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A computer-implemented method comprising receiving user preference information, based on the received user preference information, determining one or more user settings, processing data to determine a data condition, wherein, to determine the data condition, a plurality of alternate data inputs of different types are processed and normalized, and applied to a series of operations to generate a forecast having a degree of confidence, and the data condition is compared with third party information; providing an electronic notification indicative of the data condition to the user device, wherein the electronic notification includes the data condition compared with the third party information, a confidence indicator associated with the data condition, and a user prompt; in response to a single user input, the user device generating an instruction to execute the user request; and based on the instruction, executing the user request based on the determined one or more user settings.

A computer-implemented method includes a method comprising using at least one hardware processor to: receive a plurality of data from a plurality of data sources; standardize the plurality of data; tag the standardized plurality of data with one or more companies; train a prediction model to predict a metric for each of the one or more companies based on the standardized plurality of data tagged with that company and historical measurements for that company; and apply the prediction model to new data to predict the metric for at least one of the one or more companies.

A computer-implemented method comprising at least one hardware processor to: at each of one or more times, receive, from a user, a company identifier and one or more conditions, generate an alert for the company identifier and the one or more conditions, and associate the alert with the user; receive a plurality of data from a plurality of data sources in real time; update a plurality of signals based on the plurality of data, wherein each of the plurality of signals represents one of a plurality of critical indicators for one of a plurality of companies; and for each alert associated with the user, apply the one or more conditions of the alert to at least one of the plurality of signals to determine whether or not the alert is triggered.

A computer-implemented method comprising receiving user preference information, based on the received user preference information, determining one or more user settings, processing data to determine a data condition, wherein, to determine the data condition, a plurality of alternate data inputs of different types are processed and normalized, and applied to a series of operations to generate a forecast having a degree of confidence, and the data condition is compared with third party information; providing an electronic notification indicative of the data condition to the user device, wherein the electronic notification includes the data condition compared with the third party information, a confidence indicator associated with the data condition, and a user prompt; in response to a single user input, the user device generating an instruction to execute the user request; and based on the instruction, executing the user request based on the determined one or more user settings.

A computer-implemented method includes a method comprising using at least one hardware processor to: receive a plurality of data from a plurality of data sources; standardize the plurality of data; tag the standardized plurality of data with one or more companies; train a prediction model to predict a metric for each of the one or more companies based on the standardized plurality of data tagged with that company and historical measurements for that company; and apply the prediction model to new data to predict the metric for at least one of the one or more companies.

A computer-implemented method comprising receiving user preference information, based on the received user preference information, determining one or more user settings, processing data to determine a data condition, wherein, to determine the data condition, a plurality of alternate data inputs of different types are processed and normalized, and applied to a series of operations to generate a forecast having a degree of confidence, and the data condition is compared with third party information; providing an electronic notification indicative of the data condition to the user device, wherein the electronic notification includes the data condition compared with the third party information, a confidence indicator associated with the data condition, and a user prompt; in response to a single user input, the user device generating an instruction to execute the user request; and based on the instruction, executing the user request based on the determined one or more user settings.

Data with reliability; and to provide a reliable method of analyzing data and determining current trends and future trends of this data. This method is implemented in a computer, and is used to receive user preference information; to determine a plurality of user settings based on the received user preference information; and to process data to determine a data condition; and to provide a method for processing data. When determining data conditions, processing and normalizing multiple alternative data inputs of different types and applying it to a series of calculations; generating a reliable prediction; and further comparing data conditions with third party organization information.

A computer-implemented method comprises using at least one hardware processor to: receive a plurality of data related to one or more entities from a plurality of data sources; apply a predictive model to the plurality of data to predict a value of a critical indicator of performance for each of the one or more entities; generate a graphical user interface comprising a watchlist, wherein the watchlist comprises, for each of the one or more entities, an identifier of the entity, an identifier of the critical indicator predicted for the entity, a value associated with the critical indicator predicted for the entity, and a recommendation based on the value associated with the critical indicator predicted for the entity; and provide the graphical user interface to at least one user.

A computer-implemented method comprising at least one hardware processor to: at each of one or more times, receive, from a user, a company identifier and one or more conditions, generate an alert for the company identifier and the one or more conditions, and associate the alert with the user; receive a plurality of data from a plurality of data sources in real time; update a plurality of signals based on the plurality of data wherein each of the plurality of signals represents one of a plurality of critical indicators for one of a plurality of companies; and for each alert associated with the user, apply the one or more conditions of the alert to at least one of the plurality of signals to determine whether or not the alert is triggered.

A method for generating personalized investment responses that includes receiving, by a processor, input from a user, the input comprising at least one of a text or audio prompt; processing, by the processor, the input using an artificial intelligence (AI) model to generate extracted information; generating, by the processor, a plurality of responses using generative AIs with the extracted information as input; and generating, by the processor, a recommendation based on the plurality of responses.

