Sample dispatch in automated diagnostic analysis systems

Abstract

An automated diagnostic analysis system includes an input module that receives a sample container to be processed by the system. The input module includes a robot for loading sample containers from the input module to respective sample carriers for transport within the system. The system also includes a computer processor to simulate a workflow of at least some of the sample containers received at the input module to estimate a workflow completion time for each of those sample containers. The computer processor further directs the sample container with the shortest estimated workflow completion time to be loaded by the robot from the input module into a next sample carrier received at the input module. Methods of operating an automated diagnostic analysis system are also provided, as are other aspects.

Description

(19) State Intellectual Property Office (12) Invention Patent Application (10) Application Publication Number (43) Application Publication Date (21) Application Number 202480043992.8 (22) Application Date 2024.06.25 (30) Priority Data 63 / 513554 2023.07.13 US (85) PCT International Application Entering National Phase Date 2025.12.29 (86) PCT International Application Application Data PCT / US2024 / 035386 2024.06.25 (87) PCT International Application Publication Data WO2025 / 014644 EN 2025.01.16 (71) Applicant Siemens Medical Diagnostics Inc., USA Address New York, USA (72) Inventor R. Prasad A. Kapoor (74) Patent Agency China Patent Agency (Hong Kong) Limited 72001 Patent Attorneys Zhong Maojian and Liu Chunyuan (51) Int.Cl. G01N 35 / 04 (2006.01) G01N 35 / 10 (2006.01) G01N 1 / 28 (2006.01) (54) Invention Title Sample Dispatch in an Automated Diagnostic Analysis System (57) Abstract An automated diagnostic analysis system includes an input module that receives sample containers to be processed by the system. The input module includes a robot for loading the sample containers from the input module into corresponding sample carriers for transfer within the system. The system also includes a computer processor for simulating the workflow of at least some of the sample containers received at the input module to estimate the workflow completion time of each of those sample containers. The computer processor further guides the sample container with the shortest estimated workflow completion time to be loaded by the robot from the input module into the next sample carrier received at the input module. Methods for operating the automated diagnostic analysis system and other aspects are also provided.Claims 3 pages, Description 12 pages, Drawings 10 pages, CN 121464351 A 2026.02.03 CN 1 21 46 43 51 A 1. An automated diagnostic analysis system, comprising: an input module operable to receive a plurality of sample containers, the input module including a robot operable to individually load each sample container from the input module into a corresponding sample carrier received at the input module; and a computer processor and program instructions executable thereon, operable to: receive workload status of most modules in the automated diagnostic analysis system; receive travel time estimates between most modules in the automated diagnostic analysis system; and receive a workflow of each of at least some of the plurality of sample containers based on information regarding one or more analyses to be performed on a corresponding sample in each of at least some of the plurality of sample containers; Based on the workload states of most modules, the execution of the workflow of at least some of the plurality of sample containers is simulated to estimate the workflow completion time of at least some of the plurality of sample containers; and in response to at least one estimated workflow completion time not exceeding a predetermined threshold, the sample container with the shortest estimated workflow completion time is guided by a robot from the input module to the next sample carrier received at the input module. 2. The automated diagnostic analysis system of claim 1, further comprising: in response to all estimated workflow completion times exceeding a predetermined threshold, the computer processor waits for updated workload states of the majority of modules to be received, and then, based on the updated workload states of the majority of modules, repeats the simulation of the execution of the workflow of at least some of the plurality of sample containers. 3. The automated diagnostic analysis system of claim 1, wherein the input module further includes a scanning / imaging device for scanning or imaging at least some of the plurality of sample containers to obtain information about one or more analyses to be performed on the samples therein. 4. The automated diagnostic analysis system of claim 3, wherein the computer processor and programming instructions executable thereon are further operable to analyze images or scan data received from the scanning / imaging device to determine the workflow of each of at least some of the plurality of sample containers and store it in memory. 5. The automated diagnostic analysis system of claim 1, wherein information regarding one or more analyses to be performed on a corresponding sample in each of the plurality of sample containers is obtained from markings on at least some of the plurality of sample containers.6. The automated diagnostic analysis system of claim 1, wherein: the input module includes a computer processor and programming instructions; or the system controller of the automated diagnostic analysis system includes a computer processor and programming instructions; or the computer processor and programming instructions are located remotely from the input module and the system controller. 7. The automated diagnostic analysis system of claim 1, further comprising: a plurality of modules operable to perform sample container handling, sample pretreatment, sample analysis, and sample posttreatment, said plurality of modules including the input module; and a sample transport system configured to transport a plurality of sample carriers via an automated track connecting each of the plurality of modules. 8. The automated diagnostic analysis system of claim 1, wherein the computer processor and programming instructions executable thereon are operable to receive travel time estimates between modules from the system controller of the automated diagnostic analysis system. Claims 1 / 3 Page 2 CN 121464351 A 9. The automated diagnostic analysis system of claim 1, wherein: the workload state of most modules further includes time window constraints for at least some work items included in the workload state; or the workflow includes time window constraints for at least some work items included in the workflow. 10. The automated diagnostic analysis system of claim 1, wherein a workflow is received from a second computer processor, the second computer processor executing programming instructions to determine the workflow based on information regarding one or more analyses to be performed on a corresponding sample in each of at least some of the plurality of sample containers. 11. A method of operating an automated diagnostic analysis system, the method comprising: receiving, at a computer processor, workload states of a majority of modules in the automated diagnostic analysis system; receiving, at a computer processor, travel time estimates between the majority of modules in the automated diagnostic analysis system; receiving, at a computer processor, a workflow for each of at least some of the plurality of sample containers received at an input module of the automated diagnostic analysis system, the workflow being based on information regarding one or more analyses to be performed on a corresponding sample in each of at least some of the plurality of sample containers; simulating the execution of the workflow for each of the plurality of sample containers via the computer processor based on the workload states of the majority of modules to estimate the workflow completion time for each of the plurality of sample containers; and, in response to at least one estimated workflow completion time not exceeding a predetermined threshold, guiding, via the computer processor, a sample container having the shortest estimated workflow completion time to be loaded from the input module by a robot of the input module into a next sample carrier received at the input module.12. The method of claim 11, further comprising, in response to all estimated workflow completion times exceeding a predetermined threshold, performing the following operations: waiting via a computer processor for the reception of updated workload states of most of the modules to be received; and, via a computer processor, simulating the execution of the workflow of at least some of the plurality of sample containers based on the updated workload states of most of the modules. 13. The method of claim 11, further comprising scanning or imaging at least some of the plurality of sample containers received at the input module via a scanning / imaging device at the input module to obtain information about one or more analyses to be performed. 14. The method of claim 13, further comprising analyzing the images or scan data received from the scanning / imaging device via a computer processor to determine the workflow of each of at least some of the plurality of sample containers and storing it in memory. 15. The method of claim 11, wherein receiving the workflow at the computer processor comprises: obtaining information about one or more analyses to be performed from markers on each of at least some of the plurality of sample containers. 16. The method of claim 11, wherein receiving the travel time estimates between most of the modules at the computer processor comprises: receiving the travel time estimates from the system controller of the automated diagnostic analysis system. 17. The method of claim 11, wherein receiving the workload state of most modules at the computer processor comprises: receiving time window constraints for at least some of the work items included in the workload state. 18. The method of claim 11, wherein receiving the workflow at the computer processor comprises: receiving time window constraints for at least some of the work items included in the workflow. (Claims 2 / 3, Page 3, CN 121464351 A) 19. The method of claim 11, wherein receiving the workflow at the computer processor comprises: receiving the workflow from a second computer processor. 20. The method of claim 11, further comprising: performing sample container processing, sample pretreatment, sample analysis, and sample posttreatment at multiple modules including an input module of an automated diagnostic analysis system; and transferring multiple sample carriers via an automated track in a sample transport system, the automated track connecting each of the multiple modules. Claims 3 / 3 Page 4 CN 121464351 A Sample Dispatch Technology in Automated Diagnostic Analysis Systems

