Point cloud for sample identification and device configuration

Abstract

Scientific instrument support systems and related methods, computing devices, and computer-readable media for aligning scientific instruments are provided. The method includes: generating a first point cloud representing a sample with a first scientific instrument, wherein the first point cloud is in n-dimensional space and n is an integer; and generating a second point cloud representing the sample with a second scientific instrument different from the first scientific instrument, wherein the second point cloud is in an m-dimensional space different from the n-dimensional space associated with the first point cloud and wherein m is an integer. The method comprises: generating an offset between the first point cloud and the second point cloud using a transformation correlating the n-dimensional space to the m-dimensional space; and aligning an output of the second scientific instrument with an output of the first scientific instrument based on the offset.

Description

[0001] Cross - Reference to Related Applications

[0002] This application claims the priority of U.S. Provisional Patent Application No. 63 / 431,837, titled "POINT CLOUD FOR SAMPLE IDENTIFICATION AND DEVICE CONFIGURATION", filed on December 12, 2022, which is incorporated herein by reference in its entirety. Background Art

[0003] Scientific instruments can include a complex arrangement of movable parts, sensors, input and output ports, energy sources, and consumable parts. Data measured by a scientific instrument can be converted into a point cloud, thereby representing the sample to be analyzed as a multi - dimensional shape. Brief Description of the Drawings

[0004] Embodiments will be readily understood by the following detailed description in conjunction with the accompanying drawings. For ease of description, the same reference numerals denote the same structural elements. Embodiments are illustrated by way of example and not limitation in the figures of the accompanying drawings.

[0005] Figure 1 is an example three - dimensional point cloud according to various embodiments.

[0006] Figure 2 is a block diagram of an example scientific instrument support module for performing support operations according to various embodiments.

[0007] Figure 3 is a flowchart of an example method for aligning a scientific instrument according to various embodiments.

[0008] Figure 4 is an example of a graphical user interface that can be used in some or all of the methods disclosed herein.

[0009] Figure 5 is a block diagram of an example computing device that can execute some or all of the methods disclosed herein.

[0010] Figure 6 is a block diagram of an example scientific instrument support system in which some or all of the methods disclosed herein can be executed.

[0011] Figure 7 is an example charged particle microscope system incorporating the technology disclosed herein.

[0012] Figure 8is an example energy dispersive X-ray spectroscopy system incorporating the technology disclosed herein in accordance with various embodiments.

[0013] Figure 9 is a schematic diagram of a spectroscopy system incorporating the technology disclosed herein in accordance with various embodiments. DETAILED DESCRIPTION

[0014] Disclosed herein are scientific instrument support systems and related methods, computing devices, and computer-readable media. For example, in some embodiments, a method for aligning scientific instruments includes: generating, with a first scientific instrument, a first point cloud representing a sample, wherein the first point cloud is in an n-dimensional space and n is an integer; and generating, with a second scientific instrument different from the first scientific instrument, a second point cloud representing the sample, wherein the second point cloud is in an m-dimensional space different from the n-dimensional space associated with the first point cloud and wherein m is an integer. The method includes: using a transformation relating the n-dimensional space to the m-dimensional space to generate an offset between the first point cloud and the second point cloud; and aligning an output of the second scientific instrument with an output of the first scientific instrument based on the offset.

[0015] The scientific instrument support embodiments disclosed herein may achieve improved performance relative to conventional methods. For example, methods that may depend on lighting or magnification that varies may typically be used to identify a sample. Additionally, some methods of identifying a sample damage or affect the sample. The embodiments disclosed herein identify the sample in a non-destructive manner independent of variations in lighting and magnification. Thus, the embodiments disclosed herein provide improvements to scientific instrument technology (e.g., improvements in the computer technology aspects that support such scientific instruments, among other improvements).

[0016] Relative to conventional methods, the embodiments disclosed herein may achieve automatic alignment of scientific instruments using only the sample under study. For example, conventional methods may use reference markers or holders for calibration, thus wasting the operation of the scientific instruments.

[0017] The various embodiments disclosed herein may improve conventional methods to achieve the technical advantages of aligning multiple scientific instruments by using data that has been obtained when identifying the sample composition. Such technical advantages cannot be achieved by routine and conventional methods, and all users of systems incorporating such embodiments may benefit from these advantages (e.g., by assisting the user in performing technical tasks, such as quickly aligning scientific instruments within an error range through a guided human-machine interaction process). The computing and user interface features disclosed herein not only involve the collection and comparison of information, but also apply new analytical and technical techniques to change the operation of scientific instruments, including adjusting the physical configuration of scientific instruments and characterizing samples using n-dimensional data points. Thus, the present disclosure introduces functions that neither conventional computing devices nor humans can perform.

[0018] Accordingly, embodiments of the present disclosure can serve any of a number of technical purposes, such as controlling a particular technical system or method; determining how to control a machine based on measurement results; digital audio, image, or video enhancement or analysis; providing estimates and confidence intervals for biological samples; reducing the amount of sensor data to be processed; or providing faster processing of sensor data. Specifically, the present disclosure provides technical solutions to technical problems, including but not limited to identifying research samples and aligning scientific instruments.

[0019] Accordingly, the embodiments disclosed herein provide improvements to microscopy, two-beam, and spectroscopic techniques (e.g., improvements to the computer technologies that support microscopy, two-beam, and spectroscopic techniques, among other improvements).

[0020] In the following detailed description, reference is made to the accompanying drawings, which form a part of the detailed description, in which like reference numerals always indicate like parts, and in which illustrative embodiments are shown by way of example. It is to be understood that other embodiments may be utilized and structural or logical changes may be made without departing from the scope of the present disclosure. Accordingly, the following detailed description should not be taken in a limiting sense.

[0021] The various operations may be described sequentially in a manner that is most helpful in understanding the disclosed subject matter. However, the described order should not be construed as implying that these operations must be order-dependent. Specifically, these operations may not be performed in the order presented. The described operations may be performed in a different order than the described embodiments. Various additional operations may be performed and / or the described operations may be omitted in additional embodiments.

[0022] For purposes of the present disclosure, the phrases "A and / or B" and "A or B" mean (A), (B), or (A and B). For purposes of the present disclosure, the phrases "A, B, and / or C" and "A, B, or C" mean (A), (B), (C), (A and B), (A and C), (B and C), or (A, B, and C). Although some elements may be represented in the singular form (e.g., "processing device"), any suitable element may be represented by multiple instances of that element, and vice versa. For example, a set of operations described as being performed by a processing device may be implemented by having different processing devices perform different ones of those operations.

[0023] This specification uses the phrases "embodiment", "various embodiments", and "some embodiments", each of which may refer to one or more embodiments in the same or different embodiments. Additionally, terms such as "comprising", "including", "having", etc. as used with respect to embodiments of the present disclosure are synonymous. When used to describe a dimension range, the phrase "between X and Y" means a range that includes X and Y. As used herein, "apparatus" may refer to any individual device, a collection of devices, parts of a device, or a collection of parts of a device. The drawings are not necessarily to scale.

[0024] A point cloud is a set of data points that represents a three-dimensional (3D) shape or object in space. For example, each point location has a set of Cartesian coordinates (X, Y, Z). A point cloud can be generated by a 3D scanner or by photogrammetry software that measures points on the outer surface of an object. As an output of the 3D scanning process, the point cloud is used for 3D visualization of the scanned object. Figure 1 An example point cloud 1000 including a plurality of data points in Cartesian space is illustrated. The data points are not limited to representing the physical locations of the scanned sample. In some cases, the data points are located at highly interesting parts (such as high-contrast points or other points of interest). In short, the point cloud includes points in space that represent desired features (e.g., fiducials).

