Calibration of an electrochemical sensor to generate an embedding in the embedding space.

Abstract

Although an electrochemical sensor can output raw data of an electric signal in response to sensing a compound, the raw data of the electric signal can be difficult to interpret. Processing the electric signal data with a machine learning model to generate an embedding output in the embedding space allows for a better understanding of the electric signal data. Furthermore, utilizing an existing chemical property prediction model to generate other embeddings in the embedding space allows for a more accurate and efficient classification task of the electric signal data. One exemplary aspect of the present disclosure is directed to a computing system.

Claims

[Claim 1] A computing system, A sensor configured to generate an electrical signal indicating the presence of one or more compounds in the environment. A machine learning model that receives and processes the aforementioned electrical signals and is trained to generate embeddings in the embedding space, The machine learning model is trained using a training dataset that includes multiple training examples, each training example including a ground truth characteristic label applied to a set of electrical signals generated by one or more test sensors when exposed to one or more training compounds, and each ground truth characteristic label describes the properties of the one or more training compounds, the machine learning model, One or more processors, and The system includes one or more non-temporary computer-readable media that store together instructions causing the computing system to perform an operation when executed by the one or more processors, and the operation is The sensor generates sensor data indicating the presence of a specific compound in the environment, and The one or more processors process the sensor data using the machine learning model to generate an embedded output in the embedded space. A computing system that includes this. [Claim 2] The computing system according to claim 1, further comprising performing a task based on the embedded output. [Claim 3] The computing system according to claim 2, wherein the task includes providing a prediction of sensory characteristics based on the embedded output. [Claim 4] The computer system according to claim 2, wherein the task includes providing an olfactory characteristic prediction based on the embedded output. [Claim 5] The computing system according to claim 2, wherein the task is to identify a disease state based at least in part on the embedded output. [Claim 6] The computing system according to claim 2, wherein the task is to determine the state of an odor based at least in part on the embedded output. [Claim 7] The computing system according to claim 2, wherein the task is to determine whether decay has occurred based at least partially on the embedded output. [Claim 8] The computing system according to claim 2, wherein the task includes presenting a label entered by a human for display, the label entered by a human being determined by association with the embedded output of the embedded space. [Claim 9] The computing system according to claim 8, wherein the label entered by the human describes the name of a specific food item. [Claim 10] The computing system according to claim 1, wherein the machine learning model is trained in conjunction with a graph neural network, and the training comprises training the machine learning model and the graph neural network together to produce a single combined output within the embedding space. [Claim 11] The computing system according to claim 10, wherein the graph neural network is trained to take a graph-based representation of the particular compound as input and to output each of the embeddings in the embedding space. [Claim 12] The aforementioned machine learning model, Obtaining an example of compound training including electrical signal training data and respective training labels, wherein the electrical signal training data and respective training labels describe a specific training compound. The process involves using the aforementioned machine learning model to process the electrical signal training data and generate a compound embedding output. The embedded output of the compound is processed using a classification model to determine the compound label. Evaluate the loss function that evaluates the difference between the compound label and each of the training labels, and The computing system according to claim 1, which is trained by adjusting one or more parameters of the machine learning model based at least in part on the loss function. [Claim 13] The computing system according to claim 1, wherein the machine learning model is trained by supervised learning. [Claim 14] The computing system according to claim 1, wherein the sensor data describes at least one of voltage or current. [Claim 15] The computing system according to claim 1, wherein the machine learning model includes a transformer model. [Claim 16] The computing system according to claim 1, further comprising storing the embedded output. [Claim 17] The computing system according to claim 1, wherein the sensor data describes the amplitude of one or both of the voltages and / or currents of one or more electrical signals. [Claim 18] The computing system according to claim 1, wherein processing the sensor data using the machine learning model by one or more processors to generate the embedded output in the embedded space includes compressing the sensor data into a vector representation of a fixed length. [Claim 19] A computer implementation method, A computing system comprising one or more processors acquires sensor data from one or more sensors, wherein the sensor data describes an electrical signal generated by the presence of one or more compounds in the environment. The computing system processes the sensor data using a machine learning model and generates an embedded output in the embedding space, wherein the machine learning model is trained to receive and process data describing electrical signals and generate embeddings in the embedding space. The computing system determines one or more labels associated with the embedded output of the embedded space, and The computing system presents one or more labels for display. Methods that include... [Claim 20] One or more non-temporary computer-readable media that store a set of instructions causing a computing system to perform an action when executed by one or more processors, wherein the action is: The method involves acquiring sensor data using one or more sensors, wherein the sensor data describes an electrical signal generated by the presence of one or more compounds in the environment. The process involves processing the sensor data using a machine learning model and generating an embedded output in the embedded space, wherein the machine learning model is trained to receive and process data describing electrical signals and generate an embedded in the embedded space. The method involves obtaining multiple stored sensory characteristic datasets, wherein each of the multiple stored sensory characteristic datasets includes a stored embedding in the embedding space paired with each of the sensory characteristic datasets, and each of the sensory characteristic datasets is associated with each of the stored embeddings. Determining one or more sensory characteristics based on the embedded output of the embedded space and the plurality of stored sensory characteristic datasets, and To present one or more of the aforementioned sensory characteristics for display purposes, One or more non-temporary computer-readable media, including [the specified text].

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