FIELD OF THE DISCLOSURE

The present disclosure relates generally to apparatuses, systems, and methods for testing collected samples, and in particular to cloud-based apparatuses, systems, and methods for testing collected samples in online and offline conditions.

BACKGROUND

In determining the safety of the drinking water provided to the residents, or the quality of the wastewater that is released to the environment, real-time and error-free data generated from microbiological tests has the most important role in the decision making process. However, the conventional practice in this water testing and other sample-testing areas has drawbacks such as:

• testing data is not real-time since current standard tests take at least 24 hours to produce the data; and/or

• testing data is not error-free since current standard protocols heavily relies on manual transcription and communication of data.

For example, the conventional data-management process in the microbiological water testing industry follows the following steps:

• A user goes to the location of the sampling, fills the sampling bottle, and manually fills a paper-based Chain of Custody (COC) which contains information about the location, the date and time of sampling, the type of sample, and the name of the user.

• The sample and the COC is sent to a laboratory.

• After the laboratory finishes the analysis, the results are sent to the end-users.

Such a process has two main issues:

• The manual transcription of the data on the paper-based COC forms is subject to malpractice and human errors. For example, human errors in transcription of the sampling time and date may result in the rejection of the sample by the laboratory and requires resampling. A recent study showed that this process of manually transcribing sample information increases the chances of error by 33% due to mistaken information, illegible hand writing, and/or the like.

• Manual entry of the results in a database is prone to errors. It is also prone to data loss since all data about one sample is generated/stored in different location and with different tools.

The conventional data-management process may also have other drawbacks such as: • It often relies on a costly testing machine to collect and associate the sample information (such as the COC thereol) with the testing results; when such a testing machine is unavailable (which is often the case when, for example, using a low-cost rapid testing kit for sample testing in remote locations), it usually a burden to the users to ensure the association of the sample information and the testing results.

• While it is often required to repeated collect and test samples of a target area, conventional practice uses manual selection of sampling locations in a target area which, however, is not optimized to cover a well represented geographical spread. Generally, it is usually difficult to optimize the dates/times, frequencies, and locations for collecting sample in the area for testing.

SUMMARY

According to one aspect of this disclosure, there is provided a first computerized method comprising: obtaining a first identifier (ID) from a sampling container; recording a sampling timestamp; recording a sampling location; obtaining a second ID from a sample-testing device; and generating a record using the first and second IDs, the sampling timestamp, and the sampling location.

In some embodiments, said obtaining the first ID from the sampling container comprises: sensing a first machine-readable code on the sampling container to obtain the first ID.

In some embodiments, said recording the sampling timestamp comprises: recording the sampling timestamp based on a date and time of said sensing; and said recording the sampling location comprises: recording the sampling location based on a location of said sensing.

In some embodiments, said obtaining the second ID from the sample-testing device comprises: sensing a second machine-readable code on the sample-testing device to obtain the second ID.

In some embodiments, the first ID represents a sample in the sampling container.

In some embodiments, the second ID represents a sample testing conducted by the sampletesting device.

In some embodiments, the record is an electronic chain of custody (eCOC) record.

In some embodiments, at least one of the first and second ID is encoded in a quick response (QR) code, a barcode, a text, a radio frequency identification (RFID) tag, or a near field communication (NFC) tag.

In some embodiments, the first computerized method further comprises: transmitting the record to a server computer via a network. According to one aspect of this disclosure, there is provided a computing device comprising: a memory; and one or more processors for executing instructions stored in the memory for performing the first computerized method.

According to one aspect of this disclosure, there is provided one or more non-transitory computer-readable storage devices comprising computer-executable instructions, wherein the instructions, when executed, cause a processing structure to perform the first computerized method.

According to one aspect of this disclosure, there is provided a second computerized method comprising: receiving from a computing device a record comprising a fust ID, a second ID, a sampling timestamp, and a sampling location; receiving from a sample-testing device data comprising a testing result and a third ID; and associating the record and the resting result if the second ID is same as the third ID.

In some embodiments, the first ID represents a sample.

In some embodiments, the second ID represents a first sample testing.

In some embodiments, wherein the third ID represents a second sample testing conducted by the sample-testing device.

In some embodiments, the record is an eCOC record.

In some embodiments, at least one of the first, second, and third machine-readable codes is a QR code, a barcode, a text, a RFID tag, or a NFC tag.

In some embodiments, the second computerized method further comprises: storing the record and the testing result in a database.

According to one aspect of this disclosure, there is provided a computing device comprising: a memory; and one or more processors for executing instructions stored in the memory for performing the second computerized method.

According to one aspect of this disclosure, there is provided one or more non-transitory computer-readable storage devices comprising computer-executable instructions, wherein the instructions, when executed, cause a processing structure to perform the second computerized method.

