Patent Description:
The document <NPL>refers to using QR codes (two-Dimensional barcode) in medical device package without IFU, user guides and manuals to enhance patient safety, reduce cost and enhance the breadth of information available to the ultimate users. The information is accessible by taking a picture or scanning the QR code printed on a medical device package. This thesis also assesses the feasibility of implementing the QR code technology on medical device package and a case study is conducted that elaborates on the cost analysis.

Document <CIT> relates to a treatment apparatus comprising an apparatus for administering a drug to a mammal and a mobile device; wherein the mobile device is connected to a server via a network; wherein the mobile device hosts an application program; wherein the application program comprises one or more medical features; wherein the application program is configured to execute the following steps: receiving (i) an activation code and (ii) one or more parameters associated with a prescription; transmitting, by the application program, the activation code via the network from the mobile device to the server; receiving, by the application program, an authorization code from the server, wherein the authorization code indicates a validity of the activation code; activating, by the application program, one medical feature using the activation code if the activation code is valid; configuring, by the application program, the activated medical feature using at least one of the one or more parameters; generating, using the activated medical feature, data based on the one or more parameters, the generated data being indicative of how the drug shall be administered; and outputting the generated data via a user interface of the mobile device and/or sending the generated data to the apparatus for administering the drug.

Document <CIT> relates to a device configuration method for configuring a diabetes management device by a diabetes management system, the diabetes management system residing on a server computer, the method comprising: receiving, by the diabetes management system, a therapy setting instruction from a computing device, validating, by the diabetes management system, the credentials of the healthcare provider by way of a validation service based on the identification information, determining, by the diabetes management system, whether the therapy setting instruction is valid based on an association between the diabetes management system, the patient, and the healthcare provider; transmitting, by the diabetes management system, one or more parameters for configuring the feature defined by the therapy setting to the subject diabetes management device; and canceling, by the diabetes management system, the therapy setting instruction in response to the therapy setting instruction being invalid. Further, a method for issuing a prescription for configuring a diabetes management device by way of a prescription system and a diabetes management system, and a diabetes management system for processing a therapy setting instruction issued by a computing device operated by a healthcare provider is provided.

Document <CIT> discloses a method of operating a medical instrument comprising a battery powered medical appliance and a control unit. Both have Bluetooth communication modules. A first memory of the medical appliance contains a onetime password and of a password-authenticated key agreement algorithm. The control unit has a second memory with an implementation of the password-authenticated key agreement algorithm. The method comprises entering the onetime password into the data entry interface of the control unit. The method further comprises generating a Bluetooth encryption key by the medical appliance and the control unit with the onetime password by exchanging data across the wireless communication channel by executing the password-authenticated key agreement algorithm. The method further comprises storing the Bluetooth encryption key in the first memory. This document can be considered to be the closest prior art.

A method of presenting content to viewers in a computer network environment which includes scanning a barcode with a mobile device, retrieving content directly associated with the barcode, and retrieving associated proprietary content is disclosed in document <CIT>. The dual content is then separately displayed on said mobile device.

In document <CIT>, systems and methods for the wireless pairing of a personal health device (PHD) with a computing device are disclosed. In an embodiment, the PHD communicates a private key to the computing device via a first communication medium. The PHD receives from the computing device via a second wireless communication medium pairing information including the private key. The PHD can then establish a secure communication channel with the computing device by pairing the PHD to the computing device.

Document <CIT> relates to an automatic alarm device for a medical infusion apparatus. The automatic alarm device is mainly composed of a monitoring device, a mobile phone, a connecting arm, an outer arm and a liquid speed detection module, wherein the whole monitoring device is in an n shape, and the liquid speed detection module installed on the outer arm can wirelessly send the detected infusion condition to the mobile phone; an infusion system applying the automatic alarm device for the medical infusion apparatus comprises a system access module, an infusion detection module and an infusion completion prompting module; the automatic alarm device for the medical infusion apparatus can monitor the infusion condition in real time and can be recycled, energy resources are saved, the device can remotely monitor the drop-in completion time of a patient through a wechat mini application, the pressure of a nurse is greatly relieved, in addition, the device is compatible with an existing system of a hospital, the hospital does not need to replace the system, and medical resources and the medical cost are greatly saved.

Document <CIT> discloses a patient care system comprising a medical device for administering a medical treatment to a patient and a server system configured to receive and transmit data via a communications network to, respectively from users including patients and health care professionals, the server system further configured to process and store data related to patient care. The server system comprises a database configured to encrypt and store encrypted data related to patient care, an application server including patient care software components for disease management and patient information management, a communication server including a web server software application for data transfer through the internet, the patient care software components operable to receive medical device usage data comprising data on the usage of said medical device transferred through the communications network, and further operable to process said medical device usage data in conjunction with patient data to generate a report or a plurality of reports related to the treatment of the patient, the reports being accessible remotely via the communications network by registered users of the patient care system as a function of respective roles and privileges of the registered user stored in the server system.

Document <CIT>, according to one implementation, refers to a computer-implemented method of establishing a secure wireless communication connection between an insulin pump device and a mobile computing device, the method including receiving, at a mobile computing device, a device identifier for the insulin pump device; obtaining, by the mobile computing device, device information for the insulin pump device from a remote server system using the device identifier; establishing, by the mobile computing device, a secure wireless connection with the insulin pump device using, at least in part, the device information; authenticating, by the mobile computing device, the insulin pump device based on asymmetric key verification using the public key for the insulin pump; and securely communicating, by the mobile computing device and in response to authenticating the insulin pump device, information with the insulin pump device.

A mobile device may include software to retrieve information about a medical device after user self-selection of the medical device from a list. Such software requires the selected medical device to be on the list as well as user input that may disjoint the process and lead to potential human error due to an incorrect selection of the medical device from the list.

Accordingly, a need exists for alternative systems to streamline medical device configuration on a mobile device with respect to a medical device and methods of use of such systems.

For solving such problem, a medical device data manager configuration system according to the independent claim <NUM> is provided. Further, methods of operating a medical device data manager configuration system according to the independent claims <NUM> and <NUM> are provided.

Referring generally to the figures, embodiments of the present disclosure are directed to medical device data manager configuration systems to identify a medical device and configure a mobile smart device based on the identified medical device and methods of use of such systems. For example, multiple medical devices are available and provided for multiple worldwide markets. Data management solutions that communicate with these medical devices are utilized by users such as diabetic persons to more effectively manage their diabetic conditions. However, each medical device is unique in respect to its respective requirements to operate with such data management solutions, and each market may be distinct with respect to type of offered medical device and respective intended use and performance.

The medical device data manager configuration systems described herein streamline a process to select a medical device to more efficiently and accurately pair with the smart mobile device <NUM> by not requiring manual user selection, for example, of the medical device from a listing of options presented to the user. Further, by not being restricted to a listing of medical device selection options as viewable on a screen for user selection, a field of potentially identifiable medical devices able to be synched or paired with the system(s) is greatly increased. Additionally, the field of potentially identifiable medical devices may be restricted by country type and/or other restrictions, as described in greater detail further below. Additionally, removing user-based selection steps that would require additional processing steps reduces an amount of processing time along with reducing a potential of human error, thereby increasing and improving processing speed and accuracy of the systems described herein.

