24 results about "Service use" patented technology

In order to extract more contextualized metrics associated with a particular service in a microservices-based application, a method of aggregating metrics comprises analyzing tags of spans for each trace and, based on a first predetermined set of rules, extracting one or more tags pre-selected by a user of the monitoring service. A pre-selected tag may appear in one or more spans associated with a particular service in a trace and may be extracted as a numeric metric. The method comprises computing a set of metrics for the particular service using the numeric metric extracted from the one or more spans of the trace. The method also comprises determining a set of aggregated metrics over the selected time duration for the particular service by aggregating the set of metrics over multiple traces.

A network node of a communications network performs operations to determine a data traffic inactivity timer value based on subscriber event data, which identifies a subscriber and service characteristics using a communication session, being processed through a predictive model which uses historical usage data indicating idle times previously measured by charging trigger functions (CTFs) for the subscriber and the service characteristics. The operation further communicate the data traffic inactivity timer value to a CTF to configure monitoring of inactivity of the service using the communication session and to trigger a release operation related to the communication session when a data traffic inactivity timer using the data traffic inactivity timer value is determined to expire based on the monitored inactivity of the service.

【Solution means】The proxy device includes a request determination unit 105 that receives a request sent from a virtual browser and determines the security risk of the request URL specified in the request, and a request transfer unit 101 that transfers a request determined to be normal by the request determination unit 105 to a server 30 that operates the site to be analyzed, where the site to be analyzed is a target site where the service used on the site to be analyzed is specified, a response analysis unit 102 that receives and analyzes a response from the server 30, and a response return unit 103 that returns the response to the virtual browser. According to this, it is possible to prevent the analysis device 10 from receiving a response having a security risk.

Accordingly, embodiments herein disclose a method for seamless service continuity. The method includes: receiving, from a first server (110a, 112a, 118), data packets related to the service; determining that the UE (102) locates in a service area where the service is provided by both the first server (110a, 112a, 118) and a second server (110b, 112b); establishing a connection of the UE (102) with the second server (110b) based on determining that the UE (102) locates in the service area; transmitting, to the first server (110a, 112a, 118), information related to the connection of the UE (102) with the second server (110b, 112b); receiving, from the first server (110a, 112a, 118), information including a notification which indicates a transfer of application context of the service to the second server (110b, 112b) is completed; switching the connection from the first server (110a, 112a, 118) to the second server (110b, 112b) based on receiving the information including the notification; receiving, from the first server (110a, 112a, 118), end data packets related to the service, in response to switching the connection; and continuing the service using the established connection with the second server (110b, 112b) based on the received end data packets.

Monitoring network performance relating to an application-level service used by a user by obtaining first monitoring data measuring performance of a home network (11) of the user concerning the application-level service and / or type of a device used by the user, obtaining second monitoring data measuring performance of an operator network (20-40) concerning said application-level service and / or type of a device used by said user, linking the first monitoring data with the second monitoring data so as to obtain combined application-level and / or device type-level monitoring data concerning said particular service used by said particular user, and storing said combined application-level and / or device type-level monitoring data at a data lake (72).

This invention discloses a cloud-native cryptographic service method and system, relating to the fields of cloud-native technology and cryptographic security technology. It addresses the problems of low resource utilization and slow service response speed caused by deficiencies in resource scheduling, service encapsulation, and hardware interface integration. The cloud-native cryptographic service method includes: grouping and encapsulating the provided cryptographic services using containers; dynamically scheduling container resources; encapsulating the service capabilities of hardware device interfaces; dynamically allocating application-to-service routes based on the application-service authorization relationship assigned by the tenant; invoking hardware cryptographic devices to provide cryptographic services; and achieving dynamic resource allocation and deep hardware-software integration of cryptographic services through containerization and Kubernetes resource scheduling. This significantly improves resource utilization efficiency and service response speed, simplifies the application system access process, enhances service usability, and substantially reduces operation and maintenance costs and management complexity.

