Sewing machines and how to use them

Abstract

An exemplary sewing machine includes a sewing head, a needle bar that holds a needle, a motor for moving the needle during a sewing operation, a user interface, a data collection device, a data storage device, and a processor. The data collection device collects data regarding at least one of the sewing machine, an environment surrounding the sewing machine, sewing material, a sewing operation performed by the sewing machine, and one or more user interactions with the sewing machine. The data storage device stores the collected data and data related to the neural network. The processor processes the collected data through the neural network to generate processed data. Based on the processed data, the processor controls at least one of the user interface, the data storage device, and the motor.

Description

[Technical Field]

[0001] [CROSS-REFERENCE TO RELATED APPLICATIONS] This application is related to and claims the benefit of U.S. Provisional Patent Application No. 63 / 041204, filed June 19, 2020, entitled "SEWING MACHINE AND METHODS OF USING THE SAME," and U.S. Provisional Patent Application No. 63 / 175035, filed April 14, 2021, entitled "SEWING MACHINE AND METHODS OF USING THE SAME," the disclosures of which are incorporated herein by reference in their entireties.

[0002] The present invention relates generally to sewing machines and, more particularly, to control systems thereof. [Background technology]

[0003] Sewing machines can be used to create seams in single materials and to sew together various materials. A particular sewing machine can be used to create seams in a workpiece having a particular shape, to cut and sew the edges of a workpiece, to attach decorative elements to a workpiece, to cut and hem the edges of a workpiece, to attach decorative stitching and embroidery patterns to a workpiece that is held in an embroidery frame, or to cut a workpiece during the sewing process. Sewing machines can also cut, fold, roll, or otherwise process a workpiece in addition to or separate from the sewing procedure. The workpiece is moved under a needle so that a seam can be formed in the fabric. A user configures a sewing machine for a specific application by adjusting various machine parameters and by attaching a variety of different tools or accessories to the machine. Summary of the Invention

[0004] SUMMARY OF THE INVENTION Exemplary embodiments of a sewing machine, a control system for a sewing machine, and a method of using a sewing machine are disclosed herein.

[0005] The exemplary sewing machine includes a sewing head attached to an arm suspended above the sewing bed by a post, a needle bar extending from the sewing head toward the sewing bed, a needle held by the needle bar, a motor connected to the needle bar for moving the needle bar in a reciprocating motion to move the needle and sewing thread through a workpiece during a sewing operation, and a user interface for receiving commands from a user of the sewing machine and providing feedback information to the user. The exemplary sewing machine also includes a data collection device, a data storage device, and a processor. The data collection device is for collecting data regarding at least one of the sewing machine, an environment surrounding the sewing machine, sewing materials, sewing operations performed by the sewing machine, and one or more user interactions with the sewing machine. The data storage device is for storing the data collected by the data collection device as collected data and for storing data related to the neural network. The neural network is composed of a plurality of nodes. Each node of the neural network has an input connection for receiving input data, a node parameter, a computation unit for calculating an activation function based on the input data and the node parameter, and an output connection for sending output data. The processor is configured to process the collected data through the neural network to generate processed data, and to control, based on the processed data, at least one of a user interface for interacting with a user, a data storage device for storing the processed data, and a motor for modifying the sewing operation.

[0006] An exemplary method for controlling a sewing machine includes collecting data, storing the collected data in a data storage device, processing the collected data through a neural network, and controlling a user interface, a data storage device, and a motor based on the processed data. The collecting data includes collecting data related to at least one of the sewing machine, an environment surrounding the sewing machine, sewing material, a sewing operation performed by the sewing machine, and one or more user interactions with the sewing machine. The neural network in the processing stage has a plurality of nodes, each node including an input connection for receiving input data, a node parameter, a computation unit for calculating an activation function based on the input data and the node parameter, and an output connection for sending output data. During the control stage, the processor controls the user interface to interact with the user, controls the data storage device to store the processed data, and / or controls the motor to modify the sewing operation.

[0007] An exemplary control system for a sewing machine includes a data collection device, a data storage device, and a processor. The data collection device is for collecting data regarding at least one of the sewing machine, an environment surrounding the sewing machine, sewing materials, a sewing operation performed by the sewing machine, and one or more user interactions with the sewing machine. The data storage device is for storing the data collected by the data collection device as collected data and for storing data regarding a neural network. The neural network is composed of a plurality of nodes. Each node of the neural network has an input connection for receiving input data, node parameters, a computation unit for calculating an activation function based on the input data and the node parameters, and an output connection for sending output data. The processor is configured to process the collected data through the neural network to generate processed data, store the processed data in the data storage device, and control at least one of a user interface for interacting with a user and a motor for modifying the sewing operation based on the processed data.

[0008] A further understanding of the nature and advantages of the present invention is set forth in the following description and claims, particularly when considered in conjunction with the accompanying drawings in which like parts bear like reference numerals and in which: [Brief explanation of the drawings]

[0009] To further clarify various aspects of the embodiments of the present disclosure, a more particular description of certain embodiments will be made by reference to various aspects of the accompanying drawings. It will be understood that these drawings depict only typical embodiments of the present disclosure and therefore are not to be considered limiting of the scope of the present disclosure. Also, while the figures may be drawn to scale for some embodiments, the figures are not necessarily drawn to scale for all embodiments. Embodiments of the present disclosure and other features and advantages will be described and explained with additional specificity and detail through the use of the accompanying drawings.

[0010] [Figure 1] 1 shows various views and diagrams relating to an exemplary sewing machine and its systems; [Figure 2] 1 shows various views and diagrams relating to an exemplary sewing machine and its systems; [Figure 3] 1 shows various views and diagrams relating to an exemplary sewing machine and its systems; [Figure 4] 1 shows various views and diagrams relating to an exemplary sewing machine and its systems; [Figure 5] 1 shows various views and diagrams relating to an exemplary sewing machine and its systems; [Figure 6] 1 shows various views and diagrams relating to an exemplary sewing machine and its systems; [Figure 7] 1 shows various views and diagrams relating to an exemplary sewing machine and its systems; [Figure 8] 1 shows various views and diagrams relating to an exemplary sewing machine and its systems; [Figure 9] 1 shows various views and diagrams relating to an exemplary sewing machine and its systems; [Figure 10] 1 shows various views and diagrams relating to an exemplary sewing machine and its systems; [Figure 11] 1 shows various views and diagrams relating to an exemplary sewing machine and its systems; [Figure 12] 1 shows various views and diagrams relating to an exemplary sewing machine and its systems; [Figure 13] 1 shows various views and diagrams relating to an exemplary sewing machine and its systems; [Figure 14] 1 shows various views and diagrams relating to an exemplary sewing machine and its systems; [Figure 15] 1 shows various views and diagrams relating to an exemplary sewing machine and its systems; [Figure 16] 1 shows various views and diagrams relating to an exemplary sewing machine and its systems; [Figure 17] 1 shows various views and diagrams relating to an exemplary sewing machine and its systems; [Figure 18] 1 shows various views and diagrams relating to an exemplary sewing machine and its systems;

[0011] [Figure 19] Diagrams and flowcharts related to artificial intelligence and neural networks. [Figure 20] Diagrams and flowcharts related to artificial intelligence and neural networks. [Figure 21] Diagrams and flowcharts related to artificial intelligence and neural networks. [Figure 22] Diagrams and flowcharts related to artificial intelligence and neural networks. [Figure 23] Diagrams and flowcharts related to artificial intelligence and neural networks.

[0012] [Figure 24] 1A-1C illustrate various views of stitch adjustment for an exemplary sewing machine. [Figure 25] 1A-1C illustrate various views of stitch adjustment for an exemplary sewing machine. [Figure 26] 1A-1C illustrate various views of stitch adjustment for an exemplary sewing machine. [Figure 27] 1A-1C illustrate various views of stitch adjustment for an exemplary sewing machine. [Figure 28] 1A-1C illustrate various views of stitch adjustment for an exemplary sewing machine. [Figure 29] 1A-1C illustrate various views of stitch adjustment for an exemplary sewing machine. [Figure 30] 1A-1C illustrate various views of stitch adjustment for an exemplary sewing machine. [Figure 31] 1A-1C illustrate various views of stitch adjustment for an exemplary sewing machine. [Figure 32] 1A-1C illustrate various views of stitch adjustment for an exemplary sewing machine. [Figure 33] 1A-1C illustrate various views of stitch adjustment for an exemplary sewing machine.

[0013] [Figure 34] A diagram of various machine vision technologies is shown. [Figure 35] A diagram of various machine vision technologies is shown. [Figure 36] A diagram of various machine vision technologies is shown. [Figure 37] A diagram of various machine vision technologies is shown. [Figure 38] A diagram of various machine vision technologies is shown. [Figure 39] A diagram of various machine vision technologies is shown.

[0014] [Figure 40] 1A-1C illustrate various views of exemplary stitch projection features of an exemplary sewing machine. [Figure 41] 1A-1C illustrate various views of exemplary stitch projection features of an exemplary sewing machine. [Figure 42] 1A-1C illustrate various views of exemplary stitch projection features of an exemplary sewing machine.

[0015] [Figure 43] 10A-10C illustrate various diagrams relating to exemplary fabric and thread compatibility monitoring features of an exemplary sewing machine. [Figure 44] 10A-10C illustrate various diagrams relating to exemplary fabric and thread compatibility monitoring features of an exemplary sewing machine. [Figure 45] 10A-10C illustrate various diagrams relating to exemplary fabric and thread compatibility monitoring features of an exemplary sewing machine. [Figure 46] 10A-10C illustrate various diagrams relating to exemplary fabric and thread compatibility monitoring features of an exemplary sewing machine. [Figure 47] 10A-10C illustrate various diagrams relating to exemplary fabric and thread compatibility monitoring features of an exemplary sewing machine. [Figure 48] 10A-10C illustrate various diagrams relating to exemplary fabric and thread compatibility monitoring features of an exemplary sewing machine. [Figure 49] 10A-10C illustrate various diagrams relating to exemplary fabric and thread compatibility monitoring features of an exemplary sewing machine. [Figure 50] 10A-10C illustrate various diagrams relating to exemplary fabric and thread compatibility monitoring features of an exemplary sewing machine. [Figure 51] 10A-10C illustrate various diagrams relating to exemplary fabric and thread compatibility monitoring features of an exemplary sewing machine. [Figure 52] 10A-10C illustrate various diagrams relating to exemplary fabric and thread compatibility monitoring features of an exemplary sewing machine. [Figure 53] 10A-10C illustrate various diagrams relating to exemplary fabric and thread compatibility monitoring features of an exemplary sewing machine. [Figure 54] 10A-10C illustrate various diagrams relating to exemplary fabric and thread compatibility monitoring features of an exemplary sewing machine. [Figure 55] 10A-10C illustrate various diagrams relating to exemplary fabric and thread compatibility monitoring features of an exemplary sewing machine. [Figure 56] 10A-10C illustrate various diagrams relating to exemplary fabric and thread compatibility monitoring features of an exemplary sewing machine. [Figure 57] 10A-10C illustrate various diagrams relating to exemplary fabric and thread compatibility monitoring features of an exemplary sewing machine. [Figure 58] 10A-10C illustrate various diagrams relating to exemplary fabric and thread compatibility monitoring features of an exemplary sewing machine. [Figure 59] 10A-10C illustrate various diagrams relating to exemplary fabric and thread compatibility monitoring features of an exemplary sewing machine. [Figure 60] 10A-10C illustrate various diagrams relating to exemplary fabric and thread compatibility monitoring features of an exemplary sewing machine. [Figure 61] 10A-10C illustrate various diagrams relating to exemplary fabric and thread compatibility monitoring features of an exemplary sewing machine. [Figure 62] 10A-10C illustrate various diagrams relating to exemplary fabric and thread compatibility monitoring features of an exemplary sewing machine. [Figure 63] 10A-10C illustrate various diagrams relating to exemplary fabric and thread compatibility monitoring features of an exemplary sewing machine. [Figure 64] 10A-10C illustrate various diagrams relating to exemplary fabric and thread compatibility monitoring features of an exemplary sewing machine. [Figure 65]10A-10C illustrate various diagrams relating to exemplary fabric and thread compatibility monitoring features of an exemplary sewing machine.

[0016] [Figure 66] 1A-1C illustrate various views of an exemplary suture quality monitoring feature of an exemplary sewing machine. [Figure 67] 1A-1C illustrate various views of an exemplary suture quality monitoring feature of an exemplary sewing machine. [Figure 68] 1A-1C illustrate various views of an exemplary suture quality monitoring feature of an exemplary sewing machine. [Figure 69] 1A-1C illustrate various views of an exemplary suture quality monitoring feature of an exemplary sewing machine. [Figure 70] 1A-1C illustrate various views of an exemplary suture quality monitoring feature of an exemplary sewing machine.

[0017] [Figure 71] 1A-1C illustrate various diagrams of exemplary object recognition features of an exemplary sewing machine. [Figure 72] 1A-1C illustrate various diagrams of exemplary object recognition features of an exemplary sewing machine. [Figure 73] 1A-1C illustrate various diagrams of exemplary object recognition features of an exemplary sewing machine. [Figure 74] 1A-1C illustrate various diagrams of exemplary object recognition features of an exemplary sewing machine. [Figure 75] 1A-1C illustrate various diagrams of exemplary object recognition features of an exemplary sewing machine. [Figure 76] 1A-1C illustrate various diagrams of exemplary object recognition features of an exemplary sewing machine. [Figure 77] 1A-1C illustrate various diagrams of exemplary object recognition features of an exemplary sewing machine. [Figure 78] 1A-1C illustrate various diagrams of exemplary object recognition features of an exemplary sewing machine. [Figure 79] 1A-1C illustrate various diagrams of exemplary object recognition features of an exemplary sewing machine. [Figure 80] 1A-1C illustrate various diagrams of exemplary object recognition features of an exemplary sewing machine. [Figure 81]1A-1C illustrate various diagrams of exemplary object recognition features of an exemplary sewing machine.

[0018] [Figure 82] 1A-1C illustrate various diagrams of exemplary tactile feedback features of an exemplary sewing machine. [Figure 83] 1A-1C illustrate various diagrams of exemplary tactile feedback features of an exemplary sewing machine. [Figure 84] 1A-1C illustrate various diagrams of exemplary tactile feedback features of an exemplary sewing machine.

[0019] [Figure 85] 1A-1C illustrate various diagrams relating to exemplary machine diagnostic features of an exemplary sewing machine. [Figure 86A] 1A-1C illustrate various diagrams relating to exemplary machine diagnostic features of an exemplary sewing machine. [Figure 86B] 1A-1C illustrate various diagrams relating to exemplary machine diagnostic features of an exemplary sewing machine. [Figure 86C] 1A-1C illustrate various diagrams relating to exemplary machine diagnostic features of an exemplary sewing machine. [Figure 87] 1A-1C illustrate various diagrams relating to exemplary machine diagnostic features of an exemplary sewing machine. [Figure 88] 1A-1C illustrate various diagrams relating to exemplary machine diagnostic features of an exemplary sewing machine.

[0020] [Figure 89] 1 illustrates a cross-sectional view of an exemplary suture sensor.

[0021] [Figure 90] 1 illustrates a perspective view of an exemplary sewing machine.

[0022] [Figure 91] 91 shows a front view of the sewing machine of FIG. 90.

[0023] [Figure 92] 91 shows a bottom-left-front perspective view of the sewing machine of FIG. 90.

[0024] [Figure 93] 91 shows a bottom-left-rear perspective view of the sewing machine of FIG. 90.

[0025] [Figure 94] 92. A detailed view of area 92 of FIG. 92 is shown.

[0026] [Figure 95] A detailed view of area 93 in FIG. 93 is shown.

[0027] [Figure 96] 1 illustrates an exemplary process for controlling a sewing machine.

[0028] [Figure 97] 3 shows a flowchart detailing the operation of an exemplary sewing machine. [Figure 98] 3 shows a flowchart detailing the operation of an exemplary sewing machine. [Figure 99] 3 shows a flowchart detailing the operation of an exemplary sewing machine. [Figure 100] 3 shows a flowchart detailing the operation of an exemplary sewing machine. [Figure 101] 3 shows a flowchart detailing the operation of an exemplary sewing machine. [Figure 102] 3 shows a flowchart detailing the operation of an exemplary sewing machine. [Figure 103] 3 shows a flowchart detailing the operation of an exemplary sewing machine. [Figure 104] 3 shows a flowchart detailing the operation of an exemplary sewing machine. [Figure 105] 3 shows a flowchart detailing the operation of an exemplary sewing machine. [Figure 106] 3 shows a flowchart detailing the operation of an exemplary sewing machine. [Figure 107] 3 shows a flowchart detailing the operation of an exemplary sewing machine. [Figure 108] 3 shows a flowchart detailing the operation of an exemplary sewing machine. [Figure 109]3 shows a flowchart detailing the operation of an exemplary sewing machine. DETAILED DESCRIPTION OF THE INVENTION

[0029] The following description refers to the accompanying drawings which illustrate certain embodiments of the present disclosure. Other embodiments having different structure and operation do not depart from the scope of the present disclosure. Exemplary embodiments of the present disclosure are directed to sewing machines and accessories for use with sewing machines.

[0030] As described herein, when one or more components are described as connected, joined, fixed, coupled, attached, or otherwise interconnected, such interconnection may be direct between the components or may be indirect, such as through the use of one or more intermediate components. Also, as described herein, references to a "member," "component," or "portion" should not be limited to a single structural member, component, or element, but may include an assembly of components, members, or elements. Also, as described herein, the terms "substantially" and "about" are defined as at least close to (and including) a given value or state (preferably, within 10% of, more preferably, within 1% of, and most preferably, within 0.1% of).