Moreover, pre-marketing risks associated with listing an unavailable device, risks of user confusion, and/or low confidence of proper device selection from a list by the user may be risks associated with manual user selection options. Rather, the systems described herein may employ a software application tool as a data manager that is communicatively coupled to a smart mobile device to capture an image of the medical device. The software application tool may be configured to automatically identify the medical device based on the captured image to retrieve data associated with the identified medical device. The system is able to use the retrieved data to pair the identified medical device with the smart mobile device and to monitor activity of the identified medical device through use of the smart mobile device.

Reference will now be made in detail to embodiments of the medical device data manager configuration systems, and examples of such systems are illustrated in the accompanying drawings. Wherever possible, the same reference numerals will be used throughout the drawings to refer to the same or like parts. Various embodiments of the medical device data manager configuration systems will be described in further detail herein with specific reference to the appended drawings.

Referring to <FIG>, a medical device data manager configuration system <NUM> includes a smart mobile device <NUM> including a display screen <NUM> and a camera <NUM>. The medical device data manager configuration system <NUM> further includes a medical device <NUM>. The medical device may be a blood glucose meter, a continuous glucose monitor, an insulin pump, an insulin, a wellness device, or a like medical device. By way of example and not as a limitation, a wellness device may be a device configured to improve the wellness of an individual through tracking wellness data associated with the health and wellness of the individual and/or monitoring activity of the individual. Such wellness data may include, for example, vital signs including heart rate, calories consumed and burned, and cholesterol levels. The monitored activity of the individual may include a number of steps taken in a time period or another fitness activity conducted by the individual such as running, cycling, or hiking.

Further, the medical device data manager configuration system <NUM> includes a processor and a memory communicatively coupled to the processor, such as the processor <NUM> and memory component <NUM> as described with respect to <FIG> in greater detail further below. The medical device data manager configuration system <NUM> includes machine readable instructions stored in the memory that cause the medical device data manager configuration system <NUM> to perform one or more of instructions when executed by the processor.

The machine readable instructions may include instructions to use the camera <NUM> of the smart mobile device <NUM> to capture an image <NUM> of the medical device <NUM>. A software application tool <NUM> (<FIG>) on the smart mobile device <NUM> may be configured to display a reference frame <NUM> (<FIG>) on the display screen <NUM> of the smart mobile device <NUM>. The reference frame <NUM> may be configured to identify an area to position the medical device <NUM> within prior to image capture by the camera <NUM> of the smart mobile device <NUM>. Use of such a reference frame <NUM> may allow for a more robust image capture by enabling a user to capture device images that are roughly similar in size, for example. In at least one embodiment, the software application tool <NUM> on the smart mobile device <NUM> may be communicatively coupled to the camera <NUM> such that an option to capture an image may be displayed for selection and one or more menu options for selection on the display screen <NUM>. In response to selection of the capture an image option, the software application tool <NUM> provides logic instructions to turn on the camera <NUM> and place the reference frame <NUM> on the display screen <NUM> of the smart mobile device <NUM> at a predetermined location on the display screen <NUM>. Location and/or size of the reference frame <NUM> on the display screen <NUM> may be fixed and pre-determined. In response to image capture, the reference frame <NUM> disappears from the display screen <NUM> and the image <NUM> is displayed on the display screen <NUM> with an option to accept or rejection the image for user selection. In response to user acceptance of the image <NUM>, the image <NUM> is analyzed by an identification algorithm 312A to identify the medical device <NUM> in the image <NUM> as an identified medical device 108A.

The machine readable instructions may include further instructions to apply the identification algorithm 312A to the image <NUM> of the medical device <NUM>. In embodiments, the image <NUM> is a picture or a video. The image <NUM> may be a frontal image of the medical device <NUM> or a rear image of the medical device <NUM>. The image <NUM> is of the medical device <NUM> with a medical device display screen that is turned on with a back-lit background, for example, to include display information configured to be utilized as input for the identification algorithm 312A. In at least one embodiment, the identification algorithm 312A does not require the medical device <NUM> to be turned on when capturing the image <NUM> of the medical device <NUM>, as the identification algorithm 312A may be trained, as described in greater detail further below, on images of medical devices that are not turned on. Should the display information of a medical device <NUM> that is turned on for image capture as the image <NUM> be captured, however, the display information may provide identifying characteristics for use with the identification algorithm 312A to determine the identified medical device <NUM>. By way of example and not as a limitation, such identifying characteristics may include a particular display color and/or particular display text that the identification algorithm 312A may be retrained to include as input in addition to identifying characteristics already included in a trained convolutional neural network <NUM> used with the identification algorithm 312A, as illustrated in <FIG>.

The machine readable instructions may include further instructions to identify the medical device as an identified medical device 108A based on the image <NUM> of the medical device <NUM> and the identification algorithm 312A. The identification algorithm 312A may be administered through an identification tool component <NUM>, as described in greater detail below with respect to <FIG>.

In embodiments, the identification algorithm 312A may further include at least one of reading of a QUICK RESPONSE CODE ("QR code"), a serial number, a Unique Device Identifier (UDI), or a Globally Unique Identifier (GUID) of the medical device <NUM>. In at least one embodiment, a UDI may be found on the back of a medical device <NUM> and/or the medical device <NUM> may meet particular Global Harmonization Task Force (GHTF) requirements. By way of example and not as a limitation, the UDI is a unique numeric or alphanumeric code, required by the Food and Drug Administration in the United States of America, including a device identified specific to a device model of a medical device <NUM> and a production identifier. The production identifier includes current production information for the specific medical device <NUM> such as lot serial number, expiration date, and the like. The GUID may be a <NUM>-bit number created by a system, such as a Microsoft Windows® operating system or other Microsoft Windows® application or like system, to uniquely identify specific components, hardware, software, files, user accounts, database entries, and other like items. The identification algorithm 312A may be an image recognition algorithm. The image recognition algorithm may be configured to utilize a neural network, and the neural network may be customizable. The image recognition algorithm may be configured to utilize a convolutional neural network that, in a field of machine learning, for example, is a class of deep, feed-forward artificial neural networks applied for image analysis. As a non-limiting example, the image recognition algorithm is generated from a program including ALEXNET, INCEPTION (GOOGLENET), BN-INCEPTION-V2, and/or INCEPTION-V3.

The image recognition algorithm may be configured to utilize the trained convolutional neural network <NUM>. The trained convolutional neural network <NUM> may be configured to identify objects within an image to a high-level of accuracy. As an example and not a limitation, the trained convolutional neural network <NUM> is pre-trained on a subset of an image database, which may be a database <NUM> as described in greater detail below with respect to <FIG>, for example. The trained convolutional neural network <NUM> may be trained on more than a million images and configured to classify images into at least a thousand categories. The trained convolutional neural network <NUM> may pre-trained on a subset of a medical device image database <NUM>, which may be included as or within the database <NUM> as described in greater detail below, and comprises one or more coefficients configured to detect one or more types of medical devices <NUM>. The trained convolutional neural network <NUM> may be trained to detect objects such that effect of orientation, light conditions, and image depth are minimal on accuracy of identification.

