Extracting data from blockchain

Abstract

Systems and methods for extracting data from blockchain transactions may include obtaining blockchain data, processing the blockchain data to determine token data, segmenting the token data from the blockchain data, and storing the segmented token data. The systems and methods may be used to extract descriptive data (e.g., non-homogenized token titles, non-homogenized token descriptions, and / or non-homogenized token tags), content data (e.g., previews of digital resources associated with the non-homogenized tokens), and / or transaction data (e.g., data describing transaction histories of the non-homogenized tokens). The extracted data may be indexed, and then provided in a search result page based on a search query.

Description

Technical Field

[0001] The present disclosure generally relates to extracting token data from blockchain data. More specifically, the present disclosure relates to accessing a blockchain node, obtaining blockchain data, processing blockchain data to identify token data, and extracting token data. Background Art

[0002] A search engine can receive a search query and provide search results that list web pages that are determined to be responsive to the search query. A search engine can crawl the web to determine web pages that have certain terms. Search results can typically be indicated by text descriptions and / or text titles. Search results can include text results, video results, and image results.

[0003] However, searching and identifying non-fungible token search results may be difficult. Searching for non-fungible tokens may be limited to finding general web pages associated with different non-fungible tokens; however, search engines may not be able to correctly identify and verify non-fungible token search results as non-fungible tokens. Summary of the invention

[0004] Aspects and advantages of embodiments of the present disclosure will be set forth in part in the following description, or may be learned from the description, or may be learned through practice of the embodiments.

[0005] An example aspect of the present disclosure relates to a computing system. The system may include one or more processors and one or more non-transitory computer-readable media that store instructions together, and the instructions, when executed by the one or more processors, cause the computing system to perform operations. The operation may include obtaining blockchain data from a blockchain computing system. The blockchain data may include code associated with multiple non-fungible tokens. The operation may include storing the blockchain data in a blockchain log, and processing the blockchain data to determine token data associated with a non-fungible token in a plurality of non-fungible tokens. The operation may include determining that a portion of the token data includes descriptive data. In some implementations, the descriptive data may be associated with the non-fungible token. The operation may include segmenting the descriptive data from the blockchain data. In some implementations, the operation may include storing the descriptive data.

[0006] In some implementations, the descriptive data may include information determined to be associated with a particular identifying characteristic of the non-fungible token. The determination may be based on one or more criteria associated with the structure of the non-fungible token byte code. In some implementations, determining that a portion of the token data includes the descriptive data may include: parsing the token data to generate a plurality of segment data sets; processing each of the plurality of segment data sets with a machine-learned natural language processing model to generate a plurality of segment classifications; and determining that a particular segment data set in the plurality of segment data sets includes the descriptive data based on the plurality of segment classifications.

[0007] In some implementations, the operation may include obtaining standard data. The standard data may describe one or more standards for the token code. The token data may be determined based at least in part on the one or more standards. In some implementations, the code of the blockchain data may be associated with the one or more standards for the token code. In some implementations, determining that a portion of the token data includes descriptive data may include: determining token identification data associated with the non-fungible token; determining a plurality of transactions in the blockchain data based on the token identification data; and determining a portion of the token data based on the plurality of transactions. The descriptive data may include at least one of: a digital asset associated with the non-fungible token, transaction data associated with the non-fungible token, a name associated with the non-fungible token, a description associated with the non-fungible token, or a creator identification associated with a creator of the non-fungible token.

[0008] Another example aspect of the present disclosure relates to a computer-implemented method. The method may include obtaining blockchain data from a blockchain computing system by a computing system including one or more processors. The blockchain data may include code associated with a plurality of non-fungible tokens. The method may include processing the blockchain data by the computing system to determine a link in the blockchain data. The method may include obtaining a login page associated with the link by the computing system. In some implementations, the login page may be obtained based on the link. The method may include determining, by the computing system, content data associated with the login page. The content data may be associated with a non-fungible token in a plurality of non-fungible tokens. The method may include storing the content data by the computing system.

[0009] In some implementations, the link may include at least one of a uniform resource identifier or a uniform resource locator. The landing page may include a digital asset. The digital asset may be a payload of a smart contract associated with the non-fungible token. In some implementations, the content data may include a preview of the digital asset associated with the non-fungible token.

[0010] In some implementations, the method may include determining a second link based on the login page and obtaining a second login page based on the second link. The second login page may include content data. In some implementations, obtaining blockchain data may include accessing a blockchain node. The blockchain node may be associated with a specific blockchain. Obtaining blockchain data may include recursively crawling a specific blockchain.

[0011] Another example aspect of the present disclosure relates to one or more non-transitory computer-readable media that collectively store instructions that, when executed by one or more computing devices, cause one or more computing devices to perform operations. The operations may include obtaining blockchain data from a blockchain computing system. The blockchain data may include code associated with a plurality of non-fungible tokens. The operations may include processing the blockchain data to determine a first set of data associated with a particular non-fungible token in a plurality of non-fungible tokens. The operations may include processing the blockchain data to determine a second set of data associated with a particular non-fungible token in a plurality of non-fungible tokens, and storing the first set of data and the second set of data.

[0012] In some implementations, the operations may include segmenting a first set of data and a second set of data from the blockchain data. The first set of data may include free-format text data. The second set of data may include transaction data from an electronic ledger. In some implementations, the operations may include generating index data for a particular non-fungible token based on the first set of data and the second set of data.

[0013] In some implementations, processing blockchain data to determine a first set of data associated with a particular non-fungible token in a plurality of non-fungible tokens may include crawling the blockchain data to identify link data and obtaining content data associated with the link data. In some implementations, processing blockchain data to determine a second set of data associated with a particular non-fungible token in a plurality of non-fungible tokens may include determining a plurality of data sets associated with non-fungible token transactions. Non-fungible token transactions may be associated with the particular non-fungible token. Processing blockchain data to determine a second set of data associated with a particular non-fungible token in a plurality of non-fungible tokens may include generating the second set of data based on the plurality of data sets.

[0014] Other aspects of the disclosure relate to various systems, apparatuses, non-transitory computer-readable media, user interfaces, and electronic devices.

[0015] These and other features, aspects and advantages of various embodiments of the present disclosure will be better understood with reference to the following description and appended claims.The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate example embodiments of the present disclosure and, together with the description, serve to explain the relevant principles. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] A detailed discussion of the embodiments for those skilled in the art is set forth in this specification with reference to the accompanying drawings, in which:

[0017] Figure 1A A block diagram depicting an example computing system that performs token data extraction and non-fungible token indexing according to example embodiments of the present disclosure.

[0018] Figure 1B A block diagram depicting an example computing device that performs token data extraction and non-fungible token indexing according to example embodiments of the present disclosure.

[0019] Figure 2 A block diagram depicts an example token data extraction system according to an example embodiment of the present disclosure.

[0020] Figure 3 A block diagram depicts an example token data aggregation system according to an example embodiment of the present disclosure.

[0021] Figure 4 A block diagram depicts an example token data generation system according to an example embodiment of the present disclosure.

[0022] Figure 5 A block diagram depicts an example composite representation generation system according to an example embodiment of the present disclosure.

[0023] Figure 6 A flow chart depicting an example method for performing token data acquisition according to an example embodiment of the present disclosure.

[0024] Figure 7 A flow chart depicting an example method for performing token data acquisition according to an example embodiment of the present disclosure.

[0025] Figure 8 A flow chart depicting an example method for performing token data acquisition according to an example embodiment of the present disclosure.

[0026] Fig. 9A A block diagram depicting an example computing system that performs token data acquisition and indexing according to example embodiments of the present disclosure.

[0027] Fig. 9B A block diagram depicting an example computing device that performs token data acquisition and indexing according to example embodiments of the present disclosure.

[0028] Fig. 9C A block diagram depicting an example computing system that performs token data acquisition and indexing according to example embodiments of the present disclosure.

[0029] Fig.10 A block diagram depicts an example indexing system according to an example embodiment of the present disclosure.

[0030] Fig.11 A block diagram depicts an example indexed data set according to an example embodiment of the present disclosure.

[0031] Fig.12 A block diagram depicts an example search according to an example embodiment of the present disclosure.

[0032] Fig.13A Depicted is an illustration of an example search results page according to an example embodiment of the present disclosure.

[0033] Fig. 13B Depicted is an illustration of an example search results page according to an example embodiment of the present disclosure.

[0034] Fig. 13C Depicted is an illustration of an example search results page according to an example embodiment of the present disclosure.

[0035] Fig.14 A block diagram depicts an example token data identification system according to an example embodiment of the present disclosure.

[0036] Fig.15 A block diagram depicts an example index filtering system according to an example embodiment of the present disclosure.

[0037] Fig.16 A block diagram depicts an example index determination system according to an example embodiment of the present disclosure.

[0038] Fig.17 A block diagram depicts an example quality determination system according to an example embodiment of the present disclosure.

[0039] Fig.18 A block diagram depicting example token index deduplication according to example embodiments of the present disclosure.

[0040] Fig.19 A block diagram depicts an example index validity determination according to an example embodiment of the present disclosure.

[0041] Fig. 20 A block diagram depicting example token index deduplication according to example embodiments of the present disclosure.

[0042] Fig.21 A block diagram depicting example search results page generation according to an example embodiment of the present disclosure.

