 202 and / or the food type analyzer 308 receives a series of motor 214 signals from one or more sensors, such as sensor 206. The controller 202 determines a power consumption time series pattern and / or data set of the motor 214 over a first time period via the food type analyzer 308. The controller 202 identifies a plurality of time series pattern features associated with the time series pattern, and then calculates a detection vector, such as detection vector 404, based on the plurality of time series pattern features. In some embodiments, calculating the detection vector includes determining a time series pattern from the detected signal, wherein the time series pattern includes a gradient of a power curve, such as curve 303. The controller 202 and / or the food type analyzer 308 compares the position of the detection vector 404 with the positions of a plurality of known food vectors in the multidimensional feature space 400. By determining which of the first plurality of food vectors is closest to the detection vector 404 in the multidimensional feature space 400, the controller 202 and / or the food type analyzer 308 identifies the food type associated with the detection vector 404. In this case, the closest known food vector is vector 412. If the food vector 412 is known to be associated with whipped cream, the controller 202 and / or the food type analyzer 308 determines that the food being processed is whipped cream. The processor 202 can then determine how long the motor 214 and the mixing blade should rotate, such as a second time period, until the motor 214 stops to achieve more accurate and / or consistent whipped cream.

[0104] In some embodiments, an additional series of motor signals corresponding to the processed food may be detected to more accurately identify and / or confirm the type of food being processed. For example, after the controller 202 classifies the type and / or first subset of the food vector as nut butter, one or more sensors, such as sensor 206, may continue to sense an additional time period, such as 15 seconds, and provide an additional series of motor 214 signals to the controller 202 and / or food type analyzer 308. Based on analyzing this additional series of motor signals, the controller 202 may operate the motor 214 to rotate the mixing blade of the blade assembly 102 for an additional time period. These additional series of motor 214 signals may include power consumption and / or motor current trends over multiple increments or time periods (e.g., multiple 100ms time periods) output from the sensor 206 and analyzed by the controller 202 and / or food type analyzer 308. Based on its analysis, the controller 202 and / or food type analyzer 308 may determine and / or confirm the identity and / or classification of the food, and thereby determine that additional processing of the food is necessary. This determination may be based on whether the power consumption of the motor 214 detected every 100 ms is trending 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.

[0105] Figure 7Graph 700 is shown, which shows a detected increasing trend of the power load detected by, for example, sensor 206 relative to motor 214, which controller 202 can analyze to confirm whether the food being processed is a specific food classification, such as a nut butter type. Such additional sensing of motor power and / or current reduces the possibility of misclassification of food types such as nut butter. When the power consumption and / or current trend of motor 214 over a certain period of time is no longer greater than or equal to a set threshold, controller 202 and / or food type analyzer 308 can determine that processing of the food is no longer required.

[0106] In some cases, Figure 4 , when the controller 202 detects at least one characteristic associated with the processing of one or more food products, such as food products derived from the ingredients 106, the controller 202 also determines the type and / or size of one or more components, such as the blending jar 108. In this way, the controller 202 can more efficiently or easily identify food products based on the understanding that certain types of food products are most often or only processed using certain types of components and / or containers. In some embodiments, the controller 202 is configured to classify one or more food products, such as food products in the nut butter area 416, based on a detection vector, such as the detection vector 406 or 408, and by detecting the type of jar 108 used to create the food product associated with the detection vector 406 or 408. In some embodiments, the controller 202 may be configured to control the motor 214 based on only the type and / or size of one component, such as the blending jar 108, based on the recognition that certain types of food products use certain types of components and / or containers when being processed.

[0107] In some embodiments, the controller 202 is configured to identify one or more food types, such as a nut butter type associated with the detection vector 408 and a frozen drink type associated with the detection vector 402, based on applying a weighting factor to some or all food vectors in the feature space 400, such as food vectors 410 and 412. In some embodiments, the weighting factor is based on at least one of the following: the distance of the food vector from the detection vector within the multidimensional feature space 400, the food type associated with the food vector, the frequency of determining the food type, and the container type used during food processing. For example, the weighting factor can be measured and / or assigned on a scale of 0.0-1.0 or any other reasonable weighted scaling metric, which can be used to adjust the value of one or more features of the food vector and / or move the position of the food vector in the multidimensional feature space 400 to enable the controller 202 to identify the food type. In some embodiments, each food vector can be associated with a known type of food, such as the food vector 410 that can be associated with a margarita drink. In addition, some or all food vectors can be used by the controller 202 to identify the food associated with the detection vector. As previously described, the first plurality of food vectors may be based on retrieving data associated with one or more food products (e.g., food vectors 410, 426, and 428, which may be used to identify a food product associated with a particular detection vector (e.g., detection vector 402) as being associated with a margarita drink based on one or more components) in order to determine stirring conditions, such as a period of time during which stirring is performed by operating motor 214, a speed of motor 214 during a particular period of time, a temperature of the food product at a particular time and / or period of time, a pressure in a stirring and / or mixing chamber such as pitcher 108, and the like. Each food vector may define values ​​for a plurality of features.