This invention relates to the field of distributed systems technology, specifically disclosing a method, system, and storage medium for managing API gateway services using the HTTP protocol. It addresses the problem of service discovery latency and inconsistency between node states in microservice architectures. The method includes: writing service information to a remote cache when the application service starts; configuring gateway tools and specifying request processing script paths; using scripts to preferentially retrieve the service list from local shared memory, and if empty, fetching the latest list from the remote cache; employing a load balancing algorithm to forward HTTP requests, automatically trying the next service if a failure occurs, and returning a degraded response if all services fail; executing a health check script to traverse the service list, and synchronously removing services with more than a threshold number of failures from both the remote cache and shared memory.

The invention discloses a cloud service problem detection method and device, a storage medium and a program product, and relates to the technical field of internet software, and the method comprises the steps: sending a service request to an application service, enabling the application service to respond to the service request, and calling a cloud service to generate service response data; and based on a pre-constructed hook function, performing anomaly detection on the service response data to obtain an anomaly detection result, the hook function being realized based on a user-defined class loader. According to the scheme, the problem existing in the process that the application service uses the cloud service is subjected to anomaly detection, and therefore the accuracy and effectiveness of cloud service problem detection are improved.

The application discloses a polymorphic service method, device and medium for aggregating POS extended application capabilities, and comprises the following steps: creating a user account; creating a plurality of applications under the user account, accessing a plurality of API interfaces under each application, and configuring a required service capability type for each API interface; entering device information corresponding to a device having the service capability type under each service capability type; configuring a corresponding key pair for each application; initiating a calling request by a POS device to a target API interface; after receiving the calling request, performing verification on the calling request according to corresponding request parameters and a key pair corresponding to an application to which the target API interface belongs, returning device information of a corresponding service capability type after successful verification, and executing a corresponding service using a corresponding device according to the device information; and recording calling data of each calling request for statistical analysis. The application can centrally manage API interfaces of various capabilities.

This invention discloses a novel downlink scheduling scheme to maximize the number of XR users who meet their strict delay reliability requirements in 5G networks. Each XR user is assigned a predefined delay bound, within which Data Units (DUs)—encompassing packets, frames, or Service Data Units (SDUs)—must be transmitted. Failure to transmit within this bound results in a delay violation, which can degrade the user's experience. Additionally, each user has a specified reliability threshold, denoted as X %, indicating the minimum percentage of DUs that must be successfully delivered within the delay bound to meet the user's quality expectations. The proposed scheduling scheme ensures that the highest possible number of XR users achieve their required delay reliability. To accomplish this, the scheme integrates two critical components: (a) a delay tracking mechanism, and (b) a downlink scheduling strategy that optimizes scheduling based on real-time delay information.

A system (100) for optimizing multi-channel customer service using artificial intelligence (AI) and a distributed architecture, wherein the system comprises: a channel integration layer (1) configured to receive customer requests from multiple communication channels, including voice, email, chat and social media, and normalize the requests into a uniform format; a distributed message queue (2) configured to asynchronously receive and manage requests, wherein the message queue (2) ensures scalability, fault tolerance and high availability; a KL processing engine (3) comprising the following: a sentiment analysis module (31) configured to classify customer sentiment using a transformer-based model trained on labeled datasets of customer interactions; an intent detection module (32) configured to identify the customer's intent based on the requests using supervised learning algorithms trained on historical data; a task prioritization unit (33) configured to calculate a priority value for each request based on sentiment, intent and urgency; a task distribution service (4) configured to dynamically assign tasks to agents based on availability, skills and task priority; a real-time dashboard module (5) that includes the following: an agent dashboard (51) configured to display prioritized task queues with live status updates; a supervisor dashboard (52) configured to visualize query trends, sentiment distribution, backlog of tasks and agent performance metrics; where the system (100) uses a microservices architecture (6) and a cloud-based deployment (7) to dynamically scale services, maintain fault tolerance, and deliver actionable insights for optimizing customer service.

The present disclosure provides a user interface for accessing an account. The present disclosure generally relates to creating an account for a service. A request to create an account for a first service is received. In response to receiving the request to create the account for the first service, a first login option and a second login option are displayed. If an input selecting the first login option is detected, a request to create an account for the first service using first contact information of a user is transmitted. If an input selecting the second login option is detected, a request to create an account for the first service using second contact information of the user is transmitted. The second contact information is automatically generated for the service and does not reveal the first contact information of the user.