In at least one embodiment and as a non-limiting example, layers of an example trained convolutional neural network <NUM> are set forth below in TABLE <NUM>:.

Each layer in a neural network has corresponding coefficients. For example, with respect to the 25th layer in the example neural network of TABLE <NUM> above, partial coefficients related to this layer are set forth below in TABLE <NUM>:.

Such coefficients are determined during training and are set post training of the trained convolutional neural network <NUM>, as described in greater detail further below. The coefficients are derived using a large dataset that is representative of multiple types of medical devices. During the training to optimize the neural network model, coefficients self-adjust to provide for most accurate predictions. Once a model is determined and optimized, the coefficients do not change for future predictions. A determined model is then used with determined and fixed coefficients to predict a type of a medical device. New set coefficients are created only when there is a re-training of the model in the future with and using additional data for the re-training.

In at least another embodiment and as a non-limiting example, the trained convolutional neural network <NUM> as described herein is trained using ALEXNET and an IMAGENET database. In particular, ALEXNET is the pre-trained convolutional neural network that is trained on a subset of the IMAGENET database through a model that is trained on more than a million images to classify the images into a thousand categories. For use with the identification algorithm 312A with respect to medical device as described herein, ALEXNET's capabilities are leveraged to fine-tune the trained convolutional neural network <NUM> to be able to identify a type of medical device <NUM> as described herein. For example, certain layers of the ALEXNET neural network are retrained into a fine-tuned, trained convolutional neural network <NUM> using the medical device image database <NUM> that includes multiple types of medical devices, each of which may be identified as the identified medical device 108A. This trained convolutional neural network <NUM> includes a structure similar to the structure of the ALEXNET neural network yet further includes coefficients designed to detect the different types of medical devices.

Performance of this trained convolutional neural network <NUM> may be tested on a test data set that was not part of the training of the trained convolutional neural network <NUM> to confirm acceptable operation of the trained convolutional neural network <NUM>. Such a trained convolutional neural network <NUM> as described herein may be utilized in block <NUM> described further below, for example, as the network on which the identification algorithm 312A is run on the image <NUM> of the medical device <NUM> to determine the identified medical device 108A.

In at least one embodiment, and as described in greater detail below with respect to a process <NUM> of <FIG>, a process <NUM> of <FIG>, and/or a process <NUM> of <FIG>, which may be implemented by a processor <NUM> of <FIG>, a method of operating a medical device data manager configuration system may include programming logic such as at least one of the process <NUM>, the process <NUM>, and the process <NUM>.

As a non-limiting example, <FIG> illustrates a method or process <NUM> to train, retrain, and generate such a trained convolutional neural network <NUM>. In block <NUM>, a pre-trained convolutional neural network is loaded to a system. In at least one embodiment, the processor <NUM> is configured to execute one or more instructions to load the pre-trained convolutional neural network to the medical device data manager configuration system <NUM>. In block <NUM>, one or more select layers, such as final select layers, are replaced with new custom layers particular to the image and device classification problem described herein and through one or more instructions executed by the processor <NUM>. As a non-limiting example, an ALEXNET neural network is loaded to the system through one or more instructions executed by the processor <NUM>, the final three layers are removed through one or more instructions executed by the processor <NUM>, and three new custom layers are added to replace the removed final three layers through one or more instructions executed by the processor <NUM>.

In block <NUM>, a learning rate is adjusted to optimize the new custom layers. In at least one embodiment, the processor <NUM> is configured to execute one or more instructions to adjust the learning rate to optimize the new custom layers. During optimization of a neural network, a learning rate may determine how slow or fast a training is progressing. However, such a learning rate is not arbitrarily chosen for optimization. Further, if a learning rate is too small, the training will progress slowly and network coefficients will change slowly, while if the rate is too high, the training may not be able to converge for model completion. As most of the ALEXNET network is pre-trained, and only a few new layers are added to the network to replace select previous layers, the learning rate is able to be adjusted in a way that the new added layers of block <NUM> are optimized while the coefficients for the pre-trained layers do not substantially change during training.

In block <NUM>, images are balanced across each category to be classified. As set forth, the trained convolutional neural network <NUM> is pre-trained on an image database subset such as the medical device image database <NUM> of <FIG> and may be trained on more than a million images and be configured to classify the images into at least a thousand categories. In at least one embodiment, the processor <NUM> is configured to execute one or more instructions to balance the images across each such category to be classified.

In block <NUM>, the network data set, which is the entire data set from the pre-trained network now modified to include new custom layers generated through blocks <NUM>-<NUM>, may be split into a training set and a test set. In at least one embodiment, the processor <NUM> is configured to execute one or more instructions to split the network data set into the training set and the test set. As a non-limiting example, <NUM>% of the network data set may be utilized as the training set and <NUM>% of the network data set may be utilized as the test set. In block <NUM>, the convolution neural network is trained and a prediction model <NUM> is generated with the training set. In at least one embodiment, the processor <NUM> is configured to execute one or more instructions to train the convolutional neural network and generate the prediction model <NUM> with the training set. In block <NUM>, performance of the prediction model <NUM> is checked on the test set to determine confidence and accuracy levels of the prediction model <NUM> through, for example, one or more instructions executed by the processor <NUM>.

For each neural network model, a limit is set for a number of optimization steps as a number of epochs to determine a minima for a best solution. During optimization, each model searches for the minima and repeats the search until the limit is reached for the number of epochs or until the model converges. At the end of this minima search based iterative process, only one prediction model <NUM> is generated.

During the training, the system further goes through a model determination based iterative process to determine multiple prediction models <NUM>. In block <NUM>, the process <NUM> determines whether the number of iterations I completed to determine each prediction model <NUM> is less than a threshold number N set for the model determination based iterative process through, for example, one or more instructions executed by the processor <NUM>. The threshold value N may be a predetermined number, for example. If not, the process <NUM> returns to block <NUM> to repeat the minima search based iterative process to determine another prediction model <NUM> through blocks <NUM>-<NUM>. If so, the process <NUM> advances to block <NUM> to select a best performing prediction model <NUM> out of the N options of determined prediction models <NUM>. In block <NUM>, the selected best performing prediction model <NUM> is used as the trained convolutional neural network <NUM>. By repeating the model determination based iterative process N times, the process <NUM> may take advantage of different random sampling of the training set data and initial starting points. Based off different starting points and a unique distribution of the training set data, the process <NUM> may result in slightly different prediction models <NUM> with slightly different accuracies after each iteration of the model determination based iterative process. By repeating the model determination based iterative process N times through, for example, one or more instructions executed by the processor <NUM>, the process <NUM> may determine N prediction models <NUM> with slightly differences in accuracy and select the prediction model <NUM> that presents the best performance as the final model to utilize as the trained convolution neural network <NUM> as described herein.