According to one aspect of this disclosure, there is provided a third computerized method comprising: determining one or more sampling locations using one or more artificial intelligence (Al) models and based on historical sample -related data.

In some embodiments, the third computerized method further comprises: determining one or more sampling dates and times using the one or more Al models and based on the historical sample -related data.

In some embodiments, the historical sample-related data comprises at least one of: historical sampling locations in a target area; access to the historical sampling locations; historical sampling dates and times; population of one or more zones of the target area; information one or more subarea of the target area in proximity to potential contamination sources; historical testing results; and a map of the target area.

In some embodiments, the historical testing results comprises at least one of: distribution of historical samples; and severity of positive tests in one or more of the historical sampling locations.

According to one aspect of this disclosure, there is provided a computing device comprising: a memory; and one or more processors for executing instructions stored in the memory for performing the third computerized method.

According to one aspect of this disclosure, there is provided one or more non-transitory computer-readable storage devices comprising computer-executable instructions, wherein the instructions, when executed, cause a processing structure to perform the third computerized method.

According to one aspect of this disclosure, there is provided a fourth computerized method comprising: sensing a machine-readable code on a sampling container to obtain an ID; recording a sampling timestamp; recording a sampling location; receiving testing results from a sampletesting device; and generating a record using the ID, the sampling timestamp, and the sampling location.

In some embodiments, said recording the sampling timestamp comprises: recording the sampling timestamp based on a date and time of said sensing; and said recording the sampling location comprises: recording the sampling location based on a location of said sensing.

In some embodiments, the ID represents a sample in the sampling container.

In some embodiments, the record is an eCOC record.

In some embodiments, the machine -readable code is a QR code, a barcode, a text, a RFID tag, or a NFC tag.

In some embodiments, the fourth computerized method further comprises: transmitting the record to a server computer via a network.

According to one aspect of this disclosure, there is provided a computing device comprising: a memory; and one or more processors for executing instructions stored in the memory for performing the fourth computerized method.

According to one aspect of this disclosure, there is provided one or more non-transitory computer-readable storage devices comprising computer-executable instructions, wherein the instructions, when executed, cause a processing structure to perform the fourth computerized method. BRIEF DESCRIPTION OF THE DRAWINGS

For a more complete understanding of the disclosure, reference is made to the following description and accompanying drawings, in which:

FIG. 1 is a schematic diagram of a computer network system for sample testing, according to some embodiments of the present disclosure;

FIG. 2 is a schematic diagram showing a simplified hardware structure of a computing device of the computer network system shown in FIG. 1;

FIG. 3A is a schematic perspective view of a sample-testing device of the computer network system shown in FIG. 1;

FIG. 3B is a schematic perspective view of a receptacle of the sample-testing device shown in FIG. 3A;

FIG. 3C is a schematic perspective view of a disposable cartridge engageable with the receptacle shown in FIG. 3B;

FIG. 3D is a schematic diagram showing the circuit structure of the sample-testing device shown in FIG. 3 A;

FIG. 4 is a schematic diagram showing a simplified software architecture of a computing device of the computer network system shown in FIG. 1;

FIG. 5 is a schematic perspective view of a sampling container for receiving a sample to be tested by the sample-testing device shown in FIG. 3A;

FIG. 6 is a flowchart showing a sample-testing process performed by the computer network system shown in FIG. 1, according to some embodiments of this disclosure;

FIG. 7 is a screenshot of a home page displayed in a client-computing device of the computer network system shown in FIG. 1 after the user of the client-computing device has signed-in to the computer network system; and

FIG. 8 is a schematic diagram of an artificial intelligence (Al) model used by the computer network system shown in FIG. 1, according to some embodiments of this disclosure.

DETAILED DESCRIPTION

Embodiments herein disclose cloud-based apparatuses, systems, and methods for collecting and/or testing of samples such as water samples of a target area.

In some embodiments, a sample-testing system comprises one or more cloud-based databases, one or more portable or mobile client-computing devices with cameras or camera modules (such as one or more smartphones with camera modules), and one or more sample-testing devices for testing collected samples (such as testing bacteria in collected samples or conducting microbiological water tests) and storing the test results in the cloud-based database. The sample-testing system disclosed herein and the apparatuses and methods thereof unify the data management process of the sample test by for example, alleviating burden of human operations in the process and reducing or eliminating human errors therein.

Moreover, the sample-testing system and the apparatuses and methods thereof allow rapid testing in an online condition for example, when the sample -testing device is connected to the cloud-based database via a network, and further allow rapid testing in an offline condition for example, when the sample-testing device is not connected to the cloud-based database.

