Method and system for predicting the lifespan of printed labels

Abstract

The present invention is entitled "Method and system for predicting the lifespan of a printed label." Various embodiments shown herein disclose a method comprising receiving, by a processor, one or more patient characteristics associated with a first patient, the one or more patient characteristics comprising at least a type of sanitation treatment and / or a frequency of use of the sanitation treatment. Additionally, the method comprises receiving one or more image characteristics associated with an image of a patient wristband worn by the first patient. The method further comprises training a machine learning (ML) model that defines a relationship between the one or more patient characteristics and the one or more image characteristics. The ML model is used to predict the number of days until the patient wristband associated with a second patient is deemed unusable. Additionally, the method comprises generating instructions to a printing device to print a new patient wristband for the second patient based on the number of days being less than a predetermined number of days threshold.

Description

Technical Field

[0001] Exemplary embodiments of the present disclosure relate generally to printed labels, and more particularly to systems and methods for predicting the lifespan of printed labels. Background Art

[0002] Generally, printed labels that include printed machine-readable indicia fade over time. The fading of such labels renders the machine-readable indicia unreadable, which may be desirable and may lead to errors in tracking an object / person (when the machine-readable indicia is used to track the object / person). Summary of the Invention

[0003] Various embodiments described herein disclose a method comprising receiving, by a processor, one or more patient characteristics associated with a first patient, wherein the one or more patient characteristics include at least a type of hygiene treatment and / or a frequency of use of the hygiene treatment. The method also comprises receiving, by the processor, one or more image characteristics associated with an image of a patient bracelet worn by the first patient. The method also comprises training, by the processor, a machine learning (ML) model defining a relationship between the one or more patient characteristics and the one or more image characteristics, wherein the ML model is used to predict the number of days until the patient bracelet associated with a second patient is deemed unusable. In addition, the method comprises generating, by the processor, an instruction to print a new patient bracelet for the second patient based on the number of days being less than a predetermined day threshold, to a printing device.

[0004] Various embodiments shown herein disclose a central server comprising a memory device storing one or more instructions. In addition, the central server comprises a processor communicatively coupled to the memory device, the processor being configured to receive one or more patient characteristics associated with a first patient, wherein the one or more patient characteristics include at least the type of sanitary treatment and / or the frequency of use of the sanitary treatment. In addition, the processor is configured to receive one or more image characteristics associated with an image of a patient bracelet worn by the first patient. In addition, the processor is configured to train a machine learning (ML) model defining the relationship between the one or more patient characteristics and the one or more image characteristics, wherein the ML model is used to predict the number of days until the patient bracelet associated with the second patient is considered unusable. In addition, the processor is configured to generate instructions to a printing device to print a new patient bracelet for the second patient based on the number of days being less than a predetermined number of days threshold. BRIEF DESCRIPTION OF THE DRAWINGS

[0005] The description of the exemplary embodiments may be read in conjunction with the accompanying drawings. It should be understood that for simplicity and clarity of illustration, the elements shown in the drawings are not necessarily drawn to scale. For example, the dimensions of some of the elements may be exaggerated relative to other elements. Embodiments incorporating the teachings of the present disclosure are shown and described with respect to the accompanying drawings set forth herein, in which:

[0006] Figure 1 shows a system environment in which various embodiments of the present disclosure are implemented;

[0007] Figure 2 shows a block diagram of an operator computing device according to one or more embodiments described herein;

[0008] Figure 3A A flowchart illustrating a method for operating an operator computing device according to one or more embodiments described herein is shown;

[0009] Figure 3B shows a flow chart of another method for operating an operator computing device according to one or more embodiments described herein;

[0010] Figure 4 shows a block diagram of a marking scanner according to one or more embodiments described herein;

[0011] Figure 5 shows a flow chart of a method for operating a marking scanner according to one or more embodiments described herein;

[0012] Figure 6 A flowchart illustrating a method for determining a quality metric of an image according to one or more embodiments described herein is shown;

[0013] Figure 7 A block diagram illustrating a central server according to one or more embodiments described herein;

[0014] Figure 8 shows a flowchart of a method for operating a central server according to one or more embodiments described herein; and

[0015] Figure 9 A flow chart illustrating a method for predicting the number of days until a patient wristband associated with a new patient is unavailable is shown, according to one or more embodiments described herein. DETAILED DESCRIPTION

[0016] Some embodiments of the present disclosure will be described more fully hereinafter with reference to the accompanying drawings, which illustrate some, but not all, embodiments of the present disclosure. Indeed, these disclosures may be embodied in many different forms and should not be construed as limited to the embodiments set forth herein; rather, these embodiments are provided so that this disclosure will satisfy applicable legal requirements. Throughout, like reference numerals refer to like elements.

[0017] Unless the context requires otherwise, throughout this specification and the claims that follow, the word "comprise" and variations such as "include" and "have" are to be interpreted in an open-ended sense, ie, to mean "including, but not limited to."

[0018] Reference throughout this specification to "one embodiment" or "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment. Thus, the appearances of the phrases "in one embodiment" or "in an embodiment" in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, one or more particular features, structures, or characteristics from one or more embodiments may be combined in any suitable manner in one or more other embodiments.

[0019] The word “example” or “exemplary” is used herein to mean “serving as an example, instance, or illustration.” Any implementation described herein as “exemplary” is not necessarily to be construed as preferred or advantageous over other implementations.

[0020] If the specification states that a component or feature "may," "could," "would," "should," "will," "preferably," "likely," "typically," "optionally," "for example," "often," or "might" (or other such language) be included or have a characteristic, that particular component or feature is not required to be included or have that characteristic. Such components or features may optionally be included in some embodiments, or may be excluded.

[0021] The terms "electronically coupled," "electronically coupling," "electronically couple," "communicate with," "electronically communicate with," or "connect" in this disclosure mean that two or more components are connected (directly or indirectly) via wired means (such as, but not limited to, a system bus, wired Ethernet) and / or wireless means (such as, but not limited to, Wi-Fi, Bluetooth, ZigBee) so that data and / or information can be transmitted to and / or received from these components.

[0022] The term "marking" is broadly intended to include any marking or machine-readable code, including linear symbols, 2D barcodes (such as QR codes and data matrix codes), RFID tags, IR tags, near field communication (NFC) tags, and characters that can be read by a computing device (e.g., a marking scanner). A marking is typically a graphical representation of information (e.g., data), such as a product number, a package tracking number, a patient barcode symbol identifier, a medication tracking identifier, a personnel barcode symbol identifier, etc.

[0023] The term "quality" may refer to a standard or protocol based on which content can be evaluated or compared. For example, image quality can be assessed based on image sharpness, image noise, image dynamic range, and so on. In some examples, image quality can be further assessed based on the quality of certain portions of the image. For example, image quality can be assessed based on the quality of markers within the image. To this end, marker quality can be assessed based on ANSI X3.182, ISO 15415, and ISO / IEC 15416 standards.

[0024] Typical printed labels fade over time. In some cases where the printed label includes printed machine-readable indicia, the machine-readable indicia may fade over time. Indicia scanners may be unable to scan and decode the faded machine-readable indicia. Consequently, tracking objects using such faded machine-readable indicia may be prone to errors (because scanning and decoding the faded machine-readable indicia may be unsuccessful).

[0025] For example, a printed label (with machine-readable indicia) may correspond to a patient wristband printed for one or more hospitalized patients. Such patient wristbands are used to track patients within the hospital premises. Additionally or alternatively, such patient wristbands are used to track a first set of patient characteristics. In some examples, the first set of patient characteristics includes, but is not limited to, the type of illness associated with the patient, the age of the first patient, the patient's name, the patient's location within the hospital premises, the patient's history of movement within the hospital premises, and the like. If the machine-readable indicia on the patient wristband is faded, scanning and decoding of the machine-readable indicia on the patient wristband may fail, thereby resulting in erroneous monitoring of the patient.

[0026] Embodiments described herein disclose systems and methods for predicting the lifespan of printed labels. In exemplary embodiments, the printed label may correspond to a label or medium onto which a printer can print content. In some examples, the printed content may include machine-readable indicia. In exemplary embodiments, the machine-readable indicia may be configured to store a first set of object characteristics related to the object to which the printed label is attached. For example, in a hospital setting, the machine-readable indicia is printed on a patient wristband and configured to store a first set of patient characteristics. In exemplary embodiments, the first set of patient characteristics may include, but are not limited to, the name of the patient (to which the patient wristband is attached), the age of the patient (to which the patient wristband is attached), and medical conditions associated with the patient (to which the patient wristband is attached). In some examples, the patient wristband may facilitate monitoring of a patient's location within the hospital setting. For example, when a patient is moved from one ward to another, the patient wristband is scanned by a caregiver in each ward, allowing monitoring of the patient's location within the hospital setting.

[0027] Additionally or alternatively, the system includes an operator computing device configured to receive input from a caregiver associated with the patient in the hospital. For example, the caregiver may provide input to the operator computing device related to a second set of patient characteristics associated with the patient. In an exemplary embodiment, the second set of patient characteristics includes, but is not limited to, the patient's history of movement within the hospital, the type of sanitization used to disinfect the patient, the frequency of sanitization, and the like. In some examples, the first set of patient characteristics and the second set of patient characteristics may constitute the one or more patient characteristics. In some examples, the caregiver may input both the first set of patient characteristics and the second set of patient characteristics using the operator computing device. In response to inputting the one or more patient characteristics (i.e., the first set of patient characteristics and the second set of patient characteristics), the operator computing device may be configured to transmit the one or more patient characteristics to the central server.

