Abstract: A machine learning server is provided for predicting innovations in one or more scenarios. The machine learning server includes a processor and a memory in communication with the processor. The processor is configured to receive a user input, define a scenario profile based on the user input, apply a first trained machine learning model to the scenario profile to generate at least one target associated with the scenario, wherein the at least one target includes a measurable outcome, prompt for a user selection from the target, apply a second trained machine learning model to the scenario profile and the user selection from the at least one target to generate at least one candidate innovation predicted to achieve the measureable desired outcome, obtain descriptive information related to the at least one candidate innovation, and present the descriptive information related to the at least one candidate innovation.

Abstract: A computer-implemented method according to one approach includes receiving behavior data associated with a user's behavior on at least one device. The behavior data is based on one or more of an email account, a browser history, password usage and online behavior history. Risk levels associated with the behavior data are generated and role-based risk events are predicted based on the behavior data. The method further includes simulating the role-based risk events based on the risk levels, and adjusting role-based access control of the user based on results of the simulating. A computer program product for adjusting role-based access control according to another approach includes a computer readable storage medium having program instructions embodied therewith. The program instructions are readable and/or executable by a computer to cause the computer to perform the foregoing method.

Abstract: A method of shaping glass sheets is provided. A first glass sheet is shaped after it has been deposited on a first bending tool at a first position and an edge portion of the first sheet contacted to move the first sheet to a second position relative to the first bending tool, and a second glass sheet is also shaped after it has been deposited on the first bending tool at a first position and an edge portion of the second sheet contacted to move the second sheet to a second position relative to the first bending tool. The second position of the first edge portion of the first sheet is different than the second position of the first edge portion of the second glass sheet relative to the first bending tool. The first and second glass sheets may be of different sizes, and may be laminated together after each is shaped.

Abstract: A machine learning server is provided for predicting innovations in one or more scenarios. The machine learning server includes a processor and a memory in communication with the processor. The processor is configured to receive a user input, define a scenario profile based on the user input, apply a first trained machine learning model to the scenario profile to generate at least one target associated with the scenario, wherein the at least one target includes a measurable outcome, prompt for a user selection from the target, apply a second trained machine learning model to the scenario profile and the user selection from the at least one target to generate at least one candidate innovation predicted to achieve the measureable desired outcome, obtain descriptive information related to the at least one candidate innovation, and present the descriptive information related to the at least one candidate innovation.

Abstract: A method and system for providing a customer with guaranteed savings on its long-run energy charges as between a Utility and a Competitive Retailer. Preferably the method and system, including one or more computers with microprocessors for executing computer coded instructions, obtains and stores customer attributes, current energy provider, billing cycle and switching time constraints and restrictions and receives forecasted costs and prices of the Utility and the Competitive Retailer for the customer's next billing cycle. The system processes, via the computer, the customer data and the energy data for the Utility and Competitive Retailer to determine the energy provider projected to have the lower price for the next billing cycle of the customer. Preferably, the system requests a switch to the energy provider projected to have the lower price in that customer's next billing cycle, unless the customer is currently receiving service from the projected energy provider.

Abstract: The method of adhering studs to the cloth includes the step of providing a plurality of flat, aluminum studs having predetermined shapes. The backside surface of each stud is covered with a low temperature, heat sensitive adhesive. Also, a predetermined stud pattern is provided. The method includes laying the stud pattern atop the cloth. The stud pattern is then replicated on the cloth by marking the cloth. A corresponding stud is placed on each respective cloth mark. Thereafter, compressive force and heat, in the range of 130.degree.-150.degree. F., is applied to the studs and the cloth for a predetermined period of time. The heat activates the adhesive and adheres the studs to the cloth. The kit includes a plurality of studs with the heat sensitive adhesive on the backs thereof. Also, a plurality of stud patterns are included in the kit. A studed article of clothing made in accordance with the method of manufacture, is also part of this invention.