In various embodiments, the sample-testing system and the apparatuses and methods thereof simplify the process of associating sample information with the testing results, and/or optimize the scheduling (such as dates/times, frequencies, locations, and/or the like) for collecting sample in the area for testing. Herein, the terms “optimize” and “optimization” refer to the process with the results better than those of one or more conventional methods in one or more aspects.

Turning now to FIG. 1, a computer network system for collecting and/or testing of samples such as water samples of a target area is shown and is generally identified using reference numeral 100. As shown, the computer network system 100 comprises one or more server computers 102, one or more client-computing devices 104, and one or more sample-testing devices 106 such as the VeloCens™ (“VeloCens” is a trademark of Roshan Water Solutions Incorporated of Edmonton, AB, Canada) testing device offered by the Applicant of the subject application, functionally interconnected by a network 108 such as the Internet, a local area network (LAN), a wide area network (W AN), a metropolitan area network (MAN), and/or the like, via suitable wired and wireless networking connections.

The server computers 102 may be computing devices designed specifically for use as a server, and/or general-purpose computing devices acting as server computers while also being used by various users. Each server computer 102 may execute one or more server programs.

The client-computing devices 104 may be portable and/or non-portable computing devices such as laptop computers, tablets, smartphones, Personal Digital Assistants (PDAs), desktop computers, and/or the like. Each client-computing device 104 may execute one or more client application programs which sometimes may be called “apps”.

Generally, the computing devices 102 and 104 have a similar hardware structure such as a hardware structure 120 shown in FIG. 2. As shown, the computing device 102/104 comprises a processing structure 122, a controlling structure 124, one or more non-transitory computer- readable memory or storage devices 126, a network interface 128, an input interface 130, and an output interface 132, functionally interconnected by a system bus 138. The computing device 102/104 may also comprise other components 134 coupled to the system bus 138. The processing structure 122 may be one or more single-core or multiple-core computing processors such as INTEL® microprocessors (INTEL is a registered trademark of Intel Corp., Santa Clara, CA, USA), AMD® microprocessors (AMD is a registered trademark of Advanced Micro Devices Inc., Sunnyvale, CA, USA), ARM® microprocessors (ARM is a registered trademark of Arm Ltd., Cambridge, UK) manufactured by a variety of manufactures such as Qualcomm of San Diego, California, USA, under the ARM® architecture, and/or the like. When the processing structure 122 comprises a plurality of processors, the processors thereof may collaborate via a specialized circuit such as a specialized bus or via the system bus 138.

The processing structure 122 may also comprise one or more real-time processors, programmable logic controllers (PLCs), microcontroller units (MCUs), u-contro Ilers (UCs), specialized/customized processors and/or controllers using, for example, field-programmable gate array (FPGA) or application-specific integrated circuit (ASIC) technologies, and/or the like.

Generally, each processor of the processing structure 122 comprises necessary circuitries implemented using technologies such as electrical and/or optical hardware components for executing one or more processes as the implementation purpose and/or the use case maybe, to perform various tasks. In many embodiments, the one or more processes may be implemented as firmware and/or software of a plurality of instructions stored in the memory 126. Those skilled in the art will appreciate that, in these embodiments, the one or more processors of the processing structure 122, are usually of no use without meaningful firmware and/or software.

The controlling structure 124 comprises one or more controlling circuits, such as graphic controllers, input/output chipsets, and the like, for coordinating operations of various hardware components and modules of the computing device 102/104.

The memory 126 comprises one or more non-transitory computer-readable storage devices or media accessible by the processing structure 122 and the controlling structure 124 for reading and/or storing instructions for the processing structure 122 to execute, and for reading and/or storing data, including input data and data generated by the processing structure 122 and the controlling structure 124. The memory 126 may be volatile and/or non-volatile, non-removable or removable memory such as RAM, ROM, EEPROM, solid-state memory, hard disks, CD, DVD, flash memory, or the like. In use, the memory 126 is generally divided into a plurality of portions for different use purposes. For example, a portion of the memory 126 may be used as a storage memory for long-term data storing, for example, for storing files or databases. Another portion of the memory 126 may be used as a working memory for storing data during operation.

The network interface 128 comprises one or more network modules for connecting to other computing devices or networks through the network 108 by using suitable wired or wireless communication technologies such as Ethernet, WI-FI® (WI-FI is a registered trademark of Wi-Fi Alliance, Austin, TX, USA), BLUETOOTH® (BLUETOOTH is a registered trademark of Bluetooth Sig Inc., Kirkland, WA, USA), Bluetooth Low Energy (BLE), Z-Wave, Long Range (LoRa), ZIGBEE® (ZIGBEE is a registered trademark of ZigBee Alliance Corp., San Ramon, CA, USA), wireless broadband communication technologies such as Global System for Mobile Communications (GSM), Code Division Multiple Access (CDMA), Universal Mobile Telecommunications System (UMTS), Worldwide Interoperability for Microwave Access (WiMAX), CDMA2000, Long Term Evolution (LTE), 3GPP, 5G New Radio (5G NR) and/or other 5G networks, and/or the like. In some embodiments, parallel ports, serial ports, USB connections, optical connections, or the like may also be used for connecting other computing devices or networks although they are usually considered as input/output interfaces for connecting input/output devices.

