Accenture

The present disclosure discloses methods and systems for digital transformation and integration. The method includes receiving a collection of documents related to operations of an entity. Thereafter, a business process hierarchy based on the collection of documents is generated, using a model and a graph database of accumulated documents. The nodes of the business process hierarchy are divided into different levels of logical groups and includes a plurality of nodes of each level of at least four levels. Further, the business process hierarchy is displayed in a user interface. The user interface allows user to input to modify the business process hierarchy to modify or add at least one node of the business process hierarchy. Furthermore, a plurality of documents is generated, corresponding to different document types for implementing each node of the business process hierarchy, wherein the plurality of documents includes different modalities.

Method, system, and computer-readable storage media for optimizing fine-tuning of a Large Language Model (LLM) are disclosed. An adapter recommendation is generated for fine-tuning of the LLM based on input data and a desired output criterion corresponding to each of output aspects. Based on the adapter recommendation, initial coefficient values for coefficients that correspond with the output aspects and an initial ranking value are assigned. Once the coefficients and ranking value are assigned, an adaptive multi-objective low rank adaptation and uncertainty estimation are iteratively performed for a predefined number of iterations or until the desired criterion corresponding to each of the output aspects is achieved. Upon reaching the predefined number of iterations or achieving the desired criterion corresponding to each of the output aspects, an output value for each of the output aspects is derived. The derived output value is used for fine-tuning of the LLM.

Method, system, and computer-readable media (CRM) for identifying fraudulent insurance claims are disclosed. Content data is extracted from a plurality of documents related to a fulfillment request. Based upon the extracted content data, a first prompt is selected from a plurality of prompts stored in a prompts database. Further, based at least in part upon the extracted content data and based at least in part upon the selected prompt, a first verification input prompt is generated. A first answer is generated using a linguistic model based upon the first verification input prompt. The first answer is validated by comparing the first answer with the extracted content data. A fraud status label of the fulfillment request is determined based upon validation of the first answer.

Aspects of the present disclosure provide systems, methods, apparatus, and computer-readable storage media that support improved watermarking and fingerprinting of a shared dataset. To illustrate, clustering may be performed on the dataset using initial clustering parameters (e.g., a secret key) to assign each record (e.g., attribute) of the dataset to one of multiple clusters. The secret key may be selected by a user or determined automatically based on the clustering algorithm. After the clustering, the records of each cluster may be selected for embedding a portion of fingerprint data based on one or more security parameters (e.g., a hash function, priority values, even/or selection, etc.). The selected records (or portions thereof) may be replaced with corresponding portions of the fingerprint data to embed the fingerprint data within different records as watermarking. Aspects also include analyzing a dataset to verify whether watermarking is present and to extract a fingerprint.

Zusammenfassung wird geladen …

Zusammenfassung wird geladen …

Zusammenfassung wird geladen …

Zusammenfassung wird geladen …

Systems and methods for predictively responding to real-time sensor data from an computer-augmented environment are provided. A system may receive sensor data from the computer-augmented environment. The system may obtain a feature model, a prediction model, and a filter criteria. The system may derive, based on the feature model and the sensor data, a first event. The system may determine the first event is associated with an authorized event identifier in the filter criteria. The system may forecast, based on the authorized event identifier and the prediction model that a second event occurs after the first event. The system may transmit an action message to the computer-augmented environment, the action message indicative of the second event. Updates to the feature model, the prediction model, and/or the filter criteria may occur in batch using machine learning and statistical analytics while the real-time platform predictively responds to the sensor data.

Zusammenfassung wird geladen …