3835 results about "Service provisioning" patented technology

Systems and methods for providing efficient remediations for cloud environment vulnerabilities. The present systems and methods are configured to calculate the most efficient remediations to reduce cloud environments' risks, calculate a cloud environment's biggest risks and weaknesses, and provide a holistic overview of a cloud environment across different service providers. In an embodiment, steps include scanning a cloud environment for posture control data; identifying a plurality of security risk events based on the scanning; calculating a risk score for each of the plurality of security risk events; and determining and recommending one or more remediations based on the risk score of each of the plurality of security risk events and how efficient each remediation is.

The invention discloses a method for generating and constructing a database test knowledge base based on an open source large language model and retrieval enhancement, and relates to the technical field of database testing, and the method comprises the steps: building a local deployment system on a local computer system or server, and achieving the efficient local operation of the open source large language model and the stable output of language processing service; on a local computer system or a server, a retrieval enhancement generation framework is constructed, and accurate knowledge support and reliable content output are provided for local language processing service by integrating multi-modal knowledge, optimizing retrieval and generation processes and guaranteeing safety and compliance; on a local computer system or a server, a personalized database knowledge base is built, database related information is collected and processed from multiple channels and is output in an adaptive form, and the specified information requirement of local personalized language processing service is met. According to the method, efficient and stable operation of the open source large language model is realized, accurate and reliable language processing output is realized, and personalized services meet specific requirements.

An example operation may include at least one of retrieving vectors from a vector database, where the vectors include previous communication content between a source device and a service provider device, identifying an item of interest that has not been discussed in the previous communication content based on execution of a large language model (LLM) on the vectors, generating content about the item of interest, and outputting the content about the item of interest to at least one of the source device and the service provider device during an active communication session between the source device and the service provider device.

This disclosure describes a natural language question (NLQ) query service within a service provider network that provides row level security (RLS) for autocomplete during entry of NLQs and fuzzy matching in NLQ answering. The rules take the form of per-user predicates such as Tim can only see rows with region=US. In configurations a complex extraction and preprocessing pipeline to extract distinct combinations of values against RLS predicate “rule keys” is used. Those distinct values are indexed along with grouped rule keys to enable pushing down predicates at auto-complete time. This enables pushing part of RLS rule handling to ingestion time of a dataset rather than handling all RLS rule handling at query time, enabling meeting of latency goals. In configurations, a single logical document of unique cell values is split into multiple documents with a subset of rule keys to handle scalability limits.

The present invention relates to systems and methods for integrating real-world functionalities, such as healthcare services, into a virtual environment (metaverse) using digital avatars and data security measures. Users can access services through their avatars, maintaining anonymity and data security. The system securely transmits user requests and data to real-world service providers, who provide services or feedback within the virtual environment. Data security ensures confidential and tamper-proof data transmission and facilitates secure payment transactions. The invention enables the integration of various real-world services into the metaverse, enhancing its functionality and utility while preserving user privacy and security.

A method may include accessing data associated with a failure of a 5G network component. The method may include providing the data associated with the failure of the 5G network component to a first machine learning model, configured to determine and output data indicating a root cause of the failure. The method may include providing data indicating the root cause of the failure to a second machine learning model, configured to determine and output data indicating one or more service providers and respective destinations associated with the root cause. The method may include generating a service ticket may include data indicating the root cause and the respective destinations associated with the root cause. The method may include transmitting the service ticket to the respective destinations of the one or more service providers associated with the root cause of the failure.

An example operation may include one or more of receiving interaction content from a communication session between a source device and a service provider device of a service provider, identifying a search criteria from the interaction content, retrieving a subset of vectors from a plurality of vectors stored in a vector database based on the search criteria of the interaction content, wherein the subset of vectors includes previous interaction content with the service provider, generating a response for the communication session based on execution of a large language model (LLM) on the subset of vectors, and outputting the response to at least one of the source device and the service provider device during the communication session.

An example operation may include at least one of storing first interaction content with a service provider; receiving second interaction content from a communication session between a source device and a service provider device of the service provider; identifying at least one contextual attribute associated with the source device; determining a response based on execution of at least one large language models (LLMs) on the second interaction content, the at least one contextual attribute associated with the source device, and the first interaction content with the service provider; and outputting the response to at least one of the source device and the service provider device during the communication session.