[0031] 1-18 and 90-95, various views and diagrams of an exemplary sewing machine and its parts are shown. An exemplary sewing machine, such as sewing machine 100 shown in FIG. 1, includes a sewing bed or base 104 having a post 106 extending upward from one end to support an arm extending horizontally above the sewing bed. A sewing head 102 is attached to the end of the arm and may include one or more needle bars 108 for moving one or more needles 110 up and down to sew a workpiece on the sewing bed 104 below the sewing head 102. The sewing bed includes a needle plate or sewing board located below the sewing head with openings through which the needles 110 pass when making or forming a seam in the workpiece. In some sewing machines, a bobbin located below the needle plate assists in seam formation and distributes a bobbin thread that is stitched with the top thread fed through the workpiece from above by the needle. In other sewing machines, such as an overlock or serger, the bobbin thread is distributed by a looper. A user may interact with the sewing machine 100 via a wide variety of buttons, knobs, switches, and other user interface elements. A touchscreen display 112 may also be used to both present a software-based user interface to the user and receive input from the user. A projector 114 located on the sewing head 102 may be used to project one or more user interface elements onto the sewing bed 104 or a workpiece placed thereon. One or more cameras 116 located on the sewing head 102 or elsewhere around the sewing machine 100 collect information from the workpiece and the environment surrounding the sewing machine 100 that may be used to improve the performance of the sewing machine 100 and the user experience.

[0032] As used herein, "sewing machine" refers to a device that forms one or more stitches in a workpiece with a reciprocating needle and a length of sewing thread. As used herein, "sewing machine" includes, but is not limited to, sewing machines for forming specific seams (e.g., machines configured to form double stitches, chain stitches, overlock stitches), embroidery machines, quilting machines, overlock or serger machines, and the like. It should be noted that various embodiments of sewing machines and accessories are disclosed herein, and any combination of these options may be made unless specifically excluded. In other words, individual components or parts of the disclosed devices may be combined unless they are mutually exclusive or otherwise physically impossible.

[0033] A "stitch" refers to a loop formed by one or more sewing threads, at least one of which passes through a hole formed in a workpiece. The mechanical parts of a sewing machine, such as the needle, hook, looper, tensioning device, and feed mechanism, work together to form a stitch in one or more workpieces. A single repetition of this complex mechanical movement may form a stitch or stitch pattern in the workpiece. The "stitch length" of a repeat or pattern refers to the distance the workpiece is moved when the repeat is performed. Stitch length measurements vary for different types of repeats and patterns and may encompass one or more stitches in a workpiece.

[0034] A pressure bar having a presser foot also extends downward from the sewing head to press the workpiece against the sewing bed and against feed dogs that move back to front and optionally side to side to move the workpiece. The feed dogs move the workpiece in coordination with the presser foot and at a speed that can be fixed or variably controlled by the user, for example with a foot pedal. A wide variety of presser feet and other types of accessories, such as buttonhole foot presser feet, can be attached to the pressure bar to assist in forming particular types of seams or features in the workpiece. An accessory platform can also extend below the sewing head to hold special tools or accessories on or above the sewing bed.

[0035] The speed or frequency at which the needle bar is moved up and down is controlled by the user, as described above. The needle bar typically moves up and down in a cyclical motion to create a seam in the workpiece, but the needle bar can also move side to side simultaneously to create different seams, such as a zigzag seam or a tapered seam, or to change the width of the seam. The type and pitch of the stitch performed by the machine can be selected by the user through a manual interface including buttons, knobs, or levers, through a user interface presented by a computer on a touch screen, or through a voice-controlled interface.

[0036] Different types of sewing machines may include additional components for forming seams on or otherwise processing a workpiece during the sewing process. For example, in a serger, one type of sewing machine that may be used to form the edges of a workpiece, among other functions, a needle called a looper operates below the sewing bed to supply the bobbin thread to form various stitches. Sergers may also include two, three, or more needles above the needle plate and a knife for cutting the edges of the workpiece. Sewing machines may also be used to create embroidery patterns on a workpiece by including a holder for an embroidery hoop on the sewing bed (e.g., FIG. 6). The embroidery hoop holder may be actuated in at least two axes so that the sewing machine's controller can move the embroidery frame so that the needle can trace the embroidery pattern onto the workpiece.

[0037] The sewing thread used during sewing is held in various locations on the sewing machine, such as within a bobbin (FIGS. 13-15) or on a spool held by a spool holder that is part of or extends onto the sewing machine's arm (FIGS. 10-12). The sewing thread is pulled from a thread source (e.g., a bobbin or spool) to the sewing machine's needle through various other elements of the sewing machine positioned to redirect the thread so that it is smoothly withdrawn and delivered to the workpiece with minimal damage to the thread (FIGS. 7-9). The tension of the sewing thread can also be changed by various tensioning devices positioned along the thread path or within the thread source. Tensioning and distribution devices ensure that only the desired amount of thread is dispensed and that the thread that forms the seam on the workpiece is properly tightened. Loose thread can allow the seam to unravel, while tight thread can cause the seam to form incorrectly. The sewing thread tension for the top and bobbin threads may also be adjusted to ensure that the top and bottom tensions are balanced to properly form a stitch along the desired stitching path in the workpiece.

[0038] Referring now to FIG. 16 , a block diagram of a computer-based control system for a sewing machine 100 is shown. The sewing machine includes one or more data collection devices 118, one or more data storage devices 120, a processor 122, a user interface 124, and motors and actuators 126. The sewing machine 100 may also include a network interface that interfaces with the processor 122 and is used to connect the sewing machine 100 to a cloud system and / or other sewing machines or devices via a wireless network. The data collection device 118 includes a wide variety of digital, analog, active, and passive sensors, as well as software components, as described in further detail below. These sensors collect data about the sewing machine itself, the workspace or environment surrounding the sewing machine, the sewing operation being performed by the sewing machine, user interactions with the sewing machine, and the sewing material (e.g., fabric and thread) on which the sewing machine is operating. The data storage device 120 includes one or more computer memory chips for storing data collected by the data collection device 118 and the operating software of the sewing machine 100. The structure, various functions, and parameters of one or more neural networks 128 are also stored by data storage device 120. Processor 122 accesses the data stored on data storage device 120 and executes operating software to provide functionality to sewing machine 100. User interface 124 is presented to the user via touchscreen display 112 and via physical controls, such as buttons, levers, dials, lights, speakers, and actuators. Motors and actuators 126 include electromechanical actuators, motors, and general-purpose mechanical components that are controlled by the control system to cause movement of the various moving parts of the sewing machine, i.e., the needle bar, feed dog, bobbin, and looper.For example, the speed of the motor may be controlled directly by input from a foot pedal actuated by a user, or may be controlled via a computer that receives and interprets the foot pedal input before sending the signal to one or more motor controllers that control the sewing machine motors.

[0039] As used herein, a "computer" or "processor" includes, but is not limited to, any programmed or programmable electronic device or collaborative device capable of storing, retrieving, and processing data, and may be a processing unit or a distributed processing configuration. Examples of processors include microprocessors, microcontrollers, graphics processing units (GPUs), floating-point units (FPUs), reduced instruction set computing (RISC) processors, digital signal processors (DSPs), field programmable gate arrays (FPGAs), etc. A single microprocessor and / or one or more cores of multiple microprocessors, each having one or more cores, may be used to perform the operations described as being performed by a processor herein. A processor may also be dedicated to training neural networks and other artificial intelligence (AI) systems. The processor may be located locally on the sewing machine or provided at a remote location accessible via a network interface.

[0040] As used herein, a "network interface" or "data interface" includes, but is not limited to, any interface or protocol for transmitting and receiving data between electronic devices. A network or data interface may refer to a connection to a computer via a local network or through the Internet, or to a portable device, such as a mobile device or USB thumb drive, via a wired or wireless connection. A network interface may be used to form a network of computers to facilitate distributed and / or remote computing (i.e., cloud-based computing). "Cloud-based computing" refers to computing implemented on a network of computing devices remotely connected to a sewing machine via a network interface.

[0041] As used herein, "logic," which is synonymous with "circuitry," includes, but is not limited to, hardware, firmware, software, and / or combinations of each for performing one or more functions or actions. For example, based on a desired application or need, logic may include a software-controlled processor, discrete logic such as an application-specific integrated circuit (ASIC), a programmed logic device, or other processor. Logic may also be fully embodied as software. As used herein, "software" includes, but is not limited to, one or more computer-readable and / or executable instructions that cause a processor or other electronic device to perform a function, action, process, and / or behave in a desired manner. Instructions may be embodied in various forms, such as a routine, an algorithm, a module, or a program, including separate applications or code from a dynamically linked library (DLL). Software may also be implemented in various forms, such as a stand-alone program, a web-based program, a function call, a subroutine, a servlet, an application, an app, an applet (e.g., a Java applet), a plug-in, instructions stored in a memory, part of an operating system, or other types of executable instructions or interpreted instructions from which executable instructions are created.

[0042] As used herein, a "data storage device" refers to a device for non-transitory storage of code or data, e.g., a device having a non-transitory computer-readable medium. As used herein, a "non-transitory computer-readable medium" refers to any suitable non-transitory computer-readable medium for storing code or data, e.g., magnetic media, e.g., fixed disks in external hard drives, fixed disks in internal hard drives, and flexible disks, optical media, e.g., CD disks, DVD disks, and other media, e.g., ROM, PROM, EPROM, EEPROM, Flash PROM, external flash memory drives, etc.

[0043] The sewing machine's user interface may include a wide variety of input devices and means of communication with the user, such as, for example, buttons, knobs, switches, lights, displays, speakers, touch interfaces, and lights. The user interface for the sewing machine may be graphically presented to the user via one or more displays, including a touchscreen display 112 that includes a touch-sensitive overlay for detecting the position of the user's fingers touching the display. As such, the user can interact with the user interface by directly touching the screen at specific locations and by performing touch gestures, such as the touch, touch and hold gesture, pinch or spread gesture, and touch and move gesture shown in FIG. 2. The presence, position, and movement of the user's hands, fingers, or eyes may also be detected via analysis of data from optical sensors (e.g., cameras) or by sound, light, infrared radiation, or electromagnetic field disturbances via proximity sensors (e.g., FIG. 3). The graphical user interface may also be projected by one or more projectors on the sewing machine onto the sewing bed 104, a workpiece, an adjacent surface such as a wall or table, or any other suitable surface. Alternatively, the sewing machine 100 may operate without a graphical user interface via voice commands and audible feedback in the form of specific tones and / or computer voices. Haptic feedback may also be provided via actuators that vibrate various parts of the machine upon actuation or in response to a variety of workpiece or machine conditions. Audible and tactile interaction with the sewing machine is particularly useful for visually impaired users.

[0044] As seen in FIG. 17 , the sewing machine 100 may provide notifications and feedback to the user through visual, auditory, and tactile means. For example, an indication that an incorrect accessory is installed on the machine may be presented to the user via a user interface on the sewing machine's display, while an audible notification, such as a beep or computer voice, is sent to the user through the sewing machine's speaker. Notification may also be sent to the user via haptic or tactile feedback, such as through the vibration of a portion of the sewing machine 100 touched by the user. That is, the sewing machine may vibrate the sewing bed 104 to alert the user that the machine is not properly configured for the particular sewing job selected by the user. The user feels the vibration under their fingers in contact with the workpiece and sewing bed, prompting them to look at the display for further information. The sewing machine's illumination lights may also be controlled to alert the user, for example, by changing the color of a flash when an incorrect accessory is installed, prompting the user to look at the display for further information.

[0045] A projector 114 is also provided on the sewing head 102 and may be pointed downward toward the sewing bed 104 and the workpiece, as shown in FIGS. 1, 4, 92, and 94. The projector 114 is positioned to project useful information onto the workpiece to assist a user in using the sewing machine. For example, the projector 114 may project needle penetration points onto the fabric so that the user can see the needle position before making a stitch. Lines or other guides may also be projected onto the workpiece to assist the user in sewing in a straight line or along a desired path. Similar to guides, the projector 114 may project a selected stitch pattern onto the workpiece to represent the planned stitch. The projector 114 may also project an image onto the workpiece to display the selected embroidery pattern on the workpiece so that the user can position the embroidery pattern on the workpiece in a desired location. The information projected by the projector 114 may also include feedback to the user about the status of the machine or a particular sewing operation. For example, projector 114 may project a warning notice onto the workpiece that the wrong needle has been installed for the type of material being used as the workpiece. Projector 114 may also assist the user by providing visual instructions, such as still or animated images instructing the user on how to change the needle, thread the machine, or rotate the workpiece. In other words, projector 114 may be used by a computer as another means of providing feedback and instructions to the user. It should also be noted that the images projected by projector 114 may also be detected by an optical sensor such that the user's interaction with the projected images, such as by touching a button or series of buttons, allows the sewing machine to respond to the user's interaction with the projected images.

[0046] A wide variety of data collection devices 118, i.e., digital sensors, analog sensors, active sensors, passive sensors, and software components, may be used by the sewing machine 100 to acquire data regarding the sewing machine itself, the workspace or environment surrounding the sewing machine, the sewing material on which the sewing machine is operating (e.g., the fabric workpiece and the sewing thread used to form the seams), the sewing operations performed by the sewing machine, and the user's interaction with the sewing machine. A non-exhaustive list of sensor types for sewing machine 100 includes acoustic, sound, vibration, chemical, biometric, sweat, breath, fatigue detection, gas, smoke, retina, fingerprint, fluid velocity, speed, temperature, light (e.g., camera), light, infrared, ambient brightness, color, RGB color (or another color space sensor such as one using CMYK or grayscale color spaces), touch, tilt, motion, metal detector, magnetic field, humidity, moisture, imaging, photon, pressure, force, density, proximity, ultrasonic, load cell, digital accelerometer, motion, translation, friction, compressibility, audio, microphone, voltage, current, impedance, barometer, gyroscope, Hall effect, magnetometer, GPS, electrical resistance, tension, strain, and many others. The software-based data collection device 118 may include various data logs that are entered as sewing machine 100 is used. For example, a user activity log may record events involving input from a user via the user interface 124, and a system event log may record software events that occur during normal use of the sewing machine 100, which may be used for machine learning or diagnostic purposes.

[0047] Sensors may be located in a wide variety of locations on the machine and used by the sewing machine in a wide variety of ways. For example, a sewing machine may include touch and proximity sensors (e.g., the proximity sensors shown in FIG. 3 ) to provide touch control of a user interface presented on the sewing machine's display. Similar touch or proximity sensors may also be provided in other locations on the sewing machine, such as on the arm or sewing bed. Touch sensors in these other locations may be used in conjunction with the user interface presented to the user or may be used to monitor the position of the user's hand (or other foreign object, such as the user's hair or a loose sewing pin) on the machine during the sewing process for safety purposes. The sewing machine may also include eye tracking sensors that incorporate optical sensors, such as cameras or other detection means, for tracking eye position and / or the user's gaze. One or more optical sensors on the machine may be used not only to collect data about the user of the machine, but also to collect data about the workpiece, other sewing materials, such as sewing thread, and the sewing machine itself. Additional examples of the use of sensors and sensor data by sewing machines are provided throughout this disclosure. Many of the sensors used in sewing machines require calibration after installation to ensure that the data collected by the sensors and provided to the neural network is accurate. Sensor calibration may also be updated in the field periodically or when specified by the user. Sensors may be calibrated in any suitable manner. For example, camera calibration may be performed using the techniques described in U.S. Pat. No. 8,606,390, the entire contents of which are incorporated herein by reference. Cameras and other sensors may also be calibrated with techniques that use neural networks to identify characteristics of the sewing machine when calibrating the cameras, for example.

[0048] The sewing machine's one or more optical sensors may be positioned in a wide variety of locations around the sewing machine. As used herein, "optical sensor" means a sensor capable of collecting data from electromagnetic radiation (see FIG. 18) and may include, but is not limited to, sensors for detecting ultraviolet radiation, visible light, infrared radiation, and the like. Particular optical sensors may be tuned to particular wavelengths of electromagnetic radiation, such as particular wavelengths of laser light. One particular optical sensor that may be used in an exemplary sewing machine is a camera. The camera may include a lens to focus or otherwise redirect light to a sensor that receives the light data and transmits the light data to another device for processing.

[0049] One or more optical sensors may be positioned within the sewing machine to observe the workpiece during the sewing process, such as the camera 116 shown in FIGS. 1, 4, 6, 93, and 95. The optical sensor or sensors viewing the workpiece may be used to determine the color of the workpiece, the material of the workpiece, the position of the workpiece, the orientation of the workpiece, and the magnitude and direction of the workpiece's movement. The same optical sensors may also be used to detect objects within the sewing area, such as the user's hands, hazards (e.g., hair, the user's clothing, sewing pins, etc.), the type of needle installed in the sewing machine, or the type of presser foot. The optical sensors may also monitor whether the needle, presser foot, sewing plate, or accessories are properly installed and remain properly installed during use. Additional optical sensors or similar sensing devices may be positioned on the machine facing the user to provide the sewing machine's computer with information about the user, such as the user's eye position and line of sight, so that the sewing machine can determine which part of the machine the user is looking at. Tracking the user's eyes and current gaze, for example, allows the sewing machine to determine where best to illuminate the sewing bed or present useful information to the user so that important notifications or warnings are not missed. Sewing machines may include various security features to restrict access to the machine and to prevent theft of the machine. When the machine is powered on or awakened from sleep mode, for example, the user may be presented with a prompt requesting the user to prove their identity. The user may then enter a predetermined code to prove they are an authorized user to access and use the sewing machine. In addition to or instead of the predetermined code, the user may provide biometric information as proof of identity, for example, via a fingerprint sensor or facial recognition. A fingerprint sensor may be included on the sewing bed or another location where a user typically places their hand to use the machine. One or more user-facing cameras enable the sewing machine to identify the user using facial recognition technology for purposes of providing access to the machine.