[0028] In an exemplary embodiment, the system further includes a tag scanner configured to scan and decode a machine-readable tag printed on the patient wristband to retrieve a first set of patient characteristics. Additionally or alternatively, the tag scanner may be configured to determine one or more image characteristics associated with the scanned image of the patient wristband. The one or more image characteristics may include, but are not limited to, a quality metric of the patient wristband and information indicating whether decoding of the machine-readable tag was successful (hereinafter referred to as a decoding status). For example, the tag scanner may be configured to determine the quality metric of the patient wristband by determining the quality of the machine-readable tag (printed on the patient wristband) based on ANSI X3.182, ISO 15415, and ISO / IEC 15416 standards. In some examples, decoding of the machine-readable tag may fail because the machine-readable tag may have defects (such as, but not limited to, fading). Additionally or alternatively, the tag scanner may be configured to transmit the decoding status to a central server. In some examples, the decoding status may indicate a quality metric of the patient wristband. For example, if decoding of the machine-readable tag is successful, the decoding status will indicate "success." Therefore, the quality of the patient wristband is good. Similarly, if the decoding of the machine-readable indicia is unsuccessful, the decode status will indicate “unsuccessful.” Therefore, the quality of the patient bracelet has deteriorated.

[0029] In an exemplary embodiment, the central server may be configured to receive the one or more patient characteristics from the operator computing and indicia scanner devices, i.e., the second set of patient characteristics from the operator computing device and the first set of patient characteristics from the indicia scanner. Additionally or alternatively, the central server may be configured to receive the one or more image characteristics from the indicia scanner. In some examples, the central server may be configured to generate training data based on the one or more patient characteristics and the one or more image characteristics (which are associated with the images scanned by the indicia scanner). In an exemplary embodiment, the training data. In an exemplary embodiment, the training data may include one or more features and one or more tags. The one or more features of the training data may include, but are not limited to, a first time period between consecutive scans of the machine-readable indicia, the patient's current location within the hospital facility (based on the scans of the machine-readable indicia), the type of sanitizer used to disinfect the patient, the frequency of sanitizer use, the patient's age, a quality metric of the patient's wristband (received from the indicia scanner), a decoding status, and / or a disease associated with the patient. The one or more tags of the training data may include, but are not limited to, the number of days before the patient's wristband became defective.

[0030] In an exemplary embodiment, the central server may be further configured to train a machine learning (ML) model based on the training data. In an exemplary embodiment, the ML model may define one or more relationships and / or rules between the one or more features of the training data and the one or more tags of the training data. Thereafter, the central server may be configured to predict the number of days until the patient's wristband is deemed unreadable or damaged. In some examples, the central server may transmit an instruction to print a new patient wristband if the number of days is less than a predetermined threshold.

[0031] The disclosed embodiments provide several advantages. For example, they allow for proactive determination of the number of days until a patient's wristband breaks. Consequently, the central server can instruct a printer to proactively print the patient's wristband. This prevents errors in patient monitoring (due to a broken wristband).

[0032] Figure 1 A system environment 100 is shown for implementing various embodiments of the present disclosure. In an exemplary embodiment, the system environment 100 may correspond to a hospital environment in which one or more patients are being treated for health-related conditions. In an exemplary embodiment, the system environment 100 includes a landmark scanner 102, an operator computing device 104, a network 106, a central server 108, and a printing device 110.

[0033] In an exemplary embodiment, the indicia scanner 102 may correspond to a mobile device, such as a handheld indicia scanner, a portable data terminal, a mobile phone, a tablet computer, a portable computer, etc., or may be a fixed terminal, such as a fixed terminal fixed to a single location along an assembly line capable of capturing the one or more images (such as one image). In an exemplary embodiment, the image may correspond to an image of a patient wristband worn by a first patient admitted to the hospital. In an exemplary embodiment, the indicia scanner 102 may be capable of recognizing and decoding a machine-readable indicia in the image to retrieve a first set of patient characteristics associated with the first patient, such as in combination with Figure 5 Furthermore, the marker scanner 102 may be configured to determine one or more image characteristics associated with the image of the patient's wristband, such as Figure 5 Further described. Figure 4 The structure of the indicia scanner 102 is further described.

[0034] In an exemplary embodiment, in the system environment 100, the operator computing device 104 may refer to a computing device that can be configured to provide an interface to a caregiver. For example, the operator computing device 104 may be configured to provide an interface to a nurse and / or doctor working in a hospital environment. In an exemplary embodiment, through the interface, the caregiver may input one or more patient characteristics associated with a first patient, as further described in conjunction with FIG3. In an alternative embodiment, the caregiver may be configured to input only a second set of patient characteristics associated with the first patient (which is a subset of the one or more patient characteristics). Examples of the operator computing device 104 may include, but are not limited to, a personal computer, a laptop, a personal digital assistant (PDA), a mobile device, a tablet computer, or other such computing devices. In conjunction with Figure 2 The structure of the operator computing device 104 is further described.

[0035] The network 106 corresponds to the medium through which content and messages flow between various devices in the system environment 100 (e.g., the central server 108, the operator computing device 104, and the indicia scanner 102). Examples of the network 106 may include, but are not limited to, a wireless fidelity (Wi-Fi) network, a wireless area network (WAN), a local area network (LAN), or a metropolitan area network (MAN). The various devices in the system environment 100 may connect to the network 106 according to various wired and wireless communication protocols, such as, but not limited to, the Transmission Control Protocol and Internet Protocol (TCP / IP), the User Datagram Protocol (UDP), and 2G, 3G, 4G, or 5G communication protocols.

[0036] In an exemplary embodiment, the central server 108 may refer to a computing device that may be configured to communicate with the landmark scanner 102 and the operator computing device 104. The central server 108 may include one or more processors and one or more memories. The one or more memories may include computer readable code that may be executed by the one or more processors to perform predetermined operations. In addition, the central server 108 may include one or more interfaces that may facilitate communication with the landmark scanner 102 and the operator computing device 104 via the network 106. In an exemplary embodiment, the central server 108 may be configured to receive the one or more image characteristics and the one or more patient characteristics from the landmark scanner 102 and the operator computing device 104, respectively. In addition, the central server 108 may be configured to generate training data, such as Figure 8 Additionally or alternatively, the central server 108 may be configured to train an ML model capable of predicting the number of days until the patient's wristband fades, such as Figure 9As further described. Examples of central server 108 may include, but are not limited to, personal computers, laptops, personal digital assistants (PDAs), mobile devices, tablet computers, or other such computing devices. Figure 7 The structure of the central server 108 is further described.

[0037] In an exemplary embodiment, the printing device 110 may refer to a device that can reproduce content, visual images, graphics, text, etc. on a page or medium, such as a copier, printer, fax machine, or other system. Some examples of printing systems may include, but are not limited to, thermal printers, inkjet printers, laser printers, etc. In an exemplary embodiment, the printing device 110 may receive instructions to print a patient wristband from a central server.

[0038] In some examples, the scope of the present disclosure is not limited to a system environment 100 having only one indicia scanner 102. In an exemplary embodiment, the system environment 100 may have multiple indicia scanners that may be installed at multiple locations within a hospital setting. Similarly, the system environment 100 may include multiple operator computing devices.

[0039] Figure 2 1. A block diagram of an operator computing device 104 is shown, according to one or more embodiments described herein. The operator computing device 104 includes a first processor 202, a first memory device 204, a first communication interface 206, an input / output (I / O) device interface unit 208, and a patient characteristic determination unit 210.

[0040] The first processor 202 may be implemented as a device including one or more microprocessors with one or more accompanying digital signal processors, one or more processors without accompanying digital signal processors, one or more coprocessors, one or more multi-core processors, one or more controllers, processing circuits, one or more computers, various other processing elements (including integrated circuits such as, for example, application specific integrated circuits (ASICs) or field programmable gate arrays (FPGAs)), or some combination thereof. Figure 2104. Although illustrated as a single processor, in an embodiment, the first processor 202 may include multiple processors and signal processing modules. The multiple processors may be implemented on a single electronic device or may be distributed across multiple electronic devices that are collectively configured to function as the circuitry of the operator computing device 104. The multiple processors may be in operable communication with each other and may be collectively configured to perform one or more functions of the circuitry of the operator computing device 104, as described herein. In an exemplary embodiment, the first processor 202 may be configured to execute instructions stored in the first memory device 204 or otherwise accessible to the first processor 202. These instructions, when executed by the first processor 202, may cause the circuitry of the operator computing device 104 to perform one or more of the functions, as described herein.

[0041] Regardless of whether the first processor 202 is configured by a hardware approach, a firmware / software approach, or a combination thereof, the first processor may include an entity capable of performing operations according to the embodiments of the present disclosure while being configured accordingly. Thus, for example, when the first processor 202 is implemented as an ASIC, FPGA, or the like, the first processor 202 may include specially configured hardware for performing one or more operations described herein. Alternatively, as another example, when the first processor 202 is implemented as an executor of instructions (such as may be stored in the first memory device 204), the instructions may specifically configure the first processor 202 to perform one or more algorithms and operations described herein.

