Point clouds for sample identification and device configuration

Abstract

A scientific instrument support system and associated method, computing device, and computer-readable medium for aligning scientific instruments includes: generating, with a first scientific instrument, a first point cloud representing a sample, the first point cloud being in an n-dimensional space, where n is an integer; and generating, with a second scientific instrument different from the first scientific instrument, a second point cloud representing the sample, the second point cloud being in an m-dimensional space different from the n-dimensional space associated with the first point cloud, where m is an integer. The method includes generating an offset between the first and second point clouds using a transformation that relates the n-dimensional space to the m-dimensional space, and aligning an output of the second scientific instrument with the output of the first scientific instrument based on the offset.

Description

[Technical Field]

[0001] (CROSS-REFERENCE TO RELATED APPLICATIONS) This application claims priority to U.S. Provisional Patent Application No. 63 / 431,837, entitled "POINT CLOUD FOR SAMPLE IDENTIFICATION AND DEVICE CONFIGURATION," filed December 12, 2022, which is incorporated herein by reference in its entirety. [Background technology]

[0002] Scientific instruments can include complex arrangements of moving components, sensors, input and output ports, energy sources, and consumable components. Data measured by a scientific instrument can be converted into a point cloud that represents the analyzed sample as a multidimensional shape. [Brief explanation of the drawings]

[0003]

[0013] The embodiments will be readily understood by the following detailed description taken in conjunction with the accompanying drawings, in which:

[0014] To facilitate this description, like reference numerals refer to like structural elements;

[0015] The embodiments are illustrated in the figures of the accompanying drawings, by way of example, and not by way of limitation. [Figure 1] 1 is an exemplary three-dimensional point cloud, in accordance with various embodiments. [Figure 2] FIG. 1 is a block diagram of an exemplary scientific instrument support module for performing support operations, according to various embodiments. [Figure 3] FIG. 1 is a flow diagram of an exemplary method for aligning scientific instruments, according to various embodiments. [Figure 4] 1 is an example of a graphical user interface that may be used in implementing some or all of the methods disclosed herein, according to various embodiments. [Figure 5] FIG. 1 is a block diagram of an exemplary computing device that may implement some or all of the methods disclosed herein, according to various embodiments. [Figure 6]FIG. 1 is a block diagram of an exemplary scientific instrument support system in which some or all of the methods disclosed herein may be implemented, according to various embodiments. [Figure 7] 1 is an exemplary charged particle microscope system incorporating techniques disclosed herein, according to various embodiments. [Figure 8] 1 is an exemplary energy dispersive X-ray spectroscopy system incorporating techniques disclosed herein, according to various embodiments. [Figure 9] FIG. 1 is a schematic diagram of an optical spectroscopy system incorporating techniques disclosed herein, according to various embodiments. DETAILED DESCRIPTION OF THE INVENTION

[0004] Disclosed herein are scientific instrument support systems, as well as 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, the first point cloud being in an n-dimensional space, where n is an integer, and generating, with a second scientific instrument different from the first scientific instrument, a second point cloud representing the sample, the second point cloud being in an m-dimensional space different from the n-dimensional space associated with the first point cloud, where m is an integer. The method includes generating an offset between the first and second point clouds using a transformation that relates 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.

[0005] Embodiments of scientific instrument support disclosed herein may achieve improved performance compared to conventional approaches. For example, samples may typically be identified using methods that may depend on variable illumination or magnification. Additionally, some methods of identifying samples destroy or affect the sample. Embodiments disclosed herein identify samples in a non-destructive manner that is independent of variations in illumination and magnification. Thus, embodiments disclosed herein provide improvements to scientific instrument technology (e.g., improvements to the computer technology supporting such scientific instruments, among other improvements).

[0006] The embodiments disclosed herein may achieve automatic alignment of scientific instruments using only a research sample versus conventional approaches, such as using fiducial markers or holders for calibration, which may waste the operation of the scientific instrument.

[0007] Various of the embodiments disclosed herein may improve upon conventional approaches to achieve technical advantages for aligning multiple scientific instruments by using previously acquired data when identifying sample composition. Such technical advantages are not achievable with routine conventional approaches, and all users of systems incorporating such embodiments benefit from these advantages (e.g., by assisting users in performing technical tasks, such as quickly aligning scientific instruments to within a margin of error, through a guided human-machine interaction process). The computer and user interface features disclosed herein apply not only to the collection and comparison of information, but also to new analytical and technical techniques for modifying the operation of scientific instruments, including adjusting the physical configuration of their operation and characterizing samples using n-dimensional points of data. Thus, the present disclosure introduces functionality that neither conventional computing devices nor humans have been able to perform.

[0008] Thus, embodiments of the present disclosure may serve any of a number of technical purposes, such as controlling a particular technical system or process, determining how to control machinery from measurements, 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, etc. In particular, the present disclosure provides technical solutions to technical problems including, but not limited to, identifying research samples and aligning scientific equipment.

[0009] Thus, the embodiments disclosed herein provide improvements to microscopy, dual beam, and spectroscopy techniques (e.g., improvements in computer technology supporting microscopy, dual beam, and spectroscopy techniques, among other improvements).

[0010] In the following detailed description, reference is made to the accompanying drawings that form a part hereof, where like numerals refer to like parts throughout and which show, by way of illustration, embodiments that may be practiced. 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. Therefore, the following detailed description is not to be taken in a limiting sense.

[0011] Various operations may be described sequentially as multiple separate actions or operations in a manner that is most helpful in understanding the subject matter disclosed herein. However, the order of description should not be construed as implying that these operations are necessarily order dependent. In particular, these operations may not be performed in the order presented. The operations described may be performed in a different order than in the described embodiment. Various additional operations may be performed and / or described operations may be omitted in additional embodiments.

[0012] For purposes of this disclosure, the phrases “A and / or B” and “A or B” mean (A), (B), or (A and B). For purposes of this 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 referred to in the singular (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 with different ones of the operations performed by different processing devices.

[0013] This description uses the phrases "one embodiment," "various embodiments," and "some embodiments," each of which may refer to one or more of the same or different embodiments. Furthermore, terms such as "comprising," "including," and "having," when used with respect to embodiments of the present disclosure, are synonymous. When used to describe a range of dimensions, the phrase "between X and Y" represents a range that includes X and Y. As used herein, an "apparatus" may refer to any individual device, a collection of devices, a portion of a device, or a collection of portions of devices. The drawings are not necessarily drawn to scale.

