Smart container

Abstract

A system and method for detecting the presence of a specimen in a container. The container includes an image capture device configured to capture an image of the contents of the container, and a communication module configured to transmit the captured image to a remote server. The remote server processes the image using a machine vision model to generate a confidence level indicating the likelihood that a specimen is present in the container. The confidence level is compared to a predetermined threshold to determine whether to generate a specimen presence signal. The specimen presence signal is associated with a container identifier representing a physical address or location of the container and is transmitted to a user application or customer system. The user application may perform actions such as routing instructions, inventory updates, or chain-of-custody updates based on the specimen presence signal.

Description

[0001] SMART CONTAINER

[0002] FIELD OF THE INVENTION

[0003] The present invention relates to a smart container that can determine the presence or absence of items placed within the smart container and can transmit information regarding the presence or absence of items across a network.

[0004] BACKGROUND

[0005] In a health-related practice, such as a medical practice, hospital or pathology laboratory or similar, there are a multiple number of patient samples, such as tissue samples, blood samples, being moved internally from one area to another and from one location to another. For example, within the hospital setting, a patient under examination in one part of the hospital may have a blood sample taken for analysis. The blood is withdrawn from the patient direct into a blood sample tube, or similar. The blood sample tube is appropriately marked with relevant patient information, such as a unique indica that has been assigned to the patient, usually in the form of a bar code or other machine readable medium. RFID tags have also been utilised.

[0006] Similarly, if a surgical biopsy is required, the excised sample is placed in an appropriate biopsy sample tube, and also marked with relevant patient information.

[0007] In some cases, a group of sample tubes may be collated and placed within a sample bag, the sample bag also being appropriately marked with relevant patient information, such as a unique indica that has been assigned to the patient, usually in the form of a bar code or other machine readable medium.

[0008] The tubes are then placed in a container that is marked specifically for a clinical testing laboratory, which may be a clinical testing laboratory in the hospital where the patient is located, or more commonly to an external clinical / pathology testing laboratory located separately to the hospital where bulk testing procedures take place. The external clinical testing laboratory may process many thousands of samples per day. Some sample tubes must be suitably marked and recorded to track the contents, such as is the case for a biopsy tissue sample. However, the process of tracking multiple patient samples, such as blood or biopsy samples, across potentially multiple locations, including remote locations, with some samples being more important than others, or more sensitive to degradation, is complicated to implement and time intensive to manage and maintain accurate up-to-date information as to the location of a sample tube at any one time, as well the sample tubes processing state (has it been picked up for processing) or any estimated time of completion of processing.

[0009] Typically, a log book approach is used where a user manually enters or scans each sample tube / bag in a database or log book at a first location close to where the sample is taken. The sample tube / bag is then conveyed to a suitable transport vehicle, if the clinical testing laboratory is remote, where at a time when several sample tubes / bags have been collected, are then transported by the vehicle to the remote clinical testing laboratory. At times, the transporting vehicle may visit other separate locations to collect additional samples.

[0010] Upon arrival at the remote clinical testing laboratory the sample tubes / bags are manually entered or scanned in a database, usually referred to as a Lab Information System (LIS) or alternatively, a log book, and then directed to the appropriate testing area specific to the clinical test required. Upon completion of the clinical test the date is collected and recorded into the database record specific for the patient and the results collated and sent back to the remote hospital location.

[0011] Similarly, if the clinical testing laboratory is located with the hospital, the collected sample tubes / bag must also be collected from potentially multiple locations throughout the hospital before finally being taken to the internal clinical testing laboratory. Once again, the sample tubes / bags are either manually entered or scanned in a database or log book, and carried to the appropriate testing station for analysis. Results recorded from the analysis are then recorded into the database record specific for the patient, those results then being made available to the appropriate clinician.

[0012] In many cases, a particular testing laboratory may not have sufficient or appropriate equipment or human resources to provide all the requisite tests. In this case all or a subsection of the sample may be transported to another Testing Laboratory. In this scenario with sample leaves the remit of the LIS, and no information is available as to its status or whereabouts until it is processed at the receiving laboratory, and entered into the same distributed LIS, or a new independent LIS.

[0013] The process / method of collection, recording and transporting of sample tubes / bags has a number of disadvantageous, such as a lack of suitably trained personel working in a busy environment that can spend the sufficient time to process and monitor each of the sample tubes / bags coming in from multiple locations, with possible multiple testing destinations. Degradation of biological samples within the sample tubes / bags, either brought about through extended exposure to temperature, shock or light. Lack of adequate tracking of the location of sample tube / bag either within the hospital or between hospital and a remote clinical testing laboratory.

[0014] As used in the description herein and throughout the claims that follow, the meaning of “a,” “an,” and “the” includes plural reference unless the context clearly dictates otherwise. Also, as used in the description herein, the meaning of “in” includes “in” and “on” unless the context clearly dictates otherwise.

[0015] Explicitly, Tube, tub, pot or other common nomenclature for primary receptacles for pathology specimens, or any container of similar purpose, may be used interchangeably in the description herein and throughout the claims that follow. Further the term Global Positioning System (GPS) is used interchangeably with cell tower triangulation, wifi or Bluetooth based, or similar location services that provide positional information of various accuracy. Specimen bag is used interchangeably with sample bag, and specimen is used interchangeably with sample. Driver is used interchangeably with courier, and it is understood that this is in the general sense, that the courier / driver may be performing their duties without the use of a vehicle, and that the driver / courier could be replaced at any point with an autonomous system such as a drone.