[0042] Therefore, the first processor 202 used herein can refer to a programmable microprocessor, a microcomputer, or one or more multi-processor chips, which can be configured by software instructions (applications) to perform various functions including the functions of the various embodiments described above. In some devices, a plurality of processors dedicated to wireless communication functions and a processor dedicated to running other applications can be provided. Software applications can be stored in an internal memory before being accessed and loaded into the processor. The processor may include an internal memory sufficient to store application software instructions. In many devices, the internal memory can be a volatile or non-volatile memory such as a flash memory or a mixture of the two. The memory can also be located inside another computing resource (for example, to enable computer-readable instructions to be downloaded via the Internet or another wired or wireless connection).

[0043] The first memory device 204 may include suitable logic, circuitry, and / or interfaces adapted to store a set of instructions executable by the first processor 202 to perform predetermined operations. Some commonly known memory implementations include, but are not limited to, a hard disk, random access memory, cache memory, read-only memory (ROM), erasable programmable read-only memory (EPROM), and electrically erasable programmable read-only memory (EEPROM), flash memory, a magnetic cassette, a magnetic tape, a magnetic disk storage device or other magnetic storage device, a compact disk read-only memory (CD-ROM), a digital versatile disk read-only memory (DVD-ROM), an optical disk, a circuit configured to store information, or some combination thereof. In one embodiment, without departing from the scope of the present disclosure, the first memory device 204 may be integrated with the first processor 202 on a single chip.

[0044] Additionally or alternatively, the first memory device 204 can be configured to store mappings between the indicia scanners 102 and locations in the hospital facility where the indicia scanners 102 are installed. In embodiments where the system environment 100 includes multiple indicia scanners, the first memory device 204 can be configured to maintain a first lookup table that defines mappings between one or more locations in the hospital facility where the multiple indicia scanners 102 are installed. Table 1 below illustrates an exemplary first lookup table:

[0045] Tag Scanner Identification (ID) One or more locations IS-1 ICU IS-2 OPD IS-3 Orthopedics

[0046] Table 1: A first lookup table showing the mapping between the tag scanner ID and the one or more locations .

[0047] The first communication interface 206 may correspond to a communication interface that may facilitate transmitting and receiving messages and data to various devices operating in the system environment 100 via the network 106. For example, the first communication interface 206 is communicatively coupled to the central server 108 via the network 106. In some examples, the operator computing device 104 may be configured to transmit the one or more patient characteristics associated with the first patient to the central server 108 via the first communication interface 206. Examples of the first communication interface 206 may include, but are not limited to, an antenna, an Ethernet port, a USB port, a serial port, or any other port that may be suitable for receiving and transmitting data. The first communication interface 206 transmits and receives data and / or messages according to various communication protocols, such as, but not limited to, I2C, TCP / IP, UDP, and 2G, 3G, 4G, or 5G communication protocols.

[0048] The I / O device interface unit 208 may include suitable logic and / or circuitry that may enable the operator computing device 104 to be communicatively coupled to one or more sensors. In an exemplary embodiment, the one or more sensors may facilitate monitoring the movement of the first patient within the hospital premises. Some examples of the one or more sensors may include, but are not limited to, image capture devices (such as the landmark scanner 102), proximity sensors, etc. In an exemplary embodiment, the I / O device interface unit 208 may be configured to communicate with the one or more sensors using one or more known communication protocols such as I2C, a serial peripheral interface (SPI), etc. For the purposes of the ongoing description, the one or more sensors are considered to be the landmark scanner 102 (an image capture device) installed at a predetermined location within the hospital premises.

[0049] In some examples, the I / O device interface unit 208 may be further configured to present an interface to the caregiver via a display device associated with the operator computing device 104. The interface may include an input form that allows the caregiver to enter one or more patient characteristics related to the first patient. In an exemplary embodiment, the one or more patient characteristics may include, but are not limited to, the first patient's name, the first patient's age, the first patient's hospital room, the patient's travel history, the type of sanitization process used to disinfect the patient, the frequency of sanitization processes, and the like. In some examples, the scope of the present disclosure is not limited to the caregiver entering the one or more patient characteristics. In an exemplary embodiment, the operator computing device 104 may be configured to automatically determine a first set of patient characteristics from the one or more patient characteristics. In an exemplary embodiment, the first set of patient characteristics may include, but are not limited to, the first patient's name, the first patient's age, the first patient's illness, the first patient's location within the hospital facility, and the first patient's travel history within the hospital facility. Furthermore, in such an embodiment, the caregiver may only need to enter a second set of patient characteristics. In an exemplary embodiment, the second set of patient characteristics may include, but are not limited to, the type of sanitization process used to disinfect the patient, the frequency of sanitization processes, and the like.

[0050] The patient characteristics determination unit 210 may include suitable logic and / or circuitry that enables the operator computing device 104 to automatically determine a first set of patient characteristics. For example, the patient characteristics determination unit 210 may be configured to instruct the landmark scanner 102 to capture images of the patient's wristband as the patient moves through the hospital premises. Thereafter, based on the location where the landmark scanner 102 is installed or positioned, the operator computing device 104 may be configured to determine the hospital room where the first patient is admitted, as well as the first patient's movement history within the hospital premises, as further described in conjunction with FIG. Additionally or alternatively, the patient characteristics determination unit 210 may receive decoded data from the landmark scanner 102. In an exemplary embodiment, the decoded data may include information regarding the first patient's age, the first patient's name, and a medical condition associated with the first patient. In an exemplary embodiment, the patient characteristics determination unit 210 may consider the first patient's age, the medical condition associated with the first patient, the first patient's location, and the first patient's movement history as the first set of patient characteristics. The patient characteristics determination unit 210 may be implemented using one or more of an application-specific integrated circuit (ASIC) and a field-programmable gate array (FPGA).

[0051] Combine Figure 3A and Figure 3B The operation of the operator computing device 104 is further described.

[0052] Figure 3A A flowchart 300A is shown of a method for operating an operator computing device 104 according to one or more embodiments described herein.

[0053] At step 302, the operator computing device 104 may include means for presenting an interface on a display screen associated with the operator computing device 104, such as the first processor 202, the I / O device interface unit 208, etc. In an exemplary embodiment, the interface may include a form that the caregiver may fill out to enter the one or more patient characteristics. For example, the form may include one or more fields such as the name of the first patient, the age of the first patient, the ward where the first patient is staying, the patient's transport history, the type of sanitization process used to disinfect the patient, the frequency of sanitization processes, etc.

[0054] In some examples, the scope of the present disclosure is not limited to the operator computing device 104 receiving input related to the one or more patient characteristics associated with the first patient. In an exemplary embodiment, the operator computing device 104 can be configured to automatically (i.e., without input from a caregiver) determine a first set of patient characteristics from the one or more patient characteristics associated with the first patient. Flowchart 300B illustrates the automatic determination of the first set of patient characteristics.

[0055] Figure 3BA flowchart 300B is shown of a method for operating an operator computing device 104 according to one or more embodiments described herein.

[0056] At step 304, the badge scanner 102 may include means, such as the first processor 202, the I / O device interface unit 208, etc., for periodically triggering the badge scanner 102. For example, the I / O device interface unit 208 may trigger the badge scanner 102 after 30 minutes. Once triggered, the badge scanner 102 may be configured to capture an image of the field of view. If the field of view includes a patient wristband, the badge scanner 102 may be configured to decode the machine-readable indicia in the patient wristband and transmit the decoded data to the operator computing device 104. In an exemplary embodiment, the decoded data includes information regarding the name of the first patient, the age of the first patient, and a medical condition associated with the first patient. Additionally or alternatively, the badge scanner 102 may be configured to transmit a decode status of "successful" to the operator computing device 104. If the badge scanner 102 cannot identify the patient wristband within the field of view, the badge scanner 102 may be configured to transmit a message to the operator computing device 104 stating that "no patient wristband was identified in the field of view." Additionally or alternatively, if the badge scanner 102 is unable to decode a machine-readable badge in a patient's wristband (present in the field of view of the badge scanner 102 ), the badge scanner 102 may be configured to transmit a decode status of “failed” to the operator computing device 104 .

[0057] At step 306, the operator computing device 104 may include means for determining whether a patient wristband is present in the field of view of the landmark scanner 102, such as the first processor 202, the patient characteristics determination unit 210, or the like. For example, the operator computing device 104 may be configured to check for receipt of information from the landmark scanner 102 that “no patient wristband was identified in the field of view.” If the patient characteristics determination unit 210 determines that the operator computing device 104 has received the information that “no patient wristband was identified in the field of view,” the patient characteristics determination unit 210 may be configured to repeat step 304. However, if the patient characteristics determination unit 210 determines that the operator computing device 104 has not received the information that “no patient wristband was identified in the field of view,” the patient characteristics determination unit 210 may be configured to perform step 308.

[0058] At step 308 , the operator computing device 104 may include means for determining whether the decode status is “failed,” such as the first processor 202 , the patient characteristics determination unit 210 , etc. If the patient characteristics determination unit 210 determines that the decode status is “failed,” the patient characteristics determination unit 210 may be configured to perform step 310 .

[0059] However, if the patient characteristics determination unit 210 determines that the operator computing device 104 has received the decoded data, the patient characteristics determination unit 210 may be configured to perform step 312 .

[0060] At step 310 , the indicia scanner 102 may include means for presenting an interface including fields corresponding to the one or more patient characteristics to the caregiver, such as the first processor 202 , the patient characteristics determination unit 210 , the I / O device interface unit 208 , etc. Thereafter, the patient characteristics determination unit 210 may be configured to perform step 318 .

