Customized sample processing based on sample and / or sample container identification

Abstract

A system and method are provided for automatically customizing the treatment of samples in sample containers carried in a sample holder. The system and method can identify sample containers in the sample holder and / or detect various characteristics associated with the containers and / or the sample holder. This information can then be used, for example, by aspirating fluid from the sample container in a manner that takes into account the type of sample / container carrying the sample and distributing the fluid to customize the treatment of the sample.

Description

[0001] Related applications

[0002] This application relates to and claims the benefit of provisional patent application 62 / 838990, filed April 26, 2019, with the U.S. Patent and Trademark Office, entitled "Tailored Sample Handling Based on Sample and / or Sample Container Recognition," which is incorporated herein by reference in its entirety. Background Technology

[0003] Sample analyzers typically use a sample presentation unit (SPU) to support and transfer sample racks, which hold multiple sample containers, such as sample tubes or sample cups. The analyzer will also typically include a pipette that removes a portion of the sample from a sample container in the SPU and transfers it to another sample container (e.g., a sample dish) on a sample wheel. Additional pipettes may also be present, such as supplemental pipettes that transfer fluid from a sample dish to another dish (e.g., a reactor dish) on a rack in the reaction construction zone, where the fluid can be prepared for analysis, for example, by mixing with reagents and / or incubating.

[0004] While effective, analyzers like those described above can have various drawbacks. For example, due to dead space in the sample containers, relying on multiple pipettes to transfer fluid from the original sample container in the SPU to the preparation fluid for analysis and / or the final sample container for analysis can result in a reduced effective amount of sample available for analysis. Furthermore, analyzers that enable special treatment of specific samples often require the operator to manually specify the treatment on a sample-by-sample basis, introducing new potential sources of error where special treatment might be appropriate. Summary of the Invention

[0005] Generally, this disclosure relates to the differential handling of samples and their containers based on such identification and / or other information that can be automatically sensed by laboratory instruments or otherwise determined.

[0006] In a first aspect, this disclosure can be used to implement an automated clinical analyzer including a sample presentation unit and a computing device. In some such embodiments, the sample presentation unit may include a presentation channel. In some such embodiments, the computing device may be configured to perform one or more actions selected from a set. In some such embodiments, the set of actions may include: identifying the type of a sample container based on an image of a sample container in the sample presentation channel captured by a camera device; and distinguishing downstream processing for the fluid contained in the sample container based on the type. In some such embodiments, the set of actions may include: determining a target location based on identification information of the sample container in the sample presentation channel and transferring fluid from the sample container in the sample presentation channel to the target location.

[0007] In a second aspect, some embodiments, as described in the context of the first aspect, may include a group of one or more stands. In some such embodiments, each stand from the group of one or more stands may be angled relative to the presentation channel of the sample presentation unit, may be configured to translate a corresponding pipette along its length and to aspirate or dispense fluid based on commands from a computing device, and may have a portion disposed above the presentation channel of the sample presentation unit. In some such embodiments, the computing device may be configured to: determine a first fluid volume based on identification information of a sample container in the sample presentation channel, and to determine a target location and transfer fluid from the sample container in the sample presentation channel to that target location. In some such embodiments, the computing device may be configured to transfer fluid from a sample container in a sample presentation channel to a target location by sending a command from one or more of the group of stations, the command being adapted to cause the station to: position a corresponding pipette of the station above the sample container in the sample presentation channel, aspirate a first volume of fluid from the sample container in the sample presentation channel, position a corresponding pipette of the station above the target location, and dispense a second volume of fluid from the corresponding pipette of the station into a vessel at the target location.

[0008] In a third aspect, in some embodiments as described in the context of the first aspect, the computing device may be configured to: identify the type of a sample container in a sample delivery channel based on an image captured by a camera device; and to distinguish downstream processing for the fluid contained in the sample container based on that type. In some such embodiments, the computing device may be configured to identify the type of a sample container based on container shape features from images captured by the camera device.

[0009] In a fourth aspect, this disclosure can be used to implement methods for operating automated clinical analyzers. In some such embodiments, the method may include presenting a sample container in a sample presentation channel of a sample presentation unit. In some such embodiments, the method may include: a computing device performing one or more actions selected from a set of actions. In some such embodiments, the set of actions may include: identifying the type of a sample container based on an image of the sample container in the sample presentation channel captured by a camera device; and distinguishing downstream processing for the fluid contained in the sample container based on the type. In some such embodiments, the set of actions may include: determining a target location based on identification information of the sample container in the sample presentation channel and transferring fluid from the sample container in the sample presentation channel to the target location.

[0010] In a fifth aspect, in some embodiments as described in the context of the fourth aspect, the analyzer may include a set of stands. In some such embodiments, each stand from one or more stands in the set may be angled relative to the presentation channel of the sample presentation unit, may be configured to translate a corresponding pipette along its length and to aspirate or dispense fluid based on commands from a computing device, and may have a portion disposed above the presentation channel of the sample presentation unit. In some such embodiments, the computing device may be configured to: determine a first fluid volume based on identification information of a sample container in the sample presentation channel, and to determine a target location and transfer fluid from the sample container in the sample presentation channel to that target location. In some such embodiments, the computing device may be configured to transfer fluid from a sample container in a sample presentation channel to a target location by sending a command from one or more of a set of stands, the command being adapted to cause the stand to: position a corresponding pipette of the stand above the sample container in the sample presentation channel, aspirate a first volume of fluid from the sample container in the sample presentation channel, position a corresponding pipette of the stand above the target location, and dispense a second volume of fluid from the corresponding pipette of the stand into a vessel at the target location.

[0011] In a sixth aspect, in some embodiments as described in the context of the first aspect, the computing device may perform the following actions: identifying the type of a sample container based on an image of a sample container in a sample delivery channel captured by a camera device; and distinguishing downstream processing for the fluid contained in the sample container based on the type. In some such embodiments, the computing device may be configured to identify the type of sample container based on container shape features from images captured by the camera device.

[0012] In a seventh aspect, this disclosure can be used to implement a method for operating an analyzer, wherein the method includes: presenting a first sample container on a sample presentation channel of a sample presentation unit, and presenting a second sample on the sample presentation channel of the sample presentation unit. In some such embodiments, the method may further include using an imaging device to capture a first image, wherein the first image depicts the first sample container. In some such embodiments, the method may further include using an imaging device to capture a second image, wherein the second image depicts a second sample container. In some such embodiments, the method may include a computing device determining the type of the first sample container based on the first image. In some such embodiments, the method may include a computing device determining the type of the second sample container based on the second image. In some such embodiments, the method may include aspirating a first fluid volume from the first sample container based on the determined type of the first sample container, wherein the first fluid volume includes a fluid volume sufficient to perform a measurement indicated for the sample in the first sample container and an amount sufficient to fill invalid space in an intermediate sample vessel. In some such embodiments, the method may include aspirating a second fluid volume from the second sample container based on the determined type of the second sample container, wherein the second fluid volume includes a fluid volume sufficient to perform the measurement indicated for the sample in the second sample container, but excluding an amount of invalid space sufficient to fill invalid space in the intermediate sample vessel. Attached Figure Description

[0013] Figure 1 This is a top view of the example sample analyzer.

[0014] Figure 2 Depicting the installation on Figure 1 A perspective view of an exemplary sample holder at a first location in the sample presentation unit (SPU) of a sample analyzer.

[0015] Figure 3 Depicting Figure 2 The 3D diagram shows the sample holder in the second position within the SPU.

[0016] Figure 4 Depicting Figure 2 The 3D diagram shows the sample holder in the third position, where the sample holder is partially moved out of the second position of the SPU.

[0017] Figure 5 This is a front view of the example pipe rack.

[0018] Figure 6 yes Figure 5 A three-dimensional view of the pipe rack.

[0019] Figure 7 This is a front view of the example cup holder.

[0020] Figure 8 yes Figure 7 A 3D diagram of a cup holder.

[0021] Figure 9 It has multiple different types of sample tubes. Figure 5 A three-dimensional sectional view of the pipe rack in the image.

[0022] Figure 10 This is a 3D view showing a sample cup of the example type.

[0023] Figure 11 This is a three-dimensional view showing another example type of sample cup.

[0024] Figure 12 This is a three-dimensional view showing another example type of sample cup.

[0025] Figure 13 This is a three-dimensional view showing another example type of sample cup.

[0026] Figure 14 This is a three-dimensional view of the tube rack located in the delivery channel of the SPU.

[0027] Figure 15 yes Figure 14 The magnified portion of the 3D image.

[0028] Figure 16 This is another perspective view of the tube rack located in the presentation channel of the SPU, partially within the field of view of the camera unit.

[0029] Figure 17 This is another perspective view of the tube rack located in the delivery channel of the SPU, partially within the field of view of the container inspection unit.

[0030] Figure 18 This is a front view showing an example construction of a camera unit with a mounting bracket.

[0031] Figure 19 This is a flowchart of an example method for performing sample container identification on a sample rack.

[0032] Figure 20 This is a schematic diagram showing different image positions on the sample holder.

[0033] Figure 21A It is a low-exposure monochrome image of a section of a tube rack containing sample tubes.

[0034] Figure 21B It is a low-exposure monochrome image of another part of the tube rack containing sample tubes.

[0035] Figure 21CIt is a low-exposure monochrome image of a tube rack containing sample tubes, wherein the sample tubes include a capped sample tube.

[0036] Figure 22 It is a low-exposure monochrome image of a portion of a cup holder containing sample cups.

[0037] Figure 23A It is a high-exposure monochrome image of a section of a tube rack containing sample tubes.

[0038] Figure 23B yes Figure 23A A monochrome image of the corresponding sample tube that has been identified.

[0039] Figure 24 This is a flowchart of another example method for performing sample container identification on a sample rack.

[0040] Figure 25 This is a flowchart of an example method for processing an image of a sample rack with one or more containers and determining the characteristics of the containers in the sample rack.

[0041] Figure 26 yes Figure 21A Images that have the identified sample rack features.

[0042] Figure 27 yes Figure 21A An image with the identified container features.

[0043] Figure 28 yes Figure 21A An image with the identified container features.

[0044] Figure 29 yes Figure 21A Images that possess the identified histogram features.

[0045] Figure 30 yes Figure 21C Images that possess the identified histogram features.

[0046] Figure 31 This is an example category table.

[0047] Figure 32 It is a portion of the cup holder with sample cups and shows a monochrome image of the identified histogram features.

[0048] Figure 33 This is a flowchart of an example method for using a sample analyzer to add new container types and validate them for use.

[0049] Figure 34 An exemplary architecture of a computing device that can be used to implement various aspects of this disclosure is shown.

[0050] Figure 35 It is a low-exposure monochrome image of a portion of a tube rack with three sample tubes, two of which contain cups.

[0051] Figure 36 It is a low-exposure monochrome image of a portion of a cup holder containing sample cups.

[0052] Figure 37 This is a flowchart of an example method for processing samples in a customized way.

[0053] Figure 38 The example analyzer demonstrates the differential processing and default processing of samples from a pediatric cup.

[0054] Figure 39 The procedure for adding type-specific instructions is shown.

[0055] Figure 40 The procedure for customizing sample processing based on the identification of the sample contained in a pediatric cup is shown. Detailed Implementation

[0056] Various embodiments will be described in detail with reference to the accompanying drawings, wherein similar reference numerals denote similar parts and components throughout the views. Reference to the various embodiments does not limit the scope of the appended claims. Furthermore, any examples set forth in this specification are not intended to be limiting, but merely illustrate some of the many possible embodiments described in the appended claims.

[0057] Figure 1 This is a top view of an example sample analyzer. In this example, the sample analyzer is generally indicated by reference numeral 100 and is configured to analyze samples. The sample analyzer 100 includes a sample holder 102, a sample presentation unit (SPU) 104, a set of one or more stands 106, an analysis unit 108 including a reaction construction region 111, and a sample container identification unit 110. In a preferred embodiment, the set of one or more stands 106 consists of a sample pipette (and associated stand) 105 and a sample precision pipette (and associated stand) 107.

[0058] The sample rack 102 is configured to hold and transfer one or more sample containers 180. For example, the sample rack 102 can be used in various applications and can be configured to transfer one or more containers 180 inside or outside the sample analyzer 100. Figures 5 to 8As shown, one or more sample containers 180 can be positioned in the sample holder 102 in various combinations. As described herein, one or more sample containers 180 can be inserted individually and engaged with the sample holder 102. Although a single sample holder 102 is shown in this example, it should be understood that the sample analyzer 100 is configured to support multiple sample holders 102, which can be used in the sample analyzer 100 in various combinations and can be operated individually or in any combination.