[0050] A user can also associate another device with their account on the sewing machine and use that device to unlock the sewing machine. For example, an app on a smartphone or tablet can be associated with a user account so that the sewing machine can be unlocked via an app or by holding the smartphone or tablet within a predetermined range of the sewing machine. Any of these means for authenticating a user can be used individually or together to provide two-factor authentication. A phone number capable of receiving text messages can also be associated with the user's account so that a code can be sent for use in two-factor authentication. These other devices or phones can also receive alerts from the sewing machine when other attempts to access the machine fail, for example, after a predetermined number of attempts to access the sewing machine. If the sewing machine is suspected to be stolen, these other devices can be used to determine the location of the sewing machine via a GPS sensor in the sewing machine or through other means, such as a local network detected by the sewing machine. Additionally, alerts resulting from the machine being moved from its normal location or from failed attempts to access the machine can include the location of the sewing machine as determined by the built-in GPS sensor to facilitate recovery of the sewing machine, if relevant.

[0051] To process and act on the wide variety of data provided to computers located inside and / or outside the sewing machine via the above-mentioned sensors, various artificial intelligence ("AI") tools and techniques are used that enable analysis of extremely large, structured or unstructured, and changing data sets, deductive or inductive reasoning, complex problem solving, and computer learning based on historical patterns, expert input, and feedback loops (see, for example, FIG. 19). As used herein, "artificial intelligence" refers to a broad range of tools and techniques in the field of computer science that enable computers to learn and improve over time. FIG. 19 provides a non-exhaustive overview of these tools, such as symbolic artificial intelligence, machine learning, and evolutionary algorithms. As seen in FIG. 19, artificial neural networks can be used for a variety of machine learning applications and may use a variety of learning methods, including, but not limited to, statistical learning, deep learning, supervised learning, unsupervised learning, and reinforcement learning. Artificial intelligence allows the sewing machine to adapt to situations not anticipated or accurately predicted by the software programmer, facilitating sophisticated yet intuitive ways of interacting with the sewing machine to achieve desired results. That is, the AI ​​tools and techniques described herein are used by a computer integrated into or external to a sewing machine to make decisions that support or benefit the user based on data provided to the computer via sensors described herein. Although particular artificial intelligence tools (e.g., neural networks) are described below, other artificial intelligence tools may be used for the same tasks, and the description of one tool or technique should not be seen as limiting the application solely to that tool or technique unless otherwise indicated herein.

[0052] Diagrams of neural networks and associated processes are shown in Figures 20-23. As used herein, "neural network" includes, but is not limited to, multiple interconnected software nodes or neurons arranged in multiple layers, such as an input layer, hidden layer, and output layer as seen in Figure 20. Figure 20 shows a diagram of a neural network 128 including nodes 130 arranged in various layers. Like neurons in the human brain, each node 130 may have one or more input connections 132 and output connections 138 to create a many-to-many relationship with other nodes 130 in the network. That is, the output of a single node may be connected to the inputs of many different nodes, and a single node may receive the outputs of many different nodes as inputs.

[0053] Each node 130 of the network is configured to perform calculations on data from other nodes and calculate output data in conjunction with node parameters that are adjusted during the neural network training process ( FIG. 21 ). That is, a node of the network is a computational unit having one or more input connections for receiving input data from nodes in a previous layer of the network and one or more output connections for sending output data to nodes in a subsequent or next layer of the network. Each node 130 includes a computational unit 136 for calculating the result of an activation function, which may incorporate the input data received via the input connections, the input parameters associated with each input connection, and any function parameters 134, to calculate output data that may be further modified by the output parameters. For example, the input data from each input connection may be modified by that input connection's associated input parameters, e.g., weight parameters, to provide the input connection's associated weight. The result of the activation function, which may be modified by any function parameters, is sent as output data via the output connection to a node in a subsequent layer of the neural network. Any function parameter may be, for example, a threshold, such that the calculated result of the activation function is sent as other node output data only if the combined and weighted input data exceeds a threshold set by the threshold.

[0054] All forms of data available to a sewing machine, such as from sensors, software, data storage devices, and user input via software, can be processed through a neural network. The information to be processed first encounters an input layer, which performs initial processing of the input data and outputs the results to one or more hidden layers to process the output values ​​from the input layer. The information processed through the hidden layers is presented in the output layer as a confidence probability for a given result, such as the location of a detected object in an image and its classification. Software in the sewing machine's computer can receive information from one of the neural network's layers and take corresponding action to adjust sewing machine parameters and / or notify the user based on the results of the neural network processing (Figure 22).

[0055] During training of the neural network 128, the node parameters (i.e., at least one of the input parameters, function parameters, and output parameters) of each node in the neural network are adjusted via a backpropagation algorithm until the output of the neural network corresponds to the desired output for a given set of input data. Referring now to FIG. 21 , a process for training a neural network is shown. The neural network begins the training process with node parameters that may be randomized or transferred from an existing neural network. The neural network is then presented with data from a sensor to process. For example, an object may be presented to an optical sensor to provide visual data to the neural network. The data is processed by the neural network, and the output is tested so that the node parameters of the various nodes in the neural network may be updated to increase the confidence probability of the detection and classification performed by the neural network. For example, when a presser foot is presented to the optical sensor for identification by the neural network, the neural network will provide a confidence probability that the object presented to the optical sensor is located within the coordinate range of the image and can be classified as a particular presser foot. During the training process, the node parameters of the neural network's nodes are adjusted to increase the neural network's confidence that a particular answer is correct, so that when presented with particular visual data, the neural network "understands" that a particular answer is the most correct answer, even if that data is not exactly the same as what it "saw" before.

[0056] A neural network is considered "trained" when decisions made by the network reach a desired level of accuracy. A trained neural network can be characterized by a set of node parameters that were adjusted during the training process. This set of node parameters can be sent to other neural networks with the same node structure, causing those other neural networks to process data in the same manner as the initially trained network. A neural network stored on a particular sewing machine's data storage device can then be updated by downloading new node parameters, as shown in Figure 23. Note that a neural network's node parameters, such as input weight parameters and thresholds, tend to occupy significantly less storage space than the image library used for comparison with images or visual data collected by an optical sensor. Consequently, neural network files and other important files can be updated quickly and efficiently across the network. For example, the structure of the neural network, i.e., the map of connections between nodes and the activation functions calculated at each node, can also be updated in this way.

[0057] The neural network can also be continuously trained, with node parameters periodically updated based on feedback from various data sources. For example, the locally or externally stored node parameters of the neural network can be periodically updated based on data collected from sensors that match or mismatch the neural network's output. These adjusted node parameters can also be uploaded to a cloud-based system and shared with other sewing machines so that all sewing machines' neural networks improve over time. The neural network's input data can also be shared with a server or cloud-based system to provide further training information for the neural network. A large amount of data from on-site sewing machines can, through training, improve the accuracy of predictions made by the neural network.

[0058] 96, an exemplary process 200 for controlling the sewing machine 100 is shown. The process 200 includes steps 202 of collecting data, 204 of storing the collected data in a data storage device, 206 of processing the collected data through a neural network, and 208 of controlling the sewing machine based on the processed data. The collected data relates to at least one of the sewing machine, the workspace or environment surrounding the sewing machine, sewing materials (e.g., sewing thread and workpiece), sewing operations performed by the sewing machine, and user interactions with the sewing machine (e.g., recorded by a user interface or by other sensors). The neural network includes multiple nodes, each including input and output connections, node parameters, and computational units. Based on the processed data, the user interface may be controlled to interact with the user (e.g., by presenting alerts and / or prompts), the data storage device may be controlled to store the processed data (e.g., as a separate record or by updating neural network parameters), and the adjustable components may be adjusted to change the current state of the sewing machine (e.g., change the motor speed, activate a light, move the needle, or any other action that changes the sewing machine or the sewing operation performed by the sewing machine). As seen in FIG. 22 , data collected by the sewing machine's sensors may be processed locally on the sewing machine or via an external processor in a cloud-based neural network. The locally stored neural network may be pre-trained or may be a continuously updating neural network. The data processed by the neural network, whether locally or remotely, is then used by the sewing machine's software to make decisions that result in machine and / or user interaction.

[0059] 24-33 and 104-105, various diagrams and charts are shown relating to the use of artificial intelligence in a sewing machine to control the position of stitches made on fabric during sewing. The location of stitches is typically left to the user during normal sewing operations. That is, the user may be provided with various visual aids, such as guides on the needle plate, projected guides, or markings on the workpiece, and it is up to the user to maintain the sewing path in the correct position. However, because the visual aids do not control the position of the workpiece, the final position of any stitch is dependent on the user's skill in holding and advancing the fabric in the proper orientation. However, the sewing machines described herein can use one or more optical sensors and depth perception systems (described in more detail below and which may include optical sensors, projectors, ultrasonic, and thermal vision systems) to find the desired path on the workpiece and manipulate the lateral position of the needle bar and workpiece via the feed dog so that the stitches are positioned along the desired path, even if the user moves the workpiece off-path. Feed dogs used to influence the feed direction of the workpiece may include linear translation feed dogs, linear translation feed dogs combined with circular rotation feed dogs, and multi-component feed dogs having two or more independently moving parts, such as left and right parts that translate at different distances and / or speeds (similar to tank treads) to rotate the workpiece during feed. The two or more parts of the feed dog may also be positioned at different heights to accommodate sewing together fabrics having different thicknesses.

[0060] When quilting, it is common to need to stitch along an existing seam between two or more fabrics, as seen in Figure 24. Sewing along a groove tends to lift the two fabrics away from the seam, creating the appearance of a long, low, relatively deep wedge-shaped groove between the two fabrics, hence the name "groove seam." Stitching along a groove helps conceal the seam in the finished quilt. Maintaining consistent stitch placement along a groove is very difficult because the groove is moving and a very narrow target. To attempt to conceal the stitching if the stitching is damaged, a colored thread that somewhat matches the surrounding quilt pieces or even a transparent thread may be used.

[0061] Referring now to FIG. 25, an optical sensor or the optical sensor's field of view and the sewing machine's depth perception system are shown superimposed on the image of FIG. 24. As seen in FIG. 104, the user begins sewing and activates the "groove stitch" feature. As the workpiece is placed on the sewing bed, data is continuously collected from the sensors and processed through a neural network. The collected data includes data collected from cameras pointed toward the sewing area upstream and / or downstream of the needle drop location, data related to the sewing operation (e.g., stitch type and parameters), sewing thread data (e.g., thread tension), and collected data related to the sewing material (e.g., feed speed, motion vector, and workpiece topography). The data is processed through a neural network trained to detect and recognize grooves formed between two or more pieces of fabric. That is, the neural network provides a confidence probability of the groove's location and details about its appearance. When a user begins sewing and moves a workpiece into position under the needle, for example by depressing a foot pedal, pressing a button, giving a voice command, etc., the sewing machine detects and recognizes the groove and controls the position of the stitch being formed on the workpiece by swinging the needle bar and / or feeding the workpiece laterally so that the stitch is formed in the groove. The actuators shown in Figures 26-33 can be used to change the lateral position of the needle bar so that the needle is in communication with the groove and positions the stitch. As the workpiece is moved, the feed dogs can also be controlled to move the workpiece laterally to some extent during back and forth feeding of the workpiece, using the actuators shown in Figures 30-33. That is, the feed dogs are capable of movement in two axes so that the direction of the sewing path can be changed in addition to the feed rate of the workpiece, which is typically controlled by the feed dogs.

[0062] Referring now to FIG. 105, a process similar to the "groove seam" feature can be used to form a stitch at a predetermined offset distance or tolerance from a workpiece feature. As the workpiece is placed on the sewing bed, data is continuously collected from sensors and processed through a neural network. The collected data includes data collected from cameras pointed toward the sewing area upstream and / or downstream of the needle entry location, data related to the sewing operation (e.g., stitch type and parameters), thread data (e.g., thread tension), and collected data related to the sewing material (e.g., feed speed, motion vectors, and workpiece topography). The data is processed through a neural network trained to detect and recognize workpiece features, such as edges, seams, "grooves" between two fabrics, buttonholes, or pockets. That is, the neural network provides a confidence probability for the location of the feature and details about its appearance. When a user begins sewing and moves a workpiece into position under the needle, for example by depressing a foot pedal, pressing a button, giving a voice command, etc., the sewing machine detects and recognizes the feature and controls the position of the stitch formed on the workpiece by swinging the needle bar and / or feeding the workpiece laterally so that the stitch is formed a predetermined distance from the identified feature. For example, a user can specify a half-inch (1.27 centimeter) seam tolerance and begin sewing a workpiece having an edge positioned within the range of the needle bar's lateral movement. As the workpiece is moved through the sewing machine, the edge of the workpiece is detected and the seam is formed half-inch (1.27 centimeters) away from the edge without the user having to precisely follow an edge guide.

[0063] Referring now to Figures 34-39, diagrams of various computer vision systems are shown. In addition to the visual data provided by the optical sensors, the neural network can receive input from a depth perception system to provide more accurate calculation of the groove location and three-dimensional topography. The depth perception system can use any of the stereoscopic and other computer vision technologies, including, but not limited to, those shown in Figures 34-39, which calculate the distance to the workpiece using two optical sensors, two optical sensors and a projector, a projector and a single optical sensor, an optical sensor and a laser light source, a thermal vision system, or an ultrasonic vision system. Referring now to Figure 34, a passive stereo depth perception system is shown that uses two cameras (similar to human stereoscopic vision) to determine the distance to a target object based on a comparison between two images captured by the cameras. The active stereo vision system shown in Figure 35 is similar but includes a projector for projecting graphics onto the target object to improve the distance measurement by the two cameras. Projectors are also used in the structured light vision system of FIG. 36 to project lines or some other visual pattern onto a target object that can be observed by a camera to determine the distance from the camera to the target object. Another depth perception system is shown in FIG. 37, which calculates distance to a target object using a camera that measures the time it takes for light from a laser to travel from the laser to the target object and back to the camera. This distance information can also be provided to a neural network that processes visual data of the workpiece from the optical sensor to calculate the location of the grooves while the workpiece is moved under the sewing head to form a seam in the workpiece. That is, the line of points on the workpiece that are located farthest from the sewing head can be identified as the groove in the fabric.

[0064] It should also be noted that the optical sensor and depth perception system described above can have a wide variety of applications. That is, one or more optical sensors and depth perception configurations can be used to recognize the topography of fabrics and sewing threads in three dimensions to identify the type of fabric material and sewing thread already used on a workpiece. The density and type of fabric material can also be determined using an ultrasonic or thermal vision system, which can be part of the depth perception system. That is, denser materials respond differently to ultrasonic pulses than lighter materials. A laser, infrared radiation, or some other heat source can be used to heat a portion of the workpiece, which can be detected by a thermal vision system including, for example, an infrared sensor. The thermal conductivity of the fabric can then be measured and compared to known values ​​for different types of fabric. This feature can be particularly useful in embroidery processes when working on workpieces with existing stitches. The information provided by these systems can also be used to identify the type of fabric and sewing thread used on a workpiece, automatically adjust a sewing machine to sew that type of material, and recommend specific needles or other accessories to the user that can be installed on the machine for use with that workpiece. Automatic dimming can be performed to allow the user and sewing machine sensors to view the workpiece material in a manner particularly suitable for sewing (i.e., lower brightness improves visibility of highly reflective fabrics). Additionally, as described in more detail below, the sewing machine can provide recommendations and even warnings to the user based on the identified combination of thread and fabric type. The 3D topography of the workpiece can also be used to determine when to release pretension on the presser foot to more easily climb multiple layers of fabric, such as when sewing a hem.

[0065] Processing distance information along with visual data through the neural network further improves the accuracy of the information, as the neural network can be trained to consider both the appearance and shape of the workpiece when determining the location of the groove. The neural network used to process the visual and distance data may be trained elsewhere, or the node parameters and other necessary information transmitted to the sewing machine via the sewing machine's computer ( FIG. 23 ) and / or a cloud connection with the neural network may be trained during the sewing process. For example, the same or additional optical sensors can be used to observe the stitches formed on the workpiece and identify stitches that have missed the groove. Because the computer knows the stitch pitch, control data for specific missed stitches can be determined and used to adjust the node parameters of the neural network to reduce the likelihood of a missed stitch. The computer on the sewing machine can work in conjunction with the cloud-based neural network, which can provide additional computing power for processing data provided to the neural network and for training the neural network while the sewing machine is operating, so that the neural network is a continuously learning neural network.

[0066] The techniques implemented to accurately form the above-described "groove seam" may have broader applications in sewing in a wide variety of contexts to form "perfect stitches." That is, data from one or more optical sensors and a depth perception system may be processed through a neural network to provide control data to one or more motors and actuators of a sewing machine to accurately and precisely form any type of desired stitch at any specific location on a workpiece. In addition to using visual data from the optical sensors and depth perception data from the depth perception system, the perfect stitch control system may consider data from a thread tension sensor, a needle position sensor, a needle force sensor, a fabric feed rate sensor, the speed and frequency of needle bar movement, the pressure applied by the presser foot, and the feed rate of the feed dog. Data from these sensors may be processed through a neural network to predict whether an incorrect stitch is likely to be made and may instruct the control system to adjust various parameters accordingly to compensate for various factors likely causing the error. Upon processing the data provided by these sensors, decisions from the neural network can be used by the sewing machine's computer to adjust a wide variety of sewing parameters, such as thread tension, needle position, force, speed, and timing; stitch length and type; motor speed; and fabric feed settings, to actively achieve ideal stitch precision and accuracy. All of these features can be combined to correlate machine performance with the user's skill level. That is, the sewing machine can learn to work with beginner, intermediate, and advanced users, adapting machine speed, providing corrections and alerts, and recommending guides or assistance.

