Abstract: An extreme vacuum cooling (EVC) method and apparatus is disclosed for rapid cooling, food treatment, and flavor infusion in commercial kitchens and food processing plants. Operating under ultra-low pressure with adaptive chamber pressure control, the EVC apparatus eliminates liquid splash and optimizes marinating and brining, achieving substantial time and energy savings. Available in single-unit and dual-module designs, this technology and apparatus enables swift, flexible, safer, less labor-intensive, and high-throughput food preparation operations.

Abstract: Methods, systems, and apparatuses are described herein for using machine learning to generate and use synthetic profiles. A computing device may train a machine learning model to generate synthetic user profiles. The computing device may then use the trained machine learning model to generate a plurality of synthetic user profiles based on real user profile information, provide those synthetic user profiles to a quote provider via an API, then average the quotes received from that provider to determine an expected quote for the real user. The computing device may also collect a plurality of quotes from a quote provider based on synthetic user profiles, then train a second machine learning model to estimate quotes by that provider. That trained second machine learning model may be used to estimate quotes for users, and may be re-trained based on real quotes provided by the quote provider at a later time.

Abstract: A method and apparatus is disclosed relating to food preparation in commercial kitchens and food processing plants. The disclosed extreme vacuum cooling (EVC) technology and apparatus can work in ultra low pressure conditions with adaptive pressure control to avoid potential liquid splash inside the vacuum chamber caused by un-controlled low pressure conditions. Clean air is added into the vacuum chamber to allow the chamber pressure to track a setpoint trajectory that may have ramp up periods in order to avoid liquid splash events. The apparatus enables the user to cool various kinds of foods quickly, save energy, and meet food safety regulations.

Abstract: In some aspects, a computing system may authenticate a user before blockchain functions may be performed and provide obstacles to prevent brute force attacks in decentralized computing systems or other distributed computing system environments. The computing system may use a smart contract to assist with authenticating a user. As authentication attempts fail, the computing system may increase a network usage cost associated with authentication. By doing so, the security of a computing system may be improved by making it cost-prohibitive for malicious actors to perform brute force attacks. Further, a computing system may use a variety of biometric information that may provide additional verification and security when authentication is performed.

Abstract: A method and apparatus is disclosed relating to controlled environment agriculture (CEA), food processing facilities, and commercial kitchens. The inventive smart and scalable extreme vacuum cooling (EVC) equipment is based on a scalable and modular design that allows one or multiple food chamber modules to connect to one utility module so that all food chambers can share the available vacuum cooling capacity dynamically. A scalable EVC system can be built, shipped and assembled with as few as one food chamber module and one utility module to a number of food chamber modules and a utility module. The food chambers are designed with different sizes in dimension and power requirements but with the same interface to the utility module. In this way, multiple food chamber modules can operate for food rapid cooling and flavor infusion supported by only one utility module. In a CEA farm, leafy greens are harvested daily and need to be chilled rapidly to comply with USDA food safety standards.

Abstract: Systems and methods disclosed herein provide automatic expense categorization of transactions or expenditures based on a machine learning (ML) model trained using anonymized transactional data for expenditures that are stored in a public blockchain. The anonymized transactional data for the expenditure and the expense category, may be distributed throughout the blockchain network and recorded in the blockchain. In some implementations, an expenditure may be submitted to the blockchain network for expense categorization. The transactional data for the expenditure may be anonymized to remove confidential and personal identifying information (PII) before it is distributed throughout the blockchain network. Each participating node of the blockchain network may utilize the ML model to identify an expense category for the expenditure. The participating nodes may provide a consensus mechanism in order to arrive at a shared understanding of how to categorize the expenditure.

Abstract: Disclosed embodiments may include a system for generating aggregate records. The system may retrieve data corresponding to a first user. The system may receive, from the first user, first sharing preference(s) associated with a second user. The system may extract first data subset(s) from the data based on the first sharing preference(s). The system may generate block(s) of a blockchain ledger. The system may append the first data subset(s) to at least a first block of the block(s) of the blockchain ledger. The system may provide the second user with access to the first data subset(s) on the at least first block.

Abstract: This application relates to apparatus and methods for identifying fraudulent payment transfers. In some examples, a computing device determines payment transfer initiation features, and payment transfer reception features, based on previous payment transfer data. The computing device may train a machine learning fraud detection model with the payment transfer initiation features, and may train a machine learning fraud detection model with the payment transfer reception features. Once trained, the computing device may employ the machine learning fraud detection models to identify fraudulent payment transfers. For example, the computing device may determine whether a payment transfer is fraudulent when the payment transfer is initiated. Assuming the payment transfer is allowed, the computing device may determine whether the payment transfer is fraudulent when the payment is being received.

Abstract: A method and apparatus is disclosed relating to food preparation in commercial kitchens and food processing plants. The disclosed extreme vacuum cooling (EVC) technology and apparatus can work in ultra low pressure conditions with adaptive pressure control to avoid potential liquid splash inside the vacuum chamber caused by un-controlled low pressure conditions. Clean air is added into the vacuum chamber to allow the chamber pressure to track a setpoint trajectory that may have ramp up periods in order to avoid liquid splash events. The apparatus enables the user to cool various kinds of foods quickly, save energy, and meet food safety regulations.

