Abstract: Embodiments implement non-intrusive load monitoring using a novel learning scheme. A trained machine learning model configured to disaggregate device energy usage from household energy usage can be stored, where the machine learning model is trained to predict energy usage for a target device from household energy usage. Household energy usage over a period of time can be received, where the household energy usage includes energy consumed by the target device and energy consumed by a plurality of other devices. Using the trained machine learning model, energy usage for the target device over the period of time can be predicted based on the received household energy usage.

Abstract: The present disclosure relates to systems and methods for providing Online-to-Offline services. The method may include obtain first information associated with a first service request having been allocated to a service provider and having been accepted by the service provider. The method may also include obtaining second information associated with a second service request initiated via an application executed by a second requester terminal. The method may also include determining a matching parameter based on the first information and the second information by using at least one trained matching model and determining whether the matching parameter is larger than a threshold. The method may also include transmitting data associated with the second service request based on a result of the determination that the matching parameter is larger than the threshold.

Abstract: The production cost of a product is reduced without reducing the production amount of a product of the electrolytic apparatus in a fixed period. There are provided a planning apparatus, a method of generating an operating plan, and a program, the planning apparatus including an electricity rate prediction unit that predicts a transition of a future electricity rate by using an electricity rate prediction model that predicts a transition of an electricity rate in a target period based on a value of a first factor available before the target period, and an operating plan generation unit that generates an operating plan of an electrolytic apparatus in a first period in a future based on a predicted transition of the future electricity rate.

Abstract: Disclosed herein are systems and methods for identifying issues in a building. In one aspect, a method includes receiving information describing an issue with a building, accessing data that identifies (i) a plurality of components that are included in the building and (ii) features of the plurality of components, identifying one or more candidate components that have at least a threshold likelihood of being a cause of the issue, and performing some action in response to remedy the issue. For example, the method can include selecting a candidate service provider from among a plurality of service providers based on a comparison of (i) the one or more candidate components and (ii) information identifying technical qualifications for the plurality of service providers, transmitting a service request for the issue to the candidate service provider, receiving a response from the candidate service provider, and scheduling, based on the response, a service appointment.

Abstract: A system and method are disclosed for determining long-range staff planning. Embodiments include determining a baseline measurement of labor needs over a time period of one or more employees at one or more entities and modifying the baseline measurement of the labor needs over the time period based on one or more constraints that allow the one or more employees to work additional types of labor needs at the one or more entities. Embodiments further include determining working times and job assignments of the one or more employees based on one or more simulated employees that represent potential employees to the modified baseline measurement of the labor needs over the time period and storing the determined working times and job assignments in the database for the one or more employees at the one or more entities.

Abstract: A system for editing workflows as described herein, which may include a memory storing instructions and at least one processor configured to execute instructions. The instructions may cause the processor to display, on a graphical user interface an existing workflow, wherein the workflow comprises a plurality of blocks. Edits to the plurality of blocks may be received from a user, wherein edits comprise at least edits to an individual block or an interconnection between the plurality of blocks. Further, a modified workflow based on the received edits may be created and propagated to replace existing workflows within the system. Lastly, a graphical user interface may display the modified workflows for further editing.

Abstract: An example method includes obtaining data associated with a plurality of process workflows, performing an automated workflow analysis of the data at least by (i) evaluating workflow structures to identify at least one similarity between one or more portions of the process workflows, wherein the workflow structures are modeled from the process workflow data and, (ii) responsive to evaluating the workflow structures, clustering, based on the at least one similarity, the portions of the process workflows into at least one common process group that is shared between the process workflows, identifying, based on the at least one common process group shared between the process workflows, at least one process improvement that is associated with at least one of the process workflows, and outputting, by the computing system, a recommendation associated with the at least one process improvement.

Abstract: A computer-based method is provided for managing a transaction including provision of a process intelligence engine comprising a workflow aligner and process tool box, receiving deal parameters at the process intelligence engine, defining transaction subjects, each requiring the participation of at least one network partner, where each transaction subject is a requirement for achieving the objective of the transaction, defining, for each transaction subject, a plurality of subject goals to be addressed by a network partner, and defining, for each subject goal at least one action item required for satisfying the subject goal. The subject goals are then sequenced by the workflow aligner by defining prerequisites for at least one subject goal and transaction modules are defined based on the sequencing. During execution of a deal using the method, subject goals are not made available until prerequisite subject goals have been completed.

Abstract: A non-transitory computer-readable medium includes computer-executable instructions that, when executed by at least one processor, are configured to cause the at least one processor to receive an inquiry from a training system, in which the inquiry includes a request for assistance to perform a first operation, retrieve a training profile for the first operation from a database based on the inquiry, and transmit the training profile to the training system, in which the training system is configured to present image data, audio data, or both regarding the first operation based on the training profile. The computer-executable instructions are also configured to cause the at least one processor to receive variant feedback from the training system, generate an updated training profile based on the variant feedback, store the updated training profile in the database.

Abstract: A system to facilitate organized scheduling of a plurality of tasks comprises a communication device, a processing device, and a storage device. The communication device may be configured for receiving at least one schedule request from at least one first user device. The processing device may be configured for analyzing the at least one schedule request to identify a schedule type. The processing device may be configured for retrieving at least one task preset associated with the plurality of categories of the schedule type. The communication device may be further configured for transmitting the at least one task preset and receiving at least one task preset selection associated with the at least one task preset. The processing device may be further configured for generating at least one schedule reminder based on the at least one task preset selection. A corresponding method is also provided.