[0014] A point cloud is a set of data points in space that represent a three-dimensional (3D) shape or object. For example, each point location has a set of Cartesian coordinates (X, Y, Z). A point cloud can be created by a 3D scanner or by photogrammetry software that measures points on the exterior surface of an object. As the output of a 3D scanning process, the point cloud is used for 3D visualization of the scanned object. FIG. 1 illustrates an exemplary point cloud 1000 containing multiple data points in Cartesian space. The data points are not limited to representing physical locations on the scanned sample. In some examples, the data points are located in sections of high interest, such as high-contrast points or other points of interest. In essence, a point cloud includes points in space that represent desired features (e.g., fiducials).

[0015] Point clouds are not limited to 3D space. For example, a QR code can be considered a two-dimensional (2D) point cloud that includes additional information such as the scientist's name, the methodology implemented, and the timestamp at which the experiment was performed. Such metadata can be represented as points within the point cloud, with a third dimension (Z) implemented to represent the type of information. Additional dimensions can be added based on the amount of metadata 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, the fourth dimension can represent the number of detected spectra of the sample, the fifth dimension can represent the filter applied to the sample, and the sixth dimension can represent the detected compounds.

[0016] 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 may be implemented by a circuit (e.g., including electrical and / or optical components) such as a programmed computing device. The logic of the scientific instrument support module 2000 may be contained in a single computing device or may be distributed across multiple computing devices that communicate with each other as needed. An example of a computing device that may implement the scientific instrument support module 2000, alone or in combination, is discussed herein with reference to computing device 5000 of FIG. 5, and an example of a system of interconnected computing devices in which the scientific instrument support module 2000 may be implemented across one or more of the computing devices is discussed herein with reference to scientific instrument support system 6000 of FIG. 6.

[0017] The scientific instrument assistance module 2000 may include point cloud logic 2002, segmentation logic 2004, identification logic 2006, and alignment logic 2008. As used herein, the term "logic" may include an apparatus that performs a set of operations associated with the logic. For example, any of the logic elements included in the assistance module 2000 may be implemented by one or more computing devices programmed with instructions that cause one or more processing devices of the computing devices to perform the associated set of operations. In particular embodiments, a logic element may include one or more non-transitory computer-readable media having instructions that, when executed by one or more processing devices of the 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" may refer to a collection of one or more logic elements that together perform a function associated with the module. Different logic elements within a module may take the same form or different forms. For example, some logic in a module may be implemented by a programmed general-purpose processing device, while other logic in the module may be implemented by an application-specific integrated circuit (ASIC). In another example, different ones of the logic elements in a module may be associated with different sets of instructions executed by one or more processing devices. A module may not include all of the logic elements depicted in an associated figure; for example, a module may include a subset of the logic elements depicted in an associated figure when the module performs a subset of the operations discussed herein with reference to that module.

[0018] The point cloud logic 2002 may generate a point cloud representing a sample. For example, the sample is scanned by a scientific instrument. The point cloud logic 2002 receives the scanned sample and generates a three-dimensional point cloud representing the physical sample. Additionally, the scientific instrument may collect additional data associated with the sample, such as detecting chemical compounds in the sample. The point cloud logic 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 2002 generates an n-dimensional point cloud of the sample that includes all desired data associated with the sample (e.g., X coordinates, Y coordinates, and metadata associated with the sample).

[0019] In some examples, the point cloud logic 2002 is a machine learning algorithm or other artificial intelligence that is trained to analyze samples and output points in a point cloud. Exemplary 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.

[0020] The segmentation logic 2004 may apply one or more segmentation filters to the point cloud. A segmentation filter is a filter that identifies points of interest or distinctness within the point cloud. The segmentation filter may identify boundary markers that indicate the structural shape of the point cloud. In some examples, the segmentation filter identifies centroids within the point cloud. Centroids are mathematically determinable locations based on boundary markers that may not have a distinct structure 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, and when tracing around the centroid, there is no point where the trace exits the cell. By combining the centroid with the point cloud, an "augmented" point cloud may be generated by the segmentation logic 2004. In some embodiments, the centroid is used for target sampling. The results of the target sampling may be added as additional validation by the identification logic 2006 when identifying the type of sample. The centroid adds additional information about the mathematical shape of the point cloud without requiring all boundary points to be represented. Additionally, the centroid can be used by scientific instruments to capture sample information at the location identified by the centroid.

[0021] The segmentation logic 2004 may apply other filters to the point cloud instead of, or in addition to, the centroid identification filter to generate additional values ​​that can be included in the augmented point cloud. Examples of filters that may be applied include one or more of tight minimum bounding rectangle (TMBR) (which computes a minimum rectangle aligned with the major axes of the point cloud), scale-invariant feature transform (SIFT) (which detects and describes features that are invariant to scale, orientation, and illumination), keypoint-affine-invariant-z (KAZE) (which detects and describes features that are invariant to scale, rotation, and affine transformations), adaptive and generic accelerated segment 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 2004 may utilize image processing tools or libraries, such as OpenCV, to perform the filtering so that the results are included in the augmented point cloud.

[0022] In some embodiments in which the segmentation logic 2004 includes multiple filter results in the augmented point cloud, some or all of the filter results may be combined in additional dimensions of the augmented point cloud. For example, the segmentation logic 2004 may generate a hash value by applying a hash function to one or more filter results and include the hash value in the augmented point cloud instead of or in addition to the filter results. Thus, the number of filters applied to the point cloud may differ from the number of additional dimensions included in the augmented point cloud.

[0023] The identification logic 2006 may identify the type of sample based on the enhanced point cloud. For example, if the sample is known during initial configuration of the system, an enhanced point cloud may be generated and associated with the sample. This association is stored in memory. During further experiments, the generated enhanced point cloud is compared to the stored sample to determine the sample associated with the enhanced point cloud. In some examples, if the scientific instrument fails to identify or detect all the information to construct an enhanced point cloud for a given sample, the identification logic 2006 may obtain the missing information from similar enhanced point clouds.