[0108] Figure 5 is used to monitor, analyze and execute Figure 1 Flowchart of an example process 500 of one or more actions within a food processor of the present invention. Process 500 may include processing one or more food products, involving rotating one or more components via a motor (step 502). For example, this may include rotating a traction shaft coupled to a blade assembly via a motor. The motor, drive shaft, and blade assembly may be similar to Figure 1, such as motor 214, drive shaft 116, and blade assembly 102, respectively. Process 500 may also include processing one or more food products, such as food products derived from food material 106, in a blending container and / or tank 108 while blade assembly 102 is rotated by drive shaft 116 (step 504). Process 500 may further include detecting at least one characteristic associated with processing of the one or more food products during a first time period (step 506). A first series of detection signals may be generated based on the at least one characteristic detected during the first time period. For example, the first series of detection signals may include at least one of a current and a voltage associated with operation of motor 214 during the first time period via sensor 208. Process 500 may also include storing a first plurality of food vectors in a memory, such as Figure 4 410, 412, 426, and 428 of the first plurality of food vectors (step 508). Each food vector may define values ​​of a plurality of features in a multidimensional feature space, such as feature space 400. Thus, each of the first plurality of food vectors, such as food vectors 410 and 420, may be associated with a food type. For example, food vector 410 may be associated with a margarita drink, while food vector 412 may be associated with whipped cream. A first series of motor signals corresponding to at least one characteristic of a food may be processed to generate a detection vector, such as detection vector 402. A plurality of known food vectors (e.g., 410, 412, 426, and 428) may then be stored in the memory 204 and storage device 208 of the system 200 (step 508). The controller 202 may classify the first subset of food vectors into a first food category (e.g., nut butter or beverage) and control a controllable component, such as the motor 214, based on determining that the position of a detection vector, such as the detection vector 402, is within a region 418 associated with a particular food subset, group, and / or category, such as food and beverage, in the multi-dimensional feature space 400. Any of steps 502 to 510 may be performed by, for example, Figure 2 The controller 202 may be executed by a microcontroller and / or a microprocessor.

[0109] Figure 6 A process 600 is shown for identifying a food type and controlling the operation of a controllable component based on an identification of a particular food during processing of the food. The process 600 includes the controller 202 operating the controllable component during a first time period (step 602). The controllable component may include a motor, such as the motor 214, arranged to rotate the drive shaft 116 and the blade assembly 102 to mix the food 106. The controllable component may include a heater or heating element arranged in or near the tank 108 to heat the food being processed. The controllable component may include a pump and / or a valve arranged to adjust the pressure in the tank 108 while the food is being processed. The controllable component may include any device or component configured to affect the physical properties of the food during processing.

[0110] Process 600 also includes detecting at least one characteristic associated with processing of one or more food products via a monitoring device, such as sensor 206, during a first time period, wherein a first series of detection signals are generated based on the at least one characteristic detected during the first time period (step 604). Process 600 includes storing a plurality of food vectors (e.g., food vectors 410, 412, 426, and 428) in a memory, such as memory 204 and / or data storage device 208, wherein each food vector defines values ​​of a plurality of features in multidimensional feature space 400, such that each of the plurality of food vectors is associated with a food type (step 606). Then, a detection vector, such as detection vector 402, is calculated by controller 202 and / or food analyzer 308 based on the series of detection signals, wherein the detection vector defines feature values ​​of a plurality of features in multidimensional feature space 400 (step 608). Controller 202 and / or food analyzer 308 identifies one or more food types associated with a detection vector, such as detection vector 402, by determining the position of the detection vector in the multidimensional feature space relative to the position of one or more of the plurality of food vectors (e.g., food vectors 410, 426, and 428) in multidimensional feature space 400 (step 610). For example, food vector 410 may be associated with a margarita drink. Food vector 426 may be associated with another frozen drink type, and food vector 428 may be associated with peanut butter.

[0111] In one embodiment, the controller 202 may determine that the detection vector 402 is associated with a margarita drink based on the food vector 410 being closest to the detection vector 402. The controller 202 may identify the food type associated with the detection vector 402 based on the position of the detection vector 402 relative to one or more known food vectors in the feature space 400. The controller 202 and / or the food analyzer 308 may then determine one or more actions (step 612) based at least in part on the identified one or more food types. The controller 202 may control the operation of controllable components such as the motor 214 based at least in part on the determined one or more actions. For example, the one or more actions may include the controller 202 continuing to operate the controllable components within a second time period based on the identified one or more food types. The controller 202 and / or the analyzer 308 may identify the food based at least in part on performing a K-NN analysis. The controller 202 may determine how long the motor 214 and one or more components such as a mixing blade should rotate, such as a second time period, until the motor 214 stops to achieve a more accurate and / or consistent smoothie.

[0112] In some embodiments, the second time period is between 0 seconds and 30 seconds. In some embodiments, the second time period is 15 seconds. In some embodiments, the first time period is 15 seconds. In addition, the identification food may be based at least in part on K-NN classification. In addition, calculating the detection vector may include determining a time series pattern from the detection signal, wherein the time series pattern includes a gradient of the power curve. In some embodiments, the food types identified by processes 500 and 600 include one of an apple peanut butter smoothie, a ginger smoothie, a chocolate peanut butter oatmeal, a maple almond butter, a cinnamon coffee smoothie, a citrus smoothie, a basic green smoothie, a triple green smoothie, a tropical smoothie, any type of smoothie, an extract, a sauce, an ice cream, a pudding, a nut butter, a whipped cream, a margarita, a pomegranate-cashew berry, a strawberry banana, a strawberry limeade, and a frozen drink.

[0113] Figure 8 An example 800 of a food processor base 804 is included, which may be included in a blender (e.g., blender 100). Although embodiments are described herein using the example of a blender, it should be understood that the invention is not limited thereto and applies to other types of food processors. In some embodiments, the base 804 is electric and includes a base housing 806. The base 804 may also include a dial 808 and one or more static interfaces 814 (also referred to previously as user interfaces 212), both of which may affect the behavior and functionality of the blender 100. Figure 8 As shown, the static interface 814 can be embedded in the UI cover 816, which itself can be molded around the base 804 above the base housing 806. The dial 808 can include a UI screen 810, and can be collectively referred to as an interface and / or user interface (UI). In some embodiments, the UI screen 810 may include one or more / multiple indicators 820, which can be represented in different configurations across the screen edge 812 or the dial face 818. The one or more indicators 820 that include LEDs in this example may also include LCDs, OLEDs, and / or other similar methods of lighting indicators. The food data including the previously described food data can be stored in the memory 204, and may include a database and / or a lookup table. In some embodiments, the database and / or the lookup table may include a list of indicator configuration instructions and associated sensing characteristics or actions, via which the UI screen 810 can use different configurations of one or more indicators 820 to notify the user of input during the use of the food processor 100. In this way, the UI screen 810 may utilize different configurations of one or more indicators 820 to provide accessibility, information and / or feedback to a user of the food processor base 804 based on food data processed by the controller 202 and / or food analyzer 308 during operation of the blender 100.