[0016] Groupings of alternative elements or embodiments of the subject matter described herein disclosed herein are not to be construed as limitations. Each group member can be referred to and claimed individually or in any combination with other members of the group or other elements found herein. One or more members of a group can be included in, or deleted from, a group for reasons of convenience and / or patentability. When any such inclusion or deletion occurs, the specification is herein deemed to contain the group as modified thus fulfilling the written description of all Markush groups used in the appended claims.

[0017] SUMMARY OF THE INVENTION

[0018] In the following description, certain aspects and embodiments will become evident. It should be understood that the aspects and embodiments, in their broadest sense, could be practiced without having one or more features of these aspects and embodiments. It should be understood that these aspects and embodiments are merely exemplary.

[0019] The term “scan” as used herein is used interchangeable with image capture, where a device can scan an item or capture an image of an item and identify areas that contain machine readable indicia or code, such as, but not limited to, a bar code, QR code, V2 QR code, or a combination thereof, and read that machine readable indicia or code.

[0020] In general, the disclosure describes a smart container for use within a wireless network to provide verification information of the location of a sample tube / bag placed within the smart container.

[0021] In one aspect of the present invention there is a smart container including a container body for storing one or more items, at least one digital cam era / scanner in a housing positioned or located within the container body, at least one digital camera / scanner, communication circuitry configured to communicate directly to the cloud, a computing device including a processor and a memory having computer executable instructions that, when executed by the processor, cause the processor to transmit data indicative of the one or more items stored in the container body to at least one of the one or more receivers via the communication circuitry to facilitate delivery of the data indicative of the one or more items stored in the container body.

[0022] In a further aspect of the present invention, there is a smart container comprising:

[0023] (a) a receptacle portion configured to receive a specimen;

[0024] (b) an image capture device integrated with the container and positioned to capture an image of the contents of the receptacle portion; (c) a communication module configured to transmit the captured image to a remote server;

[0025] (d) the remote server comprising a machine vision model configured to analyse the captured image and generate an output indicating a confidence level of the presence of a specimen in the image;

[0026] (e) a threshold module configured to compare the confidence level to a predetermined threshold and to generate a specimen presence signal when the confidence level exceeds the threshold;

[0027] (f) a container identifier comprising a physical address or location associated with the container;

[0028] (g) wherein the container identifier is associated with the captured image and / or the specimen presence signal; and

[0029] (h) wherein the specimen presence signal and associated container identifier are configured to be transmitted to a user application for routing purposes or to be forwarded to customer software or a customer database.

[0030] In certain aspects, the specimen bag includes at least one of a biological sample, blood sample, pathology sample, or pharmaceutical product.

[0031] In certain aspects, the physical address includes at least one of a physical address data, geolocation coordinates and / or facility identifier.

[0032] In some aspects, the smart container includes one or more sensors located in the sensing module.

[0033] In preference, the one or more sensors include a movement sensor.

[0034] A further aspect of the present invention is a system when used for tracking a biological sample tube / bags, that includes having at least one central database configured to receive and store information relating to a biological sample tube / bag, wherein each biological sample tube / bag that is to be processed is associated with at least one unique identifier, for example a machine-readable code, the at least one central database being configured to communicate with a workstation and at least one smart container, the workstations being at least one of a shipping workstation, receiving workstation, management workstations, the at least one smart container being configured to read the least one unique identifier and to communicate to the central database the current location of the biological sample tube / bag access, from the central database, information relating to a biological sample tube / bag, the at least one workstation being configured to read the least one unique identifier and to access, from the central database, the information relating to the biological sample tube / bag.

[0035] A further aspect of the invention includes a remote hand-held device with a digital camera / scanner, having a display screen, such as a smart phone, configured to read the at least one unique identifier and to communicate to the central database the current location of the biological sample tube / bag access, from the central database, information relating to a biological sample tube / bag.

[0036] The machine-readable code relating to the biological specimen contained within the biological sample tube / bag can be physically applied to an outer surface of the biological sample tube / bag either in advance (pre-labelled specimen bag) of the biological sample being taken from the patient or at the time the specimen is collected from the patient.

[0037] Alternatively, the label may be applied at some point after the biological sample has been taken from the patient.

[0038] Once a unique machine-readable code is associated with a particular biological sample tube / bag registration of the biological sample tube / bag is conducted and the unique machine- readable code with unique identifier remain with the biological sample tube / bag, and information, such as the current location of the biological sample tube / bag, the date and time of scanning, and which user has scanned the sample, is recorded. For example, at collection or collation of the biological sample tubes / bags, the unique machine-readable code is recorded with the location as being “Collection Point 1”, which may correlate to a location within a hospital, such as a courier pick up location, or with greater precision being the point at where sample was collected, for example: “Level 3, Ward B, patient room #101”, at a particular date and time, eg. 11 : 13am, and by unique user identifier such as employee number 32456.

[0039] In certain aspects of the present invention, the handheld scanner such as a smart phone, may automatically capture an image of the sample during or adjacent to the scanning process that records the unique indicator, for the purposes of chain of custody validation, and problem resolution.

[0040] In certain aspects of the present invention, the biological sample tube / bag with unique identifier at “Collection Point 1”, are then collected by a courier equipped with a hand-held device, such as a smart phone or other device having a digital camera or scanner, and the biological sample tube / bag with unique identifier is taken from the smart container and scanned.

[0041] In certain aspects of the present invention, the hand-held devices periodically record the date, time and position of the device, and therefore the samples that are being transported with that user or unique encapsulating container.