[0043] Reference numerals that are repeated across multiple drawings are intended to identify like features in the various implementations. DETAILED DESCRIPTION

[0044] Overview

[0045] In general, the present disclosure relates to systems and methods for extracting blockchain data for indexing. Specifically, the systems and methods may utilize one or more processing techniques and / or one or more segmentation techniques to extract relevant data. For example, the systems and methods may include obtaining blockchain data from a blockchain computing system. The blockchain data may include code associated with a plurality of non-fungible tokens. The blockchain data may be stored in a blockchain log. The blockchain data may be processed to determine token data associated with a non-fungible token among a plurality of non-fungible tokens. The systems and methods may include determining that a portion of the token data includes descriptive data. In some implementations, the descriptive data may be associated with the non-fungible token. The descriptive data may be segmented from the blockchain data. In some implementations, the descriptive data may be stored.

[0046] The systems and methods disclosed herein may be used to implement non-fungible token searches via a search engine. For example, a search query may be received. The search engine may process the search query and may determine a plurality of search results associated with the search query. In some implementations, one or more search results may be associated with a non-fungible token. A particular search result may be associated with index data. One or more non-fungible token search results may be provided for display in a search result interface (e.g., a search result page). One or more non-fungible token search results may be provided for display in a separate panel. Alternatively and / or in addition, one or more non-fungible token search results may be provided for display adjacent to general search results. One or more non-fungible token search results may be provided along with one or more indicators indicating the properties of the non-fungible token (e.g., provided along with one or more labels, tags, and / or logos). In some implementations, one or more non-fungible token search results may be provided along with a generated preview of a digital resource based on the index data.

[0047] In some implementations, the systems and methods disclosed herein may be used for Web3 profiles, Web3 transactions, and / or Web3 identification.

[0048] Systems and methods can improve search user interfaces and user experiences by identifying and indexing relevant data that can be used to determine that a non-fungible token is responsive to a search query. Additionally and / or alternatively, the indexed data can be utilized to determine a ranking of a non-fungible token search result relative to other search results. Systems and methods can be used to determine whether a search result can be provided with a non-fungible token logo and / or whether a ranking can be improved based on an association with an authenticated non-fungible token. Additionally and / or alternatively, systems and methods can provide improved visualization of digital resources of non-fungible tokens by identifying and extracting data that can be used to generate a preview (e.g., a graphical representation) of a digital resource associated with a corresponding non-fungible token. The preview can then be provided as a thumbnail and / or a graphical representation of the non-fungible token along with the non-fungible token search results.

[0049] Systems and methods may obtain blockchain data from a blockchain computing system. The blockchain data may include code data (e.g., byte code) associated with a plurality of non-fungible tokens. The blockchain data may describe blockchain transactions that may be associated with a blockchain ledger. In some implementations, the code of the blockchain data may be associated with one or more standards for token code.

[0050] The blockchain data may be stored in a blockchain log. In some implementations, storing the blockchain data in the blockchain log may include preprocessing the blockchain data in a sandbox to determine whether the blockchain data includes vulnerabilities, viruses, and / or suspicious data. The preprocessing may utilize the sandbox environment to mitigate the spread of any potentially malicious content. Additionally and / or alternatively, the sandbox environment may be used to execute commands against a "running" node (e.g., a blockchain node that is disconnected from an external network).

[0051] The blockchain data may be processed to determine token data associated with a non-fungible token in a plurality of non-fungible tokens. The determination may be based on detection of a blockchain triggering event. Alternatively and / or in addition, the determination may be based on determining that the token data is associated with a digital resource (e.g., a digital asset).

[0052] In some implementations, systems and methods can obtain standard data. The standard data can describe one or more standards for token codes. The token data can be determined based at least in part on the one or more standards.

[0053] Systems and methods may determine that a portion of the token data includes descriptive data. The descriptive data may be associated with a non-fungible token. In some implementations, the descriptive data may include information determined to be associated with a specific identifying characteristic of the non-fungible token. The determination may be based on one or more criteria associated with the structure of the non-fungible token byte code. In some implementations, the descriptive data may include at least one of: a digital resource (e.g., a digital asset) associated with the non-fungible token, transaction data associated with the non-fungible token, a name associated with the non-fungible token, a description associated with the non-fungible token, or a creator identifier associated with a creator of the non-fungible token.

[0054] Alternatively and / or in addition, determining that a portion of the token data includes descriptive data may include: parsing the token data to generate a plurality of fragment data sets; processing each of the plurality of fragment data sets with a machine learning natural language processing model to generate a plurality of fragment classifications; and determining, based on the plurality of fragment classifications, that a particular fragment data set in the plurality of fragment data sets includes descriptive data.

[0055] Alternatively and / or in addition, determining that a portion of the token data includes descriptive data may include: determining token identification data associated with the non-fungible token; determining a plurality of transactions in the blockchain data based on the token identification data; and determining the portion of the token data based on the plurality of transactions.

[0056] In some implementations, the descriptive data may include transaction data (e.g., data describing the transaction history of a non-fungible token). The transaction data may be associated with an electronic ledger. The blockchain data may include embedded images, pointers, executable code, and other data structures that may be identified as descriptive data, which may be segmented out of the blockchain data and stored.

[0057] The descriptive data may be segmented from the blockchain data. In some implementations, multiple data strings associated with a particular non-fungible token may be identified and segmented from the blockchain data. The multiple data strings may then be aggregated and / or concatenated.

[0058] The descriptive data may then be stored. The descriptive data may be stored in a database for retrieval and processing for future tasks. The storage may be local and / or may be stored on a server computing system accessible to multiple users.

[0059] Additionally and / or alternatively, the systems and methods may be used to identify and obtain content data associated with a digital resource (e.g., a digital asset) associated with a non-fungible token. For example, the systems and methods may include obtaining blockchain data from a blockchain computing system. The blockchain data may include code associated with a plurality of non-fungible tokens. The blockchain data may be processed to determine a link in the blockchain data. A login page associated with the link may be obtained. In some implementations, a login page may be obtained based on the link. The systems and methods may determine content data associated with the login page. The content data may be associated with a non-fungible token in a plurality of non-fungible tokens. The content data may be stored.

[0060] The systems and methods may obtain blockchain data from a blockchain computing system. The blockchain data may include code data (e.g., byte code) associated with a plurality of non-fungible tokens. The blockchain data may be obtained via a blockchain node. In some implementations, the blockchain data may be obtained from a repository separate from the blockchain computing system.

[0061] In some implementations, obtaining blockchain data may include accessing a blockchain node. A blockchain node may be associated with a particular blockchain. Obtaining blockchain data may include recursively crawling a particular blockchain. Recursive crawling may occur at set intervals, trigger-based intervals (e.g., based on an event that occurs (e.g., a social media post describing a non-fungible token drop post), and / or user-specified intervals. For example, the systems and methods may intermittently and / or continuously obtain blockchain data associated with metadata of one or more particular non-fungible tokens from a blockchain in order to update the data when changes are made.

[0062] The blockchain data may be processed to determine a link in the blockchain data. The link may include at least one of a uniform resource identifier, an IPFS address, or a uniform resource locator.

[0063] Systems and methods can obtain a landing page associated with a link. In some implementations, a landing page can be obtained based on the link. The landing page can include a digital resource (e.g., a digital asset). The digital resource (e.g., a digital asset) can be the payload of a smart contract associated with a non-fungible token.

[0064] Content data associated with the login page may be obtained. The content data may be associated with a non-fungible token of the plurality of non-fungible tokens. In some implementations, the content data may include a preview of a digital asset associated with the non-fungible token.

[0065] In some implementations, the systems and methods can include determining a second link based on the landing page and obtaining a second landing page based on the second link. The second landing page can include content data.

[0066] Content data may be stored. The content data may include a preview of a digital resource (e.g., a digital asset) associated with the non-fungible token.

[0067] In some implementations, acquiring and / or generating image data based on a digital resource of a non-fungible token may include nested crawling to follow links and navigate to different landing pages. Additionally and / or alternatively, the content data may include a synthetic representation associated with the digital resource (e.g., a synthetic representation of the appearance and / or sound of the digital resource). The synthetic representation may be similar to (e.g., similar to) a preview of the digital resource. The synthetic representation may be indexed to be provided as a search result (e.g., an image search result, a video search result, and / or an audio search result), which may be displayed and / or provided adjacent to and / or with the web2.0 search results.

[0068] In some implementations, the digital resources may be accessed and processed to generate a composite representation. In some implementations, the composite representation may include a representative frame of a video digital asset. Alternatively and / or in addition, the composite representation may include compressed video, a graphics interchange format, and / or enhanced video.

[0069] Additionally and / or alternatively, the systems and methods may include identifying multiple token data sets associated with a particular non-fungible token that may be identified and stored. For example, the systems and methods may include obtaining blockchain data from a blockchain computing system. The blockchain data may include code associated with multiple non-fungible tokens. The blockchain data may be processed to determine a first set of data associated with a particular non-fungible token in the multiple non-fungible tokens. The blockchain data may be processed to determine a second set of data associated with a particular non-fungible token in the multiple non-fungible tokens. The systems and methods may include storing the first set of data and the second set of data.

[0070] The system and method can obtain blockchain data from a blockchain computing system. The blockchain data may include codes associated with multiple non-fungible tokens.

[0071] The blockchain data may be processed to determine a first set of data associated with a particular non-fungible token in a plurality of non-fungible tokens. The first set of data may include free-form text data.

[0072] In some implementations, processing the blockchain data to determine a first set of data associated with a particular non-fungible token in the plurality of non-fungible tokens may include crawling the blockchain data to identify link data and obtaining content data associated with the link data.

[0073] The systems and methods may include processing the blockchain data to determine a second set of data associated with a particular non-fungible token in the plurality of non-fungible tokens. The second set of data may include transaction data from an electronic ledger.