[0061] At step 312, the indicia scanner 102 may include means for determining a first set of patient characteristics based on the decoded data, such as the first processor 202, the patient characteristics determination unit 210, the I / O device interface unit 208, etc. As discussed, the decoded data includes information related to the name of the first patient, the age of the first patient, and the disease associated with the first patient. In an exemplary embodiment, the patient characteristics determination unit 210 may consider the name of the first patient, the age of the first patient, and the disease associated with the first patient as the first set of patient characteristics associated with the first patient.

[0062] Additionally or alternatively, at step 314, the operator computing device 104 may include means, such as the first processor 202, the patient characteristics determination unit 210, etc., for determining the location of the first patient within the hospital premises based on the location of the landmark scanner 102 from which the operator computing device 104 received the decoded data. In an exemplary embodiment, the patient characteristics determination unit 210 may reference a lookup table (Table 1) to determine the location of the landmark scanner 102 within the hospital premises. In an exemplary embodiment, the operator computing device 104 may be configured to deem the location of the landmark scanner 102 within the hospital premises as the location of the first patient within the hospital premises. Figure 1 As discussed, hospital sites may include more than one landmark scanner 102 installed at one or more locations within the hospital sites. The location of each landmark scanner is stored in a lookup table (e.g., Table 1) in the operator computing device 104 along with the corresponding ID. When the operator computing device 104 receives decoded data from one of the landmark scanners, the operator computing device 104 may be configured to determine the location of the one of the landmark scanners from the lookup table. In an exemplary embodiment, the patient characteristic determination unit 210 may be configured to add the location of the first patient to the first set of patient characteristics.

[0063] Additionally or alternatively, the patient characteristics determination unit 210 may be further configured to determine a movement history of the first patient. In an exemplary embodiment, the movement history may correspond to a list of locations within the hospital premises that the first patient has visited. In an exemplary embodiment, the patient characteristics determination unit 210 may be configured to append the determined locations of the first patient to the movement history. Furthermore, the patient characteristics determination unit 210 may be configured to add the movement history of the first patient to the first set of patient characteristics.

[0064] At step 316, the indicia scanner 102 may include means, such as the first processor 202, the patient characteristics determination unit 210, the I / O device interface unit 208, etc., for presenting an interface including fields corresponding to the second set of patient characteristics to the caregiver. In an exemplary embodiment, the second set of patient characteristics includes, but is not limited to, the type of sanitization process used to disinfect the first patient and the frequency of the sanitization process. Thereafter, the patient characteristics determination unit 210 may be configured to perform step 318.

[0065] In some examples, patient characteristics determination unit 210 may automatically determine the frequency of sanitization. For example, patient characteristics determination unit 210 may be configured to determine the frequency of sanitization based on a history of movement. In such an embodiment, patient characteristics determination unit 210 may assume that sanitization is performed on the first patient each time the first patient is moved or relocated to a new location. Therefore, based on the first patient's movement history, patient characteristics determination unit 210 may determine the frequency of sanitization. For example, the first patient was moved between locations twice within three days. In such an embodiment, patient characteristics determination unit 210 may determine the frequency of sanitization to be 0.6 times per day. Additionally or alternatively, patient characteristics determination unit 210 may determine the frequency of sanitization based on a predetermined number of times the patient is sanitized. For example, the predetermined number of times the patient is sanitized in a given day is two times per day. Therefore, the frequency of sanitization is 2.6 times per day.

[0066] At step 318 , the operator computing device 104 may include means, such as the first processor 202 , the patient characteristic determination unit 210 , or the like, for transmitting the one or more patient characteristics to the central server 108 .

[0067] Figure 4 A block diagram of a landmark scanner 102 is shown, according to one or more embodiments described herein. In an exemplary embodiment, the landmark scanner 102 can correspond to an image capture device capable of capturing an image of a corresponding field of view. The landmark scanner 102 can include a second processor 402, a second memory device 404, a second communication interface 406, an image processing unit 408, a decoder unit 410, and an image capture unit 412.

[0068] The second processor 402 may be implemented as a device including one or more microprocessors with accompanying digital signal processors, one or more processors without accompanying digital signal processors, one or more coprocessors, one or more multi-core processors, one or more controllers, processing circuits, one or more computers, various other processing elements (including integrated circuits such as, for example, application specific integrated circuits (ASICs) or field programmable gate arrays (FPGAs)), or some combination thereof. Figure 4 102. Although illustrated as a single processor in the drawings, in an embodiment, the second processor 402 may include multiple processors and signal processing modules. The multiple processors may be implemented on a single electronic device or may be distributed across multiple electronic devices that are collectively configured to function as the circuitry of the mark scanner 102. The multiple processors may be in operable communication with each other and may be collectively configured to perform one or more functions of the circuitry of the mark scanner 102, as described herein. In an exemplary embodiment, the second processor 402 may be configured to execute instructions stored in the second memory device 404 or otherwise accessible to the second processor 402. These instructions, when executed by the second processor 402, may cause the circuitry of the mark scanner 102 to perform one or more of the functions, as described herein.

[0069] Whether the second processor 402 is configured by hardware, firmware / software methods, or a combination thereof, the second processor may include an entity capable of performing operations according to the embodiments of the present disclosure when configured accordingly. Thus, for example, when the second processor 402 is implemented as an ASIC, FPGA, etc., the second processor 402 may include specially configured hardware for performing one or more operations described herein. Alternatively, as another example, when the second processor 402 is implemented as an executor of instructions (such as those that may be stored in the first memory device 204), these instructions may specifically configure the second processor 402 to perform one or more algorithms and operations described herein.

[0070] Therefore, the second processor 402 used herein may refer to a programmable microprocessor, a microcomputer or one or more multi-processor chips that can be configured by software instructions (applications) to perform various functions including the functions of the various embodiments described above. In some devices, a plurality of processors dedicated to wireless communication functions and a processor dedicated to running other applications may be provided. Software applications may be stored in an internal memory before being accessed and loaded into the processor. The processor may include an internal memory sufficient to store application software instructions. In many devices, the internal memory may be a volatile or non-volatile memory such as a flash memory or a mixture of the two. The memory may also be located inside another computing resource (e.g., enabling computer-readable instructions to be downloaded via the Internet or another wired or wireless connection).

[0071] The second memory device 404 may include suitable logic, circuitry, and / or interfaces suitable for storing a set of instructions that can be executed by the second processor 402 to perform predetermined operations. Some of the commonly known memory implementations include, but are not limited to, a hard disk, a random access memory, a cache memory, a read-only memory (ROM), an erasable programmable read-only memory (EPROM), and an electrically erasable programmable read-only memory (EEPROM), a flash memory, a cassette, a magnetic tape, a magnetic disk storage device or other magnetic storage device, a compact disk read-only memory (CD-ROM), a digital versatile disk read-only memory (DVD-ROM), an optical disk, a circuit configured to store information, or some combination thereof. In an embodiment, without departing from the scope of this disclosure, the second memory device 404 may be integrated with the second processor 402 on a single chip.

[0072] The second communication interface 406 may correspond to a communication interface that can facilitate transmitting and receiving messages and data to various devices operating in the system environment 100 via the network 106. For example, the second communication interface 406 is communicatively coupled to the central server 108 via the network 106. In some examples, the landmark scanner 102 may be configured to transmit a first set of patient characteristics associated with a first patient to the operator computing device 104 via the second communication interface 406. Furthermore, the second communication interface 406 may be configured to transmit the one or more image characteristics to the central server 108. Examples of the second communication interface 406 may include, but are not limited to, an antenna, an Ethernet port, a USB port, a serial port, or any other port suitable for receiving and transmitting data. The second communication interface 406 transmits and receives data and / or messages according to various communication protocols, such as, but not limited to, I2C, TCP / IP, UDP, and 2G, 3G, 4G, or 5G communication protocols.

[0073] The image processing unit 408 may comprise suitable logic and / or circuitry that may enable the indicia scanner 102 to determine a quality metric for an image, such as in conjunction with Figure 5 More specifically, the image processing unit 408 may be configured to compare the image with an ideal image to determine a quality metric for the image, such as in combination with Figure 6 As further described. In another embodiment, the image processing unit 408 can be configured to determine a quality metric for the image based on the quality of the machine-readable indicia in the image. In an exemplary embodiment, the image processing unit 408 can be configured to determine the quality metric for the machine-readable indicia based on one or more known quality standards such as ANSI X3.182, ISO 15415, and ISO / IEC 15416. Thereafter, the image processing unit 408 can be configured to consider the quality metric for the machine-readable indicia as the quality metric for the image. The image processing unit 408 can be implemented using one or more of an application specific integrated circuit (ASIC) and a field programmable gate array (FPGA).

