US-20260127236-A1 - ARTIFICIAL INTELLIGENCE BASED (AI-BASED) SYSTEM AND METHOD FOR AUTOMATICALLY DETERMINING DIGITAL ASSET VALUATION TRENDS IN A BLOCKCHAIN ECOSYSTEM

US20260127236A1US 20260127236 A1US20260127236 A1US 20260127236A1US-20260127236-A1

Abstract

An AI-based system and method for generating automatically determining digital asset valuation trends, is disclosed. The AI-based method includes obtaining data associated with queries corresponding to the digital asset valuation trends, from communication devices of users; analyzing the data associated with the queries to determine at least one of: purpose and context, of the queries corresponding to the digital asset valuation trends, using an AI model; extracting key information from the analyzed data associated with the queries, using the AI model; determining the digital asset valuation trends by comparing the analyzed data associated with the queries with historical data of the digital assets, using the AI model; and providing the determined digital asset valuation trends, as an output, through the user interfaces associated with the communication devices of the users.

Inventors

Ilan Rakhmanov

Assignees

ChainGPT LLC

Dates

Publication Date: 20260507
Application Date: 20250602

Claims (15)

1 . An artificial intelligence based (AI-based) method for automatically determining one or more digital asset valuation trends in a blockchain ecosystem, the AI-based method comprising: obtaining, by one or more hardware processors, data associated with one or more queries corresponding to the one or more digital asset valuation trends, from one or more communication devices of one or more users; analyzing, by the one or more hardware processors, the data associated with the one or more queries to determine at least one of: purpose and context, of the one or more queries corresponding to the one or more digital asset valuation trends, using an AI model; extracting, by the one or more hardware processors, one or more key information from the analyzed data associated with the one or more queries, using the AI model, wherein the one or more key information are corresponding to at least one of: one or more digital assets and one or more time frames for analysis of the one or more digital assets; determining, by the one or more hardware processors, the one or more digital asset valuation trends by comparing the analyzed data associated with the one or more queries with historical data of the one or more digital assets, using the AI model; providing, by the one or more hardware processors, the determined one or more digital asset valuation trends, as an output, through the one or more user interfaces associated with the one or more communication devices of the one or more users.
2 . The AI-based method of claim 1 , wherein determining the one or more digital asset valuation trends, comprises: obtaining, by the one or more hardware processors, price data associated with the one or more digital assets from one or more external sources, using the AI model through representational state transfer application programming interface (REST API); periodically updating, by the one or more hardware processors, the price data associated with the one or more digital assets at a pre-determined time interval using the AI model, wherein the AI model comprises a price update model; storing, by the one or more hardware processors, the historical data of the one or more digital assets in one or more databases, wherein the historical data comprise at least one of: historical pricing data and volume data, associated with the one or more digital assets; and comparing, by the one or more hardware processors, the periodically updated price data associated with the one or more queries with the historical data of the one or more digital assets, using the AI model, to determine the one or more digital asset valuation trends, wherein the AI model comprises a trend identification model, and wherein comparing the periodically updated price data with the historical data comprises applying predefined heuristics to the periodically updated price data associated with the one or more queries with the historical data of the one or more digital assets, to determine the one or more digital asset valuation trends.
3 . The AI-based method of claim 1 , wherein determining the one or more digital asset valuation trends, further comprises: obtaining, by the one or more hardware processors, one or more training datasets associated with the historical data of the one or more digital assets; training, by the one or more hardware processors, the AI model on the one or more training datasets associated with the historical data of the one or more digital assets, wherein the AI model comprises a price prediction model; predicting, by the one or more hardware processors, one or more price movements of the one or more digital assets, based on the trained AI model; and upon predicting the one or more price movements of the one or more digital assets, determining, by the one or more hardware processors, the one or more digital asset valuation trends by applying the predefined heuristics to the predicted one or more price movements.
4 . The AI-based method of claim 3 , further comprising: determining, by the one or more hardware processors, a probability of near-term trend formations indicating a direction of price action in digital asset markets over a predetermined time period, based on the predicted one or more price movements and one or more historical patterns associated with the historical data; and adapting, by the one or more hardware processors, the one or more users to make one or more decisions by providing one or more information related to the determined one or more digital asset valuation trends, to the one or more communication devices associated with the one or more users, using the AI model.
5 . The AI-based method of claim 1 , further comprising: periodically fetching, by the one or more hardware processors, the one or more digital asset valuation trends, from an asset valuation trend analyzing subsystem; updating, by the one or more hardware processors, the one or more digital asset valuation trends at the pre-determined time interval; storing, by the one or more hardware processors, the updated one or more digital asset valuation trends in the one or more databases; and monitoring, by the one or more hardware processors, the one or more databases storing the updated one or more digital asset valuation trends.
6 . An artificial intelligence based (AI-based) system for automatically determining one or more digital asset valuation trends in a blockchain ecosystem, the AI-based system comprising: one or more hardware processors; a memory coupled to the one or more hardware processors, wherein the memory comprises a plurality of subsystems in form of programmable instructions executable by the one or more hardware processors, and wherein the plurality of subsystems comprises: a query obtaining subsystem configured to obtain data associated with one or more queries corresponding to the one or more digital asset valuation trends, from one or more communication devices of one or more users; a query determining subsystem configured to: analyze the data associated with the one or more queries to determine at least one of: purpose and context, of the one or more queries corresponding to the one or more digital asset valuation trends, using an AI model; and extract one or more key information from the analyzed data associated with the one or more queries, using the AI model, wherein the one or more key information are corresponding to at least one of: one or more digital assets and one or more time frames for analysis of the one or more digital assets; an asset valuation trend analyzing subsystem configured to determine the one or more digital asset valuation trends by comparing the analyzed data associated with the one or more queries with historical data of the one or more digital assets, using the AI model; and an interaction generating subsystem configured to provide the determined one or more digital asset valuation trends, as an output, through the one or more user interfaces associated with the one or more communication devices of the one or more users.