[0024] In some embodiments, the identification logic 2006 may confirm the identity of a particular sample using an enhanced point cloud that includes a two-dimensional or three-dimensional image of the sample and spectroscopic data 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 (e.g., captured or generated by a visible light camera, an infrared camera, a microscope, or any other image capture device) and spectral data of the sample (e.g., spectroscopic data 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 and second enhanced point clouds may be compared, and if their similarity exceeds a threshold (e.g., the distance between the enhanced point clouds is less than a threshold), the identification logic 2006 may identify the unidentified sample as a known sample. Such embodiments may be particularly useful in applications where verifying the identity of an object is important, such as chain of custody applications to verify that a particular object is as intended, or quality assurance / quality control applications to verify that a particular object (e.g., a wafer used in electronics manufacturing, another manufactured object, pharmaceutical product, agricultural product, etc.) properly meets the visual and chemical requirements for that object. In identification use cases, the augmented point cloud may serve as a sample "fingerprint" that can be used to confirm or reject the identity of an unknown sample; in some such cases, the spectroscopic or other instrument that generates the physical or chemical data of the sample may be considered part of the identification instrument, or identification system.

[0025] The alignment logic 2008 may align the outputs of multiple scientific instruments based on the generated augmented point cloud. For example, before receiving a sample, components of the scientific instruments may be panned or rotated. Calibrating the scientific instruments to the exact same position is difficult. Additionally, when a sample is moved between scientific instruments, there is a risk of moving or changing the position of the scientific instruments from their calibrated positions.

[0026] The relative distances of points in the point clouds may result in the alignment logic 2008 identifying the degree of magnification difference between instruments having different magnifications. For example, a sample may first be analyzed with a first scientific instrument. The point cloud logic 2002 and the segmentation logic 2004 operate to generate a first enhanced point cloud of the sample as analyzed by the first scientific instrument. The sample is then analyzed with a second scientific instrument. The point cloud logic 2002 and the segmentation logic 2004 operate to generate a second enhanced point cloud of the sample as analyzed by the second scientific instrument. The alignment logic 2008 compares the first and second enhanced point clouds and determines the distance between the output of the first and second scientific instruments. If the distance exceeds an acceptable distance threshold, the alignment logic 2008 operates to adjust one of the first and second scientific instruments to better align the instruments. For example, in an optical microscope, the zoom level may be physically adjusted by adjusting the lenses within the microscope. As another example, a linear scaling factor that is applied uniformly across the captured image may be adjusted.

[0027] According to embodiments disclosed herein, the distance between two point clouds may be generated using any suitable technique. For example, if the two point clouds are in a common coordinate system, Euclidean distance, mean square distance, or any other distance metric between centroids or other representative points may be used. In some embodiments, an iterative closest point (ICP) method may be used to calculate the distance between the two point clouds. An ICP method (e.g., a point-to-point ICP method or a point-to-plane ICP method as known in the art) may be used 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 and the second point cloud is m-dimensional, where m does not equal n). In some embodiments using the ICP method, the root mean square error or other associated metric (e.g., the inverse of the fitness value) may be used as the distance measure. In some embodiments, the registration logic 2008 may utilize a point cloud computation tool or library, such as the Open3D library, to perform point cloud-related calculations.

[0028] 3 is a flow diagram of a method 3000 for aligning outputs of scientific instruments, according to various embodiments. While the operations of method 3000 may be illustrated with reference to particular embodiments disclosed herein (e.g., scientific instrument support module 2000 discussed herein with reference to FIG. 2, GUI 4000 discussed herein with reference to FIG. 4, computing device 5000 discussed herein with reference to FIG. 5, and / or scientific instrument support system 6000 discussed herein with reference to FIG. 6), method 3000 may be used in any suitable setting to perform any suitable support operations. Although the operations are illustrated in FIG. 3 in a particular order, each one once, the operations may be appropriately reordered and / or repeated as desired (e.g., different operations performed may be performed in parallel, as appropriate).

[0029] In 3002, a first operation may be performed. For example, point cloud logic 2002 of support module 2000 may perform the operation of 2002. The first operation may include generating a first point cloud of a sample using a first scientific instrument. The first point cloud may be n-dimensional, where n is an integer. The first operation may include generating a second point cloud of the sample using a second scientific instrument. The second point cloud may be m-dimensional, where m is an integer. The integer n may be equal to or different from the integer m. Additional point clouds may be generated for the sample based on the number of implemented scientific instruments.

[0030] At 3004, a second operation may be performed. For example, the segmentation logic 2004 of the assistance module 2000 may perform the operation of 3004. The second operation may include identifying a first centroid within the first point cloud. The second operation may include identifying a second centroid within the second point cloud. The centroids may be identified by applying one or more segmentation filters to the point clouds. In some examples, the second operation includes combining the first centroid with the first point cloud to generate a first augmented point cloud and combining the second centroid with the second point cloud to generate a second augmented point cloud. Additionally, in some examples, rather than identifying a first centroid and a second centroid, the second operation includes identifying a first set of centroids and a second set of centroids.

[0031] A third operation may be performed in 3006. For example, the identification logic 2006 of the assistance module 2000 may perform the operation of 3006. The third operation may include identifying a type of the sample based on the n point clouds. The type of the sample may be, for example, a compound of the sample.

[0032] In 3008, a fourth operation may be performed. For example, the alignment logic 2008 of the assistance module 2000 may perform the operation of 3008. The fourth operation may include determining a distance between the first centroid and the second centroid (or the first set of centroids and the second set of centroids). In some examples, the fourth operation includes determining a distance between the first augmented point cloud and the second augmented point cloud. The fourth operation may include aligning an output of the first scientific instrument and an output of the second scientific instrument based on the distance. For example, the second scientific instrument may be adjusted to align with the first scientific instrument based on the distance, or the outputs of the first and / or second scientific instruments may be adjusted after acquisition / generation.