[0067] As with the groove stitch example above, the sewing machine can also check for errors in stitches already formed. That is, each completed stitch can be actively monitored for quality purposes. If data collected by the sewing machine's sensors indicates that an imperfect stitch is being formed (e.g., a skipped or misaligned stitch), output data generated by the neural network can be used to determine adjustments that can be made to the sewing machine's parameters. These adjustments can be made, and the resulting stitches monitored until the stitches are perfect and error-free. Sensors can also be used to detect thread breaks so that sewing can be stopped and the sewing thread replaced. Thus, as the neural network is continually trained, stitch quality can improve over time. For example, a zigzag stitch can be controlled to maintain a specific width on either side of a fabric seam so that subsequent stitches are formed on the opposite fabric. Alternatively, when creating a simple straight stitch, the tension of the upper and lower threads can be controlled to avoid the stitch pulling to one side of the workpiece. The optical sensor can also identify pattern lines that are painted, superimposed, drawn, or projected onto the fabric, or that exist as part of the pattern on the workpiece (e.g., woven or printed on the fabric), so that the seam is formed along that line or at a fixed offset distance from the line. That is, the optical sensor can be used to detect the edge of the fabric and help the user sew along the edge of the fabric with a fixed seam tolerance. Two or more materials may have edges that the user attempts to align during sewing, and the sewing machine can detect misaligned workpieces and recommend corrections to the user.

[0068] An example of a process for detecting and adjusting sewing errors is shown in FIG. 106. While the user sews, data regarding the sewing operation (e.g., stitch type and parameters), sewing thread data (e.g., thread tension), and sewing material (e.g., workpiece feed speed, motion vector, and topography) is continuously collected from cameras pointed toward the sewing area upstream and / or downstream from the needle entry location. Input data may also be provided from a database of known sewing errors and their causes (e.g., a bunched seam may be caused by an imbalance in the tension of the upper and lower threads). The data is processed through a neural network trained to identify sewing errors, and the identified errors may be recorded and reported to the user along with contextual information, such as the sewing machine parameters at the time of the error or the workpiece movement when the error occurred. The recorded information may be used to update local and remote neural networks to improve error detection and prediction. The sewing machine may also control actuators or motors in response to the identified sewing errors to correct the error in the next stitch or prevent the recurrence of similar errors. A non-exhaustive list of sewing errors includes skipped stitches, uneven stitches, misaligned stitches, seam bunching, variable stitch density, broken bobbin, broken looper, broken needle, melted thread, broken needle, stuck needle, needle hitting the throat plate, thread cut by the needle, inconsistent thread tension, wavy seam, unthreaded needle, loose needle holder, loose presser foot, misplaced presser foot, stationary needle, stationary work piece, bunched work piece, bunched thread, thread knots, loose stitches, tangled thread, frayed thread, torn thread, variable work piece feed, bent needle, damaged looper, damaged stitch finger, misplaced looper, misplaced stitch finger, and dull fabric knife.

[0069] A continuously trained neural network, i.e., one that is trained and can be adjusted during the sewing process, can adjust numerous parameters of the sewing process in unpredictable ways to compensate for unforeseen problems that would be very difficult or impossible to anticipate and address by the user through conventional control software or by adjusting the sewing machine's settings. For example, the sewing machine can adjust the feed speed and stitch pitch in response to the user applying an external force to the sewing machine that would otherwise cause the workpiece to move and become dislodged. Adjustments to the thread tension or presser foot may also be determined to be useful by the neural network. That is, the sewing machine can learn to compensate for and even resist erroneous movements by the user to ensure that the stitches produced are accurate and precise.

[0070] The sewing machine's projector can be used in conjunction with the artificial intelligence techniques described herein to improve the location of the image projected onto the workpiece. For example, as shown in FIG. 107, a neural network can be used to identify features on the workpiece so that a sewing guide can be projected at the location of the feature or a predetermined distance from the feature. As the workpiece is placed on the sewing bed, data is continuously collected from sensors and processed through a neural network. The collected data includes data collected from cameras pointed toward the sewing area upstream and / or downstream of the needle drop location, data related to the sewing operation (e.g., stitch type and parameters), sewing thread data (e.g., thread tension), and collected data related to the sewing material (e.g., feed speed, motion vector, and workpiece topography). The data is processed through a neural network trained to detect and recognize workpiece features, such as edges, seams, "grooves" between two fabrics, buttonholes, or pockets. That is, the neural network provides a confidence probability for the location of the feature and details about its appearance. When the user begins sewing and moves the workpiece into position under the needle, for example by depressing a foot pedal, pressing a button, giving a voice command, etc., the sewing machine detects and recognizes the feature and controls the projector to project a sewing guide, such as a straight line in the feed direction, at the location of the feature or a predetermined offset distance from the identified feature. For example, the user can specify a seam tolerance of one-half inch (1.27 centimeters) and activate a sewing guide that projects a line one-half inch (1.27 centimeters) from the edge of the workpiece that moves with the workpiece as it is moved by the user, allowing the user to modify the lateral position of the workpiece to form a seam in a desired location.

[0071] 40-42, various diagrams and charts are shown relating to the use of artificial intelligence in a sewing machine to predict the path of a stitch being formed and project an image of the predicted stitch onto the fabric in front of the needle position to inform and guide the user. As with the stitch adjustment and control functions described above, an optical sensor collects visual data from the fabric workpiece and provides that data to a computer. The computer processes the data through a neural network trained to predict the sewing path based on the visual data regarding the stitches already formed and sewing machine parameters, such as needle position and speed, fabric position and speed, feed dog speed, pressure applied by the presser foot, tension of the upper and lower threads, and the speed selected by the user. The neural network processes the data and provides the predicted sewing path to the sewing machine computer, which then projects a series of stitches along the predicted path in front of the needle. The stitch type selected by the user via the user interface (FIG. 40) is incorporated into the projected image (FIG. 41) so that the user can see the shape of the stitches being formed along the predicted sewing path. As the workpiece is moved by the user or by the fabric-translating portion of the sewing machine, the projected path of the stitch also moves so that the path appears in a constant position on the workpiece. (In an embroidery machine, the projected embroidery pattern may move with the workpiece as the embroidery frame moves.) The predicted path may also be adjusted to suggest a path the user can follow to return to a deviated pattern. The projected path of the stitch may also begin at the needle entry point and extend in a straight or curved line in the feed direction that does not move as the workpiece rotates or translates.

[0072] Referring now to FIG. 42, predicted stitch lines are shown projected in black, with the formed stitches shown in blue. The projected stitches appear to be consumed by the actual stitches being formed on the workpiece. The prediction distance can also be set by the user so that only some predicted stitches are shown, or stitch lines that extend to the limits of the projector's range are shown. Embroidery patterns can also be projected in a similar manner, with the projected stitches disappearing once the pattern is formed on the workpiece. As the workpiece held by the embroidery frame is moved, the projected image also moves to track the workpiece, just as the needle follows the projection of the predicted stitched patch.

[0073] Projecting a predicted stitch path along the workpiece in front of the needle has many advantages. In some situations, a user may want to place a smooth curve of the stitch that terminates near or a specific distance from an existing feature on the workpiece. Alternatively, a user may want to avoid contacting or overlapping an existing feature on the workpiece. In these cases, a predicted sewing path that moves with the workpiece facilitates creating the desired seam in a single pass. Additional information beyond the predicted sewing path may also be provided. For example, the color of the projected stitch may change if the projected path is predicted to encounter or get too close to a workpiece feature, such as a pin, button, another seam, buttonhole, decorative element, or fabric edge, that the user has designated as an object to avoid or that the sewing machine has identified and predicts the user will want to avoid. The projected stitch may also flash in these scenarios and can be combined with other notifications, such as audio or tactile feedback, as discussed in this disclosure. Alternatively, the projected path may be automatically changed by the sewing machine to guide the user around an obstacle, with the original path and the new, changed path projected in different colors and / or with motion cues that clearly indicate that the path has changed, such as a flashing or otherwise animated arrow near the path.

[0074] Such caution signals and warnings may also be sent if the user's fingers are moved into the predicted sewing path or into the path of another component of the sewing machine, such as the presser foot or an attached accessory. It should be noted that the projector is not limited to projecting only the predicted sewing path, but can also project many other symbols and / or wording near the projected path to notify and alert the user of path changes or obstacles to watch out for. For example, the computer can instruct the projector to project a button outline around a button on a workpiece, thereby drawing the user's attention to that feature, and the neural network can identify the button on the fabric and provide button location and size data to the computer system.

[0075] As a last resort, the sewing machine can stop completely if the needle is about to hit an obstacle and the user does not respond to override the warning, for example, via a touchscreen interface or voice control system, to avoid the obstacle. FIG. 108 shows a flow diagram illustrating the use of a neural network to avoid harm to a user or damage to a sewing machine during a sewing operation. When a user begins sewing with a sewing machine, data is collected from a camera pointed toward the sewing area, and data can also be collected from other sensors, such as one or more microphones listening to the environment to capture audio cues or other expressions made by the user. The collected data is processed through a neural network trained to detect foreign objects that could be harmed by or cause damage to the sewing machine. For example, the neural network can identify a user's fingers under the presser foot or in the path of the needle. Voice data can also be useful to determine whether the user is distracted by a conversation, thereby increasing the probability of an unintended finger or hand position. Once an object is identified, the sewing machine can alert the user and stop sewing or lower the presser foot to avoid harm to the user and the sewing machine. The foreign object may not be directly in the sewing path, but may be near the path, which will cause the sewing machine to generate an alert. For example, the sewing machine may alert the user audibly or may project a warning onto the workpiece as described above. If no foreign object is detected, the sewing operation will proceed in the normal manner.

[0076] In addition to compensating for deviations from the desired sewing path, data collected by the sewing machine during sewing performed by the user can be analyzed via a neural network to detect the user's level of expertise. For example, frequent deviations from the desired sewing path may indicate a beginner, while fewer deviations may indicate an expert. Instructions and training exercises can then be suggested by the sewing machine to the user for improvement. Feedback can be shared via any single or combined means, including audio, text, video, image projection, and augmented reality configurations, from the sewing machine or a connected device. Adjustments to sewing machine settings can also be recommended to improve sewing for novice sewers and to improve the efficiency of expert sewers. The sewing machine can also suggest new opportunities and challenges for advanced users to help them further improve and expand their skill set.

[0077] An exemplary flowchart for using a neural network to detect sewing problems is shown in FIG. 109. As a user uses the sewing machine in any manner, data is collected from user-facing sensors, such as a camera, the user's real-time interactions with the user interface, a log of historical interactions with the sewing machine, and, in some cases, information about the current sewing task. The collected data is processed through a neural network trained to detect the user's skill level, as described above. If the neural network assesses the user's skill level, the sewing machine may proceed to warn the user that the difficulty of the task exceeds the detected skill level or may provide helpful tips or prompts, as appropriate. As described above, the sewing machine may also provide recommended training based on the detection of the user's skill level.

[0078] Analysis of a user's level of expertise can also be applied to interactions between the user and the sewing machine. That is, the sewing machine can detect through neural network analysis that a user is struggling to properly use a sewing machine feature and can suggest a tutorial video or instructions and provide on-screen prompts to assist the user in knowing which user interface control to interact with next. User interaction data can include the user-facing camera data described above and timing information from the user interface indicating the speed at which the user interacts with the sewing machine's settings. The timing of a user's interactions with the sewing machine can be one indicator of the user's skill level (i.e., a user who selects menu items in the user interface more quickly is more likely to be familiar with the sewing machine) and, in combination with other data, can help the sewing machine identify the user's estimated skill level. As an example, after a feature is activated, the sewing machine can highlight a button and present a pop-over message prompting the user to take the next step and use the activated feature. Input from the user-facing camera and facial recognition technology can provide further input about the user's emotional state when interacting with the sewing machine. That is, when the user appears frustrated or confused, graphical and auditory prompts may be provided, or the sewing machine may best support and coach the user through any problems they are trying to solve, without presenting further prompts that may be perceived as irritating or unhelpful.

[0079] Based on data collected from monitoring sewing machine use, the sewing machine can also provide helpful recommendations for additional products or accessories. Product advertising can be via any single or combined means, including audio, text, video, image projection, and augmented reality configurations, from the sewing machine or a connected device. When recommending products, the sewing machine or an external processor specifically collects and monitors data through real-time or retrospective data analysis, such as the user's frequency of selection and preferences for sewing accessories, programs, and machines. For example, the sewing machine can constantly track the usage of each type of sewing thread to understand typical thread purchases and recommend purchasing more of that thread when supplies are estimated to be low. Another example is when a user uses a particular presser foot for a specific purpose, but a more suitable presser foot exists; the sewing machine can recommend purchasing the more suitable option if the user has not entered it in the list of sewing accessories currently owned. The list of sewing accessories can be stored on one or both the sewing machine and the app on the connected device. This data can be sent back to the manufacturer to enable engineering, marketing, and customer service groups to improve the quality of sewing machine and other product offerings.

[0080] 43-65, there are shown various diagrams and charts relating to the use of artificial intelligence in a sewing machine to identify the thread used in the sewing machine and the textile material of the workpiece, adjust sewing parameters, and provide the user with information regarding the thread and fabric combination identified by the sewing machine. 43-49, there are shown portions of a sewing machine illustrating the paths that the thread can follow from a spool mounted on top of the machine (FIGS. 43-45) and from a bobbin mounted below the throat plate (FIGS. 46-49) to the sewing needle.

[0081] The sewing machine may include various sensors along these thread paths to detect the type of thread installed on the machine by the user. These sensors may include, but are not limited to, RGB sensors, light sensors, optical sensors, such as cameras, etc. Specific sensors may also be provided for sources of illumination and magnifiers. For example, as shown in FIG. 50, an optical thread sensor may be included on the sewing machine arm and behind the location where the spool is attached. An exemplary thread sensor 140 is shown in FIG. 87, including a tube-shaped housing 142 through which the thread 141 passes. The tube-shaped housing 142 blocks ambient light from impinging on the thread 141, so a light source 144 is provided that illuminates the thread 141 for detection by an optical sensor 146, such as a camera or RGB sensor, used to collect thread data. The sewing machine may also include sensors for detecting and adjusting parameters of the thread. The tube-shaped geometry of the sensor assembly provides a known background for illumination of the thread, thereby increasing the accuracy and precision of the thread information collected by the RGB or other sensor. The sensors provided in the tunnel housing can detect light, sound, or other parameters of the sewing thread to determine the color, density or weight, surface quality, material or fiber type, and overall quality of the sewing thread, i.e., RGB or other sensors can be used to detect the inherent characteristics of the sewing thread as it passes through the sensor housing.

[0082] Data collected by the sewing thread sensor can be transmitted to the sewing machine's computer and compared to a thread information database containing information on a wide variety of thread types and colors. Thus, the sewing machine can identify the thread and present information to the user that may not otherwise be known to the user. If a particular thread can be identified from information about the spool (manually entered by the user or detected by the machine), the detected thread characteristics of the thread can be compared to stored thread characteristics from the thread information database. The sewing machine can then detect thread characteristics that significantly differ from the stored thread characteristics, which may indicate a faulty spool of thread, and can present an alert to the user accordingly. Information about the spool of thread can be collected by an optical or other sensor located near the spool pin to which the spool is attached during sewing. Spool information can also be collected from the spool when the user holds the spool in front of an optical or other sensor located in the sewing head or another location, such as a camera in the sewing head or one or more cameras facing the user. The time and date of thread identification may be stored and associated with projection, stitch type, etc. to build a history of thread use on the sewing machine.

[0083] The sewing machine may also include sensors for detecting the current state of the sewing thread as it is manipulated by the machine and may include mechanisms for adjusting it. For example, the sewing machine may include a thread tension sensor (FIGS. 51-54), a thread distribution unit (FIGS. 55-58), and a thread tension unit (FIGS. 59-62). The sensors for detecting the inherent characteristics and current state of the sewing thread are positioned to collect data about the quality and condition of the thread as it passes from the spool or bobbin, through a tensioner, around a hook or other element of the sewing machine, and finally through the needle. As described in more detail below, optical sensors may also be used in conjunction with neural networks to detect the type of presser foot and / or needle installed on the machine.

[0084] The sewing machine also includes optical or other sensors that can be used in conjunction with neural networks to detect the type, color, reflectivity, pattern, weave direction, orientation (i.e., right side and wrong side), and topography of the fabric material or fiber used in the workpiece. An exemplary sensor for collecting data regarding the workpiece's fabric includes a source of radiation (e.g., a visible or infrared light source) provided on the sewing head and directed downward toward the workpiece. A radiation measuring device, such as an optical light sensor or infrared sensor, is provided above the sewing bed, i.e., below the workpiece. Note that the arrangement of the emitter and receiver may be reversed, i.e., the emitter may be provided on the sewing bed and the receiver may be provided on the sewing head. In this way, the amount or portion of the emitted radiation (e.g., visible or infrared light) that passes through the workpiece and, consequently, the amount of radiation reflected by the top surface of the workpiece can be detected and measured. To provide a means for determining fabric density more accurately than other techniques, the ultrasonic emitter and receiver may be arranged in a similar manner, i.e., the emitter may be located on the sewing head and the receiver may be located on the sewing bed. These emitters and detectors, i.e., for light (IR, camera), color (RGB), ultrasound, etc., can be used individually or together to determine the material or fiber type, density, and reflectivity of the workpiece material. Additional depth perception techniques described herein may also be used to detect the topography of the workpiece.