[0114] Fig.9A and Fig. 9B Examples 900 and 950 include one or more static interfaces 814 and UI screen 810 of the dial 808, respectively. In example 900, Fig.9A One or more static interfaces 814 are shown, which include prompts for users of a blender (e.g., blender 100). These prompts, which may be initiated by a user via one or more static interfaces 814, may include a power prompt 902, a manual prompt 904, a smart blend prompt 906, a mode prompt 908, and a pulse prompt 910. In some embodiments, these prompts may include buttons and / or other similar elements. In some embodiments, these static interfaces may include LED indicators on the prompts (e.g., static LED indicators 912, 914, and 916) to show the on / off state of engagement of prompts and / or other indicators associated with the food processor 100 (e.g., ring indicators 1102 and function indicators 1104). During use of the food processor 100, the user may thereby engage the manual prompt 904, the smart blend prompt 906, the mode prompt 908, and the pulse prompt 910 to initiate the smart blend ready state 1018, the manual ready state 1020, the mode ready state 1022, and the pulse run state 1028.

[0115] In example 950, Fig. 9BOne or more indicators 820 are shown within the UI screen 810, including a ring indicator 1102 and a function indicator 1104. As previously described, these indicators 820 may be configured as a ring around the edge of the UI screen 810 and may include one or more LEDs. When illuminated and / or otherwise activated in different configurations on the UI screen 810, these indicators may provide accessibility, information, and / or feedback to a user of the food processor base 804 based on food data processed by the controller 202 and / or food analyzer 308 during operation of the blender 100. These different configurations of one or more indicators 820 may be further affected by the illuminated dial, smart blending, and iQ functions, and / or used to further represent timer / speed data on the UI screen 810. For reference, throughout this disclosure, the term "function" may be used interchangeably with "mode." In some embodiments, the dial functions of the function indicator 1104 may include a start / stop function 952, a blend function 954, an add liquid function 956, an undetectable and / or install function 958, and a sensing function 960. In some embodiments, the smart blending function may include a disk, chop, small chop, and large chop functions 962 based on food data processed by the controller 202 and / or the food analyzer 308 during operation of the blender 100. In some embodiments, the iQ function may include a crush, maximum crush, thick mode, and a paste function 964, which may also be based on food data processed by the controller 202 and / or the food analyzer 308 during operation of the blender 100. In some embodiments, the timer / speed data may include low, medium, and high speed 966 and / or timer data 968. These functions and / or data (also referred to as prompts) on the UI screen 810 may include buttons and / or other similar elements. As will be discussed, these ring indicators 1102 and function indicators 1104 can illuminate in various intensities, patterns, and / or colors to indicate analysis, processing, and / or provide function selections of the food processor 100 during use.

[0116] Fig.10How functions, prompts, and / or data viewed and / or interacted with by a user via UI screen 810 may also be affected by one or more static interface 814 prompts is illustrated via process 1000. For example, a user may turn on a food processor (e.g., food processor 100) by pressing power at step 1010, which may be initiated via power prompt 902 of one or more static interfaces 814. At this point, for example, food processor 100 may be in an inserted closed state, as shown at step 1012, and one or more indicators 820 may be illuminated. In some embodiments, container 108 (also referred to as a blending container and / or blending jar 108) may not engage a recess (not shown) formed in base 104. In this case, power LED / power prompt 902 of one or more static interfaces 814 may flash for 0.5 seconds, turn off for 0.5 seconds, and all other LEDs and / or prompts may be offline. However, when the container 108 is engaged with a recess (not shown) formed in the base 104, the power LED / power prompt 902 of one or more static interfaces 814 may be illuminated / turned on, with all other LEDs and / or prompts offline.

[0117] At this point, at step 1014, the process 1000 considers whether one or more interlocks are engaged. In some embodiments, the interlocks may be located in the removable lid 110 that fits into the open top 112 of the blending jar 108. In some embodiments, the interlocks may be safety interlocks that prevent the motor 214 of the blender 100 from being operated unless the container 108 is properly engaged with the interlocks on the lid 110. If the interlocks are not properly engaged, and the food processor 100 is in an uninstalled state, then in some embodiments, step 1016 provides that the timer data 968 LED will flash for 0.5 seconds, turn off for 0.5 seconds, and all other LEDs and / or prompts are offline, except for the subsequent flashing of the installation function 958, which may flash for 0.5 seconds and turn off for 0.5 seconds. At this point, the process 1000 may restart and require the user to start the power again at step 1010. Similarly, if the user presses power and / or removes container 108 at step 1010 during use of food processor 100, steps 1012 and / or 1016 may be initiated.