[0074] The decoder unit 410 may comprise suitable logic and / or circuitry that may enable the decoder unit 410 to recognize machine-readable indicia in an image, such as Figure 5 Furthermore, the decoder unit 410 may be configured to determine a bar code symbol identifier associated with the machine-readable indicia, such as in conjunction with Figure 5 As further described. In an exemplary embodiment, the bar code symbol identifier of the machine-readable indicia may indicate the type of the machine-readable indicia. Some examples of types of machine-readable indicia may include, but are not limited to, Code 39, Code 128, Code 11, PDF417, Data Matrix Code, QR Code, Aztec Code. In an exemplary embodiment, the decoder unit 410 may be configured to decode the machine-readable indicia in the image to generate decoded data. Additionally or alternatively, the decoder unit 410 may be configured to determine a decoding status, such as Figure 5 As further described. In an exemplary embodiment, the decoding status may correspond to a flag indicating successful decoding of the machine-readable indicia in the image. The decoder unit 410 may be implemented using one or more of an application specific integrated circuit (ASIC) and a field programmable gate array (FPGA).

[0075] In an exemplary embodiment, the image capture unit 412 may include suitable logic and / or circuitry that enables the image capture unit 412 to capture an image of the field of view of the indicia scanner 102. In some examples, the image may include an image of a machine-readable indicia. In an exemplary embodiment, the image capture unit 412 may include an image sensor. In some examples, an image sensor is a solid-state device capable of generating an electrical signal corresponding to an optical signal projected onto the image sensor. Some examples of image sensors may include a color or monochrome 1D or 2D charge-coupled device (CCD), a complementary MOSFET (CMOS), a contact image sensor (CIS), or any other device capable of generating an electrical signal based on a received optical signal. In addition to the image sensor, the image capture unit 412 may include one or more optical components, such as one or more lenses, one or more gratings, and / or one or more mirrors, that may facilitate directing light from the field of view onto the image sensor. Additionally or alternatively, the image capture unit 412 may also include one or more drive units that may be capable of causing the one or more optical components to follow a predetermined path. This allows light to be focused onto the image sensor through the one or more optical components.

[0076] Figure 5 A flowchart 500 is shown of a method for operating the indicia scanner 102 according to one or more embodiments described herein.

[0077] At step 502, the landmark scanner 102 may include a device for receiving input that captures an image of the field of view of the landmark scanner 102, such as the first processor 202. In an exemplary embodiment, an operator of the landmark scanner 102 may provide input to the landmark scanner via a trigger button on the landmark scanner 102. For example, the operator may press the trigger button. In an exemplary embodiment, in response to pressing the trigger button, the trigger button of the landmark scanner 102 may generate a trigger signal indicative of the input received from the operator. In some examples, the scope of the present disclosure is not limited to the operator pressing the trigger button to provide input to the landmark scanner 102. In an exemplary embodiment, the landmark scanner 102 may be automatically triggered once the landmark scanner 102 detects an object present in the corresponding field of view. In another example, the landmark scanner 102 may receive input from a remote computer, such as an operator computing device 104 operated by a caregiver.

[0078] At step 504, the indicia scanner 102 may include means for capturing an image of the field of view of the indicia scanner 102, such as the second processor 402, an image capture unit 412, and the like. In some examples, the second processor 402 may cause the image capture unit 412 to capture an image in response to receiving a trigger signal from a trigger button. As discussed, the image capture unit 412 may include an image sensor and one or more optical components that may direct light from the field of view of the indicia scanner 102 onto the image sensor. Accordingly, the image sensor may generate an electrical signal representing the image. Additionally or alternatively, the second processor 402 may be configured to render an image based on the electrical signal received from the image sensor.

[0079] At step 506, the indicia scanner 102 may include means for determining a quality metric of the image, such as the second processor 402, the image processing unit 408, or the like. In some examples, the image processing unit 408 may be configured to compare the image to an ideal image to determine the quality metric of the image. In an exemplary embodiment, the ideal image may correspond to an image of the patient's wristband without defects (e.g., stains and / or discoloration). In some examples, in combination with Figure 6 Determination of a quality metric of the image is further described. In an exemplary embodiment, the quality metric of the image is indicative of a quality metric of the patient's wristband.

[0080] In some examples, the scope of the present disclosure is not limited to determining a quality metric for a patient bracelet based on a comparison of an image to an ideal image. In an exemplary embodiment, the image processing unit 408 can be configured to determine the quality metric for the patient bracelet by determining a quality metric of a machine-readable marking printed on the patient bracelet. Determining the quality metric for the patient bracelet based on the quality metric of the machine-readable marking will be described later in flowchart 600.

[0081] In an exemplary embodiment, the quality metric of the image indicates the quality of the patient's wristband (captured in the image). In another embodiment, the image processing unit 408 may be configured to determine the quality metric of a portion of the image. In such an embodiment, the image processing unit 408 may be configured to crop the image so that the cropped image only includes the image of the patient's wristband. Thereafter, the image processing unit 408 may be configured to determine the quality metric of the cropped image by comparing the cropped image with an ideal image. In an alternative embodiment, the image processing unit 408 may be configured to determine the quality metric of the cropped image based on the quality metric of a machine-readable marker in the cropped image.

[0082] At step 508, the mark scanner 102 may include means for identifying a machine-readable mark in the image, such as the second processor 402, the decoder unit 410, and the like. In some examples, the decoder unit 410 may be configured to identify one or more edges in the image using one or more known edge detection techniques, such as, but not limited to, a canny edge detector, a Laplacian edge detector, and the like. Thereafter, the decoder unit 410 may be configured to perform one or more morphological operations on the image (wherein the one or more edges were identified in step 502). Some examples of the one or more morphological operations may include, but are not limited to, erosion, dilation, and the like, for isolating a portion of the image that includes the machine-readable mark. For example, after the morphological operation, the image may include a patch of white pixels. The patch of white pixels may indicate the location of the machine-readable mark in the image.

[0083] In some examples, the scope of the present disclosure is not limited to using morphological operations to identify machine-readable markers in an image. In some examples, decoder unit 410 can be configured to utilize other methods to identify the first marker in the image. For example, decoder unit 410 can be configured to utilize one or more object recognition algorithms (e.g., SIFT) to identify machine-readable markers in the image.

[0084] At step 510, the indicia scanner 102 may include a device for decoding the machine-readable indicia in the image to generate decoded data, such as the second processor 402, the decoder unit 410, etc. In an exemplary embodiment, the decoder unit 410 may be configured to decode the machine-readable indicia using one or more known decoding algorithms. Before decoding the machine-readable indicia, the decoder unit 410 may be configured to determine a bar code symbol identifier associated with the machine-readable indicia. As discussed, the bar code symbol identifier may describe the type of machine-readable indicia. For example, the decoder unit 410 may be configured to identify known markings on the machine-readable indicia to determine the type of machine-readable indicia in the image. In an exemplary embodiment, the decoder unit 410 may be configured to utilize one or more known image processing techniques, such as edge detection, object recognition, image binarization, etc., to identify the known markings on the machine-readable indicia.

[0085] For example, if decoder unit 410 identifies one or more square patterns on the corners of the machine-readable marking, decoder unit 410 may identify the type of the machine-readable marking as a QR code. In another example, if decoder unit 410 identifies continuous lines on two orthogonal edges of the machine-readable marking, decoder unit 410 may identify the type of the machine-readable marking as a Data Matrix code. In yet another example, if decoder unit 410 identifies multiple parallel lines in the machine-readable marking, decoder unit 410 may be configured to identify the type of the machine-readable marking as Code 39. Thereafter, based on the known signature, decoder unit 410 may be configured to determine a barcode symbol identifier. Thereafter, the first decoder unit may be configured to decode the machine-readable marking using the one or more known decoding algorithms based on the barcode symbol identifier.

[0086] At step 512, the indicia scanner 102 may include means for determining whether the decoding of the machine-readable indicia was successful, such as the second processor 402, the decoder unit 410, etc. If the decoding of the machine-readable indicia was successful, the decoder unit 410 may be configured to perform step 514. However, if the decoding of the machine-readable indicia was unsuccessful, the decoder unit 410 may be configured to perform step 518.

[0087] At step 514, the indicia scanner 102 may include means for setting the decode status to "success," such as the second processor 402, the decoder unit 410, etc. Additionally or alternatively, at step 516, the indicia scanner 102 may include means for generating a first set of patient characteristics comprising decoded data, such as the second processor 402, the decoder unit 410, etc. As discussed, the decoded data may include the name of the first patient, the age of the first patient, and a disease associated with the first patient.

[0088] At step 518 , the indicia scanner 102 may include means, such as the second processor 402 , the decoder unit 410 , or the like, for setting the decode status to “failed.”

[0089] At step 520, the indicia scanner 102 may include means, such as the second processor 402, the second communication interface 406, etc., for transmitting the first set of patient characteristics to the operator computing device 104. In embodiments where the decode status is "failed," the second communication interface 406 may be configured to transmit only the decode status as the first set of patient characteristics to the operator computing device 104. In alternative embodiments, the second communication interface 406 may be configured to transmit the first set of patient characteristics to the central server 108.

[0090] At step 522, the indicia scanner 102 may include means for generating the one or more image characteristics, such as the second processor 402, the decoder unit 410, etc. In an exemplary embodiment, the one or more image characteristics include a decoding status and a quality metric of the image.

[0091] At step 524 , the indicia scanner 102 may include means, such as the second processor 402 , the second communication interface 406 , etc., for transmitting the one or more image characteristics to the central server 108 .

[0092] Figure 6 A flowchart 600 is shown of a method for determining a quality metric for an image according to one or more embodiments described herein.

