902 results about "Request for information" patented technology

An example operation may include one or more of storing a data store of credit card documentation, conversing with a user via a chatbot within a chat window of a software application, wherein the conversing comprises receiving natural language inputs with requests for information about a payment card, identifying a benefit obtained by the user based on the conversation, executing a large language model (LLM) on the identified benefit and the data store of the credit card documentation to generate a letter of confirmation, and generating an electronic message with the letter of confirmation attached, and transmitting the electronic message to a user device.

Embodiments described herein relate to systems and methods for automatically generating content for a generative content interface of a collaboration platform. The system may perform an intent analysis on a natural language user input to the generative content interface to determine an intent confidence score with respect to each of a set of request classifiers, the set of request classifiers comprising a first request classifier associated with a request for an action, a second request classifier associated with a request for information, and a third request classifier associated with a request for a contact. Based on the intent confidence scores of the request classifiers, the system may select a content store in which to search for content to satisfy a user's query.

An AI chatbot responds to the intent of a customer question by triggering an automatic workflow appropriate for the intention of the question. An information retrieval pipeline may be initiated to response to question corresponding to an information request. A Large Language Model may be initiated to generate a workflow to respond to other types of questions. The Large Language Model is provided with policies, tools and prompts to implement workflows. An evaluation engine evaluates factualness and helpfulness of responses to information questions and workflow intent accuracy and workflow appropriateness. Overall conversation resolution verification ay also be performed.

An illustrative method for querying multiple datasets may include generating, based on models each associated with and defining attributes of a different datasets stored in a plurality of data stores, a semantic layer defining relationships between the models and that provides a centralized application programming interface (API) for exposing the datasets by way of a common query language, receiving, by way of the centralized API, a query request for information that depends on data included in multiple datasets included in the plurality of datasets, querying, based on the query request and the relationships between the models defined by the semantic layer, the multiple datasets, and presenting, based on the querying, a query result representative of the information that depends on the data included in the multiple datasets.

In some embodiments, an electronic device receives an input corresponding to a request for information about a user-specified topic. In response, the electronic device displays information about media that is relevant to the user-specified topic, including concurrently displaying: a representation of respective media that is available for viewing on a first set of applications, the electronic device having a second set of applications downloaded on it, and a respective selectable user interface element associated with the respective media. In some embodiments, if the second set of applications includes at least one application in the first set of applications, the respective selectable user interface element is selectable to initiate a process for launching a respective application of the first set of applications.

Disclosed herein are system, method, and computer program product embodiments for using generative AI for prompt comparison. The system may receive a prompt. The prompt may include a request for information. The system may identify a stored prompt based on a similarity value between a received prompt for a large language model (LLM) and the stored prompt, the stored prompt including a response generated by the large language model (LLM). The system may generate a response using the LLM if the similarity value between the received and stored prompts is below a predefined threshold. The system may then modify the response by applying a first rule associated with a first designated phrase, and a second rule associated with a second designated phrase, where the first designated phrase comprises a banned phrase and where the second designated phrase comprises a selected phrase.

Systems and methods of the present disclosure may receive, from a user computing device, a user-provided data record query including a natural language request for information associated with one or more data sources. User persona attributes of the user may be determined, such as a user role or security parameters or both. Based on the user persona attributes a context query may be generated to obtain context attributes associated with the user-provided query. The natural language request and the context attributes are input into the model orchestration large language model (LLM) to output instructions to machine learning (ML) agents based on the context attributes. The ML agents output responses associated with the user-provided data record query based on the instructions, and the responses are input into the model orchestration LLM to output to the user computing device a natural language response based on the context attributes.

There are many ambiguous descriptions in 3GPP specification(s) how to handle the NSSRG feature.A method of a user equipment (UE) includes communicating with a communication apparatus. The method includes sending a Registration Request message. The Registration Request message includes Requested Network Slice Selection Assistance Information (NSSAI). The Requested NSSAI includes first Single Network Slice Selection Assistance Information (S-NSSAI). The first S-NSSAI and second S-NSSAI in Pending NSSAI are associated with at least one common Network Slice Simultaneous Registration Group (NSSRG) value.