[0118] If the interlock is engaged and / or remains engaged at step 1014 during process 1000, the food processor 100 may continue to enable the user to initiate various functions according to a plurality of "ready" and "run" states, including those functions affected by food data processed by the controller 202 and / or food analyzer 308 (also referred to as "smart blend" data). This implements a smart blend ready state 1018, which allows the blender 100 to perform several run states as long as the interlock remains engaged and the container is not removed. In some embodiments, the ready state may include a smart blend ready state 1018, a manual ready state 1020, and a mode ready state 1022. The run state may include a smart blend run state 1024, a manual run state 1026, a pulse run state 1028, and a mode run state 1030. The mode ready state 1022 and the smart blending ready state 1018 may proceed to initiate the smart blending operating state 1024, the manual operating state 1026, the pulse operating state 1028, and the mode operating state 1030. The user may then utilize different configurations of one or more indicators 820 via the one or more static interfaces 814 and the dial 808 to provide accessibility, information, and / or feedback to the user of the food processor base 804 based on the food data processed by the controller 202 and / or the food analyzer 308 during operation of the food processor 100. At this point, one or more indicators 820 within the UI screen 810 of the dial 808 may be illuminated and / or ready for user engagement. These one or more indicators 820 may, for example, clarify one or more states of the food processor 100 functionality depending on the previous activity of the food processor 100 and / or the food data processed by the controller 202 and / or the food analyzer 308 during operation of the food processor 100. Fig.10 The various relationships between the ready and run states and how various user inputs (activating the start / stop function indicator 952 / 1104, pressing / releasing the pulse prompt 910) may allow the user to cycle between the various ready and run states before the program is completed are described in detail. However, in some embodiments, the manual ready state 1020 may only initiate a manual run state 1026. If the unit is inactive for more than 180 seconds, the operation of the food processor 100 may time out and the food processor 100 may shut down.

[0119] Fig.11A and Fig. 11BExample ready state animations 1100 and 1125 are shown as shown on the dial 808 of the food processor 100. In the illustrative embodiment showing the smart blending ready state 1018, both 1100 and 1125 illustrate the UI flow animation via the UI screen 810 with the aid of one or more indicators 820. For example, the animation 1100 depicts the illumination of one or more LEDs including the one or more indicators 820, which, as discussed, are perceived by the user / viewer of the dial 808 / food processor 100 as an illuminated ring around the edge of the UI screen 810, as shown in the animation 1125. For clarity, the one or more indicators 820 may be referred to as one or more ring indicators 1102 when referring to those indicators that include an illuminated ring. In this manner, the one or more indicators 820 may be referred to as one or more function indicators 1104 when referring to those indicators that are represented as different configurations of the one or more indicators 820 within the one or more ring indicators 1102. In the example animation 1125, the blue ring indicator 1102 and the start / stop function indicator 1104 are illuminated at 100%, while the purple other function indicators are not illuminated because the LEDs are off. Fig. 11B As shown in animation 1125 of , these one or more function indicators 1104 may be affected by the illuminated dial, smart blending, and iQ functions, and / or used to further represent timer / speed data on the UI screen 810.

[0120] Fig. 11C An example sensing process 1150 is shown when the controller 202 and / or food analyzer 308 processes food data. When fruit ingredients 106, or any organic material that the user wishes to process through the food processor 100, are present in the container 108, the user may select the smart blending prompt 906 on one or more static interfaces 814 to prompt the controller 202 and / or food analyzer 308 to detect and collect food data during step 1110 and in accordance with the present disclosure. The user may then select the pulse prompt 910 and initiate food data processing at the pulse step 1112. In some embodiments, this causes the controller 202 and / or food analyzer 308 to identify to associate the weighted average data in one or more detection vectors within the container 108 collected during step 1110 with the appropriate time sequence for food processing during operation of the food processor 100. In some embodiments, this may also be shown in the pulse run state step 1028, when the start / stop function indicator 1104 turns off, the timer data 968 counts up, and the pulse animation begins, with all other LEDs off. When the pulse step 1112 is completed, depending on the previously processed food data and / or user input, and as Fig.10As shown, when the smart blend ready state 1018 or the manual ready state 1020 is activated, one or more indicators 820 may be illuminated at step 1114. More specifically, data processing via the controller 202 and / or the food analyzer 308 at the pulse step 1112 may then illuminate one or more function indicators 1104, thereby suggesting that the user initiate a blend 1116, crush 1118 or maximum crush 1120 and / or thickening mode 1122 sequence. This dynamic may optimize the motor 214 output, thereby optimizing the initial and / or continued processing of the food ingredients in the container 108 by rotating the mixing blade of the blade assembly 102 for an established time period and / or intensity.

[0121] Fig.11D An example ready state animation 1175 is shown that illuminates one or more ring indicators 1102, which may also occur in situations such as Fig. 11C In the step 1114 described. In the animation 1175, the illuminated portion of one or more ring indicators 1102 may change based on the automatic cycle and / or the user's selection of the processing mode via one or more function indicators 1104. For example, when the user selects between the intelligent blending functions such as disc, chop, small chop and large chop functions 962 based on the food data processed by the controller 202 and / or the food analyzer 308 during the operation of the blender 100, the segments of the ring indicator 1102 correspond to these larger or smaller outputs. In some embodiments, the different colors, patterns and / or intensities of the LEDs of the ring indicator 1102 may indicate the mode of the food processor 100. For example, during step 1114, the animation 1175 can cycle through various colors at intervals of 0.75 seconds, including 4.5 seconds / cycle. These colors and / or patterns of the ring indicator 1102 correspond to the corresponding titles and / or colors of the function indicators 1104 on the UI screen 810 and those discussed with similar names throughout this disclosure. Fig.11E It is shown that according to some embodiments, these colors can illustrate the processing modes introduced in example 950, including green for stirring 1152, turquoise for crushing 1154, blue for maximum crushing 1156, purple for thickening mode 1158, orange for dough 1160, yellow for disc 1162, and pink for chopping, small chopping, and large chopping functions 1164.

