Patent Application
20240054388
Polymers, such as plastics, may be evaluated and analyzed using various tests and analyses. For example, polymers, such as plastics, may be analyzed using flammability tests, mechanical strength tests, etc. Other processes may be performed on a sample to analyze the chemical or other properties of a plastic. For example, thermogravimetric (TGA) analyses, differential scanning calorimetry (DSC) analyses, and/or infrared spectroscopy (IR) analyses may be used as identification analyses to measure inherent properties of a polymer, such as a plastic. The results of those analyses may therefore be used to identify a type of polymer, such as a plastic, as a polymer sample of the same type, because, for example, they may have similar TGA, DSC, and IR analyses results. In this way, a polymer sample may be identified as being the same as a previously analyzed sample based on the results of such prior analyses, for example, by a trained chemist skilled at comparing results of analyses such as TGA, DSC, and IR.
Described herein are various systems, methods, computer readable media, and apparatuses for training and using machine learning models to analyze and determine matches between polymer sample analysis results with analysis results previously acquired from other samples. Once the machine learning models are trained to recognize matching polymer sample analysis results, the models may be used to determine when an analyzed sample is a same type of polymer as a previously analyzed sample. This may be valuable, for example, in a material testing program where it is desirable to determine if a new polymer sample is the same type of polymer as a previously analyzed sample.
In various embodiments, the present disclosure further provides an exemplary technically improved computer-based method that includes at least receiving, by one or more processors of one or more computing devices, training data for training of a machine learning model. The training data includes a plurality of pairs of datasets. Each of the pairs of datasets includes a reference dataset and a sample dataset. The reference dataset is indicative of first results of a first plastic sample analysis and the sample dataset is indicative of second results of a second plastic sample analysis. Each of the pairs of datasets further includes an indication, for each of the pairs of datasets, that features of the sample dataset and the reference dataset are a match. The method further includes training, by the one or more processors, a machine learning model based on the training data to determine matches between datasets. The method further includes receiving, by the one or more processors, a new sample dataset. The method further includes determining, by the one or more processors using the trained machine learning model, that the new sample dataset matches at least one of a new reference dataset, one of the reference datasets of the plurality of pairs of datasets in the training data, or one of the sample datasets of the plurality of pairs of datasets in the training data.
In various embodiments, the present disclosure provides an exemplary technically improved computer-based system that includes at least the following components of a memory and at least one processor coupled to the memory. The processor is configured to store, on the memory, a trained machine learning model. The trained machine learning model was trained with training data. The training data includes a plurality of pairs of datasets. Each of the pairs of datasets includes a reference dataset and a sample dataset. The reference dataset is indicative of first results of a first plastic sample analysis and the sample dataset is indicative of second results of a second plastic sample analysis. Each of the pairs of datasets further includes an indication, for each of the pairs of datasets, that features of the sample dataset and the reference dataset are a match. The processor is further configured to receive a new sample dataset. The processor is further configured to determine, based on the trained machine learning model, that the new sample dataset matches at least one of a new reference dataset, one of the reference datasets of the plurality of pairs of datasets in the training data, or one of the sample datasets of the plurality of pairs of datasets in the training data
In some embodiments, the present disclosure provides an exemplary technically improved non-transitory computer readable medium having instructions stored thereon that, upon execution by a computing device, cause the computing device to perform operations including receiving a dataset. The dataset includes a plurality of data pairs. Each of the data pairs includes reference data and sample data. The reference data is indicative of first results of a first plastic sample analysis. The sample data is indicative of second results of a second plastic sample analysis. The dataset further includes, an indication, for each of the data pairs, that features of the sample data and the reference data are a match. The instructions further cause the computing device to perform operations including training a machine learning model using the dataset. The instructions further cause the computing device to perform operations including receiving new sample data. The instructions further cause the computing device to perform operations including determining, using the trained machine learning model, that the new sample data matches at least one of new reference data, one of the reference data of the plurality of data pairs in the dataset, or one of the sample data of the plurality of data pairs in the dataset.
Various embodiments of the present disclosure can be further explained with reference to the attached drawings, wherein like structures are referred to by like numerals throughout the several views. The drawings shown are not necessarily to scale, with emphasis instead generally being placed upon illustrating the principles of the present disclosure. Therefore, specific structural and functional details disclosed herein, including in the various drawings and figures, are not to be interpreted as limiting, but merely as a representative basis for teaching one skilled in the art to variously employ one or more illustrative embodiments.
Various detailed embodiments of the present disclosure, taken in conjunction with the accompanying figures, are disclosed herein; however, it is to be understood that the disclosed embodiments are merely illustrative. In addition, each of the examples given in connection with the various embodiments of the present disclosure is intended to be illustrative, and not restrictive.
Described herein are methods, systems, computer readable media, etc. for training a machine learning model to match previous product analysis data performed (e.g., on a polymer sample) to new polymer sample analysis data. Since manufacturers of polymer goods, such as plastics, often update their products or release new versions of products, those manufacturers may desire to have the new or updated products tested, analyzed, and/or certified. By matching a new sample to a sample previously analyzed, a testing entity may be able to confidently certify that the polymer in the new or updated product is the same or substantially the same as the polymer (or other material) used in the previous product that was tested, analyzed, and/or certified. In doing so, the new or updated product may be tested, analyzed and/or certified more efficiently, more accurately, etc. based on matching the new or updated product to an older, previously analyzed product. Various embodiments herein include training a machine learning model to predict or determine such matches between new product samples and previously analyzed samples using datasets related to specific analyses performed on the products. Those datasets related to a particular product may be referred to herein as a fingerprint of the product, polymer, or plastic. As such, the various embodiments described herein provide for comparing the fingerprints of two polymer samples to determine if they match, even if the tester or testing entity does not know the actual chemical composition of either product-instead the fingerprint may be representative of how the products reacted to certain chemical, heat, etc. tests. For example, analyses performed on plastics may include thermogravimetric (TGA) analyses, differential scanning calorimetry (DSC) analyses, and/or infrared spectroscopy (IR) analyses. In various embodiments, other tests or analyses may be used and other types of materials than plastics may be analyzed using the various methods, apparatuses, computer readable media, and systems described herein. For example, the various embodiments herein may be used to analyze other polymers, whether natural or synthetic, including thermoplastics, thermosets, and/or elastomers.