[0025] The point cloud is not limited to 3D space. For example, a QR code can be regarded as a two-dimensional (2D) point cloud containing additional information such as the name of a scientist, the method performed, and a timestamp of the experiment. Such metadata can be represented as points within the point cloud, where the third dimension (Z) is implemented to represent the type of information. Additional dimensions can be added based on the amount of metadata to be represented by the point cloud, resulting in an "n-dimensional" point cloud. For example, the Cartesian coordinates (X, Y, Z) can represent the physical dimensions of the measured sample, while the fourth dimension represents the number of detected spectra of the sample, the fifth dimension represents the filter applied to the sample, and the sixth dimension represents the detected chemical compounds.

[0026] Figure 2 is a block diagram of a scientific instrument support module 2000 for performing alignment operations according to various embodiments. The scientific instrument support module 2000 can be implemented by a circuit (e.g., including electrical and / or optical components) such as a programmed computing device. The logical components of the scientific instrument support module 2000 can be included in a single computing device or distributed across multiple computing devices that communicate with each other as the case may be. Reference is made herein to Figure 5 the computing device 5000 of Figure 6The scientific instrument support system 6000 discusses an example of a system that interconnects computing devices, where the scientific instrument support module 2000 can be implemented across one or more of these computing devices.

[0027] The scientific instrument support module 2000 can include a point cloud logic component 2002, a segmentation logic component 2004, an identification logic component 2006, and an alignment logic component 2008. As used herein, the term "logic component" can include a device that performs a set of operations associated with that logic component. For example, any of the logic elements included in the support module 2000 can be implemented by one or more computing devices programmed with instructions such that one or more processing devices of the computing device perform the associated set of operations. In a particular embodiment, the logic element can include one or more non-transitory computer-readable media having instructions thereon that, when executed by one or more processing devices in one or more computing devices, cause the one or more computing devices to perform the associated set of operations. As used herein, the term "module" can refer to a collection of one or more logic elements that together perform a function associated with the module. Different logic elements in a module can take the same form or can take different forms. For example, some of the logic components in a module can be implemented by a programmed general-purpose processing device, while other logic components in the module can be implemented by an application-specific integrated circuit (ASIC). In another example, different logic elements in a module can be associated with different instruction sets executed by one or more processing devices. A module may not include all of the logic elements depicted in the associated drawings; for example, when a module is to perform a subset of the operations discussed herein with reference to that module, the module can include a subset of the logic elements depicted in the associated drawings.

[0028] The point cloud logic component 2002 can generate a point cloud representing a sample. For example, a sample is scanned by a scientific instrument. The point cloud logic component 2002 receives the scanned sample and generates a three-dimensional point cloud representing the physical sample. Additionally, the scientific instrument can collect additional data associated with the sample, such as detecting chemical compounds of the sample. The point cloud logic component 2002 also receives the additional data associated with the sample and adds additional dimensions to the point cloud representing the additional data. Thus, the point cloud logic component 2002 generates an n-dimensional point cloud of the sample, which includes all the desired data associated with the sample (e.g., X coordinates, Y coordinates, and metadata associated with the sample).

[0029] In some cases, the point cloud logic component 2002 is a machine learning algorithm or other artificial intelligence that is trained to analyze a sample and output points of a point cloud. Example machine learning and artificial intelligence techniques include decision tree learning, association rule learning, artificial neural networks, classifiers, inductive logic programming, support vector machines, clustering, Bayesian networks, reinforcement learning, representation learning, similarity and metric learning, sparse dictionary learning, and genetic algorithms.

[0030] The segmentation logic component 2004 may apply one or more segmentation filters to the point cloud. A segmentation filter is a filter that identifies points of interest or distinctions within the point cloud. The segmentation filter may identify boundary markers that indicate the structural shape of the point cloud. In some cases, the segmentation filter identifies centroids within the point cloud. A centroid is a mathematically determinable position based on the boundary markers, and these positions may not have distinct structures within the point cloud. As an example, the point cloud may represent a blood cell. The centroid may form the outer layer of the blood cell, such that when tracing around the centroid, the tracing will not leave the cell at any point. By combining the centroid with the point cloud, an "enhanced" point cloud may be generated by the segmentation logic component 2004. In some embodiments, the centroid is used for target sampling. When identifying the type of sample, the results of the target sampling may be added as additional verification for the identification logic component 2006. The centroid adds additional information about the mathematical shape of the point cloud without expressing each boundary point. Additionally, the centroid may be used by a scientific instrument to capture sample information at the location identified by the centroid.

[0031] As an alternative or supplement to the centroid identification filter, the segmentation logic component 2004 may apply other filters to the point cloud to generate additional values that may be included in the enhanced point cloud. Examples of filters that may be applied include one or more of the following: tight minimum bounding rectangle (TMBR) (which calculates the minimum rectangle aligned with the principal axes of the point cloud), scale-invariant feature transform (SIFT) (which detects and describes features invariant to scale, orientation, and illumination), keypoint - affine - invariant - z (KAZE) (which detects and describes features invariant to scale, rotation, and affine transformation), adaptive and generic acceleration segmentation test (AGAST) (which detects keypoints), features from accelerated segment test (FAST) (which detects corners), oriented FAST and rotated BRIEF (ORB) (which detects and describes keypoints), and accelerated - KAZE (AKAZE) (which detects and describes local features). In some embodiments, the segmentation logic component 2004 may utilize an image processing tool or library (such as OpenCV) to perform the filtering, and the results will be included in the enhanced point cloud.

[0032] In some embodiments where the segmentation logic component 2004 includes multiple filter results in the enhanced point cloud, some or all of the filter results may be combined on an additional dimension of the enhanced point cloud. For example, the segmentation logic component 2004 may generate a hash value by applying a hash function to one or more of the filter results, and may include the hash value in the enhanced point cloud as an alternative or supplement to the filter results. Thus, the number of filters applied to the point cloud may be different from the number of additional dimensions included in the enhanced point cloud.

[0033] The identification logic component 2006 may identify the type of the sample based on the enhanced point cloud. For example, when the sample is known during the initial configuration of the system, an enhanced point cloud may be generated and associated with the sample. Then this association is stored in the memory. During further experiments, the generated enhanced point cloud is compared with the stored sample to determine the sample associated with the enhanced point cloud. In some cases, if the scientific instrument fails to identify or detect all the information for constructing the enhanced point cloud of a given sample, the identification logic component 2006 may obtain the missing information from a similar enhanced point cloud.