[0093] At step 602, the indicia scanner 102 may include a device, such as the second processor 402, the image processing unit 408, etc., for converting the color scheme of the image to another color scheme. The image adopting the other color scheme is hereinafter referred to as a modified image. In an exemplary embodiment, the image processing unit 408 may be configured to convert the color scheme of the image using known methods. For example, the image processing unit 408 may be configured to convert the color scheme of the image to a 16-bit color scheme.

[0094] At step 604, the tag scanner 102 may include means for comparing the modified image to an ideal image, such as the second processor 402, the image processing unit 408, or the like. In an exemplary embodiment, the ideal image may correspond to an image that includes an image of an object without defects. For example, the ideal image may include an image of a patient's wristband that does not contain any defects (i.e., stains and / or fading). In an exemplary embodiment, the color scheme of the ideal image may be the same as the color scheme of the modified image. In an exemplary embodiment, the ideal image may be pre-stored in the tag scanner 102 during manufacture of the tag scanner 102. In an alternative embodiment, the ideal image may be retrieved from the central server 108. In such an embodiment, the central server 108 may be configured to store the ideal image, such as for later use in conjunction with Figure 8 For the purposes of the ongoing description, it will be considered that the tag scanner 102 is configured to retrieve the desired image from the central server 108 .

[0095] To compare the modified image to the ideal image, image processing unit 408 can be configured to determine an intersection between the modified image and the ideal image. In an exemplary embodiment, the intersection between the image and the ideal image facilitates highlighting common areas between the two images. Furthermore, the intersection between the image and the ideal image identifies areas of the image that are dissimilar to the ideal image. Because the ideal image of the patient's wristband is free of defects, the intersection between the ideal image and the image can facilitate identifying faded or stained areas on the patient's wristband based on the intersection between the image and the ideal image.

[0096] At step 606, the indicia scanner 102 may include means for determining whether the patient wristband has a defect, such as the second processor 402, the image processing unit 408, etc. In an exemplary embodiment, if the image processing unit 408 determines that the patient wristband has a defect, the image processing unit 408 may be configured to perform step 608. However, if the image processing unit 408 determines that the image does not have a defect, the image processing unit 408 may be configured to perform step 608.

[0097] At step 608, the landmark scanner 102 may include a device for determining a quality metric for the patient bracelet, such as the second processor 402, the image processing unit 408, or the like. In an exemplary embodiment, the image processing unit 408 may be configured to determine the percentage of pixels of the patient bracelet (in the image) that contain defects. For example, the image processing unit 408 may be configured to identify the number of pixels that represent defects in the patient bracelet. Thereafter, the image processing unit 408 may be configured to determine the percentage of total pixels (representing the patient bracelet in the image) that have defects. In an exemplary embodiment, the percentage of total pixels that represent defects in the image corresponds to the quality metric for the image.

[0098] In an embodiment, where the patient bracelet does not have any defects, the number of pixels with defects is zero. Therefore, the quality measure of the patient bracelet is 100%.

[0099] In some examples, the scope of the present disclosure is not limited to comparing an image with an ideal image to determine a quality metric of a patient bracelet. In an exemplary embodiment, the image processing unit 408 may be configured to consider a quality metric of a machine-readable mark (printed on the patient bracelet) as a quality metric of the patient bracelet. In such an embodiment, the image processing unit 408 may be configured to retrieve the machine-readable mark (printed on the patient bracelet) from the image, as identified in step 508. Thereafter, the image processing unit 408 is configured to determine a quality metric of the machine-readable mark according to one or more standards such as ANSI X3.182, ISO 15415, and ISO / IEC 15416.

[0100] Figure 7 A block diagram of the central server 108 is shown, according to one or more embodiments described herein. In the exemplary embodiment, the central server 108 includes a third processor 702, a third memory device 704, a third communication interface 706, a training data generation unit 708, a machine learning (ML) model training unit 710, and an ideal image database 712.

[0101] The third processor 702 may be implemented as a device including one or more microprocessors with accompanying digital signal processors, one or more processors without accompanying digital signal processors, one or more coprocessors, one or more multi-core processors, one or more controllers, processing circuits, one or more computers, various other processing elements (including integrated circuits such as, for example, application specific integrated circuits (ASICs) or field programmable gate arrays (FPGAs)), or some combination thereof. Figure 7 108. Although illustrated as a single processor in the drawings, in an embodiment, the third processor 702 may include multiple processors and signal processing modules. The multiple processors may be implemented on a single electronic device or may be distributed across multiple electronic devices that are collectively configured to function as the circuitry of the central server 108. The multiple processors may be in operable communication with each other and may be collectively configured to perform one or more functions of the circuitry of the indicia scanner 102, as described herein. In an exemplary embodiment, the third processor 702 may be configured to execute instructions stored in a third memory device 704 or otherwise accessible to the third processor 702. These instructions, when executed by the third processor 702, may cause the circuitry of the central server 108 to perform one or more of the functions, as described herein.

[0102] Regardless of whether the third processor 702 is configured by hardware, firmware / software methods, or a combination thereof, the third processor may include an entity capable of performing operations according to the embodiments of the present disclosure when configured accordingly. Thus, for example, when the third processor 702 is implemented as an ASIC, FPGA, etc., the third processor 702 may include specially configured hardware for performing one or more operations described herein. Alternatively, as another example, when the third processor 702 is implemented as an executor of instructions (such as those that may be stored in the first memory device 204), these instructions may specifically configure the third processor 702 to perform one or more algorithms and operations described herein.

[0103] Therefore, the third processor 702 used herein may refer to a programmable microprocessor, a microcomputer or one or more multi-processor chips that can be configured by software instructions (applications) to perform various functions including the functions of the various embodiments described above. In some devices, a plurality of processors dedicated to wireless communication functions and a processor dedicated to running other applications may be provided. Software applications may be stored in an internal memory before being accessed and loaded into the processor. The processor may include an internal memory sufficient to store application software instructions. In many devices, the internal memory may be a volatile or non-volatile memory such as a flash memory or a mixture of the two. The memory may also be located inside another computing resource (e.g., enabling computer-readable instructions to be downloaded via the Internet or another wired or wireless connection).

[0104] The third memory device 704 may include suitable logic, circuitry, and / or interfaces for storing a set of instructions that can be executed by the third processor 702 to perform predetermined operations. Some of the commonly known memory implementations include, but are not limited to, a hard disk, a random access memory, a cache memory, a read-only memory (ROM), an erasable programmable read-only memory (EPROM), and an electrically erasable programmable read-only memory (EEPROM), a flash memory, a cassette, a magnetic tape, a magnetic disk storage device or other magnetic storage device, a compact disk read-only memory (CD-ROM), a digital versatile disk read-only memory (DVD-ROM), an optical disk, a circuit configured to store information, or some combination thereof. In an embodiment, without departing from the scope of this disclosure, the third memory device 704 may be integrated with the third processor 702 on a single chip.

[0105] The third communication interface 706 may correspond to a communication interface that can facilitate transmitting and receiving messages and data to and from various devices operating in the system environment 100 via the network 106. For example, the third communication interface 706 is communicatively coupled to the landmark scanner 102 and the operator computing device 104 via the network 106. In some examples, the central server 108 may be configured to receive the one or more patient characteristics from the operator computing device 104 via the third communication interface 706. Additionally or alternatively, the central server 108 may be configured to receive the one or more image characteristics from the landmark scanner 102 via the third communication interface 706. Examples of the third communication interface 706 may include, but are not limited to, an antenna, an Ethernet port, a USB port, a serial port, or any other port suitable for receiving and transmitting data. The third communication interface 706 transmits and receives data and / or messages according to various communication protocols, such as, but not limited to, I2C, TCP / IP, UDP, and 2G, 3G, 4G, or 5G communication protocols.

[0106] The training data generation unit 708 may comprise suitable logic and / or circuitry that may enable the central server 108 to generate training data, such as Figure 8 For example, the training data generating unit 708 may be configured to generate training data based on the one or more patient characteristics and the one or more image characteristics, such as Figure 8 In an exemplary embodiment, the training data may include one or more features and one or more labels. The one or more features of the training data may include, but are not limited to, a first time period between consecutive scans of the machine-readable indicia, the patient's current location within the hospital premises (based on the scan of the machine-readable indicia), the type of sanitizer used to disinfect the patient, the frequency of sanitizer use, the patient's age, a quality metric of the patient's wristband (received from the indicia scanner), a decoding status, and / or a disease associated with the patient. The one or more labels of the training data may include, but are not limited to, the number of days before the patient's wristband had a defect.

[0107] In some examples, the training data generation unit 708 can be configured to determine the one or more features and the one or more labels of the training data, such as Figure 8 For example, the training data generation unit 708 determines a first time period between consecutive scans of the machine-readable marking (printed on the patient's wristband), such as Figure 8 Furthermore, the central server 108 may be configured to determine the number of days until the patient's wristband is printed, such as Figure 8 The training data generation unit 708 may be implemented using one or more of an application specific integrated circuit (ASIC) and a field programmable gate array (FPGA).

[0108] The ML model training unit 710 may comprise suitable logic and / or circuitry to enable the central server to train an ML model based on training data, such as Figure 8 In an exemplary embodiment, the ML model training unit 710 may be configured to train the ML model using one or more known machine learning techniques such as, but not limited to, logistic regression, K-means clustering, centroid clustering, naive Bayes, neural networks, Gaussian Copula, etc. In an exemplary embodiment, the ML model may define a mathematical relationship between the one or more features in the training data and the one or more labels. The ML model training unit 710 may be implemented using one or more of an application-specific integrated circuit (ASIC) and a field-programmable gate array (FPGA).