[0042] In some aspects of the present invention, the system further includes the capacity to associate the sample / bag indictors with an external encapsulating container with its own unique indicator, that may contain one or many samples / bags for easier data management during transit for internal or external road or airfreight movements.

[0043] In other aspects of the present invention, the smart container is in communication, remotely by way of a wireless communications device to a processing computer, such as a remote processing computer, for example cloud computing using SaaS (software as a service) in the cloud, or contained within an onboard processor contained within smart container or sensor module, wherein the processing computer incorporates machine vision processing adapted to determine, for example, the presence or absence of a biological sample tube / bag within the smart container, the number and or volume estimate of biological sample tube / bags within the smart container. The remote processing computer may in turn be in automated communication with participant LIS system, or a system of similar purpose, to exchange information on individual samples and batches of samples. The remote processing computer may also be in automated communication with remote processing computers of third party freight providers, including airline freight cargo departments, to facilitate automated booking and tracking of road and airfreight, via information exchange with their databases, in such cases that these third party services are used to move samples from point of collection to processing laboratory, or from one processing laboratory to another, or, finally from a processing laboratory back to a hospital or other such location.

[0044] In some aspects of the present invention, the system further includes image capture data analysis protocols, in which an image captured by the digital camera is recorded and analysis protocols applied to the captured image to identify an at least one unique identifier, for example a machine-readable code, on the biological sample tube / bag and applying an image blurring protocol to all areas of the bag apart from the at least one unique identifier.

[0045] Further embodiments are directed towards a system for detecting the presence of a specimen in a container

[0046] In certain embodiments, the system for detecting the presence of a specimen in a container, the system comprising: a. a container comprising: i. (i) a container body configured to receive and store one or more specimens or items; ii. an image capture device integrated with or positioned within the container body and configured to capture an image of contents of the container body; iii. a communication module configured to transmit the captured image to a remote server; b. the remote server comprising one or more processors and a memory storing computer-executable instructions which, when executed, cause the server to: i. receive the captured image from the container; ii. process the captured image using a machine vision model to generate a confidence level indicating a likelihood that a specimen is present in the container; iii. compare the confidence level to a predetermined threshold; iv. generate a specimen presence signal when the confidence level exceeds the predetermined threshold; v. associate a container identifier representing a physical address or location of the container with the specimen presence signal; and vi. transmit the specimen presence signal and associated container identifier to a user application; c. a user application configured to receive the specimen presence signal and associated container identifier from the remote server, and to initiate a routing instruction or update a customer software system or database based on the specimen presence signal.

[0047] BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 is an example of a smart container of the present invention;

[0049] Figure 2 is an example of a scanning module located within the smart container of the present invention;

[0050] Figure 3 illustrates a system of monitoring items using a smart container in accordance with the principles of the present invention;

[0051] Figure 4 and figure 5 is a flow chart illustrating steps for monitoring items using a smart container in accordance with the principles of the present invention.

[0052] DESCRIPTION

[0053] The disclosure will be described more fully hereinafter with reference to the accompanying drawings, in which example embodiments of the disclosure are shown. This disclosure may, however, be embodied in many different forms and should not be construed as limited to the example embodiments set forth herein. It will be apparent to persons skilled in the relevant art that various changes in form and detail can be made to various embodiments without departing from the spirit and scope of the present disclosure. Thus, the breadth and scope of the present disclosure should not be limited by any of the above-described example embodiments but should be defined only in accordance with the following claims and their equivalents. The description below has been presented for the purposes of illustration and is not intended to be exhaustive or to be limited to the precise form disclosed. Alternate implementations may be used in any combination to form additional hybrid implementations of the present disclosure. For example, any of the functionality described with respect to a particular device / component may be performed by another device / component. Further, while specific device characteristics have been described, embodiments of the disclosure may relate to numerous other device characteristics. Further, although embodiments have been described in language specific to structural features and / or methodological acts, it is to be understood that the disclosure is not necessarily limited to the specific features or acts described. Rather, the specific features and acts are disclosed as illustrative forms of implementing the embodiments.

[0054] As used in the description and throughout the claims that follow, when a system, engine, server, device, module, or other computing element is described as configured to perform or execute functions on data in a memory, the meaning of “configured to” or “programmed to” is defined as one or more processors or cores of the computing element being programmed by a set of software instructions stored in the memory of the computing element to execute the set of functions on target data or data objects stored in the memory.

[0055] In an embodiment of the invention being a smart container 10, having a container body 20 with an inner container area 30, defined by the container side walls (21, 22, 23, 24), and container bottom 25, in which is located an image capture device 40. In this embodiment the image capture device is located nested in a corner of the inner container area (receptacle portion) 30, however, in other embodiments the image capture device 40 may be incorporated in a side wall (21, 22, 23, 24) or attached by, for example, a clip or similar mounting mechanism, to a side wall.

[0056] The image capture device 40 has a main body 50 with a front side 51, base 52, sides 53, 54, and top 55. The front side 51 has an angled face section 60, on which there in an opening 65 through which projects a lens 70 of the image capture device. In some embodiments the image capture device is a dedicated optical scanning device, incorporating scanning computer hardware and software. Such an image capture device may be a digital camera device capable of taking images of the contents of the inner container area (receptacle portion) 30 of the smart container 10. The image capture device further includes a communications module, with communication circuitry configured to communicate with one or more receivers, and a remote cloud computing device that includes a processor and a memory having computer executable instructions. Data from the image capture device 40 is relayed to the communications module where it can be transmitted externally to a network, via Wi-Fi, LTE or other similar wireless communication, to a remote location for processing. A “network” is defined as one or more data links that enable the transport of electronic data between computer systems and / or modules and / or other electronic devices.