[0034] In some embodiments, the identification logic component 2006 may use the enhanced point cloud to confirm the identity of a specific sample, which includes a two-dimensional or three-dimensional image of the sample and spectral or other physical or chemical data. For example, a first enhanced point cloud of a known sample may include an image of the sample captured at a first time (captured or generated by, for example, a visible light camera, an infrared camera, a microscope, or any other image capture device) and spectral data of the sample (e.g., a spectrum or other chemical data), and a second enhanced point cloud of an unidentified sample may include an image of the sample captured at a second time and spectral data of the sample. The first enhanced point cloud and the second enhanced point cloud may be compared, and if their similarity exceeds a threshold (e.g., the distance between the enhanced point clouds is less than the threshold), the identification logic component 2006 may identify the unidentified sample as the known sample. Such embodiments may be particularly useful in applications where it is important to confirm the identity of an object, such as confirming that a specific object is what it purports to be in a chain of custody application, or confirming that a specific object (e.g., a wafer used in electronics manufacturing, another manufactured object, a drug, an agricultural product, etc.) fully meets the visual and chemical requirements of the object in a quality assurance / quality control application. For identification use cases, the enhanced point cloud can be used as a "fingerprint" of the sample, which can be used to confirm or reject the identity of an unknown sample; in some such cases, the spectral instrument or other instrument that generates the physical or chemical data of the sample can be considered an identification instrument or part of the identification system.

[0035] The alignment logic component 2008 can align the outputs of multiple scientific instruments based on the generated enhanced point cloud. For example, before receiving a sample, components of the scientific instrument can be translated or rotated. It is difficult to calibrate the scientific instrument to be in exactly the same position. Additionally, when the sample is moved between scientific instruments, there is a risk of collision or changing the position of the scientific instrument to deviate from the calibrated position.

[0036] The relative distances of the points within the point cloud may cause the alignment logic component 2008 to identify the degree of magnification difference between instruments with different magnifications. For example, a sample can be analyzed first with a first scientific instrument. The point cloud logic component 2002 and the segmentation logic component 2004 operate to generate a first enhanced point cloud for the sample analyzed by the first scientific instrument. Next, the sample is analyzed with a second scientific instrument. The point cloud logic component 2002 and the segmentation logic component 2004 operate to generate a second enhanced point cloud for the sample analyzed by the second scientific instrument. The alignment logic component 2008 compares the first enhanced point cloud and the second enhanced point cloud and determines the distance between the output of the first scientific instrument and the output of the second scientific instrument. If the distance exceeds an acceptable distance threshold, the alignment logic component 2008 operates to adjust one of the first scientific instrument and the second scientific instrument to better align the instruments. For example, in an optical microscope, the zoom level can be physically adjusted by adjusting the lens within the microscope. As another example, the linear scaling factor applied uniformly across the captured images can be adjusted.

[0037] According to the embodiments disclosed herein, any suitable technique can be used to generate the distance between two point clouds. For example, when the two point clouds are in a common coordinate system, the Euclidean distance, mean square distance, or any other distance metric between the centroid or other representative points can be used. In some embodiments, the iterative closest point (ICP) method can be used to calculate the distance between two point clouds. When the two point clouds are in a common coordinate system or different coordinate systems (e.g., when the first point cloud is n-dimensional, the second point cloud is m-dimensional, and m is not equal to n), the ICP method (e.g., the point-to-point ICP method or the point-to-plane ICP method known in the art) can be used; in some embodiments using the ICP method, the root mean square error or other associated metric (e.g., the reciprocal of the fitness value) can be used as the distance metric. In some embodiments, the alignment logic component 2008 can utilize a point cloud computing toolkit or library (such as the Open3D library) to perform point cloud related calculations.

[0038] Figure 3 is a flowchart of a method 3000 for aligning the outputs of scientific instruments according to various embodiments. Although reference may be made to specific embodiments disclosed herein (e.g., the scientific instrument support module 2000 discussed herein, reference herein Figure 2 discussed, the scientific instrument support module 2000, reference herein Figure 4The GUI 4000 under discussion, as referenced herein Figure 5 The computing device 5000 under discussion and / or as referenced herein Figure 6 The operation of method 3000 is illustrated by the scientific instrument support system 6000 under discussion, but method 3000 can be used with any suitable setup to perform any suitable support operation. In Figure 3 the operations are each illustrated once in a particular order, but the operations can be reordered and / or repeated as needed and as appropriate (e.g., different operations can be performed in parallel where appropriate).

[0039] At 3002, a first operation can be performed. For example, the point cloud logic component 2002 of the support module 2000 can perform the operation of 2002. The first operation can include generating a first point cloud of a sample with a first scientific instrument. The first point cloud can be n-dimensional, where n is an integer. The first operation can include generating a second point cloud of the sample with a second scientific instrument. The second point cloud can be m-dimensional, where m is an integer. The integer n can be equal to or different from the integer m. Additional point clouds can be generated for the sample based on the number of scientific instruments implemented.

[0040] At 3004, a second operation can be performed. For example, the segmentation logic component 2004 of the support module 2000 can perform the operation of 3004. The second operation can include identifying a first centroid within the first point cloud. The second operation can include identifying a second centroid within the second point cloud. The centroid can be identified by applying one or more segmentation filters to the point cloud. In some cases, the second operation includes combining the first centroid with the first point cloud to generate a first enhanced point cloud, and combining the second centroid with the second point cloud to generate a second enhanced point cloud. Additionally, in some examples, the second operation includes identifying a first set of centroids and a second set of centroids instead of identifying a first centroid and a second centroid.

[0041] At 3006, a third operation can be performed. For example, the identification logic component 2006 of the support module 2000 can perform the operation of 3006. The third operation can include identifying the type of the sample based on n point clouds. For example, the type of the sample can be the chemical compound of the sample.

[0042] At 3008, a fourth operation can be performed. For example, the alignment logic component 2008 of the support module 2000 can perform the operation of 3008. The fourth operation can include determining the distance between the first centroid and the second centroid (or the first set of centroids and the second set of centroids). In some cases, the fourth operation includes determining the distance between the first enhanced point cloud and the second enhanced point cloud. The fourth operation can include aligning the output of the first scientific instrument and the output of the second scientific instrument based on the distance. For example, the second scientific instrument can be adjusted based on the distance to be aligned with the first scientific instrument, or the output of the first scientific instrument and / or the second scientific instrument can be adjusted after acquisition / generation.

[0043] The methods disclosed herein may include interaction with a human user (e.g., via Figure 6 The user local computing device 6020 discussed above. These interactions may include providing information to the user (e.g., about scientific instruments such as Figure 6 information about the operation of a scientific instrument 6010), information about samples being analyzed or other tests or measurements performed by the scientific instrument, information retrieved from a local or remote database, or other information) or to provide a user with a means for inputting commands (e.g., for controlling a scientific instrument such as Figure 6 In some embodiments, these interactions may be performed through a graphical user interface (GUI) that includes a display device (e.g., a graphical user interface such as a display device ... Figure 5 A visual display on a display device 5010 as discussed herein that provides output to a user and / or prompts a user to provide input (e.g., via the referenced Figure 5 Other I / O devices 5012 discussed include one or more input devices such as a keyboard, mouse, trackpad, or touch screen. The scientific instrument support system disclosed herein may include any suitable GUI for interacting with a user.

[0044] Figure 4 An example GUI 4000 is depicted that can be used to perform some or all of the supporting methods disclosed herein according to various embodiments. As described above, the GUI 4000 can be provided in a scientific instrument support system (e.g., as described herein with reference to Figure 6 A computing device (e.g., a scientific instrument support system 6000 as discussed herein) Figure 5 The computing device 5000 discussed herein may include a display device (eg, Figure 5 The display device 5010 discussed above) and the user may use any suitable input device (e.g., Figure 5 Any of the input devices included in the other I / O devices 5012 discussed) and input techniques (e.g., cursor movement, motion capture, facial recognition, gesture detection, voice recognition, button actuation, etc.) interact with the GUI 4000.