Where a fingerprint match is identified, the testing entity may be able to omit other additional tests that may be more arduous or time consuming, such as physical performance tests. In other words, if a plastic product sample is determined to be the same or substantially the same plastic as a previously performance analyzed sample, those performance tests may be omitted (e.g., in a certification for the new plastic product sample, or for any other reason it is desirable to match a previously analyzed sample to a new sample). The embodiments described herein may, therefore, provide cost savings and time savings to the entity submitting the samples and to the testing entity. The embodiments described herein represent a significant improvement over methods where each sample to be matched to a previous sample is subjected to a full range of analyses and manually matched each time a product made out of a similar plastic is to be analyzed. In addition, where a manufacturer believes that the plastic in a new product is the same as the plastic in a previous product, the manufacturer may also identify the previous product that has already been analyzed. Then, a reference product used to determine a fingerprint match to a new sample may be already selected and/or specified, such that both the new product sample dataset and the reference sample dataset information may be input into a machine learning model to determine whether there is a match. In other embodiments, only a new sample may be input, and the machine learning model may identify from a set of reference data whether there are any matches between the new sample and other stored reference datasets.
Other materials than plastics may also be analyzed and matched with reference samples in a similar way. For example, thermal scanning of materials, which may include, but not limited to, various alloys, ceramics, plastics, laminates, polymers, paints, fillers, resins, adhesives, complex materials/composites, minerals, rubbers, etc. may all be analyzed in accordance with the embodiments described herein. Such materials may be related to industries such as food, environment, pharmaceutical, petrochemical areas, etc. A Fourier Transform Infrared Spectroscopy (FT-IR), or IR, for example, may be used to generate datasets for determining matches between new product samples and previously analyzed product samples, or otherwise may identify functional groups in molecules present in various samples based on the infrared absorbance or transmittance spectra. Such analyses may be used to characterize known products as well as unknown materials.
TGA may also be used, and is an analytical technique used to determine a material's thermal stability and its fraction of volatile components by monitoring the weight change that occurs as a sample is heated at a constant rate. DSC may also be used, and is a thermodynamical tool for direct assessment of the heat energy uptake, which occurs in a sample within a regulated increase or decrease in temperature. DSC may particularly be applied to monitor the changes of phase transitions in a material. One or more of these analyses or other analyses may be used to assemble a fingerprint, or unique dataset of analyses results, for a given product. Subsequent sample datasets known to match the given product may then be used to train a machine learning model to determine whether a new sample conforms to a previously known sample, and therefore can be assumed to be made from the same or a substantially similar material. The embodiments described herein may be used to characterize known products as well as unknown materials and may be able to predict the performance of known products as well as unknown materials. Where multiple analyses are used, a final determination of whether a new sample matches a previously known product reference sample may be based on multiple comparisons by a machine learning model of datasets related to those multiple different types of analyses. The various embodiments herein represent a significant technical improvement over previous methods of product analyses. In particular, the accuracy of the actual determination of a match between product sample fingerprints and reference fingerprints is hereby improved. Improved consistency in results may also be achieved, as the trained machine learning models described herein may give better or more consistent results over time while comparing plastics fingerprints than manual reviewers, thereby reducing errors performing such comparisons, which can lead to additional testing being performed (e.g., performance testing of plastics) and quality control processes being implemented that otherwise would not have to be implemented with a more accurate and consistently accurate system as described herein.
At 152, an initial plastic sample, which may be a first reference sample, may be received, for example at a facility that performs product testing, analyzing, and/or certification. That initial sample may be, for example, a plastic sample that a manufacturer desires to be tested, analyzed, and/or certified. In various embodiments, the reference sample may include multiple product samples of the same type, so that different analyses and/or tests may be carried out on different samples. This may be useful where, for example, certain performance tests are destructive, and the same sample cannot be used for all tests as it will be destroyed during the process of performing one or more of the product tests. In various examples, the analyses shown in and described with respect to
At 156, 158, 160, various analyses are performed on the reference sample received at 152. In particular, a thermogravimetric (TGA) analysis is performed at 156, an infrared spectroscopy (IR) analysis is performed at 158, and a differential scanning calorimetry (DSC) analysis may be performed at 160. The results of those three analyses may be saved as datasets that represent a fingerprint of the reference sample. Based on the tests and/or analyses performed on the sample (e.g., including performance tests), the sample may be certified at 162, and the analyses data may be saved as reference curves/datasets (e.g., a fingerprint) for later use. In a step not shown, a computing device of the organization performing the tests and/or analyses may send an electronic message to a computing device associated with the manufacturer that the certification and/or testing was successful, and the sample has been certified according to an applicable standard. In various embodiments, a sample may not be certified, but a sample may be analyzed and have its analysis data (e.g., fingerprint data) stored at 162 without certification (e.g., and without performing operation 154).
