Abstract: Systems and methods for identifying, categorizing, reporting, and/or remedying knowledge risks of a workforce. The systems and methods can operate automatically and in real-time. Risks can be identified on a per-skill basis using a plurality of risk factors. Graphical user interfaces can be generated to present knowledge risks across an entire enterprise or portion thereof in easily digestible formats. Upskilling recommendations can be generated based on categorization of knowledge risks.

Abstract: The inventive subject matter herein is directed toward improved scheduling and planning system that detects a problematic event in a workflow. When the event is detected, the system automatically notifies a set of contacts from a hierarchical contact list. The system then automatically notifies other sets of contacts depending upon the response from the first set of contacts. A cascade of different types of notifications could be triggered from a single detected event as each contact responds and provides information that could be used to trigger additional notifications.

Abstract: A computer-implemented method for improving efficiency in an electronic procurement system for sourcing resources, comprising, during digital electronic interactions of a buyer computer with one or more software platforms and without receiving explicit request for recommendations from the buyer computer: automatically generating, at a coding computer, implicit observation data of the buyer computer; automatically determining, at the coding computer, one or more active sourcing events from a plurality of sourcing events, based on at least the implicit observation data of the buyer computer; using the coding computer, causing to display at least one of the one or more active sourcing events in a graphical user interface.

Abstract: Techniques for expense report submission are disclosed. An expense report submission system receives, via a graphical user interface, a user query that corresponds to requesting whether a particular expense is allowed. The expense report submission system applies the user query to a machine learning model configured to evaluate data associated with expenses against one or more expense policy rules. The expense report submission system generates a response to the user query based at least on a result of applying the user query to the machine learning model. The response to the user query indicates whether the expense is allowed based at least on the expense reporting rule(s). The expense report submission system presents, in the graphical user interface, the response to the user query.

Abstract: A method of training a machine learning model to determine an item margin is provided. The method includes monitoring a first value for a first item having attributes and monitoring a first value for a second type of item having attributes where an attribute of the first attributes is the same as an attribute of the second attributes. The method also includes determining a first margin based on the first values. The first attributes, the second attributes, and the first margin are input as training data for the machine learning model where the machine learning model is trained with the training data. The monitoring operations for the first item and the second item are repeated to obtain a second value for the first and second items. Furthermore, the trained machine learning model is applied to the second values to determine a second margin.

Abstract: Disclosed is a resource management planning support device 1 including a learning information storage section (storage section 13), a duty recommendation processing section 110, a planning processing section (operation rescheduling planning processing section 120), and a screen processing section 140. The learning information storage section stores plan result information regarding existing operation rescheduling plans. The duty recommendation processing section 110 exercises a prior learning function to generate a duty recommendation model for recommending a duty. The planning processing section exercises a planning function to create an operation rescheduling plan by selecting a duty template suitable for a planning target train set according to the duty recommendation model generated by the duty recommendation processing section 110. The screen processing section 140 outputs a plan result display screen 147 for displaying the operation rescheduling plan created by the planning processing section.

Abstract: Embodiments of the invention relate generally to systems, methods, and apparatus for assessing consumer perception of business features, such as brands, products, and services. A graphical user interface presents a consumer with a prime associated with the business feature. The graphical user interface presents a target to be sorted by the consumer. An instruction from the consumer is received (via a user input device) to sort the target into a bin presented on the graphical user interface. Consumer response data associated with the instruction from the consumer is generated and, based on the consumer response data, the consumer's perception of the business feature is assessed.

Abstract: The present invention makes it possible to supply transportation capacity suitable for movement demand in numerous circumstances.

Abstract: A heating, ventilation, and air conditioning (HVAC) system includes a controller associated with a residence. The controller is configured to determine an expected value range for an operating parameter of a component of the HVAC system. Additionally, the controller is configured to receive a signal from a sensor indicative of a current value of the operating parameter of the component and determine if the current value of the operating parameter is outside the expected value range. Based on the determination that the current value is outside the expected value range, the controller is additionally configured to initiate a diagnostic mode of the controller. In the diagnostic mode, the controller is configured to collect diagnostic data associated with the HVAC system.

Abstract: Systems and methods for machine forward energy and energy credit purchase are disclosed. An example transaction-enabling system may include a machine having an energy requirement for a task and a controller. The controller may include a resource requirement circuit to determine an amount of an energy resource for the machine to service the energy requirement, a forward resource market circuit to access a forward resource market, and a resource distribution circuit to execute a transaction of on the forward resource market in response to the determined amount of the energy resource.

Abstract: Systems, methods, and other embodiments associated with self-tuning optimization of a replenishment policy of an item are described. In one embodiment, the method includes determining an initial replenishment policy of the item. A performance of the initial replenishment policy is determined based upon past performance of the initial replenishment policy. The initial replenishment policy is revised to get a service level of the item for future sales periods closer to a target service level of the item. Information is forwarded in real-time about the revised replenishment policy to an order fulfillment facility.

