Accenture Patente

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.

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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.

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In some implementations, an application programming interfaces (API) manager may receive, at a set of artificial intelligence (AI) APIs, a set of inputs from a set of on-site devices. Accordingly, the API manager may route the set of inputs to a corresponding set of remote servers and may receive, from at least one server of the corresponding set of remote servers, at least one response based on at least one input, from the set of inputs, routed to the at least one server. The API manager may transmit the at least one response to a corresponding device from the set of on-site devices. Further, the API manager may modify at least one API, of the set of AI APIs, based on a traffic pattern associated with the set of inputs and the at least one response.

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Methods, systems, and apparatus for generating intensity modulated radiation therapy treatment plans. In one aspect, a method includes receiving data representing an optimization problem; iteratively processing, until termination criteria are met, the received data representing the optimization problem to obtain data representing a solution to the optimization problem, comprising, for each iteration: performing a classical search algorithm on an input for the iteration to determine a first solution; providing data representing the first solution to a quantum computing resource, wherein the data representing the first solution comprises a quadratic unconstrained binary optimization formulation of the optimization problem in a local region around the first solution; obtaining data representing a second solution from the quantum computing resource; and providing the data representing the second solution as input to a subsequent iteration; and initiating an action based on the data representing a solution to the optimization problem.

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Implementations include receiving an AAG that at least partially defines a digital twin of an enterprise network and includes rule nodes each representing an attack tactic that can be used to move along a path, determining security controls each mitigating at least one rule node, executing an iteration of a simulation of a sub-set of security controls in the enterprise network, the iteration including: for each security control in the set of security controls, determining, an influence score that represents a change in a security risk from implementing the security control and a rule distribution, defining the sub-set of security controls based on the first influence scores, and reducing the AAG based on the sub-set of security controls to provide a residual AAG, determining a decrease in a graph risk value and the first AAG, and selectively implementing the sub-set of security controls in the enterprise network.

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A system may receive an item identifier and a container identifier, the item identifier being , for example, encoded in a RFID tag affixed to a physical item and the container identifier being , for example, encoded in an RFID tag affixed to a container. The system may identify, on a blockchain, an item smart contract based on the item identifier and a container smart contract based on the container identifier. The item smart contract may include environmental compliance criteria for the physical item. The system may store, on the blockchain, a mapping between an item smart contract and the container smart contract. The system may receive environmental status information derived from sensor data generated by a sensor affixed to or in the container. The system may determine the environmental status information of the container does not satisfy the environmental compliance criteria included in the item smart contract.

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A system includes a multi-layer block storage volume optimization (BSO) stack to generate a BSO token containing prescriptions to optimize block storage volume. The system may receive account information of storage accounts associated with block storage volumes; obtain respective storage regions and respective data redundancy types of the first storage account and the second storage account from the first account information; and generate the BSO token to include instructions to merge the storage accounts according to the respective storage regions and the respective data redundancy types. The system may further obtain historical resource utilization data for the block storage volume; generate historical resource utilization metrics from the historical resource utilization data; determine a resource configuration baseline based on the historical resource utilization metrics; and select a recommended resource configuration from a plurality of resource configurations based on the first resource configuration baseline.

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An automatic image annotation system receives a reference image with one or more parts annotated along with one or more query images and automatically identifies portions from the query images that are similar to the annotated parts of the reference image. The S-matrices of the reference image and the query images are obtained via singular value decomposition (SVD). Lower-dimensional images are also obtained for the reference image and the query images using a pre-trained deep learning model. The S-matrices and the lower-dimensional images of the corresponding images are combined to generate vector representations. A distance metric is calculated for the vector representation of the reference image with that of the query image. A preliminary output image with a preliminary annotation is initially generated. The preliminary annotation is further optimized to generate an optimized annotation that adequately covers the region of interest (ROI).

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