1331 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.

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.

The presently disclosed embodiments may include a computer readable medium including instructions that when executed by one or more processing devices cause the one or more processing devices to perform a method. The method may include receiving a user input, wherein the user input includes a natural language question, using a trained language model to decompose the natural language question into two or more information requests, routing each of the two or more information requests to at least one information resource, receiving two or more information responses from the at least one information resource, wherein the two or more information responses correspond to the two or more information requests, generating a natural language response to the user input based on the two or more information responses and providing the natural language response to the user.

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.

A system allows generation of information used by an integrated development environment using a machine learning-based language model, for example, a large language model. The integrated development environment is for developing applications, e.g., database applications. The system receives a natural language request for information related to development of the database application. The system determines contextual information describing a development task associated with the database application and generates a prompt for a machine learning based language model based on the natural language request and the contextual information and receives a response. The system extracts the information related to development of the database application from the response. In response to a natural request, the system may generate a database query, provide a resultset by executing the database query, automatically determine a type of chart and generate one or more charts for visually displaying the result.

Disclosed are methods and systems for secure data communication amongst computer systems. Encrypted data in a first format is accessed over a secure communication channel from a first source for a first subject. Encrypted data in a second format is accessed over a secure communication channel from a second source for the first subject. The encrypted data in the first format from the first source and in the second format from the second source is decrypted. The decrypted data in the first format from the first source and in the second format from the second source is converted to a third format. At least partly in response to the request for information from a first system, at least a portion of the data from the first source and the second source is accessed from a database The accessed data is transmitted in encrypted form to the first system.

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.

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.

A system and method of using generative AI to convert NL queries to database commands for accessing one or more databases. The method includes receiving a natural language (NL) request for information associated with a private network. The method includes providing the NL request to an artificial intelligence (AI) model trained to identify, from a plurality of access objects associated with a plurality of databases and a plurality of event types, a particular access object that provides access to one or more event datasets associated with the NL request. The method includes generating, by a processing device and using the AI model, a database request associated with the particular access object based on the NL request.

Method, data processing system, and computer-readable storage media for responding to a user query. Receiving query from user, query pertaining to request for information. Based on query, generate prompts by masking sensitive information in query. Receive responses from foundation models in response to inputting prompts. Based on responses, generate common result set. By validating common result set with sensitive information, generate response. By supplementing response with sensitive information, generate user response. Providing user response in response to query to the user.

A method for authenticating a UE in a wireless communication system disclosed herein may comprise: a step in which a first UE receives authentication mode information, wherein a second UE relaying the first UE also receives the authentication mode information, and the first UE, on the basis of the authentication mode information, requests information necessary for direct search between UEs; a step in which the first UE receives the information necessary for direct searches between UEs; and a step for connecting to a network and performing authentication via the second UE on the basis of the received information necessary for direct searches between the UEs.

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.

A method of operating a core network node in a communication system includes receiving, at a first network function, a registration message from a radio access network node to register a user equipment, UE, and, responsive to the registration message, transmitting a request for information on whether network slices associated with the UE are subject to Network Slice-Specific Authentication and Authorization, NSSAA. Responsive to the request, the method receives a response message including Single-Network Slice Selection Assistance Information, S-NSSAI, information associated with the UE, the S-NSSAI information including NSSAA status information relating to the S-NSSAI information, and determines whether to initiate an NSSAA procedure with the UE based on the S-NSSAI information. Related network nodes are disclosed.

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.

A method (100) is disclosed for managing a wireless device that is operable to connect to a communication network, wherein the communication network comprises a Radio Access Network (RAN), and wherein the wireless device has available for execution a Machine Learning (ML) model that is operable to provide an output, on the basis of which a RAN operation performed by the wireless device may be configured. The method, performed by a RAN node of the communication network, comprises, on fulfilment of a trigger condition, causing an ML model Assurance Information, MAI, Request to be sent to the wireless device (110), the MAI Request comprising an indication of the ML model to which the MAI Request relates. The method further corpses receiving, from the wireless device, an MAI Response, wherein the MAI Response comprises ML model characteristic information generated by the wireless device using the ML model (120), and configuring the RAN operation performed by the wireless device according to the received MAI Response (130).

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.

Techniques for conversational-based searching are described. A system may receive a first spoken user input, and may determine that the first spoken user input corresponds to a request for information associated with an entity. The system may retrieve item results corresponding to the entity. The system may determine a suggested user input based on the retrieved item results, and may determine output data corresponding to the suggested user input. The system may send output data to a user device, where the output data includes the item results and the suggested user input. The system may receive a second spoken user input, and may determine that the second spoken user input corresponds to the suggested user input. In response, the system may send the previously determined output data to the device.

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.

Systems and methods for authenticating parties and transactions are described herein. The systems and methods may be part of an information wallet system. The information wallet system or a separate authentication system in communication with the information wallet system may facilitate verification and / or authentication of the parties. In one embodiment, the authentication system includes an authentication server configured to receive an authentication request from a first party to authenticate a second party. The authentication server is further configured to transmit an information request the second party. The authentication server is configured to receive second party information and to configured to compare the second party information with a verified second party information. The authentication server is configured to transmit an authentication result to the first party device.

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 and systems for locational visualization of financial statements are described. In an example, a method may receive, in a financial institution system, a request to display a record associated with a financial transaction, determine a location of a merchant associated with the financial transaction using merchant information, request, from an online mapping service, a customizable map interface associated with the location of the merchant, receive the customizable map interface, and display the customizable map interface integrated into the record associated with the financial transaction.

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.