[0057] In the present embodiment of the image capture device 40, an antenna 75 is located on the top 55 for wireless transmission of data produced from the scanning module. The image capture device 40 also includes a power source to provide power to the various components contained within. The power source for the image capture device 40 may be removable for charging separately to the main body of the scanning module.

[0058] The image capture device 40 can also optionally include one or more sensor elements to detect when an item is placed into, or removed from, the inner container area 30. Such sensor elements able to detect movement of items within a localised predefined proximity of the container body 20. Alternatively, or in conjunction, the sensor may be programmed to scan items, or capture images of items, in the interior container are 30 at predetermined times intervals. For example, the processor controlling the scanning or image capture process may initiate a scan or image capture at 10, 22 30 second, 5 minute intervals.

[0059] As shown in figure 3, a biological sample tube / bag 100, may have a unique serial number or unique machine-readable indicia, 110 such as, but limited to, a bar code, QR code, V2 QR code, or a combination thereof, affixed to it. The unique serial number or unique machine- readable indicia 100 may be generated from a database and correlates to a specific patient, specific patient data, process, number or similar.

[0060] In certain embodiments, the label 110 may comprise of two or more severable duplicate stickers that encode the unique serial number. One of these severable stickers is placed on a patient form, and another may be placed in a logbook that remains at the collection point. This final severable sticker can be located in the logbook in such a way as to unambiguously associate it with the patient and episode details recorded there. This sticker may include a machine vision marker, so that the scanning step can be performed on the logbook rather than the sample bag, particularly in cases where the samples bags may be encapsulated in another external container prior to transport. Alternatively, a logbook, which has a label printed with a unique serial number in human readable format, that is peelable from the logbook and may be placed on an un-labelled specimen bag. In addition, the label includes a severable copy of the human readable format of the serial number that may be placed on the patient form, and another copy of the human readable format of the serial number that is left behind in the logbook to associate the unique collection serial number with the patient details recorded in the logbook. A machine vision format may also be left behind in the logbook to allow the scanning step to be performed on the logbook rather than on the sample bag, particularly in cases where the sample bags have been encapsulated in another container prior to transport.

[0061] The biological sample tube / bag 100 is taken to a collection point where there is the smart container 10, with an image capture device 40, the biological sample tube / bag 100 then placed into the interior (receptacle) container area 30.

[0062] The image capture device 40, captures an image of the biological sample tube / bag 100 with the label 110 and at the same time software in the image capture device can record date, time and location such as location data (provided either by internal GPS or preprogrammed location, for example “Collection Point 1”). The location and date is stored with the image data biological sample tube / bag 100, collectively and relayed to an internal communications module within the image capture device 40 and then transmitted wirelessly 120 via antenna 75 to a remote receiving station 130, or direct to a network. The network can include any one, or a combination of networks, such as a local area network, a wide area network, a telephone network, a cellular mobile network, a cable network, a wireless network, or a public / private network (Internet). Such networks may support communication technologies, such as TCP / IP, Bluetooth, and near field communication (NFC). Figure 3 shows the wireless receiver 130 having connections to a local area network (LAN) 135 with remote terminals, a cloud network (140) with a cloud hosted database 150 for processing, storage, analysis using specific user software applications, a mobile devicel60 (smart phone), or remote display 170, such as a wall mounted display screen. Software applications on a mobile computing device (smartphone) 160, provide periodic access to location data normally acquired by the smartphone and additionally access to the camera on the smartphone.

[0063] Data associated with the biological sample tube / bag 100 produced by the smart container 10 can be displayed on a track board, for example, the mobile devicel60 (smart phone), or remote display 170, providing information unique to the biological sample tube / bag 100, such as, for example, the location history of scanning events for the biological sample tube / bag 100, current estimate or actual location, scan times, indented destination, estimated time of arrival at intended destination, for example a pathology or histology laboratory “Laboratory Point 1”.

[0064] Referring to figure 4, an exemplary method / system 200 for monitoring biological sample tube / bag 100 is described. At step 205, a biological sample is obtained from a patient and placed in a sample tube / bag and a label with unique serial number presented in a human readable and a standard machine vision format (eg. barcode or QR code, in preference, a V2 QR code.) is applied to it. At step 210, the labelled sample tube / bag is placed into the smart container which is detected by the smart container and software instructs the image capture device to take an image of the labelled sample tube / bag. At step 230, the captured image is sent via the communication module to a connected network, remote processing unit, for example the cloud network 140, for processing where a processing unit analyses the captured image and calculates a confidence level of presence of a biological sample within the captured image. The processing unit then determines, based on the confidence level, if a biological sample is present, via a threshold module, that compares the calculated confidence level from the captured image against a predetermined threshold level. If the calculated confidence level is equal to or greater than the predetermined threshold level then a specimen presence signal is generated and this information (presence or absence of biological sample, location information etc) is transmitted (to a third-party system, for example a courier / transport database system or pathology related service provider (such as a laboratory preforming analysis of biological samples) or a heath related service provider (health care centre, hospital, surgery, etc). The processing unit is hosted in the cloud, running a custom trained ResNetl8 model which outputs a tensor value between 0-1 for both present and not present summing to 1. A value for present over the configurable threshold deems a courier pick up necessary.

