System monitors and methods of monitoring a system are disclosed. In one arrangement a system monitor predicts a future state of a system. A data receiving unit receives system data representing a set of one or more measurements performed on the system. A first statistical model is fitted to the system data. The first statistical model is compared to each of a plurality of dictionary entries in a database. Each dictionary entry comprises a second statistical model. The second statistical model is of the same general class as the first statistical model and obtained by fitting the second statistical model to data representing a set of one or more previous measurements performed on a system of the same type as the system being monitored and having a known subsequent state. A prediction of a future state of the system being monitored is output based on the comparison. The first statistical model and the second statistical model are each a stochastic process or approximation to a stochastic process.

System monitors and methods of monitoring a system are disclosed. In one arrangement a system monitor predicts a future state of a system. A data receiving unit receives system data representing a set of one or more measurements performed on the system. A first statistical model is fitted to the system data. The first statistical model is compared to each of a plurality of dictionary entries in a database. Each dictionary entry comprises a second statistical model. The second statistical model is of the same general class as the first statistical model and obtained by fitting the second statistical model to data representing a set of one or more previous measurements performed on a system of the same type as the system being monitored and having a known subsequent state. A prediction of a future state of the system being monitored is output based on the comparison. The first statistical model and the second statistical model are each a stochastic process or approximation to a stochastic process.

A device may receive a classification model generated based on a set of spectroscopic measurements performed by a first spectrometer. The device may store the classification model in a data structure. The device may receive a spectroscopic measurement of an unknown sample from a second spectrometer. The device may obtain the classification model from the data structure. The device may classify the unknown sample into a Kosher or non-Kosher group or a Halal or non-Halal group based on the spectroscopic measurement and the classification model. The device may provide information identifying the unknown sample based on the classifying of the unknown sample.

A device may receive a classification model generated based on a set of spectroscopic measurements performed by a first spectrometer. The device may store the classification model in a data structure. The device may receive a spectroscopic measurement of an unknown sample from a second spectrometer. The device may obtain the classification model from the data structure. The device may classify the unknown sample into a Kosher or non-Kosher group or a Halal or non-Halal group based on the spectroscopic measurement and the classification model. The device may provide information identifying the unknown sample based on the classifying of the unknown sample.

A system and method of face recognition comprising multiple phases implemented in a parallel architecture. The first phase is a normalization phase whereby a captured image is normalized to the same size, orientation, and illumination of stored images in a preexisting database. The second phase is a feature extraction/distance matrix phase where a distance matrix is generated for the captured image. In a coarse recognition phase, the generated distance matrix is compared with distance matrices in the database using Euclidean distance matches to create candidate lists, and in a detailed recognition phase, multiple face recognition algorithms are applied to the candidate lists to produce a final result. The distance matrices in the normalized database may be broken into parallel lists for parallelization in the feature extraction/distance matrix phase, and the candidate lists may also be grouped according to a dissimilarity algorithm for parallel processing in the detailed recognition phase.

An automatic artwork review system validates an artwork or a product label based on a received label specification document. Text extracted from the product label is chunked into sentences and words. Character-wise comparison is executed to identify the best match text from the label specification document for the sentence chunks from the product label. If the corresponding best match texts bears a similarity higher than a predetermined threshold to selected text including one or more sentence chunks, no errors are raised. If the similarity of the best match text to the selected text is not higher than the predetermined threshold, the specific errors occurring in the selected text and the particular portions where such errors are present are identified. The information regarding the errors can be output via one or more of an output user interface or a label compliance report.

Systems and methods for generating and annotating cell documents include extracting tables from a document using a table extraction engine. Headers are extracted for each of the tables using a header detection engine. Cells are extracted from each of the tables using a cell extraction engine. A cell document is generated for each of the cells which are each correlated to corresponding portions of the headers, each cell document recording the correlation between the cells and the headers. Each cell document is annotated to generate annotated cell documents with a cell recognition model trained to perform natural language processing on the cell documents by classifying each term in each of the cell documents and extracting relationships between the terms of each of the cell documents.

An image capture apparatus detects a subject in a captured image. The image capture apparatus further recognizes its user based on an eyeball image of the user. The image capture apparatus then selects a main subject area from among the detected subject areas, based on information regarding subjects captured in the past and stored being associated with the recognized user.

Systems, devices, and techniques are disclosed for automatic product description generation. A first set of features including labels including words may be generated from an image using a first feature extraction model. A second set of features including labels including words may be generated from the image using a second feature extraction model. A text description of a product depicted in the image may be generated by inputting the image and metadata for the image to a description generating model. The text description may include words. Each of the words may be generated by assigning probabilities to candidate words, boosting the assigned probabilities of candidate words that are similar to words of labels of the first set of features or words of labels of the second set of features, and selecting one of the candidate words based on the assigned probabilities after the boosting as a word of the text description.

Systems, devices, and techniques are disclosed for automatic product description generation. A first set of features including labels including words may be generated from an image using a first feature extraction model. A second set of features including labels including words may be generated from the image using a second feature extraction model. A text description of a product depicted in the image may be generated by inputting the image and metadata for the image to a description generating model. The text description may include words. Each of the words may be generated by assigning probabilities to candidate words, boosting the assigned probabilities of candidate words that are similar to words of labels of the first set of features or words of labels of the second set of features, and selecting one of the candidate words based on the assigned probabilities after the boosting as a word of the text description.