Predicting trends may include obtaining trend data from one or more sources, extracting a plurality of trends from the trend data, and producing permutations combining terms or concepts appearing in the plurality of trends to create trend candidates. A first term from a first trend or concept in the plurality of trends may be combined with a second term or concept from a second trend in the plurality of trends.

Predicting trends may include obtaining trend data from one or more sources, extracting a plurality of trends from the trend data, and producing permutations combining terms or concepts appearing in the plurality of trends to create trend candidates. A first term from a first trend or concept in the plurality of trends may be combined with a second term or concept from a second trend in the plurality of trends.

Provided is a federated learning model, a generation apparatus thereof, and the like that easily and suitably contribute to marketing activities. A federated learning model generation apparatus includes a local learning model acquisition unit and a federated learning model generation unit. The local learning model acquisition unit acquires a plurality of different local learning models that have learned a relationship between a plurality of customer groups respectively generated from business customer data owned by a plurality of business operators and consumption behaviors corresponding to the business operators. The federated learning model generation unit receives a predetermined consumption behavior of a customer as input data by federating at least a part of the acquired local learning models, and generates a federated learning model that outputs prospective customer data for the input data.

A tool, method, and system for assessing an incremental value of one or more items in an item assortment are disclosed. The tool can receive historical purchasing data, which can include a plurality of customers purchasing one or more items of a plurality of items in an item assortment. The tool can use the historical purchasing data for the plurality of customers to simulate execution of removal of an item from the item assortment, wherein removal of the item from the item assortment causes an incremental loss associated with the item. The tool can order any number of items of the historical purchasing data. The tool can execute scenario simulations, and the tool can account for probabilities when interacting with the historical purchasing data. The tool can display assessment data and receive inputs via an interactive user interface. The tool can launch the assessment data in a downstream application.

The present disclosure is to an Artificial Intelligence based Intellectual Property valuation system, including a valuation database that includes, as raw data, reference information, patent data, and economic statistical information, and, as extracted information processed from the raw data, statistical data and AI training dataset, a collection/refinement module that processes the raw data, computes and provides the statistical data required in a process of generating the AI training dataset or key variables, computes the AI training dataset, and stores the same, an AI module that, for outputting the key variables, trains AI models for the respective key variables, identifies, and, through the AI models, computes corresponding prediction-variable values using respective explanatory-variable values collected by the collection/refinement module to output respective key-variable values, and a valuation service module that computes a value of the target IP based on the key-variable values and generates a valuation report including the statistical data.

A program that causes a computer to function as reception means, storage means, and first determination means. The reception means receives real estate information which is information on real estate as a candidate at which a data center should be provided in a data storage system composed of a plurality of data centers coordinated with each other. The first determination means determines whether or not the data center should be provided at the real estate corresponding to the real estate information received by the reception means, based on the real estate information received by the reception means and determination criterion information stored in a storage unit by the storage means in advance.

The disclosure relates generally to an intelligent interactive platform based on Artificial Intelligence (AI) and Machine Learning (ML) components for adaptively and selectively invoking multiple virtual agents for service and service management optimization. This disclosure particularly adapts such an intelligent system to a context of hospitality service provisioning and optimization. A Virtual Agent Coordination Engine (VACE) based on a plurality of language/voice/image models is disclosed for decomposing free-form requests from customers and service personnels via a variety of access applications into request items and for adaptively selecting a subset of virtual engines to handle the requests according to the request items. The VACE is further configured to aggregate outputs from the subset of virtual agents to intelligently generate a plurality of answers/alerts/prompts/action triggers, again, based on a plurality of AI/ML models.

A system and method for utilizing deep learning and machine learning to predict the likelihood of a supplier-buyer relationship existing between two business entities using a retrieval model and a ranking model. The output is a raw supplier propensity score between 0 and 1 representing the likelihood of a supplier-buyer relationship, as well as a propensity class based on ranges of this score. A user-interactive map displays supplier-buyer relationships where the raw supplier propensity score exceeds a threshold value.

A market explanation event extraction method includes a step of acquiring, by a market explanation event extraction system, a stock price index data and unstructured data of a text type for predicting a stock price, a step of extracting, by the market explanation event extraction system, an event that influences a stock market based on the acquired unstructured data, a step of predicting, by the market explanation event extraction system, a future direction of a stock price by analyzing the extracted event, and a step of selecting, by the market explanation event extraction system, an event that matches a result of predicting the direction by comparing the result of predicting the direction and an actual stock price index, and extracting a market explanation event that explains a cause of a rise or fall of a stock price based on the selected event.

A method includes receiving historical marketing data including historical creative assets and historical performance data associated with the historical creative assets. The method further includes processing the historical marketing data to identify input features associated with the historical creative assets. The method further includes using the identified input features and the historical performance data to train a machine learning model, thereby generating a trained marketing model conditioned to the input features and the historical performance data. The method further includes providing as an input to the trained marketing model, marketing data that includes at least one creative asset, a selection of a marketing channel for placement of the creative asset therein, and performance indicators for evaluating performance of the creative asset. The method further includes receiving, as an output from the trained marketing model, predicted values for the performance indicators upon placement of the creative asset on the marketing channel.