[0065] At step 220 personnel in the health care centre, for example, may utilise a handheld device, such as an enabled smart phone, to determine the status of processing of a labelled sample tube / bag that had been previously placed in a smart container 10 , and / or its present location, and / or estimated time of arrival at a predetermined designation, for example a pathology related service provider. A processor in the image capture device 40 processes the unique data for the sample tube / bag and a communication module transmits the unique data via antenna 75 wirelessly to the receiving station 130 which can then be directed to a connected network, for example the cloud network 140, where the unique data can be, for example, processed 250 and at step 260 displayed on a track board on a display 170.

[0066] A courier, tasked with transporting the sample tube / bag from the collection point to a suitable laboratory for processing, then retrieves the sample tube / bag from the smart container, figure 5, step 270, and using a handheld device, such as a smart phone running suitable software, scans the labelled sample tube / bag. Information regarding courier scan time and location is then recorded, step 280 and transmitted to the cloud 140 to be processed in the cloud hosted database 150 for processing, storage, analysis using specific software applications. The handheld device has an internal location services module that provides periodic GPS, cell tower triangulation, or similar information regarding the location of the smartphone, step 290, which is periodically transmitted by a network to the cloud, step 300. On the cloud hosted database 150 processing of GPS data can occur, providing further information regarding estimated arrival time of courier to next destination, which may be another collection point where additional items may be retrieved, or to an intended destination, such as a laboratory, where the collected biological samples are to be tested / examined. In some circumstances, the captured GPS data can be used for optimizing a courier route to provide the courier with an efficient route to their next intended destination.

[0067] A courier, via their handheld device 160 may be either provided via an installed user application, or combination of user applications, or internet connection providing access to a cloud server, SaaS, or closed independent network, with a pre-determined route from a start location to a point of collection or a series of points of collection, with subsequent delivery to a point of processing. In some embodiments, the courier is provided, via the handheld device 160, an optimized route to collect only from points of collection that are known to have specimens ready from collection, via signals sent from a smart container to cloud server, SaaS, or closed independent network, or in part in complete alternative, information provided via other methods, such as phone calls, texts, emails, or PC or smart phone based applications, that may indicate that a specimen is ready for collection at each location.

[0068] At step 310, the captured GPS data, after processing at step 300, can be displayed on a tracking board displayed on a remote display 170, where a user can observe the last GPS location of the courier with the labelled sample tube / bag, intended arrival time / date and the next intended location, along with a status indication, such as “delivery pending”, “delivery completed”, “delivery delayed”. Such status indications are achieved in whole by in some instances, and enhanced or corroborated by in other instances, utilisation of geofences, so that arrival at specific airports, intermediary labs or hubs or other locations of interest can be provided as a status update.

[0069] When the courier arrives 320 at the laboratory destination, where the collected biological samples are to be tested / examined, the labelled sample tube / bag is scanned 330 by a scanning device at the intended destination, to provide arrival information, such as current location, current time, unique data on the label of the sample tube / bag, which is transmitted to a network and displayed on the track board on a display 170. In the case that the sample were not associated with sample bags, and / or sample bags were not associated with encapsulating containers, before transit, this may be done by the same scanning process applied at the laboratory destination, and the information used to retroactively associate a chain of custody and location history with individual samples. The display 170 may be a Human Machine Interface (HMI) that may be a wall mounted screen, or one or more other standard HMI terminals that pathology staff receiving the episodes for processing may see information on what has been located and its status including current location of the sample tube / bag / encapsulating transit container.

[0070] The courier may be a contracted courier service, a courier service provided by a testing laboratory that provides biological sample testing services, or an internal courier in a health care facility, such as a hospital. A courier may be provided the handheld device 170, such as a smart phone with an application, or combination of applications, installed on a standard smartphone, that has periodic access to the accurate and coarse location data normally acquired by the smartphone and additionally access to the camera on that smartphone, and may be either provided via the handheld device with a pre-determined route from a start location to a point of collection or a series of points of collection, with subsequent delivery to a point of processing, intended destination such as a biological sample testing location, or, an optimized route to collect only from collection that are known to have specimens ready from collection, where such data has been collected at collection points, via signals sent from smart containers 10, or from scanning events via the handheld scanner by hospital staff, and collated and processed via cloud computing 140 or in part in complete alternative, information provided via other methods, such as phone calls, texts, emails, or PC or smart phone based applications, that may indicate that a specimen is ready for collection at each location.

[0071] The present invention describes a system for moving pathology specimens from points of collection to a point of processing in a mathematically optimal manner and in such a way that also ensures chain of custody of the specimens, allowing rapid identification if an expected specimen has not arrived for processing.

[0072] Machine vision processing that may or may not incorporate machine learning may be utilised by the smart container 10, incorporated in the scanning module 40 or via cloud computing 140 in order to determine whether or not a specimen is present at the collection point, and how many specimens are available for collection. The machine vision processing may also attempt to extract information from visible machine vision or human readable markings in the optical images collected by the smart container.

[0073] The application or applications running on the courier drivers device may also require the driver to manually input the number of containers. The internet connected computing system 140 calculates, by way of solving the travelling salesman problem from a distance matrix (traffic aware travel time based) formed by the set of all collection points with specimens ready for collection and the processing point (end point), the mathematically optimal way to collect all specimens and get them to the processing point. The distance matrix or the solution to the distance matrix may be stored, to reduce repeated computations and network load. The system may use the stored solution to the distance matrix and the associated travel time and compare it to the current traffic aware travel time for the same route. If there is a difference in the two times exceeding a pre-determined value, then the distance matrix may be regenerated internally or by a third party, and the solution to the travelling salesman problem recomputed.