In embodiments, the trained convolutional neural network <NUM> and associated computations may be stored in the smart mobile device <NUM>, and the image database <NUM> (e.g., the database <NUM> of <FIG>), may be stored in a cloud networking environment (for example, referable to as "the cloud <NUM>" as shown in <FIG>, described in greater detail further below). Further, periodic retraining of the convolutional neural network may be performed in the cloud <NUM>. An incorrect classification may be configured to trigger a retraining of the trained convolutional neural network <NUM>. For example, a determination that of the incorrect classification would result in automatically sending a signal to the trained convolutional neural network <NUM> to request a retraining based on the incorrect classification. The training may occur automatically or at a scheduled time. The trained convolutional neural network <NUM> may be configured to be continuously retrained in response to a misclassification of a type of the medical device. As a non-limiting example, a misclassified image is added to a model training database with a correct label and the trained model convolutional neural network <NUM> is retrained to correctly classify the misclassified image. Further, the trained convolutional neural network <NUM> may be configured to have one or more updates made periodically as one or more updates are made to an associated convolutional network. For example, the one or more updates may be made at pre-scheduled times. In at least one embodiment, correctly identified images may be used to fine tune the trained convolutional neural network <NUM>. As a non-limiting example, if the network is identifying images correctly but with a low-range identification confidence value, such as in a range of from about <NUM>% to about <NUM>%, for example, the one or more correctly identified images may be included in the image database <NUM> along with a 'correct' label. The 'correct' label may be applied to an image after a user manually confirms a predicted device type as predicted by the trained convolutional neural network <NUM>. The 'correct' label may be further confirmed, prior to being used to retrain the trained convolutional neural network <NUM>, after establishing a communication link between the data management application of the software application tool <NUM> and the medical device <NUM> such as through a Bluetooth pairing process. During such a pairing process, a device specific identifier <NUM> as shown in <FIG> may be passed to the data management application, and this device specific identifier <NUM> may be used to confirm the device type of the identified medical device 108A. For example, the device specific identifier <NUM> may be associated with a device list <NUM> stored in a database <NUM> that is accessible by the data management application of the software application tool <NUM>. Increasing a number of representations of an image type of images capturing the medical device <NUM> in a large medical device image database <NUM> may assist with correct classification of such images with higher associated confidence values.

In embodiments, the smart mobile device <NUM> is configured to serve as a conduit to transfer the image <NUM> to a cloud server 323A of the cloud <NUM> (<FIG>), and the cloud server 323A is configured to store the image database <NUM> and the trained convolutional neural network <NUM>. For example, the image <NUM> is wirelessly transmitted to the cloud server 323A for prediction by the trained convolutional neural network <NUM> based on coefficients derived using a multitude of images stored in the image database. The trained convolutional neural network <NUM> is configured to interact with the identification algorithm 312A to predict and identify the medical device <NUM> as the identified medical device 108A as a device identification prediction based on the image <NUM> and the coefficients of the trained convolutional neural network <NUM>. Information regarding the device identification prediction of the identified medical device 108A may then be wirelessly transmitted back to the smart mobile device <NUM> to be displayed to a user, for example. One or more identification calculations associated with the identification algorithm 312A are conducted in the cloud <NUM>, and class information associated with the identified medical device 108A is transmitted to the smart mobile device <NUM> from the cloud <NUM>. In at least one embodiment, when a new image <NUM> of a medical device <NUM> is received, the image <NUM> is input into the prediction model <NUM> of the trained convolutional neural network <NUM>. The prediction model <NUM> determines a prediction of the type of the medical device as the identified medical device 108A. Images <NUM> may be stored in the image database for later use in training a new prediction model <NUM> to enhance future performance of a current prediction model <NUM>.

The identification algorithm 312A may be configured to use one or more prediction confidence probabilities indicative of a confidence level associated with identification of the identified medical device 108A. A confidence threshold value may be associated with a positive identification of the identified medical device 108A such that the positive identification occurs in response to a prediction confidence probability this is greater than or equal to the confidence threshold value. The machine readable instructions may further comprise instructions to display a name of the identified medical device 108A on a graphical user interface (GUI) <NUM> of the display screen <NUM> of the smart mobile device <NUM> in response to the prediction confidence probability being greater than or equal to the confidence threshold value. The machine readable instructions further may include instructions to display an option for a user to accept or reject the name of the identified medical device 108A. Further, the machine readable instructions may include instructions to display a factory image of the identified medical device 108A alongside the image <NUM> of the medical device <NUM> in response to the prediction confidence probability being greater than or equal to the confidence threshold value. The machine readable instructions further may include instructions to display an option for a user to accept or reject the positive identification of the identified medical device 108A.

The smart mobile device <NUM> through, for example, the software application tool <NUM>, may be configured to request a user to capture another image for analysis and identification in response to the prediction confidence probability being lower than the confidence threshold value. As a non-limiting example, the smart mobile device <NUM> is configured to request a user to capture another image of the medical device <NUM> in at least one of a different orientation or different environment in response to the prediction confidence probability being lower than the confidence threshold value.

Further, the machine readable instructions may include instructions to automatically configure the software application tool <NUM> on the smart mobile device to retrieve data associated with one or more requirements <NUM> of the identified medical device 108A. In at least one embodiment, once the device identification prediction is made to identify the identified medical device 108A, the software application tool <NUM> is configured to retrieve device specific data from the cloud <NUM>. The cloud <NUM> may host multiple device specific data, and the software application tool <NUM> may identify and retrieve the device specific data associated with the identified medical device 108A from the multiple device specific data that is stored in the cloud <NUM>. Such cloud storage may centralize the storage of the multiple device specific data to provide easy access and an ability to control and/or change such data and/or types selected for storage. Additionally or alternatively, such multiple device specific data for access by the software application tool <NUM> may be locally stored such that a remote transmission connection such as an internet connection is not required to access the device specific data. The machine readable instructions further may include instructions to display the one or more requirements <NUM> of the identified medical device 108A on the GUI <NUM> of the smart mobile device <NUM> to, for example and as described in greater detail below, inform the user of one or more requirements of the identified medical device. The GUI <NUM> is disposed on and as part of the display screen <NUM> of the smart mobile device <NUM> and is communicatively coupled to and controlled by the software application tool <NUM>.

In embodiments, the one or more requirements <NUM> of the identified medical device 108A comprise content specific to the specific to the identified medical device 108A. Such content may include at least one of onboarding content, communication management instructions, educational materials, regulatory labeling content, and one or more menu options. For example, onboard content includes content associated with user training with respect to use of the identified medical device 108A for the first thirty days of use of the identified medical device 108A. The user training may include, for example, training on how to administer a therapeutic delivery agent with the identified medical device 108A according to a prescribed treatment regime for the user by a healthcare provider or the like.