[0001] This application claims the benefit of U.S. Provisional Application No. 63 / 513,554, filed July 13, 2023, pursuant to 35 USC § 119(e). The entire contents of the patent application(s) cited above are hereby expressly incorporated herein by reference.

[0002] This disclosure relates to automated diagnostic analysis systems and methods. Background Art

[0003] In medical testing, automated diagnostic analysis systems can be used to analyze biological samples to identify analytes or other components in the samples. Biological samples can be, for example, urine, whole blood, serum, plasma, interstitial fluid, cerebrospinal fluid, and the like. Such biological samples are typically contained in sample containers (e.g., test tubes, vials, etc.), which can be transferred in sample carriers via a sample transport system (including automated tracks) to and from various modules. Various modules can perform, for example, sample container handling, sample pretreatment, sample analysis, and sample post-processing within the automated diagnostic analysis system. At any given time, the number of sample carriers present in the automated diagnostic analysis system may be hundreds or even thousands.

[0004] Sample containers are typically received at the input module of the automated diagnostic analysis system, where they are arranged in one or more trays or supports. After the sample container is loaded from the input module into a sample carrier transported by the sample transport system, the system controller can perform workflow planning based on information about one or more analyses to be performed on the biological sample in the sample container. This information can be obtained, for example, by scanning a barcode on a sample container. Workflow planning may include selecting and scheduling one or more modules to perform various actions related to one or more analyses of the sample.

[0005] However, if too many sample containers loaded into a sample carrier need to be processed by one or more of the same modules that have already been scheduled to process sample containers previously loaded into sample carriers, such workflow planning may result in processing delays due to sample carrier congestion. Such processing delays may adversely affect overall system performance (e.g., system throughput—i.e., the number of samples processed per hour, per shift, per day, etc.).

[0006] Therefore, improved workflow planning in automated diagnostic analysis systems is desired. Summary of the Invention

[0007] In some embodiments, an automated diagnostic analysis system is provided. The system includes an input module operable to receive a plurality of sample containers. The input module includes a robot operable to individually load each sample container from the input module into a corresponding sample carrier received at the input module.The automated diagnostic analysis system also includes a computer processor and program instructions executable thereon, the program instructions being operable to: (1) receive the workload status of most modules in the automated diagnostic analysis system; (2) receive travel time estimates between most modules in the automated diagnostic analysis system; (3) receive a workflow of each of at least some of the multiple sample containers based on information about one or more analyses to be performed on a corresponding sample in each of at least some of the multiple sample containers; (4) simulate the execution of the workflow of each of at least some of the multiple sample containers based on the workload status of most modules to estimate the workflow completion time of each of at least some of the multiple sample containers; and (5) in response to at least one estimated workflow completion time not exceeding a predetermined threshold, guide the sample container with the shortest estimated workflow completion time to be loaded from the input module into the next sample carrier received at the input module by a robot from the input module.

[0008] In some embodiments, a method of operating an automated diagnostic analysis system is provided. The method includes receiving, at a computer processor, the workload status of most modules in the automated diagnostic analysis system and travel time estimates between most modules in the automated diagnostic analysis system. The method further includes receiving at a computer processor a workflow for each of at least some of a plurality of sample containers received at an input module of an automated diagnostic analysis system, wherein the workflow is based on information regarding one or more analyses to be performed on a corresponding sample in each of the plurality of sample containers. The method further includes simulating the execution of the workflow for each of the plurality of sample containers via a computer processor based on the workload state of most modules to estimate the workflow completion time for each of the plurality of sample containers. In response to at least one estimated workflow completion time not exceeding a predetermined threshold, the method includes guiding a robot of the input module via a computer processor to load the sample container with the shortest estimated workflow completion time from the input module into the next sample carrier received at the input module.

[0009] Further aspects, features, and advantages of this disclosure will readily become apparent from the following detailed description and illustrations of various exemplary embodiments and implementations, including the best mode for carrying out the invention. This disclosure may also have other and different embodiments, and several details thereof may be modified in various aspects without departing from the scope of the invention. For example, although the following description relates to automated diagnostic analysis systems, selectively assigning sample containers to automated diagnostic analysis systems to increase system throughput can be easily adapted to other complex systems.This disclosure is intended to cover all modifications, equivalents, and alternatives falling within the scope of the appended claims. Brief Description of the Drawings

[0010] The drawings described below are for illustrative purposes and are not necessarily drawn to scale. Therefore, the drawings and description are to be considered illustrative in nature and not restrictive. The drawings are not intended to limit the scope of the invention in any way.

[0011] FIG1 illustrates a top schematic diagram of an automated diagnostic analysis system configured to perform one or more biological sample analyses according to embodiments provided herein.

[0012] FIG2 illustrates a side view of a sample container loaded into a sample carrier of the automated diagnostic analysis system of FIG1 according to embodiments provided herein.

[0013] FIG3 illustrates a more detailed top schematic diagram of the input module MO of FIG1 according to embodiments provided herein.

[0014] FIG4 illustrates a bar graph of example workloads of the modules of the automated diagnostic analysis system of FIG1 according to embodiments provided herein.

[0015] FIG5 illustrates a matrix of estimated travel times between modules of the automated diagnostic analysis system of FIG1 according to embodiments provided herein.

[0016] FIG6 illustrates a side view of the input module robot and scanning / imaging device assembly according to an embodiment provided herein.

[0017] FIG7 illustrates a bar graph showing a simulation of the workflow of the first sample container in the automated diagnostic analysis system of FIG1 according to an embodiment provided herein.

[0018] FIG8 illustrates a bar graph showing a simulation of the workflow of the second sample container in the automated diagnostic analysis system of FIG1 according to an embodiment provided herein.

[0019] FIG9 illustrates a bar graph showing a simulation of the first workflow of the sample container in an automated diagnostic analysis system having multiple modules performing the same functions according to an embodiment provided herein. Specification 2 / 12 pages 6 CN 121464351 A

[0020] FIG10 illustrates a bar graph showing a simulation of the second workflow of the sample container of FIG9 in an automated diagnostic analysis system having multiple modules performing the same functions according to an embodiment provided herein.

