Abstract: A method, computer system, and a computer program product for dynamic indoor navigation is provided. The present invention may include receiving a set of sensor data from at least one distance measuring sensor located in an enclosed environment. The present invention may include generating a baseline map of the enclosed environment based on the received set of sensor data. The present invention may include detecting at least one object in the enclosed environment based on the received set of sensor data. The present invention may include generating an obstacle map of the enclosed environment by plotting the detected at least one object in the generated baseline map of the enclosed environment. The present invention may include determining a movement path through the enclosed environment that avoids the detected at least one object in the generated obstacle map.

Abstract: Implementations disclosed describe, among other things, a systems and techniques for perform efficient inspection of a semiconductor manufacturing sample. The techniques include collecting optical inspection data for training sample(s) that have a plurality of defects. The techniques further include generating, using the optical inspection data, a training data set that includes descriptions, images, and ground truth classifications for the defects. The techniques further include using the training data set to train a plurality of machine learning (ML) classifiers to generate predicted classifications for the defects in the training sample(s). The techniques further include selecting, using the predicted classifications and the ground truth classifications, one or more ML classifiers that meet one or more accuracy criteria, and using the selected ML classifier(s) to classify defects in the semiconductor manufacturing sample.

Abstract: A first client device including a blockchain and associated with an environmental, social, and governance (ESG) management platform is configured to perform the following. A trading pair between two different sustainability factors is identified by the first client device. A transaction block for the trading pair is generated by the first client device. The transaction block is broadcasted by the first client device to a plurality of client devices associated with the ESG management platform. The blockchain is updated upon receipt of a network update, and the network update indicates that the transaction block was accepted by one of the plurality of client devices and a smart contract was generated based upon the transaction block being accepted.

Abstract: A computer-implemented method, computer program product, and/or computing system for providing a navigational path to an underwater robot for performing an underwater inspection is provided that includes determining one or more potential navigational paths to be undertaken by an underwater robot when traveling from a starting point to a finish point; determining an impact area of the underwater robot for each potential navigational path based upon a size and movement of the underwater robot along each respective potential navigational path; using a marine habitat map to avoid sensitive areas in an underwater ecosystem while the underwater robot travels along each of the one or more potential navigational paths; determining a navigational path from the one or more potential navigational paths that minimizes environmental impact to the underwater ecosystem as the underwater robot navigates from the starting point to the finish point; and provides the navigational path to the underwater robot.

Abstract: A system generates search query responses based on a content repository that include natural language answers and actions performed based on content items in the repository. The system receives a query associated with the content repository. Based on the query, the system retrieves a set of text chunks that are relevant to the query. At least a portion of the relevant text chunks are sent to a large language model (LLM) to cause the LLM to generate an answer description for the user query, based on the text chunks.

Abstract: Provided are techniques for data analysis and rule generation for providing a recommendation. A recommendation structure is built from features of data, where the recommendation structure comprises a plurality of rules, and where each rule of the plurality of rules is associated with a recommendation. The recommendation structure is sent to a model. A feature set comprising a feature and one or more related features is created from the features. In response to determining that a feature worthiness score of the feature set exceeds a threshold, the feature set is input to the model. A rule of the plurality of rules is received from the model, where the rule includes the feature, the one or more related features, and the associated recommendation. In response to receiving a set of values for the feature and the one or more related features, the rule is applied to the set of values to provide the recommendation.

Abstract: A method and apparatus for training a learning model for automatic defect detection and classification of at least a portion of a processed wafer include receiving labeled images having defect classification types and features for portions of a post-processed wafer, creating a first training set comprising the received labeled images, training the machine learning model to automatically classify wafer portions based on at least one detected defect in respective wafer portions using the first training set, receiving labeled wafer profiles having respective downstream yield data, creating a second training set comprising the labeled wafer profiles and training the machine learning model, using the second training set, to automatically determine a respective downstream yield of a wafer based on a respective wafer profile. The machine learning model can be applied to at least one unlabeled wafer image to determine at least one defect classification for at least one portion of a wafer.

Abstract: A method, apparatus and system for the automatic detection and measurement of chipping defects on diced wafers includes receiving an image of at least a portion of a diced wafer, aligning the received image of the at least the portion of the diced wafer, determining edges of the at least the portion of the diced wafer depicted in the aligned, received image, automatically determining at least one baseline from which to measure chipping defects on the at least the portion of the diced wafer from the determined edges, and measuring chipping defects on the at least the portion of the diced wafer using at least one determined, respective baseline. In some embodiments, the method, apparatus and system can further include applying a machine learning model to measured chipping defects to determine if a critical failure exists on the diced wafer.

Abstract: A method and apparatus for training a learning model for the automatic detection and classification of defects on wafers includes receiving labeled images of wafer defects having multiple defect classifications, creating a first training set including the received labeled images of wafer defects, training the machine learning model to automatically detect and classify wafer defects in a first stage using the first training set, blending at least one set of at least two labeled images having different classifications to generate additional labeled image data, creating a second training set including the blended, additional labeled image data, and training the machine learning model to automatically detect and classify wafer defects in a second stage using the second training set. The trained machine learning model can then be applied to at least one unlabeled wafer image to determine at least one defect classification for the at least one unlabeled wafer image.