[0033] The methods disclosed herein may include interactions with a human user (e.g., via a user local computing device 6020 discussed herein with reference to FIG. 6 ). These interactions may include providing the user with information (e.g., information regarding the operation of a scientific instrument such as the scientific instrument 6010 of FIG. E6 , information regarding a sample being analyzed or other tests or measurements performed by the scientific instrument, information retrieved from a local or remote database, or other information), or providing options for the user to enter commands (e.g., to control the operation of a scientific instrument such as the scientific instrument 6010 of FIG. 6 or to control the analysis of data generated by the scientific instrument), queries (e.g., to a local or remote database), or other information. In some embodiments, these interactions may be implemented through a graphical user interface (GUI) that includes a visual display on a display device (e.g., display device 5010 discussed herein with reference to FIG. 5) that provides output to the user and / or prompts the user to provide input (e.g., via one or more input devices such as a keyboard, mouse, trackpad, or touchscreen included in other I / O devices 5012 discussed herein with reference to FIG. 5). The scientific instrument support systems disclosed herein may include any suitable GUI for interaction with a user.

[0034] 4 depicts an exemplary GUI 4000 that may be used in implementing some or all of the assistance methods disclosed herein, according to various embodiments. As noted above, the GUI 4000 may be provided on a display device (e.g., display device 5010 discussed herein with reference to FIG. 5) of a computing device (e.g., computing device 5000 discussed herein with reference to FIG. 5) of a scientific instrument assistance system (e.g., scientific instrument assistance system 6000 discussed herein with reference to FIG. 6), and a user may interact with the GUI 4000 using any suitable input device (e.g., any of the input devices included in other I / O devices 5012 discussed herein with reference to FIG. 5) and input technique (e.g., cursor movement, motion capture, facial recognition, gesture detection, voice recognition, button activation, etc.).

[0035] GUI 4000 may include a data display region 4002, a data analysis region 4004, a scientific instrument control region 4006, and a settings region 4008. The particular number and arrangement of regions depicted in Figure 4 is merely illustrative, and any number and arrangement of regions containing any desired features may be included in GUI 4000.

[0036] The data display area 4002 may display data generated by a scientific instrument (e.g., the scientific instrument 6010 discussed herein with reference to FIG. 6). For example, the data display area 4002 may display a generated point cloud of a sample.

[0037] The data analysis area 4004 may display the results of a data analysis (e.g., the results of analyzing the data illustrated in the data display area 4002 and / or other data). For example, the data analysis area 4004 may display compounds, sample types, or other metadata associated with the samples and represented in the point cloud. In some embodiments, the data display area 4002 and the data analysis area 4004 may be combined in the GUI 4000 (e.g., to include data output from scientific instruments and several analyses of the data in a common graph or area).

[0038] The scientific instrument control area 4006 may include options that allow a user to control a scientific instrument (e.g., the scientific instrument 6010 discussed herein with reference to FIG. 6). For example, the scientific instrument control area 4006 may include interactive graphical elements that allow a user to adjust the position of the scientific instrument or components within the scientific instrument.

[0039] Settings area 4008 may include options that enable a user to control features and functionality of GUI 4000 (and / or other GUIs) and / or perform common computing operations with respect to data display area 4002 and data analysis area 4004 (e.g., saving data on a storage device such as storage device 5004 discussed herein with reference to FIG. 5, sending data to another user, labeling data, etc.).

[0040] 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 may implement 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. Furthermore, as discussed below, the computing device 5000 (or multiple computing devices 5000) that implements the scientific instrument support module 2000 may be part of one or more of the scientific instrument 6010, user local computing device 6020, service local computing device 6030, or remote computing device 6040 of Figure 6.

[0041] 5 is illustrated as having several components, any one or more of which may be omitted or duplicated as appropriate for the application and configuration. In some embodiments, some or all of the components included in computing device 5000 may be mounted on one or more motherboards and enclosed in a housing (e.g., comprising plastic, metal, and / or other materials). In some embodiments, several of these components may be fabricated on a single system-on-a-chip (SoC) (e.g., an SoC may include one or more processing devices 5002 and one or more storage devices 5004). 5, 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, computing device 5000 may not include display device 5010, but may include display device interface circuitry (e.g., connectors and driver circuits) to which display device 5010 may be coupled.

[0042] 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 and converts the 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 (GSMUs), or other processors. The processing device may include a graphics processing unit (GPU), a cryptographic processor (a dedicated processor that executes cryptographic algorithms in hardware), a server processor, or any other suitable processing device.

[0043] The computing device 5000 may include a storage device 5004 (e.g., one or more storage devices). The storage device 5004 may include random access memory (RAM) (e.g., static RAM). The storage device 5004 may include one or more memory devices, such as a static RAM (SRAM) device, a magnetic RAM (MRAM) device, a dynamic RAM (DRAM) device, a resistive RAM (RRAM) device, or a conductive-bridging RAM (CBRAM) device, a hard drive-type memory device, a solid-state memory device, a networked drive, a cloud drive, or any combination of memory devices. In some embodiments, the storage device 5004 may include 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, which 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 thereof of the methods disclosed herein.

[0044] The computing device 5000 may include an 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 for managing communications between the computing device 5000 and other computing devices. For example, the interface device 5006 may include circuitry for managing wireless communications for data transfer 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 communicate data through the use of modulated electromagnetic radiation over a non-solid medium. This term does not imply that the associated device does not include any wiring, although in some embodiments it may not. The circuitry included in interface device 4006 for managing wireless communications may implement any of several wireless standards or protocols, including, but not limited to, Wi-Fi (IEEE 802.11 family), the IEEE 802.16 standard (e.g., the IEEE 802.16-2005 amendment), Institute for Electrical and Electronic Engineers (IEEE) standards including the Long-Term Evolution (LTE) project with any amendments, updates, and / or revisions (e.g., the Advanced LTE project, the Ultra Mobile Broadband (UMB) project (also referred to as "3GPP®2"), etc.).In some embodiments, the circuitry included in the interface device 4006 for managing wireless communications is compatible with Global System for Mobile Communications (GSM), General Packet Radio Service (GPRS), Universal Mobile Telecommunications System (UMTS), High Speed ​​Packet Access (HSPA), Evolved HSPA, and the like. In some embodiments, the circuitry included in the interface device 4006 for managing wireless communications may operate in accordance with a GSM HSPA, E-HSPA, or LTE network. In some embodiments, the circuitry included in the interface device 4006 for managing wireless communications may operate in accordance with Enhanced Data for 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 included in the interface device 4006 for managing wireless communications 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 derivatives thereof, as well as any other wireless protocols designated as 3G, 4G, 5G, and beyond. In some embodiments, the interface device 4006 may include one or more antennas (eg, one or more antenna arrays) for receiving and / or transmitting wireless communications.