[0021] FIG11 illustrates a simplified block diagram of the dispatcher of the input module controller according to an embodiment provided herein.

[0022] Figure 12 illustrates a block diagram of a reinforcement learning training setup that can be employed in the assignment procedure of an input module controller according to an embodiment provided herein.

[0023] Figure 13 illustrates a flowchart of a method for operating an automated diagnostic analysis system according to an embodiment provided herein. Detailed Description

[0024] An automated diagnostic analysis system according to an embodiment described herein may include a large number of sample carriers, each sample carrier holding a sample container thereon.Each sample container may include a biological sample to be analyzed. Biological samples may be, for example, urine, whole blood, serum, plasma, interstitial fluid, cerebrospinal fluid, and the like. The automated diagnostic analysis system may also include a sample transport system for transporting sample carriers via an automated track through the system. The automated diagnostic analysis system may further include multiple modules for performing sample container handling, sample pretreatment, sample analysis, and sample post-processing. Each of the modules is connected to the sample transport system for receiving and returning sample containers via sample carriers.

[0025] One of the modules included in the automated diagnostic analysis system is an input module. The input module is configured to receive multiple sample containers to be processed by the system. The sample containers may be arranged in one or more supports or trays that are typically manually loaded into the input module. A robot of the input module loads each sample container from the input module into a corresponding sample carrier that arrives at the input module via the sample transport system. In conventional automated diagnostic analysis systems, the robot typically loads sample containers arbitrarily or simply based on the pre-arranged order of the sample containers in the supports or trays (e.g., sequentially starting with the first row and first column positions).

[0026] Sample containers typically include markings (e.g., printed labels, barcodes, RF (radio frequency) ID tags, etc.) that include information about one or more analyses to be performed on the biological sample contained therein by the automated diagnostic analysis system, and in some cases, time constraints for performing one or more of those analyses and / or one or more pre- or post-processing work items therein. Shortly after the sample container is loaded into the sample carrier, the sample carrier is typically first transferred to a scanning / imaging station or module, where the markings are scanned or imaged to determine where and how the sample container should be processed within the automated diagnostic analysis system.

[0027] The automated diagnostic analysis system may also include a system controller that communicates with the modules (including the scanning / imaging station or module), the sample transport system, and the sample carrier. The system controller may plan the system's workflow based on information about one or more analyses to be performed (e.g., information obtained from the markings on the scanned or imaged sample container). That is, the system controller can schedule and direct one or more analyses of each sample in the sample container to be performed at one or more locations within a module, some performed within a specific time period, by directing the sample carrier to the appropriate module. Such a system controller can be called a workflow planner. In some automated diagnostic analysis systems, the number of samples analyzed per day may reach hundreds or even thousands.

[0028] However, in some known automated diagnostic analysis systems, the workflow planner may cause congestion and / or bottlenecks due to directing too many sample carriers from the input module to one or more of the same modules simultaneously or approximately simultaneously.Processing delays caused by such workflow planning may prevent some sample containers from being processed within their specified time constraints, and / or may adversely affect overall system performance (e.g., system throughput—i.e., the number of samples processed per hour, per shift, per day, etc.).

[0029] An automated diagnostic analysis system according to embodiments described herein can advantageously improve workflow planning and thus overall system performance (e.g., throughput) by causing a computer processor (e.g., an input module controller) to execute programming instructions to perform dispatch operations at input modules, which dispatch operations: (1) avoid or minimize congestion and / or bottlenecks where possible at one or more of the same modules, (2) optimize module usage, (3) avoid “module starvation” (i.e., modules left idle), and / or (4) optimize system consumables (e.g., reagents, which are chemical components added to samples to facilitate or enable sample analysis).

[0030] In some embodiments, the computer processor may receive the workload status of at least a majority of modules in the automated diagnostic analysis system and travel time estimates between at least a majority of modules. The computer processor can also receive workflows (e.g., sample container markers have been scanned or imaged) of at least some sample containers that have already been received and pre-screened at the input module. The workflows are based on information from the scanned or imaged markers about one or more analyses to be performed on the samples in the sample containers to indicate the modules to be accessed by the sample containers and their order. The computer processor can further simulate the workflow of each of the pre-screened sample containers to estimate the workflow completion time of each of those sample containers. The computer processor can then guide the sample container with the shortest estimated completion time to be loaded from the input module by the robot of the input module into the next sample carrier received at the input module. This dispatch operation prioritizes the loading of sample containers into sample carriers based on the sample container workflow (which may include processing time constraints), module workload (which indicates module availability), and module-to-module travel time estimates, thereby avoiding or minimizing processing delays at one or more of the modules where possible.

[0031] According to one or more embodiments, an automated diagnostic analysis system with improved workflow planning will be explained in more detail below with reference to Figures 1-13.

[0032] FIG1 illustrates an automated diagnostic analysis system 100 configured to automatically analyze biological samples according to one or more embodiments.The automated diagnostic analysis system 100 may include multiple sample carriers 102 (only three are labeled in FIG. 1 for clarity), a sample transport system 104 including an automated track 105 and track sensors 105-S (only three are labeled), multiple modules MO-M5, and a system controller 106. The automated diagnostic analysis system 100 may include more or fewer modules and / or other components. Note that although modules MO-M5 are illustrated as having the same size and shape, they are not limited to having the same size and / or shape.

[0033] Modules MO-M5 may each be configured to perform one or more actions on a sample container or a biological sample contained in a sample container. In particular, one or more modules MO-M5 may be configured to perform sample container processing, sample pretreatment, sample analysis, or sample post-processing. For example, in some embodiments, module MO may be an input module including an input module controller 108. Module M1 may be a cap remover module, module M2 may be a centrifuge, module M3 may be a chemical analyzer module, module M4 may be an immunoassay analyzer module, and module M5 may be a sealer module. Modules M1-M5 may each include a corresponding module controller (not shown), and in other embodiments may be other types of modules.

[0034] Each sample carrier 102 may be configured to carry at least one sample container thereon. FIG2 illustrates a sample container 203 loaded into a sample carrier 202, which is an embodiment of sample carrier 102. In some embodiments, sample carrier 202 may be a passive, non-motorized puck configured to carry a single sample container 210 (e.g., via a magnet in sample carrier 202) on an automated track 105 of sample transport system 104. In other embodiments, the sample carrier 202 may be an automated carrier including an onboard drive motor (such as a linear motor) programmed via system controller 106 or input module controller 108 to move around a track and stop at pre-programmed locations (e.g., one or more of modules M0-M5). The sample carrier 202 may include a retainer 202H configured to hold the sample container 203 in a defined upright position and orientation. The retainer 202H may include a plurality of fingers or leaf springs securing the sample container 203 in or to the sample carrier 202, some of which may be movable or flexible to accommodate sample containers of different sizes. The sample carrier 202 may also include a transceiver 210 for communicating with system controller 106, input module controller 108, and other components in system 100.The sample carrier 202 may further include one or more sensors 202-S, which in some embodiments may be cameras and / or collision or position sensors. Other types of sensors may be included. The sample carrier 202 may be of other types and / or configurations, and the system 100 may include sample carriers of various types or configurations.