The present disclosure provides a user interface for accessing accounts. The present disclosure as a whole relates to creating accounts for services. A request to create an account for a first service is received. In response to receiving the request to create the account for the first service, a first login option and a second login option are displayed. If input selecting the first login option is detected, a request to create an account for the first service using a user's first contact information is transmitted. If input selecting the second login option is detected, a request to create an account for the first service using the user's second contact information is transmitted. The second contact information is automatically generated for the service and does not reveal the user's first contact information.

The purpose of the present disclosure is to automatically launch a service cooperating with an application service using a data model. A service collaboration device (101) is provided with: a data model creation unit (11) that creates data (38, 39, 40) to be processed by an application service (21) operating in collaboration with a plurality of services (A31, B32, C33), and a data model (51) that defines the relationship between the plurality of data (38, 39, 40); a data model management unit (12) that stores data processed by the data model (51) and the application service (21); an event detection unit (13) that detects, on the data model management unit (12), events occurring in the data (38, 39, 40) defined by the data model (51); and a service activation unit (14) that assigns the data in which the event has been detected to a corresponding service, which is a service associated with the data in advance, among the plurality of services, and activates the corresponding service.

The present disclosure provides methods for processing a service request in a network comprising a set of Network Function (NF) nodes, and corresponding NF nodes. The method performed at a first NF node comprises selecting (S710) an Application Program Interface (API) version for the service based on information on API versions for the service obtained by using a bootstrapping service and stored in the first NF node; constructing (S720) a request for the service using the selected API version; and transmitting (S730) the request to a Service Communication Proxy (SCP). The present disclosure further discloses corresponding methods performed at an SCP and a Network Repository Function (NRF). The present disclosure further provides a corresponding computer readable medium.

The invention belongs to the technical field of geographic information, data processing and service, and discloses a business use address and space twin standard address conversion method, which comprises the following steps of: A1, processing data; a2, standardizing the address; a3, storing and maintaining the data; and A4, improving and expanding. According to the invention, by integrating a plurality of frontier natural language processing technologies and precision algorithms, the addresses are flexibly and intelligently split and complemented, and it is ensured that each part of address information can be accurately analyzed and converted. The conversion mechanism gives consideration to the reasonability of logic and the authenticity of a real situation during design, information loss or misunderstanding is effectively avoided, the accuracy and practicability of address conversion are remarkably improved, the innovative fusion application provides powerful support for management and analysis of spatial data, and the application range is wide. And a solid foundation is laid for further development of the fields of smart cities, Internet of Things and the like.

By selecting the optimal service from among the various services used by AI, a data structure capable of enhancing the quality and speed of processing for activation triggers such as user prompts, and an information processing system using the data structure are provided. 【Solution means】A Ie - A data structure of data used by an agent, wherein the AI agent includes service information capable of identifying the service used by the AI agent and data information capable of identifying the data used by the AI, The aforementioned data information includes a service information ID indicating the service information, and the AI agent analyzes the given prompt and, based on the analysis result, Identify the aforementioned data information, and identify the service information based on the service information ID included in the identified data information. accesses the service based on the identified service information, obtains the data specified based on the data information from the service, and performs the prompt processing for the AI using the obtained data. When the aforementioned AI agent is started The processing is used.

To provide a contract inquiry system which enables efficient inquiry about contract information.SOLUTION: A service relevance calculation unit 213 calculates, for each service, service relevance which is a degree of relevance between an ancestor and the service, based on attribute information indicating attributes of the ancestor and past inquiry record information indicating past records of contract inquiry. An inquiry condition creation unit 212 selects, based on the service relevance, an inquiry target service which is a service to be subjected to contract inquiry. A contract inquiry unit 211 performs contract inquiry on the inquiry target service using personal information pertaining to the ancestor.SELECTED DRAWING: Figure 2

The application provides a user perception mapping method and system based on a service experience model, constructs a perception anchor point set of service experience features and user perception expressions, wherein the service experience features include service interaction fluency and content presentation fitness, and the user perception expressions are direct feeling descriptions of users on service use. Real-time service interaction data is analyzed for features, and real-time service experience features consistent with the service experience feature types in the perception anchor point set are extracted. The real-time service experience features are matched with the perception anchor point set by calling a service experience model, and a matching result is generated by dynamically adjusting the matching weight through an association reinforcement module. The matching result is used to perform perception expression conversion, and real-time user perception information is formed in combination with a real-time service scene. The real-time user perception information is compared with a preset perception optimization benchmark, and perception optimization guidance including a perception improvement direction and specific service adjustment suggestions is generated to provide accurate guidance for service optimization.