[0085] The data collected by the fabric sensor can be transmitted to the sewing machine's computer or any connected external processor and compared to a fabric information database containing information on a wide variety of fabric types with various colors and patterns. Thus, the sewing machine can identify the fabric of the workpiece and present information to the user that may not be known to the user. If a specific fabric can be identified from information about a batch of fabric (manually entered by the user or detected by the machine), the detected fabric characteristics of the fabric can be compared to stored fabric characteristics from the fabric information database. The sewing machine can then detect fabrics that differ significantly from the stored fabric characteristics, which may indicate a defective fabric, and present an alert to the user accordingly. The workpiece identification data can be used in combination with the stitch data to train a neural network to associate workpiece characteristics with different stitches. Consequently, the sewing machine can alert the user that a stitch is being formed on the wrong side of a workpiece facing in the wrong direction.

[0086] As shown in FIG. 63, data collected by the various sensors and other devices described above is processed by the sewing machine's computer through a neural network. The neural network is trained to provide recommendations, alerts, and warnings to the user based on the input data. That is, the neural network is trained to recognize compatible and incompatible combinations of thread, fabric, presser foot, and needle type. For example, FIG. 64 shows a fabric and thread table indicating whether heavy or light fabrics are compatible with thick or thin threads. If the sewing machine detects a possible problem with the thread and fabric combination, the user may be provided with a recommendation on the display accompanied by an audible or tactile notification. If the potential compatibility issue is more serious, the user may be alerted or even warned. In some scenarios, the sewing machine may stop all together and provide a combination of audible, tactile, and visual warnings. In addition to notifying the user of potential compatibility issues, the sewing machine may make adjustments, such as thread tension, presser foot pressure, or stitch type and speed, to improve sewing performance when using heavy or light threads and / or fabrics. Even if the correct type of thread is selected for a given fabric, the thread color may not be aesthetically pleasing given the color and / or pattern of the selected fabric. Therefore, the neural network can also be trained to advise the user on the compatibility of various threads and fabric colors and patterns, as seen in Figure 65.

[0087] An exemplary flow diagram for identifying workpieces and potential workpiece problems using a neural network is shown in FIG. 102. When a user begins sewing on a sewing machine, data is collected from a camera pointed toward the sewing area, the sewing operation, an optical sewing thread sensor, a feed speed sensor, a database of known workpiece or fabric materials, and a log of previously identified workpiece materials. The collected data is processed through a neural network trained to detect workpiece compatibility issues, damage, and other sewing thread quality issues. If the neural network identifies a workpiece and that the workpiece is incompatible with the current sewing operation and other sewing materials (e.g., a lightweight sewing thread is more likely to break when used with a thicker or heavier workpiece fabric), the sewing machine alerts the user and can continue sewing if the user chooses to disable or ignore the notification. The user is also alerted if damage or other quality issues with the workpiece are identified. The sewing machine can optionally prevent further sewing if the damage is sufficient and requires user intervention, for example, to replace or repair the workpiece.

[0088] 66-71 and 101, various diagrams and charts are shown relating to the use of artificial intelligence in a sewing machine to identify degradation in thread quality, adjust sewing parameters, and provide the user with information regarding the quality of the thread being used. As noted above, a sewing machine may include various sensors along one or more paths that the thread in the sewing machine follows from the thread source to the sewing head, such as those shown in FIGS. 43-49. These sensors may include, but are not limited to, RGB sensors, light sensors, and optical sensors, such as cameras. These sensors are positioned to collect data about the quality and condition of the thread as it passes from the spool or bobbin, through the tensioner, around the hook or other elements of the sewing machine, and finally through the needle. Additional sensors may be included, such as thermal sensors to monitor the temperature of various components involved with the thread that may cause damage to the thread.

[0089] Referring now to Figures 66 and 67, examples of the appearance and characteristics of high-quality and low-quality sutures are shown. A suture considered to be high-quality or in good condition has qualities including tight, stable fibers, consistent diameter, consistent color, consistent reflectivity, and consistent friction quality. A suture considered to be low-quality or in poor condition has qualities including loose, frayed fibers, inconsistent diameter, inconsistent color, inconsistent reflectivity, inconsistent friction quality, and poor splicing. For example, an additional light may be provided in or near a sensor, such as the tube-shaped sensor housing 142 described above, to provide a consistent light source when observing the suture, so that the suture is not misdiagnosed based on color variations in varying lighting conditions, such as sunlight, bluish white, flat light, and incandescent light. As seen in Figure 68, one or more optical sensors on the sewing machine can also detect debris buildup in areas of the sewing machine where debris is known to accumulate when low-quality sutures are used.

[0090] Referring now to FIG. 69, a flow diagram is shown for an exemplary situation in which the quality of the sewing thread used by the sewing machine is indicated. In the illustrated scenario, sensors collect data regarding the condition of the sewing thread being used in the sewing machine. The data is processed through a neural network, previously trained or continuously trained, to determine whether the sewing thread exhibits any of the markers of low-quality sewing thread. When low-quality sewing thread is detected, the user is notified via a notification means disclosed herein, such as a user interface, computer-generated voice, indicator light, or tactile feedback ( FIG. 70 ). The user can then view the sewing machine's display for further details regarding the quality of the sewing thread or request an audible explanation. The user can choose to disable the warning or take action, after which the user continues sewing. In sewing machines with multiple spools of sewing thread, the sewing machine can also track the sewing thread parameters of each spool of thread and, in some cases, notify the user as to which spool contains low-quality sewing thread.

[0091] Another flow diagram for using a neural network to detect thread problems is shown in FIG. 101. When a user begins sewing on a sewing machine, data is collected from a camera pointed toward the sewing area, the sewing operation, an optical thread sensor, other thread sensors for measuring thread tension, feed rate, and allocation, a database of known thread materials, and a log of previously identified thread materials. The collected data is processed through a neural network trained to detect thread compatibility issues, damage, and other thread quality issues. If the neural network identifies a thread and that the thread is incompatible with the current sewing operation (e.g., the thread is likely to break when used in a particular stitch), the sewing machine alerts the user and can continue sewing if the user chooses to disable or ignore the notification. The user is also alerted if damage or other quality issues with the thread are identified. The sewing machine can optionally prevent further sewing if the damage is sufficient and requires user intervention, for example, to replace or repair the thread.

[0092] The sewing machine may also include multiple suture quality sensors, such as one or more sensors 140 provided in the tubular housing 142 described above, along the suture path to determine if the quality of the suture changes along the path. For example, if a decrease in suture quality is observed after a particular feature of the suture path, the sewing machine may recommend changes to sewing parameters to reduce the likelihood that the sewing machine is causing damage to the suture. Monitoring suture quality at multiple locations along the suture path may also provide an opportunity for the sewing machine to recommend inspection of various components that may need to be repaired or replaced, such as guides that may have sharp edges causing the suture to fray. Such monitoring may also enable the sewing machine to identify improper threading of the sewing machine based on where the suture appears to deviate from the intended suture path through the sewing machine.

[0093] 71-81, various diagrams and charts are shown relating to the use of artificial intelligence in a sewing machine to recognize and identify placed objects using the machine's optical sensors and provide the user with information about the object's characteristics and its relationship to the sewing machine. The optical sensor, such as a camera, may be directed toward the sewing area or may be a front-facing or user-facing sensor that allows the user to hold an object in front of the user-facing sensor to detect the component. A library or database of identified objects is stored to enable the sewing machine to build an inventory of known objects, such as sewing machine components or accessories for use with the sewing machine. For example, the sewing machine may recognize the type of needle attached to the machine and whether the needle is properly attached (FIGS. 71-72), the type of presser foot attached to the machine and whether the presser foot is properly attached (FIG. 73), the type and characteristics of the embroidery frame attached to the machine and whether the workpiece is properly attached within the embroidery frame (FIG. 74), and whether there is a safety risk to the user's fingers and hands and the user during the current operation (FIG. 75). With respect to an embroidery hoop, the sewing machine can, for example, recognize whether the clamping mechanism for securing the embroidery hoop is secure, whether the workpiece is lying flat within the hoop, and whether all of the edges of the fabric are outside the hoop. Component quality can also be identified; i.e., the sewing machine can also detect whether a component is damaged, rusted, bent, worn, incorrectly threaded (in the case of needles and loopers), or otherwise altered from acceptable quality standards for the component. In each of these examples, a neural network is used to process visual data collected by one or more optical sensors in the sewing machine.

[0094] Referring now to FIG. 76, data collected by the sewing machine's various sensors is processed through a neural network by the sewing machine's computer to determine whether a particular object has been detected by the sewing machine and whether that object should be there. For example, as seen in FIG. 77, optical data encompassing an area including the sewing machine's presser foot may be captured. Visual data from the image is processed through a neural network to determine whether a presser foot is present, what type of presser foot is present, and whether the presser foot is properly installed. A similar determination can be made for the needle installed on the sewing machine. Once the presser foot and needle are identified, the presser foot's corresponding needle translation range is stored, and the user is notified if the needle and presser foot combination is not recommended. The user may then choose to disable the warning, such as by selecting "Expert Mode," which includes an alert about possible safety risks associated with selecting "Expert Mode." The selected stitch may also be compared to the installed presser foot and needle to determine whether they are appropriate and compatible with the selected stitch or series of stitches in the projection. If a presser foot or needle is not installed, a presser foot and needle may be recommended by the sewing machine. Upon installation of the presser foot and / or needle, the sewing machine may again inspect the presser foot and needle to verify that the appropriate presser foot and / or needle have been installed and that the needle and / or presser foot are properly installed. The sewing machine may also identify incompatibilities, for example, between the needle and the sewing plate, between the presser foot and the selected stitch pattern, and between the needle and the selected stitch pattern. For example, incompatibilities between stitch types and presser feet may be provided in a table or database of incompatibilities, or may be learned over time by monitoring sewing errors in relation to the identities of various components and the sewing operation being performed.

[0095] Another flow diagram for using a neural network to detect objects and identify fit and installation issues is shown in FIG. 100. When a user begins sewing on a sewing machine, data is collected from a camera pointed toward the sewing area, the sewing job, a database of known components and accessories previously used on the sewing machine, and a database of known components and accessories compatible with the sewing machine. The collected data is processed through a neural network trained to detect components and accessories, classify them, determine whether they are installed correctly, and determine if there are any mismatches or other problems resulting from the combination of components and accessories with the selected sewing job. If the neural network identifies a component that is incompatible with the sewing job or may cause a problem, the user is alerted and given the opportunity to disable the alert (e.g., as in the "expert mode" described above). The neural network identifies whether the component and accessory are installed correctly. If not, the user is alerted and the sewing machine may be prevented from operating until the component is removed or properly installed.

[0096] A similar determination may be made regarding an embroidery hoop that may be mounted on the sewing bed. Once the type and size of the embroidery hoop is determined, the sewing machine may notify the user if the selected embroidery pattern extends beyond the limits of the embroidery frame. The sewing machine may also inspect the edges of the fabric held within the embroidery frame to detect improper fabric loading within the hoop. If a problem with fabric loading or embroidery hoop size is detected, the user may be notified via any of the notification means described herein, such as a visual display of information on the sewing machine's display, an audible notification, or tactile feedback. During embroidery hoop identification and inspection, or if specified by the user, a camera pointed toward the sewing bed can be used to capture images of the workpiece mounted in the embroidery hoop. The entire workpiece can be captured in a single image, or the embroidery hoop can be moved to capture multiple images of the workpiece that are sewn together to form a single image of the entire workpiece. Data collected during the scanning process can be used as input to a neural network trained to recognize and predict color. This pre-trained color calibration facilitates more accurate color predictions over time as the neural network learns to correctly recognize colors. Scan data can also be used as input for a neural network trained to detect translational skips or other movement anomalies so that the embroidery hoop's actuation system can be controlled to correct for the anomalies. Other accessories may also be identified if attached to the sewing machine, and the sewing machine may provide feedback as to whether the accessory is properly installed and whether the machine is configured to operate properly with that accessory. For example, if a user attaches an accessory to the machine that is used to attach ribbon to a workpiece, the sewing machine may display information about the accessory on the screen to assist the user in the proper use of the accessory. The functionality of the sewing machine may also be limited to those that are compatible with the accessory unless such limitations are overridden by the user. The sewing machine may also display information on the screen about materials that can be used with the accessory and may recommend other accessories to the user.

[0097] 78-81, various views and diagrams of exemplary presser feet, sewing needles, and other components are shown, including features designed to make the presser feet and needles more easily recognized through object recognition techniques, such as through the use of neural networks, or by sensors, including magnetic sensors, configured to detect the component features. The presser feet, sewing needles, needle plates, or other sewing machine components may include various markers that improve the robustness of optical or other sensor-based object recognition systems. For example, the markers or markings may include patterns of two or more geometric shapes (FIGS. 78-81), stripes of color in specific locations (FIG. 80), the overall shape of the component including recognizable protrusions or depressions, filled color codes and other color treatments, reflective finishes, barcodes, QR codes, and other surface treatments that enable UV, IR, or other optical sensing technologies. The markers may include unique patterns of etched rings or lines, etched, debossed, or embossed shapes or patterns on the surface of the sewing machine component. The marker may also be comprised of different regions on the surface of the sewing machine component having surface finishes of varying reflectivity, i.e., the marker may include a first region having a first surface finish and a second region having a second surface finish. The marker may use electronic identification technologies such as near field communication (NFC) devices and radio frequency identification (RFID) devices.

[0098] Additional alternative identification information may be based on markers with magnetic field line profiles or polarity profiles for each component that can be detected by sensors when the component is attached to the sewing machine. For example, a needle may include a magnet to create a specific magnetic field that is detected only when the needle is inserted into the needle bar. Similar techniques are applied to embroidery frames to improve recognition of such frames via neural networks or other object recognition techniques.

[0099] 82-84, various diagrams and illustrations are shown relating to providing tactile feedback to a user in connection with use of a sewing machine. Haptic feedback is feedback provided to a user through a means that can be felt. For example, a control part with which a user interacts (e.g., a knob, button, pedal, lever, or slider, e.g., the component shown in FIG. 83) may vibrate slightly when a specific position is reached, and there may even be resistance to further movement of the controlled object. Micro-vibrations may also be provided through a surface of the sewing machine, such as the sewing bed, on which the user's hands and fingers may rest during use. Haptic feedback may be used to alert the user to a specific condition of the sewing machine or workpiece, or as further reinforcement to the user that an action taken by the user has been received. For example, the sewing bed may vibrate under the workpiece and the user's hand when the user deviates from the desired sewing path. Alternatively, a knob or button may vibrate to indicate that the button has been pressed or that a specific position has been reached. Haptic feedback may also be replaced with a mechanical feature that provides similar feedback, such as a detent on a knob that indicates a specific position around the knob has been reached. Haptic feedback can be used on any sewing machine surface. Vibrotactile feedback via piezoelectric sensors can be used on any surface of the sewing machine and can be used to replace mechanical user interfaces. Piezoelectric and capacitive sensors, designed to replace traditional plastic sewing machine covers, can be arranged in an array under an OLED or similar type screen. The presence of a user's finger on or near the OLED interface engages menus that are activated based on the user's request or the current sewing action and associated user interface needs, such as finger taps, slides, and scrolls for threading, adjusting thread tension, and activating or deactivating sewing accessories. Other forms of haptic feedback can include force, electrotactile, ultrasonic, air vortex ring, and thermal tactile feedback.

[0100] Referring now to FIG. 83 , a flow diagram for an exemplary situation in which haptic feedback may be used is shown. In the illustrated scenario, a user attaches a presser foot to the machine that is recognized through neural network processing of visual data received from the machine's optical or other sensors. The user then selects a particular pattern to perform or stitch to sew. The sewing machine then determines whether the combination of the particular presser foot and the selected operation is a valid combination, i.e., whether the attached presser foot can be used with the particular selected stitch, and provides haptic and other feedback to the user if the combination is not valid. This haptic feedback may be provided at the last action, for example, via a touchscreen when the user selects the stitch or other operation to perform. Simultaneously, a visual and audible alert may be provided to the user that the presser foot and selected operation are incompatible, prompting the user to install the correct presser foot or select a compatible operation ( FIG. 84 ). The interface may also provide an option for the user to disable the warning.

[0101] Referring now to FIGS. 85-88, various diagrams and charts are shown relating to the use of artificial intelligence in a sewing machine to monitor the machine's mechanical and electrical health. Operating a sewing machine produces a wide variety of sounds and mechanical vibrations, as well as fluctuations in the electrical signals that drive the machine's motors and actuators. An exemplary sewing machine includes sensors to monitor the sounds and noises, mechanical vibrations, and electrical signals to identify patterns related to the performance of associated components. Sensors may also be provided on or near various components to measure component temperatures, an increase of which may indicate excessive wear. Sensors are positioned in a variety of locations on the sewing machine, as seen in FIG. 85. Sensors may be continuously active or may be turned on to collect data during specific times, such as during startup, idle, active, and shutdown procedures.