The input interface 130 comprises one or more input modules for one or more users to input data via, for example, touch-sensitive screens, touch-sensitive whiteboards, touch-pads, keyboards, computer nice, trackballs, microphones, scanners, cameras, and/or the like. The input interface 130 may be a physically integrated part of the computing device 102/104 (for example, the touch-pad of a laptop computer or the touch-sensitive screen of a tablet), or may be a device physically separated from but functionally coupled to, other components of the computing device 102/104 (for example, a computer mouse). The input interface 130, in some implementation, may be integrated with a display output to form a touch-sensitive screen or a touch-sensitive whiteboard.

The output interface 132 comprises one or more output modules for output data to a user. Examples of the output modules include displays (such as monitors, LCD displays, LED displays, projectors, and the like), speakers, printers, virtual reality (VR) headsets, augmented reality (AR) goggles, and/or the like. The output interface 132 may be a physically integrated part of the computing device 102/104 (for example, the display of a laptop computer or a tablet), or may be a device physically separate from but functionally coupled to other components of the computing device 102/104 (for example, the monitor of a desktop computer).

The computing device 102/104 may also comprise other components 134 such as one or more positioning modules, camera modules, temperature sensors, barometers, inertial measurement units (IMUs), and/or the like.

In these embodiments, at least one of the client-computing devices 104 may be in the form of a smartphone or a tablet and comprise one or more positioning modules and one or more camera modules. Examples of the positioning modules may be one or more global navigation satellite system (GNSS) components (for example, one or more components for operation with the Global Positioning System (GPS) of USA, Global'naya Navigatsionnaya Sputnikovaya Sistema (GLONASS) of Russia, the Galileo positioning system of the European Union, and/or the Beidou system of China).

The system bus 138 interconnects various components 122 to 134 enabling them to transmit and receive data and control signals to and from each other.

The detail of an example of the one or more sample-testing devices 106 is disclosed in the PCT Patent Application Ser. No. PCT/CA2020/050593 filed on May 04, 2020, the content of which is incorporated herein by reference in its entirety. In some embodiments, the sample-testing device 106 uses a light source to illuminate a sample such as a liquid sample and uses potentiostat circuit for testing the sample.

For example, FIG. 3 A shows a sample-testing device 106 according to some embodiments of this disclosure. The sample-testing device 106 comprises a casing 142 having one or more testing units 144 and a display 146 thereon.

Each testing unit 144 has an openable lid and receiving there a respective receptacle 150. As shown in FIG. 3B, the receptacle 150 comprises an interface 152 (for example, in the form of an opening) for receiving a terminal end 170 of a disposable sampling cartridge 162 (see FIG. 3C). The interface 152 comprises a plurality of electrode connecters 154 such as a working-electrode (WE) connecter, a counter-electrode (CE) connecter, and a reference-electrode (RE) connecter for demountably engaging corresponding electrodes (such as a WE, a CE, and a RE) on the terminal end 170 of the disposable sampling cartridge 162.

As shown in FIG. 3C, the disposable sampling cartridge 162 comprises a body 164 and an outward-extending terminal end 166. The body 164 comprises a sample-receiving area 168 for receiving thereon a sample such as a water sample of about 0.4 milliliter (ml) to 0.5 ml. The terminal end 166 comprises a plurality of electrodes 170 such as a WE, a CE, and a RE. A circuit (not shown) connects the electrodes 170 to the sample-receiving area 168.

FIG. 3D shows the circuit structure of the sample-testing device 106. As shown, the sample-testing device 106 comprises a potentiostat circuit 182 connected to the electrode connectors 154 of each receptacle 150, a light source 184 such as a light-emitting diode (LED) light source, the display 146, a network module or circuit 188 (such as a Raspberry Pi module for executing software and/or firmware programs such as the user interface program, sample information collection program, data transmission program, and the like.) for connecting to the network 108 or directly to a client-computing device 104 via wired or wireless means, and a control circuit 186 (which in these embodiments also comprises a power module for powering various components such as the display 146, the potentiostat circuit 182, the LED 184, the network module 188, and the like) connecting to the potentiostat circuit 182, the light source 184, the display 146, and the network module 188. Of course, the sample-testing device 106 in various embodiments may also comprise other components such as a USB connector (not shown) for connecting to, for example, a client-computing device 104.