[0045] GUI 4000 may include a data display area 4002 , a data analysis area 4004 , a scientific instrument control area 4006 , and a settings area 4008 . Figure 4The specific number and arrangement of the regions depicted are merely illustrative, and any number and arrangement of regions (including any desired features) may be included in the GUI 4000.

[0046] The data display region 4002 may display data generated by a scientific instrument (e.g., the scientific instrument 6010 discussed herein with reference to Figure 6 the scientific instrument 6010 discussed herein). For example, the data display region 4002 may display a point cloud generated for a sample.

[0047] The data analysis region 4004 may display the results of data analysis (e.g., the results of analyzing the data illustrated in the data display region 4002 and / or other data). For example, the data analysis region 4004 may display chemical compounds, the type of sample, or other metadata associated with the sample and represented within the point cloud. In some embodiments, the data display region 4002 and the data analysis region 4004 may be combined in the GUI 4000 (e.g., to include the data output from the scientific instrument and some analysis of the data in a common graphic or region).

[0048] The scientific instrument control region 4006 may include options that allow a user to control a scientific instrument (e.g., the scientific instrument 6010 discussed herein with reference to Figure 6 the scientific instrument 6010 discussed herein). For example, the scientific instrument control region 4006 may include interactive graphical elements that allow a user to adjust the position of the scientific instrument or components within the scientific instrument.

[0049] The settings region 4008 may include options that allow a user to control the features and functions of the GUI 4000 (and / or other GUIs), and / or perform common computational operations with respect to the data display region 4002 and the data analysis region 4004 (e.g., save data on a storage device (such as the storage device 5004 discussed herein with reference to Figure 5 the storage device 5004 discussed herein), transfer data to another user, mark data, etc.).

[0050] As noted above, the scientific instrument support module 2000 may be implemented by one or more computing devices. Figure 5 is a block diagram of a computing device 5000 that can execute some or all of the scientific instrument support methods disclosed herein according to various embodiments. In some embodiments, the scientific instrument support module 2000 may be implemented by a single computing device 5000 or by multiple computing devices 5000. Additionally, as discussed below, the computing device 5000 (or multiple computing devices 5000) that implements the scientific instrument support module 2000 may be Figure 6 a part of one or more of the scientific instrument 6010, the user local computing device 6020, the service local computing device 6030, or the remote computing device 6040.

[0051] Figure 5 The computing device 5000 is illustrated as having multiple components, but any one or more of these components may be omitted or duplicated depending on the suitability for the application and settings. In some embodiments, some or all of the components included in the computing device 5000 may be attached to one or more motherboards and encapsulated in a housing (e.g., including plastic, metal, and / or other materials). In some embodiments, some of these components may be fabricated on a single system-on-chip (SoC) (e.g., the SoC may include one or more processing devices 5002 and one or more storage devices 5004). Additionally, in various embodiments, the computing device 5000 may not include Figure 5 one or more of the illustrated components, but may include interface circuitry (not shown) for coupling to one or more components using any suitable interface (e.g., a Universal Serial Bus (USB) interface, a High-Definition Multimedia Interface (HDMI) interface, a Controller Area Network (CAN) interface, a Serial Peripheral Interface (SPI) interface, an Ethernet interface, a wireless interface, or any other suitable interface). For example, the computing device 5000 may not include the display device 5010, but may include display device interface circuitry (e.g., connectors and driver circuitry) to which the display device 5010 may be coupled.

[0052] The computing device 5000 may include a processing device 5002 (e.g., one or more processing devices). As used herein, the term "processing device" may refer to any device or portion of a device that processes electronic data from registers and / or memory to transform that electronic data into other electronic data that may be stored in registers and / or memory. The processing device 5002 may include one or more digital signal processors (DSPs), application specific integrated circuits (ASICs), central processing units (CPUs), graphics processing units (GPUs), cryptographic processors (specialized processors that execute cryptographic algorithms in hardware), server processors, or any other suitable processing device.

[0053] The computing device 5000 may include a storage device 5004 (e.g., one or more storage devices). The storage device 5004 may include one or more memory devices, such as random access memory (RAM) (e.g., static RAM (SRAM) devices, magnetic RAM (MRAM) devices, dynamic RAM (DRAM) devices, resistive RAM (RRAM) devices, or conductive-bridging RAM (CBRAM) devices), hard-drive-based memory devices, solid-state memory devices, network drives, cloud drives, or any combination of memory devices. In some embodiments, the storage device 5004 may include a memory that shares a die with the processing device 5002. In such embodiments, the memory may be used as cache memory and may include, for example, embedded dynamic random access memory (eDRAM) or spin-transfer torque magnetic random access memory (STT-MRAM). In some embodiments, the storage device 4004 may include a non-transitory computer-readable medium having instructions that, when executed by one or more processing devices (e.g., the processing device 5002), cause the computing device 5000 to perform any suitable method or portion of the methods disclosed herein.

[0054] The computing device 5000 may include interface device 5006 (e.g., one or more interface devices 5006). The interface device 5006 may include one or more communication chips, connectors, and / or other hardware and software to manage communications between the computing device 5000 and other computing devices. For example, the interface device 5006 may include circuitry for managing wireless communications for transmitting data to and from the computing device 5000. The term “wireless” and its derivatives may be used to describe circuits, devices, systems, methods, techniques, communication channels, etc., that may convey data by using modulated electromagnetic radiation through a non-solid medium. The term does not imply that the associated devices do not contain any wires, although in some embodiments they may not contain any wires. The circuitry for managing wireless communications included in interface device 4006 may implement any of a plurality of wireless standards or protocols, including but not limited to Institute of Electrical and Electronics Engineers (IEEE) standards, including Wi-Fi (IEEE 802.11 series), IEEE 802.16 standards (e.g., IEEE 802.16-2005 amendment), Long Term Evolution (LTE) project, and any amendments, updates, and / or revisions (e.g., LTE-Advanced project, Ultra Mobile Broadband (UMB) project (also known as “3GPP2”), etc.). In some embodiments, the circuitry for managing wireless communications included in interface device 4006 may operate in accordance with Global System for Mobile Communications (GSM), General Packet Radio Service (GPRS), Universal Mobile Telecommunications System (UMTS), High-Speed Packet Access (HSPA), Evolved HSPA (E-HSPA), or LTE networks. In some embodiments, the circuitry for managing wireless communications included in interface device 4006 may operate in accordance with Enhanced Data GSM Evolution (EDGE), GSM EDGE Radio Access Network (GERAN), Universal Terrestrial Radio Access Network (UTRAN), or Evolved UTRAN (E-UTRAN). In some embodiments, the circuitry for managing wireless communications included in interface device 4006 may operate in accordance with Code Division Multiple Access (CDMA), Time Division Multiple Access (TDMA), Digital Enhanced Cordless Telecommunications (DECT), Evolution-Data Optimized (EV-DO), and their derivative protocols, and any other wireless protocols designated as 3G, 4G, 5G, and higher generations, etc. In some embodiments, interface device 4006 may include one or more antennas (e.g., one or more antenna arrays) for receiving and / or transmitting wireless communications.

