9383 results about "Data storing" patented technology

A system is disclosed for continuous and intermittent monitoring of physiological parameters. The system comprises at least one sensor coupled to a wearable device configured to measure physiological parameters of a user; a non-transitory data store storing data collected from the at least one sensor and computer-executable instructions; and a processor in communication with the at least one sensor and the non-transitory data store, wherein the computer-executable instructions, when executed by the processor, configure the processor to: continuously measure the physiological parameters by sampling the at least one sensor at a sampling rate to obtain repeated measurements at series of intervals configured to capture important physiological events; compare measurements of the physiological parameters to a threshold to determine a likelihood of a physiological event; and alert the user when an occurrence of the physiological event is likely.

A nonvolatile memory device includes a plurality of latch groups, an address controller, an encoder, and a buffer. The address controller controls an input address and an output address to indicate one of the plurality of latch groups. The encoder receives sector data from a latch group corresponding to the output address among the plurality of latch groups and also compresses the received sector data. The buffer stores the compressed sector data. Among the plurality of latch groups, the compressed sector data stored in the buffer is overwritten in a latch group corresponding to the input address.

A memory device includes a bank array including a plurality of memory cells, a row decoder connected to the bank array through a plurality of wordlines, and a column decoder connected to the bank array through a plurality of column select lines. The bank array may include a first region and a second region different from the first region. First metadata for first normal data stored in the first region is stored in the second region, and second metadata for second normal data stored in the second region is stored in the first region.

A federated distributed computational system enables secure, privacy-preserving biological data analysis and engineering through interconnected nodes coordinated in a distributed graph architecture. A federation manager allocates resources, manages data flow and lineage, establishes privacy boundaries, and maintains cross-institutional knowledge relationships. Each node contains a processing unit for biological data analysis, privacy preservation protocols for secure multi-party computation, a knowledge graph structure with supporting data stores, and encrypted network connections. The federation manager enforces all computation and data exchange through secure channels while maintaining privacy, security, and contractual boundaries. This architecture enables research institutions to collaborate on complex biological analyses without compromising sensitive data, facilitating breakthrough discoveries through shared computational resources while maintaining strict data privacy and security controls.

The disclosed technology is generally directed to a method for controlling a satellite system comprising at least one satellite. The method may include receiving and processing a set of control parameters associated with an orientation of the at least one satellite via a classic control model to generate a set of actions to control the orientation of the at least one satellite and storing the set of actions as data in a buffer. The processing and storing are iteratively repeated until the data stored in the buffer exceeds a threshold. When the data stored in the buffer exceeds the threshold, the method may further include iteratively implementing, based on each of the set of control parameters and the data stored in the buffer, the machine learning model to control the orientation of the at least one satellite to stabilize the at least one satellite.

The invention discloses a multi-level transaction security authentication method based on supply chain finance, and relates to the technical field of financial services, and the method comprises the steps: reading encrypted data stored in a block chain when a transaction is initiated, and calculating a dynamic credit score in combination with real-time data in a transaction request; according to the dynamic credit score, the transaction amount and the transaction frequency, carrying out risk grade division on the transaction; dynamically triggering a corresponding authentication level according to the transaction risk level, and performing privacy protection verification on sensitive data related to the transaction in combination with a zero-knowledge certification technology; after verification is passed, an adaptive hierarchical consensus mechanism is selected according to the transaction risk level to execute a consensus operation, and a consensus result is stored in the block chain; the transaction risk levels are dynamically divided, and corresponding authentication levels are triggered for different risk levels, so that the high efficiency of low-risk transactions is ensured, and fine-grained privacy protection is provided for high-risk transactions.

A method for establishing connections and forming groups in an edge computing system includes detecting and identifying devices attempting to connect to the network using a processor. The method involves authenticating detected devices with a common pre-shared key (PSK) stored in memory, forming groups of connected devices based on predefined criteria, and sharing the PSK within each group via a secured channel. It also includes creating a subnetwork or private LAN for each subscriber using network configuration data, assigning virtual pre-shared keys (vPSKs) to devices based on service requirements, determining device capabilities by analyzing received device-specific information, and identifying supported applications based on device capabilities and application compatibility data stored in memory.

Systems and methods include discovering and classifying any of data discovered by inline cloud inspection, data stored across one or more cloud services, and data stored across one or more endpoints; continuously monitoring access to and usage of classified data, wherein the monitoring is performed in real-time and includes analyzing data access patterns, user behaviors, and application interactions; evaluating a security posture of the classified data by identifying misconfigurations, compliance violations, excessive permissions, and vulnerabilities; and enforcing one or more security policies based on the evaluated security posture.

