Zure produktuak, zerbitzuak edo biak beste enpresa batengandik bereizi nahi badituzu, baliteke marka edo izen komertzial bat behar izatea. Ezagutu zer diren, zer den erregistratzeko prozedura eta zer dakarren.
Información sobre los plazos de presentación de solicitudes de transformación de marcas de la Unión Europea en marca nacional española. Más información
Arazo tekniko bat konpontzen duen edo abantaila praktiko bat duen gailu, produktu edo prozedura berri bat baduzu, hura babesteko hainbat modu daude Espainian eta beste herrialde batzuetan. Ikusi nola egin.
Zure berrikuntza zure produktuaren estetikan, apainduran edo itxuran datza? Babestu diseinu industrial baten bidez. Jakin ezazu zer eskubide ematen dituen erregistroak eta nola egin izapideak.
Mundu osoan argitaratutako patenteak informazio zientifiko, tekniko eta komertzialaren iturri baliotsua dira.
reembolso del 75% del coste de Búsquedas e Informes Tecnológicos de Patentes. Más información*
Ekintzailea edo enpresa bazara eta zure negozioaren errentagarritasuna sustatu eta hobetu nahi baduzu, zure erakundeko aktibo ukiezinak behar bezala babestuz, gune honetan aurkituko duzu behar duzuna.
86
resultados
Última actualización
27/09/2024 [09:16:00]
Resultados 50 a 75 de 86
Resumen de: US2024296389A1
Perturbed data generation for explainable Artificial Intelligence (AI) is still an evolving field and attempts are made towards to addressing the technical challenge of correlation of features that degrades generated explanations for block box models in Machine Learning (ML) or AI domain. A method and system for local explanation of black box model based on constrained perturbation and ensemble-based surrogate model is disclosed. The method disclosed averts data correlation problem by performing data perturbation around the local instance in accordance with distribution of test data set and primarily ensures the values of input features associated with the local instance stay within the feature space and does not form out of distribution scenarios (add adversary cheating). The method autogenerates labels for the perturbed data to fit or train an ensemble based surrogate model that eliminates data bias and improves accuracy of generated explanations.
Resumen de: US2024296355A1
A system of machine learning (“ML”) models for making actionable predictions regarding low-incidence events, including a generative ML model that produces synthetic minority-class records to form an augmented training data set, a predictive ML model that has been trained on the augmented training data set, a certainty ML model that produces a certainty estimate, and an explanatory model that produces an explanation. A method for producing actionable predictions of a low-incidence event by applying ML models to imbalanced class data by producing a prediction by a predictive ML model that has been trained on a data set comprising synthetic minority-class data records produced by a generative ML model, and producing a certainty estimate and an explanation. At least one of the certainty estimate or explanation determines an effective or appropriate response to the prediction. The low-incidence event may comprise risk of opioid use disorder.
Resumen de: US2024296382A1
Supporting value prediction by a first wireless device being served by a wireless communication network. The network sends, to the first wireless device, configuration information for configuring the first wireless device to, train a machine learning, ML, model to predict certain output values. The training uses input training data that at least partly are determined by operative conditions of the first wireless device and uses desired output values with the input training data. The training ends when the ML model being trained fulfills certain one or more ready criteria. The first wireless device trains the ML model based on the configuration information and sends reporting information to the network indicating that it has trained the ML model and fulfilled the ready criteria. The network determines, based on the reporting information, regarding application of the trained ML model by the first wireless device.
Resumen de: US2024296518A1
There is provided with an information processing method, Inference processing using a machine learning model having a first processing layer and a second processing layer a storing an intermediate output from the first processing layer is performed. A first intermediate output corresponding to a first input upon the first input being input into the first processing layer is output. An inference result upon (i) the first intermediate output and (ii) a second intermediate output from the first processing layer corresponding to a second input previous to the first input being input into the second processing layer, is output. The first intermediate output as the intermediate output is stored.