[0102] The collected data can be processed through neural networks trained to detect performance issues in specific sewing machine components. For example, a particular sound may be associated with two components rubbing together, which in turn indicates the need to replace a bushing or strut. Or, the voltage required to run a motor at a particular speed may be higher when motor performance is degraded, compared to a motor running at nominal conditions. Motor performance can be monitored to determine when problems arise, for example, when performing a particular task or working with a particular fabric or sewing thread material. These conditions can also be recognized via an increase in heat generated by machine components and a corresponding rise in the temperature of the particular component. More importantly, sensors used by sewing machines may be significantly more sensitive to changes in sounds or other parameters generated by sewing machine components, thus enabling earlier predictions than would otherwise be possible, such as those made by a skilled maintenance technician. Furthermore, these performance issues can be correlated with other information from the sewing machine, such as the sewing operation being performed at the time the performance problem was detected and identified. In this way, specific performance problems can be associated with specific uses of the sewing machine, and information about that relationship can be provided to engineers and maintenance technicians to better identify causes for repairs and improve future designs. As with other data collected by the sewing machine and generated by the neural network, the data can be sent to the cloud for sharing with other sewing machines to improve the training of the neural networks of all sewing machines in the network.

[0103] Referring now to FIGS. 86A-86C, flowcharts are presented illustrating various ways in which diagnostic information can be generated by a sewing machine and used by a user. When the neural network of the sewing machine's computer identifies that a correction to the machine is needed, an incident is logged and the user is notified. The user can then be instructed to perform a specific task to correct the problem, such as removing the sewing thread or moving the sewing machine to a stiffer table. After taking action, the sewing machine can be used normally, with diagnostics running at regular intervals to see if additional corrective measures are needed. If the user does not take corrective action, the motor or other actuator can be calibrated to attempt to correct the problem. If calibration does not correct the problem, the user is notified, an incident is logged, and a service request can be sent to a service provider. Calibration can also be set to occur every certain number of cycles of the motor or other component as preventative maintenance. Calibration can also be performed when changing the sewing thread used and the fabric being worked on, or for any task performed by the sewing machine.

[0104] Once a potential problem has been diagnosed, the sewing machine can notify the user of the problem by a variety of means, such as those described in this disclosure. In particular, the sewing machine can present an alert to the user via a user interface, alert the user audibly, speak to the user via computer voice, and / or send the user an email via a network connection. For example, as shown in FIG. 87, the sewing machine can present an indication to the user that service is needed and prompt the user to schedule a service request with a service dealer. Alternatively, as shown in FIG. 88, the sewing machine can suggest modifying the operating environment to improve the sewing machine's performance, as described in further detail below. If a change in the operating environment or a repair action is deemed necessary or recommended, the user can be alerted and guided to modify their work environment by using instructional illustrations, animations, and videos on the touchscreen, or localized guide lighting or other 2D or 3D static or dynamic light projections, placing the sewing machine on a firmer table to reduce vibration, or guiding the user through a simple repair or through a technician to guide the user through a more complex repair of the machine.

[0105] In the case of a software issue, the update may be installed automatically, so the user may not be aware of the update. Alternatively, the user may be guided through the software update process and contact a customer service representative through a user interface to provide support and fix the software issue. Referring now to FIG. 99, an exemplary flowchart for using a neural network with automatic software updates, as described above, is shown. If the sewing machine has not been used for a predetermined period of time, i.e., if the user has been inactive, data may be collected from the user's interaction or activity log, a user-facing camera and microphone, a network interface connected to a software update server, and a clock providing the current date and time. If a software update is available, the neural network provides an indication of whether the user will typically be away from the sewing machine long enough to install the software update before returning to the machine. If sufficient time is available, the software update is allowed to install if the user has enabled automatic updates. A similar process can be followed for calibration of various sensors, motors, and actuators.

[0106] The sewing machine may also include light sources, such as LED lights, located near various components known to wear during use, and may illuminate, for example, a yellow, orange, or red light on a particular component to indicate that the component is performing poorly and may need to be serviced or replaced. These lights may be activated when the machine is placed in a maintenance or service mode and may provide a quick status of the overall health of the machine.

[0107] Information related to the health of the sewing machine may be stored in a health log and, if necessary, transmitted to a remote customer service representative or maintenance technician to assist the remote worker in determining what maintenance may need to be performed on the machine and whether the sewing machine needs to be sent to a service center for repair. Sewing machine health data may also be automatically transmitted, with permission from the user, to a dealer, service center, and / or manufacturer so that the recipient of the data can take proactive steps to order replacement parts and notify customers that certain components of the sewing machine may soon need replacement. In a commercial environment, a sewing machine owner may choose to subscribe to a maintenance plan in which such replacement parts are supplied or service calls are automatically scheduled so that the sewing machine maintains a specified uptime.

[0108] The historical data recorded in the health log can be particularly useful when diagnosing the cause of a sewing machine failure. For example, historical temperature data can include both ambient temperature readings and temperature readings at various points throughout the machine. Ambient temperature history may reveal that the sewing machine was exposed to excessive heat, which damaged the sewing machine. Point temperature readings, i.e., temperature readings at specific locations within the sewing machine, can assist a technician in determining the root cause of the sewing machine's damage, such as wear between damaged components. Similarly, historical vibration or acceleration data can be used. Acceleration data can also indicate whether the machine experienced a drop or fall that caused the damage.

[0109] As described above, optical sensors can be used in conjunction with neural networks to detect when a user's finger or some other foreign object gets in the way of the sewing head and could cause injury to the user or damage to the machine. Similarly, neural networks can be trained to recognize whether a user's finger or other foreign object gets in the way of the presser foot, cutting accessory, or any other moving component of the sewing machine that could cause harm to the user while using the machine. When a finger or other foreign object is detected, the sewing machine can control the needle and other components to avoid the object, or can prevent further sewing if avoidance is impossible or the potential for harm is great enough to warrant prohibiting further operation of the sewing machine. For example, the sewing machine can prevent the presser foot from being lowered when a finger is detected under the presser foot. Alternatively, the sewing machine can prevent further sewing if a finger or a user's hand is detected in the sewing path. If the detected foreign object is a pin inserted into the seam, the sewing machine can adjust the feed speed or other sewing parameters to avoid the needle striking the pin.

[0110] The neural network can also take into account the orientation of the sewing machine (via an accelerometer and / or pressure sensor on the base) so that if the sewing machine is tilted, tilted enough to tip over and potentially injure the user, the sewing machine can be turned off or prevented from starting. The accelerometer can also be active when the sewing machine is in sleep or standby mode to detect machine movement and prevent power to the machine if it is moved, lifted, or tipped over. Thermal data from a temperature sensor can be fed to the neural network so that the machine automatically turns off to prevent components from overheating, or because heat buildup can be a symptom of an electrical abnormality.

[0111] User-facing proximity sensors (e.g., infrared sensors) and / or cameras can be used to monitor the presence of a user at a sewing machine so that the machine can automatically turn off to conserve energy after the user has been absent for a predetermined period of time. These user-facing sensors can also prevent activation of the sewing machine after determining, via a neural network or other means, that an unauthorized person is attempting to access the machine. For example, a neural network can be trained to recognize a child attempting to access the sewing machine. In response, the computer can prevent activation of the sewing machine and notify an authorized user of the attempted access by generating an audible sound or by sending a notification to the user via an internet connection, text message, or smartphone app. An exemplary flow diagram of a child safety feature is shown in FIG. 98. Child safety analysis can be triggered for a variety of reasons, such as after a failed attempt to access the sewing machine or when the user may wish to step away from the machine during a long embroidery stitch. Data is then collected from user-facing cameras, microphones, and various user interface elements, such as the touchscreen, buttons, and knobs. If the neural network determines that a child is attempting to access the sewing machine or approaching an operating component of the sewing machine, the sewing machine may issue an audible alert and send an alert to a mobile device assigned to an authorized user. If the child does not respond to the alert, the sewing machine may repeat the alert and stop the sewing operation to prevent harm. If the neural network determines that the failed attempt was not made by a child, the sewing machine may still issue an audible alert and send a message to the authorized user. As another example, the sewing machine may periodically monitor the environment around the sewing machine to identify the presence of a person, such as a user or a child. This periodic monitoring may be performed, for example, when a longer embroidery piece is being sewn and there is a possibility of disturbance of the embroidery workpiece or harm to the person if a child gets too close to the sewing operation in progress.If a child is detected, the sewing operation may be stopped and an alert may be sent to the user to inform them that the sewing operation has been stopped and why.

[0112] As the sewing machine is used, user-set profiles, user preferences, graphical user interface settings, feedback settings, object recognition preferences, tutorial preferences, and the like are monitored and stored. Beyond machine settings, all interactions between the user and the sewing machine may be recorded and stored. This collection of data related to interactions between the user and the sewing machine is processed through a neural network so that the sewing machine can learn how the user prefers to interact with the machine and anticipate what the user may prefer in new situations. That is, setting changes may be related to projections, stitch types, thread types, material types, and the like detected by the sewing machine or provided by the user. This collection of data enables the sewing machine to assist the user, for example, by suggesting feed speed settings for a stitch the user has not sewn based on the characteristics of the new stitch and the feed speeds the user has set for other stitch patterns. As another example, the sewing machine can remind the user about settings that are typically set, taking into account the current context, i.e., by suggesting a certain feed speed or stitch pitch for thinner materials and a different feed speed or stitch pitch for thicker materials. An exemplary workflow for recommending setting changes using a neural network is shown in FIG. 97. When a setting is changed, data is collected from the user interaction log, current real-time interactions with the user, other sensors and neural networks related to the sewing material, and data related to the current sewing job. If the neural network identifies that the user typically makes the same change in a similar context, the sewing machine prompts the user to decide whether to change the default setting. If a setting change is not typically made in a similar context, the sewing machine can prompt the user to confirm that the change was intended. The neural network can also identify other settings that might typically be changed in a similar context and suggest those other changes to the user.

[0113] The sewing machine may also occasionally suggest that the user take a break or perform exercises to improve the user's ergonomic health while using the machine. The timing of suggestions and the type of exercise and suggested break times are based on an analysis of the machine's use by a neural network trained to monitor the user's health. The user's posture may also be detected via neural network analysis of data from one or more user-facing cameras so that suggested exercises can be further customized to the user's benefit.

[0114] The condition of the user's workspace can also be detected by the sewing machine and analyzed by the neural network. An ambient light sensor can enable the neural network to consider the lighting conditions in the sewist's room and the lighting in the workspace to reduce or soften the contrast between the work area and the room. For example, the sewing machine can suggest brightening the room light to reduce eye strain caused by the contrast between the bright work surface of the sewing machine and the dark room. The sewing machine can also connect to the workspace and room lighting system, for example, through a Wi-Fi network, to automatically manage brightness adjustments. A user-facing camera can be used to determine the height of the work surface, the position of the user's chair, and other environmental conditions. For example, if the machine has access to an active control surface, such as a workbench with a controllable height, the sewing machine can make suggestions and adjustments to improve the ergonomics of the work environment. FIG. 103 shows an exemplary flow diagram demonstrating how a sewing machine can reduce strain on the user by monitoring the environment around the sewing machine. Data collected from the accelerometer, photo sensor, user-facing camera, and history logs from previous sessions can be processed through a neural network to identify health issues in the user while using the sewing machine. For example, the neural network can identify when the user tends to sit in poor posture and recommend changes, such as adjusting the user's chair. The neural network can also identify when the work surface is unstable by monitoring vibration and acceleration data and recommend adjusting the work surface so that it is flat and less prone to movement during use of the sewing machine.

[0115] As described above, data collected by various sensors on the sewing machine and data generated by monitoring how the sewing machine is being used can be stored in a database on the machine and transmitted to a remote server. The data transmitted to the various remote servers can be collected in a central database and used to analyze the machine's performance and the user's sewing behavior across a much larger data set. So-called "big data" analysis can reveal patterns that would not otherwise be detectable in smaller data sets. The results of this analysis can be fed back to the machine's neural network or a remote neural network operating to support the machine's operation, thereby improving the quality of the results determined by the neural network. Big data analysis can also help research and development teams improve quality control processes in factories and the testing of various components performed in laboratory environments. For example, failure modes that could not be predicted during the machine's initial development can be identified through big data analysis, and future parts and processes can be modified in response.

[0116] While various inventive aspects, concepts, and features of the present disclosure may be described and illustrated herein as embodied in combination in exemplary embodiments, these various aspects, concepts, and features may be used in many alternative embodiments, either individually or in various combinations and subcombinations thereof. Unless expressly excluded herein, all such combinations and subcombinations are intended to be within the scope of the present application. Furthermore, while various alternative embodiments of various aspects, concepts, and features of the present disclosure may be described herein, such as alternative materials, structures, configurations, methods, devices, and components, form, fit, and function alternatives, such descriptions are not intended to be a complete or exhaustive list of available alternative embodiments, whether currently known or later developed. Those skilled in the art may readily adopt one or more of the inventive aspects, concepts, or features into additional embodiments and applications within the scope of the present application, even if such embodiments are not expressly disclosed herein.

[0117] Furthermore, while some features, concepts, or aspects of the present disclosure may be described herein as being preferred configurations or methods, such description is not intended to imply that such features are required or necessary unless expressly indicated as such. Still further, while example or representative values ​​and ranges may be included to aid in understanding the present application, such values ​​and ranges should not be construed in a limiting sense, and are intended to be critical values ​​or ranges only when expressly indicated as such.