[0109] In an exemplary embodiment, the ideal image database 712 may correspond to a repository of one or more ideal images of one or more patient wristbands worn by one or more patients in the hospital. In an exemplary embodiment, the third processor 702 may be configured to update the ideal image database 712 with new images, such as in conjunction with Figure 8 Some examples of the ideal image database 712 may include, but are not limited to, SQL databases, Mongo DB, and the like.

[0110] Figure 8 A flowchart 800 is shown of a method for operating the central server 108 according to one or more embodiments described herein.

[0111] At step 802 , the central server 108 may include means, such as the third processor 702 , the third communication interface 706 , or the like, for receiving the one or more patient characteristics (associated with the first patient) from the operator computing device 104 .

[0112] At step 804, the central server 108 may include means for determining whether the first set of characteristics associated with the first patient corresponds to characteristics of a new patient, such as the third processor 702, the training data generation unit 708, or the like. In an exemplary embodiment, the training data generation unit 708 may be configured to compare the first set of patient characteristics to previously received first sets of patient characteristics associated with other patients. If the first set of patient characteristics matches one of the previously received first sets of patient characteristics, the training data generation unit 708 may be configured to determine that the first set of patient characteristics associated with the first patient does not correspond to a new patient. Therefore, the training data generation unit 708 may be configured to perform step 806. However, if the training data generation unit 708 determines that the first set of patient characteristics corresponds to a new patient, the training data generation unit 708 may be configured to perform step 818.

[0113] At step 806, the central server 108 may include means for receiving the one or more image characteristics from the landmark scanner 102, such as the third processor 702, the training data generation unit 708, and the like. In some examples, before receiving the one or more image characteristics associated with the image captured by the landmark scanner 102, the training data generation unit 708 may be configured to receive a request to access an ideal image. Upon receiving the request, the training data generation unit 708 may be configured to retrieve the ideal image associated with the patient wristband worn by the first patient from the ideal image database 712. Furthermore, the training data generation unit 708 may be configured to transmit the ideal image to the landmark scanner 102. Thereafter, the training data generation unit 708 receives the one or more image characteristics associated with the image of the patient wristband worn by the first patient.

[0114] At step 810, the central server 108 may include means, such as the third processor 702, the training data generation unit 708, etc., for determining whether the quality metric of the patient wristband (received in the one or more image characteristics) is less than a predetermined quality threshold. In an exemplary embodiment, the predetermined quality threshold may correspond to a quality metric below which the machine-readable marking printed on the patient wristband is unreadable and / or undecodable. If the training data generation unit 708 determines that the quality metric of the patient wristband is less than the predetermined quality threshold, the training data generation unit may be configured to perform step 812. However, if the training data generation unit 708 determines that the quality metric is greater than the predetermined quality threshold, the training data generation unit 708 may be configured to repeat step 802.

[0115] In some examples, the predetermined quality threshold may correspond to a percentage of defective pixels in an image above which a barcode in the image is unreadable. In some examples, the predetermined quality threshold is pre-stored in the central server 108. In an alternative embodiment, the training data generation unit 708 may determine the predetermined quality threshold based on historical data (i.e., data received before receiving the one or more patient characteristics). For example, the training data generation unit 708 may determine the percentage of defective pixels in an image of a patient's wristband (in the historical data) in which barcode decoding was unsuccessful. Thereafter, the training data generation unit 708 may determine the minimum percentage of defective pixels in the historical data as the predetermined quality threshold. For example, the training data generation unit 708 may determine that barcode decoding fails when 10% of the image pixels are defective. Therefore, the training data generation unit 708 may determine 10% as the predetermined quality threshold.

[0116] In some examples, the scope of the present disclosure is not limited to performing step 810 to determine whether the quality of the patient bracelet has deteriorated. In an exemplary embodiment, the training data generation unit 708 can be configured to determine the quality of the patient bracelet based on the decoding status received in the one or more image characteristics associated with the image of the patient bracelet. If the decoding status is "failed", the training data generation unit 708 can be configured to determine that the quality of the patient bracelet has deteriorated. However, if the decoding status is "successful", the training data generation unit 708 can be configured to determine that the quality of the patient bracelet has not deteriorated.

[0117] At step 812, the central server 108 may include a device, such as the third processor 702, the training data generation unit 708, etc., for determining the number of days before the patient's wristband developed a defect (such as staining and / or fading). In an exemplary embodiment, the training data generation unit 708 may be configured to determine a difference between a first timestamp at which the training data generation unit 708 first received the one or more patient characteristics associated with the first patient and a second timestamp at which the training data generation unit 708 last received the one or more patient characteristics associated with the first patient. In an exemplary embodiment, the difference between the first timestamp and the second timestamp corresponds to the number of days before the patient's wristband developed a defect.

[0118] At step 814, the central server 108 may include a device for generating training data, such as the third processor 702, the training data generation unit 708, etc. In an exemplary embodiment, the training data generation unit 708 may be configured to define the one or more features and the one or more tags of the training data. In some examples, the training data generation unit 708 may be configured to determine the number of days before the patient's wristband developed a defect as the one or more tags. In addition, the training data generation unit 708 may be configured to determine the patient's current location (retrieved from the one or more patient characteristics), the type of sanitizer used to disinfect the patient (retrieved from the one or more patient characteristics), the frequency of sanitizer use (retrieved from the one or more patient characteristics), the patient's age (retrieved from the one or more patient characteristics), the quality metric of the patient's wristband (retrieved from the one or more image characteristics), the decoding status (retrieved from the one or more patient characteristics), and / or the disease associated with the patient (retrieved from the one or more patient characteristics) as the one or more features of the training data.

[0119] At step 816, the central server 108 may include means for training a machine learning model, such as the third processor 702, an ML model training unit 710, and the like. In an exemplary embodiment, the ML model training unit 710 may be configured to train the ML model using one or more machine learning techniques, such as, but not limited to, logistic regression, K-means clustering, centroid clustering, naive Bayes, neural networks, Gaussian linker dependency, and the like. For example, the ML model training unit 710 may be configured to identify one or more clusters in the training data using the elbow theorem. In an exemplary embodiment, the one or more clusters may define one or more relationships between the one or more features and the one or more labels. For example, the ML model training unit may define a cluster that defines a relationship between the age of a first patient and the number of days before the patient's wristband developed a defect. Similarly, the ML model training unit 710 may be configured to define additional clusters in the training data. Thereafter, the ML model training unit may be configured to determine the centroid of each cluster. In an exemplary embodiment, the one or more clusters and corresponding centroids may correspond to the trained ML model. In an exemplary embodiment, the ML model training unit 710 may be configured to store the ML model in the third memory device 704 .

[0120] At step 818, the central server 108 may include means for transmitting a request to obtain an image of the patient's wristband to the tag scanner 102, such as the third processor 702, the training data generation unit 708, etc. At step 820, the central server 108 may include means for receiving the image of the patient's wristband from the tag scanner 102, such as the third processor 702, the training data generation unit 708, etc. In some examples, the training data generation unit 708 may be configured to store the image of the patient's wristband as an ideal image of the patient's wristband (associated with the first patient) in the ideal image database 712. Thereafter, the third processor 702 may be configured to repeat step 802.

[0121] In some examples, the scope of the present disclosure is not limited to the central server 108 performing steps 818 and 820. In an alternative embodiment, the central server 108 transmits a request to retrieve the one or more patient characteristics to the operator computing device 104. Thereafter, the central server 108 can be configured to generate an image for printing the patient wristband based on the one or more patient characteristics. This image is considered an ideal image for the patient wristband. For example, the central server 108 can be configured to generate an image of a barcode based on the first set of patient characteristics. In this example, this image of the barcode is considered an ideal image.

[0122] Figure 9A flowchart 900 is shown of a method for predicting the number of days until a patient wristband associated with a new patient is unavailable, according to one or more embodiments described herein.

[0123] At step 902 , the central server 108 may include means for receiving the one or more patient characteristics associated with the new patient, such as the third processor 702 . In an exemplary embodiment, the third processor 702 may receive the one or more patient characteristics associated with the new patient from the operator computing device 104 .

[0124] At step 904 , the central server 108 may include means, such as the third processor 702 , for using the ML model trained in step 816 to predict the number of days until the patient wristband associated with the new patient becomes unusable.

[0125] At step 906, the central server 108 may include a device, such as the third processor 702, for determining the number of the second day that has passed since the patient wristband associated with the new patient was created. In an exemplary embodiment, the third processor 702 may be configured to determine the number of the second day that has passed since the patient wristband was created using the method described in step 812.

[0126] At step 908, the central server 108 may include means, such as the third processor 702, for determining whether the difference between the number of days until the patient wristband associated with the new patient becomes unavailable and the second number of days is less than a predetermined threshold number of days. If the third processor 702 determines that the difference is less than the predetermined threshold number of days, the third processor 702 may be configured to execute step 910. However, if the third processor 702 determines that the difference is greater than the predetermined threshold number of days, the third processor 702 may be configured to repeat step 902. In some examples, the predetermined threshold number of days is less than the number of days until the patient wristband associated with the new patient becomes unavailable. Furthermore, in some examples, the predetermined threshold number of days is defined during configuration of the software. In an alternative embodiment, the predetermined threshold number of days is configurable and may be defined in real time (i.e., during execution of flowchart 800).