A database query processing method includes receiving a natural language request for information contained within a database from a user in an application session, prompting a large language model to generate a SQL request, and receiving a particular SQL request from the large language model that is parsed to identify a command to access one or more database structures. A security predicate is appended to the command, creating a modified SQL request, to enforce one or more database access constraints constraining a user-authenticated client device that submitted the request that is not enforced in a database session between the application and a database. The modified SQL request is used to access data in the database session, and a visualization of the accessed data is caused to be displayed in the application session.

A method, a system, and a computer program product for transmission of information over a network. A validity token is generated based on a received contactless card identifier associated with a contactless card. The validity token is generated upon validating the contactless card using the contactless card identifier, and transmitted to a first computing device. A second computing device is identified based on an information request received from the first computing device. The information request includes a first public key associated with the first computing device and the validity token. An encrypted response to the request and a second public key associated with the second computing device are sent to the first computing device. The second computing device generates the encrypted response and encrypts it using a shared key generated using the first public key. The first computing device decrypts the response upon generation of the shared key using the second public key.

Methods for a network data analytics function (NWDAF) of a communication network. Such methods include receiving, from user plane function (UPF) of the communications network, a first subscription request for an analytic related to user plane (UP) traffic congestion and sending to the UPF a second subscription request for information related to traffic between the UPF and a radio access network (RAN). Such methods also include receiving the traffic-related information from the UPF, in accordance with the second subscription request, and computing the analytic related to UP traffic congestion, based on the received traffic-related information. Such methods also include sending the computed analytic to the UPF, in accordance with the first subscription request. Embodiments also include complementary methods for UPFs, as well as NWDAFs and UPFs configured to perform such methods.

Methods and apparatuses are described herein for configuration of background data transfers (BDTs) between local area data networks (LADNs). An apparatus may receive a message indicating a request from a user equipment (UE) for a data transfer of data originating from the UE. The apparatus may send, to a database, a request for subscription information associated with the UE and a policy profile associated with the UE to determine whether there is an existing BDT policy. The apparatus may receive, from the database, a response indicating whether there is an existing BDT policy that can be re-used. The apparatus may determine, based on the received response, a BDT policy for the data transfer and a LADN to service the data transfer. The apparatus may send, to the LADN via a radio access network (RAN) node, a notification message of an arrival time and data rate for the data transfer.

The invention discloses a gateway plug-in loading and optimizing method and device, equipment and a medium, and relates to the technical field of gateway plug-in management, and the method comprises the steps: collecting target information of a user request entering a gateway, so as to obtain corresponding collection data; the target information comprises a request source ip, a request path, parameter information, a request frequency, a user agent, a request method and a response state code; identifying the user request based on the collected data by using a pre-trained identification model so as to determine a request type of the user request according to a corresponding identification result; determining a target plug-in matched with the user request based on the request type, and loading the target plug-in to obtain a corresponding execution result; and optimizing a plug-in priority, a plug-in strategy parameter and an execution path of the target plug-in based on the execution result. Therefore, the gateway performance is improved, the maintenance cost is reduced, and the system stability is enhanced.

The present disclosure provides example network devices and communication methods. One example method includes transmitting, by a first network device, according to a network protocol, a packet to a second network device. The packet includes loopback request information in a header of a layer of a network model. A loopback reply packet is received. The loopback reply packet includes, in a payload, at least a portion of the packet including at least one header of the packet. The loopback request information requests the second network device to transmit the loopback reply packet to the first network device.

Systems and methods of the present disclosure may receive, from a user computing device, a user-provided data record query including a natural language request for information associated with one or more data sources. User persona attributes of the user may be determined, such as a user role or security parameters or both. Based on the user persona attributes a context query may be generated to obtain context attributes associated with the user-provided query. The natural language request and the context attributes are input into the model orchestration large language model (LLM) to output instructions to machine learning (ML) agents based on the context attributes. The ML agents output responses associated with the user-provided data record query based on the instructions, and the responses are input into the model orchestration LLM to output to the user computing device a natural language response based on the context attributes.