[0118] Also, while various aspects, features, and concepts may be expressly identified herein as inventive or forming part of the present disclosure, such identification is not intended to be exclusive; rather, there may be aspects, concepts, and features of the invention that are fully described herein without being expressly identified as such or as part of a particular disclosure, which disclosure is instead set forth in the appended claims. Descriptions of exemplary methods or processes are not limited to including every step as required in all instances, nor should the order in which steps are presented be construed as required or necessary unless expressly indicated as such. The terms used in the claims have their full ordinary meaning and are not limited in any way by the description of embodiments in the specification. 。 [Item 1] A sewing machine, a sewing head attached to an arm suspended above the sewing bed by a pillar; a needle bar extending from said sewing head toward said sewing bed, said needle bar holding a needle for forming a stitch in a workpiece with sewing thread; an adjustable component of the sewing machine, the adjustable component being adjustable to change the current state of the sewing machine; a user interface for receiving input from a user of the sewing machine and communicating feedback information to the user; at least one data collection device for collecting data regarding at least one of the sewing machine, an environment surrounding the sewing machine, sewing materials, a sewing operation performed by the sewing machine, and one or more interactions of the user with the sewing machine; at least one data storage device for storing data collected by said data collection device as collected data and for storing data relating to a neural network, said neural network including a plurality of nodes, each node comprising: an input connection for receiving input data; At least one node parameter; a computing unit for computing an activation function based on the input data and the at least one node parameter; and Includes an output connection for transmitting output data; at least one processor, said at least one processor comprising: processing the collected data through the neural network to generate processed data; configured to control, based on the processed data, at least one of the user interface for interacting with the user, the data storage device for storing the processed data, and the adjustable component for changing the current state of the sewing machine; A sewing machine equipped with [Item 2] the data collection device includes an optical sensor for collecting data from a field of view that encompasses at least a portion of any of the needle, presser foot, throat plate, and sewing accessory; the processor is configured to process the data generated by the optical sensor through the neural network to generate object detection data and object classification data related to at least one of the needle, the presser foot, the throat plate, and the sewing accessory. Item 1. The sewing machine according to item 1. [Item 3] the processor is configured to control the user interface to present the object detection data and the object classification data to the user. Item 2. The sewing machine described in item 2. [Item 4] 4. The sewing machine of claim 2, wherein the object classification data includes object position data and object orientation data. [Item 5] Item 5. The sewing machine of item 4, wherein the processor is configured to determine whether at least one of the needle, the presser foot, the needle plate, and the sewing accessory is incorrectly installed based on the object position data and the object orientation data. [Item 6] 6. The sewing machine of claim 5, wherein the processor is configured to control the user interface to alert the user based on a determination that any of the needle, the presser foot, the throat plate, and the sewing accessory is incorrectly installed. [Item 7] 7. The sewing machine of claim 2, wherein the processor is configured to determine whether an incompatible combination exists between at least two of the needle, the presser foot, the throat plate, and the sewing accessory based on the object classification data and user input received from the user interface. [Item 8] Item 8. The sewing machine of item 7, wherein the processor is configured to prevent operation of a motor of the sewing machine based on a determination that a mismatch exists. [Item 9] 9. The sewing machine of claim 7, wherein the processor is configured to control the user interface to alert the user based on a determination that a mismatch exists. [Item 10] 10. The sewing machine of any one of items 1 to 9, wherein the adjustable components include at least one of a motor, an actuator, a light, a speaker, a thread tensioner, a thread distributor, a knife, a presser foot, a needle bar, and a feed mechanism. [Item 11] 11. The sewing machine according to any one of items 1 to 10, further comprising a projector for projecting a sewing guide onto at least one of the workpiece and the sewing bed. [Item 12] Item 12. The sewing machine of item 11, wherein the user interface elements include at least one sewing guide. [Item 13] the data collection device includes at least one of an optical sensor, an ultrasonic vision system, and a thermal vision system for collecting sewing material data including workpiece data; the processor is configured to process the workpiece data through the neural network to generate workpiece detection data and workpiece classification data. The sewing machine according to any one of items 1 to 12. [Item 14] Item 14. The sewing machine of item 13, wherein the workpiece classification data includes at least one of color, pattern, reflectivity, thickness, density, fiber type, weave type, weave orientation, topography, orientation, and damage. [Item 15] the data collection device includes at least one of an optical sensor, a color sensor, a light sensor, a camera, and a thermal sensor for collecting sewing material data, including sewing thread data; the processor is configured to process the suture data through the neural network to generate suture classification data. The sewing machine according to any one of items 1 to 14. [Item 16] Item 16. The sewing machine of item 15, wherein the thread classification data includes at least one of color, density, thickness, surface quality, material, and damage. [Item 17] 17. The sewing machine of any one of items 1 to 16, wherein the processor is configured to determine whether an incompatible combination exists between the workpiece and the sewing thread based on workpiece classification data and sewing thread classification data. [Item 18] 18. The sewing machine of any one of items 1 to 17, wherein the processor is configured to determine whether an incompatible combination exists between at least two of the workpiece, the sewing thread, the needle, the presser foot, the throat plate, and the sewing accessory based on workpiece classification data, sewing thread classification data, and object classification data. [Item 19] Item 19. The sewing machine of any one of items 17 and 18, wherein the processor is configured to control the user interface to alert the user based on a determination that a mismatch exists. [Item 20] 20. The sewing machine of any one of items 13 to 19, wherein the processor is configured to process workpiece data through the neural network to generate feature detection data and feature classification data regarding at least one of seams, edges, pockets, buttonholes, patterns, weave orientations, defects, and seams of the workpiece. [Item 21] Item 21. The sewing machine of item 20, wherein the processor is configured to control a projector to project a sewing guide at a location of a detected feature in the feature detection data. [Item 22] Item 21. The sewing machine of item 20, wherein the processor is configured to control a projector to project the seam stitches at a predetermined distance from a location of a detected feature in the feature detection data. [Item 23] a swing motor for swinging the needle bar laterally during a sewing operation; and a feed mechanism for translating the workpiece forward, backward, leftward, and rightward and for rotating the workpiece; The sewing machine according to any one of items 20 to 22, further comprising: [Item 24] Item 24. The sewing machine of item 23, wherein the processor is configured to control the oscillating motor and the feed mechanism to form stitches in the workpiece at locations of detected features in the feature detection data. [Item 25] Item 24. The sewing machine of item 23, wherein the processor is configured to control the oscillating motor and the feed mechanism to form a stitch on the workpiece at a predetermined distance from a location of a detected feature in the feature detection data. [Item 26] the data collection device includes an optical sensor for collecting optical data from a field of view encompassing the needle, the workpiece, the suture, and at least a portion of a stitch formed in the workpiece; the processor is configured to process the optical data through the neural network to generate seam recognition data, seam classification data, sewing operation data, and sewing error data. The sewing machine according to any one of items 1 to 25. [Item 27] Item 27. The sewing machine of item 26, wherein the sewing error data includes sewing operation error events including at least one of a skipped stitch, an uneven stitch, an offset stitch, a bunched seam, a varying stitch density, a broken bobbin, a broken looper, a broken needle, a melted thread, a broken needle, a stuck needle, a needle striking the throat plate, a thread cut by the needle, inconsistent thread tension, a wavy seam, an unthreaded needle, a loose needle holder, a loose presser foot, a misplaced presser foot, a stationary needle, a stationary work piece, a bunched work piece, a bunched thread, a thread knot, a loose stitch, a tangled thread, a frayed thread, a torn thread, a variation in work piece feed, a bent needle, a damaged looper, a damaged stitch finger, a misplaced looper, a misplaced stitch finger, and a dull fabric knife. [Item 28] 28. The sewing machine of claim 26, wherein the processor processes the sewing error data and at least one of object recognition data, object classification data, workpiece classification data, thread classification data, stitch recognition data, stitch classification data, and sewing operation data through the neural network to generate error relationship data. [Item 29] 29. The sewing machine of claim 28, wherein the processor controls the user interface to communicate the error-related data to the user. [Item 30] 30. The sewing machine of claim 28, wherein the processor controls at least one of the adjustable component and the actuator based on the error-related data to prevent a sewing operation error event. [Item 31] the data collection device includes at least one of a microphone, an accelerometer, a temperature sensor, a current sensor, and a voltage sensor for collecting sewing machine data including performance data; the processor is configured to process the performance data through the neural network to generate at least one of defect classification data and failure prediction data. The sewing machine according to any one of items 1 to 30. [Item 32] the data collection device includes at least one of a microphone, an accelerometer, a temperature sensor, a photosensor, and an optical sensor having a field of view that includes at least a portion of the user for collecting environmental data; the processor is configured to process the environmental data through the neural network to generate at least one of object detection data, object classification data, user detection data, user classification data, user command data, and environmental classification data. The sewing machine according to any one of items 1 to 31. [Item 33] the data collection device includes at least one of a microphone, an optical sensor having a field of view that includes at least a portion of the user, and a user interaction log of the user interface for collecting user interaction data; the processor is configured to process the user interaction data through the neural network to generate skill level classification data for estimating a skill level of the user. The sewing machine according to any one of items 1 to 32. [Item 34] Item 34. The sewing machine of item 33, wherein the processor controls the user interface to recommend training exercises based on the skill level classification data. [Item 35] 35. The sewing machine of any one of items 1 to 34, wherein the processor is configured to update the node parameters of the neural network based on the processed data generated by the neural network and the collected data. [Item 36] Item 36. The sewing machine of any one of items 34 and 35, wherein the processor is configured to update the node parameters of the neural network according to a plurality of trained node parameters of the trained neural network. [Item 37] Item 37. The sewing machine of item 36, wherein the trained node parameters are downloaded via a network interface. [Item 38] 38. The sewing machine according to any one of items 34 to 37, wherein the node parameters include at least one of an input parameter, a function parameter, and an output parameter. [Item 39] Item 39. The sewing machine of item 38, wherein the input parameter is a weight parameter associated with the input connection. [Item 40] Item 39. The sewing machine according to item 38, wherein the function parameter is a threshold value. [Item 41] transmitting the collected data to a remote processor for processing the collected data through a remote neural network to generate remote processed data; 41. The sewing machine of any one of items 1 to 40, further comprising a network interface configured to receive the remote processed data. [Item 42] Item 42. The sewing machine of any one of items 1 to 41, wherein the processor is configured to determine whether at least one of the needle, presser foot, throat plate, and sewing accessory is damaged based on object classification data. [Item 43] Item 43. The sewing machine of any one of items 1 to 42, wherein the processor is configured to control the user interface to alert the user based on a determination that any of the needle, presser foot, throat plate, and sewing accessory is damaged. [Item 44] Item 44. The sewing machine of any one of items 1 to 43, wherein the processor is configured to prevent operation of the sewing machine based on a determination that any of the needle, presser foot, throat plate, and sewing accessory is damaged. [Item 45] 45. The sewing machine of any one of items 1-44, wherein the data collection device comprises at least one of an acoustic sensor, a sound sensor, a vibration sensor, a chemical sensor, a biometric sensor, a sweat sensor, a breath sensor, a fatigue sensor, a gas sensor, a smoke sensor, a retina sensor, a fingerprint sensor, a fluid velocity sensor, a speed sensor, a temperature sensor, an optical sensor (e.g., a camera), a photo sensor, an infrared sensor, an ambient brightness sensor, a color sensor, an RGB color sensor, a CMYK color sensor, a grayscale color sensor, a touch sensor, a tilt sensor, a motion sensor, a metal sensor, a magnetic field sensor, a humidity sensor, a moisture sensor, an imaging sensor, a photon sensor, a pressure sensor, a force sensor, a density sensor, a proximity sensor, an ultrasonic sensor, a load cell, a digital accelerometer, a translation sensor, a friction sensor, a compressibility sensor, a sound sensor, a microphone sensor, a voltage sensor, a current sensor, an impedance sensor, a barometer, a gyroscope, a Hall effect sensor, a magnetometer, a GPS receiver, an electrical resistance sensor, a tension sensor, and a strain sensor. [Item 46] Item 46. The sewing machine of any one of items 1 to 45, wherein the data collection device includes at least one of a user activity log and a system event log. [Item 47] 1. A method of controlling a sewing machine, comprising: collecting data regarding at least one of the sewing machine, an environment surrounding the sewing machine, sewing materials, a sewing task performed by the sewing machine, and one or more interactions of a user with the sewing machine; storing the collected data in a data storage device; processing the collected data through a neural network, the neural network including a plurality of nodes, each node comprising: an input connection for receiving input data; Node parameters; a computing unit for computing an activation function based on the input data and node parameters; and Includes an output connection for transmitting output data; controlling, based on the processed data, at least one of a user interface for interacting with the user, the data storage device for storing the processed data, and an adjustable component for altering at least one of a state of the sewing machine and a performance of the sewing machine; A method comprising: [Item 48] 1. A control system for a sewing machine, comprising: a data collection device for collecting data regarding at least one of the sewing machine, an environment surrounding the sewing machine, sewing materials, a sewing operation performed by the sewing machine, and one or more interactions of a user with the sewing machine; a data storage device for storing data collected by the data collection device as collected data and for storing data relating to a neural network, the neural network including a plurality of nodes, each node comprising: an input connection for receiving input data; Node parameters; a computing unit for computing an activation function based on the input data and node parameters; and Includes an output connection for transmitting output data; a processor, the processor comprising: processing the collected data through the neural network to generate processed data; storing the processed data in said data storage device; and configured to control, based on the processed data, at least one of a user interface for interacting with the user and an adjustable component for modifying a current state of the sewing machine; A control system comprising: [Item 49] Item 49. The control system of item 48, wherein the data collection devices include a local data collection device and a remote data collection device. [Item 50] 50. The control system of any one of items 48 and 49, wherein the data storage device includes a local data storage device and a remote data storage device. [Item 51] A component for a sewing machine, comprising: a mounting portion for removably mounting the component to the sewing machine; and a marker indicating at least one of the identity and orientation of the component, the marker being detectable via a detection system of the sewing machine; A component for a sewing machine comprising: [Item 52] Item 52. The component for a sewing machine according to item 51, wherein the component includes at least one of a needle, a needle plate, a presser foot, and an embroidery frame. [Item 53] 53. The component for a sewing machine of any one of items 51 and 52, wherein the marker includes a plurality of geometric shapes. [Item 54] 54. The component for a sewing machine according to any one of items 51 to 53, wherein the marker comprises a colored stripe. [Item 55] 55. The component for a sewing machine according to any one of items 51 to 54, wherein the marker includes a filled-in color code. [Item 56] 56. The component for a sewing machine of any one of items 51 to 55, wherein the marker includes a reflective finish. [Item 57] 57. A component for a sewing machine according to any one of items 51 to 56, wherein the marker comprises an ultraviolet-reactive surface treatment. [Item 58] 58. The component for a sewing machine according to any one of items 51 to 57, wherein the marker comprises an infrared-responsive surface treatment. [Item 59] 59. A component for a sewing machine according to any one of items 51 to 58, wherein the marker comprises a magnet having a pronounced magnetic field line profile. [Item 60] 60. A component for a sewing machine according to any one of items 51 to 59, wherein the marker is invisible to the naked eye of a user of the sewing machine. [Item 61] Item 61. A component for a sewing machine according to any one of items 51 to 60, wherein the marker includes a near-field communication device. [Item 62] Item 62. A component for a sewing machine according to any one of items 51 to 61, wherein the marker comprises a radio frequency identification device. [Item 63] 63. A component for a sewing machine according to any one of items 51 to 62, wherein the marker comprises at least one pattern of etched rings and etched lines. [Item 64] 64. A component for a sewing machine according to any one of items 51 to 63, wherein the marker includes at least one of an etched portion, a debossed portion, and an embossed portion. [Item 65] 65. A component for a sewing machine according to any one of items 51 to 64, wherein the marker comprises a first region having a first surface finish and a second region having a second surface finish. [Item 66] A sewing machine, a sewing bed, the sewing bed including a feed mechanism and a throat plate, on which a workpiece may be placed and which moves the workpiece across the sewing bed; a sewing head disposed on the sewing bed; a needle bar extending to a distal end toward said sewing bed; at least one needle attached to the distal end of the needle bar; an accessory bar extending to a distal end toward said sewing bed; at least one accessory attached to the distal end of the accessory bar; a camera having a field of view encompassing at least a portion of any of the sewing bed, the throat plate, the workpiece, the needle, and the accessory, the camera generating camera data signals relating to the portion of the sewing bed, the throat plate, the workpiece, the needle, and the accessory within the field of view of the camera; a user interface configured to present sewing information to a user and to receive input from the user of the sewing machine; at least one component recognition neural network trained to identify at least one of a needle plate from a plurality of needle plates, a needle from a plurality of needles, and an accessory from a plurality of accessories from the camera data signal, the at least one component recognition neural network generating a component identity data signal including the identified component; receiving an indication from the user interface of a stitch pattern selected by the user of the sewing machine; receiving an indication of the identified component via the component identity data signal from the at least one component recognition neural network; determining whether the stitching pattern selected by the user is compatible with the identified components; at least one processor configured to control the user interface to present an indication of compatibility between the selected stitching pattern and the identified component; A sewing machine equipped with [Item 67] Item 67. The sewing machine of item 66, wherein the accessory bar is a presser bar and the at least one accessory is a presser foot. [Item 68] the at least one component recognition neural network is trained to determine whether at least one of the throat plate, the needle, and the accessory is incorrectly installed, and the at least one component recognition neural network generates a misassembly data signal including the incorrectly installed component; the processor is configured to control the user interface to present an indication of the mis-installed component. Item 66 or 67. A sewing machine according to item 66 or 67. [Item 69] Item 69. The sewing machine of item 68, wherein the processor is configured to prevent operation of the sewing machine while the incorrectly installed component remains incorrectly installed. [Item 70] the component identity data signal includes data regarding two or more identified components; the processor is configured to determine whether an incompatibility exists between the two or more identified components. The sewing machine according to any one of items 66 to 69. [Item 71] A sewing machine, a sewing bed, the sewing bed including a feed mechanism and a throat plate, on which a workpiece may be placed and which moves the workpiece across the sewing bed; a sewing head disposed above said sewing bed, said sewing head including a needle bar extending to a distal end toward said sewing bed, said needle bar holding a needle for performing a sewing operation on said workpiece; a control system for the sewing machine having a plurality of settings for modifying at least one of a current state of the sewing machine and a performance characteristic of the sewing machine; a user interaction log including at least one of historical data of current state and historical data of performance characteristics; a user interface configured to present sewing information to a user and to receive input from the user of the sewing machine, the input received from the user changing settings of the control system; a neural network trained to identify a setting context for the changed setting from the current state of the sewing machine, the performance characteristics of the sewing machine, and the user interaction log, the neural network generating a setting context data signal including the identified setting context; receiving an indication of the changed settings of the control system from the user interface; receiving an indication of the identified configuration context from the neural network via the configuration context data signal; determining a proposed default configuration change based on the changed configuration and the identified configuration context; at least one processor configured to control the user interface to present the suggested default setting changes; A sewing machine equipped with [Item 72] the processor: determining suggested relevant configuration changes based on the changed configuration and the identified configuration context; Item 72. The sewing machine of item 71, configured to control the user interface to present the suggested associated setting changes. [Item 73] A sewing machine, a workspace sensor, the workspace sensor comprising: a camera facing away from the sewing machine, the camera generating a camera data signal; and at least one of the microphones generating a microphone data signal; a user interface configured to present sewing information to a user and to receive input from the user of the sewing machine; Speaker; a neural network trained to identify from at least one of the camera data signal, the microphone data signal, and the input received by the user interface that a child is attempting to interact with the sewing machine, the neural network generating a child detection data signal; at least one processor, said at least one processor comprising: receiving from the neural network an indication of a child detected from the child-detection data signal; and configured to control the speaker to generate an audible alert based on the indication of the detected child; A sewing machine equipped with [Item 74] a user device configured to receive an alert from the sewing machine; the processor is configured to send an alert to the user device based on the indication of the detected child. Item 73. The sewing machine described in item 73. [Item 75] A sewing machine, a data storage device for storing a current version of software for operating said sewing machine, said current version of software having a version number and an installation date; a workspace sensor, the workspace sensor comprising: a camera facing away from the sewing machine, the camera generating a camera data signal; and at least one of the microphones generating a microphone data signal; User interaction logs containing user activity data, including time data and weekday data; a clock for generating clock data indicating a current time and a current day of the week; a data interface for receiving an indication that a new version of software is available and for receiving said new version of software; a neural network trained to identify typical available times and typical available periods from at least one of the camera data signal, the microphone data signal, the user activity data, and the clock data, the neural network generating available time data and available day of the week data; at least one processor, said at least one processor comprising: receiving the indication from the data interface that the new version of software is available; determining an installation period for installing the new version of software; receiving the available time data and the available day of the week data from the neural network; determining an installation time and an installation date for the new version of the software based on the installation period, the available time data, and the available day of the week data; and configured to install the new version of software at the installation time and on the installation date based on a determination that the sewing machine is not being used; A sewing machine equipped with [Item 76] A sewing machine, needles for forming seams on workpieces with sewing thread; a sewing thread sensor, said sewing thread sensor generating a sewing thread data signal; a user interface configured to present sewing information to a user and to receive input from the user of the sewing machine; a thread classification neural network trained to identify characteristics of the thread from the thread data signal, the characteristics including at least one of color, density, surface quality, and fiber type, the thread classification neural network generating a thread classification data signal; and at least one processor, said at least one processor comprising: receiving an indication of at least one suture characteristic from the suture classification neural network via the suture classification data signal; and configured to control the user interface to present an indication of the at least one suture characteristic; A sewing machine equipped with [Item 77] Item 77. The sewing machine of item 76, wherein the sewing thread sensor includes a tube-shaped housing through which the sewing machine's sewing thread moves during a sewing operation, a light source disposed within the tube-shaped housing, and an optical sensor disposed within the tube-shaped housing. [Item 78] the processor: receiving a stitch pattern selected by the user from the user interface; determining whether the stitch pattern selected by the user is compatible with the at least one sewing thread characteristic; and and controlling the user interface to present an indication of compatibility between the selected stitch pattern and the at least one sewing thread characteristic. 78. The sewing machine according to item 76 or 77. [Item 79] A sewing machine, needles for forming seams on workpieces with sewing thread; a workpiece sensor, the workpiece sensor generating a workpiece data signal; a user interface configured to present sewing information to a user and to receive input from the user of the sewing machine; a workpiece classification neural network trained to identify characteristics of the workpiece from the workpiece data signal, the characteristics including at least one of material type, color, density, reflectivity, weave direction, pattern, orientation, and topography, the workpiece classification neural network generating a workpiece classification data signal; and at least one processor, said at least one processor comprising: receiving an indication of at least one workpiece characteristic from the workpiece classification neural network via the workpiece classification data signal; and configured to control the user interface to present an indication of the at least one workpiece characteristic; A sewing machine equipped with [Item 80] 80. The sewing machine of claim 79, wherein the workpiece sensor includes at least one camera pointed toward a sewing bed on which the workpiece is placed during a sewing operation. [Item 81] the processor: receiving a stitch pattern selected by the user from the user interface; determining whether the stitch pattern selected by the user is compatible with the at least one workpiece characteristic; and and controlling the user interface to present an indication of compatibility between the selected stitching pattern and the at least one workpiece characteristic. Item 79 or 80. The sewing machine according to item 79 or 80. [Item 82] a sewing thread sensor, said sewing thread sensor generating a sewing thread data signal; a sewing thread classification neural network trained to identify characteristics of the sewing thread from the sewing thread data signal, the characteristics including at least one of color, density, surface quality, and fiber type, and the sewing thread classification neural network generating a sewing thread classification data signal; at least one processor, said at least one processor comprising: receiving an indication of at least one sewing thread characteristic from the sewing thread classification neural network via the sewing thread classification data signal; determining whether an incompatibility exists between the suture and the workpiece based on the suture characteristics and the workpiece characteristics; and configured to control the user interface to present an indication of compatibility of the suture and the workpiece; The sewing machine according to any one of items 79 to 81, further comprising: [Item 83] A sewing machine, a sewing bed, the sewing bed including a feed mechanism and a throat plate, on which a workpiece may be placed and which moves the workpiece across the sewing bed; a sewing head disposed on the sewing bed; a needle bar extending to a distal end toward said sewing bed; at least one needle attached to the distal end of the needle bar, the needle being threaded with a suture; an accessory bar extending to a distal end toward said sewing bed; at least one accessory attached to the distal end of the accessory bar; a camera having a field of view encompassing at least a portion of any of the sewing bed, the throat plate, the workpiece, the needle, and the accessory, the camera generating camera data signals relating to the portion of the sewing bed, the throat plate, the workpiece, the needle, and the accessory within the field of view of the camera; an adjustable component of the sewing machine, the adjustable component being adjustable to change the current state of the sewing machine; a user interface configured to present sewing information to a user and to receive input from the user of the sewing machine; an object recognition neural network trained to recognize and classify at least one of the needle plate, the at least one needle, the at least one accessory, the workpiece, the sewing thread, the embroidery frame, and a foreign object from the camera data signal, the object recognition neural network generating an object recognition data signal and an object classification data signal; at least one processor, said at least one processor comprising: receiving from the object recognition neural network an indication of at least one recognized object from the object recognition data signal; receiving from the object recognition neural network an indication of at least one classification of the recognized object from the object classification data signal; and configured to control, based on the classification of the recognized object, at least one of the user interface for presenting the classification to the user and the adjustable component for modifying a current state of the sewing machine; A sewing machine equipped with [Item 84] Item 84. The sewing machine of item 83, wherein the adjustable component is a motor that moves the needle bar in a sewing operation. [Item 85] Item 85. The sewing machine of item 83 or 84, wherein the classification includes a position of the recognized object, and the processor is configured to determine that the recognized object is at risk of damage based on the position of the recognized object. [Item 86] Item 86. The sewing machine of item 85, wherein the processor is configured to control the user interface to present an indication that the recognized object is at risk of damage. [Item 87] Item 87. The sewing machine of item 85 or 86, wherein the processor is configured to adjust the adjustable component to prevent operation of the sewing machine. [Item 88] A sewing machine, a workspace sensor, the workspace sensor comprising: a camera facing away from the sewing machine, the camera generating a camera data signal; an accelerometer that generates a vibration data signal; a gyroscope for generating an orientation data signal; and a photosensor that generates a brightness data signal; a user interface configured to present sewing information to a user and to receive input from the user of the sewing machine; a workspace neural network trained to identify at least one of work surface height, work surface stability, user posture, chair position, and lighting from at least one of the camera data signal, the vibration data signal, the orientation data signal, and the lighting data signal, the workspace neural network generating an ergonomic health data signal; and at least one processor, said at least one processor comprising: receiving the ergonomic health data signal from the workspace neural network; and configured to control the user interface to present recommendations for changes to the workspace to improve the ergonomic health of the user; A sewing machine equipped with [Item 89] A sewing machine, a sewing bed, the sewing bed including a feed mechanism and a throat plate, on which a workpiece may be placed and which moves the workpiece across the sewing bed; a sewing head disposed on the sewing bed; a needle bar extending to a distal end toward said sewing bed; a needle attached to the distal end of the needle bar; a swing motor for swinging the needle bar laterally; a camera having a field of view encompassing at least a portion of the workpiece, the camera generating a camera data signal relating to the portion of the workpiece within the field of view of the camera; a workpiece neural network trained to identify a topography of the workpiece from the camera data signal, the workpiece neural network generating a workpiece topography data signal; at least one processor, said at least one processor comprising: receiving the workpiece topography data signal from the workpiece neural network; determining a location of a seam in the workpiece from the workpiece topography data signal; and configured to control the swing motor to move the needle bar laterally so that the needle penetrates the seam of the workpiece during a sewing operation; A sewing machine equipped with [Item 90] A sewing machine, a sewing bed, the sewing bed including a feed mechanism and a throat plate, on which a workpiece may be placed and which moves the workpiece across the sewing bed; a sewing head disposed on the sewing bed; a needle bar extending to a distal end toward said sewing bed; a needle attached to the distal end of the needle bar; a swing motor for swinging the needle bar laterally; a camera having a field of view encompassing at least a portion of the workpiece, the camera generating a camera data signal relating to the portion of the workpiece within the field of view of the camera; a feature detection neural network trained to identify at least one feature of the workpiece from the camera data signal, the feature including at least one of an edge, a hole, a seam, and the feature detection neural network generating a feature location data signal; at least one processor, said at least one processor comprising: receiving the feature location data signals from the feature detection neural network; determining the location of the workpiece feature from the feature location data signal; and configured to control the oscillating motor to move the needle bar laterally during a sewing operation so that the needle penetrates the workpiece a predetermined distance from the location of the feature; A sewing machine equipped with [Item 91] A sewing machine, a sewing bed, the sewing bed including a feed mechanism and a throat plate, on which a workpiece may be placed and which moves the workpiece across the sewing bed; a sewing head disposed on the sewing bed; a needle bar extending to a distal end toward said sewing bed; a needle attached to the distal end of the needle bar, the needle having a sewing thread threaded therethrough for forming a stitch in the workpiece during a sewing operation; a camera having a field of view encompassing at least a portion of any of the sewing bed, the throat plate, the workpiece, the needle, and the sewing thread, the camera generating camera data signals relating to the portion of the sewing bed, the throat plate, the workpiece, the needle, and the sewing thread within the field of view of the camera; a user interface configured to present sewing information to a user and to receive input from the user of the sewing machine; a sewing error neural network trained to identify sewing errors occurring during the sewing operation from the camera data signal, the sewing error neural network generating a sewing error data signal including the identified sewing errors; at least one processor, said at least one processor comprising: receiving an indication of the identified sewing error from the sewing error neural network via the sewing error data signal; and configured to control the user interface to present an indication of the sewing error; A sewing machine equipped with [Item 92] Item 92. The sewing machine of item 91, wherein the identified sewing errors include at least one of a skipped stitch, an uneven stitch, an offset stitch, a bunched seam, varying stitch density, a broken bobbin, a broken looper, a broken needle, melted thread, a broken needle, a stuck needle, a needle striking the throat plate, thread cut by the needle, inconsistent thread tension, a wavy seam, an unthreaded needle, a loose needle holder, a loose presser foot, a misplaced presser foot, a stationary needle, a stationary work piece, a bunched work piece, a bunched thread, a thread knot, a loose stitch, a tangled thread, a frayed thread, torn thread, variations in workpiece feed, a bent needle, a damaged looper, a damaged stitch finger, a misplaced looper, a misplaced stitch finger, and a dull fabric knife. [Item 93] A sewing machine, a sewing bed, the sewing bed including a feed mechanism and a throat plate, on which a workpiece may be placed and which moves the workpiece across the sewing bed; a sewing head disposed above said sewing bed, said sewing head including a needle bar for moving a needle that penetrates a workpiece with a sewing thread during a sewing operation; an adjustable component of the sewing machine, the adjustable component being adjustable to change the current state of the sewing machine; a first data source generating a first data signal including first data having a first data type; a second data source generating a second data signal including second data having a second data type, said second data type being different from said first data type; a processor, the processor comprising: receiving the first data signal from the first data source; receiving the second data signal from the second data source; processing the first data signal and the second data signal through an artificial intelligence unit to generate a third data signal including third data; configured to control the adjustable component to change the current state of the sewing machine based on the third data; A sewing machine equipped with [Item 94] Item 94. The sewing machine of item 93, wherein the first data source is a camera. [Item 95] Item 95. The sewing machine according to item 93 or 94, wherein the artificial intelligence unit is a neural network.