After the reference sample has been received, optionally tested, analyzed, and optionally certified at 152, 154, 156, 158, 160, and 162, a new sample and reference data may be received at 164. The receiving of a new sample at 164 may be remote in time from the receiving of the initial plastic sample at 152. In other words, the analysis data may be saved as fingerprint data at 162 and may be stored for some time before it is used to compare to analysis data collected for the new sample received at 164. For example, the manufacturer may desire to update a product or release a new version of a product that uses a material that is the same as or similar to the material already analyzed in the reference sample. As such, the manufacturer may send a new product sample or samples (e.g., plastic samples), and may also identify a previously analyzed and certified product to which the new product sample is believed to be similar. In this way, the new product sample may be analyzed, and those analysis results may be compared to the analysis results of the previously analyzed reference sample. In various embodiments, some tests performed on the reference sample, such as physical performance tests, may not be performed on the new product sample received. As described herein, those additional tests may be omitted if it is determined that the new product sample and the reference sample are matches (e.g., made from the same or substantially the same material). The identification of the reference sample purported to be the same as the new sample may, for example, include other information such as a source of the reference or new product sample (e.g., identity of manufacturer, country/city/region of origin, supplier of material for the manufacturer, etc.).
At 166, 168, and 170, the TGA, IR, and DSC analyses are performed on the new product sample. The outputs of these analyses may be a dataset and may be represented in various ways, such as a curve, table, comma-separated-values (CSV) file, etc. As described herein, these analysis results may also represent a fingerprint of the new sample. At 172, it is determined manually whether the new sample analysis results (e.g., the new sample fingerprint) match the reference sample analysis results (e.g., the reference fingerprint). In other words, a qualified chemist, for example, may compare the respective TGA, IR, and DSC results for the reference and new product samples to determine if the analysis results indicate a match (e.g., indicate that the reference and new product samples are made from the same or substantially the same material). If there is a match (e.g., the comparison passes), the new sample may be certified similarly to the reference sample. If there is not a match, the system may automatically generate and send a report to the manufacturer (or a computing device associated with the new product sample) that a match was not found with the new sample. The qualified chemist may also assemble such a report or communication, or may approve the sending of an automatically generated report/communication. In addition, the fingerprint of the reference may be stored along with the new sample fingerprint along with an indication that the reference and new sample fingerprints are a matched pair. Information that two fingerprints of analyzed samples are not a match may also be stored. In other words, a database or other type of data storage may contain the reference fingerprint data, the new sample fingerprint data, and information indicating whether the reference and new sample fingerprints are a match. In various embodiments as described herein, new samples may be matched to initial samples even if certification is not involved (e.g., in a process similar to the process 150 but omitting the operation 154 and the portion of operation 162 relating to certification).
As described herein, a machine learning model may be stored on and trained using one or more of any of the computing components described herein (e.g., in a distributed computing environment), such as any of the components in
At 254, the training data is transformed for the respective machine learning model being trained. The training data may be formatted, cleaned up, cropped, etc. to make the data usable for purposes of training a machine learning model. In various embodiments, any type of pre-processing may be performed on the data assembled at 252.
For example, hexadecimal data may be converted to decimal data, noise filtering may be performed, scaling of the data may be performed, curve smoothing may be performed, curve data sampling and/or cropping may be performed to better align reference and sample curves in a matched pair, etc. In various embodiments, some or all of these pre-processing functions may be used. In various embodiments, additional types of pre-processing may also be performed. In addition, pre-processing may include a feature engineering step that detects and/or extracts features from analysis result data, so that features determined or extracted from the analysis data may be input into a machine learning model to train the machine learning model. In various embodiments, extracted features alone may be used to train a machine learning model, raw data alone may be used to train a machine learning model, or extracted features together with raw data may be used to train a machine learning model (e.g., where raw data is tagged to identify extracted features within the raw data). Feature identification/extraction may include one or more of curve derivation processes, segmentation processes, filtering, local minima and/or maxima identification, peaks/events detection and/or isolation, and/or targeted events determination.
In various embodiments, the training of a machine learning model may also involve splitting available training data into a set used for training and a set used for verification that trained model is working correctly. For example, 70% of available training data may be used to train a model, and the remaining 30% may be used after the model is trained to verify that the model is working correctly. Other possible percentage splits between training/verification data, other than the 70/30 already described, may include 90/10, 85/15, 80/20, 75/25, 65/35, 60/40, 55/45, or 50/50, in various examples. As such the training data may be a first portion of a total available training data, and a second portion of the total available training data may be used to verify a model after it is trained. In other words, a computing system may determine, using the trained machine learning model, whether sample datasets in the second portion of the total available training data match reference datasets in the second portion of the total available training data, the reference datasets of the plurality of pairs of datasets in the training data (e.g., the first portion of the total available training data), and/or the sample datasets of the plurality of pairs of datasets in the training data (e.g., the first portion of the total available training data). Then, the system may receive an input from a user via a user interface indicating whether matches for one or more of the sample datasets in the second portion of the total available training data match were successfully determined (e.g., receive user verification that the machine learning model is trained successfully). A machine learning model may be considered to be successfully trained if a predetermined percentage of the datasets used to confirm the model is working are successfully matched as verified by the user inputs.