Abstract: This application relates to apparatus and methods for identifying fraudulent payment transfers. In some examples, a computing device determines payment transfer initiation features, and payment transfer reception features, based on previous payment transfer data. The computing device may train a machine learning fraud detection model with the payment transfer initiation features, and may train a machine learning fraud detection model with the payment transfer reception features. Once trained, the computing device may employ the machine learning fraud detection models to identify fraudulent payment transfers. For example, the computing device may determine whether a payment transfer is fraudulent when the payment transfer is initiated. Assuming the payment transfer is allowed, the computing device may determine whether the payment transfer is fraudulent when the payment is being received.

Abstract: This application relates to apparatus and methods for identifying fraudulent payment transfers. In some examples, a computing device determines payment transfer initiation features, and payment transfer reception features, based on previous payment transfer data. The computing device may train a machine learning fraud detection model with the payment transfer initiation features, and may train a machine learning fraud detection model with the payment transfer reception features. Once trained, the computing device may employ the machine learning fraud detection models to identify fraudulent payment transfers. For example, the computing device may determine whether a payment transfer is fraudulent when the payment transfer is initiated. Assuming the payment transfer is allowed, the computing device may determine whether the payment transfer is fraudulent when the payment is being received.

Abstract: Systems and methods disclosed herein provide automatic expense categorization of transactions or expenditures based on a machine learning (ML) model trained using anonymized transactional data for expenditures that are stored in a public blockchain. The anonymized transactional data for the expenditure and the expense category, may be distributed throughout the blockchain network and recorded in the blockchain. In some implementations, an expenditure may be submitted to the blockchain network for expense categorization. The transactional data for the expenditure may be anonymized to remove confidential and personal identifying information (PII) before it is distributed throughout the blockchain network. Each participating node of the blockchain network may utilize the ML model to identify an expense category for the expenditure. The participating nodes may provide a consensus mechanism in order to arrive at a shared understanding of how to categorize the expenditure.

Abstract: There is herein described energy storage systems. More particularly, there is herein described thermal energy storage systems and use of energy storable material such as phase change material in the provision of heating and/or cooling systems in, for example, domestic dwellings.

Abstract: Systems and methods disclosed herein provide automatic expense categorization of transactions or expenditures based on a machine learning (ML) model trained using anonymized transactional data for expenditures that are stored in a public blockchain. The anonymized transactional data for the expenditure and the expense category, may be distributed throughout the blockchain network and recorded in the blockchain. In some implementations, an expenditure may be submitted to the blockchain network for expense categorization. The transactional data for the expenditure may be anonymized to remove confidential and personal identifying information (PII) before it is distributed throughout the blockchain network. Each participating node of the blockchain network may utilize the ML model to identify an expense category for the expenditure. The participating nodes may provide a consensus mechanism in order to arrive at a shared understanding of how to categorize the expenditure.

Abstract: There is herein described energy storage systems. More particularly, there is herein described thermal energy storage systems and use of energy storable material such as phase change material in the provision of heating and/or cooling systems in, for example, domestic dwellings.

Abstract: The exemplary embodiments provide an automated control compliance evidence manager that is responsible for gathering evidence of compliance with controls. The control compliance evidence manager may operate on an ongoing basis. In some exemplary embodiments, the evidence is gathered and available for review in real time or near real time. The steps for gathering the compliance evidence may be specified at the time a control is established so that there is consistency in what is gathered and how the evidence is gathered. A user may be required to provide an itemization of what evidence is to be gathered and where. The evidence may be stored in an immutable fashion. In some exemplary embodiments, the evidence may be cryptographically hashed or otherwise encrypted and referenced by a secure distributed ledger, like a blockchain. The distributed ledger may be visible to concerned parties.

Abstract: Systems and methods disclosed herein provide automatic expense categorization of transactions or expenditures based on a machine learning (ML) model trained using anonymized transactional data for expenditures that are stored in a public blockchain. The anonymized transactional data for the expenditure and the expense category, may be distributed throughout the blockchain network and recorded in the blockchain. In some implementations, an expenditure may be submitted to the blockchain network for expense categorization. The transactional data for the expenditure may be anonymized to remove confidential and personal identifying information (PII) before it is distributed throughout the blockchain network. Each participating node of the blockchain network may utilize the ML model to identify an expense category for the expenditure. The participating nodes may provide a consensus mechanism in order to arrive at a shared understanding of how to categorize the expenditure.

Abstract: The present invention provides methods for producing a protein having one or more desired properties, the method comprising: (a) a library design step, (b) a library testing step; and (c) a learning step, in which the sequence variants are each assigned a fitness score based at least in part on the result of the library testing step, and a machine learning algorithm uses the fitness score of each of the sequence variants to train a model to predict the fitness score for new sequence variants, and wherein the machine learning model trained in step (c) is used to design a new library of sequence variants. The present invention also provides a system for producing a protein having one or more desired properties, said system adapted to implement the method of the invention.

Abstract: There is herein described energy storage systems. More particularly, there is herein described thermal energy storage systems and use of energy storable material such as phase change material in the provision of heating and/or cooling systems in, for example, domestic dwellings.

Abstract: There is herein described energy storage systems. More particularly, there is herein described thermal energy storage systems and use of energy storable material such as phase change material in the provision of heating and/or cooling systems in, for example, domestic dwellings.