Abstract: A method and system for generating a unique character string based on attribute information are disclosed. The method includes receiving transaction information of a non-standard transaction, extracting various attributes from the received transaction information, and dynamically generating a character string using a combination of the various attributes. The method further includes identifying a vendor identifier that corresponds with a set of attributes stored at a vendor device that matches with the various attributes of the dynamically generated character string, and consolidating the vendor identifier with the dynamically generated unique identifier for generating a unique consolidated identifier for the non-standard transaction.

Abstract: An embodiment of the present invention is directed to storing a loan asset on a blockchain by using a secured note as a unique secured token (or non-fungible token (NFT)). An embodiment of the present invention may provide tokenization of a note to service a residential mortgage debt on a private blockchain. As recognized by an embodiment of the present invention, property may be uniquely represented by various characteristics including property address and/or property characteristics, such as bedrooms, bathrooms, garage, etc. Accordingly, an embodiment of the present invention may represent the underlying collateral (property) as a unique NFT.

Abstract: Bandwidth of High Bandwidth Memory (HBM) and scratch pad memory used by an Fully Homomorphic Encryption (FHE) accelerator in a System-on-Chip (SoC) during FHE relinearization is reduced by including a key generator module in the SoC. The key generator module to generate FHE public keys from a seed that is input to the SoC. The seed used by the on-die key generator module to generate FHE relinearization public keys locally within the scratch pad memory units in the SoC.

Abstract: The present invention relates to ashwagandha root extract that potentiates the complete spectrum of natural active principles present in the Ashwagandha root. The composition is prepared by a process that uses no organic solvent media to extract active principles. The compositions are further potentiated with milk or with biproducts of milk like whey, milk solids, curd etc.

Abstract: A machine learning (Ml)-based computing method for forecasting financial transactions is disclosed. The ML-based computing method includes receiving inputs from users; generating cash flow data including historical cash flow data and real-time cash flow data, based on the inputs received from the users; determining a growth factor in a week and month level based on the historical cash flow data and the forecast period; determining average cash flow data in a week and month level based on the historical cash flow data, the forecast period, a lookback period received from the users, and the growth factor; generating forecast cash flow data in at least one of a week, month, quarter, and year level for the forecast period using a ML model; and providing an output of the forecast cash flow data to the one or more users on a user interface associated with one or more electronic devices.

Abstract: One or more example embodiments provides a computer-implemented method of predicting secondary sites of a primary tumour. The method includes obtaining a whole-slide image of a primary tumour of a patient; obtaining a primary site descriptor of the primary tumour of the patient; inputting the whole-slide image and the primary site descriptor to a deep learning system previously trained to predict a metastasis probability for an appearance of a secondary tumour for one or more body parts of patients based on a particular whole-slide image and an associated particular primary site descriptor of a particular primary tumour; and outputting a secondary site prediction for at least one body part of the patient, wherein a secondary site prediction comprises a descriptor of the at least one body part and the metastasis probability associated with the at least one body part.

Abstract: The present disclosure relates to transaminase polypeptides capable of aminating a dicarbonyl substrate, and polynucleotides, vectors, host cells, and methods of making and using the transaminase polypeptides.

Abstract: A machine learning based computing method for distributing financial transactions is disclosed. The machine learning based computing method includes steps of: receiving one or more user inputs from one or more users; generating one or more data from the one or more inputs received from the one or more users; converting the month level forecast cash flow data to the week level forecast cash flow data using a machine learning model; converting the week level forecast cash flow data to day level forecast cash flow data using the machine learning model; and providing an output of at least one of: the month level forecast cash flow data, the week level forecast cash flow data, and the day level forecast cash flow data to the one or more users on a user interface associated with one or more electronic devices.

Abstract: A computer-implemented method for providing a treatment response prediction for a patient suffering from a cancerous disease, comprises: obtaining a whole slide image of the patient showing a tissue sample relating to the cancerous disease; providing a prediction function configured to derive a treatment response prediction for one or more treatment options from whole slide images; and applying the prediction function to the whole slide image to provide the treatment response prediction.

Abstract: Systems, methods, and computer-readable media for generating responses to natural-language queries regarding items in unstructured documents are disclosed. An application instance that includes one or more machine learning models receives, from a subscriber computing system, a query and document comprising unstructured data. Based on the unstructured data, the application instance generates a searchable data structure using a machine learning model. A query response is generated by performing a semantic search on the searchable data structure. The query response is provided to a target application.