The education materials may include educational content associated with the identified medical device 108A providing during a setup associated with the identified medical device 108A. As a non-limiting example, the educational content may include information about a compatible test strip, information about use of the compatible test strip with the identified medical device, and/or information about related calibration testing procedures. In embodiments, the setup associated with the identified medical device 108A may include configuration of the software application tool <NUM> to present instructions through a start-up wizard or first time user flow process of the software application tool <NUM> that is presented to the user to guide the user through setup of the identified medical device 108A. In at least one embodiment, the identified medical device 108A may be an identified blood glucose (BG) meter, and the start-up wizard for the identified blood glucose meter may include static and audio and/or video based instructional content as descriptions for instructions regarding, for example, setting BG test reminders, warning/alert thresholds, specific steps for wireless communication pairing, on-device feature configuration such as configuration of BG target range settings, and acquiring a blood sample. In at least one other embodiment, the identified medical device 108A may be an identified continuous blood glucose (BG) meter, and the start-up wizard for the identified continuous blood glucose meter may include static and audio and/or video based instructional content as descriptions for instruction regarding, for example, setting calibration test reminders, setting warning/alert thresholds, sounds used for specific audible alarms, specific steps for wireless communication pairing, sensor insertion, and on-device feature configuration such as configuration of continuous glucose target range settings.

The smart mobile device <NUM> may configured to be communicatively coupled to the identified medical device 108A. As an example and not a limitation, the machine readable instructions may include instructions to pair the smart mobile device <NUM> and the identified medical device 108A. The machine readable instructions may further include instructions to automatically provide device specific pairing instructional information to a user regarding pairing prior to pairing the smart mobile device <NUM> and the identified medical device 108A. By way of example and not as a limitation, a given identified medical device 108A may provide a PIN code on the display screen <NUM> during pairing when triggered to provide the PIN code through a user selection, such as through the user pressing an appropriate front-panel button on the identified medical device 108A. Devices without front-panel buttons may alternative include a printed fixed PIN code, such as a fixed PIN code printed upon a back of the identified medical device 108A. A user may be instructed where to find the printed fixed PIN code for pairing based on visual characteristics of the meter image, such as the identified medical device 108A including or not including such front-panel buttons. An additional security such as out of band pairing may be used to pair the smart mobile device <NUM> and the identified medical device 108A. Instruction may be provided for non-wireless communication methods for transferring data from the identified medical device 108A to a data repository, such as through instructions outlining required systems and steps to transfer data from the identified medical device 108A to a data repository through a protocol that is implement for a Universal Serial Bus (USB) device.

The machine readable instructions may further include instructions to automatically perform firmware version checks associated with the identified medical device 108A and/or install firmware updates associated with the identified medical device 108A. Upon identification of and subsequent connection to an identified medical device 108A, various device management tasks may be directed through use of the software application tool <NUM> and the identified medical device 108A. Such tasks may include downloading a firmware/software update for the identified medical device 108A for installation by a host system. Updates to associated systems in the field may occur after launch of the systems. The recognition of the identified medical device 108A could accordingly trigger a desired handling and update to the identified medical device 108A by an associated host system. Additionally, communication of device specific messaging from a manufacturer to an end user may occur any time after launch of the identified medical device 108A. By updating system responses in the cloud <NUM>, timely messaging would thus be able to be enabled after recognition and launch of the identified medical device 108A. Further, as the identified medical device 108A may be configured to support one or more complex data analysis algorithms including at least a pattern recognition algorithm of one or more collected measurements, the smart mobile device <NUM> through the software application tool <NUM> may be configured to support one or more graphical tools configured to display one or more patterns based on the pattern recognition algorithm to a user. A particular medical device <NUM> may support more metadata, such as flags or annotations, than other medical devices <NUM>. As such, recognizing a specific identified medical device 108A may allow the medical device data manager configuration system <NUM> to configure itself to support the metadata properly in terms of storage and visualization. The one or more patterns may be displayed to the user on the GUI <NUM> of the display screen <NUM> of the smart mobile device <NUM>.

In embodiments, the identified medical device 108A is associated with one or more device specific labeling requirements. The instructions to automatically configure the software application tool <NUM> on the smart mobile device <NUM> to retrieve data associated with one or more requirements <NUM> of the identified medical device <NUM> may include instructions to retrieve and use the one or more device specific labeling requirements for the identified medical device 108A. For example, instructions to retrieve and use the one or more device specific labeling requirements for the identified medical device 108A from a database in which the labeling requirements are stored may further include instructions to display a unit of measure required for the identified medical device when displaying one or more measurement results on the GUI <NUM> of the display screen <NUM> of the smart mobile device <NUM>. Additionally or alternatively, instructions to retrieve and use the one or more device specific labeling requirements for the identified medical device 108A may include instructions to display a warning, precaution, and/or limitation statement specific to and required by a health authority for the identified medical device 108A, such as a prescription device distribution control symbol (Rx). The one or more device specific labeling requirements to the identified medical device 108A may vary by country. For example, each country may have a specific, separately lead health authority setting country-specific regulations with respect to use of the identified medical device 108A, such as with respect to blood glucose unit of measure (e.g., mg/dL or mmol/L), local distributor contact information, operating condition limits (e.g., temperature and humidity), or direction regarding electronic labeling. Country or configuration specific information may further be useful to target removal and corrected activities with respect to identified adulterated devices in place of extending a recall activity for all distributed devices sharing a product name. The medical device data manager configuration system <NUM> may be configured to identify a country and retrieve country information related to the identified country through a signal generated by a global positioning system (GPS) sensor. Additionally or alternatively, the medical device data manager configuration system <NUM> may be configured to identify the country and retrieve country information related to the identified country through an image recognition algorithm as described herein that is further configured to recognize country specific information from the image <NUM> and/or another captured image.

Referring to <FIG>, a system <NUM> for implementing a computer and software-based method to utilize the medical device data manager configuration system, as shown in <FIG>, is illustrated and may be implemented along with using a graphical user interface (GUI) that is accessible at a user workstation (e.g., a computer <NUM>), for example. The system <NUM> includes a communication path <NUM>, one or more processors <NUM>, a memory component <NUM>, an identification tool component <NUM>, a storage or database <NUM> that may include the medical device image database <NUM>, an artificial intelligence component <NUM>, a network interface hardware <NUM>, a server <NUM>, a network <NUM>, and at least one computer <NUM>. The various components of the system <NUM> and the interaction thereof will be described in detail below.

While only one application server <NUM> and one user workstation computer <NUM> is illustrated, the system <NUM> can include multiple workstations and application servers containing one or more applications that can be located at geographically diverse locations across a plurality of industrial sites. In some embodiments, the system <NUM> is implemented using a wide area network (WAN) or network <NUM>, such as an intranet or the Internet, or other wired or wireless communication network that may include a cloud computing-based network configuration (for example, the cloud <NUM> including the cloud server 323A). The workstation computer <NUM> may include digital systems and other devices permitting connection to and navigation of the network. Other system <NUM> variations allowing for communication between various geographically diverse components are possible. The lines depicted in <FIG> indicate communication rather than physical connections between the various components.

As noted above, the system <NUM> includes the communication path <NUM>. The communication path <NUM> may be formed from any medium that is capable of transmitting a signal such as, for example, conductive wires, conductive traces, optical waveguides, or the like, or from a combination of mediums capable of transmitting signals. The communication path <NUM> communicatively couples the various components of the system <NUM>. As used herein, the term "communicatively coupled" means that coupled components are capable of exchanging data signals with one another such as, for example, electrical signals via conductive medium, electromagnetic signals via air, optical signals via optical waveguides, and the like.