Claims

1. A sewing machine, a sewing bed having a feed mechanism and a throat plate, on which a workpiece is placed and across which the workpiece can be moved; a sewing head disposed on the sewing bed, the sewing head comprising: a needle bar extending to a distal end toward the sewing bed; a needle attached to the distal end of the needle bar; an accessory bar extending to a distal end toward the sewing bed; an accessory attached to the distal end of the accessory bar; a sewing head having a a camera having a field of view that encompasses at least a portion of any of the sewing bed, the throat plate, the workpiece, the needle, and the accessory, the camera generating camera data signals relating to the portions of the sewing bed, the throat plate, the workpiece, the needle, and the accessory within the field of view of the camera; a user interface configured to present information to a user of the sewing machine and to receive input from the user of the sewing machine; a component recognition neural network trained to identify at least one of the needle plate, the needle, and the accessory from the camera data signals, the component recognition neural network generating component recognition data signals related to the identified components; 1. A processor, comprising: receiving an indication from the user interface of a stitch pattern selected by the user of the sewing machine; receiving the component recognition data signal from the component recognition neural network; determining whether the stitching pattern selected by the user is compatible with the identified components; Controlling the user interface to present an indication of compatibility between the selected stitching pattern and the identified component. The processor and A sewing machine equipped with

2. the component recognition neural network is trained to determine whether at least one of the throat plate, the needle, and the accessory is incorrectly installed, and the component recognition neural network generates an incorrect assembly data signal including the incorrectly installed component; The sewing machine of claim 1 , wherein the processor is configured to control the user interface to present an indication of the incorrectly installed component.

3. The sewing machine of claim 2 , wherein the processor is configured to alter operation of the sewing machine while the incorrectly installed component remains incorrectly installed.

4. The sewing machine according to claim 1 , wherein the accessory bar is a presser bar and the accessory is a presser foot.

5. the component recognition neural network is trained to determine whether the presser foot is incorrectly installed, the component recognition neural network generating an incorrect assembly data signal including the incorrectly installed presser foot; The sewing machine of claim 4 , wherein the processor is configured to control the user interface to provide an indication of the incorrectly installed presser foot.

6. The sewing machine of claim 5 , wherein the processor is configured to alter operation of the sewing machine while the incorrectly installed presser foot remains incorrectly installed.

7. the component recognition neural network is trained to determine the orientation of the presser foot, the component recognition neural network generating a component orientation data signal including the orientation of the presser foot; The sewing machine of claim 4 , wherein the processor is configured to control the user interface to provide an indication of the orientation of the presser foot.

8. A sewing machine, a sewing bed having a feed mechanism and a throat plate, on which a workpiece is placed and across which the workpiece can be moved; a sewing head disposed on the sewing bed, the sewing head comprising: a needle bar extending to a distal end toward the sewing bed; a needle attached to the distal end of the needle bar, the needle having a suture threaded therethrough; an accessory bar extending to a distal end toward the sewing bed; an accessory attached to the distal end of the accessory bar; a sewing head having a a camera having a field of view that encompasses at least a portion of any of the sewing bed, the throat plate, the workpiece, the needle, and the accessory, the camera generating camera data signals relating to the portions of the sewing bed, the throat plate, the workpiece, the needle, and the accessory within the field of view of the camera; a user interface configured to present information to a user of the sewing machine and to receive input from the user of the sewing machine; an object recognition neural network trained to detect and classify recognized objects from the camera data signals into at least one of the needle plate, the at least one needle, the at least one accessory, the workpiece, the sewing thread, an embroidery hoop, and a foreign object, the object recognition neural network generating object detection data signals related to at least one of a position and an orientation of the recognized objects and object classification data signals related to an identity of the recognized objects; 1. A processor, comprising: receiving from the object recognition neural network an indication of at least one of the position and the orientation of the recognized object from the object detection data signal; receiving from the object recognition neural network an indication of the identity of the recognized object from the object classification data signal; Controlling the user interface to present at least one of the position, the orientation, and the identity of the recognized object to the user. The processor and A sewing machine equipped with

9. The processor: determining whether the recognized object is at least one of incorrectly placed and damaged based on at least one of the position and the orientation of the recognized object; Controlling the user interface to present an indication of the incorrect placement or damage to the recognized object to the user.

9. The sewing machine according to claim 8, wherein the sewing machine is configured as follows:

10. 10. The sewing machine of claim 9, wherein the processor is configured to alter operation of the sewing machine while the recognized object remains at least one of incorrectly placed and damaged.

11. the recognized object comprises a first recognized object and a second recognized object; The processor: receiving from the object recognition neural network an indication of at least one of a first position and a first orientation of the first recognized object from the object detection data signal; receiving from the object recognition neural network an indication of at least one of a second position and a second orientation of the second recognized object from the object detection data signal; Determine whether an incompatibility exists between the first recognized object and the second recognized object based on the first position, the first orientation, the second position, and the second orientation. The sewing machine according to any one of claims 8 to 10, wherein the sewing machine is configured as follows.

12. The sewing machine of claim 11 , wherein the processor is configured, based on the determination that an incompatibility exists, to control the user interface to indicate to the user that the incompatibility exists.

13. The sewing machine of claim 11 or 12, wherein the processor is configured to alter operation of the sewing machine based on the determination that an incompatibility exists.

14. the recognized object comprises a first recognized object and a second recognized object; The processor: receiving from the object recognition neural network an indication of a first identity of the first recognized object from the object classification data signal; receiving from the object recognition neural network an indication of a second identity of the second recognized object from the object classification data signal; determining whether an incompatibility exists between the first recognized object and the second recognized object based on the first identity and the second identity; The sewing machine according to any one of claims 8 to 13, wherein the sewing machine is configured as follows.

15. The sewing machine of claim 14 , wherein the processor is configured, based on the determination that an incompatibility exists, to control the user interface to indicate to the user that the incompatibility exists.

16. 16. The sewing machine of claim 14 or 15, wherein the processor is configured to alter operation of the sewing machine based on the determination that an incompatibility exists.

17. The processor: determining a replacement component for at least one of the first recognized object and the second recognized object based on the first identity, the second identity, and the existence of the incompatibility, wherein installation of the replacement component resolves the existence of the incompatibility; and controlling the user interface to present the alternative components to the user; 17. The sewing machine of claim 14, configured to:

18. the object detection data signal further comprises a velocity of the recognized object; the recognized object comprises a first recognized object and a second recognized object; The processor: receiving from the object recognition neural network an indication of at least one of a first position, a first orientation, and a first velocity of the first recognized object from the object detection data signal; receiving from the object recognition neural network an indication of at least one of a second position, a second orientation, and a second velocity of the second recognized object from the object detection data signal; determining whether a collision will occur or has already occurred between the first recognized object and the second recognized object based on a first identity of the first recognized object and a second identity of the second recognized object; 18. The sewing machine according to claim 8, wherein the sewing machine is configured as follows:

19. 20. The sewing machine of claim 18, wherein the processor is configured to alter operation of the sewing machine based on the determination that the first recognized object and the second recognized object will collide or have already collided.

20. A sewing machine, a sewing bed having a feed mechanism and a throat plate, on which a workpiece can be placed and moved across the sewing bed; a sewing head disposed above the sewing bed, the sewing head having a needle bar that moves a needle through a workpiece with a sewing thread during a sewing operation; an input data source generating an input data signal including input data, the input data having first data having a first data type and second data having a second data type, the first data type comprising optical data; 1. A processor, comprising: receiving the input data signal; processing the input data signal through an artificial intelligence unit to generate an output data signal including output data; updating the parameters of the artificial intelligence unit based on the output data; The processor and A sewing machine equipped with

21. 21. The sewing machine of claim 20, wherein the artificial intelligence unit is a neural network.

22. A sewing machine as described in claim 20 or 21, wherein the second data type is different from the first data type.

23. The sewing machine of claim 20, wherein the second data type is component compatibility data generated from a component database.

24. The sewing machine of claim 20, wherein the second data type is stitch indication data generated by user interaction with a user interface.

25. 25. The sewing machine of any one of claims 20 to 24, wherein the first data type includes at least one of color data generated by a camera and brightness data generated by the camera.

Images