To test a sample, the sample is first applied to the sample-receiving area 168 of a disposable sampling cartridge 162, and the terminal end 166 of the disposable sampling cartridge 162 is extended into the receptacle 150 to engage the electrodes 170 on the terminal end 166 with the corresponding electrode connectors 154 in the receptacle 150. The control circuit 186 then controls the light source 184 to illuminate the sample in the sample-receiving area 168 of the disposable sampling cartridge 162, and controls the potentiostat circuit 182 to test the sample via the electrode connectors 154 and the electrodes 170.

The operational status of the sample-testing device 106 and the test results may be displayed on the display 146. The test results may also be transmitted to the server computer 102 via the network module 188 and the network 108.

Referring back to FIG. 1, a use may use a sample-testing device 106 to test collected samples and transmit the testing results to the server computer 102 via the network 108. As part of the testing process, the user may also use a client-computing device 104 such as a smartphone or a tablet to record information related to the collected samples and the testing thereof. As those skilled in the art will appreciate, the server computer 102 and client-computing device 104 execute suitable software programs for collaborating with the sample-testing device 106 to test the samples, storing testing results, and performing other related functions (such as analyzing testing results, inquiry testing and analytic results, and/or the like).

FIG. 4 shows a simplified software architecture 200 of the computing device 102 or 104. The software architecture 200 comprises an application layer 202, an operating system 204, a logical input/output (I/O) interface 206, and a logical memory 208. The application layer 202, operating system 204, and logical I/O interface 206 are generally implemented as computerexecutable instructions or code in the form of software programs or firmware programs stored in the logical memory 208 which may be executed by the processing structure 122.

The application layer 202 comprises one or more application programs 212 executed by or performed by the processing structure 122 for performing various tasks.

The operating system 204 manages various hardware components of the computing device 102 or 104 via the logical I/O interface 206, manages the logical memory 208, and manages and supports the application programs 212. The operating system 204 is also in communication with other computing devices (not shown) via the network 108 to allow the application programs 212 to communicate with programs running on other computing devices. As those skilled in the art will appreciate, the operating system 204 may be any suitable operating system such as MICROSOFT® WINDOWS® (MICROSOFT and WINDOWS are registered trademarks of the Microsoft Corp., Redmond, WA, USA), APPLE® OS X, APPLE® iOS (APPLE is a registered trademark of Apple Inc., Cupertino, CA, USA), Linux, ANDROID® (ANDROID is a registered trademark of Google Inc., Mountain View, CA, USA), or the like. The computing devices 102 and 104 of the computer network system 100 may all have the same operating system, or may have different operating systems.

The logical I/O interface 206 comprises one or more device drivers 214 for communicating with respective input and output interfaces 130 and 132 for receiving data therefrom and sending data thereto. Received data may be sent to the application layer 202 for being processed by one or more application programs 212. Data generated by the application programs 212 may be sent to the logical I/O interface 214 for outputting to various output devices (via the output interface 132).

The logical memory 208 is a logical mapping of the physical memory 126 for facilitating the application programs 212 to access. In this embodiment, the logical memory 208 comprises a storage memory area that may be mapped to a non-volatile physical memory such as hard disks, solid-state disks, flash drives, and the like, generally for long-term data storage therein. The logical memory 208 also comprises a working memory area that is generally mapped to high-speed, and in some implementations, volatile physical memory such as RAM, generally for application programs 212 to temporarily store data during program execution. For example, an application program 212 may load data from the storage memory area into the working memory area, and may store data generated during its execution into the working memory area. The application program 212 may also store some data into the storage memory area as required or in response to a user’s command.

In a server computer 102, the application layer 202 generally comprises one or more server-side application programs 212 which provide(s) server functions for managing network communication with client-computing devices 104 and facilitating collaboration between the server computer 102 and the client-computing devices 104. Herein, the term “server” may refer to a server computer 102 from a hardware point of view, or to a logical server from a software point of view, depending on the context.

In these embodiments, the server computer 102 also comprises a database for storing testing results and related data.

On the client side, the client-computing device 104 comprises a client-side application program 212 such as a sample information collection app using web-technologies and running on the iOS and/or Android® operation systems for collecting necessary information of samples to be tested and sent to the server computer 102 and the sample-testing device 106 for testing collected samples. A process for testing a collected sample is now described. In these embodiments, the collected sample is received in a sampling container 240 such as a sampling bottle shown in FIG. 5, wherein the sampling container 240 comprises a quick response (QR) code 242 (also denoted a “sample QR code” hereinafter) encoding therein a unique identifier (ID) of the sampling container 240 which may be used to represent the ID of the sample in the sampling container 240 (also denoted a “sample ID” hereinafter).

