If you want to distinguish your goods, services or both from those of another company, you may need a trademark or trade name. Find out what they are, what their registration procedure is and what it involves.
Información sobre los plazos de presentación de solicitudes de transformación de marcas de la Unión Europea en marca nacional española. Más información
If you have a new device, product or procedure that solves a technical problem or has a practical advantage, there are different ways to protect it in Spain and other countries. Find out how.
Does your innovation lie in the aesthetics, ornamentation or appearance of your product? Protect it through industrial design. Find out what rights registration confers and how to proceed.
Patents published worldwide are a valuable source of scientific, technical and commercial information.
reembolso del 75% del coste de Búsquedas e Informes Tecnológicos de Patentes. Más información*
If you are an entrepreneur or a company and you want to boost and improve the profitability of your business by adequately protecting the intangible assets of your organisation, in this space you will find what you need.
70
results.
Updated on
01/09/2024 [07:35:00]
Absstract of: WO2024177749A1
The present disclosure relates to ranking electronic advertisements using one or more machine learning algorithms for a targeted audience associated with an aircraft flight.
Absstract of: WO2024175595A1
The invention relates to a method (500) for automatically generating an individualised electronic sports training schedule (100-1, 100-2) comprising a plurality of sessions for improving the physical performance of a user, the method comprising: a step (510) of collecting individual data of the user via a personal electronic device (200); a step (520) of analysing the collected data by means of machine learning algorithms on a digital analysis platform (300); and a step (540) of generating a first individualised schedule (100-1) on the basis of the collected and analysed data; a step (550) of measuring in real-time, at least during a session, physiological or performance parameters of the user, and/or of modifying the individual data; a step (560) of dynamically adapting the schedule on the basis of the parameters and/or the data of the preceding step by executing machine learning algorithms; and a step (570) of generating a second individualised schedule (100-2) that is better suited to the user.
Absstract of: 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.
Absstract of: 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.
Absstract of: 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.
Absstract of: 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.
Absstract of: 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.
Absstract of: 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.
Absstract of: 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.
Absstract of: 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.
Absstract of: US2024289724A1
A system and method of generating a plurality of actionable insights is disclosed herein. A computing system retrieves data corresponding to a work procedure. Each work procedure includes a plurality of steps. The computing system generates a predictive model for each actionable insight using a plurality of machine learning models by generating an input training based on the retrieved work procedure data and learning, by the plurality machine learning models, a metric corresponding to each actionable insight based on each respective input training set. The input data set for each actionable insight includes actionable insight specific information. The computing system receives a request to generate a plurality of actionable insights for a current work procedure. The computing system generates, via the predictive models, a plurality of metrics for a plurality of actionable insights based on data corresponding to the current work procedure.
Absstract of: US2024290422A1
Disclosed herein are methods for identifying mutations from a patient sample, by evaluating, using a computer having a machine learning classifier, a candidate variant against a plurality of decision trees trained to detect mutations in the candidate variant with a gradient boosting algorithm.
Absstract of: 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.
Absstract of: 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.
Absstract of: 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.
Absstract of: 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.
Absstract of: EP4421693A1
Methods are provided of training a computing module implementing a Machine Learning (ML) model, said methods comprising designating a main computing-node, and performing an iteration loop until an ending condition is satisfied, each of the iterations including: training the computing-module with ML model update using coordinates of a first selection of data-elements in the main computing-node; designating as candidates those computing-nodes parent-child related with the main computing-node based on a candidate-designation policy; calculating, for each of the candidate computing-nodes, a gradient denoting a measure of loss of the ML model using coordinates of a second selection of data-elements in the candidate; and designating as main computing-node one of the candidates depending on the gradients previously calculated, so as to initiate next iteration with said new main computing-node. Training systems and computer programs suitable for performing such training methods are also provided.
Absstract of: US2024281713A1
Introduced here is a process for training a machine learning model to predict a characteristic of a textile through analysis of spectral information. The spectral information may include spectra generated by a spectroscopy instrument, for example. The machine learning model can be used for a variety of applications, including automated sorting of textiles for recycling and automated analyzing of textiles for quality control.
Absstract of: US2023120826A1
A method for machine learning-based data matching and reconciliation may include: ingesting a plurality of records from a plurality of data sources; identifying company associated with each of the plurality of records; assigning a unique identifier to each uniquely identified company; matching each of the records to one of the uniquely identified companies using a trained company matching machine learning engine; identifying a primary company record in the matching records and associating other matching records with the primary company record; matching each of the records to a contact using a trained contact matching machine learning engine; identifying a primary contact record in the matching records and associating other matching records with the primary contact record; synchronizing the plurality of records in a graph database using the unique identifier; receiving feedback on the matching companies and/or matching contacts; and updating the trained company matching machine learning engine.