7 . The AI-based system of claim 6 , wherein the asset valuation trend analyzing subsystem is further configured to: obtain price data associated with the one or more digital assets from one or more external sources, using the AI model through representational state transfer application programming interface (REST API), periodically update the price data associated with the one or more digital assets at a pre-determined time interval using the AI model, wherein the AI model comprises a price update model; store the historical data of the one or more digital assets in one or more databases, wherein the historical data comprise at least one of: historical pricing data and volume data, associated with the one or more digital assets; and compare the periodically updated price data associated with the one or more queries with the historical data of the one or more digital assets, using the AI model, to determine the one or more digital asset valuation trends, wherein the AI model comprises a trend identification model, and wherein comparing the periodically updated price data with the historical data comprises applying predefined heuristics to the periodically updated price data associated with the one or more queries with the historical data of the one or more digital assets, to determine the one or more digital asset valuation trends.
8 . The AI-based system of claim 6 , wherein the asset valuation trend analyzing subsystem is further configured to: obtain one or more training datasets associated with the historical data of the one or more digital assets; train the AI model on the one or more training datasets associated with the historical data of the one or more digital assets, wherein the AI model comprises a price prediction model; predict one or more price movements of the one or more digital assets, based on the trained AI model; and upon predicting the one or more price movements of the one or more digital assets, determine the one or more digital asset valuation trends by applying the predefined heuristics to the predicted one or more price movements.
9 . The AI-based system of claim 6 , wherein the asset valuation trend analyzing subsystem is further configured to: determine a probability of near-term trend formations indicating a direction of price action in digital asset markets over a predetermined time period, based on the predicted one or more price movements and one or more historical patterns associated with the historical data; and adapt the one or more users to make one or more decisions by providing one or more information related to the determined one or more digital asset valuation trends, to the one or more communication devices associated with the one or more users, using the AI model.
10 . The AI-based system of claim 6 , further comprising a real-time data updating subsystem is configured to: periodically fetch the one or more digital asset valuation trends, from an asset valuation trend analyzing subsystem; update the one or more digital asset valuation trends at the pre-determined time interval; store the updated one or more digital asset valuation trends in the one or more databases; and monitor the one or more databases storing the updated one or more digital asset valuation trends.
11 . A non-transitory computer-readable storage medium having instructions stored therein that when executed by one or more hardware processors, cause the one or more hardware processors to execute operations of: obtaining data associated with one or more queries corresponding to the one or more digital asset valuation trends, from one or more communication devices of one or more users; analyzing the data associated with the one or more queries to determine at least one of: purpose and context, of the one or more queries corresponding to the one or more digital asset valuation trends, using an AI model; extracting one or more key information from the analyzed data associated with the one or more queries, using the AI model, wherein the one or more key information are corresponding to at least one of: one or more digital assets and one or more time frames for analysis of the one or more digital assets; determining the one or more digital asset valuation trends by comparing the analyzed data associated with the one or more queries with historical data of the one or more digital assets, using the AI model; providing the determined one or more digital asset valuation trends, as an output, through the one or more user interfaces associated with the one or more communication devices of the one or more users.
12 . The non-transitory computer-readable storage medium of claim 11 , wherein determining the one or more digital asset valuation trends, comprises: obtaining price data associated with the one or more digital assets from one or more external sources, using the AI model through representational state transfer application programming interface (REST API), periodically updating the price data associated with the one or more digital assets at a pre-determined time interval using the AI model, wherein the AI model comprises a price update model; storing the historical data of the one or more digital assets in one or more databases, wherein the historical data comprise at least one of: historical pricing data and volume data, associated with the one or more digital assets; and comparing the periodically updated price data associated with the one or more queries with the historical data of the one or more digital assets, using the AI model, to determine the one or more digital asset valuation trends, wherein the AI model comprises a trend identification model, and wherein comparing the periodically updated price data with the historical data comprises applying predefined heuristics to the periodically updated price data associated with the one or more queries with the historical data of the one or more digital assets, to determine the one or more digital asset valuation trends.
13 . The non-transitory computer-readable storage medium of claim 11 , wherein determining the one or more digital asset valuation trends, further comprises: obtaining one or more training datasets associated with the historical data of the one or more digital assets; training the AI model on the one or more training datasets associated with the historical data of the one or more digital assets, wherein the AI model comprises a price prediction model; predicting one or more price movements of the one or more digital assets, based on the trained AI model; and upon predicting the one or more price movements of the one or more digital assets, determining the one or more digital asset valuation trends by applying the predefined heuristics to the predicted one or more price movements.
14 . The non-transitory computer-readable storage medium of claim 11 , further comprising: determining a probability of near-term trend formations indicating a direction of price action in digital asset markets over a predetermined time period, based on the predicted one or more price movements and one or more historical patterns associated with the historical data; and adapting the one or more users to make one or more decisions by providing one or more information related to the determined one or more digital asset valuation trends, to the one or more communication devices associated with the one or more users, using the AI model.
15 . The non-transitory computer-readable storage medium of claim 11 , further comprising: periodically fetching the one or more digital asset valuation trends, from an asset valuation trend analyzing subsystem; updating the one or more digital asset valuation trends at the pre-determined time interval; storing the updated one or more digital asset valuation trends in the one or more databases; and monitoring the one or more databases storing the updated one or more digital asset valuation trends.