FIG. 6 is a flowchart showing the sample-testing process 300. As shown, the process 300 starts (step 302) when a user collects a sample such as a water sample into a sampling container 240 and launches the sample information collection app in the user’s a client-computing device 104 (such as the user of a smartphone or a tablet), for example, by tapping on the icon of the sample information collection app on the screen of the smartphone 104. At step 304, the user operates the sample information collection app to use the camera of the smartphone 104 to scan or otherwise optically sense the sample QR code 242 of the sampling container 240. The sample information collection app then decodes or otherwise obtains the ID of the sampling container 240 and use it as the sample ID.

At step 306, the sample information collection app automatically records essential information of the sample. For example, the sample information collection app may record the user’s name as the sampler name, record the “current” date and time (that is, the date and time of scanning the sample QR code 242 of the sampling container 240) as the sampling timestamp of the sample, and record the “current” location (obtained using the positioning module of the smartphone 104) as the sampling location of the sample.

At step 308, the user uses the sample information collection app to input sampling information such as the type of sample (for example, drinking water, surface water, treated sewage, or the like) and may input comments and/or observations such as the smell or color of the sample, residual chlorine (if measured onsite by other tools), and/or the like. The sample information collection app then generates an electronic chain of custody (eCOC) record (such as an eCOC form) using the sample ID and the information obtained at steps 306 and 308, and stores the eCOC record locally in the smartphone 104. The information obtained at steps 306 and 308 is thus associated with the sample ID and subsequently with the collected sample.

At step 310, the user operates the sample information collection app to use the camera of the smartphone 104 to scan or otherwise optically sense a QR code of the sample-testing device 106 (denoted a “testing QR code” hereinafter) such as a QR code shown on the display 146 thereof, printed on the body 142 thereof, or printed on a label on the body 142 thereof.

More specifically, the user uses the supplies in a test kit to process the sample in the sampling container 240 such as reducing the volume of the sample to between 0.4 ml and 0.5 ml by pouring the sample into a large syringe (equipped with a syringe filter), pushing the water through the filter to capture any bacteria, and re-suspending the bacteria into 0.4 ml to 0.5 ml of reagent by sucking back the liquid reagent from its specific container (in the test kit) through the syringe filter, and/or the like. The processed sample is then transferred to the sampling area 168 of a disposable cartridge 162, and the user engages the disposable cartridge 162 with a receptacle 150 of a testing unit 144 of the sample -testing device 106 to start the sample testing. In some embodiments, the sample-testing device 106 may automatically detect the testing unit 144 that receives the disposable cartridge 162. In some other embodiments, the display 146 may be a touchscreen and displays the available testing units 144. The user may select the testing unit 144 that receives the disposable cartridge 162 by tapping on the testing unit 144 shown on the display 146. Other methods of indicating the testing unit 144 to be used are also available, for example, by pressing a button corresponding to the testing unit 144 that receives the disposable cartridge 162 to notify the testing unit 144 to be used. The sample-testing device 106 then generates a testing QR code which encodes a randomly generated, unique ID (denoted a “testing ID” hereinafter) such as a globally unique ID (GUID) representing the “current” sample testing.

After scanning the testing QR code, the sample information collection app decodes or otherwise obtains the testing QR code, determines the testing ID, and records the testing ID in the eCOC record.

The testing of the sample is then conducted by using the sample-testing device 106 as described above (step 312). At this step, the sample-testing device 106 may guide the user through the sample preparation process step-by-step and then perform the testing and post-processing of the testing data. After sample testing, the sample-testing device 106 may display the results on the screen 146 in a table format that includes, for example, time of the test, sample ID, absence or presence of bacteria, concentration of the bacteria, and/or the like.

As those skilled in the art will appreciate, steps 302 to 312 do not require connection to the network 108 and may be performed “offline” at locations where connection to the network 108 is unavailable. When the user’s smartphone 104 is able to connect to the network 108 (for example, the user later moves to a location where connection to the network 108 is available), the user’s smartphone 104 connects to the network 108 and transmits the sampling information such as the eCOC record to the server computer 102 either automatically or under the instruction of the user (step 314).

Similarly, when the sample-testing device 106 is able to connect to the network 108, the sample-testing device 106 connects to the network 108 and transmits the testing results and the testing ID to the server computer 102 either automatically or under the instruction of the user (step 316). The server computer 102 then uses the testing ID received from the user’s smartphone 104 and the testing ID received from the sample-testing device 106 to associate the sampling information (for example, the eCOC record) with the testing results having the same testing IDs, and store the matched sampling information and testing results in the database. The process 300 is then ended (step 318).

Those skilled in the art will appreciate that the embodiments described above are for illustrative purpose only and variations thereof are readily available. For example, in the sampletesting process 300 shown in FIG. 6, step 314 may be automatically performed after step 308 and before step 310 when the user’s smartphone 104 is connected to the network 108 at the time the sampling information has been inputted (that is, after step 308).