Abstract: A machine-learning ecosystem includes a correlation module for building at least one prediction model based on at least one data input including at least one input parameter and at least one output parameter, the prediction model relating the output parameter to the input parameter. The correlation module performs at least one threshold check on the prediction model to assess the robustness of the prediction model. The ecosystem further includes a decision module communicatively coupled to the correlation module and receiving the prediction model from the correlation module. Based on a verification check at the decision module, a confirmation, a deferral, or a rejection of the prediction model is sent from the decision module to the correlation module.

Abstract: Systems and methods are provided to predict customer behavior during a digital transaction at a point of sale. The disclosed systems and methods can collect information regarding a merchant and the merchant's business as well as information about the current sales environment in which the merchant is operating. From the collected information, the disclosed systems and methods can process the collected information to generate a prediction of future customer behavior in real-time or near real-time.

Abstract: There is provided an information processing apparatus for efficiently processing various products. The information processing apparatus includes a determiner that receives signals from sensors respectively provided in a plurality of machine tools, determines operation statuses of the plurality of machine tools, and derives a plurality of usable machine tools, a receiver that receives product specifying data for specifying a product to be manufactured, a decider that decides, based on the product specifying data, a necessary machine tool required to process the product, and a presenter that searches for the necessary machine tool from the plurality of usable machine tools, and presents information concerning a use machine tool as a search result.

Abstract: In an example, a method comprises defining an object comprising a plurality of discrete components, each of which comprises a set of discrete formulas having a predefined order. The method initiates an evaluation of each discrete component by looping over each formula of that discrete component to produce a result set according to the predefined order by evaluating without specifying a dimensionality to produce the result set with a single row, or evaluating the formula with a specified dimensionality to produce the result set with a plurality of rows. The method displays a calculation user interface, determines that an action exists that is associated with at least one formula of at least one discrete component, displays a user-selectable element corresponding to the action, and automatically pauses computation of at least a portion of the evaluation until input selecting the user-selectable element is received.

Abstract: System and methods for dynamic replenishment in a facility are disclosed. A mobile inventory processing device reads a machine readable tag affixed to a perishable item and transmits an indication of the perishable item to a stock inventory processing device. The stock inventory processing device retrieves a stock inventory value corresponding to the perishable item and a historic daily sales value for the perishable item from a database. The stock inventory processing device determines a restocking value based on whether the historic daily sales value exceeds the stock inventory value. The stock inventory processing device transmits a second indication to the mobile inventory processing device. The second indication correspond to insufficient shelf stock of the perishable item. The mobile inventory processing device receives the second indication and outputs an auditory alert in response to the second indication.

Abstract: A method includes obtaining multiple inputs associated with a worker in a manufacturing environment. The method also includes performing a fuzzy logic process on the multiple inputs to generate multiple outputs, where the multiple outputs are associated with performance by the worker of a task in the manufacturing environment. The method further includes providing instructions to an electronic device to display a specified output among the multiple outputs while the worker performs the task in the manufacturing environment, where the specified output includes instruction information for performing the task.

Abstract: Artificial intelligence (AI)-based techniques are provided that predict a quality score for a product-related data structure associated with one or more products. One method comprises obtaining data for a given product-related data structure; evaluating a plurality of first features related to a customer account associated with the given product-related data structure using the obtained data; evaluating a plurality of second features related to the given product-related data structure using the obtained data; processing at least some of the first features and the second features using at least one model that provides a predicted quality score for the given product-related data structure; and applying one or more thresholds to the predicted quality score to determine an acceptance status related to the given product-related data structure. A weighting of the first features and the second features can be learned during a training phase.

Abstract: Techniques are disclosed for intelligently managing software development. In one example, a method for managing software development, includes receiving, by a computing system, a request to review source code written by a first developer, determining, by the computing system, a software skill set for the source code review, selecting, by the computing system, one or more selected source code reviewers from the pool of source code reviewers based on the software skill set and respective reputation scores for a pool of source code reviewers, assigning, by the computing system, one or more portions of the source code for code review to each of the selected source code reviewers, and determining, by the computing system, a consensus verification output on the code review based on review input from a majority of the selected source code reviewers.

Abstract: Techniques for expense report submission are disclosed. An expense report submission system receives, via a graphical user interface, a user query that corresponds to requesting whether a particular expense is allowed. The expense report submission system applies the user query to a machine learning model configured to evaluate data associated with expenses against one or more expense policy rules. The expense report submission system generates a response to the user query based at least on a result of applying the user query to the machine learning model. The response to the user query indicates whether the expense is allowed based at least on the expense reporting rule(s). The expense report submission system presents, in the graphical user interface, the response to the user query.