Description

CROSS REFERENCE TO RELATED APPLICATION(S) This Application is a continuation of a non-provisional patent application filed in the US having patent application Ser. No. 18/939,662, filed on Nov. 7, 2024, titled “ARTIFICIAL INTELLIGENCE BASED (AI-BASED) SYSTEM AND METHOD FOR GENERATING NEWS ARTICLES IN A BLOCKCHAIN ECOSYSTEM”. TECHNICAL FIELD Embodiments of the present disclosure relate to blockchain technology, and more particularly relate to an artificial intelligence based (AI-based) system for automatically determining one or more digital asset valuation trends in multi-faceted blockchain ecosystem by leveraging artificial intelligence (AI) and data analysis techniques to streamline processes, improve security, and make blockchain technology more accessible to a diverse user base. BACKGROUND Blockchain technology has gained significant prominence as a decentralized and secure digital ledger system. The blockchain technology underpins various applications, including news aggregation, cryptocurrencies, digital contracts, non-fungible tokens (NFTs), and decentralized finance (DeFi). The distributed nature of blockchain technology ensures transparency and trust among participants, making blockchain technology a cornerstone of innovation in numerous industries. Despite the considerable potential of blockchain technology, it presents challenges for mainstream adoption. For instance, digital contract development demands proficiency in blockchain-specific languages like Solidity, creating a barrier to entry for non-developers and impeding the swift deployment of blockchain solutions. Furthermore, ensuring the security and integrity of digital contracts is of paramount importance, as vulnerabilities result in severe and catastrophic consequences. Additionally, the realm of cryptocurrency landscapes is characterized by instability and complexity, creating hurdles for both investors and enthusiasts seeking to navigate this cryptocurrency landscape. While the cryptocurrency landscape is vital to stay informed about cryptocurrency trends and market data, accomplishing this often requires a significant investment of time and expertise. Moreover, the rise of NFTs has brought about a new and distinctive digital asset category. However, the creation and management of NFTs generally entail proficiency in coding and a deep understanding of blockchain technology. In addition to the challenges in blockchain technology and cryptocurrency management, staying up to date with the rapidly evolving world of blockchain technology and digital currencies is crucial for informed decision-making. The proliferation of news and information on the internet overwhelms individuals seeking reliable and relevant updates on blockchain, cryptocurrencies, and related technologies. There are various technical problems with blockchain technology in the prior art. These technical problems encompass complexities related to digital contract development, which demand expertise in blockchain-specific languages and pose barriers for non-developers. Additionally, the security and integrity of digital contracts present ongoing concerns, as vulnerabilities have severe consequences. The cryptocurrency landscape is marked by volatility and intricacies, making it challenging for investors and enthusiasts to navigate and stay informed about market trends. Moreover, the emergence of NFTs has introduced a unique digital asset class, but the process of creating and managing NFTs typically involves specialized coding skills and blockchain knowledge. Therefore, there is a need for a system to address the aforementioned issues by providing am AI-based system and method to streamline blockchain processes, enhance security, simplify digital contract development, automate NFT management, determining/analyzing digital asset valuation trends to offer insights into the digital asset valuation trends, and facilitate efficient news aggregation. SUMMARY This summary is provided to introduce a selection of concepts, in a simple manner, which is further described in the detailed description of the disclosure. This summary is neither intended to identify key or essential inventive concepts of the subject matter nor to determine the scope of the disclosure. In accordance with an embodiment of the present disclosure, an artificial intelligence based (AI-based) method for automatically determining one or more digital asset valuation trends in a blockchain ecosystem, is disclosed. The AI-based method includes obtaining, by one or more hardware processors, data associated with one or more queries corresponding to the one or more digital asset valuation trends, from one or more communication devices of one or more users. The AI-based method further includes analyzing, by the one or more hardware processors, the data associated with the one or more queries to determine at least one of: purpose and context, of the one or more queries corresponding to the one or more digital asset valuat