[0074] In some implementations, processing the blockchain data to determine the second set of data associated with a particular non-fungible token in the plurality of non-fungible tokens may include determining a plurality of data sets associated with non-fungible token transactions. The non-fungible token transactions may be associated with the particular non-fungible token. Processing the blockchain data to determine the second set of data associated with the particular non-fungible token in the plurality of non-fungible tokens may include generating the second set of data based on the plurality of data sets.

[0075] Additionally and / or alternatively, the systems and methods may segment the first set of data and the second set of data from the blockchain data. Segmentation may involve utilizing a machine learning segmentation model.

[0076] The first set of data and the second set of data may be stored. In some implementations, the first set of data and the second set of data may be stored in a database. Alternatively and / or in addition, the first set of data and the second set of data may be provided to an index computing system to index data associated with a particular non-fungible token.

[0077] Index data for a particular non-fungible token may then be generated based on the first set of data and the second set of data. The index data may include a preview of a digital resource associated with the non-fungible token. Additionally and / or alternatively, the index data may include a title, description, transaction history, and / or publisher information associated with the non-fungible token. Generating the index data may involve processing the content data using one or more machine learning models.

[0078] In some implementations, the systems and methods can obtain and / or determine more and more context with each nested crawl. A merge of the context data can then be generated and / or stored.

[0079] Text data, transaction data, and / or media data (e.g., images, video, and / or audio) may be acquired via different techniques. For example, text data may be acquired using natural language processing; transaction data may be acquired using token ID detection, segmentation, and concatenation; and media data may be acquired by navigating to different landing pages based on the identified uniform resource identifier.

[0080] In some implementations, the systems and methods may include obtaining lightweight natural language text data describing data stored on the blockchain (e.g., an open standard file format and data exchange format that can use human-readable text to store data objects (including attribute-value pairs and arrays)). The lightweight natural language text data can be parsed, nested data can be obtained, and / or bag-of-words data can be obtained. In some implementations, the bag-of-words can be obtained by a machine learning model to determine the signs and relevance for search and ranking.

[0081] In some implementations, different blockchains may include different protocols (e.g., different languages, different syntax, different data structures, and / or different reference protocols) and / or different formatting types. Systems and methods may adapt to different blockchains (e.g., adapt to different protocols and / or different formatting types). Adaptability may include using one or more conformer blocks to adapt to different protocols and / or different formatting types. Adaptability may include using one or more machine learning models to process blockchain data from different blockchains. Alternatively and / or in addition, adaptability may utilize deterministic functions and / or heuristics. Systems and methods may learn the protocol of a new blockchain and then map to a common setting.

[0082] Additionally and / or alternatively, the systems and methods may identify, acquire, and / or aggregate transaction data associated with one or more non-fungible tokens. The transaction data may describe a transaction history associated with the non-fungible token. For example, the transaction data may include primitives such as timestamps, addresses involved, and / or lineage of data over time. The acquired transaction data of different non-fungible tokens may be compared to generate comparative data. In some implementations, fuel prices, transaction volumes, and / or mint data may be compared to generate comparative data.

[0083] In some implementations, token data may be determined, segmented, and / or connected based on a token ID and / or a contract address. For example, systems and methods may process blockchain data to identify a token ID and / or a contract address. Token data associated with a token ID and / or a contract address may be searched and / or determined. Token data may be segmented from blockchain data. The segmented data may then be aggregated.

[0084] Systems and methods may identify and / or obtain data associated with the reliability of non-fungible token data. For example, data associated with block verification in a blockchain (e.g., data associated with chain data). Additionally and / or alternatively, composite signals may be obtained and considered to determine block reliability. Composite signals may be determined based on processing blockchain data of a blockchain log. Calls (e.g., calls issued to a peer-to-peer network) may be processed.

[0085] The systems and methods of the present disclosure provide many technical effects and benefits. As an example, the systems and methods may provide systems and methods for extracting token data associated with one or more non-fungible tokens. For example, the systems and methods disclosed herein may process blockchain data to identify and extract multiple token data sets associated with one or more non-fungible tokens, and then may further process the multiple token data sets to generate index data.

[0086] Another technical benefit of the systems and methods of the present disclosure is the ability to utilize the identified data (e.g., token ID and / or contract address) to determine transaction data to extract to obtain the transaction history of a particular non-fungible token associated with a blockchain. For example, the systems and methods disclosed herein can process blockchain data to identify various data sets associated with a particular non-fungible token in the blockchain. The various data sets can be segmented from the rest of the blockchain data and can then be aggregated.

[0087] Another example of technical effects and benefits relates to increased computing efficiency and improved computing system functionality. For example, the systems and methods disclosed herein can utilize token data extraction to significantly reduce the computing power for indexing, searching, and displaying non-fungible token data. In addition, by identifying digital resources, processing digital resources, and generating synthetic representations that can be smaller in size than the original digital resources, the systems and methods disclosed herein can reduce the computing power used to generate index databases.

[0088] Referring now to the accompanying drawings, example embodiments of the present disclosure will be discussed in greater detail.

[0089] Example Apparatus and Systems

[0090] Figure 1A A block diagram depicting an example computing system 100 that performs token data extraction and non-fungible token indexing according to an example embodiment of the present disclosure. The system 100 includes a user computing system 130, a server computing system 110, a creator computing system 150, and a blockchain computing system 170 communicatively coupled via a network 180.

[0091] User computing system 130 may be any type of computing device, such as, for example, a personal computing device (e.g., a laptop or desktop computer), a mobile computing device (e.g., a smartphone or tablet computer), a gaming console or controller, a wearable computing device, an embedded computing device, or any other type of computing device.

[0092] The user computing system 130 includes one or more processors 132 and memory 134. The one or more processors 132 may be any suitable processing device (e.g., a processor core, a microprocessor, an ASIC, an FPGA, a controller, a microcontroller, etc.) and may be one processor or a plurality of processors operatively connected. The memory 134 may include one or more non-transitory computer-readable storage media, such as RAM, ROM, EEPROM, EPROM, flash memory devices, disks, etc., and combinations thereof. The memory 134 may store data 136 and instructions 138 executed by the processor 132 to cause the user computing system 130 to perform operations.

[0093] The user computing system 130 may also include one or more user input components that receive user input. For example, the user input component may be a touch-sensitive component (e.g., a touch-sensitive display screen or a touchpad) that is sensitive to the touch of a user input object (e.g., a finger or a stylus). The touch-sensitive component may be used to implement a virtual keyboard. Other example user input components include a microphone, a traditional keyboard, or other means by which a user can provide user input.

[0094] The server computing system 110 includes one or more processors 112 and memory 114. The one or more processors 112 may be any suitable processing device (e.g., a processor core, a microprocessor, an ASIC, an FPGA, a controller, a microcontroller, etc.) and may be one processor or a plurality of processors operatively connected. The memory 114 may include one or more non-transitory computer-readable storage media, such as RAM, ROM, EEPROM, EPROM, flash memory devices, magnetic disks, etc., and combinations thereof. The memory 114 may store data 118 and instructions 116 executed by the processor 112 to cause the server computing system 110 to perform operations.

[0095] In some implementations, the server computing system 110 includes or is otherwise implemented by one or more server computing devices. Where the server computing system 110 includes multiple server computing devices, such server computing devices may operate according to a sequential computing architecture, a parallel computing architecture, or some combination thereof.

[0096] The blockchain computing system 170 includes one or more processors and memory. The one or more processors may be any suitable processing device (e.g., a processor core, a microprocessor, an ASIC, an FPGA, a controller, a microcontroller, etc.) and may be a processor or multiple processors operatively connected. The memory may include one or more non-transitory computer-readable storage media, such as RAM, ROM, EEPROM, EPROM, flash memory devices, disks, etc., and combinations thereof. The memory may store data and instructions executed by the processor to enable the blockchain computing system 170 to perform operations. In some implementations, the blockchain computing system 170 includes one or more server computing devices or is otherwise implemented by one or more server computing devices.

[0097] The network 180 may be any type of communication network, such as a local area network (e.g., an intranet), a wide area network (e.g., the Internet), or some combination thereof, and may include any number of wired or wireless links. In general, communications over the network 180 may be conducted via any type of wired and / or wireless connection using a variety of communication protocols (e.g., TCP / IP, HTTP, SMTP, FTP), encodings or formats (e.g., HTML, XML), and / or protection schemes (e.g., VPN, secure HTTP, SSL).

[0098] The computing system 100 may include multiple applications (e.g., applications 1 to N). Each application may communicate with a central intelligence layer. Example applications may include a text messaging application, an email application, a dictation application, a virtual keyboard application, a browser application, etc. In some implementations, each application may communicate with the central intelligence layer (and the models stored therein) using an API (e.g., a public API across all applications).

[0099] The central intelligence layer can communicate with a central device data layer. The central device data layer can be a centralized data repository for computing system 100. In some implementations, the central device data layer can communicate with many other components of the computing device, such as, for example, one or more sensors, a context manager, a device state component, and / or additional components. In some implementations, the central device data layer can communicate with each device component using an API (e.g., a private API).

[0100] Additionally and / or alternatively, Figure 1AAn exemplary computing system 100 is depicted that can be used to implement token data extraction and indexing according to aspects of the present disclosure. The system 100 has a user-server architecture that includes a server 110 that communicates with one or more user computing systems 130 via a network 180. However, the present disclosure can be implemented using other suitable architectures that can include any number of computing systems communicating via a network 180.