At 256, the machine learning model is trained with the assembled and transformed training data. Once sufficient historical data is received and processed by the algorithmic methods and machine learning models at 256, patterns may be recognizable to be able to perform the matching between historical data (reference) and samples under scrutiny. Different types of machine learning models may be used in various embodiments, or combinations of different types of models may be used together as a machine learning model. A trained machine learning model may continue to be further trained and/or refined using additional usage data after it is used as a trained machine learning model, as further shown in and described with respect to
In various embodiments, when a new sample is a received a reference sample that purportedly matches the new sample may or may not be identified. That is, there may be a known reference sample that is believed to be match for the new sample, or there may not be one provided. In other words, a new plastic sample may or may not have a known purported reference curve or fingerprint that the new sample should have similar data to. In such instances, a manufacturer or source of the new sample may not specify a previously analyzed product that the new sample is similar to. In such instances, a machine learning model may be trained to compare the new sample fingerprint data to multiple possible reference datasets and determine whether one of them is a match for, or substantially similar to, the new sample fingerprint data. In other words, the trained machine learning model may compare a new sample fingerprint to a concurrently input reference fingerprint when one is provided, or the trained machine learning model may compare a new sample fingerprint to a plurality of previously stored reference fingerprints to determine a match from, or whether it is substantially similar to one or multiples from the plurality of previously stored fingerprints. In the embodiments where a new sample fingerprint is compared to a plurality of previously stored reference fingerprints, the reference fingerprints may be fingerprints from the training data, may be other, previously input new sample fingerprints, or may be any other fingerprints stored in a server or database of other previously analyzed samples.
At 304, 306, and 308, the new sample may have TGA, IR, and DSC analyses performed on it in order to generate a fingerprint for the new sample. This fingerprint data, along with an identification of a purported matching reference sample fingerprint, may be transmitted to and received by one or more processors of one or more computing devices implementing the trained machine learning model(s). In other words, the new fingerprint data may be input into one or more trained machine learning model(s) and may be used at 310 to determine whether there is one or more matches for the new fingerprint data of the new product sample. For example, the trained machine learning model(s) may be used to determine whether the subsequent new product sample fingerprint data matches the purported reference fingerprint data for each of the TGA, IR, and DSC analyses, if a purported reference fingerprint is identified. As described herein, in other embodiments, no identification of a purportedly matching reference sample may be received, and therefore the machine learning model(s) may determine a match from a plurality of reference fingerprint datasets. In other words, the one or more processors implementing the trained machine learning model(s) may determine that the new sample dataset/fingerprint matches at least one of a new reference dataset (e.g., a reference dataset not previously analyzed by the machine learning model(s)), one of the reference datasets of the plurality of pairs of datasets in the training data used to train the machine learning model(s), one of the sample datasets of the plurality of pairs of datasets in the training data used to train the machine learning model(s), etc.
In instances where the new sample data set is determined to match, or be substantially similar to a reference dataset, that reference dataset may be retrieved, for example from a computer memory, server, database, etc. or otherwise received at or used by one or more processors implementing the machine learning model(s), even if that reference dataset has never been used or considered by the machine learning model before (e.g., the reference dataset was not part of the training data used to train the machine learning model). In other words, the machine learning model(s), once trained, may be able to determine matches between a new sample dataset/fingerprint and a reference dataset/fingerprint even if the machine learning model(s) has not seen or processed either of the sample dataset/fingerprint and/or reference dataset/fingerprint before.
As discussed herein with respect to
At 312, the results of the machine learning model(s) analysis may be output to a user display. In this way, a user at the testing facility or associated with the testing facility may see whether there was a positive match or indication that the samples were substantially similar to one another. In various embodiments, the results may also be output or otherwise sent as a message to one or more computing devices, such as a computing device of the manufacturer of the reference sample and/or the subsequent new product sample being currently analyzed for a matching fingerprint. In other words, the results of an analysis may be transmitted to the manufacturer who sent the product for testing and/or certification (or for any other reason). In various embodiments, the subsequent new sample for which a match was identified by the machine learning model(s) may be certified by the entity running the product tests and/or analyses, since it is determined to be a match for a reference product sample. An application programming interface (API) may also be used to implement the methods described herein on a server or other computing system. As such, any computing device with access to the API may cause the methods described herein to be implemented. As such, any computing device with access to the API may also include the display to which results of the methods described herein are output.
An output from the trained machine learning model(s) at 312 may also include a confidence score (degree of matching between a new sample fingerprint and one or more reference fingerprints) and/or an explanation of the output. For example, the output results may include events or features extracted from the various analyses curves passed through one or more models, where the events or features match or mismatch between a new and reference fingerprints, data indicative of a degree to which events or features mismatch (e.g., difference in height, difference in position, etc.). A user display may also show results for different curve comparisons and associated data about each (e.g., each of the TGA, IR, and DSC curve comparisons).
At 314, a user may also optionally enter an input indicating whether the results of the machine learning model(s) are accurate. In some embodiments, this step may not be performed. In various embodiments, this step may be performed on all outputs where there is match determined, may be performed on a subset of all outputs from the machine learning model(s), or may not be performed on the outputs of the machine learning model(s). This input may be used to confirm whether the machine learning model(s) are accurately determining matches or not. That information on whether the outputs are correct or not may be used to refine or retrain a model (e.g., as shown in and described with respect to
For example, an analysis machine may age and therefore produce different results over time. In another example, environmental factors may change in an analysis environment over time, which may also cause drift in analysis results. As such, this drift in analysis results may represent a change over time in the distribution of input data for a machine learning model, and original training data may not be representative of later in time analysis result data. In such instances, a trained model may provide inaccurate outputs, and it may be desirable to refine or retrain the model using updated or new training data. Such data drift may be detected by a time series analysis of data and a comparison of a probability density function for a recent sample of data against original training data. Such analysis may be performed automatically by a computing device to monitor for data drift as additional datasets are acquired via product analysis. In such embodiments, a computing device may output an alert message that drift may affect results of a machine learning model, and/or may automatically trigger a machine learning model to be retrained or refined using newer analysis data.