[0035] The sample container 203 may include a cap 203C, a tubular body 203T, and a label 203L, which may include identification information 203I (e.g., markings) such as barcodes, alphanumeric characters, RF (radio frequency) ID tags, or combinations thereof. The identification information 203I may be machine-readable at various locations within the automated diagnostic analysis system 100 (such as, for example, at each of modules MO-M5 (which may include scanning / imaging devices) and at various locations around the automated track 105 where the sensors 105-S are located). The biological sample 212 to be analyzed may be contained in the sample container 203. Biological samples can be, for example, urine, whole blood, serum, plasma, interstitial fluid, cerebrospinal fluid, or the like. In some embodiments, as shown in FIG2, biological liquid sample 212 may include serum or plasma portion 212SP and sedimented blood portion 212B.

[0036] Returning to FIG1, sample transport system 104 may be configured to transport sample containers to and from each of modules MO-M5 via respective sample carriers 102 and tracks 105. Track 105 may include multiple interconnected segments configured to allow unidirectional or bidirectional transport of sample containers. Track 105 may be a collection of rails (e.g., monorail or multirail), conveyor belts, conveyor belt chains, movable platforms, or any other suitable type of transport mechanism. Track 105 may be circular, elliptical, or any other suitable shape or configuration or combination thereof, and in some embodiments, may be a closed track.

[0037] The system controller 106 can communicate directly with each of the sample carrier 102, sample transfer system 104, and modules M0-M5 via wired and / or wireless connections as shown, or via network 114, each of which includes a suitable communication device (e.g., a transceiver). Network 114 can be, for example, a local area network (LAN), a wide area network (WAN), or other suitable communication network, including wired and wireless networks. The system controller 106 can be housed as part of the automated diagnostic analysis system 100 or can be located remotely from the automated diagnostic analysis system 100.

[0038] System controller 106 may communicate with one or more databases or similar sources (represented in FIG. 1 as Laboratory Information System (LIS) 116) to receive sample information, including, for example, patient information, analysis to be performed on each sample, time and date of acquisition of each sample, medical institution information, tracking and routing information, and / or any other information related to the sample to be analyzed.

[0039] System controller 106 may include a user interface 118, which may include a display for enabling a user to access various control and status displays and input commands and / or data into system controller 106.

[0040] System controller 106 may also include a computer processor 106P, a memory 106M, and programming instructions 106PI (e.g., software, programs, algorithms, and the like). Programming instructions 106PI may be stored in memory 106M and executed by processor 106P. Workflow planning (WFP) algorithm 106WFP may also be stored in memory 106M and executed by processor 106P. The memory 106M may further have one or more artificial intelligence (AI) algorithms stored therein for performing or facilitating various preprocessing and postprocessing actions and / or sample analysis. The system controller 106 may alternatively or additionally include other processing devices / circuits (including microprocessors, A / D converters, amplifiers, filters, etc.), transceivers, interfaces, device drivers, and / or other electronic devices.

[0041] The system controller 106 may be configured to operate and / or control various components of the system 100 via communication therewith, including sample carriers 102, sample transfer system 104, and modules MO-M5. Specifically, for example, the system controller 106 may control the movement of each sample carrier 102 to and from any module in modules MO-M5 via the sample transfer system 104 and to and from any other component (not shown) in the system 100. System controller 106 can plan the workflow of system 100 based on information received from, for example, LIS 116, user interface 118, and / or information obtained from the scanning or imaging of sample container markings (e.g., identification information 203I). That is, system controller 106 can be operable to schedule and guide one or more analyses of each sample contained in the respective sample container 102 to be performed at one or more of modules M0-M5, and in some cases scheduled to be performed according to a specific time. System controller 106 can be considered a workflow planner.

[0042] FIG3 illustrates a more detailed view of the input module M0 of FIG1 according to one or more embodiments.Input module M0 can be configured to receive one or more supports or trays 320A, 320B (some labeled) of sample containers 303. Input module M0 may also include sensors (not shown) and a robot 322, wherein robot 322 is configured to grasp each sample container 303 and move it from the supports or trays 320A, 320B to an empty sample carrier 102B received at input module M0 via track segment 305B. Sensors can be configured to detect sample containers 303 in supports or trays 320A, 320B and guide robot 322 accordingly. As shown in FIG3, sample carrier 102A loaded with sample containers 303 from input module M0 can leave input module M0 via track segment 305A. Note that in some embodiments, sample carrier 102 is not limited to the direction of travel as described herein with respect to sample carriers 102A and 102B.

[0043] In some embodiments, the input module M0 may be an input / output module (IOM), wherein a sample container to be processed may be received and loaded into a sample carrier 102, and after processing, may be returned to the IOM and unloaded from the sample carrier 102 back into a holder or tray 320A, 320B for removal from the IOM (and the automated diagnostic analysis system 100). In other embodiments, the input module M0 may be a batch input module (BIM) or a cool / storage module (RSM).

[0044] Returning to FIG1, according to one or more embodiments, the input module controller 108 may include a computer processor 108P, a memory 108M, and a dispatch program 108D (e.g., programming instructions, software programs, AI algorithms, or the like) stored in the memory 108M and executable by the processor 108P. The input module controller 108 may also include a user interface 124, which may include a display for enabling a user to access one or more control and status displays and input commands and / or data into the input module controller 108. The input module controller 108, including suitable communication devices (e.g., transceivers), can communicate directly with the system controller 106, sample carrier 102, sample transfer system 104, and modules M1-M5 via wired and / or wireless connections or via network 114.

[0045] According to one or more embodiments, a dispatcher 108D executing on processor 108P is configured to receive the workload status of each (or at least most of the modules M1-M5) in response to a request from dispatcher 108D. The workload status of a module indicates whether the module is currently processing a sample container and / or whether other sample containers are scheduled to be processed at the module, and in some embodiments, indicates whether any time constraints are associated with work items included in the processing.In other words, the workload status represents the availability of a module to process sample containers. In response to a request from dispatcher 108D, the workload status of each (or at least most) of modules M1-M5 can be received from system controller 106—provided that such information is known to system controller 106. Alternatively, in response to a separate request sent from dispatcher 108D to each module M1-M5, dispatcher 108D can receive the workload status directly from each (or most) of modules M1-M5.

[0046] FIG4 illustrates an example workload status 400 of modules M1-M5 that can be received by dispatcher 108D according to one or more embodiments. As shown, module M1 is processing a first sample container from time t1 (the time when the workload status is created) to time t3 and is scheduled to process a second sample container from time t7 to time t9. This indicates that module M1 is available to process one or two sample containers between time t3 and time t7, and is available again to process sample containers after time t9. Similarly, module M2 is scheduled to process sample containers from time t2 to time t4, and is available to process other sample containers after time t4. As shown, module M3 is scheduled to process two sample containers and will not be available to process other sample containers until after time t5. Module M4 is currently reloaded with reagents (i.e., chemical components added to the sample to facilitate or enable sample analysis) and is scheduled to process sample containers until time t8. And module M5 is currently processing sample containers and will not be available until the predetermined module calibration is completed at time t6. Other embodiments of workload states may be received by dispatcher 108D.