[0059] SPU 104 operates to support sample holder 102 and transfer sample holder 102 to various positions. (Refer to...) Figures 2 to 9 Example operation of sample rack 102 is further described and illustrated.

[0060] Figure 1 The set of one or more stands 106 shown includes two pipettes for extracting fluid from sample containers in sample holder 102: a sample pipette 105 and a precision sample pipette 107. (As shown) Figure 1 As shown, the sample pipette 105 is preferably mounted on a stand that intersects with the sample rack presentation channel 128 and the sample wheel 129. In operation, the sample pipette 105 aspirates one or more samples (i.e., portions of the sample) along with sufficient additional fluid to account for ineffective space and excess fluid drawn from the sample container in the sample rack 102, and dispenses each aspirated sample (along with the additional fluid to account for ineffective space and excess fluid) into a new sample container in the sample wheel 129. Subsequently, when one sample is to be analyzed, the sample precision pipette 107 aspirates the sample from the sample container in the sample wheel and dispenses it into a reactor dish in the reaction construction zone 111.

[0061] For illustration, consider a scenario requiring 100 μL of fluid for the assay (e.g., HBsAg assay). In some embodiments, to provide the required fluid, the sample pipette can aspirate 182 μL from the sample container. This 182 μL represents the 100 μL required for the assay plus a 5 μL expected excess from the sample precision pipette plus 60 μL to account for the dead volume in the sample dish, multiplied by 1.1 to provide an additional 10% excess margin for the sample pipette itself. 165 μL of the aspirated fluid can then be dispensed into the sample dish, while the additional 17 μL excess is retained in the tip of the sample pipette. The sample precision pipette can then aspirate 105 μL of fluid from the sample dish and dispense the required 100 μL into the reaction dish, while the 5 μL excess is retained in the tip of the sample precision pipette. The same type of procedure can be followed if two 100 μL samples are required. Initially, the sample pipette can aspirate 363 μL of fluid from the sample container, and 165 μL of this fluid can be dispensed into each of the two sample dishes, while 33 μL will be retained as an over-pump in the tip of the sample pipette. Then, the precision pipette can aspirate 105 μL from the first sample dish and dispense 100 μL into the first reaction dish, and aspirate 105 μL from the second sample dish and dispense 100 μL into the second reaction dish.

[0062] Furthermore, in some embodiments, the precise sample pipette 107 may be mounted on a stand that intersects not only the sample wheel 129 but also the sample holder delivery channel 128. In these types of embodiments, the precise sample pipette 107 and its associated stand may be configured such that the precise sample pipette 107 can directly aspirate fluid from the sample container in the sample holder 102. In some embodiments where this functionality is present, this allows for the direct transfer of samples from the original sample container to the vessel in the reaction construction zone 111, thereby avoiding any fluid loss into the wasted space of the intermediate sample vessel in the sample wheel 129. For example, in the case where 100 μL of fluid is required for measurement, the precise sample pipette can aspirate 105 μL and dispense 100 μL directly into the reaction vessel, thereby avoiding volume loss due to wasted space in the sample container or over-absorption by the sample pipette.

[0063] Of course, it should be understood that, although Figure 1The analyzer shown includes multiple pipettes, but this type of multi-pipette configuration is not applicable in all embodiments. For example, in some embodiments, only a single pipette may be mounted on the stand intersecting the sample wheel 129, the sample rack presentation channel 128, and the reaction construction area 111. Therefore, the discussion set forth above should be understood as illustrative only and not as limiting.

[0064] In addition to the previously mentioned reaction construction zone 111, the analysis unit 108 operates to analyze the sample initially introduced into the sample analyzer 100 in the container 180 on the sample rack 102. The analysis unit 108 includes subsystems for transferring vessels, dispensing reagents into reaction vessels, incubating, mixing, washing, delivering substrates, and reading the light intensity of the chemiluminescent reaction.

[0065] The sample container identification unit 110 operates to identify the type of container 180 in the sample rack 102. An example of the sample container identification unit 110 is shown and described herein.

[0066] Reference Figures 2 to 4 This illustrates an example operation of the sample rack 102, which holds one or more sample containers 180 within the sample analyzer 100 and transfers one or more sample containers 180 within the sample analyzer 100. Specifically, Figure 2 A perspective view depicts an exemplary sample holder 102 installed at a first location in the SPU of the sample analyzer 100. Figure 3 A three-dimensional view of the sample holder at the second position in the SPU is depicted, and Figure 4 A perspective view of the sample holder at a third position is depicted, where the sample holder is partially moved out of the second position of the SPU. As described below, sample holder 102 Figure 2 Located in loading channel 124, in Figure 3 Located at the intersection of loading channel 124 and presentation channel 128, and in Figure 4 It is located in the delivery channel 128.

[0067] In some embodiments, sample rack 102 is loaded with one or more sample containers 180 before being loaded into sample analyzer 100 (e.g., its SPU 104). In other embodiments, sample rack 102 is loaded with one or more sample containers 180 after being loaded into sample analyzer 100 (e.g., its SPU 104). In yet another embodiment, sample rack 102 is partially loaded with one or more sample containers 180 before being loaded into sample analyzer 100, and one or more additional sample containers 180 may be loaded into sample rack 102 subsequently.

[0068] SPU 104 operates to transfer sample rack 102, thereby transferring the sample container 180 held in sample rack 102. In some embodiments, SPU 104 is configured to transfer sample rack 102 to various locations or stations within sample analyzer 100. Figure 2 As shown, SPU 104 includes a lateral movement segment 120 (i.e., a load-unload channel) and a transverse movement segment 122 (i.e., a delivery channel). As depicted, the lateral movement segment 120 is substantially perpendicular to the transverse movement segment 122. The lateral movement segment 120 includes a load channel 124 and an unload channel 126. The delivery channel 128 of the transverse movement segment 122 is positioned between the load channel 124 and the unload channel 126.

[0069] In some embodiments, the lateral movement segment 120 includes a pusher 130 that travels the sample holder 102 along the loading channel 124 and the unloading channel 126. The lateral movement segment 122 includes a carrier 132 that travels the sample holder 102 along the presentation channel 128. The loading channel 124 includes a first track 136 (i.e., the rear loading track) and a second track 138 (i.e., the pre-loading track). The presentation channel 128 includes a third track 140 (i.e., the rear carrier track, the first hook-shaped clamp, etc.) and a fourth track 142 (i.e., the front carrier track, the second hook-shaped clamp, etc.). The unloading channel 126 includes a fifth track 144 (i.e., the rear unloading track) and a sixth track 146 (i.e., the pre-unloading track). The first track 136 and the fifth track 144 are aligned with each other. Similarly, the second track 138 and the sixth track 146 are aligned with each other and are substantially parallel to the first track 136 and the fifth track 144. When the carrier 132 is in the receiving position (e.g., see...), Figure 2 The third track 140 is aligned with the first track 136 and the fifth track 144, and the fourth track 142 is aligned with the second track 138 and the sixth track 146.

[0070] The sample holder 102 may include mounting features configured to load the sample holder 102 into the SPU 104. In some embodiments, the mounting features include a first hook 160 disposed at a first end 164 and a second hook 162 disposed at a second end 166 opposite the first end 164. To load the sample holder 102 into the SPU 104, the first hook 160 engages with rails 136, 140 and / or 144, and the second hook 162 engages with rails 138, 142 and / or 146. To facilitate placement of the sample holder 102 into the SPU 104, a handle 168 (see, for example, see...) Figure 2The sample rack 102 can be set into the sample holder 102 and can be manually grasped by an operator. In some embodiments, the sample rack 102 can be loaded into the SPU 104 via an automated device (e.g., by a robot, pick-and-place device, etc.), although it is possible that in some embodiments, the sample rack 102 can also be loaded manually. It is also possible that some embodiments can support both automated and manual loading (e.g., the first side of the analyzer, such as its left side, can have an automated connection for automated loading, while the second side of the analyzer, such as its front, can be provided with a backup option for manual loading in case of automated failure).

[0071] When multiple sample racks 102 are held by SPU 104, the sample racks 102 are typically loaded into SPU 104 at loading channel 124. Therefore, sample racks 102 can be stacked within SPU 104. For example, the front portion 150 of one sample rack 102 may abut the rear portion 152 of another sample rack 102. When more than two sample racks 102 are held by SPU 104, the front portion 150 of one sample rack 102 may abut the rear portion 152 of another sample rack positioned in front of said one sample rack 102, and the rear portion 152 of said one sample rack 102 may abut the front portion 150 of another sample rack positioned behind said one sample rack 102. The abutting sample racks 102 can thus be configured as a stack. The rear portion 152 of the last sample rack 102 may abut the pusher 130.

[0072] One or more sample racks 102 can be loaded into SPU 104 at a time. For example, a first hook 160 can engage with rail 136 and a second hook 162 can engage with rail 138 to load sample rack 102 into loading channel 124. If needed (e.g., when other sample racks in sample rack 102 have already been positioned within SPU 104), pusher 130 can retract (e.g., move away from already positioned sample rack 102), thus making room for newly added sample rack 102. While one or more sample racks 102 are being loaded into SPU 104, pusher 130 can be moved (e.g., towards sample rack 102), thus removing any excess space between pusher 130 and sample rack 102. One or more sample racks 102 can be loaded into SPU 104 before, during, or after sample racks 102 have been positioned within SPU 104.

[0073] In order to move the sample rack 102 (and thus the sample containers loaded thereon) through / into the sample analyzer 100, the pusher 130 can cause the sample rack 102 to travel and thus position at least one sample rack of the sample rack 102 into the delivery channel 128 when the carrier 132 is in the receiving position (see, for example, see...). Figure 2 and Figure 3 (Movement between). As it moves from loading channel 124 to presentation channel 128, the first hook 160 will engage from track 136 to track 140, and the second hook 162 will engage from track 138 to track 142. To further move the sample holder 102 (and thus further move the sample container) through / into the sample analyzer 100 (e.g., through...) Figure 4 (door 170), the carrier 132 can travel from the receiving position and thus allow at least one sample rack in the sample rack 102 to travel further along the presentation channel 128 (see, for example, see...). Figure 3 and Figure 4 (The movement between) enters the sample analyzer 100. When it reaches a predetermined position within the sample analyzer 100, samples can be removed from one or more sample containers and / or processed and / or analyzed by the sample analyzer 100 and / or in other ways within the sample analyzer 100.

[0074] In order to pass through / remove sample rack 102 from sample analyzer 100 (and thus remove the sample containers loaded thereon), carrier 132 can retract from a predetermined position to a receiving position and thereby remove at least one sample rack from sample rack 102 from sample analyzer 100 along presentation channel 128 (e.g., see...). Figure 4 and Figure 3 (Movement between). In order to (e.g., through) Figure 4 When the door 170 in the middle reaches the receiving position, the carrier 132 positions at least one sample rack in the sample rack 102 along the lateral moving section 120. The pusher 130 then causes the sample rack 102 to travel and thus position at least one sample rack in the sample rack 102 into the unloading channel 126 when the carrier 132 is in the receiving position (see, for example, see...). Figure 2 and Figure 3The movement between the sample racks, however, involves the pusher 130 or the stacking of sample racks 102 pushing at least one sample rack from the carrier 132 into the unloading channel 126. As the sample rack moves from the presentation channel 128 to the unloading channel 126, the first hook 160 transfers engagement from track 140 to track 144, and the second hook 162 transfers engagement from track 142 to track 146. To further move the sample rack 102 (and thus further move the sample container) through / out of the sample analyzer 100, another sample rack 102 can be similarly pushed from the carrier 132 into the unloading channel 126, thereby pushing at least one sample rack from the sample rack 102 along the unloading channel 126. Similarly, the sample rack 102 can be driven away from the end of the unloading channel 126 (e.g., into a waste container) and thus unloaded from the sample analyzer 100.

[0075] Alternatively, to unload sample rack 102 from SPU 104, a first hook 160 may disengage from tracks 136, 140, and / or 144, and a second hook 162 may disengage from tracks 138, 142, and / or 146. To facilitate removal of sample rack 102 from SPU 104, an operator may manually grip handle 168. Sample rack 102 may be unloaded from SPU 104 via manual or automated means (e.g., by a robot, pick-and-place device, etc.). Unloading channel 126 (similar to loading channel 124) may hold multiple sample racks 102 simultaneously. Sample rack 102 is typically unloaded from SPU 104 at unloading channel 126.