Absstract of: US2024281675A1
One embodiment provides a method for generating a digital standard utilizing a trained machine-learning model, the method including: training at least one machine-learning model to generate digital standards from underlying standards utilizing a schema, wherein the training includes: receiving, for unstructured information within the underlying standards, a plurality of annotated underlying standards including a set of underlying standards having annotations identifying a classification of conceptual units within the set of underlying standards and corresponding to the schema; and teaching, for structured information within the underlying standards, the at least one machine-learning model patterns delineating information as belonging to conceptual units within the schema; and deploying the at least one trained machine-learning model to convert a second set of underlying standards to the digital standards, wherein the second set of underlying standards is different than the set of underlying standards. Other aspects are described and claimed.
Absstract of: US2024281869A1
An online concierge system allows users to order items from a warehouse having multiple physical locations, allowing a user to order items at any given warehouse location. To select a warehouse location for a warehouse selected by a user, the online concierge system identifies a set of items that the user has a threshold likelihood of purchasing from prior orders by the user. For each of a set of warehouse locations, the online concierge system applies a machine-learned item availability model to each item of the identified set. From the availabilities of items of the set at each warehouse location of the set, the online concierge system selects a warehouse location. The online concierge system identifies an inventory of items from the selected warehouse location to the user for inclusion in an order.
Absstract of: WO2024173131A1
A system and method for generating industrial process in a computing environment is disclosed. The system receives multimedia input from users, analyzes multimedia input, to determine semantics associated with multimedia inputs, using at least one of natural language and symbolic processing techniques, and deep learning technique. Furthermore, the system determines process specifications and descriptions in industrial process, using at least one of a dataset and library comprising representations of industrial processes in plurality of configurations, using machine learning (ML) models. Additionally, the system combines a plurality of types of process specifications and descriptions. Further, the system generates industrial processes corresponding to the multimedia inputs, based on combining the plurality of types of process specifications and descriptions. Furthermore, the system outputs the generated industrial processes, on at least one of display of a user device, and external devices.
Absstract of: US2024281268A1
Systems and techniques that facilitate computing touchpoint journey recommendations are provided. In various embodiments, an input component can receive a computing context of a client and a computing profile of a client. In various instances, the client can be engaged in a computing touchpoint journey. In various embodiments, a prediction component can predict, via a first machine learning classifier, a negative event likely to occur on the computing touchpoint journey. In various cases, the first machine learning classifier can receive as input the computing context and the computing profile and can generate as output the predicted negative event. In various embodiments, a decision component can recommend in real-time, via a second machine learning classifier, a computing touchpoint to which to transfer the client. In various aspects, the second machine learning classifier can receive as input the computing context, the computing profile, and the predicted negative event and produce as output the recommended computing touchpoint. In various embodiments, an execution component can transfer the client to the recommended computing touchpoint. In various embodiments, a computing touchpoint journey component can record computing touchpoint journeys traversed by various clients and trains the first and second machine learning classifiers on the recorded computing touchpoint journeys.
Absstract of: US2024281727A1
In some embodiments, an artificial intelligence system for communicating predicted hours of operation to a client device may be provided. In some embodiments, prediction models may be generated using a randomly selected subset of a training data set comprising: a group of transaction authorizations associated with a merchant group related to the merchant, and a ground truth data set for the merchant group identifying hours of operation of merchants of the merchant group. A request for hours of operations of a merchant may be received from a client device. A set of transaction authorizations associated with the merchant may be obtained, and the prediction models may be used to determine prediction indications for a plurality of time intervals based on the set of transaction authorizations. The prediction indications for the time intervals may be communicated to the client device.
Nº publicación: US2024281715A1 22/08/2024
Applicant:
CAPITAL ONE SERVICES LLC [US]
Capital One Services, LLC
Absstract of: US2024281715A1
A computer-implemented method for using a machine-learning trained predictive engine to predict failures, comprising receiving, from a user using a graphical user interface, electronic transaction data comprising an item and candidate transaction terms, the candidate transaction terms comprising a first transaction term, the electronic transaction data being associated with a candidate transaction. The method may determine, by a machine learning predictive engine, a likelihood of success of the candidate transaction based on the electronic transaction data, and may determine, by the machine learning predictive engine, based on the first transaction term, a second transaction term that, together with the first transaction term, increases the likelihood of success of the candidate transaction above a predetermined threshold. An indication of the second transaction term and an indication of the likelihood of success of the candidate transaction may be displayed on the graphical user interface.
BOPI
Electronic site