In another example, a trained model itself may drift overtime to eventually produce incorrect results. Concept or model drift may be a change in the statistical properties of the output variables (e.g., the prediction results). This may be due to changes in the external environment or in the real-world usage of the predictions, and may be detected by monitoring changes in the feedback received from users. If feedback indicates that a model is producing inaccurate results, training data may need to be re-labelled based the user feedback/inputs, or new training data may be used to retrain or refine a model. For instance, if user feedback directly contradicts a matched pair of datasets in training data, this may indicate concept drift that should be corrected.
At 404, new training data may be assembled, similar to 252 of
In any case, at 512, historical TGA curve pairs (including match or mismatch information for the curves) along with information about events/features identified in those curve pairs are thereby assembled at 512, for both reference and sample materials of each pair. At 514, a machine learning model is therefore trained using the historical TGA curve pairs (including match or mismatch data) as well as the event/feature annotations for those curves. At 516, a trained conformity classifier (e.g., a model that determines or classifies how closely one TGA curve conforms to another TGA curve) may be output. Such a model may be used at 310 of
In
In any case, at 612, historical DSC curve pairs (including match or mismatch information for the curves) along with information about events/features identified in those curve pairs are thereby assembled at 612, for both reference and sample materials of each pair. At 614, a machine learning model is therefore trained using the historical DSC curve pairs (including match or mismatch data) as well as the event/feature annotations for those curves. At 616, a trained conformity classifier (e.g., a model that determines or classifies how closely one DSC curve conforms to another DSC curve) may be output. Such a model may be used at 310 of
In
In any case, at 712, historical IR curve pairs (including match or mismatch information for the curves) along with information about events/features identified in those curve pairs are thereby assembled at 712, for both reference and sample materials of each pair. At 714, a machine learning model is therefore trained using the historical IR curve pairs (including match or mismatch data) as well as the event/feature annotations for those curves. At 716, a trained conformity classifier (e.g., a model that determines or classifies how closely one IR curve conforms to another IR curve) may be output. Such a model may be used at 310 of
In
In various embodiments, a manufacturer themselves may also perform analyses that generate datasets to be analyzed using the methods described herein. For example, a manufacturer may perform TGA, IR, or DSC analyses on a new product sample that they would like to get compared to a previously tested, analyzed, and/or certified sample. The manufacturer may send those datasets to a computing device that implements the machine learning models (e.g., the certification/testing entity), so that the new product sample dataset may be compared to one or more previously analyzed reference datasets. As described herein, that manufacturer may also include an indication of a previously analyzed sample that is purportedly a match for, or substantially similar to, the new product sample. While the testing/certification entity may or may not use the analysis datasets sent by the manufacturer for final testing/certification, the manufacturer may, in any case, be able to get a preliminary result based on the output of the machine learning model indicating whether their new product sample would be a match with an existing reference dataset. In this way, a manufacturer can determine the likelihood of whether a new product will get certified on the basis of a previous certification, or if all new certification/testing may need to be performed on the new product (e.g., including performance tests that may take longer to complete than, for example, a subset of tests, such as IR, DSC, and/or TGA scans/analyses).
An example user interface for displaying results of a machine learning comparison between a new sample and a reference is also described herein. In such examples, data indicative of whether a match for the new sample dataset was identified may be sent to a display of a user computing device. An x-y graph may be displayed on the display that may include a reference curve representing a reference dataset for a particular type of analysis (e.g., TGA, DSC, IR) and a sample curve representing a sample dataset for a particular type of analysis (e.g., TGA, DSC, IR). In an example, a reference curve and sample curve may be overlaid on the same x-y graph displayed on a display, such as shown in
A user may also be able to select a portion of the user interface to decide which curve data (e.g., TGA, DSC, IR) is displayed. The user interface may also display data related to different identified features or events, the classifications of those features or events, etc., for different curves (e.g., reference or sample curves for any analysis). A button of the user interface may be selected to cause one or more of the conformity classifiers described herein to run a compare analysis on two curves, for example. The user interface may also allow a user to select a different reference curve to run a comparison against a new curve for one or more analyses (e.g., TGA, DSC, IR).
In various embodiments, different aspects are described with respect to
Throughout the specification, the following terms take the meanings explicitly associated herein, unless the context clearly dictates otherwise. The phrases “in one embodiment” and “in some embodiments” as used herein do not necessarily refer to the same embodiment(s), though it may. Furthermore, the phrases “in another embodiment” and “in some other embodiments” as used herein do not necessarily refer to a different embodiment, although it may. Thus, as described below, various embodiments may be readily combined, without departing from the scope or spirit of the present disclosure.
In addition, the term “based on” is not exclusive and allows for being based on additional factors not described, unless the context clearly dictates otherwise. In addition, throughout the specification, the meaning of “a,” “an,” and “the” include plural references. The meaning of “in” includes “in” and “on.”
It is understood that at least one aspect/functionality of various embodiments described herein can be performed in real-time and/or dynamically. As used herein, the term “real-time” is directed to an event/action that can occur instantaneously or almost instantaneously in time when another event/action has occurred. For example, the “real-time processing,” “real-time computation,” and “real-time execution” all pertain to the performance of a computation during the actual time that the related physical process (e.g., a user interacting with an application on a mobile device) occurs, in order that results of the computation can be used in guiding the physical process.
As used herein, the term “dynamically” and term “automatically,” and their logical and/or linguistic relatives and/or derivatives, mean that certain events and/or actions can be triggered and/or occur without any human intervention. In some embodiments, events and/or actions in accordance with the present disclosure can be in real-time and/or based on a predetermined periodicity of at least one of: nanosecond, several nanoseconds, millisecond, several milliseconds, second, several seconds, minute, several minutes, hourly, several hours, daily, several days, weekly, monthly, etc.