[0101] System 100 includes a server 110, such as, for example, a web server. Server 110 may be one or more computing devices implemented as a parallel computing system and / or a distributed computing system. Specifically, multiple computing devices may act together as a single server 110. Server 110 may have one or more processors 112 and a memory 114. Server 110 may also include a network interface for communicating with one or more remote computing devices (e.g., user devices) 130 via a network 180.

[0102] The processor 112 may be any suitable processing device, such as a microprocessor, a microcontroller, an integrated circuit, or other suitable processing device. The memory 114 may include any suitable computing system or medium, including but not limited to non-transitory computer-readable media, RAM, ROM, a hard drive, a flash drive, or other memory devices. The memory 114 may store information accessible by the processor 112, including instructions 116 that may be executed by the processor 112. The instructions 116 may be any set of instructions that, when executed by the processor 112, cause the processor 112 to provide desired functionality.

[0103] Specifically, the instructions 116 may be executed by the processor 112 to implement index adjustment (e.g., index deduplication). The user profile database 120 may be configured to store multiple user profiles associated with multiple users using one or more user computing systems 130. In some implementations, the user profile database 120 may be configured to facilitate one or more interactions. Facilitating one or more interactions may involve sending data to and receiving data from the blockchain computing system 170 using a blockchain application programming interface (API) 122. For example, the server computing system 110 may utilize the blockchain API 122 to update one or more ledgers 172 of the blockchain computing system 170. One or more ledgers 172 may be associated with one or more tokens 174. One or more tokens 174 may include one or more non-fungible tokens, which may include scripts associated with digital assets (e.g., image data, video data, text data, potential encoding data, domain data, audio data, augmented reality asset rendering data, and / or virtual reality asset rendering data). Specifically, the script may reference a specific digital asset offered for sale. Digital assets may include image data, text data, video data, potentially encoded data, domain names, virtual property, augmented reality assets, virtual reality assets (e.g., virtual reality environments and / or virtual reality objects for interacting in the environments), smart contracts, physical authentication, etc. In some implementations, one or more ledgers 172 may be associated with a cryptocurrency that may be used to conduct transactions in a physical marketplace and / or a virtual marketplace.

[0104] It should be understood that the term "element" may refer to computer logic for providing desired functionality. Therefore, any element, function and / or instruction may be implemented in hardware, dedicated circuits, firmware and / or software that controls a general purpose processor. In one implementation, an element or function is a program code file stored on a storage device, loaded into a memory and executed by a processor, or may be provided from a computer program product such as a computer executable instruction stored in a tangible computer readable storage medium such as RAM, a hard disk or an optical or magnetic medium.

[0105] The memory 114 may also include data 118 that may be retrieved, manipulated, created, or stored by the processor 112. The data 118 may include search result data, ranking data, image data (e.g., digital maps, satellite images, aerial photographs, street-level photographs, synthetic models, paintings, personal images, portraits, etc.), video data, audio data, text data (e.g., books, articles, blogs, poems, etc.), potentially encoded data, blockchain address data, tables, vector data (e.g., vector representations of roads, plots of land, buildings, etc.), points of interest data (e.g., places such as islands, cities, restaurants, hospitals, parks, hotels, and schools), or other data or related information. As an example, the data 118 may be used to access information and data associated with a particular digital asset, website, search result, blockchain, etc.

[0106] The data 118 may be stored in one or more databases. The one or more databases may be connected to the server 110 via a high bandwidth LAN or WAN, or may also be connected to the server 110 via a network 180. The one or more databases may be split so that they are located in multiple locations.

[0107] Server 110 may exchange data with one or more user computing systems 130 via network 180. Figure 1A 1, two user computing systems 130 are shown, but any number of user computing systems 130 may be connected to the server 110 via the network 180. The user computing system 130 may be any suitable type of computing device, such as a general purpose computer, a special purpose computer, a navigation device, a laptop computer, a desktop computer, an integrated circuit, a mobile device, a smart phone, a tablet computer, a wearable computing device, a display coupled to one or more processors and / or embedded with one or more processors, or other suitable computing devices. In addition, the user computing system 130 may be a plurality of computing devices acting together to perform an operation or computing action.

[0108] Similar to server 110, user computing system 130 may include processor 132 and memory 134. Memory 134 may store information accessible by processor 132, including instructions and data that may be executed by the processor. As an example, memory 134 may store data 136 and instructions 138.

[0109] Instructions 138 may provide instructions for implementing a browser, non-fungible token purchases, and / or a number of other functions. Specifically, a user of user computing system 130 may exchange data with server 110 by using a browser to access a website accessible at a particular URL. The token data identification and non-fungible token index of the present disclosure may be provided as an element of a user interface of a website and / or application.

[0110] Data 136 may include data related to running a specialized application on user computing system 130. Specifically, the specialized application may be used to exchange data with server 110 via network 160. Data 136 may include user device readable code for providing and implementing aspects of the present disclosure. Additionally and / or alternatively, data 136 may include data related to previously input or received data. For example, data 136 may include data related to past occurrences of a specialized application.

[0111] The user computing system 130 may include various user input devices for receiving information from the user, such as a touch screen, a touch pad, data entry keys, a speaker, a mouse, a motion sensor, and / or a microphone suitable for speech recognition. In addition, the user computing system 130 may have a display for presenting information (such as a user interface), displaying digital assets, displaying pop-up windows or application elements displayed in an interface, and / or other forms of information.

[0112] The user computing system 130 may also include a user profile 140, which may be used to identify the user of the user computing system 130. The user may optionally use the user profile 140 to conduct one or more transactions, which may then be recorded on one or more ledgers 172 of the blockchain computing system 170. The user profile 140 may describe user information, which may include an identification number and / or payment account information. For example, the user profile 140 may include data associated with a crypto wallet, which may be linked to a browser application via an application extension and / or embedding.

[0113] User computing system 130 may further include a graphics processing unit. Processor 132 may use the graphics processing unit to perform index adjustments. In some embodiments, user computing system 130 performs any and all index adjustments.

[0114] User computing system 130 may include a network interface for communicating with server 110 over network 180. Network interface may include any component or configuration suitable for communicating with server 110 over network 180, including, for example, one or more ports, transmitters, wireless cards, controllers, physical layer components, or other items for communicating according to any currently known or later developed communication protocol or technology.

[0115] The network 180 may be any type of communication network, such as a local area network (e.g., an intranet), a wide area network (e.g., the Internet), or some combination thereof. The network 180 may also include a direct connection between the user computing system 130 and the server 110. In general, communication between the server 110 and the user computing system 130 may be conducted via a network interface using any type of wired and / or wireless connection, using a variety of communication protocols (e.g., TCP / IP, HTTP), encodings or formats (e.g., HTML, XML), and / or protection schemes (e.g., VPN, secure HTTP, SSL).

[0116] In some implementations, the exemplary computing system 100 may include one or more creator computing systems 150. The one or more creator computing systems 150 may be used to generate images, videos, prose, poetry, audio, etc., which may then be offered for sale. The one or more creator computing systems 150 may include one or more processors 152, which may be used to perform one or more operations to implement the systems and methods disclosed herein. The one or more creator computing systems 150 may include one or more memory components 154, which may be used to store data 156 and one or more instructions 158. The data 156 may include data related to one or more applications, one or more media data sets, etc. The instructions 158 may include one or more operations for implementing the systems and methods disclosed herein.

[0117] One or more creator computing systems 150 may store data associated with one or more digital assets 160 and / or one or more creator profiles 162. One or more digital assets 160 may include text data, image data, video data, audio data, potentially encoded data, domain data, or various other data formats. One or more creator profiles 162 may include information associated with one or more "creators" of one or more digital assets 160. One or more creator profiles 162 may include identification data, transaction data, and / or encrypted wallet data.

[0118] Additionally and / or alternatively, the exemplary computing system 100 may include one or more blockchain computing systems 170. The one or more blockchain computing systems 170 may include multiple computing devices for decentralized data storage, such that multiple "blocks" may be distributed throughout a network of computing devices to provide a secure data storage system, which may include one or more ledgers 172 and one or more tokens 174. In some implementations, each of the one or more tokens 174 may be associated with at least a portion of the one or more ledgers 172.

[0119] Blockchain may refer to a system configured to securely record information. Blockchain may include a decentralized system that may make it extremely difficult to change information. Blockchain may include a digital ledger of transactions that may be replicated and distributed across a network of computing systems. Each block in the chain may include multiple transactions. When a new transaction occurs on the blockchain, a record of the transaction may be added to the ledger of each computing device. Blockchain may be used to track the exchange of currency and / or digital assets via recording transactions on a digital ledger, which may be propagated throughout the decentralized system. Currencies exchanged and tracked via the blockchain computing system 170 may be referred to as cryptocurrencies.

[0120] The tokens 174 may include one or more non-fungible tokens. Non-fungible tokens may be minted on a blockchain associated with the blockchain computing system 170. A non-fungible token (NFT) may be a certificate of authenticity of a digital asset. NFTs are not interchangeable, so their value depends on the price anyone might be willing to pay for the asset. NFTs may be printed on a blockchain so that their scarcity and authenticity may be maintained. A digital asset may be defined as anything that is stored digitally and is uniquely identifiable, which an organization may use to realize value. Examples of digital assets may include tweets, social media comments, documents, audio, images, videos, logos, website domains, slide presentations, spreadsheets, CSS files and formats, executable code, and / or websites.