[0122] Other ring indicators 1102 may be displayed, such as white for variable speed / pulse 1166, red for error / add liquid 1168, and other errors indicating some kind of malfunction and similarly resulting in a red ring indicator 1102. For example, if the controller 202 and / or the food analyzer 308 determine that more liquid needs to be added to the contents of the container 108, the motor 214 will stop running and, in addition to the LED ring indicator similarly illuminated 100% in red, the "add liquid" indicator 1168 will become 100% illuminated as a function indicator 1104 on the UI screen 810. All other LEDs may be off and / or display a segmented reading "AL", referring to adding liquid. In some embodiments, when the interlock is reengaged after adding more liquid to the container 108, the smart blend ready state 1018 will return to the default state. As previously mentioned, the user must disengage the interlock and reengage it within 180 seconds, otherwise the food processor 100 may return to the off state plugged in 1012. Additional error and / or notification indicators via 1168 may include "Error 1: Runtime Protection Mode," which may occur when the food processing runtime via the blade assembly 102 exceeds 180 seconds. In some embodiments, if the controller 202 and / or food analyzer 308 determines that the runtime exceeds this length, the motor 214 will stop running and the static interface 814 and dial 808 become disabled. More specifically, all static interface 814 LEDs may flash in unison with the timer data 968, flashing "E1" in a 0.25 second on, 0.25 second off pattern until the food processor 100 is unplugged. In some embodiments, the food processor 100 must be unplugged to reset and cancel the runtime protection mode because the release of the interlock and idle timeout may not reset the food processor 100. However, this error may only apply to a single interlock.

[0123] FIG. 12A to FIG. 12D Animation 1210 , 1220 , 1230 , and 1240 views of the smart stir sensing animation of step 1110 are shown. Fig. 12A Animation 1210 shows that in some embodiments, during intelligent stirring sensing of the contents of the container 108, the animation on the UI screen 810 includes the ring indicator 1102 as four illuminated LEDs for clockwise rotation, which can be seen in animations 1220 and 1240. In the example animations 1220 to 1240, the blue ring indicator 1102 and the start / stop function indicator 1104 are illuminated at 100%, the yellow timer data 968 is illuminated at 10%, the function indicator 1104 flashes for 0.5 seconds, turns off the orange pulse for 0.5 seconds according to 1110 sensing, and the purple function indicator 1104 is not illuminated. Fig. 12C The clockwise rotation of the timer data 968 during the sensing period is shown via animation 1230 . Fig.12D Via animation 1240, it is shown that in some embodiments, during sensing, the color can fade from dark blue, magenta, orange, and cyan to the next color at 0.5 second intervals, with each sequence comprising 2 seconds. In some embodiments, the smart blend ready state 1018 has begun because the smart blend prompt 906 is illuminated and the ring indicator 1102 is as shown. FIG. 11A to FIG. 11C In some embodiments, only the start / stop function indicator 1104 is available to the user for use in starting steps 1024 and 1112. The rotary dial 808 may be disabled, and all other LEDs next to the timer data 968 are off.

[0124] FIG. 13A to FIG. 13C Animation 1310 , 1320 , and 1330 views of the smart blending operation animation of steps 1024 and 1112 are shown. Fig.13A Animation 1310 shows that in some embodiments, after the sensing in step 1110, during operation of the food processor 100 and as discussed in steps 1024 and 1112, food data is processed by the controller 202 and / or the food analyzer 308 via smart blending. The animation on the UI screen 810 may include a ring indicator 1102 as an LED that turns off in a clockwise rotation, which can be seen in the three views of the animation 1310, showing the beginning of the smart blending program, the smart blending program being 50% complete, and the end of the smart blending program, respectively, from left to right. Animations 1320 and 1330 further illustrate this example embodiment of the smart blending program. Again, in the example animations 1320 and 1330, the blue ring indicator 1102 and the start / stop function indicator 1104 are illuminated at 100%, the function indicator 1104 pulses orange at 0.5 seconds on, 0.5 seconds off based on sensing in 1110, and the purple function indicator 1104 is not illuminated. As shown in Fig.10 As described in step 1024, according to FIG. 13A to FIG. 13C When the example process begins, the motor 214 is in operation and the start / stop function indicator 1104 is illuminated. In some embodiments, when the sensing animation begins, the function indicator 1104 of the sensing function 960 pulses orange by flashing for 0.5 seconds and turning off for 0.5 seconds. Once the controller 202 and / or the food analyzer 308 completes the sensing 1110, the intelligent blending process at step 1112 is initiated. The process includes illuminating the function indicators 1104 for the iQ functions, including crush, maximum crush, thick mode, and paste function 964, unless no program is detected, in which case the function indicator can be illuminated at 100%, thereby initiating standard blending. In this case, and as Fig. 13B As shown, the stirring function 954 can be illuminated at 100%. Fig. 13CAnimation 1330 in FIG. 1 provides that, in some embodiments, the LED for ring indicator 1102 may be green to show stirring 1116 and / or 1152 modes, such as Fig. 11C and Fig.11E As introduced in .

[0125] FIG. 14A to FIG. 14C Animation 1410, 1420, and 1430 views are shown when a user selects and engages a mode ready state 1022 and a mode running state 1030 during operation of the dial 808 of the food processor 100. In some embodiments, if the user engages the mode prompt 908 to initiate the mode ready state 1022, the manual and smart blend prompts 906 and 910, respectively, may be turned off. The start / stop function indicator 1104 can be illuminated at 100% and the LED for the timer data 968 can be turned off. In some cases, if the smart blend processing determines that a particular program is appropriate based on food data processed by the controller 202 and / or food analyzer 308 during operation of the blender 100, such as the disc, chop, small chop, and large chop functions 962, the processing animation begins with the blend function 954 illuminated at 100%, as shown. Fig. 13B As shown. In some embodiments, these function indicators 1104 for the blending function 954 may then pulse for 0.5 seconds and turn off for 0.5 seconds. The animation on the UI screen 810 may include the ring indicator 1102 as an illuminated LED in various groups of static LEDs to illustrate and correspond to the corresponding function indicators 1104 and program initiation after the sensing of step 1110 and the smart blending process step 1112. For example, the animation 1410 includes four views of the function indicator 1104, showing the smart blending programs for chopping, small chopping, large chopping, and disk functions 962 from left to right, respectively.