[0055] In some embodiments, interface device 5006 may include circuitry for managing wired communications, such as electrical communication protocols, optical communication protocols, or any other suitable communication protocol. For example, interface device 5006 may include circuitry that supports communication according to Ethernet technology. In some embodiments, interface device 5006 may support both wireless and wired communications and / or may support multiple wired communication protocols and / or multiple wireless communication protocols. For example, a first set of circuitry of interface device 5006 may be dedicated to short-range wireless communication such as Wi-Fi or Bluetooth, while a second set of circuitry of interface device 5006 may be dedicated to long-range wireless communication such as Global Positioning System (GPS), EDGE, GPRS, CDMA, WiMAX, LTE, EV-DO, etc. In some embodiments, a first set of circuitry of interface device 5006 may be dedicated to wireless communication, while a second set of circuitry of interface device 5006 may be dedicated to wired communication.

[0056] Computing device 5000 may include battery / power circuitry 5008. Battery / power circuitry 5008 may include one or more energy storage devices (e.g., batteries or capacitors) and / or circuitry for coupling components of computing device 5000 to an energy source (e.g., AC line power) separate from computing device 5000.

[0057] Computing device 5000 may include a display device 5010 (e.g., multiple display devices). Display device 5010 may include any visual indicator, such as a heads-up display, a computer monitor, a projector, a touchscreen display, a liquid crystal display (LCD), a light-emitting diode display, or a flat panel display.

[0058] Computing device 5000 may include other input / output (I / O) devices 5012. Other I / O devices 5012 may include, for example, one or more audio output devices (e.g., speakers, headphones, earbuds, sirens, etc.), one or more audio input devices (e.g., a microphone or a microphone array), a positioning device (e.g., a GPS device known in the art that communicates with a satellite-based system to receive the location of computing device 5000), an audio codec, a video codec, a printer, sensors (e.g., a thermocouple or other temperature sensor, a humidity sensor, a pressure sensor, a vibration sensor, an accelerometer, a gyroscope, etc.), an image capture device such as a camera, a keyboard, a cursor control device such as a mouse, a stylus, a trackball, or a touchpad, a barcode reader, a quick response (QR) code reader, or a radio frequency identification (RFID) reader.

[0059] The computing device 5000 can have any suitable form factor appropriate for its applications and settings, such as a handheld or mobile computing device (e.g., a cellular phone, smartphone, mobile Internet device, tablet computer, laptop computer, netbook computer, ultrabook computer, personal digital assistant (PDA), ultra-mobile personal computer, etc.), a desktop computing device, or a server computing device or other networked computing component.

[0060] One or more computing devices implementing any of the scientific instrument support modules or methods disclosed herein can be part of a scientific instrument support system. Figure 6 FIG. 6000 is a block diagram of an exemplary scientific instrument support system in which some or all of the scientific instrument support methods disclosed herein can be performed according to various embodiments. The scientific instrument support modules and methods disclosed herein (e.g., Figure 2 the scientific instrument support module 2000 of Figure 3 and

[0061] the method 3000 of Figure 5 can be implemented by one or more of the scientific instrument 6010, the user local computing device 6020, the service local computing device 6030, or the remote computing device 6040 of the scientific instrument support system 6000. Figure 5 Any one of the scientific instrument 6010, the user local computing device 6020, the service local computing device 6030, or the remote computing device 6040 can include any of the embodiments of the computing device 5000 discussed herein with reference to

[0062] FIG. 5000, and any one of the scientific instrument 6010, the user local computing device 6020, the service local computing device 6030, or the remote computing device 6040 can take the form of any suitable embodiment of the computing device 5000 discussed herein with reference to Figure 5 FIG. 5002. The processing devices 6002 included in different devices of the scientific instrument 6010, the user local computing device 6020, the service local computing device 6030, or the remote computing device 6040 can take the same form or different forms. The storage device 6004 can take any suitable form, including those discussed herein with reference to Figure 5in the form of any of the storage devices 6004 discussed, and the storage devices 6004 included in the different devices of the scientific instrument 6010, the user local computing device 6020, the service local computing device 6030, or the remote computing device 6040 may take the same form or different forms. The interface device 6006 may take any suitable form, including any of the interface devices 5006 discussed herein with reference to Figure 5 in the form of any of the interface devices 5006 discussed, and the interface devices 6006 included in the different devices of the scientific instrument 6010, the user local computing device 6020, the service local computing device 6030, or the remote computing device 6040 may take the same form or different forms.

[0063] The scientific instrument 6010, the user local computing device 6020, the service local computing device 6030, and the remote computing device 6040 may communicate with other elements of the scientific instrument support system 6000 via the communication path 6008. The communication path 6008 may communicatively couple the interface devices 6006 of different elements among the elements of the scientific instrument support system 6000 (as shown), and may be a wired or wireless communication path (e.g., according to any of the communication technologies discussed for the interface device 6006 of the computing device 5000 herein with reference to Figure 5 the communication technologies). Figure 6 The specific scientific instrument support system 6000 depicted includes communication paths between each pair of the scientific instrument 6010, the user local computing device 6020, the service local computing device 6030, and the remote computing device 6040, but this specific implementation of "fully connected" is merely illustrative, and in various embodiments, various communication paths in the communication path 6008 may not exist. For example, in some embodiments, the service local computing device 6030 may not have a direct communication path 6008 between its interface device 6006 and the interface device 6006 of the scientific instrument 6010, but may communicate with the scientific instrument 6010 via the communication path 6008 between the service local computing device 6030 and the user local computing device 6020 and the communication path 6008 between the user local computing device 6020 and the scientific instrument 6010.

[0064] The scientific instrument 6010 may include any suitable scientific instrument, such as a charged particle microscope system. Figure 7Shows an example charged particle microscope system 7000 according to an embodiment of the present disclosure. The charged particle microscope system 7000 can be a scanning electron microscope (SEM). The SEM system 7000 includes an electron source 7010 that emits an electron beam 7011 along an emission axis 7110 towards a focusing column 7012. In some embodiments, the focusing column 7012 can include one or more of a condenser lens 7121, an aperture 7122, a scanning coil 7123, and an upper objective lens 7124. The focusing column 7012 focuses the electrons from the electron source 7010 into a small spot on the sample 7014. Different positions of the sample can be scanned by adjusting the direction of the electron beam via the scanning coil 7123. For example, by operating the scanning coil 7123, the incident beam 7112 can be deflected (as shown by the dashed line) to be focused on different positions of the sample 7014. The sample 7014 can be thin enough not to impede the transmission of most of the electrons in the electron beam 7011.

[0065] The sample 7014 can be held by a sample holder 7013. Electrons 7101 passing through the sample 7014 can enter a projector 7116. In one embodiment, the projector 7116 can be a part separated from the focusing column. In another embodiment, the projector 7116 can be an extension of the lens field from a lens in the focusing column 7012. The projector 7116 can be adjusted by a controller 7030 such that the direct electrons passing through the sample strike a disk-shaped bright-field detector 7115, while the diffracted or scattered electrons more strongly deflected by the sample are detected by a dark-field detector 7019. Signals from the bright-field detector and the dark-field detector can be amplified by an amplifier 7022 and an amplifier 7021, respectively. Signals from the amplifiers 7021 and 7022 can be transmitted to an image processor 7024, which can form an image of the sample 7014 based on the detected electrons. The SEM system 7000 can detect signals from one or more of the bright-field detector and the dark-field detector simultaneously.