Resumen de: US2024296354A1
A system and a method are disclosed for adjusting communication settings based on user segmentation. An activity-based communication management system retrieves behavioral and demographic data of at least one user. The system inputs the behavioral data and the demographic data into machine learning models. For each of the machine learning models, the system receives a respective activity parameter characterizing a predicted activity occurring within a time window. The system determines, based on the received activity parameters, a category to which the behavioral data and demographic data belong. The system subsequently adjusts a plurality of communication settings based on the determined category. The activity-based communication management system may provide user segmentation using both empirical activity parameters (e.g., historical behavioral data) and predicted activity parameters.
Resumen de: US2024296388A1
Computer-implemented techniques encompass using distinct machine learning sub-models to score respective types of candidate content for the purpose of providing personalized content suggestions to end-users of a content management system. The relevancy scores generated by the distinct sub-models are mapped to expected end-user interaction scores of the candidate content scored. Content suggestions are provided at end-users' computing devices where the suggested content is selected from the candidate content based on the expected end-user interaction scores of the candidate content. For each distinct sub-model, a normalizing mapping function is solved using an optimizer that maps the relevancy scores generated by the sub-model for the candidate content to expected end-user interaction scores for the candidate content. The expected end-user interaction scores are comparable across the distinct sub-models and can be used to rank content suggestions across the distinct sub-models.
Resumen de: EP4425387A1
Perturbed data generation for explainable Artificial Intelligence (AI) is still an evolving field and attempts are made towards to addressing the technical challenge of correlation of features that degrades generated explanations for block box models in Machine Learning (ML) or AI domain. A method and system for local explanation of black box model based on constrained perturbation and ensemble-based surrogate model is disclosed. The method disclosed averts data correlation problem by performing data perturbation around the local instance in accordance with distribution of test data set and primarily ensures the values of input features associated with the local instance stay within the feature space and does not form out of distribution scenarios (add adversary cheating). The method autogenerates labels for the perturbed data to fit or train an ensemble based surrogate model that eliminates data bias and improves accuracy of generated explanations.
Resumen de: US2023130974A1
Various embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for perform predictive data analysis operations using natural language input data. For example, certain embodiments of the present invention utilize systems, methods, and computer program products that perform predictive data analysis operations by using sentence embedding machine learning models that are trained in coordination with similarity-based machine learning models.
Resumen de: US2024289364A1
The present solution provides systems and methods to receive a data set comprising a representation of one or more questions from a survey and provide the data set as input to each of a plurality of machine learning models trained to predict a domain associated with the one or more questions. The systems and methods can receiving as output a first domain prediction for the domain from each of the plurality of machine learning models and determine a second domain prediction for the domain for each question of the one or more questions based on applying a function to each of the first domain predictions. The systems and methods can select, based on the data set and the second domain prediction, an enumerated list of one or more answers from an answer set and cause a display of the enumerated list via a user interface for a selection.
Resumen de: US2024291834A1
Access to emails delivered to an employee of an enterprise is received. An incoming email addressed to the employee is acquired. A primary attribute is extracted from the incoming email by parsing at least one of: (1) content of the incoming email or (2) metadata associated with the incoming email. It is determined whether the incoming email deviates from past email activity, at least in part by determining, as a secondary attribute, a mismatch between a previous value for the primary attribute and a current value for the primary attribute, using a communication profile associated with the employee, and providing a measured deviation to at least one machine learning model.
Resumen de: WO2024178006A1
A method may include determining, based at least on a knowledge graph, a plurality of biological interaction profiles associated with a plurality of drugs. The knowledge graph being representative of a plurality of interactions between a variety of drugs, proteins, and a hierarchy of biological functions. Each biological interaction profile may be representative of the effects of a corresponding drug being propagated through protein-protein interactions and biological functions. A liver injury prediction model may be trained, based on a training dataset including the biological interaction profiles, a probability of drug induced liver injury. The liver injury prediction model to may be applied to determine, based on the biological interaction profile of a drug, the probability of liver injury associated with the drug. In some cases, the liver injury prediction model may further determine the probability of liver injury based on the molecular fingerprint and/or the molecular properties of the drug.
Resumen de: US2024289307A1
Embodiments provide systems, methods, and computer program products that utilize artificial intelligence/machine learning to process database change data and correlated performance data to predict the impact of database changes and generate rules with respect to database changes to prevent undesired behavior or promote increased performance.