[0076] Reference Figures 5 to 8 An example of a sample rack 102 containing a container 180 is shown. Figure 5 This is a front view of an example pipe rack, and Figure 6 yes Figure 5 A three-dimensional view of the pipe rack. Figure 7 This is a front view of the example cup holder, and Figure 8 yes Figure 7 A 3D diagram of a cup holder.

[0077] The sample rack 102 includes a sample rack slot 190, which can hold a container 180. The sample rack slot 190 can define a container position 334, as shown below. Figures 21A to 21C As shown.

[0078] In some embodiments, the sample holder 102 includes, for example: Figure 5 and Figure 6The tube rack 102A is shown. In the example shown, the tube rack 102A is loaded with tubes 182 of different sizes (i.e., examples of containers 180), such as the first tube 182A, the second tube 182B, and the third tube 182C. In this example, one of the sample rack slots 190 in the tube rack 102A is empty. As described herein, different types of tubes 182 can be identified by the sample container identification unit 110.

[0079] In other embodiments, the sample holder 102 includes, as shown below: Figure 7 and Figure 8 The cup holder 102B is shown. In the example shown, the cup holder 102B is loaded with cups 184 of different sizes (i.e., examples of containers 180), such as the first cup 184A, the second cup 184B, and the third cup 184C. In this example, four sample holder slots in the sample holder slots 190 of the cup holder 102B are empty. As described herein, different types of cups 184 can be identified by the sample container identification unit 110.

[0080] Figure 9 It is a sample holder 102 for holding various types of sample tubes 182, for example. Figure 5 and Figure 6 A perspective sectional view of the tube rack 102A. As shown, the tube rack 102A is configured to accommodate sample tubes 182 of different sizes.

[0081] Figures 10 to 13 Various types of sample cups 184 are shown. As shown, the sample cups 184 can be of various types, and the cup holder 102B is configured to accommodate sample cups 184 of different sizes.

[0082] Reference Figures 14 to 18 An example of the sample container identification unit 110 is described with respect to sample rack 102. Figures 14 to 18 The sample container identification unit 110 is shown primarily for tube rack 102A. However, it is understood that the sample container identification unit 110 can also be used and operated similarly for cup rack 102B.

[0083] In particular, Figure 14 This is a perspective view of the tube rack 102 located in the delivery channel 128 of SPU 104. Figure 15 yes Figure 14 An enlarged view of pipe rack 102. Figure 14 and Figure 15 In the image, the tube rack 102 is shown partially within the field of view of the camera unit of the sample container identification unit 110. Figure 16 This is another perspective view of the tube rack 102 located in the presentation channel 128 of the SPU 104, which is partially within the field of view of the camera unit of the sample container identification unit 110. Figure 17 This is another perspective view of the tube rack 102 located in the presentation channel 128 of the SPU 104, which is partially within the field of view of the container detection unit of the sample container identification unit 110.

[0084] The sample container identification unit 110 operates to identify containers 180 in the sample rack 102 and detect various features associated with the containers 180 to determine their type. For example, the sample container identification unit 110 operates to detect a container identifier 186, such as a barcode or QR code set to the container 180. As described herein, the container identifier 186 is used to verify the containers 180 in the sample rack 102. The container identifier 186 can be set to any suitable location on the container 180. Figure 5 , Figure 6 and Figure 15 In the example shown, container identifier 186 is set on the outside of sample tube 182. Container identifier 186 can similarly be set on the outside of sample cup 184.

[0085] Furthermore, the sample container identification unit 110 operates to identify the sample rack 102. For example, the sample container identification unit 110 operates to detect a sample rack identifier 188, such as a barcode or QR code set to the sample rack 102. As described herein, the sample rack identifier 188 is used to verify the sample rack 102. The sample rack identifier 188 can be set at any suitable location on the sample rack 102. Figure 5 , Figure 6 and Figure 15 In the example shown, the sample rack identifier 188 is positioned at the front of the sample rack 102, adjacent to the first end 164 of the sample rack 102. Other locations for the sample rack identifier 188 within the sample rack 102 are also possible. The sample rack identifier 188 may be positioned on the tube rack 102A and / or the cup rack 102B.

[0086] In some embodiments, the sample container identification unit 110 includes a camera unit 202, a container detection unit 204, a screen 206, and a computing device 208. The camera unit 202 can be fixed to the SPU 104 using a mounting bracket 210.

[0087] The camera unit 202 operates to detect and identify the sample rack 102 and the containers 180 within it, and to determine the characteristics of the sample rack 102 and the containers 180 therein. Such characteristics of the containers 180 can be used to identify the type of container 180, as discussed herein. The camera unit 202 is positioned in front of the sample rack 102, which is movable relative to the camera unit 202.

[0088] As described herein, camera unit 202 is operable to read identifiers associated with sample rack 102 and containers 180 therein. Furthermore, camera unit 202 is operable to locate, analyze, and inspect sample rack 102 and containers 180 therein. Camera unit 202 can be connected to computing device 208 for various processing tasks. An example of camera unit 202 includes the ADVANTAGE 100 series, available from Cognex Corporation (Natick, MA).

[0089] The camera unit 202 can be supported in the sample analyzer 100 using a mounting bracket 210. The mounting bracket 210 is configured to space the camera unit 202 from the sample holder 102 and to position the camera unit 202 relative to the sample holder 102 at an instantaneous position such that the camera unit 202 can have a field of view (FOV) over the container 180 being inspected and / or the sample holder 102. (See reference...) Figure 18 An example of mounting bracket 210 is further described and illustrated.

[0090] The camera unit 202 may include a light source 203, such as an LED, operable to emit light toward the sample holder 102 (and toward the screen 206). The screen 206 is used to project light back in the field of view (FOV) direction of the camera unit 202 by reflecting the light toward the aperture of the camera unit. An example of the camera unit 202 includes a model named ADVANTAGE 102, such as part number ADV102-CQBCKFS1-B, available from Cognex Corporation (Nartick, Massachusetts).

[0091] Container detection unit 204 operates to detect the presence of container 180 in sample rack 102. Container detection unit 204 is arranged to scan sample rack 102 as it moves relative to container detection unit 204. In the example shown, container detection unit 204 is arranged on one side of sample rack 102, while the other side of sample rack 102 faces camera unit 202. As described herein, container detection unit 204 can partially or completely detect sample rack 102 and determine the location of any container in sample rack 102 (e.g., such as...). Figures 21A to 21C Is container position 334 shown empty?

[0092] The container detection unit 204 can use various sensors. In some examples, the container detection unit 204 includes various types of photoelectric sensors. For example, the container detection unit 204 includes a reflector-type photoelectric sensor (also known as a reflective light blocker or light reflector), which positions a light emitting element and a light receiving element on the same surface (so that they face the same direction) and is configured to detect the presence and position of an object based on reflected light from a target object. An example of such a reflector-type photoelectric sensor is the GP2A25J0000F series available from Sharp Corporation (Osaka, Japan). The container detection unit 204 can also use other types of light sensors such as light blockers (also known as transmissive light sensors), which consist of light emitting elements and light receiving elements aligned face-to-face in a single package and function by detecting light obstruction when a target object enters between the two elements.

[0093] Screen 206 is arranged to be used with camera unit 202 to improve image capture by camera unit 202. Screen 206 is arranged opposite camera unit 202 such that sample holder 102 is positioned between camera unit 202 and screen 206. Screen 206 is used to project light back in the field of view (FOV) direction of camera unit by reflecting light toward the aperture of camera unit.

[0094] Screen 206 is made of one or more materials that can provide different levels of reflectivity. Furthermore, screen 206 includes materials configured to increase the scanning range of barcodes or other identifiers. For example, screen 206 includes a retroreflective sheet, an example of which includes 3M materials available from 3M Corporation (Maplewood, MN). TM Scotchlite TM Sheet material 7610.

[0095] The computing device 208 is connected to the camera device unit 202 and operates to process data transmitted from the camera device unit 202, such as performing image processing and evaluation. Additionally, the computing device 208 is connected to the container detection unit 204 and operates to detect the presence of containers in the sample holder. The computing device 208 may be included in a reference... Figure 34 At least some of the components included in the example computing device shown and described.

[0096] In some embodiments, the computing device 208 performs software applications that process and evaluate images from the camera unit 202 and determine various features associated with the sample holder 102 and / or the containers 180 within the sample holder 102. An example of such software application is Cognex In-Sight Vision Software, available from Cognex Corporation (Natick, Massachusetts), which provides various tools such as edge detection (“Edge”), pattern matching (“PatternMatch”), histogram analysis (“Histogram”), and barcode detection (“ReadIDMax”). In other embodiments, the camera unit 202 may be an intelligent camera capable of determining such features itself, in which case such features are provided to the computing device 208 for use in its further processing. An example of such an intelligent camera is the Advantage 102 from Cognex Corporation (Natick, Massachusetts).

[0097] Reference Figure 18 The mounting bracket 210 is configured to position the camera unit 202 in front of the sample holder 102 and facing the front portion 150 of the sample holder 102. The camera unit 202 is spaced apart from the front portion 150 of the sample holder 102 by a distance L1, which can range from approximately 100 mm to approximately 200 mm, while the height H1 of the sample holder 102 can range from approximately 50 mm to approximately 100 mm. The height H1 of the sample holder 102 can be defined as the distance between the bottom 156 and the top 158 of the sample holder 102 (see also...). Figure 5 In some embodiments, the mounting bracket 210 is configured to support the camera unit 202 at an angle A relative to the bottom 156 of the sample holder 102, such that the field of view (FOV) covers the entire height of the container 180 housed in the sample holder 102. In some embodiments, angle A can be in the range of about 90 degrees to about 120 degrees. In other embodiments, other ranges for distance L1, height H1, and angle A are also possible.

[0098] Figure 19 This is a flowchart of an example method 300 for performing sample container identification on sample rack 102. In some embodiments, method 300 may be performed at least in part by the sample container identification unit 110 and related devices in the sample analyzer 100. See also... Figures 20 to 2 3 is used to describe method 300.

[0099] Method 300 may begin at operation 302, in which sample holder 102 is operated to move relative to sample container recognition unit 110 toward first image position 330A.

[0100] The sample rack 102 can be moved relative to the sample container identification unit 110 to multiple predetermined image positions 330, such that different portions of the sample rack 102 are observed and captured by the sample container identification unit 110. For example, the camera unit 202 of the sample container identification unit 110 may have a field of view (FOV) limited to a portion of the sample rack 102. Therefore, in order to examine the entire sample rack 102 (i.e., all sample rack slots 190 of the sample rack 102), the sample rack 102 is moved relative to the camera unit 220, such that the camera unit 220 captures multiple images at multiple positions (i.e., image positions 330). Each image shows a portion of the sample rack 102 at a specific position (i.e., a specific image position) of the sample rack 102. Each portion of the sample rack 102 (i.e., sample rack portion 332) may include one or more container positions 334 accommodating one or more containers 180. As described herein, the container positions 334 of the sample rack 102 correspond to the sample rack slots 190 of the sample rack 102.

[0101] like Figure 20 As shown, in some embodiments, the sample rack 102 has three image positions 330 (e.g., a first image position 330A, a second image position 330B, and a third image position 330C). At each image position 330, the camera unit 202 is configured to have a field of view (FOV) that captures a portion of the sample rack 102 (i.e., sample rack section) 332. In the example shown, the camera unit 202 may capture an image of the first sample rack section 332A when the sample rack 102 is at the first image position 330A, an image of the second sample rack section 332B when the sample rack 102 is at the second image position 330B, and an image of the third sample rack section 332C when the sample rack 102 is at the third image position 330C. The image of each sample rack section 332 may show one or more container positions 334.

[0102] exist Figures 21A to 21CIn the example shown, the first image 350 was captured when the sample rack 102 was in the first image position 330A. The first image 350 shows a first sample rack portion 332A of the sample rack 102, which includes a first container position 334A and a second container position 334B within the sample rack 102. The second image 352 was captured when the sample rack 102 was in the second image position 330B. The second image 352 shows a second sample rack portion 332B of the sample rack 102, which includes a third container position 334C and a fourth container position 334D within the sample rack 102. The third image 354 was captured when the sample rack 102 was in the third image position 330C. The third image 354 shows a third sample rack portion 332C of the sample rack 102, which includes a fifth container position 334E, a sixth container position 334F, and a seventh container position 334G within the sample rack 102.