[0127] At step 910, the central server 108 may include means, such as the third processor 702, for transmitting instructions to print a new patient wristband to the printing device 110. Because the predetermined number of days threshold is less than the number of days until the patient wristband associated with the new patient becomes unusable, the central server 108 may print the new patient wristband before the patient wristband becomes unreadable.

[0128] In some examples, the scope of the present disclosure is not limited to having three separate computing devices (the indicia scanner 102, the operator computing device 104, and the central server 108) to perform the aforementioned operations. In an exemplary embodiment, the system environment 100 may include only one computing device that is capable of performing the operations of the indicia scanner 102, the operator computing device 104, and the central server 108.

[0129] In an exemplary embodiment, the scope of the present disclosure is not limited to predicting the lifespan of a patient wristband in a hospital setting. The embodiments of the present disclosure may be applicable to any printed label utilized in any domain. For example, the systems and methods of the present disclosure may be used to predict printed labels in a logistics environment. In such an embodiment, a caregiver may utilize the operator computing device 104 to transmit one or more object characteristics instead of the one or more patient characteristics. In an exemplary embodiment, the one or more object characteristics may include, but are not limited to, the destination of the object, the object's transfer history, the object's storage temperature, the type of sanitization used to disinfect the object, and / or the frequency of the sanitization. In an exemplary embodiment, upon receiving the one or more object characteristics, the central server 108 may be configured to train an ML model using the one or more object characteristics and the one or more image characteristics. Thereafter, the central server 108 may be configured to utilize the ML model to predict the lifespan of a printed label attached to a new object based on the one or more object characteristics associated with the new object.

[0130] For example, in a shipping warehouse, the operator computing device 104 may input the one or more object characteristics, which may include the number of times the object has been handled by an operator, the number of times the object has been sanitized, the object's movement history, the object's storage temperature, the type of sanitization used to disinfect the object, and / or the frequency of sanitization. For example, the operator of the operator computing device 104 may input that the object is sanitized once every five hours, that the object is handled by ten different workers in the warehouse, that the object has been previously handled at five different locations, etc. Thereafter, the central server 108 may be configured to train an ML model using the one or more object characteristics and the one or more image characteristics. Thereafter, the central server 108 may be configured to utilize the ML model to predict the lifespan of a printed label attached to a new object based on the one or more object characteristics associated with the new object.

[0131] In this specification and the accompanying drawings, exemplary embodiments of the present disclosure have been disclosed. The present disclosure is not limited to such exemplary embodiments. The use of the term "and / or" includes any and all combinations of one or more of the associated listed items. The accompanying drawings are schematic representations and are not necessarily drawn to scale. Unless otherwise indicated, specific terms are used in a generic and descriptive sense and not for purposes of limitation.

[0132] The above detailed description has described various embodiments of devices and / or processes through the use of block diagrams, flow charts, schematics, examples, and instances. To the extent that such block diagrams, flow charts, schematics, and instances include one or more functions and / or operations, each function and / or operation within such block diagrams, flow charts, schematics, or instances may be implemented individually and / or collectively by a variety of hardware.

[0133] In one embodiment, examples of the present disclosure may be implemented via an application specific integrated circuit (ASIC). However, the embodiments disclosed herein may be equivalently implemented in whole or in part in a standard integrated circuit as one or more computer programs running on one or more computers (e.g., one or more programs running on one or more computer systems), one or more programs running on one or more processing circuits (e.g., microprocessing circuits), one or more programs running on one or more processors (e.g., microprocessors), firmware, or nearly any combination thereof.

[0134] In addition, those skilled in the art will recognize that the exemplary mechanisms disclosed herein may be distributed as program products in a variety of tangible forms, and that the exemplary embodiments apply equally regardless of the particular type of tangible instruction-bearing medium used to actually perform the distribution. Examples of tangible instruction-bearing media include, but are not limited to, the following: recordable media such as floppy disks, hard drives, CD ROMs, digital tapes, flash drives, and computer memory.

[0135] The various embodiments described above can be combined with one another to provide additional embodiments. For example, two or more of the exemplary embodiments described above can be combined to, for example, improve the safety of laser printing and reduce the risks associated with laser-related accidents and injuries. These and other changes can be made to the present systems and methods in light of the above detailed description. Accordingly, the present disclosure is not limited by the disclosure, but rather its scope will be determined by the following claims.

Claims

1. A central server, comprising: a memory device storing one or more instructions; and a processor communicatively coupled to the memory device, wherein the processor is configured to: receiving one or more patient characteristics associated with a first patient, wherein the one or more patient characteristics include at least a type of hygiene treatment and / or a frequency of use of the hygiene treatment, receiving one or more image characteristics associated with an image of a patient wristband worn by the first patient, training a machine learning (ML) model that defines a relationship between the one or more patient characteristics and the one or more image characteristics, wherein the ML model is used to predict the number of days until the patient wristband associated with a second patient is deemed unusable, and generating an instruction to a printing device to print a new patient wristband for the second patient based on the number of days being less than a predetermined threshold number of days, Wherein the processor is further configured to receive the one or more patient characteristics associated with the second patient, wherein the ML model predicts the number of days based on the one or more patient characteristics associated with the second patient.

2. The central server of claim 1, wherein the one or more image characteristics include a quality measure of a patient's wristband in the image.

3. The central server of claim 2, wherein the processor is further configured to compare whether a quality metric of the patient's wristband in the image is less than a predetermined quality threshold, wherein the predetermined quality threshold corresponds to a percentage of defective pixels in the image. 4 . The central server of claim 3 , wherein the processor is further configured to: in response to determining that the quality metric of the patient bracelet is less than the predetermined quality threshold, determine a number of days that have passed since the patient bracelet was printed.

5. The central server of claim 1 , wherein the one or more image characteristics include a decode status of a machine-readable indicia printed on the patient bracelet, wherein the decode status indicates a quality metric of the patient bracelet in the image, wherein the quality metric includes a percentage of total pixels indicating defects.

6. The central server of claim 1 , wherein the processor is further configured to generate training data based on the one or more patient characteristics and the one or more image characteristics associated with the first patient, wherein the training data comprises one or more features and one or more labels, wherein the one or more features comprise a current location of the first patient, a type of sanitization treatment used to disinfect the first patient, a frequency of sanitization treatment use, an age of the first patient, a quality metric of the patient wristband, a decoding status of a machine-readable marking printed on the patient wristband, and / or a disease associated with the first patient.

7. The central server of claim 1, wherein the processor is further configured to determine whether the one or more patient characteristics associated with the first patient correspond to patient characteristics of a new patient.

8. The central server of claim 7, wherein in response to determining that the first patient corresponds to a new patient, the processor is further configured to transmit instructions to a badge scanner to transmit an image of the patient's wristband. 9 . The central server of claim 8 , wherein the processor is configured to store the image of the patient bracelet as an ideal image of the patient bracelet worn by the first patient.

10. A method for predicting the life of a printed label, comprising: receiving, by a processor, one or more patient characteristics associated with a first patient, wherein the one or more patient characteristics include at least a type of hygiene treatment and / or a frequency of use of the hygiene treatment; receiving, by a processor, one or more image characteristics associated with an image of a patient wristband worn by the first patient; and training, by a processor, a machine learning (ML) model that defines a relationship between the one or more patient characteristics and the one or more image characteristics, wherein the ML model is used to predict a number of days until the patient wristband associated with a second patient is deemed unusable; generating, by the processor, an instruction to a printing device to print a new patient wristband for the second patient based on the number of days being less than a predetermined day threshold; and The one or more patient characteristics associated with the second patient are received, by a processor, wherein the ML model predicts the number of days based on the one or more patient characteristics associated with the second patient. The method of claim 10 , wherein the one or more image characteristics include a quality measure of a patient's wristband in the image.

12. The method of claim 11, further comprising comparing, by the processor, whether a quality metric of the patient bracelet in the image is less than a predetermined quality threshold, wherein the predetermined quality threshold corresponds to a percentage of defective pixels in the image.

13. The method of claim 12, further comprising determining, by the processor, a number of days that have passed since the patient bracelet was printed in response to determining that the quality metric of the patient bracelet is less than the predetermined quality threshold.

14. The method of claim 10, wherein the one or more image characteristics comprises a decode status of machine-readable indicia printed on the patient bracelet, wherein the decode status indicates a quality metric of the patient bracelet in the image, wherein the quality metric comprises a percentage of defective pixels in the image.

15. The method of claim 10 , further comprising generating training data based on the one or more patient characteristics associated with the first patient and based on the one or more image characteristics, wherein the training data comprises one or more features and one or more labels, wherein the one or more features comprise a current location of the first patient, a type of sanitization treatment used to disinfect the first patient, a frequency of use of the sanitization treatment, an age of the first patient, a quality metric of the patient wristband, a decoding status of a machine-readable marking printed on the patient wristband, and / or a disease associated with the first patient.

16. The method of claim 10, further comprising determining, by the processor, whether the one or more patient characteristics associated with the first patient correspond to patient characteristics of a new patient.

17. The method of claim 16, further comprising transmitting, by the processor, instructions to a marker scanner to transmit an image of the patient's wristband in response to determining that the first patient corresponds to a new patient.

18. The method of claim 17, further comprising storing, by the processor, the image of the patient bracelet as an ideal image of a patient bracelet worn by the first patient.

Images