[0045] In some embodiments, the interface device 5006 may include circuitry for managing wired communications, such as electrical, optical, or any other suitable communications protocol. For example, the interface device 5006 may include circuitry to support communications according to Ethernet technology. In some embodiments, the interface device 5006 may support both wireless and wired communications, and / or may support multiple wired and / or multiple wireless communications protocols. For example, a first set of circuits in the interface device 5006 may be dedicated to short-range wireless communications, such as Wi-Fi or Bluetooth, and a second set of circuits in the interface device 5006 may be dedicated to long-range wireless communications, such as global positioning system (GPS), EDGE, GPRS, CDMA, WiMAX, LTE, EV-DO, or others. In some embodiments, the first set of circuits in the interface device 5006 may be dedicated to wireless communications, and the second set of circuits in the interface device 5006 may be dedicated to wired communications.

[0046] The computing device 5000 may include a battery / power circuit 5008. The battery / power circuit 5008 may include one or more energy storage devices (e.g., batteries or capacitors) and / or circuitry for coupling components of the computing device 5000 to an energy source separate from the computing device 5000 (e.g., AC line power).

[0047] The computing device 5000 may include a display device 5010 (e.g., multiple display devices). The 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.

[0048] The computing device 5000 may include other input / output (I / O) devices 5012. The other I / O devices 5012 may include, for example, one or more audio output devices (e.g., speakers, headsets, earphones, alarms, etc.), one or more audio input devices (e.g., microphones or microphone arrays), a location device (e.g., a GPS device that communicates with a satellite-based system to receive the location of the computing device 5000, as is known in the art), an audio codec, a video codec, a printer, sensors (e.g., thermocouples or other temperature sensors, humidity sensors, pressure sensors, vibration sensors, accelerometers, gyroscopes, etc.), an image capture device such as a camera, a keyboard, a cursor control device such as a mouse, stylus, trackball, or touchpad, a barcode reader, a Quick Response (QR) code reader, or a radio frequency identification (RFID) device. The device may include a radio frequency identification (RFID) reader.

[0049] The computing device 5000 may have any form factor suitable for its use and configuration, such as a handheld or mobile computing device (e.g., a mobile 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.

[0050] One or more computing devices implementing any of the scientific instrument support modules or methods disclosed herein may be part of a scientific instrument support system. Figure 6 is a block diagram of an exemplary scientific instrument support system 6000 in which some or all of the scientific instrument support methods disclosed herein may be implemented, according to various embodiments. The scientific instrument support modules and methods disclosed herein (e.g., scientific instrument support module 2000 of Figure 2 and method 3000 of Figure 3) may be implemented by one or more of the scientific instrument 6010, user local computing device 6020, service local computing device 6030, or remote computing device 6040 of the scientific instrument support system 6000.

[0051] Any of the scientific instrument 6010, the user local computing device 6020, the service local computing device 6030, or the remote computing device 6040 may include any of the embodiments of the computing device 5000 discussed herein with reference to FIG. 5, and any 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 form of any suitable embodiment of the embodiments of the computing device 5000 discussed herein with reference to FIG. 5.

[0052] The scientific instrument 6010, the user local computing device 6020, the service local computing device 6030, or the remote computing device 6040 may each include a processing device 6002, a storage device 6004, and an interface device 6006. The processing device 6002 may take any suitable form, including any form of the processing device 5002 discussed herein with reference to Figure 5, and the processing devices 6002 included in different ones 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 storage device 6004 may take any suitable form, including any form of the storage devices 6004 discussed herein with reference to Figure 5, and the storage devices 6004 included in different ones 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 forms of the interface device 5006 discussed herein with reference to FIG. 5, and the interface devices 6006 included in different ones 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 or different forms.

[0053] 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 communication paths 6008. The communication paths 6008 may communicatively couple the interface devices 6006 of the different elements of the scientific instrument support system 6000, as shown, and may be wired or wireless communication paths (e.g., according to any of the communication techniques discussed herein with reference to the interface device 6006 of the computing device 5000 of FIG. 5 ). While the particular scientific instrument support system 6000 depicted in FIG. 6 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, this “fully connected” implementation is merely illustrative, and in various embodiments, various ones of the communication paths 6008 may not be present. 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 instead may communicate with the scientific instrument 6010 via a communication path 6008 between the service local computing device 6030 and the user local computing device 6020, and a communication path 6008 between the user local computing device 6020 and the scientific instrument 6010.

[0054] The scientific instrument 6010 may include any suitable scientific instrument, such as a charged particle microscope system. FIG. 7 shows an example of an exemplary charged particle microscope system 7000, according to one embodiment of the present disclosure. The charged particle microscope system 7000 may 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 toward a focusing column 7012. In some embodiments, the focusing column 7012 may 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 electrons from the electron source 7010 into a small spot on the sample 7014. By adjusting the direction of the electron beam via the scanning coil 7123, different locations on the sample may be scanned. For example, by operating scan coil 7123, incident beam 7112 can be shifted (as shown by the dashed line) to focus at a different location on sample 7014. Sample 7014 can be thin enough not to prevent the transmission of most of the electrons in electron beam 7011.

[0055] The sample 7014 may be held by a sample holder 7013. Electrons 7101 passing through the sample 7014 may enter a projector 7116. In one embodiment, the projector 7116 may be a separate component from the focusing column. In another embodiment, the projector 7116 may be an extension of the lens field of view from a lens in the focusing column 7012. The projector 7116 may be adjusted by a controller 7030 so that direct electrons passing through the sample impinge on a disk-shaped bright-field detector 7115, while diffracted or scattered electrons that are more strongly deflected by the sample are detected by a dark-field detector 7019. Signals from the bright-field and dark-field detectors may be amplified by amplifiers 7022 and 7021, respectively. The signals from amplifiers 7021 and 7022 may be sent to an image processor 7024, which may form an image of the sample 7014 from the detected electrons. The SEM system 7000 can simultaneously detect signals from one or more bright field and dark field detectors.