[0066] The controller 7030 can manually or automatically control the operation of the imaging system 7000 in response to operator instructions or according to computer-readable instructions stored in a non-transitory memory 7032. The controller 7030 can be configured to execute the computer-readable instructions and control the various components of the imaging system 7000. For example, the controller 7030 can adjust the scanning position on the sample by operating the scanning coil 7123. The controller 7030 can adjust the profile of the incident beam by adjusting one or more apertures and / or lenses in the focusing column 7012. The controller 7030 can adjust the sample orientation relative to the incident beam by adjusting the sample holder 7013. The controller 7030 can be further coupled to a display 7031 to display notifications and / or an image of the sample. The controller 7030 can receive user input from a user input device 7033. The user input device 7033 can include a keyboard, a mouse, or a touch screen.

[0067] Although the SEM system has been described by way of example, it should be understood that the electron source can also be used in other charged particle beam microscope systems (such as a transmission electron microscope (TEM) system and a dual beam microscope system). The current discussion of SEM imaging is provided only as an example of a suitable imaging modality.

[0068] The scientific instrument 6010 can include a CPM / EDX system. Figure 8 An example configuration of the CPM / EDX system 210 is illustrated. The CPM / EDX system 210 can be a scanning electron microscope with energy dispersive X-ray spectroscopy (SEM / EDX) system. The CPM / EDX system 210 can include a particle optical column 315 mounted on a vacuum chamber 306. Inside the particle optical column 315, electrons generated by an electron source 312 are modified by a condenser lens system 314 before being focused onto a sample 302 by a lens system 316. The incident beam 304 can scan the sample 302 by operating a scanning coil 313. The sample can be held by a sample stage 308.

[0069] The CPM / EDX system 210 can include multiple detectors for detecting various emissions from the sample 302 in response to irradiation by the incident beam 304. A first detector 303 can detect X-rays emitted from the sample 302. In one example, the detector 303 can be a multi-channel photon counting EDX detector. A second detector 301 can detect electrons, such as backscattered and / or secondary electrons emitted from the sample 302. In one example, the detector 301 can be a segmented electron detector.

[0070] The scientific instrument 6010 can alternatively include an optical microscope system that utilizes spectroscopic techniques (such as Raman spectroscopy, Fourier transform infrared (FTIR) spectroscopy, laser induced fluorescence, or others). Figure 9 An example configuration of the spectroscopic system 10 is illustrated. The system can include an optical camera 15 that can observe the sample by using an optical path 17. The system can include a laser or other light source 10 that is directed through an optical relay and focused onto the sample at a sample location 16 by using a microscope objective 12. Light returning from the sample can be collected by the microscope objective 12 and directed through another optical path to a spectrometer for collecting a characteristic spectral data set. This process can be repeated across regions of the sample, thereby generating a hyperspectral data set. It should be understood that the spectrometer system can be arranged in many different geometries and have various additional features, and embodiments of the present invention can be used in any such various embodiments of the spectrometer system.

[0071] Discussed in more detail Figure 9, the spectral system 10 may include an optical microscope depicted within the dashed / dotted line 11. The microscope 11 may include an objective optical element 12 and an eyepiece optical element 14 (depicted herein as lenses, but reflective elements (e.g., mirrors) may also be used in place of refractive elements such as lenses). Light from a sample located at the sample position 16 may be transmitted through the objective optical element 12 along the microscope beam path 17 to the eyepiece optical element 14 to form an image, which may be directly observed by an observer through the eyepiece optical element 14 or observed using a camera 15 and a video display terminal (not shown).

[0072] Molecular spectroscopy of the sample may also be performed. An illumination beam 21 may be provided from a light source 20 (depicted herein as a laser, but other light sources may also be used) through a beam path adjuster 22 to a mirror 24, which redirects the illumination beam 21 onto a path towards a mirror 26. The mirror 26 may deflect the illumination beam 21 onto a path that coincides with the microscope beam path 17. The objective 12 may focus the illumination beam 21 onto a focal point 28, such that any sample at that point interacts with the illumination beam 21 and scatters, emits, or otherwise delivers light having different wavelength content along a return beam path 30 after being collected by the objective optical element 12. The return beam 30 may be deflected by the mirror 26 onto a path that coincides with the illumination beam path 21 and may be allowed to pass through the mirror 24 (which may be a dichroic mirror selected to allow wavelengths within one or more ranges other than the illumination beam 21 to pass through). The return beam 30 may pass through a beam path adjuster 34 (i.e., a set of optical elements capable of offsetting the axis of the return beam 30) and through an input lens 35, which focuses the beam 30 onto the spectrometer input aperture 36 of a spectrometer 37. The spectrometer 37 may be configured to spatially disperse the light wavelengths in the return beam 30 (e.g., by a Czerny-Turner monochromator or other device, not shown), and then these wavelengths are incident on a detector 38, which detects the intensity of light at various wavelengths to provide an output signal characterizing the properties of the sample.

[0073] Beam path adjusters 22 and 34 can be provided to precisely align the illumination beam 21 and the return beam 30 with the foci 28 and the spectrometer input aperture 36. Adjustment signals are fed to the beam path adjusters 22, 34 by a control system 44 that depends on inputs from a detector 38 (discussed below) and from an alignment unit 39 located on or within the sample stage 40 of the microscope 11. The alignment unit 39 includes a stage entrance aperture 41 that is positioned by the operator by observing the alignment unit 39 with the eyepiece optics 14 and / or the camera 15 to coincide with the central axis of the microscope beam 17. The alignment unit 39 can include a stage light source 60 (e.g., a high-intensity light-emitting diode (LED)) and a stage light sensor 65 (e.g., a silicon photodiode positioned to receive light transmitted through the LED / stage light source 60) actuated by a line 62 in communication with the control system 44, where the stage light sensor 65 transmits a stage light sensor output signal along a line 68 to the control system 44 in response to the reception of light. The control system 44 can perform alignment by turning on the stage light source 60 and then adapting the beam path adjuster 34 until the return beam 30 from the stage light source 60 is recorded at maximum intensity on the detector 38, indicating that in the case where the return beam 30 is generated by the illumination beam 21 from the light source 20, such return beam 30 will also be well-aligned with the spectrometer input aperture 36 and the detector 38. Similarly, the beam path adjuster 22 can be adapted by the control system 44 until the stage light sensor 65 measures a maximum output from the light source 20, indicating that the illumination beam 21 is correctly aligned. In other words, the input or reference beam 21 for spectrometry is optimized via the beam path adjuster 22 by signals from the stage light sensor 65 in the alignment unit 39 (where the stage light sensor 65 is excited by the light source 20), and the return beam 30 for spectrometry is optimized via the beam path adjuster 34 by signals from the detector 38 in the spectrometer 37 (where the detector 38 is excited by the stage light source 60). Note that the control system 44 can communicate with the light source 20 via a line 46, with the beam adjuster 22 via a line 47, with the detector 38 via a line 48, and with the beam adjuster 34 via a line 49, as well as communicate with the stage light sensor 65 via a line 68 and with the stage light source 60 via a line 62. Once alignment is achieved, the alignment unit 39 can be removed from the sample stage 40 (if it is not built-in) so that the microscope system 11 can be used to analyze a sample.