In some embodiments, the system 100 comprises a plurality of users and the server computer 102 establishes and maintains an account for each user. In these embodiments, each user is associated with a separate account and is assigned with a unique token as an identifier of the user’s account. The users may be employees of a plurality of organizations. Therefore, the server computer 102 associates the user accounts with the corresponding organizations and implements necessary security methods to prevent unauthorized information access.

In these embodiments, the server computer 102 associates above-described sampling information and corresponding testing results with the user’s account.

In some embodiments, the server computer 102 allows users to use their client-computing devices 104 to access some or all sampling data and testing results. Upon request, the server computer 102 may present requested data in a table format and may optionally allow the user to download the requested data as a report.

In some embodiments, the server computer 102 may overlay user-requested data (such as testing results) on a map at corresponding testing locations for visualizing the testing results, thereby allowing the user to plan sampling locations and allow the user to quickly notice issues if test results are constantly positive in specific areas.

In some embodiments, the system 100 requires a user to sign-in to the user’s account in order to transmit above-described sampling information to the server computer 102 (step 314 of the process 300). For example, the user may use another client-computing device 104’ (such as the user’s office computer) to sign-in to the user’s account by entering the username and password. After the server computer 102 successfully verifies the user’s identity, the server computer 102 communicate with the client-computing device 104 and display thereon a home page or dashboard for user’s further operation.

FIG. 7 shows an exemplary home page 400 which shows the user’ s token 402 in text format and a QR code 404 encoding the user’s token 402. The home page 400 also displays a menu 406 having a plurality of menu items for the user to select and perform various functionalities. The home page 400 further displays a plurality of information and function blocks 408 for displaying a summary of various information and for the user to select and perform related functionalities.

The user may operate the sample information collection app on the smartphone 104 to scan the QR code 404 in the home page 400 to connect sample information collection app to the user’s account in the server computer 102. Then, the sample information collection app transmits abovedescribed sampling information to the server computer 102. The server computer 102 associates above-described sampling information with the user’s account.

In some embodiments, the system 100 may comprise a “superadmin” account for monitoring all users’ activities. The superadmin account may also generate the QR codes for the users of an organization for various purposes (for example, for printing and distribution).

In some embodiments, the server computer 102 may comprise one or more server-side application programs 212 for analyzing the testing results.

In above embodiments, the sampling information (such as the eCOC record) uses the date and time when the smartphone 104 scans the sample QR code 242 of the sampling container 240 (denoted the “scanning date and time”) as the sampling date and time (that is, the sampling timestamp) of the sample. In some embodiments, the sampling timestamp may be determined based on the date and time of scanning the sample QR code 242 of the sampling container 240. For example, the sampling timestamp may be automatically determined by adjusting the scanning date and time using a predefined time offset.

In above embodiments, the sampling information (such as the eCOC record) uses the location of the smartphone 104 (obtained using the positioning module of the smartphone 104) when the smartphone 104 scans the sample QR code 242 of the sampling container 240 (denoted the “scanning location”) as the sampling location of the sample. In some embodiments, the sampling location may be determined based on the location of the smartphone 104 when the smartphone 104 scans the sample QR code 242 of the sampling container 240. For example, the sampling location may be automatically determined by adjusting the scanning location using a predefined location offset.

As those skilled in the art will appreciate, the characteristics of the samples (and those of the target objects the samples represent) may be correlated in accordance with various factors such as time, location, animal activities, human activities, business activities, and/or the like. For example, the characteristics of a water sample collected from the upstream of a river may be correlated to some extent with those of the downstream of the river. Therefore, if a water sample collected from the upstream of a river exhibits some noticeable characteristics in the testing (such as a high level of bacteria), there may be a need to collect a sample from the downstream of the river for testing.

Moreover, some utilities need to get samples from their distribution systems depending on their sizes. For example, smaller utilities tend to get 3 to 5 samples per week and larger ones such as metropolitan utilities may need to get 40 to 50 samples per week from their distribution systems. As obtaining samples for testing at every location in the distribution system is generally not feasible, people usually collect samples for testing at selected locations in the distribution system. Moreover, it is often required to repeatedly collect and test samples from the distribution systems. Therefore, the sampling locations and sampling dates/times need to be representative for the whole distribution system with a meaningful geographical spread. Depending on the situations of the distribution systems, the sampling locations of one sampling round may be different to those of other sampling rounds.

Repeatedly selecting the sampling locations and sampling dates/times for such samples via a manual process may cause significant burden to the users. Moreover, such a manual selection may have a drawback that the variation or fluctuation of the water quality or contamination may make the manual selection difficult, and the manually selected sampling locations, date/time, and sampling frequencies may be either more than enough or insufficient.