[0103] In some embodiments, the images 350, 352, and 354 captured by the camera unit 202 of the sample container identification unit 110 may be low-exposure monochrome images. Figures 21A to 21C Images 350, 352, and 354 shown are for a tube rack 102A with sample tubes 182. Figure 22 Image 356 shows a portion of a cup holder 102B with sample cup 184. Figure 35 Image 357 shows a portion of a tube rack with three sample tubes 182, wherein two of the three sample tubes 182 have sample cups 184 inserted thereon. Figure 36 Image 358 shows a portion of a cup holder with sample cup 184.

[0104] At operation 304, as the sample rack 102 moves toward the first image position 330A, the presence of one or more containers 180 in the sample rack portion 332A of the sample rack 102 is detected. As described herein, the container detection unit 204 can be operated to perform container presence detection. The sample rack portion 332A is the portion of the sample rack 102 located at or near the first image position 330A within the field of view (FOV) of the camera device unit 202 of the sample container identification unit 110. In some embodiments, the container detection unit 204 can be operated to detect the presence of containers in the sample rack portion (e.g., the first sample rack portion 332A) of the sample rack 102 as the sample rack 102 moves toward the first image position 330A. In other embodiments, the presence of containers can be detected when the sample rack 102 is located at or near the first image position 330A.

[0105] At operation 306, it is determined whether any container 180 exists in sample rack portion 332A of sample rack 102. If any container 180 exists ("yes" in this operation), method 300 proceeds to operation 308. If no container 180 is detected ("no" in this operation), method 300 proceeds to operation 316, in which sample rack 102 moves to the next image position 330 (e.g., 330B after 330A). Therefore, if no container is found at a particular image position 330, sample rack 102 can bypass that particular image position. For example, sample rack 102 can jump to the next image position 330 without performing container recognition operations at the particular image position (e.g., operations 308 and 310), thus saving time and resources.

[0106] At operation 308, the sample container identification unit 110 operates to detect one or more container identifiers 186 associated with container 180. The sample container identification unit 110 may further operate to verify container 180 based on the detected container identifiers 186. In some embodiments, the sample holder 102 stops at image position 330 for identifier detection. For example, as... Figure 23A As shown, the sample container identification unit 110 (e.g., its imaging device unit 202) operates to capture an image 340 of the sample holder 102 containing a portion of the sample tube 182. In some embodiments, image 340 is a high-exposure monochrome image for identifier detection. Once image 340 is captured, the sample container identification unit 110 operates to identify a container identifier 186 in image 340 and read the container identifier 186 for verification of the container 180 (i.e., sample tube 182 in this example). Figure 23B As shown in rectangle 344, container identifier 186 is identified in image 340. Various image processing methods can be used to identify and read container identifiers. An example of such image processing methods is Cognex In-Sight Vision Software, available from Cognex Corporation (Nartick, Massachusetts), which offers various tools such as edge detection (“Edge”), pattern matching (“PatternMatch”), histogram analysis (“Histogram”), and barcode detection (“ReadIDMax”).

[0107] Furthermore, the sample container identification unit 110 can operate to detect the sample rack identifier 188 set to the sample rack 102 and verify the sample rack 102 based on the sample rack identifier 188. The sample rack identifier 188 is detected and read in a manner similar to that of the container identifier 186 described above. For example, as... Figure 23AAs shown, the image 340 captured by the sample container identification unit 110 (e.g., its camera unit 202) may include a portion of the sample holder 102 having a sample holder identifier 188. Once image 340 is captured, the sample container identification unit 110 operates to identify the sample holder identifier 188 in image 340 and read the sample holder identifier 188 for verification of the container 180. Figure 23B The sample holder identifier 188 is identified in image 340, as shown by rectangle 346. Various image processing methods can be used to identify and read sample holder barcodes. An example of such image processing methods is Cognex In-Sight Vision Software, available from Cognex Corporation (Nartick, Massachusetts), which provides various tools such as edge detection (“Edge”), pattern matching (“PatternMatch”), histogram analysis (“Histogram”), and barcode detection (“ReadIDMax”).

[0108] At operation 310, the sample container identification unit 110 operates to determine the characteristics of the container 180. In some embodiments, the sample holder 102 remains stationary to determine the container characteristics. As described herein, the sample container identification unit 110 operates to examine an image of the sample holder 102 having the container 180 (e.g., ...). Figures 21A to 21C , Figure 22 , Figure 35 and Figure 36 Images 350, 352, 354, 356, 357, and 358 are processed to determine various features associated with container 180, such as the dimensions of each container (e.g., height and width) and the presence of a lid on the container. As described in more detail below, such features can be used to identify the type of container. Various image processing methods can be used to determine such features of the containers in the sample rack. An example of such an image processing method is Cognex In-Sight Vision Software, available from Cognex Corporation (Nartick, Massachusetts), which offers various tools such as edge detection (“Edge”), pattern matching (“PatternMatch”), histogram analysis (“Histogram”), and barcode detection (“ReadIDMax”).

[0109] At operation 312, it is determined whether the entire sample rack 102 has been inspected. In some embodiments, it is determined whether the sample rack 102 has moved past all predetermined image positions 330. In other embodiments, it is determined whether all sample rack portions 332 of the sample rack 102 have been captured by the imaging unit 202. In still other embodiments, it is determined whether all container positions 334 of the sample rack 102 have been captured by the imaging unit 202.

[0110] If it is determined that the entire sample holder 102 has been inspected ("Yes" in this operation), method 300 proceeds to operation 314, in which the sample holder 102 is moved to another location inside or outside the sample analyzer 100 for subsequent processing (e.g., moved to a new location on the presentation channel 128 from which fluid will be drawn from the sample vessel). Otherwise ("No" in this operation), method 300 proceeds to operation 316, in which the sample holder 102 is moved to the next image position 330 (e.g., 330B after 330A). As the sample holder 102 moves to the next image position 330, or as the sample holder 102 is located at or near the next image position 330, operation 304 and subsequent operations are performed as described above. In some embodiments, if the sample holder barcode reading has already been completed once when performing operation 304 and subsequent operations, the sample holder barcode reading can be omitted (as shown in operation 308).

[0111] Figure 37 This is a flowchart of an example method for processing samples in a customized manner based on identification information, such as information that can be customized based on... Figure 19 Obtained using the method shown. First, in Figure 37 During the process, it is determined 3701 whether the user has specified instructions on how to handle the contents of a particular container. This can be achieved, for example, by the computing device 208 using an identifier provided by a barcode attached to the container and matching that identifier with test instructions previously stored in the memory of the computing device 208 to determine whether the user has provided a specific type of treatment for the sample in the container (e.g., a specific volume of fluid for sampling). If such user-specified container-specific instructions exist, fluid can be aspirated from the container and dispensed according to those instructions 3702. For example, if the user has specified two samples, a sample of volume X and a sample of volume Y should be taken from the specific sample, the computing device 208 can send instructions to the sample holder 105, which instruct the sample holder 105 to aspirate the first sample of volume X and dispense it into a first sample dish on the sample wheel 129, and to aspirate the second sample of volume Y and dispense it into a second sample dish on the sample wheel 129.

[0112] In such Figure 37 In the described process, if no container-specific processing instructions are specified, the process can continue to determine the type of the sample in container 3703. This can be achieved, for example, by using processes such as... Figure 19 The identifier (e.g., barcode) identified during the processing is used to retrieve a test instruction for a sample included in the container, and the sample is then considered to have a type based on the indicated test (e.g., if a hepatitis B test has been indicated for the sample, then the sample can be identified as having the "hepatitis B test" type). Similarly, in some implementations, the type of sample can be determined based on the characteristics of the container. For example, if the height and width of the container are consistent with those of a container as a small-volume cup or a pediatric cup, then the sample can be identified as having the "pediatric" type. As another example of how the type can be determined, in some cases, the user can explicitly specify the "type" of a particular sample, in which case the type can be determined as the user-specified type.

[0113] After determining the type (3703), it can be checked (3704) whether any type-specific instructions exist. This can be achieved, for example, by the computing device 208 checking the memory to determine if any instructions previously set to be used to process samples of the determined type already exist. For example, in the case of HIV testing, positive results must be confirmed multiple times to avoid errors; therefore, with this in mind, an instruction can be defined in the analyzer's memory stating that when aspirating fluid from a sample container of the "HIV test" type, a sufficiently large volume should be aspirated to run not only the initial test but also the reflective test required to avoid errors. As another example, considering the small volume of fluid available for samples of the "pediatric" type, an instruction can be defined stating that fluid should be aspirated from the "pediatric" sample using a sample pipette that can dispense the fluid directly into the reactor dish in the analyzer's reaction construction zone, rather than dispensing the fluid into sample dishes in the sample wheel, thereby avoiding unnecessary loss of effective volume due to ineffective space in intermediate sample dishes.

[0114] Other, more complex methods for checking whether 3704 contains specific instructions are also possible. For illustration, consider a case where the sample 3703 is determined to have multiple types, one type for tests to be performed by a first analytical element (e.g., a photometer in the analyzer), and another type for tests to be performed by a second analytical element (e.g., a flow cell coupled to an external region of the analyzer via a track). In this case, checking 3704 might involve applying the rule that for a sample having a type corresponding to a test to be performed by multiple analytical elements, a sample should be created for each of the analytical elements to be used to test the sample. According to this disclosure, other variations (e.g., the computing device 208 can determine whether there are inconsistent instructions associated with different types and resolve such inconsistencies by using a hierarchy of instructions or by providing a warning and requesting further input from the user) are also possible and will be readily apparent to those skilled in the art. Therefore, the above discussion of type-specific processing instructions for checking 3704 should be understood as illustrative only and not as limiting.

[0115] exist Figure 37 During the process, if there are type-specific instructions for a particular sample, fluid 3705 can be aspirated and dispensed from the sample container according to those instructions; or if no type-specific instructions exist, fluid 3706 can be aspirated and dispensed according to the default processing instructions used by the analyzer. For example, if the analyzer has a default behavior of aspirating fluid and dispensing it into sample vessels on the sample wheel for storage until it is subsequently transferred to a reactor vessel, instructions for "pediatric" type vessels can be used to cause the analyzer to aspirate fluid directly from the sample container into the reactor vessel, such as... Figure 38 and Figure 40 As shown. Alternatively, in the absence of such type-specific instructions, the analyzer's default behavior can be used to simply process the samples. This process can then be repeated for each sample container in the sample rack, allowing the analyzer to properly process all samples.

[0116] Figure 24 This is a flowchart of another example method 400 for performing sample container identification for sample rack 102. In some embodiments, method 400 may be performed at least in part by SPU 104, sample container identification unit 110 and / or other devices in sample analyzer 100.

[0117] Method 400 may begin at operation 402, in which the sample holder 102 is moved into the presentation channel 128. In some embodiments, the carrier 132 is operated to travel the sample holder 102 into the presentation channel 128, for example from... Figure 3The position shown in the image is moved to Figure 4 The location shown in the image.

[0118] As shown, the sample rack 102 is oriented to move along the presentation channel 128 toward the sample container identification unit 110, such that the first sample rack portion 332A of the sample rack 102 (which includes the first container position 334A and the second container position 334B in this example) first approaches the sample container identification unit 110.

[0119] At operation 404, the sample container identification unit 110 operates the container detection unit 204 to detect the presence of any container 180 in the first sample rack portion 332A of the sample rack 102. Figure 19 Operation 304 similarly performs operation 404. In the example shown, the first sample rack portion 332A of the sample rack 102 includes a first container position 334A and a second container position 334B, and therefore, the container detection unit 204 operates to detect whether either the first container position 334A or the second container position 334B is occupied by the container 180, or whether both the first container position 334A and the second container position 334B are occupied by the container 180.

[0120] Thus, as Figure 17 As the sample rack 102 is introduced into the presentation channel 128 and moves toward the first image position 330A, the container detection unit 204 performs a first fly-by check for the presence of containers in the first sample rack portion 332A of the sample rack 102.