[0074] The user local computing device 6020 can be a computing device local to the user of the scientific instrument 6010 (e.g., according to any of the embodiments of the computing device 5000 discussed herein). In some embodiments, the user local computing device 6020 can also be local to the scientific instrument 6010, but this is not necessarily the case; for example, the user local computing device 6020 in the user's home or office can be remote from the scientific instrument 6010 but communicate with the scientific instrument such that the user can use the user local computing device 6020 to control and / or access data from the scientific instrument 6010. In some embodiments, the user local computing device 6020 can be a laptop computer, a smart phone, or a tablet device. In some embodiments, the user local computing device 6020 can be a portable computing device.

[0075] The service local computing device 6030 can be a computing device local to an entity that services the scientific instrument 6010 (e.g., according to any of the embodiments of the computing device 5000 discussed herein). For example, the service local computing device 6030 can be a local device of the manufacturer of the scientific instrument 6010 or a third-party service company. In some embodiments, the service local computing device 6030 can communicate with the scientific instrument 6010, the user local computing device 6020, and / or the remote computing device 6040 (e.g., via the direct communication path 6008 or via multiple "indirect" communication paths 6008, as described above) to receive data regarding the operation of the scientific instrument 6010, the user local computing device 6020, and / or the remote computing device 6040 (e.g., self-test results of the scientific instrument 6010, calibration coefficients used by the scientific instrument 6010, measurement results of sensors associated with the scientific instrument 6010, etc.). In some embodiments, the service local computing device 6030 can communicate with the scientific instrument 6010, the user local computing device 6020, and / or the remote computing device 6040 (e.g., via the direct communication path 6008 or via multiple "indirect" communication paths 6008, as described above) to send data to the scientific instrument 6010, the user local computing device 6020, and / or the remote computing device 6040 (e.g., to update the programmed instructions (such as firmware) in the scientific instrument 6010 to initiate the execution of a test or calibration sequence in the scientific instrument 6010, to update the programmed instructions (such as software) in the user local computing device 6020 or the remote computing device 6040, etc.). The user of the scientific instrument 6010 can communicate with the service local computing device 6030 using the scientific instrument 6010 or the user local computing device 6020 to report problems with the scientific instrument 6010 or the user local computing device 6020, thereby requesting a technician visit to improve the operation of the scientific instrument 6010, to order consumables or replacement parts associated with the scientific instrument 6010, or for other purposes.

[0076] The remote computing device 6040 can be a computing device that is remote from the scientific instrument 6010 and / or the user local computing device 6020 (e.g., according to any of the embodiments of the computing device 5000 discussed herein). In some embodiments, the remote computing device 6040 can be included in a data center or other large-scale server environment. In some embodiments, the remote computing device 6040 can include network-attached storage (e.g., as part of the storage device 6004). The remote computing device 6040 can store data generated by the scientific instrument 6010, perform analysis on the data generated by the scientific instrument 6010 (e.g., according to programmed instructions), facilitate communication between the user local computing device 6020 and the scientific instrument 6010, and / or facilitate communication between the service local computing device 6030 and the scientific instrument 6010.

[0077] In some embodiments, Figure 6 one or more of the elements of the scientific instrument support system 6000 illustrated in may not be present. Additionally, in some embodiments, Figure 6 multiple of the various elements of the scientific instrument support system 6000 of may be present. For example, the scientific instrument support system 6000 can include multiple user local computing devices 6020 (e.g., different user local computing devices 6020 associated with different users or located in different locations). In another example, the scientific instrument support system 6000 can include multiple scientific instruments 6010, all of which communicate with the service local computing device 6030 and / or the remote computing device 6040; in such embodiments, the service local computing device 6030 can monitor the multiple scientific instruments 6010, and the service local computing device 6030 can cause updates or other information to be "broadcast" to the multiple scientific instruments 6010 simultaneously. Different scientific instruments among the scientific instruments 6010 in the scientific instrument support system 6000 can be close to each other (e.g., in the same room) or far from each other (e.g., on different floors of a building, in different buildings, in different cities, etc.). In some embodiments, the scientific instrument 6010 can be connected to an Internet of Things (IoT) stack that allows the scientific instrument 6010 to be commanded and controlled via web-based applications, virtual or augmented reality applications, mobile applications, and / or desktop applications. Any of these applications can be accessed by a user operating the user local computing device 6020, which communicates with the scientific instrument 6010 via an intermediate remote computing device 6040. In some embodiments, the scientific instrument 6010 can be sold by a manufacturer together with one or more associated user local computing devices 6020 as part of the local scientific instrument computing unit 6012.

[0078] In some embodiments, the different scientific instruments 6010 included in the scientific instrument support system 6000 can be different types of scientific instruments 6010; as previously described. In some such embodiments, the remote computing device 6040 and / or the user local computing device 6020 can combine data from different types of scientific instruments 6010 included in the scientific instrument support system 6000.

[0079] The following paragraphs provide various examples of the embodiments disclosed herein.

[0080] Example 1 is a method of identifying a sample, the method comprising: receiving, by a computing device, image data representing the sample, the image data being generated by an imaging device; receiving, by the computing device, physical or chemical data representing the sample, the physical or chemical data being generated by a scientific instrument different from the imaging device; generating, by the computing device, an enhanced point cloud based on the image data and the physical or chemical data; and providing, by the computing device, the enhanced point cloud to an identification system for comparison with one or more additional enhanced point clouds to identify the sample.

[0081] Example 2 includes the subject matter of Example 1 and further specifies that the physical or chemical data includes spectral data.

[0082] Example 3 includes the subject matter of any one of Examples 1 to 2 and further specifies that the image data includes two-dimensional or three-dimensional image data, and generating the enhanced point cloud includes combining the two-dimensional or three-dimensional image data with one or more additional dimensions representing the physical or chemical data.

[0083] Example 4 includes the subject matter of any one of Examples 1 to 3 and further specifies that the enhanced point cloud includes one or more dimensions representing one or more filters applied to the image data.

[0084] Example 5 includes the subject matter of any one of Examples 1 to 4 and further specifies that the enhanced point cloud includes one or more dimensions representing a hash value.

[0085] Example 6 includes the subject matter of any one of Examples 1 to 5 and further specifies that the sample is a manufactured object or an agricultural product.

[0086] Example 7 includes the subject matter according to any one of Examples 1 to 6, and further specifies that the computing device is part of the identification system, and the method further includes: comparing the enhanced point cloud with one or more additional enhanced point clouds by the computing device generating a distance between the enhanced point cloud and each of the one or more additional enhanced point clouds; and when the distance between the enhanced point cloud and the enhanced point cloud associated with a known sample meets one or more distance criteria, identifying, by the computing device, the sample as corresponding to the known sample.

[0087] Example 8 is a method for aligning the outputs of different scientific instruments, the method including: generating, at least in part, a first point cloud representing a sample using a first scientific instrument, wherein the first point cloud is in an n-dimensional space and n is an integer; generating, at least in part, a second point cloud representing the sample using a second scientific instrument different from the first scientific instrument, wherein the second point cloud is in an m-dimensional space different from the n-dimensional space associated with the first point cloud and wherein m is an integer; generating an offset between the first point cloud and the second point cloud using a transformation relating the n-dimensional space to the m-dimensional space; and aligning the output of the second scientific instrument with the output of the first scientific instrument based on the offset.

[0088] Example 9 includes the subject matter according to Example 8, and further specifies that m is equal to n.

[0089] Example 10 includes the subject matter according to Example 8, and further specifies that m is different from n.