Methods, systems, and devices for programmable metadata and related operations are described. A method may include receiving signaling that indicates a set of rules for transitions of states of metadata at a memory device storing the metadata. The memory device may receive a command from a host device associated with a set of data after receiving the set of rules. The memory device may transition metadata associated with the set of data stored at the memory device from a first state to a second state based in part on the set of rules and the command. The memory device may execute the command received from the host device.

Methods, systems, and devices for programmable metadata and related operations are described. A method may include receiving signaling that indicates a set of rules for transitions of states of metadata at a memory device storing the metadata. The memory device may receive a command from a host device associated with a set of data after receiving the set of rules. The memory device may transition metadata associated with the set of data stored at the memory device from a first state to a second state based in part on the set of rules and the command. The memory device may execute the command received from the host device.

The invention relates to a power field Text2SQL (Structured Query Language) data analysis method based on LLM (Logical Language Model) and RAG (Random Access Gateway), and solves the defects that a retrieval result contains noise and redundancy and a large model still has a large number of illusion phenomena due to the absence of a vectorization model in a specific field and the singleness of knowledge base types and dimensions in the RAG technology compared with the prior art. The method comprises the following steps: constructing a data set of a Text2SQL data analysis method in the power field; constructing a multi-dimensional text knowledge base of the Text2SQL data analysis method in the power field; fine adjustment of an LLM base of a Text2SQL data analysis method in the electric power field; constructing an RAG retrieval module of the Text2SQL data analysis method in the power field; and constructing an Agent data analysis module of the Text2SQL data analysis method in the power field, and performing data analysis processing. According to the method, related information required by Text2SQL data analysis in the electric power field can be obtained by a large language model through retrieval on the basis of an RAG technology, meanwhile, the privacy and timely updating of the data are ensured by field data stored in a knowledge base form, and the model training cost is also greatly reduced by the LLM-based Text2SQL data analysis method taking a prompt project as a main part; and by setting the mixed retrieval parameters, the retrieval result has relatively high accuracy, and the effect of Text2SQL data analysis is ensured.

A data processing system implements receiving a plurality of natural language prompts for a language model; storing the prompts in a privacy protecting datastore in which the prompts are inaccessible from outside of the privacy protecting datastore; analyzing the prompts stored to generate embedding vectors representing the plurality of natural language prompts; storing the embedding vectors in the privacy protecting datastore; analyzing the embedding vectors using a clustering algorithm to generate clusters of related embedding vectors in the privacy protecting datastore; generating, for each cluster, theme information summarizing the embedding vectors of the cluster; and storing the theme information associated with each cluster of the plurality of clusters in a second datastore outside of the privacy protecting datastore, the second datastore being accessible for querying the data stored therein.

A disclosed method facilitates AI-generation of a customized email per a methodology that significantly reduces the risk of the customized email including hallucinated facts or undesirable personal identity information (PII). The method includes identifying an email template and a recipient identifier that identifies a recipient of the customized email based on user inputs to an email application; mining contextual data stored in association with the recipient identifier; generating a large language model (LLM) prompt based on the email template and the contextual data; providing the LLM prompt as input to a trained large language model (LLM); receiving the customized email as an output from the LLM; and returning the customized email to the email application for display within a user interface.

The present disclosure pertains to natural language techniques for querying data stored in feature stores using zero shot learning. In a particular aspect, a computer-implemented method includes receiving a natural language query for retrieving features from a feature store, generating an input prompt by appending a script to the natural language query, and then using a large language model to determine tables or databases from the feature store that are relevant to the natural language query, retrieve metadata for the tables or databases from the feature store, determine feature groups comprising features relevant to the natural language query, and generate a programming language query based on the input prompt, the metadata, and the groups. A list of features within the feature groups that are accessible within the feature store may then be retrieved by executing the programming language query on the feature store.

A method for processing a query for data stored in a distributed database includes receiving, at an edge device, the query for data stored in the distributed database from a query device. The method includes causing, by the edge device, the query to be stored on a dynamic ledger maintained by the distributed database. The method includes detecting, by the edge device, that summary data has been stored on the dynamic ledger. The method includes generating, by the edge device, an approximate response to the query based on the summary data stored on the dynamic ledger. The method includes transmitting, to the query device, the approximate response.

A large language model (LLM) message generation system and method tor generating a response to a user message. The method includes: obtaining a text document specified by a user; chunking the text document into a plurality of chunks; generating text gloss data for each of the plurality of chunks based on the chunk and a predetermined prompt; storing the text gloss data for each chunk into a vector data store along with an identifier for the chunk and / or tine chunk itself; receiving a user message; querying the vector data store with a vector data store query, wherein the vector data store query is generated based on the user message; obtaining chunk(s) based on querying the vector data store with the vector data store query; generating a language model input based on the one or more identified chunks; and generating a response message based on inputting the language model input.