The exchange of resource allocation assistance information between a requester User Equipment (UE) and an assistant UE allows the requester to make better resource allocation decisions. The assistance information may include a blacklist, a whitelist, resource allocation information, a candidate resource set, and measurement information, for example. Both UEs may have an assistance configuration to assist in the exchange of resource allocation assistance information, that may be obtained from a third apparatus, such as a base station. The configuration may include information related to when to trigger assistance, identities of UEs, and types of assistance information, for example. The requester may, based on the assistance information received, modify a candidate resource set at a Medium Access Control (MAC) layer. The requester may, at the MAC layer, determine a configured sidelink grant for a selected destination using assistance information targeting this destination.

A computer using the systems and methods described herein can use a task agent to automatically select and manage different applications and agents to use to generate results in response to requests for information. The computer can iteratively execute the task agent to generate the results. For instance, the task agent can identify a set of applications to use to generate a result in response to an information request. The task agent can identify the set of applications based on an intent of the information request. The task agent can iteratively execute different sequences of applications from the set of applications to query and retrieve data from different data sources until determining the data that is necessary to generate a response to the request has been retrieved. The computer can generate the response from the retrieved data and present the response on a user interface.

Implementations for messaging for inter-user equipment (UE) coordination are described, such as related to providing ways for handling scenarios for inter-UE coordination where portions of requests for inter-UE coordination information and / or replies to requests for inter-UE coordination information are not successfully received.

A stateful chatbot system leverages generative AI to provide an interface by which users can retrieve information from backend IoT databases of a security provider via natural language queries. Upon receiving a natural language query that corresponds to a request for information from the database, the chatbot generates a corresponding database query having a format compatible with the database. The chatbot comprises a generative model adapted to generate database queries based on natural language queries via prompt engineering using natural language and database query pairs. The chatbot queries the database with the generated database query, retrieves results comprising data / metadata that satisfy the query, and generates a summary of the results, both of which it presents as a response to the user's query. The chatbot also has access to a vulnerability database from which it can obtain information about known vulnerabilities documented therein to respond to user queries.

To improve user convenience.SOLUTION: A work support system (1) provided herein is capable of supporting work of users. A user input information acquisition unit (201) acquires user input information regarding input by a user. An API request generation unit (203) generates an API request for a destination API from among multiple APIs related to work support based on the user input information and AI. An API request transmission unit (204) sends the API request to the destination API.SELECTED DRAWING: Figure 4

An illustrative data storage management system uses a control layer that controls information content presented to users and ensures information privacy between diverse tenants and / or resellers who share the system. The system populates a relationship database as transactions roll in (intake processing), and uses information in the relationship database later when processing responses (output processing). The relationship database comprises associations between e.g., a company ID and any number of entities that were created by or on behalf of the company or that are related to the company's service in the system. The control layer parses raw results that are responsive to requests for information and prevents others' information from being included in the responsive message(s). The techniques disclosed herein are not limited to shared systems managed by service providers, and may be implemented in fully owned and operated systems to add security and privacy among diverse users and / or departments.

A database query processing method includes receiving a natural language request for information contained within a database, categorizing the natural language request in a particular category from a plurality of categories, and selecting a large language model and one or more database structures for which a SQL request is to be directed based in part on the particular category. The selected large language model is prompted to generate a SQL request directed to the selected set of one ore more database structures and the received SQL request is validated for the particular category. The validated particular SQL request is used to access data within the database and one or more visualizations is caused to be displayed based at least in part on a result of the validated particular SQL request.

A generative interactive video computer-implemented method and system applies trained computer-implemented neural networks to generate a sequence of images, which may comprise a video, and which contains identified objects, and then delivers the sequence of images to a user. The sequence of images may be further generated in accordance with an inference of a preference from user behavioral information. The identified objects may be provided to the system in the form of natural language and / or images. The system then generates and delivers to users natural language-based responses to user requests for information with respect to the identified objects, including attributes that are associated with the identified objects. Users may direct the system to generate video-based virtual environments that include representations of the identified objects.

A distributed computing system may include a code repository server configured to store code, a code deployment server configured to receive a deployment of the code, and a development and operations (DevOps) server configured to construct a pipeline between the code repository server and the code deployment server. The DevOps server may be configured to execute a source code management program to receive a request for information regarding a target resource that has been deployed using the pipeline, generate a permissions model for the target resource, the permissions model including one or more permissions, each permission authorizing a managed identity to execute an action related to the target resource, determine a permissions usage history of the permissions contained in the permissions model, filter the permissions model based on the permissions usage history, and generate a list of events determined to have occurred as the filtered permissions model.