[0074] The courier may simply pick up the specimens that were included in the smart containers at the known collection points, or, in preference, will use the labelled biological sample tube / bag or, in some instances, the logbook, to connect together the specimen bag that contains the specimens, the log book, and the patient form that is included with the specimen bag. Where the labelled biological sample tube / bag or logbook is used, then the courier uses the handheld device provided to the courier to capture an optical image of the machine vision presentation of the unique serial number applied to the specimen bag, at time of collection, the courier drivers device then sends this information including the image (with patient details automatically blurred or degraded) to the internet connected computing system 140. The courier driver repeats the image collection and data transmission for every specimen bag collected at every location and then returns to the processing point with the collected specimen bags.

[0075] At time of capture of optical image of the machine vision label, the system may, with machine vision processing on the courier drivers device or the internet connected computing system 140attempt to estimate the number of specimens contained inside the specimen bag and their type. The system may also attempt to use text recognition to retrieve patient details or any other useful information included on the sample containers or specimen bag. Alternatively, the applications in the courier drivers device may require the driver to manually input the number of specimens contained in the specimen bag, for transmission of that information to the internet connected computing system 140.

[0076] In addition, the specimen bags may have one or many specimens enclosed, and each of the enclosed specimens may be in tubes or tubs that have their own unique serial numbers or machine vision labels attached. The specimen bags themselves, may be enclosed further in an encapsulating container for transport, with its own unique serial number or machine vision label attached. These may also be scanned in as part of the process and, may alternatively be directly used in place of the scanning of specimen bags as an intermediary. The system may or may not allow the driver to use the courier drivers device to take an optical image of the logbook or patient form to additionally allow patient details to be associated with the specimen bag unique serial number in the system, or as a substitute for the scanning of the specimen bags or tubes directly, in such a case as when the specimens and specimen bags are already encapsulated in a transport container at the time of collection. Finally, these nested associations can be done at time of receipt and processing at the destination lab, to retrospectively associate chain of custody history data to individual specimens or episodes (collection of individual specimen tubes from a single collection event).

[0077] While the courier is working through collection points, the track board on the display 170 is updated each time a new specimen bag is scanned and may display updated information on the current location of the driver, how many specimen bags and individual specimens have been collected at each collection point and in aggregate, and, estimated time of arrival of the driver and their collected specimens at the processing point. The track board on the display 170 may also display a map view showing the location of all inbound specimens and specimen bags, including work routed via third party services. The track board on the display 170 may also display aggregate information on current inbound work and predicted workload at the processing point.

[0078] In certain embodiments, if the internet connected computing system 140 receives data from any smart container 10 at a collection point, while the courier is in transit between collection points or a collection point and the processing point, the optimal route will be recomputed, and, if the diversion results in delays to the delivery of the already collected specimens that are within pre-determined tolerances, then the courier will be dynamically re-routed to include the new collection point in their route before returning to the processing point.

[0079] Upon arrival at the processing point, the courier hands directly or indirectly to processing staff or processing automation equipment. The processing staff scan every specimen bag machine vision label with a separate scanning device, or another method such as a standard barcode scanner attached to a terminal located on the network 135 or in communication with the cloud 140. As each specimen bag is scanned, the track board on the display 170 is updated to display a reduced number of specimen bags (and or specimens) in route from that driver. If the courier driver is late, or almost arrived, visual feedback is provided by the track board on the display 170. Additionally, if the courier registered as arrived by a geofence or other mechanism such as the scanning of any of the specimen bag unique serial numbers collected by the driver, and some portion of the specimen bags or contained specimens have not been scanned by the staff or automated equipment after a pre-determined period of time, then visual feedback will be provided to the staff at the processing point by the track board on the display 170. Once all specimen bag unique serial numbers associated with a single courier are scanned, then the entry on the track board on the display 170 for that courier driver is removed. The system may only supply an entry for a courier driver once the first specimen bag is scanned in a sequence of collections (that may only be 1 collection point) via individual machine vision codes on the enclosed specimens, or by a manual count, the number of contained specimens in each specimen is checked off against the number of contained specimens recorded by the system at time of collection. Similar to the case where a deficit of specimen bags has been recorded as arriving at the collection point, a deficit of contained specimens may block the removal of an entry from the track board on the display 170 and trigger supplementary alerts and alarms. Supplementary information such as the pending workload for pickup, map views of all available specimens for pickup and all work in transit, and predictive workload arrival volumes based on calculated courier driver times and estimated travel times, may be supplied via similar HMI’s to the trackboard.

[0080] If patient details were or were not associated with the unique specimen bag serial number at time of collection from a collection point, then, at time of receipt and scanning at the processing point, patient details may now be associated with the unique specimen bag’s serial number, and it’s contained specimen’s serial number at this point. Conversely, the specimen bag unique serial number may be associated with the LIS or an independent system or database, to permanently record chain of custody from collection point to processing point, at the least, via automated communication with over the internet 140 or private network.

[0081] The role of the courier in the described method may be replaced by a drone or other nonhuman system.