As used herein, the term “runtime” corresponds to any behavior that is dynamically determined during an execution of a software application or at least a portion of software application.
In some embodiments, exemplary inventive, specially programmed computing systems/platforms with associated devices are configured to operate in the distributed network environment, communicating with one another over one or more suitable data communication networks (e.g., the Internet, satellite, etc.) and utilizing one or more suitable data communication protocols/modes such as, without limitation, IPX/SPX, X.25, AX.25, AppleTalk™, TCP/IP (e.g., HTTP), Bluetooth™, near-field wireless communication (NFC), RFID, Narrow Band Internet of Things (NBIOT), 3G, 4G, 5G, GSM, GPRS, WiFi, WiMax, CDMA, satellite, ZigBee, and other suitable communication modes.
The material disclosed herein may be implemented in software or firmware or a combination of them or as instructions stored on a machine-readable medium, which may be read and executed by one or more processors. A machine-readable medium may include any medium and/or mechanism for storing or transmitting information in a form readable by a machine (e.g., a computing device). For example, a machine-readable medium may include read only memory (ROM); random access memory (RAM); magnetic disk storage media; optical storage media; flash memory devices; electrical, optical, acoustical or other forms of propagated signals (e.g., carrier waves, infrared signals, digital signals, etc.), and others.
The aforementioned examples are, of course, illustrative and not restrictive.
As used herein, the term “user” shall have a meaning of at least one user. In some embodiments, the terms “user”, “subscriber” “consumer” or “customer” should be understood to refer to a user of an application or applications as described herein, and/or a consumer of data supplied by a data provider. By way of example, and not limitation, the terms “user” or “subscriber” can refer to a person who receives data provided by the data or service provider over the Internet in a browser session or can refer to an automated software application which receives the data and stores or processes the data.
In some embodiments, referring to
In some embodiments, the exemplary network 105 may provide network access, data transport and/or other services to any computing device coupled to it. In some embodiments, the exemplary network 105 may include and implement at least one specialized network architecture that may be based at least in part on one or more standards set by, for example, without limitation, Global System for Mobile communication (GSM) Association, the Internet Engineering Task Force (IETF), and the Worldwide Interoperability for Microwave Access (WiMAX) forum. In some embodiments, the exemplary network 105 may implement one or more of a GSM architecture, a General Packet Radio Service (GPRS) architecture, a Universal Mobile Telecommunications System (UMTS) architecture, and an evolution of UMTS referred to as Long Term Evolution (LTE). In some embodiments, the exemplary network 105 may include and implement, as an alternative or in conjunction with one or more of the above, a WiMAX architecture defined by the WiMAX forum. In some embodiments and, optionally, in combination of any embodiment described above or below, the exemplary network 105 may also include, for instance, at least one of a local area network (LAN), a wide area network (WAN), the Internet, a virtual LAN (VLAN), an enterprise LAN, a layer 3 virtual private network (VPN), an enterprise IP network, or any combination thereof. In some embodiments and, optionally, in combination of any embodiment described above or below, at least one computer network communication over the exemplary network 105 may be transmitted based at least in part on one of more communication modes such as but not limited to: NFC, RFID, Narrow Band Internet of Things (NBIOT), ZigBee, 3G, 4G, 5G, GSM, GPRS, WiFi, WiMax, CDMA, satellite and any combination thereof. In some embodiments, the exemplary network 105 may also include mass storage, such as network attached storage (NAS), a storage area network (SAN), a content delivery network (CDN) or other forms of computer or machine readable media.
In some embodiments, the exemplary server 106 or the exemplary server 107 may be a web server (or a series of servers) running a network operating system, examples of which may include but are not limited to Microsoft Windows Server, Novell NetWare, or Linux. In some embodiments, the exemplary server 106 or the exemplary server 107 may be used for and/or provide cloud and/or network computing. Although not shown in
In some embodiments, one or more of the exemplary servers 106 and 107 may be specifically programmed to perform, in non-limiting example, as authentication servers, search servers, email servers, social networking services servers, SMS servers, IM servers, MMS servers, exchange servers, photo-sharing services servers, advertisement providing servers, financial/banking-related services servers, travel services servers, or any similarly suitable service-base servers for users of the member computing devices 101-104.
In some embodiments and, optionally, in combination of any embodiment described above or below, for example, one or more exemplary computing member devices 102-104, the exemplary server 106, and/or the exemplary server 107 may include a specifically programmed software module that may be configured to send, process, and receive information using a scripting language, a remote procedure call, an email, a tweet, Short Message Service (SMS), Multimedia Message Service (MMS), instant messaging (IM), internet relay chat (TRC), mIRC, Jabber, an application programming interface, Simple Object Access Protocol (SOAP) methods, Common Object Request Broker Architecture (CORBA), HTTP (Hypertext Transfer Protocol), REST (Representational State Transfer), or any combination thereof.