[0121] Figure 1B Describing can be done by Figure 1ABlock diagram of an example blockchain 50 used by a blockchain computing system 170 of an exemplary computing system 100 of FIG. The example blockchain 50 may include a plurality of blocks that may be used to store data having one or more cryptographic features. The blockchain 50 may be stored on a decentralized computing system including a plurality of computing devices. The blockchain 50 may be a public blockchain (e.g., a blockchain that is open and has no access restrictions, so that anyone with Internet access can send transactions or verify transactions as part of a decentralized distributed system), a private blockchain (e.g., a blockchain that provides access based on permissions set by a network administrator), or a hybrid blockchain (e.g., a blockchain with a combination of unrestricted blocks and restricted blocks). The blockchain 50 may include a proof-of-work feature that may include one or more cryptographic forms of proof. Proof-of-work may be provided when requesting an update to the blockchain 50 (e.g., requesting an update to a ledger based on a new transaction). Proof-of-work may indicate that a device or a group of devices has performed a certain amount of computation, which may then be verified by other parties. Once verified, the blockchain 50 may be updated, or may remain unchanged in response to a verification failure. The proof-of-work feature may be used to mitigate the computational cost of each device in the system having to perform the same computational function and check to determine that the request is valid for updating the blockchain 50.

[0122] Each block may include a hash, a previous hash associated with the hash of the previous block, and data. In some implementations, each block may include a random number. A hash may be a fixed-length hash value that may serve as a fingerprint of a particular block. A hash value may be generated based on a hash function and may change each time a change is made to the data of that particular block. A previous hash may include a hash value of a block immediately preceding a particular block. A previous hash may be used to ensure that downstream true values ​​remain unchanged unless appropriate verification is performed. The data may include transaction data (e.g., a transaction ledger), a timestamp, a value associated with a cryptocurrency value, a non-fungible token (e.g., a non-fungible token including a script that references a digital asset, random number data, and / or general blockchain data. A random number (i.e., a number that is used only once) may be a number added to a block in a blockchain that may satisfy a difficulty level constraint when a block is rehashed. A random number may be a number that a blockchain miner is solving for an incentive (e.g., cryptocurrency).

[0123] The blockchain 50 may include one or more security protocols and / or features. The blockchain 50 may include an encryption system. For example, the blockchain 50 may verify that the blockchain 50 is valid by ensuring that the stored previous hash stored in the block matches the hash value of the previous block from the last block back to the first block (e.g., the genesis block). In some implementations, the blockchain 50 may include proof-of-work verification, which may rely on verifying the computational proof before implementing changes to the stored data (e.g., the stored ledger). Proof-of-work verification may take seconds, minutes, and / or hours based in part on the number of blocks in the blockchain 50. Additionally and / or alternatively, the blockchain 50 may be implemented on a distributed decentralized computing system. In some implementations, each computing device in the distributed decentralized computing system may store a portion (e.g., a block in a plurality of blocks) or all of the blocks in the blockchain 50. Therefore, the system can verify the data by ensuring that the data is uniform in most (if not all) distributed systems. Before adding new data, each node of the distributed system may be checked for tampering.

[0124] The data may include data associated with a cryptocurrency value (e.g., a ledger associated with a particular cryptocurrency value), data associated with a digital asset (e.g., a non-fungible token minted on blockchain 50 that may include a script associated with a digital asset), data associated with a smart contract (e.g., a smart contract that includes conditions that automatically initiate actions in response to criteria being met), and / or timestamp data (e.g., timestamp data for block creation, minting, transactions, etc.).

[0125] Specifically, Figure 1B A first block 10, a second block 20, a third block 30, a fourth block 40, and an nth block 60 are depicted. Although five blocks are depicted, any number of blocks may be used. The first block 10 may be a genesis block (e.g., the first overall block in a blockchain). The first block 10 may include a corresponding first hash 12 (e.g., a hash value associated with the first block 10). The first block 10 may include a first previous hash 14 (e.g., if the first block 10 has a block before it in the blockchain 50, the hash of the previous block may be stored on the first block 10). Additionally and / or alternatively, the first block 10 may include data 16 and a random number 18.

[0126] The second block 20 may be subsequent to the first block 10. The second block 20 may include a corresponding second hash 22 (e.g., a hash value associated with the second block 20). The second block 20 may include a second previous hash 24 (e.g., the second previous hash 24 may be the same as or reference the first hash 12). Additionally and / or alternatively, the second block 20 may include data 26 and a random number 28.

[0127] The third block 30 may be subsequent to the second block 20. The third block 30 may include a corresponding third hash 32 (e.g., a hash value associated with the third block 30). The third block 30 may include a third previous hash 34 (e.g., the third previous hash 34 may be the same as or reference the second hash 22). Additionally and / or alternatively, the third block 30 may include data 36 and a random number 38.

[0128] Additionally and / or alternatively, the fourth block 40, the nth block 60, and other potential blocks may include corresponding hashes, corresponding previous hashes, and data. The first data 16, the second data 26, the third data 36, ​​and the data of the other blocks may include overlapping data, may be different, and / or the same, such that the data is repeated for all blocks. In some implementations, each block may be associated with a different transaction (e.g., a different mint, a different sale, etc.). The first random number 18, the second random number 28, the third random number 38, and the random numbers of the other blocks may be different and may be solved during minting.

[0129] The data in each block may include ledger data that may include timestamps, the assets and / or cryptocurrencies exchanged, the participants involved in the transaction, and / or various other information.

[0130] In some implementations, multiple different blockchains can be used for the systems and methods disclosed herein. Different blockchains can include different configurations. Different blockchains can include parallel chains, side chains, shared blocks, different chains, different permissions, different purposes, different numbers of blocks, and / or different hash functions and / or different hash value lengths.

[0131] In some implementations, the systems and methods may include one or more machine learning model computing systems 900. One or more machine learning models may be used for various tasks to achieve token data identification, acquisition, indexing, and deduplication.

[0132] Fig. 9A A block diagram depicts an example computing system 900 that performs token data identification, acquisition, indexing, and deduplication according to an example embodiment of the present disclosure. System 900 includes a user computing device 902, a server computing system 930, and a training computing system 950 that are communicatively coupled via a network 980.

[0133] The user computing device 902 may be any type of computing device, such as, for example, a personal computing device (e.g., a laptop or desktop computer), a mobile computing device (e.g., a smartphone or tablet computer), a gaming console or controller, a wearable computing device, an embedded computing device, or any other type of computing device.

[0134] The user computing device 902 includes one or more processors 912 and memory 914. The one or more processors 912 can be any suitable processing device (e.g., a processor core, a microprocessor, an ASIC, an FPGA, a controller, a microcontroller, etc.) and can be one processor or multiple processors operatively connected. The memory 914 can include one or more non-transitory computer-readable storage media, such as RAM, ROM, EEPROM, EPROM, flash memory devices, disks, etc. and combinations thereof. The memory 914 can store data 916 and instructions 918 executed by the processor 912 to cause the user computing device 902 to perform operations.

[0135] In some implementations, the user computing device 902 may store or include one or more token index models 920. For example, the token index model 920 may be or may otherwise include various machine learning models, such as a neural network (e.g., a deep neural network) or other types of machine learning models, including nonlinear models and / or linear models. The neural network may include a feedforward neural network, a recurrent neural network (e.g., a long short-term memory recurrent neural network), a convolutional neural network, or other forms of neural networks. Reference Figure 4 and Fig.11 Discuss example token indexing model 920.

[0136] In some implementations, one or more token indexing models 920 may be received from a server computing system 930 via a network 980, stored in a user computing device memory 914, and then used or otherwise implemented by one or more processors 912. In some implementations, a user computing device 902 may implement multiple parallel instances of a single token indexing model 920 (e.g., to perform parallel token data indexing across multiple instances of token data describing non-fungible tokens).

[0137] More specifically, the token indexing model may include one or more detection models, one or more segmentation models, one or more classification models, one or more enhancement models, one or more generation models, and / or one or more feature extractor models. The token indexing model may process blockchain data and / or web page data to generate index data describing index information associated with one or more corresponding non-fungible tokens. In some implementations, the one or more token indexing models may include one or more token identification models. The one or more token identification models may process blockchain data to identify token data, and the one or more segmentation models may segment token data from the blockchain data. The segmented token data may then be indexed via one or more additional machine learning models of the token indexing model.

[0138] Additionally or alternatively, one or more token index models 940 may be included in or otherwise stored and implemented by a server computing system 930 that communicates with the user computing device 902 according to a client-server relationship. For example, the token index model 940 may be implemented by the server computing system 940 as part of a web service (e.g., a token index service). Thus, one or more models 920 may be stored and implemented at the user computing device 902, and / or one or more models 940 may be stored and implemented at the server computing system 930.

[0139] The user computing device 902 may also include one or more user input components 922 that receive user input. For example, the user input component 922 may be a touch-sensitive component (e.g., a touch-sensitive display screen or a touchpad) that is sensitive to the touch of a user input object (e.g., a finger or a stylus). The touch-sensitive component may be used to implement a virtual keyboard. Other example user input components include a microphone, a traditional keyboard, or other devices by which a user can provide user input.

[0140] The server computing system 930 includes one or more processors 932 and memory 934. The one or more processors 932 can be any suitable processing device (e.g., a processor core, a microprocessor, an ASIC, an FPGA, a controller, a microcontroller, etc.) and can be one processor or multiple processors operatively connected. The memory 934 can include one or more non-transitory computer-readable storage media, such as RAM, ROM, EEPROM, EPROM, flash memory devices, disks, etc. and combinations thereof. The memory 934 can store data 936 and instructions 938 executed by the processor 932 to cause the server computing system 930 to perform operations.

[0141] In some implementations, the server computing system 930 includes or is otherwise implemented by one or more server computing devices. Where the server computing system 930 includes multiple server computing devices, such server computing devices may operate according to a sequential computing architecture, a parallel computing architecture, or some combination thereof.