[0047] According to one or more embodiments, a dispatcher 108D executing on processor 108P is also configured to receive travel time estimates between (or at least between most) modules MO-M5 in response to a request from dispatcher 108D. In some embodiments, dispatcher 108D may request travel time estimates from system controller 106, wherein travel time estimates may be stored in memory 106M and may have been provided by, for example, the system manufacturer as part of the specifications of automated diagnostic analysis system 100. In other embodiments, travel time estimates may be determined during a testing or initial operation period of automated diagnostic analysis system 100, wherein the travel time of sample carriers 102 between modules may be measured and averaged over a period of time (e.g., a day, a week, a month, etc.) and stored in system controller memory 106M or input module controller memory 108M.In some other embodiments, the travel time estimate may be calculated based on the sample carrier velocity and the track length between modules, and then stored in memory 106M or 108M. Any suitable method for estimating the travel time between modules of the automated diagnostic analysis system can be used.

[0048] FIG5 illustrates a matrix of example travel time estimates 500 between modules M0-M5 of the automated diagnostic analysis system 100, which can be received by dispatcher 108D according to one or more embodiments. As shown, the travel time estimate 500 between modules M0-M5 is represented by estimated times et1-et15. For example, the travel time estimate of a sample carrier transferred from module M2 to module M5 is et12 (and, depending on the track layout and whether the track is unidirectional or bidirectional, the travel time estimate of a sample carrier transferred from module M5 to module M2 may also be et12, as shown, or may be different). Alternatively, the estimated travel time between modules may be received by dispatcher 108D in other suitable forms or formats.

[0049] According to one or more embodiments, a dispatch program 108D executing on processor 108P is further configured to receive, in response to a request from dispatch program 108D, a workflow for each of at least some of the sample containers received in input module M0. The workflow, based on information about one or more analyses to be performed on the sample in the sample container, indicates the modules to be accessed by the sample container and their order. For example, a first workflow may indicate that a first sample container will access modules M1, M3, and M5 (in that order) after leaving input module M0, while a second workflow may indicate that a second sample container will access modules M2, M3, M4, and M5 (in that order) after leaving input module M0, or access modules M2, M4, M3, and M5 (in that order). The information on which the workflow is based may be obtained, for example, from sample container markings (e.g., identification information 203I) from scanning or imaging, LIS 116, user interface 118 and / or 124 and / or combinations thereof. For example, in some cases, the input module M0 can scan or image a mark on a sample container received therein and obtain a sample container identification number from the mark. The input module controller 108 can then communicate with LIS 116 (e.g., via network 114) to obtain sample analysis instructions associated with the identification number. The sample analysis instructions indicate one or more analyses to be performed on the sample in the sample container and any time constraints associated with that one or more analyses. In other cases, scan or imaging data from the sample container mark may include analysis instructions.

[0050] FIG6 illustrates an example input module robot and scanning / imaging device assembly 600 that may be included in the input module M0 according to one or more embodiments.Assembly 600 can be controlled by input module controller 108 (FIG. 1). In other embodiments, assembly 600 can be directly controlled by system controller 106 (FIG. 1) or another (e.g., remote) controller. Assembly 600 includes robot 622 and scanning / imaging device 626 attached to robot 622. Specification 7 / 12 pages 11 CN 121464351 A

[0051] Robot 622 is operable to grasp sample containers 603 and 603A and transfer them from / to a holder or tray 620 to / from sample carrier 102 or 202. Robot 622 includes gripper 628 operable in three dimensions (e.g., X, Y, and Z or R, θ, and Z). Gripper 628 is coupled via translation motor 630M to telescopic arm 630 movable in the horizontal direction (- / + X), as shown. A telescopic arm 630 is attached to an upright portion 632, which is movable in the vertical direction (- / + Y) via a vertical motor 632M. The telescopic arm 630 is also capable of rotating about the upright portion 632 in the angular direction (+ / - θ) via a rotary motor 632R. The upright portion 632 can be mounted to the frame 634 of the input module. A gripper 628 may include two gripper fingers 628A and 628B, which can be opened and closed by an actuation mechanism 628M. A rotary actuator 628R is operable to rotate the gripper fingers 628A and 628B to any specified rotational position / orientation in the angular direction (+ / - θ2) about an axis 636. The robot 622 can be any suitable robot capable of moving a sample container received at the input module to a sample carrier also received at the input module / moving a sample container from the sample carrier.

[0052] The scanning / imaging device 626 is operable to scan or image identification information 603I of sample container 603A, as shown in FIG. 6. Identification information 603I may include, for example, a barcode, and may be the same as identification information 203I of sample container 203 (FIG. 2). The scanning / imaging device 626 may include a scanner or digital camera 626C mounted to a vertical support 626S, which is attached to a telescopic arm 630. Other embodiments of the scanning / imaging device 626 are possible. To ensure that identification information 603I is scanned or imaged by the scanning / imaging device 626, sample container 603A may be scanned or imaged multiple times as the clamp 628 is rotated incrementally about axis 636 (e.g., in increments of 45 degrees, 90 degrees, or 120 degrees). Scanning or imaging data from scanning / imaging device 626 can be analyzed at input module controller 108 via processor 108P, which executes appropriate scanning / imaging software stored in memory 108M.Based on sample analysis instructions obtained directly from scan or imaging data of identification information 603I or retrieved from LIS 116 via network 114, for example, based on a patient identification number scanned or imaged from identification information 603I, input module controller 108 executes appropriate workflow planning software stored in memory 106M via processor 108P to determine the workflow of sample container 603A and provide it to dispatcher 108D. In other embodiments, scan or imaging data from scanning / imaging device 626 may be processed at system controller 106 or another (e.g., remote) controller, wherein the workflow determined for sample container 603A is determined and sent to and received by dispatcher 108D.

[0053] According to one or more embodiments, dispatcher 108D, executed on processor 108P, is also configured to simulate each of the workflows to determine an estimated workflow completion time for each of the workflows. Figures 7 and 8 illustrate example simulations of first and second sample containers according to one or more embodiments, both of which are currently located at input module M0.

[0054] Figure 7 illustrates a workflow simulation 700 of a first sample container (container 1) having workflows of modules M2, M4, and M5 after leaving input module M0 in the automated diagnostic analysis system 100, which currently has, for example, a workload 400 (Figure 4). As shown, container 1 is processed immediately at module M2 after sample container 703A has been processed at module M2. Container 1 is then processed at module M4, which is available after sample container 703B has been processed at module M4. Arrow 738A indicates the estimated travel time between modules M2 and M4, as shown, for example, in the travel time estimate 500 of Figure 5. Container 1 is then processed at module M5, which is available after calibration. Arrow 738B indicates the estimated travel time between modules M4 and M5, which again can be shown, for example, in the travel time estimate 500 of Figure 5. Workflow simulation 700 indicates that container 1 has an estimated workflow completion time ct1, where ct1 can be the elapsed time measured from time t1.