The invention provides a wireless communication and computing resource collaborative optimization method and system. The method provided by the invention comprises the following steps: establishing an initialized global model matched with a business demand for each service provider, and matching service resources of each service provider according to the business demand; establishing a service agent corresponding to each service provider; each service agent generates an optimal strategy based on the corresponding service resources and the state information of other service providers; sending the initialized global model to a distribution client according to an optimal strategy; each distribution client trains the received to-be-trained model based on the distributed service resources, and sends the trained model to the corresponding service provider; and aggregating the received trained models by each service agent to obtain a global model of the corresponding service. The method and the system provided by the invention are used for realizing collaborative optimization of wireless communication and computing resources and improving the overall performance and the resource utilization efficiency of federated learning.

The invention provides a security verification method for a large model service operation environment, a medium, equipment and a product, and relates to the technical field of computers, large model services are deployed in a container group created in a virtual machine, and the virtual machine is deployed in a hardware-based trusted execution environment, so that the security verification of the large model service operation environment is realized. The method comprises the following steps: acquiring environment measurement corresponding to a running environment of a large model service through a remote attestation service deployed in a hardware-based trusted execution environment, the environment measurement at least comprising a first measurement corresponding to the trusted execution environment in which a virtual machine is located and a container measurement under a container stack dimension corresponding to a container group; the remote attestation service generates a remote attestation report according to the environmental metrics to enable a user to validate the security of the operating environment through the remote attestation report. According to the invention, not only can the attack from the IaaS layer be shielded, but also the credibility verification under the dimension of the virtual machine and the credibility verification under the dimension of the container stack can be provided, so that the credibility of the user to the service provided by the service provider is improved.

In one embodiment, a method of training one or more artificial intelligence (AI) models for language-based communication prompts with a service provider is disclosed. The training method includes generating industry specific labels used to fine tune a large language model; providing an industry specific database associated with the industry specific labels to fine tune the large language model; reading the industry specific database into the large language model; adjusting the parameters of the large language model to recognize industry specific terms associated with servicing equipment within the industry; adjusting the parameters of the large language model to discover the intent associated with the industry specific terms; and adjusting the parameters of the large language model for industry specific tasks including questions and answer tasks, named entity recognition, classification tasks, and machine translations tasks.

Distributed media content history and preferences can be stored and maintained by media playback systems including one or more blockchain-capable playback devices. Content record sets, such as content experience record sets and content network record sets, can be stored via distributed ledgers and updated at least in part based on media consumption events performed or detected by playback devices, service providers, or other participants. Such distributed data can also be accessed to facilitate playback of media content for particular users, devices, households, or environments.

The disclosure relates to a communication method and system for converging a 5th-Generation (5G) communication system for supporting higher data rates beyond a 4th-Generation (4G) system with a technology for Internet of Things (IoT). The disclosure may be applied to intelligent services based on the 5G communication technology and the IoT-related technology, such as smart home, smart building, smart city, smart car, connected car, health care, digital education, smart retail, security and safety services. A method and an apparatus for handling configured grant (CG) resource for small data transmission (SDT) are provided.

Techniques and systems can obtain a first private key usable with a classical cryptography algorithm and a second private key usable with a post-quantum cryptography algorithm based on classical and post-quantum public keys hosted by a computer-implemented storage of an online service provider. A plurality of keys to perform a cryptography operation on data hosted by the computer-implemented storage can be generated, the plurality of keys generated based on at least the first and second private keys and a cryptography derivation function identified in the computer-implemented storage. The plurality of keys can be used to perform the cryptography operation on the data hosted by the computer-implemented storage.

Techniques are disclosed for generating a ranked list of content for a user account of a service provider. A server device can determine that a first user account of a service provider is linked with a second user account of the service provider. The server device can then determine whether the first user account has been registered with the service provider a time period less than a threshold time. In response, the server device can determine interaction information associated with the second user account and generate, using a ranking model taking the interaction information as an input, a ranked list of content offered by the service provider. The server device can then cause the user device to present content at a graphical user interface of the user device according to the ranked list of content.

A method performed by a User Equipment having a first functional component implementing an enabler function. The method comprises performing an Edge Application Server (EAS) discovery to discover information regarding one or more first network functions implementing EASs as part of an initial service provisioning. The method further comprises selecting at least one first network function for implementing an initial EAS of the one or more first network functions, for providing service to a second functional component implementing application function in the UE. The method further comprises determining a second network function implementing Edge Enabler Server (EES) according to the selected at least one first network function as an initial EAS. The method further comprises transmitting, to the determined second network function, a message for triggering at least one of service session context handling and traffic influence. The message comprises an EAS endpoint of the selected initial EAS.