[0142] As described above, the server computing system 930 may store or otherwise include one or more machine-learned token index models 940. For example, the model 940 may be or may otherwise include a variety of machine-learned models. Example machine-learned models include neural networks or other multi-layer nonlinear models. Example neural networks include feedforward neural networks, deep neural networks, recurrent neural networks, and convolutional neural networks. Figure 4 and Fig.11 Example model 940 is discussed.

[0143] User computing device 902 and / or server computing system 930 may train models 920 and / or 940 via interaction with training computing system 950 communicatively coupled via network 980. Training computing system 950 may be separate from server computing system 930 or may be part of server computing system 930.

[0144] The training computing system 950 includes one or more processors 952 and a memory 954. The one or more processors 952 may be any suitable processing device (e.g., a processor core, a microprocessor, an ASIC, an FPGA, a controller, a microcontroller, etc.) and may be one processor or a plurality of processors operatively connected. The memory 954 may include one or more non-transitory computer-readable storage media, such as RAM, ROM, EEPROM, EPROM, flash memory devices, disks, etc., and combinations thereof. The memory 954 may store data 956 and instructions 958 executed by the processor 952 to cause the training computing system 950 to perform operations. In some implementations, the training computing system 950 includes or is otherwise implemented by one or more server computing devices.

[0145] The training computing system 950 may include a model trainer 960 that trains the machine learning models 920 and / or 940 stored at the user computing device 902 and / or the server computing system 930 using various training or learning techniques such as, for example, error back propagation. For example, a loss function may be back propagated through the model to update one or more parameters of the model (e.g., based on the gradient of the loss function). Various loss functions may be used, such as mean squared error, likelihood loss, cross entropy loss, hinge loss, and / or various other loss functions. Gradient descent techniques may be used to iteratively update the parameters in multiple training iterations.

[0146] In some implementations, performing error back-propagation may include performing truncated back-propagation through time. The model trainer 960 may perform a variety of generalization techniques (eg, weight decay, backoff, etc.) to improve the generalization capabilities of the model being trained.

[0147] Specifically, the model trainer 960 can train the token index model 920 and / or 940 based on a set of training data 962. The training data 962 can include, for example, training blockchain data, training web page data, training transaction data, ground truth labels, ground truth index information, and / or ground truth segmentation masks.

[0148] In some implementations, if the user has provided consent, the training examples may be provided by the user computing device 902. Thus, in such implementations, the model 920 provided to the user computing device 902 may be trained by the training computing system 950 based on user-specific data received from the user computing device 902. In some cases, this process may be referred to as personalizing the model.

[0149] The model trainer 960 includes computer logic for providing the desired functionality. The model trainer 960 can be implemented in hardware, firmware, and / or software that controls a general purpose processor. For example, in some implementations, the model trainer 960 includes a program file stored on a storage device, loaded into a memory, and executed by one or more processors. In other implementations, the model trainer 960 includes one or more sets of computer executable instructions stored in a tangible computer readable storage medium such as RAM, a hard disk, or an optical or magnetic medium.

[0150] The network 980 can be any type of communication network, such as a local area network (e.g., an intranet), a wide area network (e.g., the Internet), or some combination thereof, and can include any number of wired or wireless links. In general, communications through the network 980 can be conducted via any type of wired and / or wireless connection using a variety of communication protocols (e.g., TCP / IP, HTTP, SMTP, FTP), encodings or formats (e.g., HTML, XML), and / or protection schemes (e.g., VPN, secure HTTP, SSL).

[0151] The machine learning models described in this specification can be used for a variety of tasks, applications, and / or use cases.

[0152] In some implementations, the input of the machine learning model of the present disclosure may be image data. The machine learning model may process the image data to generate an output. As an example, the machine learning model may process the image data to generate an image recognition output (e.g., recognition of image data, potential embedding of image data, encoded representation of image data, hash of image data, etc.). As another example, the machine learning model may process the image data to generate an image segmentation output. As another example, the machine learning model may process the image data to generate an image classification output. As another example, the machine learning model may process the image data to generate an image data modification output (e.g., a change in image data, etc.). As another example, the machine learning model may process the image data to generate an encoded image data output (e.g., an encoded representation and / or compressed representation of image data, etc.). As another example, the machine learning model may process the image data to generate a prediction output.

[0153] In some implementations, the input of the machine learning model of the present disclosure may be text or natural language data. The machine learning model may process the text or natural language data to generate an output. As an example, the machine learning model may process the natural language data to generate a language encoding output. As another example, the machine learning model may process the text or natural language data to generate a potential text embedding output. As another example, the machine learning model may process the text or natural language data to generate a classification output. As another example, the machine learning model may process the text or natural language data to generate a text segmentation output. As another example, the machine learning model may process the text or natural language data to generate a semantic intent output. As another example, the machine learning model may process the text or natural language data to generate an amplified text or natural language output (e.g., text or natural language data with higher quality than the input text or natural language, etc.). As another example, the machine learning model may process the text or natural language data to generate a predicted output.

[0154] In some implementations, the input of the machine learning model of the present disclosure may be speech data. The machine learning model may process the speech data to generate an output. As an example, the machine learning model may process the speech data to generate a speech recognition output. As another example, the machine learning model may process the speech data to generate a speech translation output. As another example, the machine learning model may process the speech data to generate a potential embedding output. As another example, the machine learning model may process the speech data to generate an encoded speech output (e.g., an encoded representation and / or compressed representation of the speech data, etc.). As another example, the machine learning model may process the speech data to generate a text representation output (e.g., a text representation of the input speech data, etc.). As another example, the machine learning model may process the speech data to generate a predicted output.

[0155] In some implementations, the input of the machine learning model of the present disclosure may be latent coded data (e.g., a latent space representation of an input, etc.). The machine learning model may process the latent coded data to generate an output. As an example, the machine learning model may process the latent coded data to generate a recognition output. As another example, the machine learning model may process the latent coded data to generate a reconstruction output. As another example, the machine learning model may process the latent coded data to generate a search output. As another example, the machine learning model may process the latent coded data to generate a re-clustering output. As another example, the machine learning model may process the latent coded data to generate a prediction output.

[0156] In some implementations, the input to the machine learning model of the present disclosure may be statistical data. The machine learning model may process the statistical data to generate an output. As an example, the machine learning model may process the statistical data to generate an identification output. As another example, the machine learning model may process the statistical data to generate a prediction output. As another example, the machine learning model may process the statistical data to generate a classification output. As another example, the machine learning model may process the statistical data to generate a segmentation output. As another example, the machine learning model may process the statistical data to generate a segmentation output. As another example, the machine learning model may process the statistical data to generate a visualization output. As another example, the machine learning model may process the statistical data to generate a diagnostic output.

[0157] In some cases, the machine learning model can be configured to perform a task that includes encoding input data for reliable and / or efficient transmission or storage (and / or corresponding decoding). For example, the task can be an audio compression task. The input can include audio data, and the output can include compressed audio data. In another example, the input includes visual data (e.g., one or more images or videos), the output includes compressed visual data, and the task is a visual data compression task. In another example, the task can include generating an embedding for the input data (e.g., input audio or visual data).

[0158] In some cases, the input includes visual data, and the task is a computer vision task. In some cases, the input includes pixel data of one or more images, and the task is an image processing task. For example, the image processing task may be image classification, where the output is a set of scores, each score corresponding to a different object class and representing the possibility that one or more images depict an object belonging to an object class. The image processing task may be object detection, where the image processing output identifies one or more regions in one or more images, and for each region, identifies the possibility that the region depicts an object of interest. As another example, the image processing task may be image segmentation, where the image processing output defines the corresponding possibility of each category in a set of predetermined categories for each pixel in one or more images. For example, a set of categories may be foreground and background. As another example, a set of categories may be object classes. As another example, the image processing task may be depth estimation, where the image processing output defines a corresponding depth value for each pixel in one or more images. As another example, the image processing task may be motion estimation, where the network input includes multiple images, and the image processing output defines the motion of the scene depicted at the pixel between images in the network input for each pixel of one of the input images.

[0159] In some cases, the input includes audio data representing a spoken utterance, and the task is a speech recognition task. The output may include a text output mapped to the spoken utterance. In some cases, the task includes encrypting or decrypting the input data. In some cases, the task includes a microprocessor execution task, such as branch prediction or memory address translation.

[0160] Fig. 9AAn example computing system that can be used to implement the present disclosure is shown. Other computing systems may also be used. For example, in some implementations, the user computing device 902 may include a model trainer 960 and a training data set 962. In such implementations, the model 920 may be both trained and used locally at the user computing device 902. In some of such implementations, the user computing device 902 may implement the model trainer 960 to personalize the model 920 based on user-specific data.

[0161] Fig. 9B A block diagram depicts an example computing device 970 performing in accordance with an example embodiment of the present disclosure. The computing device 970 may be a user computing device or a server computing device.

[0162] The computing device 970 includes multiple applications (e.g., applications 1 to N). Each application contains its own machine learning library and machine learning model. For example, each application can include a machine learning model. Example applications include text messaging applications, email applications, dictation applications, virtual keyboard applications, browser applications, etc.

[0163] like Fig. 9B As shown, each application can communicate with multiple other components of the computing device (such as, for example, one or more sensors, a context manager, a device state component, and / or additional components). In some implementations, each application can communicate with each device component using an API (e.g., a public API). In some implementations, the API used by each application is specific to the application.