Abstract: A system and method are disclosed for solving a supply chain planning problem modeled as a linear programming (LP) problem. Embodiments further include receiving a multi-period matrix formulation of a least a portion of an LP supply chain master planning problem representing a supply chain planning problem for a supply chain network and having a planning horizon divided into time buckets separated by time-bucket boundaries, mapping constraints of the LP supply chain master planning problem and variables of the LP supply chain master planning problem to the time buckets, calculating a quantity of cross-over variables for the constraints and the time buckets, selecting one or more decomposition boundaries from the time-bucket boundaries, and formulating at least two time-based decomposed subproblems by decomposing the LP supply chain master planning problem at the one or more decomposition boundaries.

Abstract: A learning unit 81 generates a plurality of sample groups from samples to be used for learning, and generates a plurality of prediction models while inhibiting overlapping of a sample group to be used for learning among the generated sample groups. An optimization unit 82 generates an objective function based on an explained variable predicted by the prediction model and based on a constraint condition for optimization, and optimizes a generated objective function. An evaluation unit 83 evaluates an optimization result by using a sample group that has not been used in learning of a prediction model used for generating an objective function targeted for the optimization.

Abstract: Disclosed herein are techniques and tools for verifying data for semantic correctness and/or verifying data for network correctness. In one respect, a method includes receiving an input defining at least two master nodes and at least one master link, each master node having at least one or more respective data properties populated with master node data and the master link having at least one or more master link data, the master nodes and master link defining a master semantic network, importing source data into a second semantic network, comparing the source data to the master node data and making a first determination that the source data reflects a data relationship defined by the master node data, and based on the first determination, populating the source data into the second semantic network, wherein the source data populated within the second semantic network reflects the data relationship defined by the master node data and the master link data.

Abstract: A method comprises displaying visual representations of a plurality of product alternatives each including at least one attribute variant to a respondent, receiving from the respondent an indication of a preferred one of the plurality of product alternatives, transmitting a request to the respondent to identify at least one attribute variant of a non-preferred product alternative that is preferred by the respondent to the corresponding attribute variant of the preferred one of the plurality of product alternatives and receiving a response from the respondent identifying at least one attribute variant of a non-preferred product alternative that is preferred by the respondent to the corresponding attribute variant of the preferred one of the plurality of product alternatives.

Abstract: Techniques are described for cognitive defined network management (CDNM) that seek to perform real-time collection and analysis of raw network data from across a disaggregated wireless network and to dynamically orchestrate network management functions substantially in real time, accordingly. For example, a multi-modal artificial intelligence (AI) engine is trained to normalize the heterogeneous raw network data into homogeneous so-called “golden record data.” A repository of historical golden records can be maintained for generating data models for use in training AI network management applications. An orchestrator can operate to directing execution of pre-developed network management workflows based on results obtained from querying the trained AI network management applications with newly received (real-time) golden records.

Abstract: A vehicle and a method of controlling the same are provided. The vehicle may include a storage configured to store map information and store information, an inputter configured to receive an input of a destination and an item list from a user, and a controller configured to generate a traveling route of the vehicle based on the stored map information and the received destination, and determine at least one store of the plurality of stores that is located within a predetermined distance with respect to the generated travelling route and sells at least one item included in the received list of items, wherein the controller modifies the generated travelling route such that the vehicle passes through at least one store of the plurality of stores based on a location of at least one store of the plurality of stores.

Abstract: A system for predicting and prescribing actions for impacting policymaking outcomes may include at least one processor configured to access first information scraped from the Internet to identify, for a particular pending policy, information about a plurality of policymakers slated to make a determination on the pending policy. The processor may parse the scraped first information to determine an initial prediction relating to an outcome of the pending policy. The processor may access second information to identify an action likely to change at least one of the initial prediction and the propensity of at least one policymaker, to thereby generate a subsequent prediction corresponding to an increase in a likelihood of achieving the desired outcome. The processor may display to the system user a recommendation to take the action in order to increase the likelihood of achieving the desired outcome.

Abstract: The present disclosure relates to a method and system for exchanging packets of information pertaining to an instrument. Data pertaining to the instrument is obtained from internal and external sources, which may be governmental or non-governmental. The obtained data undergoes a process of clustering and dimensional reduction to arrive at cleaned and optimised data attributes. A predictive model is built using those data attributes. A testing provision is included in the proposed method and system that allows for validation of the constructed model by using test data and comparing the predictions with actual values. Upon validation, the model predicts one or more packets of information that can have a bearing on the exchange of packets pertaining to the instrument.

Abstract: A system and method for aggregating and tracking content is described. In one aspect, a computer-implemented method includes forming a consolidated graphical user interface that tracks activities related to a project identifier. The project identifier identifies aggregated content. A project search query is received on the consolidated graphical user interface. The system identifies the project identifier based on the project search query, and presents at least one of a content item, a file, a folder, a user identifier, a user message, a meeting identifier, and a website identifier associated with the project identifier. The system registers selected items with the project identifier with a user of the consolidated graphical user interface, and forms the aggregated content assigned to the project identifier and to the user based on the registered selection.

Abstract: An example method for gathering a plurality of data sets for a particular process is provided. Each data set indicates transitions between different stages for a corresponding occurrence of the particular process. The method includes generating stage transition data based on the plurality of data sets. The stage transition data indicates an aggregate value for each distinct transition. The method includes determining a root stage based on the plurality of data sets. The method includes selecting each additional stage in the pipeline of stages. Each additional stage is sequentially selected based on a dynamically determined path constructed to reduce a value of a cost function. The method includes selectively modifying the pipeline of stages responsive to detecting an improvement to the value of the cost function. The method also includes generating a command to perform the particular process using the modified pipeline of stages.