In some embodiments, member computing devices 202a through 202n may also comprise a number of external or internal devices such as a mouse, a CD-ROM, DVD, a physical or virtual keyboard, a display, or other input or output devices. In some embodiments, examples of member computing devices 202a through 202n (e.g., clients) may be any type of processor-based platforms that are connected to a network 206 such as, without limitation, personal computers, digital assistants, personal digital assistants, smart phones, pagers, digital tablets, laptop computers, Internet appliances, and other processor-based devices. In some embodiments, member computing devices 202a through 202n may be specifically programmed with one or more application programs in accordance with one or more principles/methodologies detailed herein. In some embodiments, member computing devices 202a through 202n may operate on any operating system capable of supporting a browser or browser-enabled application, such as Microsoft™ Windows™, and/or Linux. In some embodiments, member computing devices 202a through 202n shown may include, for example, personal computers executing a browser application program such as Microsoft Corporation's Internet Explorer™, Apple Computer, Inc.'s Safari™, Mozilla Firefox, and/or Opera. In some embodiments, through the member computing client devices 202a through 202n, users 212a through 212n, may communicate over the exemplary network 206 with each other and/or with other systems and/or devices coupled to the network 206. As shown in
In some embodiments, at least one database of exemplary databases 207 and 215 may be any type of database, including a database managed by a database management system (DBMS). In some embodiments, an exemplary DBMS-managed database may be specifically programmed as an engine that controls organization, storage, management, and/or retrieval of data in the respective database. In some embodiments, the exemplary DBMS-managed database may be specifically programmed to provide the ability to query, backup and replicate, enforce rules, provide security, compute, perform change and access logging, and/or automate optimization. In some embodiments, the exemplary DBMS-managed database may be chosen from Oracle database, IBM DB2, Adaptive Server Enterprise, FileMaker, Microsoft Access, Microsoft SQL Server, MySQL, PostgreSQL, and a NoSQL implementation. In some embodiments, the exemplary DBMS-managed database may be specifically programmed to define each respective schema of each database in the exemplary DBMS, according to a particular database model of the present disclosure which may include a hierarchical model, network model, relational model, object model, or some other suitable organization that may result in one or more applicable data structures that may include fields, records, files, and/or objects. In some embodiments, the exemplary DBMS-managed database may be specifically programmed to include metadata about the data that is stored.
As also shown in
According to some embodiments, the exemplary inventive computer-based systems/platforms, the exemplary inventive computer-based devices, components and media, and/or the exemplary inventive computer-implemented methods of the present disclosure may be specifically configured to operate in or with cloud computing/architecture such as, but not limiting to: infrastructure a service (IaaS), platform as a service (PaaS), and/or software as a service (SaaS).
As used herein, the terms “computer engine” and “engine” identify at least one software component and/or a combination of at least one software component and at least one hardware component which are designed/programmed/configured to manage/control other software and/or hardware components (such as the libraries, software development kits (SDKs), objects, etc.).
Examples of hardware elements may include processors, microprocessors, circuits, circuit elements (e.g., transistors, resistors, capacitors, inductors, and so forth), integrated circuits, application specific integrated circuits (ASIC), programmable logic devices (PLD), digital signal processors (DSP), field programmable gate array (FPGA), logic gates, registers, semiconductor device, chips, microchips, chip sets, and so forth. In some embodiments, the one or more processors may be implemented as a Complex Instruction Set Computer (CISC) or Reduced Instruction Set Computer (RISC) processors; x86 instruction set compatible processors, multi-core, or any other microprocessor or central processing unit (CPU). In various implementations, the one or more processors may be dual-core processor(s), dual-core mobile processor(s), and so forth.
Examples of software may include software components, programs, applications, computer programs, application programs, system programs, machine programs, operating system software, middleware, firmware, software modules, routines, subroutines, functions, methods, procedures, software interfaces, application program interfaces (API), instruction sets, computing code, computer code, code segments, computer code segments, words, values, symbols, or any combination thereof. Determining whether an embodiment is implemented using hardware elements and/or software elements may vary in accordance with any number of factors, such as desired computational rate, power levels, heat tolerances, processing cycle budget, input data rates, output data rates, memory resources, data bus speeds and other design or performance constraints.
One or more aspects of at least one embodiment may be implemented by representative instructions stored on a machine-readable medium which represents various logic within the processor, which when read by a machine causes the machine to fabricate logic to perform the techniques described herein. Such representations, known as “IP cores,” may be stored on a tangible, machine readable medium and supplied to various customers or manufacturing facilities to load into the fabrication machines that make the logic or processor. Of note, various embodiments described herein may, of course, be implemented using any appropriate hardware and/or computing software languages (e.g., C++, Objective-C, Swift, Java, JavaScript, Python, Perl, QT, etc.).
In some embodiments, one or more of exemplary inventive computer-based systems/platforms, exemplary inventive computer-based devices, and/or exemplary inventive computer-based components of the present disclosure may include or be incorporated, partially or entirely into at least one personal computer (PC), laptop computer, ultra-laptop computer, tablet, touch pad, portable computer, handheld computer, palmtop computer, personal digital assistant (PDA), cellular telephone, combination cellular telephone/PDA, television, smart device (e.g., smart phone, smart tablet or smart television), mobile internet device (MID), messaging device, data communication device, and so forth.
As used herein, the term “server” should be understood to refer to a service point which provides processing, database, and communication facilities. By way of example, and not limitation, the term “server” can refer to a single, physical processor with associated communications and data storage and database facilities, or it can refer to a networked or clustered complex of processors and associated network and storage devices, as well as operating software and one or more database systems and application software that support the services provided by the server. Cloud components and cloud servers are examples.