Abstract: A system and method include determination of a source tenant system, determination of a target tenant system, determination of target configuration data associated with the target tenant system, comparison of the source configuration data and the target configuration data to identify similar configuration data and dissimilar configuration data, and presentation a first hierarchy of the source configuration data and a second hierarchy of the target configuration data along with indicators of the similar configuration data and the dissimilar configuration data.

Abstract: The present disclosure provides engineered proline hydroxylase polypeptides for the production of hydroxylated compounds, polynucleotides encoding the engineered proline hydroxylases, host cells capable of expressing the engineered proline hydroxylases, and methods of using the engineered proline hydroxylases to prepare compounds useful in the production of active pharmaceutical agents.

Abstract: The present disclosure relates to engineered ketoreductase polypeptides for the preparation of hydroxyl substituted carbamate compounds, and polynucleotides, vectors, host cells, and methods of making and using the ketoreductase polypeptides.

Abstract: Methods, systems, and apparatus are provided for generating an image. A personalized text prompt is generated by processing an input embedding using a transformer model followed by a first fully connected neural network. The input embedding comprises a multi-dimensional embedding vector associated with a user profile and a plurality of user items. A scored label set is generated identifying a user's preferences by processing a set of attributes for the plurality of user items using a second fully connected neural network. The image is generated by processing the personalized text prompt and the scored label set using a diffusion model.

Abstract: Audio data is received via a client device, where the audio data captures a first utterance and a second utterance from different users. The audio data can be processed to determine a first transcription of the first utterance and a second transcription of the second utterance. The audio data can be further processed to determine a first account of a first user that provides the first utterance and a second account of a second user that provides the second utterance. A first natural language description including a first user ID of the first account and the first transcription can be generated, and a second natural language description including a second user ID of the second account and the second transcription can be generated. A generative model can be applied to process the first and natural language descriptions, to generate a response responsive to the first and second utterances.

Abstract: The present disclosure relates to semiconductor structures and, more particularly, to heterojunction bipolar transistors (HBTs) with a buried trap rich region and methods of manufacture. The structure includes: a heterojunction bipolar transistor comprising a collector region, a base region and an emitter region; and at least one non-single-crystal semiconductor region in the collector region of the heterojunction bipolar transistor.

Abstract: A system comprises a IoT resource and a computing device of a user. The computing device comprises a processor that executes a personal privacy app that receives data about the IoT resource and communicates a preference setting for the user with respect to the IoT device. The preference setting is based on the data received about the IoT resource.

Abstract: The present disclosure relates to a system, a method, and a product for using deep learning models to quantify and/or improve trust in conversations. The system includes a non-transitory memory storing instructions executable to construct a deep-learning network to quantify trust scores; and a processor in communication with the non-transitory memory. The processor executes the instructions to cause the system to: obtain a trust score for each voice sample in a plurality of audio samples, generate a predicated trust score by the deep-learning network based on each voice sample in the plurality of audio samples, wherein the deep-learning network comprises a plurality of branches and an aggregation network configured to aggregate results from the plurality of branches, and train the deep-learning network based on the predicated trust score and the trust score for each voice sample to obtain a training result.

Abstract: The present disclosure generally relates to a system and method for the simultaneous transport of Crude Oil and Naphtha in a cross-country crude oil pipeline. The system includes the batch/zone of crude blend followed by lighter crude to clean and dissolve the asphaltene content of the crude blend. The naphtha batch/zone is sent securely in between the batch/zone of water. To carry out the cleaning of inner surface of the pipe various types of pigs have been used, the pigs have been designed to scrap the inner surface of the pipe and collect the foreign particle as well. The light crude oil is used to dissolve the asphaltene and resin components of crude blend, pigs are used to clean the surface and as well as a separating barrier in between the two layers.

Abstract: An intelligent scoring system includes a scorecard tool, a user input device, a memory component, a processor; and machine-readable instructions. The scorecard tool includes a scorecard and a neural network model. The machine-readable instructions stored in the memory component cause the intelligent scoring system to: receive a parameter rating for the scorecard for a first portion of a plurality of business performance parameters; automatically input the parameter rating for the scorecard for a second portion of the plurality of business performance parameters; associate a weighting with each parameter rating; estimate a parameter outcome score for each of the first portion and the second portion of the plurality of business performance parameters based on each parameter rating and weighting; automatically estimate an overall outcome score; and automatically generate a recommendation for improving the level of performance of the business unit.