[0056] The controller 7030 may control the operation of the imaging system 7000 either manually in response to operator commands or automatically in accordance with computer-readable instructions stored in non-transitory memory 7032. The controller 7030 may be configured to execute the computer-readable instructions and control various components of the imaging system 7000. For example, the controller 7030 may adjust the scan location on the sample by manipulating the scan coil 7123. The controller 7030 may adjust the profile of the incident beam by adjusting one or more apertures and / or lenses in the focusing column 7012. The controller 7030 may adjust the sample orientation relative to the incident beam by adjusting the sample holder 7013. The controller 7030 may further be coupled to a display 7031 for displaying notifications and / or images of the sample. The controller 7030 may receive user input from a user input device 7033. The user input device 7033 may include a keyboard, a mouse, or a touch screen.

[0057] Although an SEM system is described as an example, the electron source may be a transmission electron microscope (TEM). It should be understood that the present invention may also be used in other charged particle beam microscope systems, such as transmission electron microscopy (TEM) systems and dual beam microscope systems. The present discussion of SEM imaging is provided merely as an example of one suitable imaging modality.

[0058] The scientific instrument 6010 may include a CPM / EDX system. FIG. 8 illustrates an exemplary configuration of the CPM / EDX system 210. The CPM / EDX system 210 may be a scanning electron microscopy with energy dispersive X-ray spectroscopy (SEM / EDX) system. The CPM / EDX system 210 may include a particle optical column 315 mounted on a vacuum chamber 306. Within the particle optical column 315, electrons generated by an electron source 312 are modified by a compound lens system 314 and then focused onto the sample 302 by a lens system 316. The incident beam 304 may be scanned across the sample 302 by operating a scan coil 313. The sample may be held by a sample stage 308.

[0059] The CPM / EDX system 210 may include multiple detectors that detect various emissions from the sample 302 in response to illumination by the incident beam 304. The first detector 303 may detect X-rays emitted from the sample 302. In one example, the detector 303 may be a multi-channel photon-counting EDX detector. The second detector 301 may detect electrons, such as backscattered and / or secondary electrons, emitted from the sample 302. In one example, the detector 301 may be a segmented electron detector.

[0060] The scientific instrument 6010 may alternatively be used for Raman spectroscopy, Fourier-transform infrared (FTIR), or other similar applications. Optical spectroscopy systems may include optical microscopy systems that utilize optical spectroscopy techniques such as infrared (FTIR) spectroscopy, laser-induced fluorescence, or others. FIG. 9 illustrates an exemplary configuration of an optical spectroscopy system 10. The system may include an optical camera 15 that can view the sample through the use of an optical path 17. The system may include a laser or other light source 10 that is directed through an optical relay and focused onto the sample at sample position 16 through the use of a microscope objective 12. Light returning from the sample may be collected by the microscope objective 12 and directed through another optical path to a spectrograph that is used to collect a characteristic spectral dataset. This process may be repeated over an area of ​​the sample, resulting in a hyperspectral dataset. Spectrometer systems can be laid out in many different shapes and with various additional features, and it is understood that embodiments of the present invention may be utilized with any such variety of spectrometer system embodiments.

[0061] Considering Figure 9 in more detail, optical spectroscopy system 10 may include an optical microscope depicted within phantom / dashed line 11. Microscope 11 may include objective optics 12 and eyepiece optics 14 (depicted here as a lens, although reflective elements, such as mirrors, may be used instead of refractive elements such as lenses). Light from a sample located at sample position 16 may be transmitted through objective optics 12 to eyepiece optics 14 on microscope beam path 17 to form an image that can be viewed by a viewer either directly through eyepiece optics 14 or using a video camera 15 and video display terminal (not shown).

[0062] Molecular spectroscopy measurements may also be performed on the sample. An illumination light beam 21 may be provided from a light source 20 (depicted here as a laser, although other light sources may be used) via a beam path adjuster 22 to a mirror 24, which redirects the illumination beam 21 onto a path toward a mirror 26. The mirror 26 may deflect the illumination beam 21 onto a path coincident with the microscope beam path 17. The objective lens 12 focuses the illumination beam 21 onto a focal point 28, thereby causing any sample at this point to interact with the illumination beam 21 and, after being collected by the objective optics 12, scatter, emit, or otherwise transmit light having different wavelength components along a return beam path 30. The return beam 30 may be deflected by the mirror 26 onto a path coincident with the illumination beam path 21 and allowed to pass through the mirror 24 (which may be a dichroic mirror selected to pass wavelengths along one or more ranges other than that of the illumination beam 21). Return beam 30 may pass through a beam path adjuster 34 (i.e., a set of optical elements that can shift the axis of return beam 30) and through an input lens 35 that focuses beam 30 onto a spectrograph input aperture 36 of a spectrograph 37. Spectrograph 37 may be configured to spatially distribute the wavelengths of light in return beam 30 (e.g., by a Czerny-Turner monochromator or other arrangement (not shown)), which then incidents on a detector 38 that detects the intensity of light at various wavelengths to provide an output signal that characterizes a property of the sample.

[0063] Beam path adjusters 22 and 34 may be provided to precisely align illumination beam 21 and return beam 30 with focal spot 28 and spectrograph input aperture 36. Beam path adjusters 22 and 34 may be supplied with adjustment signals by control system 44, which relies on inputs from detector 38 (discussed below) and from an alignment unit 39 located on or within sample stage 40 of microscope 11. Alignment unit 39 includes a stage entrance aperture 41 that is positioned by an operator to coincide with the central axis of microscope light beam path 17 by the operator viewing alignment unit 39 with eyepiece optics 14 and / or video camera 15. The alignment unit 39 may include a stage light source 60, such as a high-intensity light-emitting diode (LED), operated by line 62 in communication with the control system 44, and a stage light sensor 65, such as a silicon photodiode, positioned to receive light transmitted through the LED / stage light source 60, which in response to receiving the light, emits a stage light sensor output signal along line 68 to the control system 44. The control system 44 may perform alignment by turning on the stage light source 60 and then adjusting the beam path adjuster 34 until the return beam 30 from the stage light source 60 presents a maximum intensity on the detector 38, thereby indicating that such return beam 30, when generated via the illumination light beam path 21 from the light source 20, is also well aligned with the spectrograph entrance aperture 36 and the detector 38. Similarly, the beam path adjuster 22 may be controlled 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 path 21 is properly aligned.In other words, the input or datum beam 21 for the spectroscopic measurement is optimized by signals from a stage light sensor 65 in the alignment unit 39 via the beam path adjuster 22 (the stage light sensor 65 is stimulated by the light source 20), and the return beam 30 for the spectroscopic measurement is optimized by signals from a detector 38 in the spectrograph 37 via the beam path adjuster 34 (the detector 38 is stimulated by the stage light source 60). Note that the control system 44 may communicate with the light source 20 via line 46, the beam path adjuster 22 via line 47, the detector 38 via line 48, the beam path adjuster 34 via line 49, the stage light sensor 65 via line 68, and the stage light source 60 via line 62. Once alignment is achieved, the alignment unit 39 may be removed from the sample stage 40 (if not incorporated therein) so that the microscope 11 may be used to analyze a sample.