An online system receives user reviews for items and generates a review embedding for each review. The system receives a request for information describing an item from a client device associated with a user. Responsive to the request, the system identifies the item and contextual information associated with a current session of the user, and generates a user embedding based on the contextual information. The system compares the user embedding to a set of review embeddings for the item, identifies a set of reviews for the item based on the comparison, and generates a prompt including the identified set of reviews and a request to summarize, for the user, the identified set of reviews. The system provides the prompt to a large language model to obtain a summarized review for the item and sends a user interface including the item and summarized review for display to the client device.

The invention relates to the technical field of quality supervision and early warning management, in particular to an AI-based subway engineering construction quality supervision and early warning management system and method, and the system comprises a process data fusion module which obtains the sensor monitoring data deviation degree, the material detection qualified rate and the personnel operation specification conformity of a shield tunneling process. According to the method, a quality vulnerability index for expressing process state fluctuation is established by fusing a sensor monitoring data deviation degree, a material detection qualification rate and a personnel operation specification conformity, and an accumulated risk assessment mode based on a process chain conduction relation is formed by combining dynamic quantification of an upstream process defect on a downstream influence probability. And the capability of identifying the coupling influence between the working procedures is enhanced. And synchronously analyzing information request metadata, scheduling personnel hour data, access control behavior records and rectification cycle time, and constructing a multi-dimensional collaborative efficiency index sequence covering information circulation efficiency, personnel operation load and problem solving efficiency.

The disclosure relates to a 5G or 6G communication system for supporting a higher data transmission rate. A method performed by a vertical application layer (VAL) server in a wireless communication system is provided. The method comprises transmitting, to a location management server, a user equipment (UE) information request message including a first VAL service area identity (ID) of a first VAL service area and receiving, from the location management server, UE information as a response to the UE information request message. The UE information includes a list of at least one UE whose location is within an application defined proximity range of the first VAL service area corresponding to the first VAL service area ID, and the application defined proximity range includes a range in which the UE information is required.

Various embodiments leverage artificial intelligence in identifying and potentially resolving compliance issues (e.g., with regulatory requirements, client-specified requirements, certification conditions, etc.), or preventing violations of law, rules and regulations. The AI can be configured to automatically generate requests for information. For example, a system analysis component can be configured to identify a specific compliance target (e.g., a branch location) and select or automatically generate questions to collect responsive information to ensure compliance, identify potential violations, and define any evidence required to identify or resolve issues (e.g., prove compliance, support potential violations, flagged issues, etc.). According to one example, the system can use trained AI models to analyze a set of rules and / or requirements to efficiently build questionnaires to address or demonstrate compliance.

The invention provides a routing information processing method and system, an electronic device and a storage medium, and the method comprises the steps: receiving a target service request, extracting request label information corresponding to the target service request, the request label information being used for representing business information associated with the target service request, matching the request label information with each route, and sending the matching result to the target service request; each route comprises at least one first-level route and at least one second-level route, each first-level route is an entrance route of the at least one second-level route, and if a target route matched with the request label information exists, the target service request is forwarded to a service node corresponding to the target route through the target route. By adopting the method, the service request processing capability of different service units under the same function module is improved, and the fine control capability of cluster nodes is also improved.

The invention belongs to the technical field of operation and maintenance monitoring, and discloses a unified monitoring data collection method and system, electronic equipment and a storage medium, and the method comprises the steps: registering a monitoring object at a server side, pushing the configuration information of the monitoring object to a configuration center, initiating a configuration information request to the configuration center by a unified collector, and reading the configuration information of the configuration center; the unified collector collects, analyzes, formats and cleans the monitoring data based on the configuration information through a user-defined plug-in to obtain index data in a format required by Prometheus, and pushes the index data to a router of the unified collector; and the router of the unified collector performs exposure processing on the index data after routing grouping according to a preset rule, and the index data is acquired and analyzed by the monitoring database to generate an alarm event and a notification. Intelligent configuration management can be realized, the system toughness is improved through hierarchical shake resistance, and the security of the system is ensured through an encryption mechanism.