[0126] Fig. 14B It is shown via animation 1420 that the food processor 100 and / or the user can cycle between function indicators 1104 associated with particular functions (e.g., chopping, small chopping, large chopping, and disc function 962). In this way, the LED groups illuminated via indicators 1102 and 1104 can change based on the user's selection of a processing mode, as the user can turn the dial 808 counterclockwise to cycle from large chopping to small chopping to chopping to disc functions and clockwise to cycle from disc to chopping to small chopping to large chopping, respectively. In some embodiments, no continuous scrolling is available. For example, when a user selects between chopping, large chopping, and small chopping functions 962, the ring indicator 1102 corresponds to these larger or smaller outputs. In Fig. 14BIn the example animation 1420 in FIG. 1 , the blue ring indicator 1102, the start / stop function indicator 1104, and the currently selected smart blending program are illuminated at 100%, while the function indicator 1104 of the available but currently unselected smart blending program is shown in green and illuminated at 20%. Again, Fig. 14C The animation 1430 in FIG. 140 provides that in some embodiments, the LED ring indicator 1102 can match the animation corresponding to the default function, such as pink to show the large chopping / small chopping / chopped mode 1164, such as Fig.11E During this time, in some embodiments, the timer data 968 may be illuminated and reduced to a "countdown" via the UI screen 810 to illustrate the default function / mode time for each program. Optionally, as Fig.11E The color of the ring indicator 1102 shown can be changed by turning the dial 808 to the corresponding iQ function color. In some embodiments, if the pulse prompt 910 of the static interface 814 (e.g., static indicator) is pressed during operation, the food processor can return to a ready state, such as the smart blend ready state 1018, the manual ready state 1020, and the mode ready state 1022. However, in some embodiments, if the variable speed 1166 is engaged while the smart blend program is running, the food processor can return to the variable speed ready state.

[0127] FIG. 15A to FIG. 15C The manual ready state 1020 and subsequent animations are shown via animations 1510, 1520, and 1530. FIG. 15B to FIG. 15C In the example animations 1520 to 1530 in FIG. 1 , the blue ring indicator 1102 , the start / stop function indicator 1104 , and the currently selected smart blending program are illuminated at 100%, while the function indicator 1104 of the available but currently unselected smart blending program is shown in green and illuminated at 20%. Fig.15A The ring indicator 1102 can be illuminated in a "bottom-up" pattern that increases upwardly via both sides to illustrate and correspond to the speed selected by the user and / or food processor 100 for operation of the blade assembly 102 based on communication between the motor 214 and the controller 202 and / or the food analyzer 308. For example, the animation 1510 shows 1.5 LEDs illuminated to illustrate speed 1 (low), 11.5 LEDs illuminated to illustrate speed 5 (medium), and 24 LEDs illuminated to illustrate speed 10 (high), respectively. Fig.16A A precise example speed implementation can be seen in Table 1600 of FIG. Fig. 15BThe function indicator 1104 showing the timer data 968 via animation 1520 may reflect and / or be configured by the user via the dial 808 to reflect these different speed modes, example speeds being 1, 5, and 10. In some embodiments, this may apply when the container 108 is a 72 oz can. Fig. 15C It is shown via animation 1530 that the user can more conveniently alternate between these modes via the function indicators 1104 of the low, medium and high speed 966 modes. In some embodiments, this may apply when the container 108 is a 24 ounce can.

[0128] For example, if the user engages the manual prompt 904 of the static interface 814, the blue ring indicator 1102, the start / stop function indicator 1104, and the manual prompt 904 are illuminated at 100%. Fig. 15B As shown in the animation 1520 of , when the container / tank 108 is installed, the timer data 968 may read "01", and the user may turn the dial 808 clockwise and / or counterclockwise to increase and / or decrease the speed from "10" up and down. In some embodiments, the ring indicator 1102 correspondingly grows from 1 at the bottom center in the lighting group to grow incrementally as it approaches 10, with a full ring indicator 1102 illumination appearing. If the installed container / tank 108 is 24 ounces, the timer data 968 may read "--" and default to the "low" mode 966. In this example, these LEDs may be 100% illuminated, while all other (medium and high) modes may be 20% illuminated. As mentioned, and as Fig. 15C As shown in the animation 1530 of , the user can rotate the dial 808 clockwise to cycle from low to high, and rotate the dial counterclockwise to cycle from high to low. In some embodiments, there is no continuous scrolling available, and if the user engages the manual prompt 904 again, nothing happens, and all other LEDs are turned off.

[0129] As discussed, Fig.16A Table 1600 shows a precise example speed implementation for food processor 100. Correspondingly, Fig. 16B Example lighting clusters and / or intensities of the ring indicator 1102 are shown via animation 1650 , showing half-lighting and full-lighting trends that meet the metrics set forth in table 1600 .