Abstract: In some aspects, control circuitry receives and stores a user-specified time duration to associate with a skip-forward command. When the user later issues a skip-forward command to advance playback of a media asset to a desired location, the control circuitry retrieves the stored time duration, and determines an expected overshoot value associated with the skip-forward command. The control circuitry then compensates for the expected overshoot by calculating a new location in the media asset based on both the retrieved time duration and expected overshoot value. Upon advancing to the new location, the media asset is played back in a fast-forward mode, e.g., at a speed higher than normal speed.

Abstract: Exemplary embodiments relate to a system for recovering a conversation between a user and the system when the system is unable to properly respond to a user's input. The system may process the user input and determine an error condition exists. The system may query one or more storage systems to identify candidate text data based on their semantic similarity to the user input. The storage systems may store data related to past frequently entered inputs and/or user-generated inputs. Alternative text data is selected from the candidate text data, and presented to the user for confirmation.

Abstract: In some aspects, control circuitry receives and stores a user-specified time duration to associate with a skip-forward command. When the user later issues a skip-forward command to advance playback of a media asset to a desired location, the control circuitry retrieves the stored time duration, and determines an expected overshoot value associated with the skip-forward command. The control circuitry then compensates for the expected overshoot by calculating a new location in the media asset based on both the retrieved time duration and expected overshoot value. Upon advancing to the new location, the media asset is played back in a fast-forward mode, e.g., at a speed higher than normal speed.

Abstract: The subject matter of this specification can be implemented in, among other things, methods, systems, computer-readable storage medium. A method can include a processing device storing a plurality of feature vectors representative of previously processed image frames that correspond to various substrate processing defects. The method further includes receiving first image data comprising one or more image frames indicative of a first substrate processing defect. The method further includes determining a first feature vector corresponding to the first image data. The method further includes determining a selection of the plurality of feature vectors based on a proximity between the first feature vector and each of the selection of the plurality of feature vectors. The method further includes determining second image data comprising one or more image frames corresponding to the selection of the plurality of embedding vectors and performing an action based on determining the second image data.

Abstract: In some aspects, control circuitry receives and stores a user-specified time duration to associate with a skip-forward command. When the user later issues a skip-forward command to advance playback of a media asset to a desired location, the control circuitry retrieves the stored time duration, and determines an expected overshoot value associated with the skip-forward command. The control circuitry then compensates for the expected overshoot by calculating a new location in the media asset based on both the retrieved time duration and expected overshoot value. Upon advancing to the new location, the media asset is played back in a fast-forward mode, e.g., at a speed higher than normal speed.

Abstract: A computer system includes memory hardware configured to store instructions and a database, and processor hardware configured to execute the instructions. The instructions include receiving input from a user. The instructions include querying the database to: obtain a label text associated with a specified national drug code (NDC), obtain a field associated with the label text, and obtain a set of field options associated with the field. The instructions include building a data structure including the label text, the field, and the set of field options. The instructions include converting the data structure to a dynamic web interface for displaying the label text, the field, and the set of field options. The instructions include receiving a user selection of a field option of the set of field options, updating the field to reflect the user selection, and generating a label by printing the label text including the updated field.

Abstract: Embodiments are directed to a human-machine interface including one or more sensors configured to detect myoelectrical signals of a user and an electronic device configured to provide a set of training data based on the myoelectric signals as input to a classifier. The electronic device can receive one or more sets of feature data each associated with movement classification from the classification model and display a user interface including a multi-dimensional plot comprising at least two dimensions that are each based on a component dimension determined by the classifier and used to determine the first and second sets of the feature data. The plot can include visual elements corresponding to the sets of feature data. The user device can receive a second set of detected myoelectric signals and display a graphical icon on the multi-dimensional plot indicating a current feature based on the second set of myoelectric signals.

Abstract: A computer system includes memory storing a database including a label text table for storing multiple label texts each associated with a national drug code (NDC), a fields table, and a field options table. A processor is configured to execute instructions including receiving drug input from a user indicative of a specified NDC, querying the database to obtain a label text associated with the specified NDC, at least one field associated with the obtained label text, and multiple field options associated with the obtained field, building a data structure, converting the data structure to a user interface for displaying the obtained label text, the obtained field, and the multiple obtained field options, receiving a user selection of one of the multiple obtained field options, and printing the label text including the updated field to a label and/or saving the label text including the updated field to a record database.

Abstract: Exemplary embodiments relate to a system for recovering a conversation between a user and the system when the system is unable to properly respond to a user's input. The system may process the user input and determine an error condition exists. The system may query one or more storage systems to identify candidate text data based on their semantic similarity to the user input. The storage systems may store data related to past frequently entered inputs and/or user-generated inputs. Alternative text data is selected from the candidate text data, and presented to the user for confirmation.

Abstract: Methods and systems are disclosed herein for accurately training a machine learning model with a reduced training data set. A large number of data records may be parsed. Each record may be reduced to a set of symbols representing the composition of each record. A user may assign a classification to each symbol within each record. Records with identical arrangements and classifications of symbols may be grouped together, and a representative sample of data records from each group may be fed into the model as the reduced training data set.

Abstract: In some aspects, control circuitry receives and stores a user-specified time duration to associate with a skip-forward command. When the user later issues a skip-forward command to advance playback of a media asset to a desired location, the control circuitry retrieves the stored time duration, and determines an expected overshoot value associated with the skip-forward command. The control circuitry then compensates for the expected overshoot by calculating a new location in the media asset based on both the retrieved time duration and expected overshoot value. Upon advancing to the new location, the media asset is played back in a fast-forward mode, e.g., at a speed higher than normal speed.