[0121] The container detection unit 204 may include one or more sensors of various types. In some examples, the container detection unit 204 includes various types of photoelectric sensors. For example, the container detection unit 204 includes a reflector-type photoelectric sensor (also known as a reflective light blocker or light reflector), which positions a light emitting element and a light receiving element on the same surface (so that they face the same direction) and is configured to detect the presence and position of an object based on reflected light from the target object. An example of such a reflector-type photoelectric sensor is the GP2A25J0000F series available from Sharp Corporation (Osaka, Japan). Other types of photoelectric sensors may also be used in the container detection unit 204.

[0122] At operation 406, if any container 180 is detected in the first sample rack portion 332A of sample rack 102, the sample container identification unit 110 operates to store information indicating that the sample rack includes at least one container. For example, if at operation 404 it is determined that sample rack 102 (e.g., its first sample rack portion 332A) includes one or two containers 180, the sample container identification unit 110 operates to set a container presence flag (“at least one container presence flag”) to true.

[0123] At operation 408, the sample holder 102 continues to move to the first image position 330A and stops at the first image position 330A. For example, the carrier 132 operates to continuously move the sample holder 102 to the first image position 330A and stop the sample holder 102 at the first image position 330A.

[0124] As described herein, the first image position 330A can be the position of the sample holder 102 relative to the camera unit 202, where the container 180 fixed at the first container portion 332A, including the first container position 334A and the second container position 334B, can be at least partially captured by the camera unit 202. Figure 21A and Figure 23A As shown. In the example shown, the sample holder identifier 188, which is set to the sample holder 102, can also be observed at the first image position 330A.

[0125] At operation 410, the sample container identification unit 110 operates the camera device unit 202 to read the container identifier 186 of each container 180 contained in the first sample rack portion 332A of the sample rack 102 (which includes the first container position 334A and / or the second container position 334B). Operation 410 is similar to Figure 19 Operation 308. In some embodiments, the camera unit 202 operates to capture an image of the first sample holder portion 332A of the sample holder 102 (e.g., Figure 21A The first image 350 is processed to detect and read the container identifier 186 of the container 180 at the first container position 334A and the second container position 334B (e.g., the first image 350 in the image is processed to detect and read the container identifier 186 of the container 180 at the first container position 334A and the second container position 334B). Figure 23A and Figure 23B (As shown).

[0126] Once container identifier 186 is read, sample container identification unit 110 can identify container 180 based on the detected container identifier 186. Sample container identification unit 110 can store identification information of container 180 (e.g., container ID).

[0127] In some implementations, the sample container identification unit 110 operates to compare the detected container identifier 186 with user-provided information (e.g., user input about the container that can be received via the input device of the sample analyzer 100) and determine whether the container identifier 186 matches the user input. The sample container identification unit 110 may operate to store information indicating that a particular container location 334 (e.g., 334A and / or 334B) includes a container 180 that does not match the user input. For example, the sample container identification unit 110 may operate to mark the container locations 334 (e.g., first container location 334A and / or second container location 334B) of the sample rack 102 that hold containers with mismatched container identifiers 186.

[0128] Additionally, the sample container identification unit 110 further operates the camera device unit 202 to read the sample rack identifier 188 of the sample rack 102. In the example shown, the sample rack identifier 188 is set to be adjacent to the first sample rack portion 332A of the sample rack 102 (near the first end 164 of the sample rack 102). Therefore, the image of the first sample rack portion 332A of the sample rack 102 (e.g., Figure 21A The first image (350) includes a sample holder identifier 188 of the sample holder 102. The sample container recognition unit 110 processes the image to detect and read the sample holder identifier 188 of the sample holder 102.

[0129] Once the sample rack identifier 188 is read, the sample container identification unit 110 can identify the sample rack 102 based on the detected sample rack identifier 188. The sample container identification unit 110 can store the identification information of the sample rack 102 (e.g., sample rack ID).

[0130] Various image processing methods can be used to identify and read identifiers 186 and 188. One example of such image processing methods is Cognex In-Sight Vision Software, available from Cognex Corporation (Nartick, Massachusetts), which offers a variety of tools such as edge detection (“Edge”), pattern matching (“PatternMatch”), histogram analysis (“Histogram”), and barcode detection (“ReadIDMax”).

[0131] At operation 412, the sample container identification unit 110 can be operated to determine whether the detected sample rack identifier 188 is valid. If the sample rack identifier 188 is determined to be valid ("Yes" in this operation), method 400 proceeds to operation 414. Otherwise ("No" in this operation), method 400 jumps to operation 448, in which sample rack 102 is moved to unloading channel 126. At operation 448, the sample analyzer 100 can be operated to alert the user to the invalidity of the sample rack determined at operation 412. The alert can be of various types, such as a visual and / or auditory warning or notification via the sample analyzer 100.

[0132] At operation 414, the sample container identification unit 110 can operate the camera device unit 202 to determine the characteristics of the container 180 at the first sample rack portion 332A of the sample rack 102. Figure 19 Operation 310 in the middle is similarly executed as operation 414.

[0133] For example, the sample container recognition unit 110 operates to recognize an image of the first sample holder portion 332A of the sample holder 102 (e.g. Figure 21A The first image 350 is processed to determine various features associated with container 180, such as the dimensions of each container (e.g., height and width) and the presence of a lid on the container. As described in more detail below, such features can be used to identify the type of container. (Refer to...) Figure 25 Describe and illustrate a detailed example method for performing operation 414.

[0134] Additionally, the sample container identification unit 110 can operate the camera device unit 202 to determine the features of the sample rack 102 in a similar manner to determining the container features. In some embodiments, an image of the first sample rack portion 332A of the sample rack 102 (e.g., Figure 21A The first image (350) is processed to determine the sample rack features. In other embodiments, the sample rack features can be determined using a sample rack identifier 188 identified from the captured image.

[0135] In some embodiments, the data on container features and / or sample rack features obtained above can be stored in the sample container identification unit 110. In some embodiments, if a container has predetermined undesirable features (e.g., uncovered, unapproved, and / or inappropriate container location), the sample container identification unit 110 can store information indicating that a particular container location 334 (e.g., 334A and / or 334B) includes a container 180 that does not match the user input. For example, the sample container identification unit 110 can be operated to mark the container locations 334 (e.g., first container location 334A and / or second container location 334B) of the sample rack 102 that hold containers with such undesirable features.

[0136] At operation 416, the sample holder 102 moves toward the second image position 330B. As described herein, the second image position 330B can be the position of the sample holder 102 relative to the imaging device unit 202, where the container 180 fixed at the second container portion 332B, including the third container position 334C and the fourth container position 334D, can be at least partially captured by the imaging device unit 202, such as... Figure 21B As shown.

[0137] At operation 418, the sample container identification unit 110 operates the container detection unit 204 to detect the presence of any container 180 in the second sample rack portion 332B of the sample rack 102. Figure 19 Operation 418 is performed similarly to operation 304 or operation 404 above. In the example shown, the second sample rack portion 332B of the sample rack 102 includes a third container position 334C and a fourth container position 334D, and therefore the container detection unit 204 operates to detect whether either the third container position 334C or the fourth container position 334D is occupied by the container 180, or to detect whether both the third container position 334C and the fourth container position 334D are occupied by the container 180.

[0138] Thus, as the sample holder 102 moves toward the second image position 330B, the container detection unit 204 performs a second instant check on the presence of containers in the second sample holder portion 332B of the sample holder 102.

[0139] At operation 420, if any container 180 is detected in the second sample rack portion 332B of sample rack 102, the sample container identification unit 110 operates to store information indicating that the sample rack includes at least one container. For example, if at operation 418 it is determined that sample rack 102 (e.g., its second sample rack portion 332B) includes one or two containers 180, the sample container identification unit 110 operates to set a container presence flag (“at least one container presence flag”) to true.

[0140] At operation 422, it is determined whether any container exists at the second sample rack portion 332B of sample rack 102 (e.g., one or both of the third container position 334C and the fourth container position 334D). If it is determined that any container exists at the second sample rack portion 332B (“Yes”), method 400 proceeds to operation 424. Otherwise (“No”), method 400 jumps to operation 448.

[0141] At operation 424, the sample holder 102 is stopped and brought to rest at the second image position 330B.

[0142] At operation 426, the sample container identification unit 110 operates the camera device unit 202 to read the container identifier 186 of each container 180 contained in the second sample rack portion 332B of the sample rack 102 (which includes the third container position 334A and / or the fourth container position 334D). Operation 418 is similar. Figure 19 Operation 308 or operation 410 above. In some embodiments, the camera unit 202 operates to capture an image of the second sample holder portion 332B of the sample holder 102 (e.g., Figure 21B The second image 352 is processed to detect and read the container identifier 186 of the container 180 at the third container position 334C and the fourth container position 334D.

[0143] Once container identifier 186 is read, sample container identification unit 110 can identify container 180 based on the detected container identifier 186. Sample container identification unit 110 can store identification information of container 180 (e.g., container ID).

[0144] In some implementations, the sample container identification unit 110 operates to compare the detected container identifier 186 with user-provided information (e.g., user input about the container that can be received via the input device of the sample analyzer 100) and determine whether the container identifier 186 matches the user input. The sample container identification unit 110 may operate to store information indicating that a particular container location 334 (e.g., 334C and / or 334D) includes a container 180 that does not match the user input. For example, the sample container identification unit 110 may operate to mark the container locations 334 (e.g., first container location 334C and / or second container location 334D) of the sample rack 102 that hold containers with mismatched container identifiers 186.

[0145] In some implementations, the sample container identification unit 110 further operates to cross-check whether the container 180 identified at the second image position 330B matches (or is compatible with) the identifier of the sample rack 102 (e.g., the sample rack ID found at operation 410).

[0146] At operation 428, the sample container identification unit 110 can operate the camera device unit 202 to determine the characteristics of the container 180 at the second sample rack portion 332B of the sample rack 102. Figure 19 Operation 310 in the middle or operation 414 above are executed similarly to operation 414.

[0147] For example, the sample container recognition unit 110 operates to recognize an image of the second sample holder portion 332B of the sample holder 102 (e.g., Figure 21B The second image (352) is processed to determine various features associated with container 180, such as the dimensions of each container (e.g., height and width) and the presence of a lid on the container. As described in more detail below, such features can be used to identify the type of container. (Refer to...) Figure 25 Describe and illustrate a detailed example method for performing operation 428.

[0148] In some embodiments, the data on the container features obtained above can be stored in the sample container identification unit 110. In some embodiments, if a container has predetermined undesirable features (e.g., uncovered, unapproved, and / or inappropriate container location), the sample container identification unit 110 can store information indicating that a particular container location 334 (e.g., 334C and / or 334D) includes a container 180 that does not match the user input. For example, the sample container identification unit 110 can be operated to mark the container locations 334 (e.g., third container location 334C and / or fourth container location 334D) of the sample rack 102 that hold containers with such undesirable features.

[0149] At operation 430, the sample holder 102 moves toward the third image position 330C. As described herein, the third image position 330C can be the position of the sample holder 102 relative to the imaging device unit 202, where the container 180 fixed at the third container portion 332C, including the fifth container position 334E, the sixth container position 334F, and the seventh container position 334G, can be at least partially captured by the imaging device unit 202, such as... Figure 21C As shown.

[0150] At operation 432, the sample container identification unit 110 operates the container detection unit 204 to detect the presence of any container 180 in the third sample rack section 332C of the sample rack 102. Figure 19Operation 432 is performed similarly to operation 304 or operation 404 or 418 above. In the example shown, the third sample rack portion 332C of the sample rack 102 includes a fifth container position 334E, a sixth container position 334F, and a seventh container position 334G, and therefore, the container detection unit 204 operates to detect whether any or all of the fifth container position 334E, the sixth container position 334F, and the seventh container position 334G are occupied by one or more containers 180.

[0151] Thus, as the sample holder 102 moves toward the third image position 330C, the container detection unit 204 performs a third instant check on the presence of containers in the third sample holder portion 332C of the sample holder 102.

[0152] At operation 434, if any container 180 is detected in the third sample rack portion 332C of sample rack 102, the sample container identification unit 110 operates to store information indicating that the sample rack includes at least one container. For example, if at operation 432 it is determined that sample rack 102 (e.g., its third sample rack portion 332B) includes one or two containers 180, the sample container identification unit 110 operates to set a container presence flag (“at least one container presence flag”) to true.

[0153] At operation 436, the sample container identification unit 110 operates to determine the state (true or false) of the container presence flag ("at least one container presence flag"). If the state is true ("true"), method 400 proceeds to operation 438. Otherwise ("false"), method 400 jumps to operation 448.