The present disclosure relates to systems and methods for retrieving telematics data as well as for training machine learning models. A method for retrieving telematics data may comprise operating at least one processor to: provide a plurality of databases, each database storing at least telematics data originating from a plurality of telematics devices installed in a plurality of vehicles; receive a natural language request from a user, the natural language request comprising at least one textual question relating to the telematics data stored within one of the plurality of databases; generate, using a large language model (LLM) that does not have access to the plurality of databases, an executable query for retrieving a portion of the telematics data that is responsive to the natural language request from the database by inputting into the LLM at least: a contextual prompt, the contextual prompt providing to the LLM at least one or more features of the database, an expected structure of the executable query, and one or more example natural language requests and corresponding executable query outputs, and the natural language request; execute the executable query for retrieving the portion of the telematics data from the database; and return at least the portion of the telematics data to the user, whereby the natural language request is responded to without providing the LLM with access to the telematics data stored on the plurality of databases.

The invention relates to the technical field of scene rendering, in particular to a building information model scene rendering method and system based on parallel computing, and the method comprises the following steps: based on scene data stored in a building information model, analyzing space coordinates of geometric structure nodes of a building, and associating the geometric structure nodes with material attributes; and analyzing a data partition relationship according to the illumination distribution, and generating a geometric material illumination analysis result. According to the method, deep integration of geometry, materials and illumination is realized by analyzing spatial coordinates of geometric nodes and material attributes and combining illumination partition analysis, the data organization efficiency of a complex scene and the analysis precision of an illumination path are improved, light energy in a noise area is optimized by analyzing light propagation and energy distribution in a segmented manner, refraction and reflection paths are corrected, and the accuracy of the light path analysis is improved. And through texture boundary and scene geometry conjoint analysis, texture fracture is repaired, detail expression is enhanced, efficient analysis and optimization of complex building scene data are realized, and the high-precision rendering requirement is met.

An exemplary machine-learning-based method for generating a response to a natural-language query from an entity associated with an organization comprises: obtaining a sub-query from the natural-language query; determining if a cache associated with the organization stores a matching historical sub-query: if the cache stores the matching historical sub-query, retrieving a historical sub-response associated with the historical sub-query from the cache as a sub-query response; if the cache does not store any matching historical sub-query: inputting the sub-query into a cache-based enhancement machine-learning model to enhance the sub-query, wherein the cache-based enhancement machine-learning model is trained based on historical query data stored in the cache associated with the organization; and inputting the enhanced sub-query into a language model to generate the sub-query response; and generating the response to the natural-language query based on the sub-query response.

The invention discloses a BMC intrusion protection method and device, a BMC and computer equipment. The method is applied to the BMC in the computer equipment. The BMC comprises a service module and a security detection module which are in communication isolation, and storage media accessed by the service module and the security detection module are isolated. The business module is used for providing a management function for parts in the computer equipment, and the security detection module is used for implementing security detection on the BMC. The method comprises the steps that the security detection module obtains data stored in the storage medium associated with the service module, analyzes the security situation of the data sensing service module, and obtains a security detection result. And when the security detection result indicates that the BMC suffers from the APT attack, restarting the BMC in a degraded starting mode. Through a deep defense scheme, namely on the basis of defense, intrusion detection capability is added to the BMC, a dynamic defense chain of defense, detection, degradation and recovery is formed, the APT attack resisting capability of the BMC is improved, and the BMC is ensured to be safe and credible.

The invention provides improved methods and corresponding systems for the sharing, storage, creation and accessing of data stored on a blockchain eg the Bitcoin blockchain. It may form part of a protocol for searching the blockchain for content / data. A method in accordance with the invention may be used for associating or linking data stored within (separate / different) blockchain transactions to enable the identification, retrieval and / or sharing of data stored therein. Additionally, or alternatively, it facilitates identification of transactions (TX) in a blockchain which store content / data that needs to be shared, transmitted, stored and / or accessed by a user. Such a method comprises the step of mapping a mnemonic to: 1) a public key (PK) associated with the transaction (TX); and 2) the transaction ID (TXID) of the transaction (TX).

The present invention relates to a system (100) and method (300) for automated product validation, identification, and transaction management in shopping environments. The system (100) integrates a smart shopping cart (102) equipped with at least one camera (106), a load cell (108), and a user interface (110). An AI model trained on a dataset of high-resolution, 360° product images generate unique image embeddings for each product. The system performs multimodal validation by comparing the extracted image embeddings, measured weight data, and scanned barcode information with reference data stored in a vector database. Further, discrepancies, such as unscanned products or mismatches, are flagged, and payment is blocked until resolved.