As noted above, the system <NUM> includes the processor <NUM>. The processor <NUM> can be any device capable of executing machine readable instructions. Accordingly, the processor <NUM> may be a controller, an integrated circuit, a microchip, a computer, or any other computing device. The processor <NUM> is communicatively coupled to the other components of the system <NUM> by the communication path <NUM>. Accordingly, the communication path <NUM> may communicatively couple any number of processors with one another, and allow the modules coupled to the communication path <NUM> to operate in a distributed computing environment. Specifically, each of the modules can operate as a node that may send and/or receive data. The processor <NUM> may process the input signals received from the system modules and/or extract information from such signals.

As noted above, the system <NUM> includes the memory component <NUM> which is coupled to the communication path <NUM> and communicatively coupled to the processor <NUM>. The memory component <NUM> may be a non-transitory computer readable medium or non-transitory computer readable memory and may be configured as a nonvolatile computer readable medium. The memory component <NUM> may comprise RAM, ROM, flash memories, hard drives, or any device capable of storing machine readable instructions such that the machine readable instructions can be accessed and executed by the processor <NUM>. The machine readable instructions may comprise logic or algorithm(s) written in any programming language such as, for example, machine language that may be directly executed by the processor, or assembly language, object-oriented programming (OOP), scripting languages, microcode, etc., that may be compiled or assembled into machine readable instructions and stored on the memory component <NUM>. Alternatively, the machine readable instructions may be written in a hardware description language (HDL), such as logic implemented via either a field-programmable gate array (FPGA) configuration or an application-specific integrated circuit (ASIC), or their equivalents. Accordingly, the methods described herein may be implemented in any conventional computer programming language, as pre-programmed hardware elements, or as a combination of hardware and software components. In embodiments, the system <NUM> may include the processor <NUM> communicatively coupled to the memory component <NUM> that stores instructions that, when executed by the processor <NUM>, cause the processor to perform one or more functions as described herein.

Still referring to <FIG>, as noted above, the system <NUM> comprises the display such as a GUI on a screen of the computer <NUM> for providing visual output such as, for example, information, graphical reports, messages, or a combination thereof. The computer <NUM> may include one or more computing devices across platforms, or may be communicatively coupled to devices across platforms, such as mobile smart devices including smartphones, tablets, laptops, and/or the like or medical devices such as blood glucose meters, insulin pumps, continuous glucose monitors, and the like. The display on the screen of the computer <NUM> is coupled to the communication path <NUM> and communicatively coupled to the processor <NUM>. Accordingly, the communication path <NUM> communicatively couples the display to other modules of the system <NUM>. The display can include any medium capable of transmitting an optical output such as, for example, a cathode ray tube, light emitting diodes, a liquid crystal display, a plasma display, or the like. Additionally, it is noted that the display or the computer <NUM> can include at least one of the processor <NUM> and the memory component <NUM>. While the system <NUM> is illustrated as a single, integrated system in <FIG>, in other embodiments, the systems can be independent systems.

The system <NUM> comprises the identification tool component <NUM> to identify a medical device <NUM> as an identified medical device 108A through application of an identification algorithm 312A as described herein and an artificial intelligence component <NUM> to train and provide machine learning capabilities to a neural network associated with the identification algorithm 312A as described herein. The identification tool component <NUM> and an artificial intelligence component <NUM> are coupled to the communication path <NUM> and communicatively coupled to the processor <NUM>. As will be described in further detail below, the processor <NUM> may process the input signals received from the system modules and/or extract information from such signals.

Data stored and manipulated in the system <NUM> as described herein is utilized by the artificial intelligence component <NUM>, which is able to leverage a cloud computing-based network configuration such as the cloud <NUM> to apply Machine Learning and Artificial Intelligence. This machine learning application may create models that can be applied by the system <NUM>, to make it more efficient and intelligent in execution. As an example and not a limitation, the artificial intelligence component <NUM> may include components selected from the group consisting of an artificial intelligence engine, Bayesian inference engine, and a decision-making engine, and may have an adaptive learning engine further comprising a deep neural network learning engine.

The system <NUM> includes the network interface hardware <NUM> for communicatively coupling the system <NUM> with a computer network such as network <NUM>. The network interface hardware <NUM> is coupled to the communication path <NUM> such that the communication path <NUM> communicatively couples the network interface hardware <NUM> to other modules of the system <NUM>. The network interface hardware <NUM> can be any device capable of transmitting and/or receiving data via a wireless network. Accordingly, the network interface hardware <NUM> can include a communication transceiver for sending and/or receiving data according to any wireless communication standard. For example, the network interface hardware <NUM> can include a chipset (e.g., antenna, processors, machine readable instructions, etc.) to communicate over wired and/or wireless computer networks such as, for example, wireless fidelity (Wi-Fi), WiMax, Bluetooth, IrDA, Wireless USB, Z-Wave, ZigBee, or the like.

Still referring to <FIG>, data from various applications running on computer <NUM> can be provided from the computer <NUM> to the system <NUM> via the network interface hardware <NUM>. The computer <NUM> can be any device having hardware (e.g., chipsets, processors, memory, etc.) for communicatively coupling with the network interface hardware <NUM> and a network <NUM>. Specifically, the computer <NUM> can include an input device having an antenna for communicating over one or more of the wireless computer networks described above.

The network <NUM> can include any wired and/or wireless network such as, for example, wide area networks, metropolitan area networks, the Internet, an Intranet, the cloud <NUM>, satellite networks, or the like. Accordingly, the network <NUM> can be utilized as a wireless access point by the computer <NUM> to access one or more servers (e.g., a server <NUM>). The server <NUM> and any additional servers such as the cloud server 323A generally include processors, memory, and chipset for delivering resources via the network <NUM>. Resources can include providing, for example, processing, storage, software, and information from the server <NUM> to the system <NUM> via the network <NUM>. Additionally, it is noted that the server <NUM> and any additional servers can share resources with one another over the network <NUM> such as, for example, via the wired portion of the network, the wireless portion of the network, or combinations thereof.

<FIG> illustrates a method of operating or process <NUM> for operating the medical device data manager configuration system <NUM>. In at least one embodiment, the processor <NUM> is configured to execute one or more instructions to implement the process <NUM>. In block <NUM>, an image <NUM> of a medical device <NUM> is captured. For example, the image <NUM> may be captured by a camera <NUM> of a smart mobile device <NUM> as described herein and through one or more instructions executed by the processor <NUM>. In block <NUM>, a data manager software application tool <NUM> of the smart mobile device <NUM> is used to identify the medical device <NUM> as an identified medical device 108A based on the image <NUM> as described herein. For example, an identification algorithm 312A is applied as described herein to the image <NUM> of the medical device <NUM> through one or more instructions executed by the processor <NUM> to identify the medical device <NUM> as an identified medical device 108A based on the image <NUM> of the medical device <NUM> and the identification algorithm 312A. In block <NUM>, the data manager software application tool <NUM> on the smart mobile device <NUM> is configured to retrieve data associated with one or more requirements of the identified medical device 108A. In at least one embodiment, the processor <NUM> is configured to execute one or more instructions to retrieve data associate with the one or more requirements of the identified medical device 108A.