[0064] The user local computing device 6020 may be a computing device that is local to a user of the scientific instrument 6010 (e.g., according to any of the embodiments of computing device 5000 discussed herein). In some embodiments, the user local computing device 6020 may also be local to the scientific instrument 6010, but need not be; for example, a user local computing device 6020 in a user's home or office may be remote from but communicate with the scientific instrument 6010 such that the user may 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 may be a laptop, smartphone, or tablet device. In some embodiments, the user local computing device 6020 may be a portable computing device.

[0065] The servicing local computing device 6030 may be a computing device (e.g., according to any of the embodiments of computing device 5000 discussed herein) that is local to an entity that provides services to the scientific instrument 6010. For example, the servicing local computing device 6030 may be local to the manufacturer of the scientific instrument 6010 or a third-party service company. In some embodiments, the servicing local computing device 6030 may communicate with the scientific instrument 6010, the user local computing device 6020, and / or the remote computing device 6040 (e.g., via a direct communication path 6008 or via multiple "indirect" communication paths 6008, as discussed 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., results of self-tests of the scientific instrument 6010, calibration coefficients used by the scientific instrument 6010, measurements of sensors associated with the scientific instrument 6010, etc.). In some embodiments, the service local computing device 6030 may communicate with the scientific instrument 6010, the user local computing device 6020, and / or the remote computing device 6040 (e.g., via a direct communication path 6008 or via multiple "indirect" communication paths 6008, as discussed above) and transmit data to the scientific instrument 6010, the user local computing device 6020, and / or the remote computing device 6040 (e.g., to update programmed instructions such as firmware in the scientific instrument 6010, to initiate the performance of a test or calibration sequence in the scientific instrument 6010, to update programmed instructions such as software in the user local computing device 6020 or the remote computing device 6040, etc.).A user of the scientific instrument 6010 may use the scientific instrument 6010 or the user local computing device 6020 to communicate with the service local computing device 6030 to report a problem with the scientific instrument 6010 or the user local computing device 6020, to request 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.

[0066] The remote computing device 6040 may be a computing device (e.g., according to any of the embodiments of computing device 5000 discussed herein) that is remote from the scientific instrument 6010 and / or the user local computing device 6020. In some embodiments, the remote computing device 6040 may be included in a data center or other large-scale server environment. In some embodiments, the remote computing device 6040 may include network-attached storage (e.g., as part of the storage device 6004). The remote computing device 6040 may store data generated by the scientific instrument 6010, perform analysis of the data generated by the scientific instrument 6010 (e.g., according to programmed instructions), facilitate communications between the user local computing device 6020 and the scientific instrument 6010, and / or facilitate communications between the service local computing device 6030 and the scientific instrument 6010.

[0067] In some embodiments, one or more of the elements of the scientific instrument support system 6000 illustrated in Figure 6 may not be present. Furthermore, in some embodiments, more than one of various of the elements of the scientific instrument support system 6000 of Figure 6 may be present. For example, the scientific instrument support system 6000 may include multiple user local computing devices 6020 (e.g., different user local computing devices 6020 associated with different users or at different locations). In another example, the scientific instrument support system 6000 may include multiple scientific instruments 6010 that all communicate with a servicing local computing device 6030 and / or a remote computing device 6040; in such an embodiment, the servicing local computing device 6030 may monitor these multiple scientific instruments 6010, or the servicing local computing device 6030 may trigger updates or other information to the multiple scientific instruments 6010 simultaneously. Different ones of the scientific instruments 6010 in the scientific instrument support system 6000 may be located near 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 instruments 6010 may be connected to an Internet-of-Things (IoT) stack that enables command and control of the scientific instruments 6010 through web-based applications, virtual or augmented reality applications, mobile applications, and / or desktop applications. Any of these applications may be accessed by a user operating a user local computing device 6020 that communicates with the scientific instruments 6010 by way of an intervening remote computing device 6040. In some embodiments, the scientific instruments 6010 may be sold by a manufacturer as part of a local scientific instrument computing unit 6012, along with one or more associated user local computing devices 6020.

[0068] In some embodiments, different ones of the scientific instruments 6010 included in the scientific instrument support system 6000 may be different types of scientific instruments 6010, as described above. In some such embodiments, the remote computing device 6040 and / or the user local computing device 6020 may combine data from different types of scientific instruments 6010 included in the scientific instrument support system 6000. [Example]

[0069] The following paragraphs provide various examples of the embodiments disclosed herein.

[0070] Example 1 is a method of identifying a sample, the method including: receiving, by a computing device, image data representing the sample, the image data generated by an imaging device; receiving, by the computing device, physical or chemical data representing the sample, the physical or chemical data 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.

[0071] Example 2 includes the subject matter described in Example 1, further specifying that the physical or chemical data includes spectroscopic data.

[0072] Example 3 includes the subject matter of any of Examples 1 and 2, further specifying 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 of data representing physical or chemical data.

[0073] Example 4 includes the subject matter of any of examples 1-3, further specifying that the enhanced point cloud includes one or more dimensions representing one or more filters applied to the image data.

[0074] Example 5 includes the subject matter of any of examples 1-4, further specifying that the augmented point cloud includes one or more dimensions that represent the hash value.

[0075] Example 6 includes the subject matter of any of Examples 1-5, and further specifies that the sample is a manufactured object or agricultural product.