[0055] Figure 8 illustrates a workflow simulation 800 of a second sample container (container 2) having workflows of modules M1, M4, and M5 after leaving input module M0 in the automated diagnostic analysis system 100, which currently has a workload 400 (Figure 4). As shown, container 2 is immediately processed at module M1 after sample container 803A has already been processed at module M1.Container 2 is then processed at module M4, which becomes available after sample container 803B has been processed at module M4. Arrow 838A indicates the estimated travel time between modules M1 and M4, as shown, for example, in the travel time estimate 500 of Figure 5. Container 2 is then processed at module M5, which becomes available after calibration. Arrow 838B indicates the estimated travel time between modules M4 and M5, which can be shown, for example, in the travel time estimate 500 of Figure 5, and can be the same as that indicated by arrow 738B of Figure 7. Workflow simulation 800 indicates that container 2 has an estimated workflow completion time ct2, where ct2 can be the elapsed time as measured from time t1.

[0056] According to one or more embodiments, a dispatch program 108D executed on processor 108P is also configured to simulate a variable workflow of the sample container to determine the estimated workflow completion time for each variation of the workflow. That is, in an automated diagnostic analysis system with redundant modules (two or more modules performing (one or more) the same function), dispatcher 108D is configured to simulate workflow changes, where there is a choice of which redundant module to access and / or a choice of the order in which some modules can be accessed.

[0057] Figures 9 and 10 illustrate corresponding example simulations of a sample container (container 3) located at input module M0 in an embodiment of an automated diagnostic analysis system (not shown), which also has modules M1, M2, two M3s (denoted as M3-1 and M3-2), two M4s (denoted as M4-1 and M4-2), and M5. The variable workflow of container 3 is M2, M3 (or M4), and M4 (or M3). The automated diagnostic analysis system of this embodiment has a workload represented by an active bar with white text and a black background in the bar charts of Figures 9 and 10.

[0058] Figure 9 illustrates a workflow simulation 900 of a first workflow change of container 3 as determined by dispatcher 108D according to one or more embodiments. After container 3 leaves input module M0, the first simulation workflow of container 3 has a series of modules M2, M3-1, and M4-2. As part of the simulation, dispatcher 108D initially selected one of the two M3 modules for container 3 to be accessed before one of the two M4 modules. Dispatcher 108D also selected container 3 to access module M3-1 instead of module M3-2 based on the earlier availability of module M3-1, which processes container 3, and selected container 3 to access module M4-2 instead of module M4-1 based on the availability of module M4-2 when container 3 is ready to be processed by one of the two M4 modules (note that M4-1 has been scheduled to process the sample container approximately when container 3 is ready to be processed by one of the two M4 modules).Arrow 938A indicates the estimated travel time between modules M2 and M3-1, as shown in the travel time estimate that can be received by dispatcher 108D, which can be similar to the travel time estimate 500 in Figure 5. Arrow 938B indicates the estimated travel time between modules M3-1 and M4-2, which again can be shown in the travel time estimate received by dispatcher 108D. The first workflow variation workflow simulation 900 indicates that container 3 has an estimated workflow completion time ct3, where ct3 can be the elapsed time as measured from time t1.

[0059] Figure 10 illustrates a second workflow variation workflow simulation 1000 of container 3 as determined by dispatcher 108D according to one or more embodiments. After container 3 leaves input module M0, the second simulated workflow of container 3 has a series of modules M2, M4-1, and M3-2. As part of this simulation, dispatcher 108D has now selected one of the two M4 modules for container 3 to access before one of the two M3 modules, and has selected container 3 to access module M4-1 instead of module M4-2 based on the slightly earlier availability of module M4-1 handling container 3. In some embodiments, dispatcher 108D may also consider travel time estimates when choosing between two modules with temporally close availability. Arrow 1038A indicates the estimated travel time between modules M2 and M4-1, as shown in the travel time estimates that can be received by dispatcher 108D, which may be similar to the travel time estimate 500 in Figure 5. Therefore, for example, if the estimated travel time from module M2 to module M4-2 is longer than the estimated travel time from module M2 to module M4-2, resulting in container 3 arriving at module M4-1 later than container 3 arrives at module M4-2, then dispatcher 108D will instead select module M4-2 to receive container 3 after module M2.

[0060] To complete the second workflow change from module M4-1 to one of modules M3-1 and M3-2, dispatcher 108D may first consider module availability. Given that both modules M3-1 and M3-2 are available to process container 3 after being processed at module M4-1, dispatcher 108D may then consider the estimated travel time from module M4-1 to each of the available modules M3-1 and M3-2 as a determining factor. In this example, the estimated travel time from module M4-1 to module M3-2 (indicated by arrow 1038B) is determined to be less than the estimated travel time from module M4-1 to module M3-1, so dispatcher 108D selects module M3-2 to process container 3 after module M4-1.The second workflow variation simulation 1000 indicates that container 3 has an estimated workflow completion time ct4, where ct4 can be the elapsed time measured from time t1.

[0061] Although Figures 7 and 8 only illustrate the simulation of the workflow of two sample containers (container 1 and container 2), and Figures 9 and 10 only illustrate the simulation of two workflow variations of container 3, all workflow variations of the pre-screened sample containers and all sample container workflows at the input module M0 can be simulated by dispatcher 108D to determine the corresponding estimated completion time.

[0062] After completing the workflow simulation of each sample container with a workflow received by dispatcher 108D, dispatcher 108D is further configured to guide the robot of input module M0 to load the sample container with the shortest estimated workflow completion time from the input module into the next sample carrier received at the input module. Therefore, for example, referring to Figures 7 and 8, and assuming that containers 1 and 2 are the only pre-screened sample containers among the sample containers in the nearest tray or support loaded into input module M0 (i.e., sample containers with a workflow received by dispatcher 108D), dispatcher 108D can guide the robot to load container 2 into the next sample carrier received at input module M0 because the estimated completion time ct2 of container 2 is shorter (less) than the estimated completion time ct1 of container 1.

[0063] Similarly, referring to Figures 9 and 10, dispatcher 108D can determine that the second workflow variation of modules M2, M4-1, and M3-2 of container 3 is optimal because the estimated completion time ct4 is shorter (less) than the estimated completion time ct3 of the first workflow variation of modules M2, M3-2, and M4-2 of container 3. Furthermore, dispatch procedure 108D can therefore use the estimated completion time ct4 of container 3 to determine which pre-screened sample container has the shortest estimated workflow completion time, which in turn determines which sample container will be loaded next into the available sample carrier arriving at input module M0.