In some embodiments, the server computer 102 may execute one or more server-side application programs 212 to use an artificial intelligence (Al) method such as a machine learning method with one or more trained Al models to determine the sampling date and time and the sampling location, and/or determine future sampling dates and times, sampling frequencies, and/or sampling locations.

FIG. 8 is a schematic representation of an Al model 450, which generally comprises an input layer 452, one or more hidden layers 454, and an output layer 456. The input layer 452 comprises one or more input nodes 462, the output layer 456 comprises one or more output nodes 466, and each hidden layer 454 comprises a plurality of nodes 464. The nodes of adjacent layers are interconnected. Therefore, in each hidden layer 454, each node thereof receives data from the nodes of the previous layer (which may be the input nodes 462 of the input layer 452 or the nodes 464 of a hidden layer 454), processes the received data, and outputs the processed data to the nodes of the next layer (which may be the nodes 464 of a hidden layer 454 or the output nodes 466 of the output layer 456).

The one or more Al models 450 may be any suitable Al models such as support vector machines (SVM), neural networks, random forest, perception back-propagation, clustering models, and/or the like. The Al model may be trained using historical sample-related data, for example, the historical sampling locations, the access to the historical sampling locations (for example, some locations such as libraries may have easy access while other locations such as private houses are harder to access), historical sampling dates and times, zonal population, subarea information, historical testing results and/or trend (such as the spread or distribution of historical samples, severity of positive tests in one or more locations, and/or the like), a map of the target area, visual presentation of results on the map, and/or the like; also see the parameters described below) and/or the like.

In some embodiments, the Al model may take one or more of the following parameters as input:

• locations of previous samples (that is, previous sampling locations recorded, for example, via scanning the QR coded bottles);

• time of previous samples (that is, previous sampling dates and times recorded, for example, via scanning the QR coded bottles);

• zonal population (that is, the population of one or more zones of the target area, which may depend on how a jurisdiction divides the target area to different zones);

• ease of access to a sampling location (for example, ease of access to a public library, a private house with a tap outside, a private house with no tap outside, and/or the like);

• proximity to one or more water-related subareas of the target area in proximity to potential contamination sources such as one or more agriculture subareas, one or more rivers, and/or the like, with a possibility of overflow.

With these input parameters, the Al model may then output sampling locations (which may be new sampling locations and/or previous sampling locations to be revisited) in the target area (which may be a large geographical spread) to be visited in a future round of sampling, the sampling date and time for each of the output sampling locations in one or more future rounds of sampling, and/or the sampling frequency for each of the output sampling locations.

The system 100 described herein thus provides a solution for simple, accurate, and error- free data management during sample testing. Moreover, the system 100 allows the collection of sampling information and/or the sample testing to be performed both offline (that is, without connection to the network 108) and online (that is, with connection to the network 108).

In some embodiments, the sample-testing device 106 may be directly connected to the user’s client-computing device 104 (such as smartphone) via a suitable wired or wireless mechanism (such as a USB cable or Bluetooth®), and transmit the testing results to the user’s client-computing device 104. In these embodiments, the client-computing device 104 may only need to scan the sample QR code 242 of the sampling container 240, and does not need to scan any testing QR code of the sample-testing device 106. The client-computing device 104 then generates an eCOC record using the sampling information it collected and the testing results it received from the sample-testing device 106, and transmits the eCOC record (having the sample ID) to the server computer 102 when connecting to the network 108.

In some of above embodiments, a test kit may be used for processing the sample before testing. In some embodiments, the test kit may comprise a QR code. The user may use the clientcomputing device 104 to scan the test-kit QR code for recording which test kit was used to in a testing for tracing the test kit information if an abnormal result is obtained.

While QR codes are used in above embodiments, those skilled in the art will appreciate that, in some other embodiments, other machine-readable codes such as barcodes, text (which may be recognized using optical character recognition (OCR) methods), and/or the like, may be used.

In some embodiments, the sample ID may be encoded in other suitable formats such as encoded in a radio frequency identification (RFID) tag or a near field communication (NFC) tag on the sampling container 240. The client-computing device 104 may comprise a suitable reader such as a RFID reader or a NFC reader to read to RFID tag or the NFC tag to retrieve the sample ID.

In some embodiments, the testing ID may be encoded in other suitable formats such as encoded in a radio frequency identification (RFID) tag or a near field communication (NFC) tag on the sampling container 240. The client-computing device 104 may comprise a suitable reader such as a RFID reader or a NFC reader to scan, read, or otherwise wirelessly sense the RFID tag or the NFC tag to retrieve the testing ID.

Those skilled in the art will appreciate that the above-described embodiments or portions thereof may be combined as needed or desired.

Although embodiments have been described above with reference to the accompanying drawings, those of skill in the art will appreciate that variations and modifications may be made without departing from the scope thereof as defined by the appended claims.