[0154] At operation 438, it is determined whether any container exists at the third sample rack portion 332C of sample rack 102 (e.g., any or all of the fifth container position 334E, the sixth container position 334F, and the seventh container position 334G). If it is determined that any container exists at the third sample rack portion 332C (“Yes”), method 400 proceeds to operation 440. Otherwise (“No”), method 400 jumps to operation 446.

[0155] At operation 440, the sample holder 102 is stopped and brought to rest at the third image position 330C.

[0156] At operation 442, the sample container identification unit 110 operates the camera device unit 202 to read the container identifier 186 of each container 180 contained in the third sample rack portion 332C of the sample rack 102 (which includes the fifth container position 334E, the sixth container position 334F, and the seventh container position 334G). Operation 418 is similar to... Figure 19Operation 308 or operations 410 or 426 above. In some embodiments, the camera unit 202 operates to capture an image of the third sample holder portion 332C of the sample holder 102 (e.g., Figure 21C The third image 354 is processed to detect and read the container identifier 186 of the container 180 at the fifth container position 334E, the sixth container position 334F and the seventh container position 334G.

[0157] Once the container identifier 186 is read, the sample container identification unit 110 can identify the container 180 based on the detected container identifier 186. The sample container identification unit 110 can store identification information of the sample in the container 180 (e.g., sample ID, representing the patient ID associated with the physician's test instructions for the sample request).

[0158] In some implementations, the sample container identification unit 110 operates to compare the detected container identifier 186 with user-provided information (e.g., user input about the container that can be received via the input device of the sample analyzer 100) and determine whether the container identifier 186 matches the user input. The sample container identification unit 110 may operate to store information indicating that a particular container location 334 (e.g., 334E, 334F, and / or 334G) includes containers 180 that do not match the user input. For example, the sample container identification unit 110 may operate to mark the container locations 334 (e.g., fifth container location 334E, sixth container location 334F, and / or seventh container location 334G) of the sample rack 102 that hold containers with mismatched container identifiers 186.

[0159] In some implementations, the sample container identification unit 110 further operates to cross-check whether the container 180 identified at the third image position 330C matches (or is compatible with) the identifier of the sample rack 102 (e.g., the sample rack ID found at operation 410).

[0160] At operation 444, the sample container identification unit 110 can operate the camera device unit 202 to determine the characteristics of the container 180 at the third sample rack portion 332C of the sample rack 102. Figure 19 Operation 414 is performed similarly to operation 310 or operations 414 or 428 above.

[0161] For example, the sample container recognition unit 110 operates to view an image of the third sample holder portion 332C of the sample holder 102 (e.g., Figure 21CThe third image (354) is processed to determine various features associated with container 180, such as the dimensions of each container (e.g., height and width) and the presence of a lid on the container. As described in more detail below, such features can be used to identify the type of container. (Refer to...) Figure 25 Describe and illustrate a detailed example method for performing operation 444.

[0162] In some embodiments, the data on the container features obtained above can be stored in the sample container identification unit 110. In some embodiments, if a container has predetermined undesirable features (e.g., uncovered, unapproved, and / or inappropriate container location), the sample container identification unit 110 can store information indicating that a particular container location 334 (e.g., 334E, 334F, and / or 334G) includes a container 180 that does not match the user input. For example, the sample container identification unit 110 can be operated to mark the container locations 334 (e.g., fifth container location 334E, sixth container location 334F, and / or seventh container location 334G) of the sample rack 102 that hold containers with such undesirable features.

[0163] At operation 446, sample rack 102 is moved to a sampling and / or pipetting system for sample processing.

[0164] In some implementations, information output from the SPU with sample container identification unit 110 to the sampling and / or pipetting system includes information about a barcode, which can be used to prioritize sample aspiration and indicate the type of sample (e.g., small volume, STAT, and calibration samples). Information from the SPU with sample container identification unit 110 may also include visual information, such as the container type determined from a library of container types. Information that can be provided to the sample pipette may include initiating level sensing to detect the starting position of fluid (top of the container), the maximum permissible depth of travel during aspiration (ineffective fluid volume or bottom of the container), and the internal geometry of the sample container (for accurate aspiration in case of any additional offset required by the SPU and pipette). This information may also include type-specific or sample-specific instructions for the sampling and / or pipetting system (e.g., pipette stands 105, 107) to facilitate aspiration as previously described in... Figure 37 The sample is processed in the manner described in the context.

[0165] At operation 448, once sample processing has been performed at operation 446, the sample rack 102 moves to the unloading channel 126. Furthermore, the sample analyzer 100 can be operated to alert the user to various information, such as the invalidity of the sample rack determined at operation 412, the status of the container presence flag (i.e., false) determined at operation 436, or the completion of sample processing performed at operation 446. Alerts can be of various types, such as visual and / or auditory warnings or notifications issued by the sample analyzer 100.

[0166] As described above, if no container is found at a specific image location 330, the sample holder 102 can bypass that specific image location. For example, the sample holder 102 can jump to the next image location 330 without performing a container recognition operation at the specific image location. Therefore, the bypass algorithm surrounding visual inspection can save time. The main instrument has a cycle time (e.g., 8 seconds), and the SPU operation is partially independent of the main instrument, but ideally completes within 8 seconds. For example, if multiple inappropriate sample holders are present, the bypass allows them to be quickly removed. Therefore, due to the bypass, the main instrument does not need to wait for the SPU to complete its operation.

[0167] Figure 25 This is a flowchart of an example method 500 for processing an image of a sample rack having one or more containers and determining the features of the containers in the sample rack. In some embodiments, method 500 is used to perform... Figure 24 Operations 414, 428, and 444 are described herein. In some embodiments, method 500 may be performed at least in part by other means in SPU 104, sample container identification unit 110, and / or sample analyzer 100. See also... Figures 26 to 32 Let's describe method 300.

[0168] Method 500 may begin at operation 502, in which sample holder reference 520 is identified in the captured image. In some embodiments, a first hook 160 (also referred to herein as a lug) of sample holder 102 is used as sample holder reference 520. The first hook 160 may be detected in an image (e.g., first image 350) captured when sample holder 102 is in a first stop position (e.g., first image position 330A).

[0169] For example, the edge 522 of the sample holder 102 ( Figure 5 The sample holder reference 520 is pre-determined. Figure 26As shown, the sample container recognition unit 110 can identify the predetermined edge 522 of the sample holder 102 in the first image 350. In this illustration, the identified edge 522 of the sample holder 102 is indicated by a line 524, which is an icon representing the recognition of the edge 522 by the imaging device unit 202. In this embodiment, the X-axis assumes that the sample holder 102 is fully engaged.

[0170] At operation 504, the sample container identification unit 110 operates to create one or more regions of interest 528 (also referred to herein as height regions of interest). In some implementations, three regions of interest 528 (including 528A, 528B, and 528C) are created relative to the sample holder reference 520, for example, by offsetting from the sample holder reference 520 on the Y-axis.

[0171] exist Figure 27 In the example shown, in image 350, a first region of interest 528A is created and positioned centered on the sample holder reference 520 on the Y-axis. A second region of interest 528B is created and positioned offset from the first region of interest 528A by a predetermined distance on the Y-axis (e.g., in...). Figure 27 (The middle part is 200 pixels). Create a third region of interest 528C and arrange it so that it is offset from the second region of interest 528B by a predetermined distance on the Y-axis (e.g., in...). Figure 27 (The middle region is 200 pixels). Alternatively, a third region of interest 528C can be created by offsetting from the first region of interest 528A.

[0172] For each of the regions of interest 528, the sample container identification unit 110 operates to detect the top tube edges 530 (e.g., 530A, 530B, and 530C) and determine the height of the associated container 180. Figure 27 In the example shown, the height of container 180 associated with the second region of interest 528B is measured to be 1178.34 pixels, and the height of container 180 associated with the third region of interest 528C is measured to be 1193.10 pixels.

[0173] In some implementations, a result indicating that no container was detected can be generated instead of reporting the container's height. For example, if no container is present in the first region of interest 528A, a result indicating that no container was detected will be output. In other implementations, the sample container identification unit 110 operates to determine the X-coordinate measurement of the sample holder using the top tube edge 530A in the first region of interest 528A.

[0174] At operation 506, the sample container identification unit 110 operates to create one or more regions of interest 534 (hereinafter also referred to as width of interest regions) for container width (or diameter) detection. In some embodiments, the width of interest regions 534 are created at a predetermined distance above the sample holder 102 (on the X-axis) and centered on the height of interest region 528. The width of interest regions 534 are arranged to transversely to the height of interest region 528. In some embodiments, the width (i.e., the Y-axis distance) of each width of interest region 534 can be preset, for example, at... Figure 28 The width is 250 pixels.

[0175] For each of the width-of-interest regions 534, the sample container identification unit 110 operates to detect the two opposite sides 536A and 536B of the container and determine the width of the associated container 180. Figure 28 In the example shown, the width of container 180 associated with region of interest 534A is measured to be 152.99 pixels (i.e., the pixel distance between opposite sides 536A and 536B), and the width of container 180 associated with region of interest 534B is measured to be 151.74 pixels (i.e., the pixel distance between opposite sides 536A and 536B).

[0176] At operation 508, the sample container identification unit 110 operates to create one or more regions of interest 540 for histogram analysis (also referred to herein as histogram regions of interest).

[0177] In some implementations, for example, three histogram regions of interest 540 (including 540A, 540B, and 540C) are created relative to the top of each height region by offsetting from the top tube edge 530 on the X-axis. In some implementations, the histogram regions of interest 540 are created at a predetermined distance on the X-axis from the top tube edge 530 (e.g., 5 pixels from the top tube edge 530) when the container is detected. In some implementations, the size of each histogram region of interest 540 may be predetermined.

[0178] Once the histogram region of interest 540 is created, histogram values ​​are obtained for each histogram region of interest 540. Figure 29 In the example shown, the histogram value of region of interest 540B associated with the second region of interest 528B is measured to be 177.62, and the histogram value of region of interest 540C associated with the third region of interest 528C is measured to be 42.53.

[0179] In some implementations, histogram analysis at point 508 can also detect the presence of a lid on the container. For example... Figure 30As shown, the measurement results of the histogram region 540 of interest can indicate the presence or absence of a cover. In some embodiments, a low histogram value can indicate the presence of a cover at that location, while a high histogram value can indicate the absence of a cover at that location. Figure 30 In the example, the average histogram value of the region of interest 540D above the lid 542 of container 180 was measured to be 16.08 (a relatively low value), while the average histogram value of the regions of interest 540E and 540F on the uncovered container 180 was measured to be 145.81.

[0180] At operation 510, the sample container identification unit 110 operates to compare the information obtained in the above operation with the classification table 550. Figure 31 For example, for each container, the height value, width value, and / or histogram value can be compared with the values ​​in classification table 550, and the type of container can be determined based on this comparison.

[0181] like Figure 31 As shown, a classification table 550 is provided to classify different types of containers (first column) based on height, width, and histogram values. For each type of container, the height, width, and histogram values ​​can be set to a minimum, maximum, and average value. For example, if the height value obtained in method 500 is between 90 and 137, the width value obtained in method 500 is between 137 and 154, and the histogram value obtained in method 500 is between 70 and 300, then the container to be identified can be identified as a 12mm x 65mm or 13mm x 75mm pipe with a lid (second row of table 550).

[0182] Alternatively, in some implementations, fewer than, for example, can be used when identifying the container type. Figure 31All the information shown in the table is illustrated. For example, in some implementations, containers can be classified as small (e.g., pediatric containers) or non-small based solely on height and histogram data, rather than also relying on width. In such implementations, a container with a height between 16 and 50 and a histogram value between 0 and 30 can be considered small, while a container with a larger height or histogram value can be considered non-small. Additionally, in some implementations, information such as that discussed above can be used to determine if there are errors in container classification. For example, if the height and histogram information do not match (e.g., the height matches a small container, but the histogram matches a non-small container), this can be identified as an error that should draw the user's attention, so he or she can manually specify the correct type of container. Similarly, in some implementations, gaps may exist in the information used to classify various types of containers (e.g., heights between 16 and 50 would be considered small containers, while heights greater than 85 would be considered large containers), which can be similarly used to identify errors for remediation (e.g., in cases where measurements fall into a gap). Therefore, the above discussion on the classification of container types should be understood as illustrative only, and not as restrictive.