[0130] 17A to 17C The manual run state 1026 and subsequent animations are shown via animations 1710 , 1720 , and 1730 . Fig.17A Animation 1710 shows that in some embodiments, during the manual run state 1026, the animation on the UI screen 810 includes a ring indicator 1102 as four illuminated LEDs for clockwise rotation, which can be seen in animations 1720 and 1730. Fig. 17B As shown in animation 1720 in FIG. 1 , the blue ring indicator 1102 , the timer data 968 , and the start / stop function indicator 1104 are illuminated at 100%, and the purple function indicator 1104 is not illuminated. Fig. 17C The clockwise rotation of the ring indicator 1102 during the manual run state 1026 is shown via animation 1730. Once the user has initiated the example sequence of the manual ready state 1024 and has decided at his or her discretion to only non-specifically process / stir the contents of the container 108, he or she may select his or her preferred speed animation based on the timer data 968, as shown in FIG. FIG. 15A to FIG. 16B 10." Again, when the container / jar 108 is installed, the timer data 968 may read "01," and the user may turn the dial 808 clockwise and / or counterclockwise to increase and / or decrease the speed from "10" up and down. In some embodiments, the ring indicator 1102 correspondingly grows from 1 at the bottom center in the lighting group to grow incrementally as it approaches 10, with a full ring indicator 1102 illumination appearing. If the installed container / jar 108 is 24 ounces, the timer data 968 may read "--" and default to "low" mode 966. In this example, these LEDs may be 100% illuminated, while all other (medium and high) modes may be 20% illuminated. The user may turn the dial 808 clockwise to cycle from low to high, and counterclockwise to cycle from high to low. They may then select the start / stop function indicator to begin food processing, such as Fig. 17B As shown in animation 1720. Fig. 17CAs shown in the animation 1730 of , the countdown of the timer data 968 can be described in the ring indicator 1104 that rotates clockwise, rotating around the face of the UI screen 810 for a complete cycle of 2 seconds every 0.5 seconds. In some embodiments, no continuous scrolling is available, and if the user engages the smart blending prompt 906 and / or the pulse prompt 910 at runtime, the food processor 100 returns to at least one of the aforementioned ready states (e.g., smart blending ready state 1018, manual ready state 1020, and mode ready state 1022) via the UI screen 810 again. The elements of the computer include one or more processors for executing instructions and one or more storage area devices for storing instructions and data. Typically, the computer will also include one or more machine-readable storage media, or be operably coupled to one or more machine-readable storage media to receive data from one or more machine-readable storage media or transmit data to one or more machine-readable storage media, or both, and the machine-readable storage medium is, for example, a mass storage device for storing data, such as a magnetic disk, a magneto-optical disk, or an optical disk. Non-transitory machine-readable storage media suitable for containing computer program instructions and data include all forms of non-volatile storage areas, including, for example, semiconductor storage area devices, such as erasable programmable read-only memory (EEPROM), electrically erasable programmable read-only memory (EEPROM), and flash memory area devices; magnetic disks, such as internal hard disks or removable disks; magneto-optical disks; and compact disk read-only memory (CD-ROM) and digital versatile disk read-only memory (DVD-ROM).

[0131] Fig.18 is used to monitor, identify, determine and execute Fig.10 1800 for controlling one or more actions within a food processor of a food processor. The process 1800 may include operating a controllable component via one or more user interfaces configured to receive user input and operably coupled to the base (step 1802). For example, the one or more user interfaces may include multiple indicators, such as a static interface 814 (e.g., a static indicator) and / or multiple indicators 820. The base, controllable components, and user interfaces may be similar to Figure 1 and / or Figure 8 Those described, for example, are base 804, motor 214, drive shaft 116, blade assembly 102, user interface 212, dial 808, and one or more static interfaces 814, respectively.

[0132] Process 1800 may also include detecting, via a monitoring device, at least one characteristic associated with processing one or more food products and generating at least one detection signal (step 1804). For example, the monitoring device may include Figure 2Detecting at least one characteristic associated with processing of one or more food products may include the controller 202 and / or the food analyzer 308 monitoring, sensing, and processing food data originating from the food product 106, the controller 202 and / or the food analyzer 308 also determining the type and / or size of one or more components (e.g., the blending jar 108).

[0133] As previously described, at least one characteristic associated with processing one or more food products may involve the controller 202 and / or the food analyzer 308 communicating with the memory 204, which may store a plurality of food product vectors, e.g. Figure 4 402. The food vectors 410, 412, 426, and 428 of the present invention may be described. Each food vector may define values ​​of a plurality of features in a multidimensional feature space, such as feature space 400. Thus, each of a plurality of food vectors, such as food vectors 410 and 420, may be associated with a food type. For example, food vector 410 may be associated with a margarita drink, while food vector 412 may be associated with whipped cream. A series of motor signals corresponding to at least one characteristic of a food may be processed to generate a detection vector, such as detection vector 402. A plurality of known food vectors (e.g., 410, 412, 426, and 428) may then be stored in the memory 204 and storage device 208 of the system 200. The controller 202 and / or the food analyzer 308 may classify the first subset of food vectors into a first food category (e.g., nut butter or beverage) and control a controllable component, such as the motor 214, based on determining that the position of a detection vector, such as the detection vector 402, is within an area 418 associated with a particular food subset, group, and / or category, such as food and beverage, in the multidimensional feature space 400. The at least one detection signal may include at least one of a current and a voltage associated with the operation of the motor 214 via the sensor 208 and may be based on at least one characteristic associated with processing one or more foods. The controller 202 and / or the food analyzer 308 may then receive the at least one detection signal (step 1806).

[0134] The controller 202 and / or the food analyzer 308 may then identify the food type and control the operation of the controllable component based on the at least one detection signal received (step 1808). The controllable component may include a motor, such as the motor 214, arranged to rotate the drive shaft 116 and the blade assembly 102 to mix the food 106. The controllable component may include a heater or heating element arranged in or near the tank 108 to heat the food being processed. The controllable component may include a pump and / or a valve arranged to adjust the pressure in the tank 108 while the food is being processed. The controllable component may include any device or component configured to affect the physical properties of the food during processing.

[0135] Process 1800 also includes activating a first indicator of a plurality of indicators on a user interface, the first indicator indicating that at least one of detection and identification is in progress (step 1810). In some embodiments, the first indicator includes the lighting of static LED indicators 912, 914, 916, ring indicator 1102, and / or function indicator 1104. The controller 202 and / or food analyzer 308 may then determine one or more food processing actions based at least in part on the identified one or more food types (step 1812). The controller 202 and / or food analyzer 308 may control the operation of controllable components such as motor 214 based at least in part on the determined one or more actions (step 1814). For example, one or more actions may include the controller 202 operating the controllable component for a period of time based on the identified one or more food types with the aid of a specific processing mode (e.g., stirring 1152) and / or speed. In some embodiments, this may involve activating a second indicator of a plurality of indicators on a user interface. In some embodiments, the controller 202 and / or the food analyzer 308 may continue to monitor and detect at least one characteristic associated with processing one or more food products, generate subsequent detection signals, and determine that a subsequent food processing action (e.g., a blending blade) should be rotated, for example, for a second time period until the motor 214 is stopped to achieve a more accurate and / or consistent smoothie. Any of steps 1802 to 1814 may be performed by a microcontroller and / or a microprocessor, for example, respectively. Figure 2 and Figure 3 The controller 202 and / or the food analyzer 308 may be used to perform the above steps.