[0076] Example 7 includes the subject matter of any of Examples 1-6, further specifying that the computing device is part of an identification system, and the method further includes comparing, by the computing device, the enhanced point cloud to the one or more additional enhanced point clouds by generating distances between the enhanced point cloud and individual ones of the one or more additional enhanced point clouds; and identifying, by the computing device, the sample as corresponding to the known sample when a distance between the enhanced point cloud and the enhanced point cloud associated with the known sample satisfies one or more distance criteria.

[0077] Example 8 is a method of aligning 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, the first point cloud being in an n-dimensional space, where 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, the second point cloud being in an m-dimensional space different from the n-dimensional space associated with the first point cloud, where m is an integer; generating an offset between the first point cloud and the second point cloud using a transform that aligns the n-dimensional space with 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.

[0078] Example 9 includes the subject matter of Example 8, further specifying that m is equal to n.

[0079] Example 10 includes the subject matter described in Example 8, further specifying that m is different from n.

[0080] Example 11 includes the subject matter of any of Examples 8-10, further specifying that the first scientific instrument and the second scientific instrument have different magnifications.

[0081] Example 12 includes the subject matter of any of Examples 8-11, further specifying that the first point cloud includes two-dimensional or three-dimensional image data and one or more additional dimensions of data representing one or more filters applied to the image data.

[0082] Example 13 includes the subject matter of any of Examples 8-12, further specifying that the first point cloud includes one or more dimensions that represent the hash value.

[0083] Example 14 includes the subject matter of any of Examples 8-13, and further specifies that the sample is a manufactured object or agricultural product.

[0084] Example 15 includes the subject matter of any of examples 8-14, further including adjusting one or more settings of the first scientific instrument or the second scientific instrument based on the offset.

[0085] 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, the first point cloud being in an n-dimensional space, where n is an integer, the first point cloud being based at least in part on data generated by the first scientific instrument, the first point cloud including output of one or more filters on 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, the second point cloud being in an m-dimensional space, where m is an integer, the second point cloud being based at least in part on data generated by the second scientific instrument, the second point cloud including output of one or more filters on 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 transform that aligns the n-dimensional space to the m-dimensional space; and outputting, by the computing device, an identification or alignment result based at least in part on the offset.

[0086] Example 17 includes the subject matter of example 16, further specifying that the data generated by the first scientific instrument and the data generated by the second scientific instrument include physical or chemical data.

[0087] Example 18 includes the subject matter of Example 17, further specifying that the physical or chemical data includes spectroscopic data.

[0088] Example 19 includes the subject matter of any of Examples 16-18, further specifying that the first point cloud includes two-dimensional or three-dimensional image data and one or more additional dimensions of data representing physical or chemical data.

[0089] Example 20 includes the subject matter of any of examples 16-19, further specifying that the first point cloud includes one or more dimensions that represent the hash value.

[0090] Example 21 is a method for aligning scientific instruments, the method including: generating, using a first scientific instrument, a first n-dimensional point cloud representing a sample; generating, using a second scientific instrument, a second n-dimensional point cloud representing the sample; identifying a first centroid in the first n-dimensional point cloud; identifying a second centroid in 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.

[0091] Example 22 is one or more non-transitory computer-readable media having instructions that, when executed by one or more processing devices of a scientific instrument support device, cause the scientific instrument support device to perform a method described in any of Examples 1 to 21.

[0092] Example 23 is a scientific instrument support system including a computing device configured to perform the method according to any one of Examples 1 to 21.

[0093] Example 24 is a scientific instrument support system including means for carrying out the method according to any one of Examples 1 to 21.

Claims

1. 1. A method for identifying a sample, comprising: receiving, by a computing device, image data representative of the sample, the image data being generated by an imaging device; receiving, by the computing device, physical or chemical data representative of a sample, the physical or chemical data being generated by a scientific instrument distinct 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 of claim 1 , wherein the physical or chemical data comprises spectroscopic data.

3. 3. The method of claim 1 or 2, wherein the image data comprises two-dimensional or three-dimensional image data, and generating the enhanced point cloud comprises combining the two-dimensional or three-dimensional image data with one or more additional dimensions of data representing the physical or chemical data.

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

5. The method of any one of claims 1 to 4, wherein the augmented point cloud comprises one or more dimensions representing hash values.

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

7. the computing device is part of the identification system; comparing, by the computing device, the augmented point cloud with the one or more additional augmented point clouds by generating distances between the augmented point cloud and each one of the one or more additional augmented point clouds; 7. The method of claim 1, further comprising: identifying, by the computing device, the sample as corresponding to the known sample when the distance between the enhanced point cloud and an enhanced point cloud associated with a known sample satisfies one or more distance criteria.

8. 1. A method for aligning outputs of different scientific instruments, comprising: generating, at least in part using a first scientific instrument, a first point cloud representing the sample, said first point cloud being in n-dimensional space, where n is an integer; generating a second point cloud representing the sample using at least in part a second scientific instrument different from the first scientific instrument, the second point cloud being in an m-dimensional space different from the n-dimensional space associated with the first point cloud, where m is an integer; generating an offset between the first cloud of points and the second cloud of points using a transformation that aligns the n-dimensional space with 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.

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

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

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

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

13. The method of any one of claims 8 to 12, wherein the first point cloud includes one or more dimensions that represent hash values.

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

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

16. 1. A method for comparing scientific instrument data, comprising: receiving, by a computing device, a first cloud of points representing a first sample, the first cloud of points being in an n-dimensional space, where n is an integer, the first cloud of points being based at least in part on data generated by a first scientific instrument, the first cloud of points including an output of one or more filters on some or all of the data generated by the first scientific instrument; receiving, by the computing device, a second cloud of points representing a second sample, the second cloud of points being in an m-dimensional space, where m is an integer, the second cloud of points being based at least in part on data generated by a second scientific instrument, the second cloud of points including the output of one or more filters on some or all of the data generated by the second scientific instrument; generating, by the computing device, an offset between the first cloud of points and the second cloud of points using a transformation that aligns the n-dimensional space with the m-dimensional space; and outputting, by the computing device, an identification or alignment result based at least in part on the offset.

17. The method of 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 of claim 17 , wherein the physical or chemical data comprises spectroscopic data.

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

20. The method of any one of claims 16 to 19, wherein the first point cloud includes one or more dimensions that represent hash values.

Images