[0183] like Figure 32 As shown, in the case where sample holder 102 is a cup holder 102B with sample cups 184, the same method 500 can be applied to identify the type of sample cups 184. As described above, the measurement results of the histogram region 540 of interest indicate what type of cup is present. The histogram data can be combined with other measurements such as height and width (diameter) to determine the type of cup in cup holder 102B.

[0184] Figure 33 This is a flowchart of an example method 600 for adding and validating a new container in classification table 550 (i.e., container library, list of approved containers, etc.).

[0185] At step 602, the user inputs information about the new sample container. This information may include the container type, internal geometry, volume, manufacturer part number, external dimensions, etc. At step 604, the user loads the container of interest onto the sample rack to add it to the classification table 550 via software (i.e., SW). The user further fills the container to its maximum volume and loads the sample rack 102 into the loading channel 124 of the SPU 104.

[0186] When a user enters new sample container information (at operation 602), the sample analyzer 100 (e.g., the software application described herein) operates to prompt the user to use a washing buffer or deionized water to provide maximum volume, place the new sample container in the sample holder 102, and load it onto the SPU 104 (at operation 604). At operation 602, the information may include information about the manufacturer, part number, container type (e.g., tube or cup), plasma or serum gel matrix in the tube, internal container geometry, insert / cup (i.e., cup placed inside the tube), and / or capacity.

[0187] Then, at operation 606, the SPU (including the sample container identification unit 110 therein) operates to identify the dimensions of the sample rack and the containers therein. In some embodiments, the information obtained includes the height in the sample rack (e.g., the position where the pipette should begin level sensing and stepping from its original position), the diameter, and the histogram value at the top of each container.

[0188] At operation 608, determine whether the new sample container is a gel or an insert / cup, etc. If operation 608 determines the container is an insert / cup, etc., then at operation 610, the sample pipette moves to detect the bottom of the container. If operation 608 determines the container is a gel tube, the sample pipette begins sampling from near the top of the fluid in the container.

[0189] In steps 608 to 618, the sample analyzer 100 (i.e., the instrument) processes the new container and observes the characteristics of the new container as measured by various detection functions of the sample analyzer 100. For example, in order to measure the volume in step 616, all fluid from the container is transferred to a sample dish (i.e., SV) and the sample dish is transferred to a washing wheel (i.e., WW).

[0190] As in some implementations, users can use, for example... Figure 33 The process shown adds a new container to the container library. In some implementations, it may allow users to add containers that can be used in containers such as... Figure 37 The process shown uses new type-specific instructions. Figure 39 The document provides examples of procedures that can be used to add such type-specific instructions.

[0191] In such Figure 39In the illustrated process, a user adding type-specific instructions can initially specify the type of instructions provided to them (3901). This can be accomplished, for example, by the following processing by computing device 208: retrieving information from the memory of computing device 208 indicating what types might be encountered on the analyzer (e.g., what types of tests the analyzer can perform, what types of containers are included in the library, etc.), and then presenting an interface with one or more drop-down menus for those types (e.g., a drop-down menu for container types, a drop-down menu for test types, etc.), from which the user specifies the type of instructions he or she is adding. Similarly, in some embodiments that support type-specific instruction specifications, the user can be given the option to enter information into fields corresponding to the data (e.g., tests to be performed, etc.) that will be provided to the analyzer for each sample, and then, when processing the sample, the information of the sample can be compared with the information added by the user to determine the type of sample. According to this disclosure, other methods (e.g., allowing the user to select or enter types as a combination of free text from a pre-specified type menu) are also possible and will be readily apparent to those skilled in the art.

[0192] After specifying type 3901, various processing parameters for that type can be added. For example, a user can specify aspiration volume 3902, which is the amount of fluid to be aspirated from a sample container containing a sample of the specified type. Similarly, in some implementations, a user can specify dispensing targets 3903, such as sample wheels (for types that should initially be added to sample vessels before being transferred to individual reaction vessels) or reaction build-up zones (for types that should be dispensed directly into reaction vessels without first being dispensed into intermediate sample vessels). Some implementations may also allow the user to specify processing targets 3904, such as using external equipment connected to the analyzer to perform a specific type of test.

[0193] Figure 34 Exemplary architectures of computing devices that can be used to implement aspects of this disclosure are shown, including sample analyzer 100 or various systems of sample analyzer 100, such as sample container identification unit 110 and other sub-units or sub-devices. Furthermore, one or more devices or units included in the system of sample analyzer 100 can also be used... Figure 34 The computing device shown is implemented using at least some components. Such a computing device is designated herein by reference numeral 700. The computing device 700 is used to execute the operating system, application programs, and software modules (including software engines) described herein.

[0194] In some embodiments, computing device 700 includes at least one processing device 702, such as a central processing unit (CPU). Various processing devices are available from various manufacturers, such as Intel or AMD. In this example, computing device 700 also includes system memory 704 and a system bus 706, which couples various system components, including system memory 704, to processing device 702. System bus 706 is one of any number of types of bus architectures, including: a memory bus or memory controller; a peripheral bus; and a local bus using any of a variety of bus architectures.

[0195] Examples of computing devices suitable for computing device 700 include desktop computers, laptop computers, tablet computers, and mobile devices (such as smartphones, etc.). A mobile digital device or other mobile device, or other device configured to process digital instructions.

[0196] System memory 704 includes read-only memory 708 and random access memory 710. A basic input / output system 712 containing basic routines for transferring information within computing device 700, for example, during startup, is typically stored in read-only memory 708.

[0197] In some embodiments, the computing device 700 further includes an auxiliary storage device 714, such as a hard disk drive, for storing digital data. The auxiliary storage device 714 is connected to the system bus 706 via an auxiliary storage interface 716. The auxiliary storage device and its associated computer-readable medium provide the computing device 700 with non-volatile storage of computer-readable instructions (including application programs and program modules), data structures, and other data.

[0198] While the exemplary environment described herein employs a hard disk drive as a secondary storage device, other types of computer-readable storage media are used in other embodiments. Examples of these other types of computer-readable storage media include magnetic tape cassettes, flash memory cards, digital video disks, Bernoulli cassette tapes, compact disk read-only memory, digital universal disk read-only memory, random access memory, or read-only memory. Some embodiments include non-transitory media.

[0199] Multiple program modules, including an operating system 718, one or more application programs 720, other program modules 722, and program data 724, can be stored in the auxiliary storage device 714 or the memory 704.

[0200] In some embodiments, computing device 700 includes input devices that enable a user to provide input to computing device 700. Examples of input devices 726 include keyboard 728, pointer input device 730, microphone 732, and touch-sensitive display 740. Other embodiments include other input devices 726. Input devices are typically connected to processing device 702 via input / output interfaces 738 coupled to system bus 706. These input devices 726 can be connected via any number of input / output interfaces, such as parallel ports, serial ports, game ports, or universal serial buses. In some possible embodiments, wireless communication between the input devices and interface 738 is also possible and includes infrared, Wireless technology, WiFi technology (802.11a / b / g / n, etc.), cellular and / or other radio frequency communication systems.

[0201] In this example embodiment, the touch-sensitive display device 740 is also connected to the system bus 706 via an interface such as a video adapter 742. The touch-sensitive display device 740 includes a touch sensor for receiving input from a user when the user touches the display. Such a sensor can be a capacitive sensor, a pressure sensor, or other touch sensor. The sensor detects not only contact with the display but also the location of the contact and its movement over time. For example, a user can move a finger or stylus on the screen to provide handwriting input. In some embodiments, the handwriting input is evaluated and converted into text input.

[0202] In addition to the display device 740, the computing device 700 may include various other peripheral devices (not shown), such as speakers or printers.

[0203] The computing device 700 also includes a communication device 746 configured to establish communication over a network. In some embodiments, when used in a local area network (LAN) or wide area network (WAN) environment (e.g., the Internet), the computing device 700 is typically connected to a network via a network interface, such as a wireless network interface 748. Other possible embodiments use other wired and / or wireless communication devices. For example, some embodiments of the computing device 700 include an Ethernet network interface or a modem for communication over a network. In still other embodiments, the communication device 746 is capable of short-range wireless communication. Short-range wireless communication is one-way or two-way short- to medium-range wireless communication. Short-range wireless communication can be established according to various technologies and protocols. Examples of short-range wireless communication include radio frequency identification (RFID), near field communication (NFC), Bluetooth, and Wi-Fi.

[0204] Computing device 700 typically includes at least some form of computer-readable medium. Computer-readable medium includes any available medium accessible by computing device 700. As examples, computer-readable media include computer-readable storage media and computer-readable communication media.

[0205] Computer-readable storage media include volatile and non-volatile, removable and non-removable media implemented in any means configured to store information such as computer-readable instructions, data structures, program modules or other data. Computer-readable storage media include, but are not limited to, random access memory, read-only memory, electrically erasable programmable read-only memory, flash memory or other memory technologies, compact disk read-only memory, digital multifunction disk or other optical storage devices, magnetic tape cassettes, magnetic tape, disk storage devices or other magnetic storage devices, or any other medium that can be used to store desired information and is accessible by the computing device 700.

[0206] Computer-readable communication media typically embody computer-readable instructions, data structures, program modules, or other data in the form of modulated data signals, such as carrier waves or other transmission mechanisms, and include any information transmission medium. The term "modulated data signal" refers to a signal whose characteristics are set or altered in a manner that encodes information within the signal. For example, computer-readable communication media include: wired media, such as wired networks or direct wired connections; and wireless media, such as acoustic, radio frequency, infrared, and other wireless media. Any combination of the above is also included within the scope of computer-readable media.

[0207] The various embodiments described above are provided by way of illustration only, and based on this disclosure, various modifications and combinations of the described embodiments will be apparent to those skilled in the art, and can be implemented by those skilled in the art without much experimentation. For example, although Figure 19 and Figure 24The processing includes steps of detecting identifiers of sample holders and / or containers for use in subsequent processing; however, some embodiments may omit this type of detection, or may detect identifiers as indicated but omit their use in subsequent processing. For example, in embodiments that implement the function of dispensing fluid aspirated from a pediatric sample directly into a reaction dish rather than first dispensing the fluid into an intermediate sample dish, the fluid processing may be performed purely based on detecting fluid in the pediatric sample cup, and the identifiers on the container itself or its sample holder may not be used. In fact, in some embodiments, type-specific processing functionality can be provided entirely independently of any type of machine vision or image processing. For example, using the assumption that the correct sample is loaded into the correct position, the type of sample can be determined based on the position of the sample in the sample holder. As another illustration, in some cases, techniques such as those described herein for identifying sample containers based on their shape can potentially be used in laboratory automation to determine which machine a particular container should be routed to (e.g., identifying a container as a small-volume or pediatric container may result in that container being routed directly to a diagnostic instrument rather than being sent to a separate sampler). Therefore, given the possibility of such variations and combinations, the protection provided by this document or any related document should not be limited to the material expressly disclosed herein, but rather, instead, should be defined by the claims of such document when interpreting these claims in accordance with the broadest reasonable interpretation of these documents and any express definitions provided for their terms.

Claims

1. An automated clinical analyzer, comprising: a) Sample presentation unit (104), which includes a sample presentation channel (128); as well as b) A computing device (208) configured to perform one or more actions selected from a set of the following: i) Identify the type of the sample container (180) in the sample presentation channel (128) captured by the camera device (202), and distinguish the downstream processing for the fluid contained in the sample container (180) based on the type. as well as ii) Based on the identification information of the sample container (180) in the sample delivery channel (128), determine the target location and transfer the fluid from the sample container (180) in the sample delivery channel (128) to the target location; c) The analyzer includes a set of benches (106), wherein the set of benches (106) comprises one or more benches (106) and is from each of the set of benches (106): i) The sample presentation channel (128) is set at an angle relative to the sample presentation unit (104); ii) Configured to: translate the corresponding pipette along its length and cause the corresponding pipette to aspirate or dispense fluid based on a command from the computing device (208); and iii) Having a portion disposed above the sample presentation channel (128) of the sample presentation unit (104); d) The computing device (208) is configured to use the identification information of the sample container (180) in the sample presentation channel (128): i) Determine the first fluid volume; and ii) Determine the target location and transfer the fluid from the sample container (180) in the sample presentation channel (128) to the target location; as well as e) The computing device (208) is configured to transfer fluid from the sample container (180) in the sample presentation channel (128) to the target location by sending a command to a station from the set of stations (106) to the station: i) Position the corresponding pipette of the stand above the sample container (180) in the sample presentation channel (128); ii) Aspirate the first fluid volume from the sample container (180) in the sample presentation channel (128); iii) Position the corresponding pipette on the rack above the target location; and iv) Dispense a second volume of fluid from the appropriate pipette on the stand into the vessel at the target location.