[0136] The elements of the different embodiments described can be combined to form other embodiments not specifically described previously. Elements can be omitted from the previously described system without adversely affecting its operation or the operation of the entire system in general. In addition, various individual elements can be combined into one or more individual elements to perform the functions described in this specification.

[0137] Other embodiments not specifically described in this specification are also within the scope of the following claims.

Claims

1. A food processor system, comprising: a user interface, the user interface comprising a plurality of indicators, the user interface being configured to receive user input; a monitoring device configured to detect at least one characteristic associated with processing one or more food products and to generate at least one detection signal; as well as A controller, the controller being configured to: receiving the at least one detection signal; identifying one or more food types based on the at least one detection signal received; activating a first indicator of the plurality of indicators on the user interface, the first indicator indicating that at least one of the detecting and the identifying is in progress; determining one or more food processing actions based at least in part on the identified one or more food types; as well as Operation of the controllable component is controlled based at least in part on the determined one or more food processing actions.

2. The food processor system of claim 1 , further comprising a memory configured to: i) storing a plurality of food vectors, each of the plurality of food vectors being associated with a food type, and ii) storing a plurality of indicator configuration instructions associated with the plurality of indicators, a first indicator configuration instruction associated with detecting the at least one characteristic and a second indicator configuration instruction associated with activating the controllable component.

3. The food processor system of claim 2, wherein the first indicator configuration instruction comprises activating the first indicator of the plurality of indicators on the user interface.

4. The food processor system of claim 2, wherein the second indicator configuration instructions include activating a second indicator of the plurality of indicators on the user interface.

5. The food processor system of claim 1 , wherein controlling the operation of the controllable component based at least in part on the determined one or more food processing actions comprises: The user input is first received via the user interface based on activation of one or more of the plurality of indicators.

6. The food processor system of claim 1, wherein the user interface includes one or more static prompts.

7. The food processor system of claim 1, wherein the user interface comprises a rotatable dial.

8. The food processor system of claim 1, wherein said monitoring device activates a sensing indicator when said monitoring device detects said at least one characteristic.

9. The food processor system of claim 1, wherein a stirring indicator of the plurality of indicators is activated when the controllable component is activated.

10. The food processor system of claim 1, wherein when the controllable component is deactivated, a stirring indicator of the plurality of indicators is deactivated.

11. The food processor system of claim 7, wherein the rotatable dial is configured to display the first and second indicators of the plurality of indicators.

12. The food processor system of claim 9, wherein displaying the first and second indicators of the plurality of indicators comprises illuminating one or more LEDs.

13. The food processor system of claim 11, wherein the controller receives the user input via the user interface based on activation of one or more of the plurality of indicators when a user rotates the rotatable dial.

14. The food processor system of claim 2, wherein the plurality of indicators include a function indicator and a ring indicator.

15. The food processor system of claim 14, wherein the plurality of indicators convey controllable component processing mode, function, intensity, length and / or speed.

16. The food processor system of claim 1, wherein detecting at least one characteristic associated with processing of the one or more food products comprises determining a type and / or size of one or more components, Wherein activating the plurality of indicators is based on the determined type and / or size of the one or more components.

17. The food processor system of claim 1, wherein the controller is further configured to identify the one or more food types associated with the detection vector by determining which of a plurality of food vectors in a multi-dimensional feature space is closest to the detection vector, Wherein the controller is further configured to identify one or more food types associated with the detection vector by determining a position of the detection vector in the multidimensional feature space relative to positions of two or more of the plurality of food vectors in the multidimensional feature space.

18. The food processor system of claim 17, wherein the controller is configured to control operation based on applying a weighting factor to each of two or more of the first plurality of food vectors, the weighting factor being based on at least one of a distance of the food vector from the detection vector, a frequency of determining a food type, and a type of container used during food processing, Wherein the controller activates the plurality of indicators based at least in part on the applied weighting factors.

19. A method of processing food products via a controllable component configured to process one or more food products, comprising: operating the controllable components via one or more user interfaces configured to receive user input and operably coupled to the base, wherein the one or more user interfaces include a plurality of indicators; detecting, via a monitoring device, at least one characteristic associated with processing one or more food products and generating at least one detection signal; receiving the at least one detection signal; identifying one or more food types based on the at least one detection signal received; activating a first indicator of the plurality of indicators on the user interface, the first indicator indicating that at least one of the detecting and the identifying is in progress; determining one or more food processing actions based at least in part on the identified one or more food types; as well as Operation of the controllable component is controlled based at least in part on the determined one or more food processing actions.

20. A non-transitory computer-readable storage medium storing instructions, the instructions comprising a plurality of food processing instructions associated with a food processing sequence, the plurality of food processing instructions, when executed by a computer, causing the computer to perform a method for processing food using a food processor via controllable components configured to process one or more food products, the method comprising: operating the controllable components via one or more user interfaces configured to receive user input and operably coupled to the base, wherein the one or more user interfaces include a plurality of indicators; detecting, via a monitoring device, at least one characteristic associated with processing one or more food products and generating at least one detection signal; receiving the at least one detection signal; identifying one or more food types based on the at least one detection signal received; activating a first indicator of the plurality of indicators on the user interface, the first indicator indicating that at least one of the detecting and the identifying is in progress; determining one or more food processing actions based at least in part on the identified one or more food types; as well as Operation of the controllable component is controlled based at least in part on the determined one or more food processing actions.

Images