[0082] Those skilled in the art will appreciate that the present disclosure may be practiced in network computing environments with many types of computer system configurations, including in-dash vehicle computers, personal computers, desktop computers, laptop computers, message processors, handheld devices, multi-processor systems, microprocessorbased or programmable consumer electronics, network PCs, minicomputers, mainframe computers, mobile telephones, PDAs, tablets, pagers, routers, switches, various storage devices, etc. The disclosure may also be practiced in distributed system environments where local and remote computer systems, which are linked (either by hardwired data links, and / or wireless data links) through a network, such a LAN or cloud network.

[0083] Further, where appropriate, the functions described herein may be performed in one or more of hardware, software, firmware, digital components, or analogue components. This may include application specific integrated circuits (ASICs) that are programmed to carry out one or more of the systems and procedures described.

[0084] Conventional specimen handling systems rely on manual inspection, which are prone to errors, labour-intensive, and difficult to scale. The present system and device improves automation, reliability, and scalability by employing image-based specimen detection combined with thresholding and dynamic routing.

[0085] In certain embodiments, the user application is configured to perform one or more automated actions in response to receiving the specimen presence signal and associated container identifier. The actions may include generating a routing instruction for a delivery or collection vehicle; updating an inventory management system; triggering a billing event; updating a chain-of-custody record associated with the container; generating an audit log recording the specimen presence signal, container identifier, and a timestamp; and providing a notification to a user. Such automated actions can facilitate efficient logistics, inventory tracking, billing, and regulatory compliance in systems employing the described smart container and remote processing architecture.

[0086] In certain embodiments, a predetermined threshold is employed to assess whether the confidence level generated by the machine vision model is sufficient to indicate the presence of a specimen within the container. The threshold may be adjustable by a user or system administrator, may be dynamically updated based on historical data or system feedback, or may be stored in a database accessible to the remote server. In some embodiments, multiple thresholds may be defined, enabling different actions to be triggered depending on the level of confidence (for example, low, medium, or high certainty of specimen presence). The use of adjustable and dynamic thresholds enables the system to be tuned for different applications, specimen types, or operational environments, thereby enhancing accuracy and adaptability.

[0087] Features of the invention include a smart container comprising: a. a receptacle portion configured to receive a specimen; b. an image capture device integrated with the container and positioned to capture an image of the contents of the receptacle portion; c. a communication module configured to transmit the captured image to a remote server; d. the remote server comprising a machine vision model configured to analyse the captured image and generate an output indicating a confidence level of the presence of a specimen in the image; e. a threshold module configured to compare the confidence level to a predetermined threshold and to generate a specimen presence signal when the confidence level exceeds the threshold; f. a container identifier comprising a physical address or location associated with the container; g. wherein the container identifier is associated with the captured image and / or the specimen presence signal; and h. wherein the specimen presence signal and associated container identifier are configured to be transmitted to a user application for routing purposes or to be forwarded to customer software or a customer database.

[0088] The smart container comprising: o a container body configured to receive and store one or more items or specimens; o an image capture device positioned within or integrated with the container body and configured to capture an image of contents of the container body; o a communication module configured to transmit the captured image to a remote server; o a computing device comprising a processor and a memory storing computerexecutable instructions which, when executed by the processor, cause the processor to:

[0089] ■ transmit the captured image to the remote server via the communication module;

[0090] ■ receive from the remote server a confidence level indicating a likelihood that a specimen or item is present in the container; ■ compare the received confidence level to a predetermined threshold; and

[0091] ■ generate a specimen presence signal when the confidence level exceeds the predetermined threshold; o a container identifier representing a physical address or location associated with the container, the container identifier being associated with the specimen presence signal; and o wherein the specimen presence signal and associated container identifier are configured to be transmitted to a user application or customer system to facilitate routing, tracking, or inventory management.

[0092] In some forms, the smart container includes one or more sensors located in the sensing module.

[0093] In some forms the one or more sensors modules are contained within a housing positioned or located within the container body. In some forms the one or more sensors include a movement sensor.

[0094] There is also a method of detecting the presence of a specimen in a container, the method comprising:

[0095] • capturing an image of contents of a container using an image capture device integrated with the container;

[0096] • transmitting the captured image to a remote server via a communication module;

[0097] • processing the captured image using a machine vision model hosted on the remote server to generate a confidence level indicating a likelihood that a specimen is present in the container;

[0098] • comparing the confidence level to a predetermined threshold;

[0099] • generating a specimen presence signal when the confidence level exceeds the threshold;

[0100] • associating a container identifier representing a physical address or location of the container with the captured image and / or the specimen presence signal; and transmitting the specimen presence signal and associated container identifier to a user application or a customer software system or database for further processing.

[0101] In some forms, the method includes where the container is a smart container as previously described.

[0102] There is also a system for detecting the presence of a specimen in a container, the system comprising:

[0103] • a container comprising: i. a container body configured to receive and store one or more specimens or items; ii. an image capture device integrated with or positioned within the container body and configured to capture an image of contents of the container body; iii. a communication module configured to transmit the captured image to a remote server;

[0104] • the remote server comprising one or more processors and a memory storing computer-executable instructions which, when executed, cause the server to: i. receive the captured image from the container; ii. process the captured image using a machine vision model to generate a confidence level indicating a likelihood that a specimen is present in the container; iii. compare the confidence level to a predetermined threshold; iv. generate a specimen presence signal when the confidence level exceeds the predetermined threshold; v. associate a container identifier representing a physical address or location of the container with the specimen presence signal; and vi. transmit the specimen presence signal and associated container identifier to a user application;

[0105] • a user application configured to receive the specimen presence signal and associated container identifier from the remote server, and to initiate a routing instruction or update a customer software system or database based on the specimen presence signal.