Resumen de: US2024289325A1
A largely automated method of categorizing spend data is provided that does not require a prior in-depth knowledge of an organization's transactional data. Natural language processing is applied to text data from transactional data to generate a consolidated cleaned data set (CDS) containing information for categorization. Logs for transactions are clustered based on similarity, forming the minimal data set (MDS). An automated algorithm selects a subset of high-value clusters that are categorized by requesting users to manually categorize one or more representative logs from each cluster of the subset. A model is then trained using the subset of manually categorized clusters and used to predict spend categories for the remaining logs with high accuracy. The AI engine automatically analyzes the predictions based on client context and either auto-tunes the machine learning model or identifies a new subset of clusters to be manually categorized. This loop may continue until 95%-100% of the spend is categorized.
Resumen de: US2024289688A1
Systems and methods for training machine learning models are disclosed. An example method includes receiving historical event timing data including event data for a first portion including events from a first time period, and a second portion comprising events from a second time period not including the first time period, predicting, based on the first portion of the historical event timing data, a first plurality of predicted events, the first plurality of predicted events corresponding to the second time period, determining a first subset of predicted events to be accurate predictions based at least in part on comparing the first plurality of predicted events to the historical events occurring within the second time period, generating training data based at least in part on the first subset of the first plurality of predicted events, and training the machine learning model based at least in part on the training data.
Resumen de: US2024289643A1
An enterprise's data source relevant to their customers is obtained at predefined intervals of time. The data is processed through classification machine learning models (MLMs) and labeled with features. The labeled data is provided as input to an attrition predicting MLM and one or more lists are provided as output identifying customers likely to leave the enterprise and customers with a high likelihood of remaining with the enterprise when provided an incentive to do so. The one or more lists are provided to enterprise interfaces and/or promotion systems for mitigating customer attrition. In an embodiment, results for the attrition predicting MLM are compared against results predicted by a Recency, Frequency, Monetary (RFM) analyzer in view of subsequent actual observed results for the customers with the enterprise. A continuous feedback loop for retaining the attrition prediction MLM is processed based on the comparison to improve the prediction MLM's F1 accuracy metric.
Resumen de: US2024289594A1
Certain aspects of the present disclosure provide techniques and apparatus for improved hidden Markov model (HMM)-based machine learning. A sequence of observations is accessed. A hidden Markov model (HMM) comprising a set of transition probabilities, a set of emission probabilities, a transition coefficient hyperparameter, and an emission coefficient hyperparameter is also accessed, and a first output inference from the HMM is generated based on the sequence of observations.
Resumen de: US2024289645A1
The disclosed embodiments include computer-implemented systems, apparatuses, and processes that automatically generate and provision a system of machine learning models specifically configured and trained for providing a signal output indicative of a prediction and associated confidence metrics derived via retraining the prediction model for providing the initial prediction and comparing outputs of the set of machine learning models to expected thresholds for the prediction and generating, based on the comparison, a set of actions to be performed on a networked computing environment relating to one or more transactions associated with the prediction.
Resumen de: US2024289876A1
The disclosed embodiments include computer-implemented systems, apparatuses, and processes that automatically generate and provision for presenting actionable icons on a graphical user interface (GUI) of a computer device. The method includes providing electronic data transfers and attributes to a predictive machine learning model to predict future data transfers and data transfer trends; dynamically determining prior factors historically influencing data transfers and thereby a set of factors likely to influence the predicted future data transfers; automatically triggering a digital nudge to the computer device, across a communications network, to automatically present the predicted future data transfers and the set of factors as one or more interactive visual insight icons on the graphical user interface; and, responsive to a determination of engagement with the visual insight icons, triggering generating one or more action icons on the graphical user interface, customized to adjusting the predicted future data transfers.
Resumen de: US2024289681A1
A device comprises a processor. The processor is configured to: generate training vectors based on data related to communication with users; convert the training vectors into optimized vectors to be input into a machine learning unit; apply the machine learning unit to the optimized vectors to construct decision trees for determining probabilities of making a successful call during different time windows; generate a list pf calls and calling times based on the determined probabilities; and forward the list to an automatic dialer.