<FIG> illustrates another process <NUM> for operating the medical device data manager configuration system <NUM>. In at least one embodiment, the processor <NUM> is configured to execute one or more instructions to implement the process <NUM>. In block <NUM>, an image <NUM> of the medical device <NUM> is captured with a camera <NUM> of the smart mobile device <NUM> as described herein and through, for example, one or more instructions executed by the processor <NUM> to capture the image <NUM> with the camera <NUM>. In block <NUM>, an identification algorithm 312A as described herein is run on the image <NUM> through application of the identification algorithm 312A by the identification tool component <NUM>, for example, to the image <NUM> of the medical device <NUM>. The medical device <NUM> is identified as an identified medical device 108A based on the image <NUM> of the medical device <NUM> and the identification algorithm 312A as described herein. In at least one embodiment, the processor <NUM> is configured to execute one or more instructions to run the identification algorithm 312A on the image <NUM> and identify the identified medical device 108A. In at least another embodiment, the identification algorithm 312A identified the identified medical device 108A based on the image <NUM> through use of the trained convolutional neural network <NUM> as described herein and above, such as the trained convolutional neural network that is fine-tuned and retrained for customizable use from an ALEXNET neural network trained on an IMAGENET database and further trained on the medical device image database <NUM> that includes multiple types of medical devices. Using the trained convolutional neural network, the medical device <NUM> in the image <NUM> is identified as the identified medical device 108A based on the applied coefficients and prediction model <NUM> of the trained convolutional neural network <NUM>. In at least one embodiment, the identified medical device 108A is determined by the trained prediction model <NUM> based on one or more device features. Such features are created within the trained convolutional neural network <NUM> using convolution of the image <NUM> and pre-determined filters applied in early layers of the trained convolutional neural network <NUM> as described above. Once a prediction model <NUM> is fine-tuned and retrained as described above, this prediction model <NUM> may be used to identify the medical device <NUM> of the image <NUM> to predict and determine the identified medical device 108A.

In block <NUM>, the software application tool <NUM> determines whether the identified medical device <NUM> is an acceptable identification of the medical device <NUM> through, for example, instructions to make such a determination executed by the processor <NUM>. As described above, the identification algorithm 312A may use one or more prediction confidence probabilities indicative of a confidence level associated with identification of the identified medical device 108A such that the matched type of medical device <NUM> is assigned a confidence level as a prediction confidence probability that must be greater than or equal to the confidence threshold value prior to being set as the identified medical device 108A.

As an additional or alternative non-limiting example, a user is presented with an option on the GUI <NUM> on the display screen <NUM> of the smart mobile device <NUM> of whether to accept the identified medical device 108A as acceptable or not. In at least one embodiment, the processor <NUM> is configured to execute one or more instructions to present the user with the option and receive a selection of the user whether to accept or reject the identification of the identified medical device 108A. In response to user rejection of the option to accept the identified medical device 108A, the software application tool <NUM> may be configured to capture another image <NUM> of the medical device <NUM> through use of the camera <NUM> to repeat the steps in blocks <NUM> and <NUM> until the user accepts the option to accept the identified medical device 108A. Alternatively, the software application tool <NUM> may be configured to repeat the steps in blocks <NUM> and <NUM> and present the user with an option to accept another identified medical device 108A until the user accepts the option. The software application tool <NUM> may further be configured to process the misclassified image <NUM> in an image database through, for example, use of the processor <NUM> to execute instruction steps as described herein to retrain a convolutional neural network such that the convolutional neural network is more likely to correctly identify the medical device <NUM> in a future application of the identification algorithm 312A. In at least one embodiment, in response to user rejection of the option to accept the identified medical device 108A, the software application tool <NUM> may be configured to display a warning that the medical device <NUM> in the image <NUM> is an unrecognized medical device type.

In response to acceptance by the user of the option to accept the identified medical device 108A, the process <NUM> proceeds to block <NUM>. In block <NUM>, one or more requirements of the identified medical device 108A are retrieved, including setup content. For example, the software application tool <NUM> on the smart mobile device <NUM> is automatically configured to retrieve data associated with one or more requirements of the identified medical device as retrieved data including at least setup content through, for example, one or more instructions executed by the processor <NUM>. In at least one embodiment, the retrieved data may include country specific settings such as date and time display, metric units, and the like and update the software application tool <NUM> and/or identified medical device 108A with such settings. Further, menu options may be created and/or populated in the software application tool <NUM> associated with the identified medical device 108A and may include a list of compatible test strips, usage of test strips, pairing instructions such as whether manual or automatic, and the like. As described above, the software application tool <NUM> includes a GUI <NUM> on the display screen <NUM> of the smart mobile device <NUM>. At least a portion of the retrieved data may be displayed to the user on the GUI <NUM> on the display screen <NUM> of the smart mobile device <NUM>. The portion of the retrieved data may be displayed during setup of the software application tool <NUM> based on the identified medical device 108A and/or during use of the software application tool <NUM> to monitor the identified medical device 108A after pairing through, for example, one or more instructions executed by the processor <NUM>.

For example, in block <NUM>, retrieved data such as the setup content is utilized to pair the identified medical device 108A with the smart mobile device <NUM>. The software application tool <NUM>, for example, is paired with the identified medical device 108A through, for example, one or more instructions executed by the processor <NUM> and based on the setup content such that the smart mobile device <NUM> is communicatively coupled to the identified medical device 108A. In block <NUM>, activity of the paired identified medical device 108A is monitored with the smart mobile device <NUM>. As a non-limiting example, the software application tool <NUM> of the smart mobile device <NUM> is configured to monitor as a monitored activity the identified medical device 108A through, for example, one or more instructions executed by the processor <NUM>. The monitored activity may include an administration of a prescribed treatment regime for the user through use of the identified medical device to administer the prescribed treatment regime.

In embodiments, the software application tool <NUM> may be configured to provide an alert on the GUI <NUM> and to the user of a failure in the administration of the prescribed treatment regime based on the monitored activity of the identified medical device by the software application tool <NUM>. As a non-limiting example, the alert may be an audio, visual, and/or tactile alert provided to the user through the smart mobile device <NUM> upon detection of the failure in the administration of the prescribed treatment regime.

In medical device data manager configuration systems described herein, a software application tool <NUM> communicatively coupled to a smart mobile device <NUM> is configured to apply an identification algorithm 312A such as an image recognition algorithm through an identification tool component <NUM> to an image <NUM> captured by the smart mobile device <NUM>. The software application tool <NUM> is thus able to automatically identify an identified medical device 108A through use of the identification algorithm 312A and a database <NUM> such as, for example, an image database on the cloud <NUM> or other database storage location. Such automatic identification streamlines a process to identify the medical device <NUM> as an identified medical device <NUM> to pair with the smart mobile device <NUM> by not requiring user selection, for example, of the medical device <NUM> from a listing of options, for example.