An example operation may include one or more of receiving interaction content from a communication session between a source device and a service provider device of a service provider, identifying a plurality of contextual attributes of the communication session based on execution of at least one large language models (LLMs) on the interaction content, converting the interaction content and the plurality of contextual attributes of the communication session into vectorized data based on execution of an additional LLM, labelling the vectorized data with identifiers of the plurality of contextual attributes, and storing the vectorized data within a vector database.

Provided is a process for mobile-initiated authentications to web services. Credential values of the user are established within a trusted execution environment of the mobile device and representations are transmitted to a server. The user of the mobile device may authenticate with the mobile device to the server, which may convey access to a web-based service from a relying device that executes a client authentication component to report user sessions to the server. The user may select the relying device from the mobile device to cause the relying device to present credentials to the web-service to login, authenticate, or otherwise obtain user-level permission for the user on the relying device. The user of the mobile device may authenticate with the mobile device to the server, and may initiate the authentication process from the mobile device, without inputting credentials corresponding to the web-service on the relying device.

Systems, methods, and instrumentalities are disclosed for service provisioning and providing access to a PALS hosting network. In examples, a WTRU may send a first message to a first network node if a configuration failure event is detected, wherein the first network node is associated with a first network, wherein the configuration failure event is associated with a second network, and wherein the first message indicates an update request for a network configuration for a local service. The WTRU may receive a second message from the first network node, wherein the second message indicates a response to the update request, indicates an updated network configuration, and indicates a slice identifier associated with the second network The WTRU may receive a third message from the first network node, wherein the third message indicates policy information associated with the local service. The WTRU may send a fourth message to a second network node, wherein the second network node is associated with the second network, wherein the fourth message is based on the policy information and the updated network configuration, and wherein the fourth message indicates a registration request associated with the slice identifier.

A virtual machine controller service of a service provider network may provide a virtual machine controller environment where applications associated with machine controllers (e.g., programmable logic controllers (PLCs), programmable automation controllers (PACs), etc.) can execute and data can be provided as input to the executing applications. An example process may include receiving a request to create a virtual controller instance, and creating the virtual controller instance in a service provider network based at least in part on the request. The example process may further include executing, on the virtual controller instance, an application that is associated with a machine controller that is operable within a facility, and sending data as input to the application to elicit a response from the application. This example process can be used for training personnel and / or for validating machine controller applications prior to their deployment on a physical machine controller in a facility.

Embodiments described herein relate to computer systems and methods for digital asset custodian that seamlessly integrates off-chain multi-party computation (MPC) signing with on-chain party identity authentication. The system comprises a service provider node, multiple computation nodes equipped with robust secure hardware, and a Blockchain accessible to all participating nodes. The nodes deliver a threshold signing service with utmost security—ensuring that no single node can recover the private key, while simultaneously recording and mutually authenticating each node's identity on-chain. The system boasts intrinsic support for hierarchical address creation and signing, facilitated by an enhanced protocol. Its scalability, particularly in the quantity of computation nodes, is complemented by efficient communication tailored to the demands of the MPC protocol.

Techniques described herein include providing personal data of users of a payment service to a data service when such users have granted sharing permissions. Then, a query may be configured to determine which of the users are eligible for a promotion from a third party service. The query may be sent to a data service and a query result may be received from the data service based on the third party service provisioning a temporary viewing permission to the payment service. Eligible promotions may be presented to individual users based on the query results.

There are provided systems and methods for a fine-tuning system for large language models trained for open-ended domain-specific tasks. An online transaction processor or other service provider may provide computing services and platforms to entities, which may include chatbots, information retrieval systems, question-and-answer systems, and the like. To provide better LLM training and fine-tuning, which may improve LLM performance in answering users' questions in an automated manner, the service provider may implement a fine-tuning system that may utilize automated annotations of training data, such as query and response pairs. An LLM may be prompted to determine an annotation to such pairs, and the annotations may be used to label the training data. A fine-tuning system and operations may then be implemented to fine-tune the LLMs using different processes including question-answering, retrieval augmented generation, or a continuous fine-tuning based on a size of the training data.