[0164] Fig. 9C A block diagram depicts an example computing device 990 performing in accordance with an example embodiment of the present disclosure. The computing device 990 may be a user computing device or a server computing device.

[0165] The computing device 990 includes a plurality of applications (e.g., applications 1 to N). Each application communicates with a central intelligence layer. Example applications include text messaging applications, email applications, dictation applications, virtual keyboard applications, browser applications, etc. In some implementations, each application can communicate with the central intelligence layer (and the models stored therein) using an API (e.g., a public API across all applications).

[0166] The central intelligence layer includes multiple machine learning models. For example, Fig. 9CAs shown, a corresponding machine learning model (e.g., model) can be provided for each application and managed by the central intelligence layer. In other implementations, two or more applications can share a single machine learning model. For example, in some implementations, the central intelligence layer can provide a single model (e.g., a single model) for all applications. In some implementations, the central intelligence layer is included in the operating system of the computing device 990 or is otherwise implemented by the operating system.

[0167] The central intelligence layer can communicate with the central device data layer. The central device data layer can be a centralized data repository for computing devices 990. Fig. 9C As shown, the central device data layer can communicate with multiple other components of the computing device (such as, for example, one or more sensors, a context manager, a device state component, and / or additional components). In some implementations, the central device data layer can communicate with each device component using an API (e.g., a private API).

[0168] Example system layout

[0169] Figure 2 A block diagram depicts an example token data extraction system 1600 according to an example embodiment of the present disclosure. Specifically, the example token data extraction system 1600 can obtain blockchain data 1604 from a blockchain node 1602, and can process the blockchain data 1604 to extract token data 1616, which can then be used for one or more tasks (e.g., non-fungible token indexing, searching, and / or analysis).

[0170] For example, the system may access the blockchain node 1602 via one or more application programming interfaces. Blockchain data 1604 may then be obtained via the blockchain node 1602. The blockchain data 1604 may describe a plurality of blockchain transactions. In some implementations, the blockchain data 1604 may include code data describing the byte code of the blockchain.

[0171] The acquired blockchain data 1604 may be added to a blockchain log 1606. The blockchain log 1606 may be updated at given intervals and / or based on triggering events. The blockchain log 1606 may then be accessed to obtain the stored blockchain data 1608, which may then be processed by an identification model 1610 to generate identification data 1612. The identification data 1612 may describe a data set determined to be associated with a particular non-fungible token. Identification may be based on an identified token ID, an identified contract address, an identified blockchain triggering event, an identified digital resource reference, and / or based on one or more criteria. The identification model 1610 may include a machine learning detection model, a machine learning classification model, and / or a machine learning semantic model.

[0172] The identification data 1612 may then be processed by a segmentation model 1614 (and / or an extraction model) to segment the identified data set from the blockchain data 1608 to generate token data 1616. In some implementations, the segmentation model 1614 processes the acquired blockchain data 1608 and the identification data 1612 acquired from the blockchain log 1606. Alternatively and / or in addition, the segmented data sets may be concatenated 1620 (and / or assembled) to generate the token data 1616.

[0173] Figure 3 A block diagram depicts an example token data aggregation system 1700 according to an example embodiment of the present disclosure. Specifically, the example token data aggregation system 1700 can obtain blockchain data 1702, determine data associated with a particular non-fungible token 1704, and generate token data 1712.

[0174] For example, blockchain data 1702 may be obtained from a blockchain and / or a repository. The blockchain data may be processed to determine a specific non-fungible token 1704 associated with the blockchain data 1702. The blockchain data 1702 may be further processed to determine data associated with a specific non-fungible token 1704. The token data aggregation system 1700 may identify, segment, and / or generate descriptive data 1706, content data 1708, and / or transaction data 1710 associated with a specific non-fungible token 1704. The descriptive data 1706 may be based on free-format text data embedded in and / or referenced in the blockchain code. The content data 1708 may be associated with a digital resource (e.g., a digital asset) of a specific non-fungible token 1704.

[0175] Content data 1708 may include a composite representation of a digital resource. In some implementations, the composite representation may be generated by following one or more reference addresses to obtain a digital resource, which may then be processed to generate the composite representation.

[0176] The transaction data 1710 may be generated by identifying electronic ledger transactions associated with a particular non-fungible token 1704 and aggregating the data to generate transaction history data for the particular non-fungible token 1704. The transaction data 1701 may be identified based on a token ID and / or contract address associated with a particular non-fungible token 1704.

[0177] The descriptive data 1706, content data 1708, and transaction data 1710 may be aggregated to generate token data 1712. The token data 1712 may then be processed to generate index data for a particular non-fungible token 1704.

[0178] Figure 4 A block diagram depicts an example token data generation system 1800 according to an example embodiment of the present disclosure. Specifically, the example token data generation system 1800 can obtain blockchain data 1802, process the blockchain data 1702 with one or more models, and output token data 1818.

[0179] For example, blockchain data 1802 may be obtained from a blockchain log and / or a third-party repository. Blockchain data 1802 may be processed with identification model 1804 to generate identification data 1806. The identification data may describe a subset of blockchain data 1802 associated with a particular non-fungible token.

[0180] The identification data 1806 and the blockchain data 1802 may be processed using a segmentation model 1808 (and / or an extraction model) to segment (and / or extract) a subset of the blockchain data 1802 associated with a particular non-fungible token. In some implementations, the segmentation model 1808 may generate first data 1810, second data 1812, and third data 1814 by segmenting the subsets from the blockchain data 1802. The first data 1810, the second data 1812, and the third data 1814 may be based on different portions of the blockchain data 1802 and / or may be associated with different types of data associated with the non-fungible token. For example, the first data 1810 may be associated with a title, description, or community of the non-fungible token; the second data 1812 may be associated with a digital resource of the non-fungible token; and the third data 1814 may be associated with a transaction history of the non-fungible token.

[0181] The first data 1810, the second data 1812, and the third data 1814 may be processed using the generation model 1816 to generate token data 1818. The token data 1818 may include data associated with identification characteristics of the non-fungible token. The token data 1818 may include data describing a token ID 1820 and / or a contract address 1822 associated with the non-fungible token. The token ID 1820 and / or the contract address 1822 may be identified by the identification model 1804 and may be included in the identification data 1806.

[0182] Figure 5 A block diagram depicts an example composite representation generation system 1900 according to an example embodiment of the present disclosure. Specifically, the example composite representation generation system 1900 can obtain blockchain data 1902, process the blockchain data 1902, and generate a composite representation 1916 associated with a digital resource 1912 of a non-fungible token.

[0183] For example, the blockchain data 1902 may be processed to determine a first link 1904 associated with a non-fungible token. The first link 1904 may include a reference address associated with a digital resource 1912 of the non-fungible token. The first link 1904 may include a uniform resource identifier and / or a uniform resource locator.

[0184] The first link 1904 can be used to navigate to a first login address 1906. The first login address 1906 (e.g., a first login page) can be processed to determine and / or obtain a digital resource 1912. Alternatively and / or in addition, the first login address 1906 can be processed to determine a second link 1908 associated with the digital resource 1912. The second link 1908 can include a uniform resource identifier and / or a uniform resource locator. The second link 1908 can be used to navigate to a second login address 1910. The second login address 1910 can include a web page with one or more digital resources. The second login address 1910 can be processed to determine and / or obtain a digital resource 1912.

[0185] The digital asset 1912 may be processed using the representation model 1914 to generate a composite representation 1916. The representation model 1914 may be configured and / or trained to process the digital asset 1912 to generate a preview of the digital asset 1912. Alternatively and / or in addition, the representation model 1914 may be trained to determine and / or generate a media content item that includes a semantically-aware representation of the digital asset 1912.

[0186] The composite representation 1916 may include a compressed version of the digital resource 1912. Alternatively and / or in addition, the composite representation 1916 may include a representative frame or segment associated with the digital resource 1912. In some implementations, the composite representation 1916 may include a rendering of the embedded code embedded in the blockchain.

[0187] Fig.10 A block diagram depicts an example indexing system 200 according to an example embodiment of the present disclosure. In some implementations, the indexing system 200 is configured to receive blockchain data 202 describing a blockchain that stores data associated with a plurality of non-fungible tokens, and provide output data 220 describing a plurality of index data sets associated with the plurality of non-fungible tokens as a result of receiving the input data 202. Thus, in some implementations, the indexing system 200 may include one or more models and / or one or more functions for identifying token data and extracting relevant information to generate index data.

[0188] Specifically, the example indexing system 200 may include obtaining blockchain data 202 associated with a blockchain. The blockchain data 202 may include the code of the blockchain. Additionally and / or alternatively, the blockchain data 202 may include script data associated with a plurality of non-fungible tokens (e.g., data describing scripts that may be deployed to interact with smart contract code of the blockchain data 202).

[0189] The blockchain data 202 may be processed to identify first token data of a first non-fungible token 204, second token data of a second non-fungible token 206, third token data of a third non-fungible token 208, and nth token data of an nth non-fungible token 210. Identifying the token data sets may involve parsing the blockchain data and determining whether each parsed segment is associated with one or more non-fungible token characteristics. Alternatively and / or in addition, identifying the token data may include crawling the blockchain data to search for specific characteristics, structures, and / or features associated with the token data.

[0190] The first token data associated with the first non-fungible token 204 may be processed to generate first index data 212 associated with the first non-fungible token 204. The second token data associated with the second non-fungible token 206 may be processed to generate second index data 214 associated with the second non-fungible token 206. The third token data associated with the third non-fungible token 208 may be processed to generate third index data 216 associated with the third non-fungible token 208. The nth token data associated with the nth non-fungible token 210 may be processed to generate nth index data 218 associated with the nth non-fungible token 210.