A method for generating personalized investment responses comprising receiving, by a processor, input from a user, the input comprising at least one of a text prompt or an audio prompt; processing, by the processor, the input using a first artificial intelligence (AI) model to generate extracted information; generating, by the processor, a plurality of responses using generative AIs with the extracted information as input; connecting, by the processor, the plurality of responses to real-time market data and exclusive datasets to improve quality and relevance of the plurality of responses; and generating, by the processor, a personalized investment recommendation to the user based on the plurality of responses.

A method for generating personalized investment responses that includes receiving, by a processor, input from a user, the input comprising at least one of a text prompt or an audio prompt; processing, by the processor, the input using a first artificial intelligence (AI) model to generate extracted information; generating, by the processor, a plurality of responses using generative AIs with the extracted information as input; connecting, by the processor, the plurality of responses to real-time market data and exclusive datasets to improve quality and relevance of the plurality of responses; and generating, by the processor, a personalized investment recommendation to the user based on the plurality of responses.

A method for generating personalized investment responses comprises receiving, by a processor, input from a user, the input comprising at least one of a text prompt or an audio prompt. The method comprises processing the input using a first artificial intelligence model to generate extracted information. The method comprises generating a plurality of responses using generative artificial intelligence models with the extracted information as input, wherein when processor resources are exhausted, performing distributed computing by utilizing user devices to generate responses locally. The method comprises connecting the responses to real-time market data and exclusive datasets. The method comprises copying a subset of responses and performing stochastic perturbation. The method comprises computing a consistency score for each response by comparing against stochastically perturbed variants. The method comprises scoring responses based on consistency scores to select top responses and generating a personalized investment recommendation.

A method for performing investment portfolio balancing that includes receiving, by a processor, data associated with a user; receiving, by the processor, market condition information; generating, by the processor, a plurality of investment recommendations using a first artificial intelligence (AI) model with the data and the market condition information as input; ranking, by the processor, the plurality of investment recommendations based on coherence and accuracy using at least one second AI model; generating, by the processor, finalized investment recommendations based on ranked plurality of investment recommendations; and presenting, by the processor, the finalized investment recommendations to the user for selection, wherein selection of the finalized investment recommendations rebalances an investment portfolio of the user.

A method for performing investment portfolio adjustment that includes receiving, by a processor, user portfolio associated with a user; receiving, by the processor, a plurality of news data from various real-time information providers; analyzing and weighing, by the processor, the plurality of news data based on the user portfolio using an artificial intelligence (AI) model; and adjusting, by the processor, the user portfolio based on a weighed plurality of news data.

A method for generating a personalized newsletter. The method may include receiving and analyzing news articles from various sources in real-time or at predetermined intervals; storing and managing user demographic information, financial portfolio data, interests, preferences, and financial education levels; processing the analyzed news articles and user information using a Generative Artificial Intelligence (AI) to create personalized content based on the user's demographic information, financial portfolio, interests, preferences, and financial education level; assembling the personalized content into a newsletter with varying lengths and details tailored to the user; and sending the personalized newsletter to the user via email or other communication methods, wherein the tone and style of the personalized content are adjusted based on the user's preferences.

The present disclosure provides a computer-implemented system for generating, governing, and distributing personalized financial communications for financial institution clients across multiple jurisdictions. The system includes a contextual aggregation module assembling a real-time context window by fusing portfolio data, market events, investment committee views, product documentation, macroeconomic indicators, and engagement patterns. The system includes a content generation module, compliance verification module generating an audit log, investment committee alignment module, engagement intelligence module, multi-tenant architecture, multi-language generation module, cross-border regulatory compliance engine, extended content intelligence module, deep personalization engine, an AI decision explainability module producing structured decision records stored in an immutable audit archive, an agent programming transparency module maintaining human-interpretable representations of AI agent logic, and an adversarial AI protection module.

A method for generating personalized investment responses that includes receiving, by a processor, input from a user, the input comprising at least one of a text prompt or an audio prompt; processing, by the processor, the input using a first artificial intelligence (AI) model to generate extracted information; generating, by the processor, a plurality of responses using generative AIs with the extracted information as input; connecting, by the processor, the plurality of responses to real-time market data and exclusive datasets to improve quality and relevance of the plurality of responses; and generating, by the processor, a personalized investment recommendation to the user based on the plurality of responses.

A computer-implemented system and method for securing financial service computing environments against exploitation by autonomous AI software agents operating on client computing devices. The present invention provides an integrated framework specifically designed for the emerging threat paradigm where legitimate autonomous AI agents—including AI assistants with computer-use capabilities, robotic process automation systems, and agentic AI platforms—coexist with financial service interfaces on shared computing devices and may be subject to adversarial manipulation. The system comprises an agent classification engine, a behavioral biometric analysis module, a computational environment isolation mechanism employing novel visual encryption techniques, a real-time pre-trade transaction validation engine, an adversarial input sanitization pipeline implementing novel temporal injection detection, and a forensic analysis engine employing generative adversarial network techniques for self-evolving threat detection with federated cross-institutional intelligence sharing.