An example operation may include one or more of receiving interaction content from a communication session between a source device and a service provider device, executing a large language model (LLM) on the interaction content, wherein the LLM comprises a plurality of attention heads which are configured to simultaneously identify a mood and an item of interest from the interaction content, generating a response to the interaction content based on the mood and the item of interest, and outputting the response to at least one of the source device and the service provider device during the communication session.

Distributed media content history and preferences can be stored and maintained by media playback systems including one or more blockchain-capable playback devices. Content record sets, such as content experience record sets and content network record sets, can be stored via distributed ledgers and updated at least in part based on media consumption events performed or detected by playback devices, service providers, or other participants. Such distributed data can also be accessed to facilitate playback of media content for particular users, devices, households, or environments.

The invention discloses a method for dynamically calling multiple models for a deep learning algorithm and a service architecture. The method comprises the steps that a client sends a request to a scheduling service through a standard AP I interface; after the scheduling service receives the request, key information of the request is analyzed and extracted to generate a task queue, and a Server server is selected; the scheduling service acquires state data provided by the Agent service in real time, and performs task allocation according to the load condition of the Server; the scheduling service stores the selected task information into a database, and marks a'to-be-processed 'state in a task state table; after receiving the scheduling task, the Server calls a model management service to load a specified reasoning model; and S6, after the reasoning task is executed, the reasoning service pool informs a scheduling service of a result through a callback mechanism. The method has the advantages that the processing capacity and efficiency of the deep learning task are improved, the requirements of different tasks are met by dynamically calling multiple models, meanwhile, resource use is optimized, and the expandability of the system is guaranteed.

To provide a return device or the like that allows a person to receive service provision itself as a return from a local government by making a donation through hometown tax payment.SOLUTION: A return device 10 enables a doner to make a donation to a local government using a donor terminal 30, and in return for the donation, to receive service provision at a store of a service providing return service provider belonging to the local government. The return device includes: a memory processing unit that stores information about the local government and information about the service providing return service provider belonging to the local government; a communication processing unit that receives requests from the donor terminal 30, transmits screen configuration information to the donor terminal 30, and also transmits a payment execution request to an external server; and a control processing unit.SELECTED DRAWING: Figure 2

Some embodiments improve the security of service principals, service accounts, and other application identity accounts by detecting compromise of account credentials. Application identity accounts provide computational services with access to resources, as opposed to human identity accounts which operate on behalf of a particular person. Authentication attempt access data is submitted to a machine learning model which is trained specifically to detect application identity account anomalies. Heuristic rules are applied to the anomaly detection result to reduce false positives, yielding a compromise assessment suitable for access control mechanism usage. Embodiments reflect differences between application identity accounts and human identity accounts, in order to avoid inadvertent service interruptions, improve compromise detection for application identity accounts, and facilitate compromise containment and recovery efforts by focusing on credentials individually. Aspects of familiarity measurement, model feature selection, and a model feature engineering pipeline are also described.

A computer system finetunes a machine-learned language model to generate a personalized response to a user request. The system may generate a user representation for each of a plurality of users by applying a transformer model to a sequence of tokens representing a sequence of activities of the user. The system may train an evaluation model coupled to receive a user representation and a response to a user request and generate an estimated evaluation score indicating a level of personalization of the response to the user. The system may finetune a first machine-learned language model to generate a second machine-learned language model. The finetuned machine-learned language model is configured to provide personalized responses for customer services at an online concierge system.

The invention provides an information pushing method and system based on big data matching and GPS positioning, and the method comprises the steps: receiving position data, processing the position data through a Kalman filtering algorithm, predicting a movement trend through combining a timestamp and velocity vector information, and employing a trajectory prediction algorithm, and generating a potential region of interest list; using a spatial clustering analysis algorithm to analyze historical position data of the user, identifying a behavior mode and a preference location type of the user, and constructing a user interest model according to the behavior mode and the preference location type; utilizing the user interest model to configure a dynamic adaptive geo-fencing system; sequencing the service providing points by adopting a weighted K nearest neighbor algorithm, and combining geographic position information in the service recommendation list with other sensor data by utilizing a multi-modal fusion algorithm to generate a customized information packet; according to the technical scheme provided by the invention, the accuracy of the position information is improved, the prediction precision and timeliness are enhanced, and personalized service recommendation is enhanced.