Examples of the present disclosure describe systems and methods for monitoring services provided by a service provider to a consumer using a smart contract architecture. In one example, smart contract terms are provided to a DeFi application running on a blockchain, and a smart contract (e.g., a SLA smart contract) is constructed on the blockchain. Once the service provider begins providing service to the consumer, the system may monitor the service performance of the service. If the service performance dips below a certain agreed-upon threshold based on the smart contract, a trigger event may occur. Additionally, the analysis of the service performance data may indicate a service deficiency, wherein a remedial action is suggested to the service provider to be applied. The service deficiency and remedial action may be stored on the blockchain for future review.

Techniques for reducing energy emissions of a service used by a user, wherein the method comprises the steps of: requesting, by a user equipment, a service from a network; determining the operating units required to run the service and the associated parameters requested for the service; retrieving operational energy data of the service, wherein the operational energy data comprises information of energy consumption and / or corresponding energy sources; comparing the operational energy data of the service with an energy profile and creating an energy report based on the comparison; providing the energy report to the user by signaling means of the UE, providing the energy report to an operating system of the UE, a QoS adapter and / or an application of the UE providing the service; and changing operating units and / or associated parameters of the service based on the energy report in order to reduce energy emissions of the service.

The embodiment of the application discloses a data migration method based on a distributed lock and a related device, at least relates to cloud technology and the like, reduces the data migration times of service data between service nodes, improves the stability of server load, and reduces the delay of service migration. The method comprises the following steps: obtaining an access request sent by a service using device, and obtaining first lock information based on the access request, wherein the first lock information comprises a first oscillation protection time, and the first oscillation protection time is used to indicate the effective protection time of the distributed lock when the second service node accesses the service data while holding the first lock information; when the current running time is not less than the first oscillation protection time, receiving service data sent by a storage service node, and then sending an access response message to the service using device, wherein the access response message comprises the service data, and the access response message is used to instruct the service using device to perform an access operation on the service data.

The present invention discloses a service recommendation method based on the interaction of location and preference features, comprising: obtaining identification information, location information, and historical call records of users and services; obtaining a service set corresponding to the user and a user set corresponding to the service based on the historical call information and location information of the user and service, respectively; fusing and extracting the interest preferences of the user and service using a multi-head attention fusion network and an interest feature extraction network; subjecting the interest preferences, location information, and identification information of the user and service to feature interaction, inputting the information into a deep residual neural network, and outputting a service quality prediction result; and selecting the service with the best service quality prediction result as the final recommended service. The present invention addresses the problems of data redundancy and imprecision by locating the interest preferences of users and services, integrating the location information and interest preferences of users and services into the prediction of service quality, and performing linear and nonlinear transformations on the features to improve the accuracy of predictions and recommendations.

Disclosed in the embodiments of the present disclosure is an intelligent service method used for an Internet-of-Things device. The method comprises: performing face identification on a user to determine identity information of the user; determining personality information of a plurality of personalities associated with the identity information; from amongst the personality information of the plurality of personalities, selecting the personality information of one personality as initial personality information; according to a first service mode correspondingly set for the initial personality information, starting to provide service for the user by means of speech interaction; in the service process, collecting a speech uttered by the user and / or an action performed by the user to generate corresponding user real-time performance data; analyzing the user real-time performance data by using a pre-trained large model to obtain an analysis result; according to the analysis result, determining whether the user has switched to another personality among the plurality of personalities; and if so, automatically switching from the first service mode to a second service mode correspondingly set for the personality information of said another personality, and according to the second service mode, continuing to provide service for the user.