[0090] Example 11 includes the subject matter according to any one of Examples 8 to 10, and further specifies that the first scientific instrument and the second scientific instrument have different magnifications.

[0091] Example 12 includes the subject matter according to any one of Examples 8 to 11, and further specifies that the first point cloud includes two-dimensional or three-dimensional image data and one or more additional dimensions representing one or more filters applied to the image data.

[0092] Example 13 includes the subject matter according to any one of Examples 8 to 12, and further specifies that the first point cloud includes one or more dimensions representing hash values.

[0093] Example 14 includes the subject matter according to any one of Examples 8 to 13, and further specifies that the sample is a manufactured object or an agricultural product.

[0094] Example 15 includes the subject matter according to any one of Examples 8 to 14, and further includes: adjusting one or more settings of the first scientific instrument or the second scientific instrument based on the offset.

[0095] Example 16 is a method of comparing scientific instrument data, the method comprising: receiving, by a computing device, a first point cloud representing a first sample, wherein the first point cloud is in an n-dimensional space and n is an integer, the first point cloud being at least partially based on data generated by a first scientific instrument, and the first point cloud including outputs of one or more filters for some or all of the data generated by the first scientific instrument; receiving, by the computing device, a second point cloud representing a second sample, wherein the second point cloud is in an m-dimensional space and m is an integer, the second point cloud being at least partially based on data generated by a second scientific instrument different from the first scientific instrument, and the second point cloud including outputs of one or more filters for some or all of the data generated by the second scientific instrument; using, by the computing device, a transformation that correlates the n-dimensional space with the m-dimensional space to generate an offset between the first point cloud and the second point cloud; and outputting, by the computing device, an identification or alignment result at least partially based on the offset.

[0096] Example 17 includes the subject matter of Example 16 and further specifies that the data generated by the first scientific instrument and the data generated by the second scientific instrument include physical or chemical data.

[0097] Example 18 includes the subject matter of Example 17 and further specifies that the physical or chemical data includes spectral data.

[0098] Example 19 includes the subject matter of any one of Examples 16 to 18 and further specifies that the first point cloud includes two-dimensional or three-dimensional image data and one or more additional dimensions representing physical or chemical data of the data.

[0099] Example 20 includes the subject matter of any one of Examples 16 to 19 and further specifies that the first point cloud includes one or more dimensions representing hash values.

[0100] Example 21 is a method for aligning scientific instruments, the method comprising: generating, with a first scientific instrument, a first n-dimensional point cloud representing a sample; generating, with a second scientific instrument, a second n-dimensional point cloud representing the sample; identifying a first centroid within the first n-dimensional point cloud; identifying a second centroid within the second n-dimensional point cloud; determining a distance between the first centroid and the second centroid; and aligning the second scientific instrument with the first scientific instrument based on the distance.

[0101] Example 22 is one or more non-transitory computer-readable media having instructions thereon that, when executed by one or more processing devices of a scientific instrument support device, cause the scientific instrument support device to perform the method of any one of Examples 1 to 21.

[0102] Example 23 is a scientific instrument support system, which includes a computing device configured to execute the method according to any one of Examples 1 to 21.

[0103] Example 24 is a scientific instrument support system, which includes components for executing the method according to any one of Examples 1 to 21.

Claims

1. A method of identifying a sample, the method comprising: Receiving, by a computing device, image data representing the sample, the image data being generated by an imaging device; Receiving, by the computing device, physical or chemical data representing the sample, the physical or chemical data being generated by a scientific instrument different from the imaging device; Generating, by the computing device, an enhanced point cloud based on the image data and the physical or chemical data; And Providing, by the computing device, the enhanced point cloud to an identification system for comparison with one or more additional enhanced point clouds to identify the sample.

2. The method according to claim 1, wherein the physical or chemical data includes spectral data.

3. The method according to any one of claims 1-2, wherein the image data includes 2D or 3D image data, and generating the enhanced point cloud includes combining the 2D or 3D image data with one or more additional dimensions of data representing the physical or chemical data.

4. The method according to any one of claims 1-3, wherein the enhanced point cloud includes one or more dimensions representing one or more filters applied to the image data.

5. The method according to any one of claims 1-4, wherein the enhanced point cloud includes one or more dimensions representing a hash value.

6. The method according to any one of claims 1-5, wherein the sample is a manufactured object or an agricultural product.

7. The method according to any one of claims 1-6, wherein the computing device is part of the identification system, and the method further comprises: Comparing, by the computing device, the enhanced point cloud with the one or more additional enhanced point clouds by generating a distance between the enhanced point cloud and each of the one or more additional enhanced point clouds; And Identifying, by the computing device, the sample as corresponding to the known sample when the distance between the enhanced point cloud and the enhanced point cloud associated with the known sample satisfies one or more distance criteria.

8. A method of aligning outputs of different scientific instruments, the method comprising: Generating, at least in part, a first point cloud representing a sample using a first scientific instrument, wherein the first point cloud is in an n-dimensional space and n is an integer; Generating, at least in part, a second point cloud representing the sample using a second scientific instrument different from the first scientific instrument, wherein the second point cloud is in an m-dimensional space different from the n-dimensional space associated with the first point cloud and wherein m is an integer; Generating an offset between the first point cloud and the second point cloud using a transformation relating the n-dimensional space to the m-dimensional space; And Aligning the output of the second scientific instrument with the output of the first scientific instrument based on the offset.

9. The method according to claim 8, wherein m is equal to n.

10. The method according to claim 8, wherein m is different from n.

11. The method according to any one of claims 8-10, wherein the first scientific instrument and the second scientific instrument have different magnifications.

12. The method according to any one of claims 8-11, wherein the first point cloud comprises two-dimensional or three-dimensional image data and one or more additional dimensions representing one or more filters applied to the image data.

13. The method according to any one of claims 8-12, wherein the first point cloud comprises one or more dimensions representing hash values.

14. The method according to any one of claims 8-13, wherein the sample is a manufactured object or an agricultural product.

15. The method according to any one of claims 8-14, the method further comprising: Adjusting one or more settings of the first scientific instrument or the second scientific instrument based on the offset.

16. A method for comparing scientific instrument data, the method comprising: Receiving, by a computing device, a first point cloud representing a first sample, wherein the first point cloud is in an n-dimensional space and n is an integer, the first point cloud is at least partially based on data generated by a first scientific instrument, and the first point cloud comprises outputs of one or more filters for some or all of the data generated by the first scientific instrument; Receiving, by the computing device, a second point cloud representing a second sample, wherein the second point cloud is in an m-dimensional space and m is an integer, the second point cloud is at least partially based on data generated by a second scientific instrument different from the first scientific instrument, and the second point cloud comprises outputs of one or more filters for some or all of the data generated by the second scientific instrument; Generating, by the computing device, an offset between the first point cloud and the second point cloud using a transformation that correlates the n-dimensional space with the m-dimensional space; And Outputting, by the computing device, an identification or alignment result at least partially based on the offset.

17. The method according to claim 16, wherein the data generated by the first scientific instrument and the data generated by the second scientific instrument comprise physical or chemical data.

18. The method according to claim 17, wherein the physical or chemical data comprises spectral data.

19. The method according to any one of claims 16-18, wherein the first point cloud comprises two-dimensional or three-dimensional image data and one or more additional dimensions representing physical or chemical data.

20. The method according to any one of claims 16-19, wherein the first point cloud comprises one or more dimensions representing hash values.

Images