Fujitsu Corporation

Fujistu Limited

Sanam Mirzazad Barijough

Wei-Peng Chen

Ajay Chander

Jorjeta Gueorguieva Jetcheva

Ramya Malur Srinivasan

A method may include obtaining multiple search paths, each search path including a previous search query and multiple search materials. The method may include generating multiple relevancy scores corresponding to the search paths. The method may include obtaining a current search query of a new search. The method may include identifying one or more previous search queries that match the current search query. The method may include identifying a first search path and a second search path based on the first search path and the second search path corresponding to the matching previous search queries. The method may include comparing a first relevancy score of the first search path with a second relevancy score of the second search path. The method may include presenting the search materials of the first search path in response to the first relevancy score being greater than the second relevancy score.

Crowdsourced search result generation

A method of collaborative scheduling includes receiving group inputs and receiving environmental data. The method includes calculating collaborative schedule information based on the environmental data, traits, and the group inputs that include feasible schedules and total schedule costs associated therewith that are based on an individual member performance cost, a flexible task violation cost, and a behavior cost. The method includes receiving a selection of a feasible schedule as a preferred schedule and determining a first trait and a second trait of each member based thereon. The first trait includes selfishness assumed when the feasible schedule selected places a higher total schedule cost on other members. The second trait includes selflessness assumed when the feasible schedule selected places a lower total schedule cost on the other members. The method includes generating a collaborative schedule as the feasible schedule selected by the members.

Collaborative scheduling

A method may include receiving a goal of a user. The method may also include obtaining progress data related to accomplishment of the goal by the user from a sensor. The method may further include determining, based on the progress data, a progress level of the first user. The progress level may correspond to progress of the first user with respect to accomplishment of the goal. Moreover, the method may include generating a conversation with the user by a virtual agent based on the progress level.

Generating conversations for behavior encouragement

A method to prioritize email such that a number of interruptions experienced by a user is reduced may include obtaining a threshold cognitive load of a user and a threshold importance; in response to receiving an email in an inbox of the user, determining an importance of the received email; comparing the importance thereof with the threshold importance; and based on the importance thereof not satisfying the threshold importance, determining a priority thereof with respect to emails in the inbox. The method includes determining a cognitive cost of the received email and that the user is handling a first group of emails, and comparing the priority of the received email with a priority of emails of the first group of emails; obtaining a current cognitive load of the user; and identifying a second group of emails; and presenting the second group of emails to the user.

Electronic message management based on cognitive load

A system configured for curtailment event implementation based on uncertainty of energy demand flexibility of sites includes a processor and a non-transitory computer-readable medium communicatively coupled to the processor and having encoded therein programming code executable by the processor to perform operations. The operations may include receiving energy usage data from meters at sites and uncertainties of energy demand flexibility for each site. The uncertainties of energy demand flexibility of each site may be zero, may fit a single probability distribution, may fit a set of probability distributions, or may belong to a set of demand flexibilities. The operations may include clustering the sites based on the uncertainties. The operations may include solving group-specific formulations for groups. The operations may include identifying sites to include in the DR event using the solved-for group-specific parameters. The operations may include implementing the DR event at the identified sites to reduce energy usage.

Energy curtailment event implementation based on uncertainty of demand flexibility

A method of providing advice to a user may be provided. A method may include receiving a topic from the user. The method may also include presenting one or more potential advice conversations to a human professional, wherein each potential advice conversation of the one or more potential advice conversations includes one or more advice statements. Further, the method may include selecting an advice statement from the one or more advice statements, and presenting the selected advice statement to the user upon receiving approval of the selected advice statement from the human professional.

Advice engine systems

A method of demand response (DR) assessment may include reading energy economics data from a third party for an interval of a DR event period. The energy economics data may include a day-ahead demand, a day-ahead locational marginal pricing (LMP), and a real-time LMP. The method may also include estimating a real-time energy demand for the interval. Based on the energy economics data and the estimated real-time energy demand, the method may also include determining a demand imbalance for the interval. The method may also include calculating a cost of a DR event that exploits the demand imbalance. The method may also include selecting an energy curtailment request amount for the interval that optimizes profitability of the DR event.

Demand response event assessment

A method includes receiving input data pertaining to a user. Based on the input data, the method includes rendering a graphical user interface (GUI) that includes an initial icon arrangement (arrangement) presentable in a virtual environment. The initial icon arrangement includes a set of icons representative of portions of an overall time period represented by the initial icon arrangement. The method includes determining whether a portion of the input data is representable in the initial icon arrangement. If so, the method includes quantifying the second portion of the input data with a target and a timeframe, associating the target and the timeframe with the second portion of the input data, modifying icons in the arrangement that correspond to the timeframe to represent the target, rendering the modified icons as an opportunity layer on the arrangement, and displaying the arrangement with the opportunity layer in the virtual environment.

Lightweight virtual environment

A method of demand response (DR) event issue assessment is described. The method may include receiving parameters that may include one or more of contractual parameters, ambient condition data, historical data, and energy price data. The method may include predicting a customer demand and a customer energy curtailment based on one or more of the parameters. The method may further include calculating a customer participation likelihood based on one or more of the parameters. The method may include determining a price threshold based on one or more of the parameters, the customer energy curtailment, the customer demand, and the customer participation likelihood. The price threshold may represent an energy price at which issuing a DR event is more profitable than not issuing the DR event.

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Mountain View, CA, United States of America

Sanam Mirzazad Barijough