Such an automated data configuration system streamlines and more accurately and effectively adapts digital or data management solutions from a data manager such as the software application tool <NUM> to the identified medical device 108A on demand while minimizing dependencies on user involvement and know-how. Based on acceptance by the software application tool <NUM> through either an acceptable confidence value of the identification as described herein for automated acceptance or through user input of an acceptance of the identification of the presented identified medical device 108A, the software application tool <NUM> is configured to automatically retrieve data associated with the identified medical device 108A and to pair the identified medical device 108A with the smart mobile device <NUM>. The user may then utilize the smart mobile device <NUM> to monitor activity of the identified medical device 108A such as use of the identified medical device 108A to administer a prescribed treatment regime to the user and user adherence to the prescribed treatment regime through use of the identified medical device 108A.

A medical device data manager configuration system including a medical device, a smart mobile device including a camera, a processor, a memory communicatively coupled to the processor, and machine readable instructions stored in the memory that cause the medical device data manager configuration system to perform at least the following when executed by the processor: machine readable instructions stored in the memory that cause the medical device data manager configuration system to perform at least the following when executed by the processor; apply an identification algorithm to the image of the medical device; identify the medical device as an identified medical device based on the image of the medical device and the identification algorithm; and automatically configure a software application tool on the smart mobile device to retrieve data associated with one or more requirements of the identified medical device.

The machine readable instructions may further comprise instructions to display the one or more requirements of the identified medical device on a graphical user interface (GUI) of the smart mobile device.

The one or more requirements of the identified medical device may comprise content specific to the specific to the identified medical device, the content comprises at least one of onboarding content, communication management instructions, educational materials, regulatory labeling content, and one or more menu options.

The education materials may comprise educational content associated with the identified medical device providing during a setup associated with the identified medical device.

The educational content may comprise at least one of information about a compatible test strip, information about use of the compatible test strip with the identified medical device, and information about related calibration testing procedures.

The machine readable instructions may further comprise instructions to pair the smart mobile device and the identified medical device.

The machine readable instructions may further comprise instructions to automatically provide device specific pairing instructional information to a user regarding pairing prior to pairing the smart mobile device and the identified medical device.

The machine readable instructions may further comprise instructions to at least one of automatically perform firmware version checks and install firmware updates associated with the identified medical device.

The identification algorithm may comprise at least one of reading of a QR code and a serial number of the medical device.

The identification algorithm may comprise an image recognition algorithm.

The image recognition algorithm may be configured to utilize a trained convolutional neural network, the trained convolutional neural network configured to identify objects within an image to a high-level of accuracy.

The trained convolutional neural network and associated computations may be stored in the smart mobile device and an image database may be stored in a cloud networking environment, the trained convolutional neural network configured to be pre-trained on a subset of the image database.

The smart mobile device may be configured to serve as a conduit to transfer the image to a cloud server of a cloud networking environment, and the cloud server may be configured to store an image database and a convolutional network that may be configured to interact with the identification algorithm to identify the medical device based on the image.

One or more identification calculations associated with the identification algorithm may be conducted in the cloud networking environment, and class information associated with the identified medical device may be transmitted to the smart mobile device from the cloud networking environment.

The software application tool may be configured to display a reference frame on a display screen of the smart mobile device, the reference frame configured to identify an area to position the medical device within prior to image capture by the camera of the smart mobile device.

A method of operating a medical device data manager configuration system, including capturing an image of a medical device through a camera on a smart mobile device; applying an identification algorithm to the image of the medical device; identifying the medical device as an identified medical device based on the image of the medical device and the identification algorithm; automatically configuring a software application tool on the smart mobile device to retrieve data associated with one or more requirements of the identified medical device as retrieved data, wherein the software application tool comprises a GUI on a display screen of the smart mobile device; pairing the software application tool with the identified medical device based on the retrieved data such that the smart mobile device is communicatively coupled to the identified medical device; monitoring, as a monitored activity of the identified medical device by the software application tool, an administration of a prescribed treatment regime for the user through use of the identified medical device to administer the prescribed treatment regime; and providing an alert on the GUI and to the user of a failure in the administration of the prescribed treatment regime based on the monitored activity of the identified medical device by the software application tool.

The method may further include presenting an option to the user to accept the identified medical device, and automatically configuring a software application tool on the smart mobile device to retrieve the retrieved data in response to acceptance by the user of the option to accept of the identified medical device.

The method may further include displaying to a user at least a portion of the retrieved data on the GUI on the display screen of the smart mobile device to inform the user of one or more requirements of the identified medical device.

A method of operating a medical device data manager configuration system, including capturing an image of a medical device through a camera on a smart mobile device; applying an identification algorithm to the image of the medical device; identifying the medical device as an identified medical device based on the image of the medical device and the identification algorithm; presenting an option to the user to accept the identified medical device; in response to acceptance by the user of the option to accept of the identified medical device, automatically configuring a software application tool on the smart mobile device to retrieve data associated with one or more requirements of the identified medical device as retrieved data including at least setup content; pairing the software application tool with the identified medical device based on the setup content such that the smart mobile device is communicatively coupled to the identified medical device; and monitoring as a monitored activity the identified medical device by the software application tool of the smart mobile device.

The monitored activity may comprise an administration of a prescribed treatment regime for the user through use of the identified medical device.

The method may include the medical device data manager configuration system.

A processor for a medical device data manager configuration system including a medical device and a smart mobile device including a camera, the processor configured to execute machine readable instructions stored in a memory communicatively coupled to the processor to perform at least the following; use the camera of the smart mobile device to capture an image of the medical device; apply an identification algorithm to the image of the medical device; identify the medical device as an identified medical device based on the image of the medical device and the identification algorithm; and automatically configure a software application tool on the smart mobile device to retrieve data associated with one or more requirements of the identified medical device.

The smart mobile device may be communicatively coupled to the processor.

The medical device data manager configuration system may be communicatively coupled to the processor.

The processor may include the medical device data manager configuration system.

It is noted that recitations herein of a component of the present disclosure being "configured" or "programmed" in a particular way, to embody a particular property, or to function in a particular manner, are structural recitations, as opposed to recitations of intended use. More specifically, the references herein to the manner in which a component is "configured" or "programmed" denotes an existing physical condition of the component and, as such, is to be taken as a definite recitation of the structural characteristics of the component.

It is noted that the terms "substantially" and "about" and "approximately" may be utilized herein to represent the inherent degree of uncertainty that may be attributed to any quantitative comparison, value, measurement, or other representation. These terms are also utilized herein to represent the degree by which a quantitative representation may vary from a stated reference without resulting in a change in the basic function of the subject matter at issue.

Claim 1:
A medical device data manager configuration system comprising:
- a smart mobile device including a camera;
- a medical device being pairable with the smart mobile device;
- a processor;
- a memory communicatively coupled to the processor; and
- machine readable instructions stored in the memory that cause the medical device data manager configuration system to perform at least the following when executed by the processor:
- use the camera of the smart mobile device to capture an image of the medical device;
- apply an identification algorithm to the image of the medical device, wherein the identification algorithm comprises an image recognition algorithm configured to utilize a trained convolutional neural network which is configured to identify objects within an image to a high-level of accuracy;
- identify the medical device as an identified medical device based on the image of the medical device and the identification algorithm; and
- automatically configure a software application tool on the smart mobile device to retrieve data associated with one or more requirements of the identified medical device.