In some embodiments, as detailed herein, one or more of the computer-based systems of the present disclosure may obtain, manipulate, transfer, store, transform, generate, and/or output any digital object and/or data unit (e.g., from inside and/or outside of a particular application) that can be in any suitable form such as, without limitation, a file, a contact, a task, an email, a message, a map, an entire application (e.g., a calculator), data points, and other suitable data. In some embodiments, as detailed herein, one or more of the computer-based systems of the present disclosure may be implemented across one or more of various computer platforms such as, but not limited to: (1) Linux™, (2) Microsoft Windows™, (3) OS X (Mac OS), (4) Solaris™, (5) UNIX™ (6) VMWare™, (7) Android™, (8) Java Platforms™, (9) Open Web Platform, (10) Kubernetes or other suitable computer platforms. In some embodiments, illustrative computer-based systems or platforms of the present disclosure may be configured to utilize hardwired circuitry that may be used in place of or in combination with software instructions to implement features consistent with principles of the disclosure. Thus, implementations consistent with principles of the disclosure are not limited to any specific combination of hardware circuitry and software. For example, various embodiments may be embodied in many different ways as a software component such as, without limitation, a stand-alone software package, a combination of software packages, or it may be a software package incorporated as a “tool” in a larger software product.
For example, exemplary software specifically programmed in accordance with one or more principles of the present disclosure may be downloadable from a network, for example, a website, as a stand-alone product or as an add-in package for installation in an existing software application. For example, exemplary software specifically programmed in accordance with one or more principles of the present disclosure may also be available as a client-server software application, or as a web-enabled software application. For example, exemplary software specifically programmed in accordance with one or more principles of the present disclosure may also be embodied as a software package installed on a hardware device.
In some embodiments, illustrative computer-based systems or platforms of the present disclosure may be configured to handle numerous concurrent users that may be, but is not limited to, at least 100 (e.g., but not limited to, 100-999), at least 1,000 (e.g., but not limited to, 1,000-9,999), at least 10,000 (e.g., but not limited to, 10,000-99,999), at least 100,000 (e.g., but not limited to, 100,000-999,999), at least 1,000,000 (e.g., but not limited to, 1,000,000-9,999,999), at least 10,000,000 (e.g., but not limited to, 10,000,000-99,999,999), at least 100,000,000 (e.g., but not limited to, 100,000,000-999,999,999), at least 1,000,000,000 (e.g., but not limited to, 1,000,000,000-999,999,999,999), and so on.
In some embodiments, exemplary inventive computer-based systems/platforms, exemplary inventive computer-based devices, and/or exemplary inventive computer-based components of the present disclosure may be configured to output to distinct, specifically programmed graphical user interface implementations of the present disclosure (e.g., a desktop, a web app., etc.). In various implementations of the present disclosure, a final output may be displayed on a displaying screen which may be, without limitation, a screen of a computer, a screen of a mobile device, or the like. In various implementations, the display may be a holographic display. In various implementations, the display may be a transparent surface that may receive a visual projection. Such projections may convey various forms of information, images, and/or objects. For example, such projections may be a visual overlay for a mobile augmented reality (MAR) application.
In some embodiments, exemplary inventive computer-based systems/platforms, exemplary inventive computer-based devices, and/or exemplary inventive computer-based components of the present disclosure may be configured to be utilized in various applications which may include, but not limited to, gaming, mobile-device games, video chats, video conferences, live video streaming, video streaming and/or augmented reality applications, mobile-device messenger applications, and others similarly suitable computer-device applications.
As used herein, the term “mobile electronic device,” or the like, may refer to any portable electronic device that may or may not be enabled with location tracking functionality (e.g., MAC address, Internet Protocol (IP) address, or the like). For example, a mobile electronic device can include, but is not limited to, a mobile phone, Personal Digital Assistant (PDA), Blackberry™ Pager, Smartphone, or any other reasonable mobile electronic device.
As used herein, the terms “proximity detection,” “locating,” “location data,” “location information,” and “location tracking” refer to any form of location tracking technology or locating method that can be used to provide a location of, for example, a particular computing device/system/platform of the present disclosure and/or any associated computing devices, based at least in part on one or more of the following techniques/devices, without limitation: accelerometer(s), gyroscope(s), Global Positioning Systems (GPS); GPS accessed using Bluetooth™; GPS accessed using any reasonable form of wireless and/or non-wireless communication; WiFi™ server location data; Bluetooth™ based location data; triangulation such as, but not limited to, network based triangulation, WiFi™ server information based triangulation, Bluetooth™ server information based triangulation; Cell Identification based triangulation, Enhanced Cell Identification based triangulation, Uplink-Time difference of arrival (U-TDOA) based triangulation, Time of arrival (TOA) based triangulation, Angle of arrival (AOA) based triangulation; techniques and systems using a geographic coordinate system such as, but not limited to, longitudinal and latitudinal based, geodesic height based, Cartesian coordinates based; Radio Frequency Identification such as, but not limited to, Long range RFID, Short range RFID; using any form of RFID tag such as, but not limited to active RFID tags, passive RFID tags, battery assisted passive RFID tags; or any other reasonable way to determine location. For ease, at times the above variations are not listed or are only partially listed; this is in no way meant to be a limitation.
In some embodiments, the exemplary inventive computer-based systems/platforms, the exemplary inventive computer-based devices, and/or the exemplary inventive computer-based components of the present disclosure may be configured to securely store and/or transmit data by utilizing one or more of encryption techniques (e.g., private/public key pair, Triple Data Encryption Standard (3DES), block cipher algorithms (e.g., IDEA, RC2, RC5, CAST and Skipjack), cryptographic hash algorithms (e.g., MD5, RIPEMD-160, RTRO, SHA-1, SHA-2, Tiger (TTH), WHIRLPOOL, RNGs).
While one or more embodiments of the present disclosure have been described, it is understood that these embodiments are illustrative only, and not restrictive, and that many modifications may become apparent to those of ordinary skill in the art, including that various embodiments of the inventive methodologies, the inventive systems/platforms, and the inventive devices described herein can be utilized in any combination with each other. Further still, the various steps may be carried out in any desired order (and any desired steps may be added and/or any desired steps may be eliminated).