The invention discloses a method and a system for determining memory access permission of equipment and electronic equipment. The method is applied to an input and output physical memory protection (IOPMP) unit and can comprise the steps that a memory access request of equipment is obtained, at least one IOPMP entry matched with the equipment in the IOPMP unit is determined, the memory access request is used for making the equipment request to access data stored in a memory address corresponding to the IOPMP unit, and the IOPMP entry is used for representing a data structure of the IOPMP unit; the memory access request is verified in at least one IOPMP entry, at least one verification result is obtained, and the verification result is used for representing whether the device has the access permission in the corresponding IOPMP entry or not; based on the verification result, an access permission result corresponding to the memory access request is generated, and the access permission result is used for representing whether the device is allowed to access data stored in the memory address or not. The technical problem that the memory access permission of the equipment cannot be effectively determined is solved.

The disclosure relates to systems, methods, and computer-readable media for an artificial intelligence (AI) conversational agent configured for enhanced user interaction in multi-channel contact centers. An example method includes receiving a query regarding a particular property of an asset during a real-time conversation, classifying an intent of the query into a predefined category, triggering, based on the classifying of the intent, a finetuned large language model for the predefined category to answer the query, generating, by the finetuned large language model, a response to the query based on at least one of an information data model and a knowledge graph data store, and outputting the response to the query.

Systems, apparatus, articles of manufacture, and methods are disclosed to test an automated query language by interfacing with a query endpoint, generating a first test suite based on a plurality of combinations of a plurality of attributes corresponding to an API specification, transmitting the first test suite to be executed on the query endpoint, the first test suite to include instructions to cause the query endpoint to access data stored in a database accessible to a plurality of secondary endpoints, obtaining a response time corresponding to the access of the data, comparing the response time corresponding to the access of the data with a service level agreement required response time, and in response to the response time corresponding to the access of the data being longer than the service level agreement required response time, notifying a developer of the plurality of secondary endpoints.

Apparatuses, systems, and techniques for classifying one or more computer programs executed by a host device as being ransomware using a machine learning (ML) detection system. An integrated circuit is coupled to physical memory of a host device via a host interface. The integrated circuit hosts a hardware-accelerated security service to protect one or more computer programs executed by the host device. The security service obtains a series of snapshots of data stored in the physical memory and extracts a set of features from each snapshot of the series of snapshots, each snapshot representing the data at a point in time. The security service classifies a process of the one or more computer programs as ransomware or non-ransomware using the set of features and outputs an indication of ransomware responsive to the process being classified as ransomware.

Systems, methods, devices and non-transitory, computer-readable storage mediums are disclosed for a wearable multimedia device and cloud computing platform with an application ecosystem for processing multimedia data captured by the wearable multimedia device. In an embodiment, a method for using a machine learning model to provide data corresponding to a query comprises receiving a query for data stored in one or more databases. A prompt file is determined for the query. The prompt file and the query are provided as input for a machine learning model configured to generate a prediction for the query. Training the machine learning model can include updating network parameters in the machine learning model based on the prompt file. The prediction generated for the query is received. The prediction comprises a predicted database query corresponding to the query, and a level of accuracy is determined for the predicted database query.

An information handling system includes first and second storages, a basic input / output system (BIOS), and an embedded controller. The first storage stores telemetry data associated with the information handling system. The second storage includes a boot partition. The BIOS stores the telemetry data in the first storage. The embedded controller receives the telemetry data stored in the first storage from the BIOS. The embedded controller provides the telemetry data to the boot partition of the second storage over an out-of-band communication channel.

An electronic hardware wallet for conducting cryptocurrency transactions, blockchain transactions, or other secure communications is embodied on a monolithic integrated circuit (IC) die supported on a single substrate. The monolithic semiconductor device can include a non-volatile data store for storing application software executable by the multi-core processor, and the secure element can include a secure data store for storing secret data (e.g., a private key) for use in a secure electronic transaction. In some embodiments, the secure element can include hardware logic embodying a cryptocurrency algorithm associated with executing the secure electronic transaction and can have a limited and selective communication bus between the secure element and the multi-core processor. The electronic hardware wallet can communicatively couple with one or more other devices to facilitate a multi-party computation (MPC) algorithm for authenticating the cryptocurrency algorithm and validating the secure electronic transaction.

A storage system has storage devices and a storage controller that includes a processing device. The processing device is to receive a request for portions of data stored at locations of one or more storage devices. The processing device is to generate a file at a storage device. The file corresponds to a view of the portions of data stored at the locations of the one or more storage devices. The processing device is to identify the portions of data that are stored at the locations of the one or more storage devices. The processing device is to provide the view comprising the portions of data.