2. The analyzer according to claim 1, wherein, The computing device (208) is configured to determine the target location by selecting from a group consisting of: a) Sample wheel (129); and b) Reaction construction region (111).

3. The analyzer according to claim 2, wherein, The set of stands consists of a single sample precision pipette stand (107) operable to position the corresponding pipette of the stand above the sample presentation unit (104), the sample wheel (129) and the reaction construction area (111).

4. The analyzer according to claim 2, wherein, The set of test stands includes: a) A sample pipette stand (105) operable to position the corresponding pipettes of the stand above the sample presentation unit (104) and the sample wheel (129) but not above the reaction construction area (111); and b) A sample precision pipette stand (107) operable to position the corresponding pipettes of the stand above the sample presentation unit (104), the sample wheel (129), and the reaction construction area (111).

5. The analyzer according to claim 1, wherein: a) The identification information of the sample container (180) is the container type; as well as b) The computing device (208) is configured to determine the container type based on container shape information captured by the camera device (202) coupled to the clinical analyzer.

6. The analyzer according to claim 1, wherein: a) The identification information of the sample container (180) is the identification of the sample in the sample container (180); b) The computing device (208) is configured to determine the sample in the sample container (180) based on one or more of the following: i) the identifier (186) on the sample container (180); and ii) The position of the sample container (180) in the sample rack (102).

7. The analyzer according to claim 1, wherein, The computing device (208) is configured with instructions that, when executed, are operable for: a) Determine whether the sample container (180) contains pediatric samples based on the identification information of the sample container (180); and b) Based on the determination that the sample container (180) contains pediatric samples: i) Send a command to the platform that is adapted to cause the corresponding pipette on the platform to directly dispense fluid drawn from the sample container (180) into the reactor dish; as well as ii) Send a command to the reagent pipette of the automated clinical analyzer, the command being adapted to cause the reagent pipette of the automated clinical analyzer to dispense reagents into the reaction dish.

8. The analyzer according to claim 1, wherein: a) The computing device (208) is configured to determine the test type based on the identification information of the sample container (180); and b) The computing device (208) is configured to determine the first fluid volume based on the determined test type.

9. The analyzer according to claim 1, wherein, The computing device (208) is configured to: a) Determine the test type based on the identification information of the sample container (180); b) Determine whether the fluid in the sample container (180) should be sampled in multiple portions; and c) Based on the determination that the fluid in the sample container (180) should be sampled in multiple portions, send the following command to a station from the set of stations, the command being adapted to cause the station to: i) Dispense the first sample fluid drawn from the sample container (180) into the first sample dish in the sample wheel (129); as well as ii) Dispense the second sample fluid drawn from the sample container (180) into the second sample dish in the sample wheel (129).

10. The analyzer according to claim 1, wherein: a) The computing device (208) is configured with data indicating, for each of a set of test types, corresponding sample processing information including the volume of fluid to be aspirated, wherein the set of test types includes one or more test types; and b) The computing device (208) is configured to determine the first fluid volume based on sample processing information corresponding to the type of test to be used to process the sample when the sample container (180) is determined to contain a sample to be processed using a test having a type from the set of test types.

11. The analyzer according to claim 10, wherein: a) The computing device (208) is configured with instructions adapted to, when executed, present an interface operable by a user to specify sample processing information corresponding to a specific test type; and b) The computing device (208) is configured to apply sample processing information, specified by the user as corresponding to the specific test type, to a plurality of samples to be processed using the specific test type, without requiring the user to re-enter the sample processing information for each of the plurality of samples to be processed using the specific test type.

12. The analyzer according to claim 1, wherein, The computing device (208) is configured to determine the first fluid volume based on a test instruction for the sample in the sample container (180).

13. The analyzer according to claim 1, wherein, The computing device (208) is configured to determine the first fluid volume by performing steps including the following processes: a) Determine the number of specimens to be created from the sample in the sample container (180); and b) Determine the volume of fluid sufficient for each of the determined number of samples.

14. The analyzer according to claim 13, wherein, The computing device (208) is configured to determine the volume of fluid sufficient for each of the determined number of samples based on the following manner: combining the available volume of each sample with the amount of ineffective space for each sample.

15. The analyzer according to claim 1, wherein, The difference between the first fluid volume and the second fluid volume is at least an excess of the suction volume.

16. The analyzer according to claim 1, wherein: a) The computing device (208) is configured to: identify the type of the sample container (180) based on an image of the sample container (180) in the sample presentation channel (128) captured by the camera device (202); and to distinguish downstream processing for the fluid contained in the sample container (180) based on the type; b) The computing device (208) is configured to identify the type of the sample container (180) based on the container shape features from the image captured by the camera device (202).

17. The analyzer according to claim 16, wherein, The container shape features include the container height.

18. A method for operating an automated clinical analyzer, the method comprising: a) Presenting a sample container (180) in the sample presentation channel (128) of the sample presentation unit (104); b) The computing device (208) performs one or more actions selected from the set of the following: i) Identify the type of the sample container (180) in the sample presentation channel (128) captured by the camera device (202), and distinguish the downstream processing for the fluid contained in the sample container (180) based on the type. as well as ii) Based on the identification information of the sample container (180) in the sample delivery channel (128), determine the target location and transfer the fluid from the sample container (180) in the sample delivery channel (128) to the target location; c) The analyzer includes a set of benches (106), wherein the set of benches (106) comprises one or more benches (106) and is from each of the set of benches (106): i) The sample presentation channel (128) is set at an angle relative to the sample presentation unit (104); ii) Configured to: translate the corresponding pipette along its length and cause the corresponding pipette to aspirate or dispense fluid based on a command from the computing device (208); and iii) Having a portion disposed above the sample presentation channel (128) of the sample presentation unit (104); d) The computing device (208) is configured to use the identification information of the sample container (180) in the sample presentation channel (128): i) Determine the first fluid volume; and ii) Determine the target location and transfer fluid from the sample container (180) in the sample presentation channel (128) to the target location; and e) The computing device (208) is configured to transfer fluid from the sample container (180) in the sample presentation channel (128) to the target location by sending a command to a station from the set of stations (106) to the station: i) Position the corresponding pipette of the stand above the sample container (180) in the sample presentation channel (128); ii) Aspirate the first fluid volume from the sample container (180) in the sample presentation channel (128); iii) Position the corresponding pipette on the rack above the target location; and iv) Dispense a second volume of fluid from the appropriate pipette on the stand into the vessel at the target location.

19. The method according to claim 18, wherein, The computing device (208) is configured to determine the target location by selecting from a group consisting of: a) Sample wheel (129); and b) Reaction construction region (111).

20. The method according to claim 19, wherein, The set of stands consists of a single sample precision pipette stand (107) operable to position the corresponding pipette of the stand above the sample presentation unit (104), the sample wheel (129) and the reaction construction area (111).

21. The method according to claim 19, wherein, The set of test stands includes: a) A sample pipette stand (105) operable to position the corresponding pipettes of the stand above the sample presentation unit (104) and the sample wheel (129) but not above the reaction construction area (111); and b) A sample precision pipette stand (107) operable to position the corresponding pipettes of the stand above the sample presentation unit (104), the sample wheel (129), and the reaction construction area (111).

22. The method of claim 18, wherein: a) The identification information of the sample container (180) is the container type; as well as b) The computing device (208) is configured to determine the container type based on container shape information captured by the camera device (202) coupled to the clinical analyzer.

23. The method of claim 18, wherein: a) The identification information of the sample container (180) is the identification of the sample in the sample container (180); b) The computing device (208) is configured to determine the sample in the sample container (180) based on one or more of the following: i) the identifier (186) on the sample container (180); and ii) The position of the sample container (180) in the sample rack (102).

24. The method according to claim 18, wherein, The computing device (208) is configured with instructions that, when executed, are operable for: a) Determine whether the sample container (180) contains pediatric samples based on the identification information of the sample container (180); and b) Based on the determination that the sample container (180) contains pediatric samples: i) Send the following command to the platform, the command being adapted to cause the corresponding pipette of the platform to directly dispense fluid drawn from the sample container (180) into the reactor dish; as well as ii) Send a command to the reagent pipette of the automated clinical analyzer, the command being adapted to cause the reagent pipette of the automated clinical analyzer to dispense reagents into the reaction dish.

25. The method according to claim 18, wherein: a) The computing device (208) is configured to determine the test type based on the identification information of the sample container (180); and b) The computing device (208) is configured to determine the first fluid volume based on the determined test type.

26. The method according to claim 18, wherein, The computing device (208) is configured to: a) Determine the test type based on the identification information of the sample container (180); b) Determine whether the fluid in the sample container (180) should be sampled in multiple portions; and c) Based on the determination that the fluid in the sample container (180) should be sampled in multiple portions, send the following command to a station from the set of stations, the command being adapted to cause the station to: i) Dispense the first sample fluid drawn from the sample container (180) into the first sample dish in the sample wheel (129); as well as ii) Dispense the second sample fluid drawn from the sample container (180) into the second sample dish in the sample wheel (129).

27. The method according to claim 18, wherein: a) The computing device (208) is configured with data indicating, for each of a set of test types, corresponding sample processing information including the volume of fluid to be aspirated, wherein the set of test types includes one or more test types; and b) The computing device (208) is configured to determine the first fluid volume based on sample processing information corresponding to the type of test to be used to process the sample when the sample container (180) is determined to contain a sample to be processed using a test having a type from the set of test types.

28. The method of claim 27, wherein: a) The computing device (208) is configured with instructions adapted to, when executed, present an interface operable by a user to specify sample processing information corresponding to a specific test type; and b) The computing device (208) is configured to apply sample processing information, specified by the user as corresponding to the specific test type, to a plurality of samples to be processed using the specific test type, without requiring the user to re-enter the sample processing information for each of the plurality of samples to be processed using the specific test type.

29. The method according to claim 18, wherein, The computing device (208) is configured to determine the first fluid volume based on a test instruction for the sample in the sample container (180).

30. The method according to claim 18, wherein, The computing device (208) is configured to determine the first fluid volume by performing steps including the following processes: a) Determine the number of specimens to be created from the sample in the sample container (180); and b) Determine the volume of fluid sufficient for each of the determined number of samples.

31. The method according to claim 30, wherein, The computing device (208) is configured to determine the volume of fluid sufficient for each of the determined number of samples based on the following manner: combining the available volume of each sample with the amount of ineffective space for each sample.

32. The method according to claim 18, wherein, The difference between the first fluid volume and the second fluid volume is at least an excess of the suction volume.

33. The method according to claim 18, wherein: a) The computing device (208) performs the following actions: identifying the type of the sample container (180) in the sample presentation channel (128) based on an image captured by the camera device (202); and distinguishing the downstream processing for the fluid contained in the sample container (180) based on the type; and b) The computing device (208) is configured to identify the type of the sample container (180) based on the container shape features from the image captured by the camera device (202).

34. The method according to claim 33, wherein, The container shape features include the container height.

35. A method for operating an automated clinical analyzer, the method comprising: a) The first sample container is presented on the sample presentation channel (128) of the sample presentation unit (104); b) Presenting a second sample container on the sample presentation channel (128) of the sample presentation unit (104); c) Capture a first image using a camera device (202), wherein the first image depicts the first sample container; d) Using the camera device (202), a second image is captured, wherein the second image depicts the second sample container; e) The computing device (208) determines the type of the first sample container based on the first image; f) The computing device (208) determines the type of the second sample container based on the second image; g) Based on the determined type of the first sample container, aspirate a first fluid volume from the first sample container, wherein the first fluid volume includes a fluid volume sufficient to perform the measurement indicated for the sample in the first sample container and an amount sufficient to fill the invalid space in the intermediate sample vessel; and h) Based on the determined type of the second sample container, a second fluid volume is drawn from the second sample container, wherein the second fluid volume includes a fluid volume sufficient to perform the measurement indicated for the sample in the second sample container but does not include an amount of invalid space sufficient to fill the invalid space in the intermediate sample vessel.

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