[0064] Based on the simulation of the sample container workflow, as the input module robot 322 (FIG. 3) or robot 622 (FIG. 6) loads the sample container into the next sample carrier 102 or 202 arriving at input module M0, dispatch procedure 108D can transfer the workflow of the sample container to one or more of the next sample carrier 102 or 202, sample transfer system 104, and / or system controller 106. Such transfer indicates that the next sample carrier 102 or 202 does not need to first access the scanning / imaging station or module as a sample carrier loaded at input module M0 with a sample container that has not yet been pre-screened will. Sample containers that have not yet been pre-screened at input module M0 are typically transferred first to the scanning / imaging station or module to obtain analysis instructions and determine the workflow of the sample container.

[0065] In some embodiments, after completing the workflow simulation, the dispatcher 108D may not dispatch any sample containers from the input module M0. In these embodiments, a predetermined threshold may be applied to the estimated workflow completion time. For example, in response to all estimated workflow completion times exceeding the predetermined threshold, the dispatcher 108D may not dispatch any sample containers from the input module M0. Instead, the dispatcher 108D may wait for a preset amount of time before requesting and receiving updated workload status from the module. The preset amount of time for waiting for updated workload status may allow mitigation of any situation that causes the estimated workflow completion time to exceed the predetermined threshold. The dispatcher 108D may then repeat the simulation of the sample container workflow based on the updated workload status and again apply the predetermined threshold to the estimated workflow completion time to determine whether to dispatch any sample containers from the input module M0. In response to at least one estimated workflow completion time not exceeding the predetermined threshold, the dispatcher 108D may dispatch a sample container with the shortest estimated workflow completion time not exceeding the predetermined threshold.

[0066] In some embodiments, a predetermined threshold may represent a completion time considered excessively long. For example, an estimated completion time exceeding a predetermined threshold may indicate congestion or bottlenecks at one or more modules where sample containers are already waiting to be processed. Such a predetermined threshold may be determined based on the number of modules in the system (and any redundancy therein), the average number of modules accessed by each sample container, the average number of sample containers in the system at any given time, the known processing speed of the modules (e.g., the number of sample containers / samples processed per unit time), the expected maximum acceptable completion time, and / or one or more other system performance targets (e.g., the total number of samples to be processed per hour, per shift, per day, etc.). Such a predetermined threshold may be determined in any suitable manner.

[0067] In other embodiments, a predetermined threshold may represent one or more time constraints associated with one or more workflows. For example, if each of containers 1, 2, and 3 has a time constraint indicating, for example, a maximum allowable completion time, and the workflow simulation for each indicates that each estimated completion time exceeds its corresponding predetermined threshold (i.e., its corresponding maximum allowable completion time), then dispatcher 108D may not dispatch any of containers 1, 2, and 3 from input module MO. Instead, dispatch procedure 108D can wait a preset amount of time before receiving updated workload status from the requesting and receiving modules, repeat the simulation of the sample container workflow, and apply a predetermined threshold to the estimated workflow completion time to determine whether any of containers 1, 2, and 3 should be dispatched from input module M0.

[0068] Advantageously, simulation of the sample container workflow by the dispatcher 108D before loading the sample containers into the sample carrier can improve system performance, optimize the use of system consumables (e.g., reagents), balance module workload, and / or avoid module idleness (i.e., module shelving) by optimizing the workflow sequence of redundant modules in the automated diagnostic analysis system and / or by prioritizing the loading of pre-screened sample containers into the sample carrier based on workflow simulation.

[0069] In some embodiments, the dispatcher 108D may employ a computer program called a CP-SAT solver (e.g., see developers.google.com / optimization / cp / cp_solver), which relates to solving an allocation problem. In other embodiments, the dispatcher 108D may employ a computer program called ScheduleNet (e.g., see [link]), which relates to solving a scheduling problem using reinforcement learning (RL). In still other embodiments, the dispatcher 108D may employ a deep neural network to solve the scheduling problem and output an optimal sample container workflow. Figure 11 illustrates an example embodiment of a dispatch procedure 108D employing a neural network 1140. System state 1142 (which includes module workload states, such as, for example, workload state 400) and workflow 1144 (which includes a pre-screened sample container workflow) can be vectorized inputs to the neural network 1140, which outputs priority-ranked containers 1146 to be dispatched (which can be, as simulated, sample containers with the shortest estimated workflow completion time, and, in cases where the workflow includes a variable sequence of modules and / or the automated diagnostic analysis system includes redundant modules, also as simulated, the optimal workflow sequence). The neural network 1140 can be trained in a reinforcement learning (RL) setting 1200 having, for example, a deep neural network (“agent”) and a simulator (“environment”) for the system, as shown in Figure 12. In this RL embodiment, the neural network 1140 can operate by internally modeling the system using only partial system state information (i.e., workload states from fewer than all modules in the automated diagnostic analysis system) to make optimal predictions for the sample containers to be dispatched from the input module M0. Other types of software products can be used to perform the functionality of the dispatch procedure 108D described herein.

[0070] Although shown as stored in memory 108M and executed by processor 108P of input module controller 108, in other embodiments, dispatch program 108D may be stored in system controller 106 and executed in system controller 106, or may be stored in and executed in another memory and computer processor that is remote from and in communication with input module M0 and system controller 106.

[0071] FIG13 illustrates a method 1300 of operating an automated diagnostic analysis system according to one or more embodiments. At process block 1302, method 1300 may include receiving the workload states of most (if not all) modules in the automated diagnostic analysis system at a computer processor. For example, referring to FIGS. 1 and 4, processor 108P executing dispatch program 108D of input module controller 108 (or alternatively system processor 106P executing dispatch program 108D) may receive the workload states of modules M1-M5, such as workload state 400.

[0072] At process block 1304, method 1300 may include receiving, at a computer processor, travel time estimates between most (if not all) modules in the automated diagnostic analysis system. For example, referring to Figures 1 and 5, processor 108P executing dispatch program 108D of input module controller 108 may receive travel time estimate 500.

[0073] At process block 1306, method 1300 may include receiving, at a computer processor, a workflow for each of at least some of a plurality of sample containers received at the input module of the automated diagnostic analysis system, wherein the workflow is based on information regarding one or more analyses to be performed on a corresponding sample in each of the plurality of sample containers. For example, referring to Figures 7-10, processor 108P executing dispatch program 108D of input module controller 108 may receive workflows for containers 1, 2, and 3.

[0074] At process block 1308, method 1300 may include simulating the execution of a workflow for each of at least some of the plurality of sample containers via a computer processor based on received module workload states, to estimate the workflow completion time for each of at least some of the plurality of sample containers. Referring again to Figures 7-10 and continuing with the above example, the execution of the workflows for containers 1, 2, and 3 may be simulated by processor 108P executing dispatch program 108D, as illustrated by workflow simulations 700, 800, 900, and 1000.

[0075] And at process block 1310, in response to at least one estimated workflow completion time not exceeding a predetermined threshold, method 1300 may include guiding a robot via a computer processor to load a sample container with the shortest estimated workflow completion time into the next sample carrier received at the input module. Continuing again with the above example in conjunction with Figures 7 and 8, processor 108P executing dispatch procedure 108D may guide robot 322 (Figure 3) or robot 622 (Figure 6) to load container 2 with a shorter estimated workflow completion time (et2) than container 1 (et1) into the next sample carrier 102 or 202 received at input module M0 (Figures 1 and 3).