A computer-implemented system for securing autonomous AI agents in a multi-agent computer network includes a pre-execution security validation engine configured to intercept proposed actions before execution and validate them against constraint sets, operating synchronously in a critical execution path such that no agent action proceeds without cryptographic authorization. The engine includes a pre-execution validator configured to issue cryptographically signed authorization tokens using ECDSA or RSA signatures for compliant actions and block non-compliant actions with violation identification. The system includes a central orchestration engine managing inter-agent communications via encrypted channels conforming to FIPS 140-2 Level 3 or FIPS 140-3 cryptographic standards. The system includes an immutable hash-chain logger configured to record agent actions using SHA-256 cryptographic hash blocks wherein each block comprises a hash of a previous block to form a tamper-evident chain structure.

A computer-implemented AI copilot system for financial advisors comprises a contextual aggregation module configured to integrate client portfolio data, investment committee views, product documentation, market data, and client risk profiles. The system comprises a proactive intelligence engine configured to monitor portfolios, identify opportunities and risks, and generate prioritized action suggestions. The system comprises a personalized content generator configured to produce client-specific talking points and rebalancing proposals aligned with investment committee views. The system comprises an automated compliance module configured to verify recommendations against regulatory suitability standards and generate compliance documentation. The system comprises an activity logging system configured to maintain cryptographically-signed audit trails. The system comprises an adversarial AI security module configured to detect prompt injection attacks and behavioral anomalies. The system comprises an AI safety module configured to verify factual grounding, detect hallucinated outputs, and validate numerical accuracy.

A computer-implemented system for generating and delivering compliance-verified Next Best Actions (NBA) and Next Best Offers (NBO) for financial professionals is provided. The system comprises an omnisource data fusion engine configured to ingest and normalize data from structured and unstructured sources to produce a Client Context Graph. The system comprises a multi-agent NBA generation architecture with specialized AI agents coordinated by an orchestration layer for ensemble synthesis of recommendations. The system comprises a multi-jurisdictional compliance governance engine configured to validate each NBA and NBO against regulatory requirements before delivery. The system comprises a cross-instance collaborative intelligence network enabling client-dedicated instances to share anonymized patterns via a privacy-preserving message bus. The system comprises an adaptive multimedia output generation engine producing outputs in multiple formats with compliance annotations.

An autonomous self-improving artificial intelligence system for financial advisory services comprises one or more processors and non-transitory computer-readable storage media storing instructions that implement integrated subsystems. A multi-regulator compliance orchestration engine maintains machine-readable regulatory rule representations and dynamically assembles applicable regulatory profiles. An immutable activity tracking subsystem records system decisions in a cryptographically verifiable chain using Merkle tree hash structures. An adaptive security governance framework enforces behavioral guardrails at five hierarchical levels comprising individual inference, session, portfolio, institution, and system-wide constraints. An external adversarial defense layer implements a five-layer defense-in-depth architecture. An evolutionary self-optimization subsystem generates competing software variants, evaluates variants, and manages deployment. A predictive regulatory adaptation module analyzes regulatory data sources using natural language processing to trigger pre-emptive compliance variant generation. A federated self-improvement protocol enables cross-institutional optimization sharing.

A computer-implemented system for securing a financial service computing environment against exploitation by autonomous AI software agents includes an agent classification engine configured to detect autonomous AI software agents on a client computing device, generate a multi-dimensional computational fingerprint for each detected agent based on process execution patterns, API call sequences, memory allocation patterns, network communication characteristics, rendering engine interaction patterns, input event timing distributions, and resource consumption profiles, classify each entity into one of four categories comprising a human user, an authorized autonomous AI software agent operating within established behavioral parameters, an authorized autonomous AI software agent exhibiting anomalous behavior indicative of mid-session compromise, or an unauthorized entity, and continuously monitor the computational fingerprint to detect drift. The system includes a behavioral biometric analysis engine, a computational environment isolation mechanism implementing visual encryption, and a pre-trade transaction validation engine.

This patent covers Streetbeat's core platform architecture for deploying multiple specialized AI instances across financial domains (investments, insurance, personal finance, crypto) that communicate through a secure inter-instance layer to generate unified 'next best action' recommendations. Key innovations include a multi-jurisdictional compliance engine spanning CONSOB, FINRA, SEC, FCA, BaFin, ESMA, and FINMA with automatic conflict resolution; a five-level behavioral guardrail framework (inference, session, portfolio, institution, emergency shutdown) integrated with the Guardian Angel security system; universal data ingestion from heterogeneous sources (transactions, handwritten notes, voice, email, social media); Merkle-tree-based immutable audit logging with sub-millisecond retrieval; and adaptive AI personality profiles for multi-channel delivery.