Resumen de: US2024289839A1
The present disclosure relates to ranking electronic advertisements using one or more machine learning algorithms for a targeted audience associated with an aircraft flight. For example, one or more embodiments described herein include a computer-implemented method comprising executing a learn-to-rank algorithm to train a machine learning model on a training dataset that includes electronic advertisements with associated scores characterizing a relevancy between the electronic advertisements and a defined query. The computer-implemented method can also comprise applying the trained machine learning model to rank a set of electronic advertisements based on a feature vector characterizing input data that includes flight details of an aircraft.
Resumen de: WO2024178038A1
Techniques are disclosed that pertain to training a machine learning model to generate audio data similar to a music generator program. A computer system, executing a rules-based music generator program, selects and combines multiple musical expressions to generate audio data. The computer system trains a machine learning model to select and combine musical expressions to generate music compositions. The machine learning model receives generator information by the generator program that indicates expression selection decisions to generate the audio data, mixing decisions to generate the audio data, and first audio information output based on the generator program's expression selection decisions and the mixing decisions. The computer system compares the generator information to expression selection decisions, mixing decisions, and second audio information generated by the machine learning model based on the machine learning model's expression selection decisions and mixing decisions. The computer system updates the machine learning model based on the comparing.
Resumen de: WO2024177033A1
A learning model generation device 10 comprises: a graph generation unit 11 which generates, from a data group including biometric information of persons and information indicating the presence or absence of occurrence of diseases in the persons, a graph composed of nodes representing data points and edges representing relationships between the nodes; a graph supplementation unit 12 which supplements the generated graph for a deficiency therein; and a model generation unit 13 which generates, from the supplemented graph, a data group in which the deficiency is supplemented, performs machine learning using the generated data group as training data, and generates a prediction model for predicting the occurrence of diseases in a person.
Resumen de: US2024288500A1
A method and apparatus for battery charge/discharge profile analysis is provided. The method includes training a machine learning model using a plurality of training charge/discharge profiles as a training dataset, where each training charge/discharge profile includes training section classification information, and the training section classification information is a dataset in which an identification number of any one of a plurality of charge/discharge control sections performed in a sequential order in an activation process is allocated to each time index; inputting a target charge/discharge profile acquired through the activation process of a battery cell to the machine learning model; and acquiring target section classification information for the input target charge/discharge profile from the machine learning model. The target section classification information is a dataset in which the identification number of any one of the plurality of charge/discharge control sections is allocated to each time index of the target charge/discharge profile.
Resumen de: US2024290440A1
Chemical formulations for chemical products can be represented by digital formulation graphs for use in machine learning models. The digital formulation graphs can be input to graph-based algorithms such as graph neural networks to produce a feature vector, which is a denser description of the chemical product than the digital formulation graph. The feature vector can be input to a supervised machine learning model to predict one or more attribute values of the chemical product that would be produced by the formulation without actually having to go through the production process. The feature vector can be input to an unsupervised machine learning model trained to compare chemical products based on feature vectors of the chemical products. The unsupervised machine learning model can recommend a substitute chemical product based on the comparison.
Nº publicación: US2024290460A1 29/08/2024
Solicitante:
PEDDINTI KALADHAR [US]
PEDDINTI SRIMAYE [US]
Peddinti Kaladhar,
Peddinti Srimaye
Resumen de: US2024290460A1
A system for habitual behavior data collection and the artificial intelligence analysis thereof for data interpretation and associated insightful output, the system embodied in a platform having a user interface module for collecting intrinsic descriptive analytical data comprising at least and a software interface module for receiving extrinsic descriptive analytical data from a plurality of computer devices, wherein the extrinsic descriptive analytical data at least partially derived from a habitual behavior output having associated purchase inputs indicative of a purchase thereof. The platform inputs the intrinsic and extrinsic descriptive analytical data into a trained artificial intelligence module, the artificial intelligence module having been trained using a machine learning algorithm having as input habitual behavior training data, so as to generate insightful output.
BOPI
Sede Electrónica