[0076] While this disclosure is susceptible to various modifications and alternatives, specific method and apparatus embodiments have been shown by way of example in the drawings and described in detail herein. However, it should be understood that the specific methods and apparatus disclosed herein are not intended to limit this disclosure or the following claims. Instruction manual, page 12 / 12, 16 CN 121464351 A, Figure 1; Instruction manual, Figure 1 / 10, page 17 CN 121464351 A, Figure 2; Instruction manual, Figure 2 / 10, page 18 CN 121464351 A, Figure 3; Figure 4; Instruction manual, Figure 3 / 10, page 19 CN 121464351 A, Figure 5; Instruction manual, Figure 4 / 10, page 20 CN 121464351 A, Figure 6; Instruction manual, Figure 5 / 10, page 21 CN 121464351 A, Figure 7; Figure 8; Instruction manual, Figure 6 / 10, page 22 CN 121464351 A, Figure 9; Instruction manual, Figure 7 / 10, page 23 CN 121464351 A, Figure 10; Figure 11; Instruction manual, Figure 8 / 10, page 24 CN 121464351 A, Figure 12; Instruction manual, Figure 9 / 10, page 25 CN 121464351 Figure 13, Appendix to the Instruction Manual, Page 10 / 10, 26 CN 121464351 A.

Claims

1. An automated diagnostic analysis system, comprising: An input module operable to receive multiple sample containers, the input module including a robot operable to individually load each sample container from the input module into a corresponding sample carrier received at the input module; as well as A computer processor and program instructions executable thereon, which are operable to: Receives the workload status of most modules in the automated diagnostic analysis system; Receive travel time estimates between most modules in the automated diagnostic analysis system; Based on information about one or more analyses to be performed on a corresponding sample in each of at least some of the multiple sample containers, a workflow is received for each of the multiple sample containers. Based on the workload status of most modules, the execution of the workflow of at least some of the plurality of sample containers is simulated to estimate the workflow completion time of at least some of the plurality of sample containers. as well as In response to at least one estimated workflow completion time not exceeding a predetermined threshold, the sample container with the shortest estimated workflow completion time is guided by the robot from the input module to the next sample carrier received at the input module.

2. The automated diagnostic analysis system according to claim 1, further comprising: In response to all estimated workflow completion times exceeding a predetermined threshold, the computer processor waits for updated workload states of most modules to be received, and then, based on the updated workload states of most modules, repeats the simulation of the execution of the workflow of at least some of the plurality of sample containers.

3. The automated diagnostic analysis system according to claim 1, wherein, The input module further includes a scanning / imaging device for scanning or imaging at least some of the plurality of sample containers to obtain information about one or more analyses to be performed on the samples therein.

4. The automated diagnostic analysis system according to claim 3, wherein, The computer processor and the programming instructions executable thereon are further operable to analyze images or scan data received from the scanning / imaging device to determine the workflow of at least some of the plurality of sample containers and store them in memory.

5. The automated diagnostic analysis system according to claim 1, wherein, Information about one or more analyses to be performed on a corresponding sample in each of the plurality of sample containers is obtained from markings on at least some of the plurality of sample containers.

6. The automated diagnostic analysis system according to claim 1, wherein: The input module includes a computer processor and programming instructions; or The system controller of an automated diagnostic analysis system includes a computer processor and programming instructions; or The computer processor and programming instructions are located away from the input module and system controller.

7. The automated diagnostic analysis system according to claim 1, further comprising: Multiple modules operable to perform sample container processing, sample pretreatment, sample analysis, and sample posttreatment, said multiple modules including an input module; as well as The sample transport system is configured to transport multiple sample carriers via an automated track connecting each of multiple modules.

8. The automated diagnostic analysis system according to claim 1, wherein, The computer processor and the programming instructions executable thereon are operable to receive travel time estimates between modules from the system controller of the automated diagnostic analysis system.

9. The automated diagnostic analysis system according to claim 1, wherein: The workload status of most modules further includes time window constraints for at least some of the work items included in the workload status; or The workflow includes time window constraints for at least some of the work items included in the workflow.

10. The automated diagnostic analysis system according to claim 1, wherein, The workflow is received from a second computer processor, which executes programming instructions to determine the workflow based on information about one or more analyses to be performed on a corresponding sample in at least some of the plurality of sample containers.

11. A method for operating an automated diagnostic analysis system, the method comprising: Receives the workload status of most modules in the automated diagnostic analysis system at the computer processor; The computer processor receives travel time estimates between most modules in the automated diagnostic analysis system. A workflow in which a computer processor receives at least some of each of a plurality of sample containers received at an input module of an automated diagnostic analysis system, the workflow being based on information about one or more analyses to be performed on a corresponding sample in each of the plurality of sample containers. The execution of the workflow of at least some of the plurality of sample containers is simulated by a computer processor based on the workload status of most of the modules in order to estimate the workflow completion time of at least some of the plurality of sample containers. as well as In response to at least one estimated workflow completion time not exceeding a predetermined threshold, a sample container with the shortest estimated workflow completion time is guided by a robot at the input module to be loaded from the input module into the next sample carrier received at the input module, via a computer processor.

12. The method of claim 11, further comprising, in response to all estimated workflow completion times exceeding a predetermined threshold, performing the following operations: The computer processor awaits updates to the workload status of most modules; and The simulation of the execution of the workflow of at least some of the plurality of sample containers is repeated by a computer processor based on the updated workload status of most modules.

13. The method of claim 11, further comprising scanning or imaging at least some of the plurality of sample containers received at the input module via a scanning / imaging device at the input module to obtain information about one or more analyses to be performed.

14. The method of claim 13, further comprising analyzing images or scan data received from the scanning / imaging device via a computer processor to determine the workflow of at least some of the plurality of sample containers and storing it in a memory.

15. The method according to claim 11, wherein, The workflow received at the computer processor includes obtaining information about one or more analyses to be performed from markers on at least some of the plurality of sample containers.

16. The method according to claim 11, wherein, The estimated travel times between most modules are received at the computer processor, including: The system controller of the automated diagnostic analysis system receives the estimated travel time.

17. The method according to claim 11, wherein, Receiving the workload status of most modules at the computer processor includes receiving time window constraints for at least some of the work items included in the workload status.

18. The method according to claim 11, wherein, Receiving a workflow at the computer processor includes receiving time window constraints for at least some of the work items included in the workflow.

19. The method according to claim 11, wherein, Receiving workflows at the computer processor includes receiving workflows from a second computer processor.

20. The method of claim 11, further comprising: Sample container processing, sample pretreatment, sample analysis, and sample post-processing are performed at multiple modules, including the input module of the automated diagnostic analysis system. as well as Multiple sample carriers are transported via an automated track in a sample transport system, the automated track connecting each of multiple modules.

Images