[0106] The present system, method and smart container achieves a technical effect by integrating an image capture device into a container, transmitting image data to a remote server for processing by a machine vision model optimised for the container geometry and specimen types, and applying thresholding logic to generate a robust, machine-implementable specimen presence signal. This signal is used to trigger physical -world actions such as routing instructions, inventory updates, and chain-of-custody records, thereby improving the automation, reliability, and throughput of specimen handling systems. The use of dynamic thresholding and model-based image analysis enables adaptation to varying environmental and operational conditions, further enhancing the robustness of the system. As a result, the invention improves the operation of physical logistics and specimen handling systems.

[0107] The description has been presented for the purposes of illustration and description. It is not intended to be exhaustive or to limit the present disclosure to the precise form disclosed. Many modifications and variations are possible in light of the above teaching. Further, it should be noted that any or all of the aforementioned alternate implementations may be used in any combination desired to form additional hybrid implementations of the present disclosure. For example, any of the functionality described with respect to a particular device or component may be performed by another device or component. Further, while specific device characteristics have been described, embodiments of the disclosure may relate to numerous other device characteristics. Further, although embodiments have been described in language specific to structural features and / or methodological acts, it is to be understood that the disclosure is not necessarily limited to the specific features or acts described. Rather, the specific features and acts are disclosed as illustrative forms of implementing the embodiments.

Claims

CLAIMS1. A smart container comprising: a. a receptacle portion configured to receive a specimen; b. an image capture device integrated with the container and positioned to capture an image of the contents of the receptacle portion; c. a communication module configured to transmit the captured image to a remote server; d. the remote server comprising a machine vision model configured to analyse the captured image and generate an output indicating a confidence level of the presence of a specimen in the image; e. a threshold module configured to compare the confidence level to a predetermined threshold and to generate a specimen presence signal when the confidence level exceeds the threshold; f. a container identifier comprising a physical address or location associated with the container; g. wherein the container identifier is associated with the captured image and / or the specimen presence signal; and h. wherein the specimen presence signal and associated container identifier are configured to be transmitted to a user application for routing purposes or to be forwarded to customer software or a customer database.

2. A smart container comprising: a. a container body configured to receive and store one or more items or specimens; b. an image capture device positioned within or integrated with the container body and configured to capture an image of contents of the container body; c. a communication module configured to transmit the captured image to a remote server; d. a computing device comprising a processor and a memory storing computerexecutable instructions which, when executed by the processor, cause the processor to: i. transmit the captured image to the remote server via the communication module;ii. receive from the remote server a confidence level indicating a likelihood that a specimen or item is present in the container; iii. compare the received confidence level to a predetermined threshold; and iv. generate a specimen presence signal when the confidence level exceeds the predetermined threshold; e. a container identifier representing a physical address or location associated with the container, the container identifier being associated with the specimen presence signal; and f. wherein the specimen presence signal and associated container identifier are configured to be transmitted to a user application or customer system to facilitate routing, tracking, or inventory management.

3. The smart container of claim 1, wherein the smart container includes one or more sensors located in the sensing module.

4. The smart container of any one of the above claims, wherein the one or more sensors modules are contained within a housing positioned or located within the container body.

5. The smart container of any one of the above claims, wherein the one or more sensors include a movement sensor.

6. A method of detecting the presence of a specimen in a container, the method comprising: a. capturing an image of contents of a container using an image capture device integrated with the container; b. transmitting the captured image to a remote server via a communication module; c. processing the captured image using a machine vision model hosted on the remote server to generate a confidence level indicating a likelihood that a specimen is present in the container; d. comparing the confidence level to a predetermined threshold;e. generating a specimen presence signal when the confidence level exceeds the threshold; f. associating a container identifier representing a physical address or location of the container with the captured image and / or the specimen presence signal; and g. transmitting the specimen presence signal and associated container identifier to a user application or a customer software system or database for further processing.

7. The method of claim 6, wherein the container is a smart container of any one of claims 1-5.

8. A system for detecting the presence of a specimen in a container, the system comprising: a. a container comprising: i. a container body configured to receive and store one or more specimens or items; ii. an image capture device integrated with or positioned within the container body and configured to capture an image of contents of the container body; iii. a communication module configured to transmit the captured image to a remote server; b. the remote server comprising one or more processors and a memory storing computer-executable instructions which, when executed, cause the server to: i. receive the captured image from the container; ii. process the captured image using a machine vision model to generate a confidence level indicating a likelihood that a specimen is present in the container; iii. compare the confidence level to a predetermined threshold; iv. generate a specimen presence signal when the confidence level exceeds the predetermined threshold; v. associate a container identifier representing a physical address or location of the container with the specimen presence signal; andvi. transmit the specimen presence signal and associated container identifier to a user application; c. a user application configured to receive the specimen presence signal and associated container identifier from the remote server, and to initiate a routing instruction or update a customer software system or database based on the specimen presence signal.

9. The system of claim 8, wherein the user application is configured to generate a routing instruction for a delivery or collection vehicle based on the specimen presence signal.

10. The system of claim 8, wherein the user application is configured to trigger a billing event based on the specimen presence signal.

11. The system of claim 8, wherein the user application is configured to update a chain- of-custody record associated with the container.

12. The system of claim 8, wherein the user application generates an audit log recording the specimen presence signal, associated container identifier, and a timestamp.

13. The system of claim 8, wherein the user application is configured to provide a notification to a user when the specimen presence signal is received.

Images