[0191] The first index data 212, the second index data 214, the third index data 216, and the nth index data 218 may be stored in an index database 220, which may then be used for various tasks (e.g., non-fungible token searches). The index data set may include index information associated with a plurality of index term fields. The index data set may include a title of the non-fungible token, a reference to a digital resource payload, a descriptor, and / or various other index terms for annotating and / or characterizing various aspects of the non-fungible token that may be searched.

[0192] Fig.11 A block diagram depicting an example index data set 300 according to an example embodiment of the present disclosure. In some implementations, index data 310 may be generated based on blockchain data and / or web page data 326. The blockchain data may be processed to identify a subset of the blockchain data describing token data 302. The token data 302 may be processed to determine one or more index terms for index data 310 generation. Additionally and / or alternatively, one or more additional index terms for one or more index term fields may be determined using web page data 326 associated with a non-fungible token that is the same as or related to a non-fungible token associated with the token data 302.

[0193] Additionally and / or alternatively, the token data 302 and / or the web page data 326 may be processed with one or more machine learning models 324 to generate one or more outputs that may be used as index items for the index data 310. Alternatively and / or additionally, the input data for the one or more machine learning models 324 may be obtained from other data sources. In some implementations, the index data 310 may include data describing the blockchain 312 on which the non-fungible token is minted. The index data 310 may include references 314 (e.g., URIs) to digital resources, time data 316 (e.g., minting time, first digital resource release time, and / or the time difference between two times), transaction data 318 (e.g., purchase time, purchase amount, purchase frequency, identities of buyers and bidders, and / or auction event data), metadata 320 (e.g., stored by a blockchain, a marketplace, and / or another data source), other machine learning data 322, and / or other derived data.

[0194] Fig.12A block diagram depicting an example search 400 according to an example embodiment of the present disclosure. The example search 400 may include receiving a search query 402. The search query 402 may include one or more words, one or more images, and / or one or more other forms of input. The search query 402 may be processed by a search engine 404, which may then access a non-fungible token index database 406 to determine one or more non-fungible token search results 410 responsive to the search query 402. Additionally and / or alternatively, the search query 402 may be processed by a search engine 404, which may then access a web database 408 to determine one or more general web results 412 responsive to the search query 402. A search results page 414 may be generated utilizing the one or more non-fungible token search results 410 and / or the one or more general web results 412. The search results page 414 may then be provided for display.

[0195] FIG. 13A to FIG. 13C Different example search results page configurations are depicted. Although three configurations are depicted, other variations may be utilized to provide search results for display. The different variations depicted may include a search interface 502 that may include a search query input box 504, one or more non-fungible token search results (e.g., a first non-fungible token search result 510, a second non-fungible token search result 512, a third non-fungible token search result 514, and / or a fourth non-fungible token search result 516), one or more general web results (e.g., a first general web result 520, a second general web result 522, a third general web result 524, and / or a fourth general web result 526), ​​and / or a knowledge panel 506. The search query input box 504 may be configured to receive and / or display one or more search queries. One or more non-fungible token search results and one or more general web results may be determined and provided based on a determined responsiveness to the input search query. Additionally and / or alternatively, the knowledge panel 506 may include structured data associated with a topic determined to be responsive to the input query.

[0196] Fig.13AAn illustration of an example search results page 500 is depicted in accordance with an example embodiment of the present disclosure. The example search results page 500 includes a search query input box 504 displayed at the top of a search interface 502 and a knowledge panel 506 displayed in a side panel of the search interface 502. Additionally, the example search results page 500 includes a non-fungible token search results section that displays one or more non-fungible token search results (e.g., a first non-fungible token search result 510, a second non-fungible token search result 512, a third non-fungible token search result 514, and / or a fourth non-fungible token search result 516). The example search results page 500 may include a separate general web results section that may display one or more general web results (e.g., a first general web result 520, a second general web result 522, a third general web result 524, and / or a fourth general web result 526). Additionally and / or alternatively, one or more non-fungible token search results and one or more general web results may be provided in different formats (e.g., non-fungible token search results may be provided with image thumbnails, while general web results may be displayed only with text).

[0197] Fig. 13B An illustration of an example search results page 540 according to an example embodiment of the present disclosure is depicted. The example search results page 540 includes a search query input box 504 displayed at the top of the search interface 502 and a knowledge panel 506 displayed in a side panel of the search interface 502. The example search results page 540 may include one or more non-fungible token search results (e.g., a first non-fungible token search result 510, a second non-fungible token search result 512, a third non-fungible token search result 514, and / or a fourth non-fungible token search result 516), and one or more general web results (e.g., a first general web result 520, a second general web result 522, a third general web result 524, and / or a fourth general web result 526) may be displayed in a mixed format such that different types of search results may be adjacent to each other. The order may be determined based on a score determined based on responsiveness to the search query and / or based on other factors. The sorting may be based solely on the score without preference for the type of result.

[0198] Fig. 13CAn illustration of an example search results page 580 is depicted according to an example embodiment of the present disclosure. The example search results page 580 may include one or more non-fungible token search results (e.g., first non-fungible token search results 510, second non-fungible token search results 512, third non-fungible token search results 514, and / or fourth non-fungible token search results 516), and may provide one or more general web results (e.g., first general web result 520, second general web result 522, third general web result 524, and / or fourth general web result 526) for display with a preview of a media content item.

[0199] Fig.14 A block diagram depicting an example token identification system 1000 according to an example embodiment of the present disclosure. In some implementations, the example token identification system 1000 may be configured to receive blockchain data 1004 from a blockchain and / or repository 1002 associated with a plurality of non-fungible tokens and, as a result of receiving the blockchain data 1004, provide identification data 1014 that may be used to determine data to index 1016. Thus, in some implementations, the token identification system 1000 may include an identification block (e.g., a machine-learned token identification model) that is operable to determine which subsets of data in a blockchain are associated with non-fungible tokens to index.

[0200] Specifically, Fig.14 A block diagram depicting an example token data identification system 1000 according to an example embodiment of the present disclosure. For example, the token data identification system 1000 may include accessing a blockchain and / or a repository 1002 to obtain blockchain data 1004. The blockchain may be accessed via a blockchain node. In some implementations, the blockchain and / or the repository 1002 may be accessed via an application programming interface to obtain the blockchain data 1004. The repository may be a repository that stores the blockchain data 1004 outside of the blockchain computing system. The repository may be generated by a third-party computing system using one or more third-party application programming interfaces.

[0201] Blockchain data 1004 may include script data associated with multiple non-fungible tokens. Multiple non-fungible tokens may be associated with multiple corresponding electronic ledgers. Blockchain data 1004 may be associated with a specific blockchain and / or multiple different blockchains. Blockchain data may include byte code data associated with a blockchain. Alternatively and / or in addition, blockchain data may be associated with a leaf page of a web platform.

[0202] The blockchain data 1004 may be processed by one or more blocks to generate identification data 1014. The identification data 1014 may describe one or more identified token data sets. In some implementations, identifying one or more token data sets may include processing the blockchain data using a machine learning token identification model.

[0203] Processing the blockchain data 1004 to generate the identification data 1014 may include determining that one or more subsets of the blockchain data 1004 describe one or more token data sets associated with one or more non-fungible tokens. The determination may include parsing the blockchain data 1004. Parsing may be based on code semantics, data substructures, one or more code identifiers, and / or based on a plurality of other factors. Alternatively and / or in addition, the blockchain data 1004 may be processed to determine that one or more subsets of the blockchain data 1004 are associated with one or more blockchain triggering events 1006. One or more subsets associated with the one or more blockchain triggering events 1006 may be determined as candidate token data sets. The one or more blockchain triggering events 1006 may be associated with one or more smart contracts that include data describing an "if-then" event that results in a transfer of a payload (e.g., a digital resource) in response to a transaction.

[0204] The one or more candidate token data sets may be further processed to determine whether the blockchain trigger event 1006 is associated with a non-fungible token. For example, the blockchain trigger 1006 event may be processed to determine an output action associated with the blockchain trigger event 1006. The output action may be processed to determine whether the output action is associated with a digital resource (e.g., a digital asset). If the output action is associated with a digital resource associated with a non-fungible token, the candidate token data may be identified as token data, and the token data may then be indexed 1016.

[0205] Alternatively and / or in addition, token data can be identified based on one or more criteria 1008. The one or more criteria 1008 can be standards associated with a unified structure for non-fungible tokens (e.g., an EIP standard and / or an ERC standard). For example, the blockchain data 1004 can be crawled to search for one or more strings associated with one or more standard non-fungible token strings (e.g., strings associated with a unified interface template and / or predetermined strings associated with a "function" and / or "event" portion of the script data). If a subset of the blockchain data meets a threshold level of criteria (e.g., the subset includes a unified interface template and / or meets one or more criteria), the subset can be determined to be token data, which can then be utilized to generate identification data 1014.

[0206] In some implementations, the candidate token data and / or the identified token data may be processed to determine a quality 1010 of a non-fungible token associated with the token data. The quality 1010 may be determined based on heuristic data and / or based on output data of one or more machine learning models. In some implementations, the quality 1010 of the non-fungible token may be based on the determined authenticity or validity of the non-fungible token (e.g., whether the non-fungible token is suspicious), based on whether the digital resource associated with the non-fungible token includes a distinguishing feature (e.g., the digital resource amount exceeds white noise or is only slightly different from a previously minted digital resource), based on the degree of vulgarity or offensiveness (e.g., if the digital resource provides family-friendly content that does not include offensive language or imagery, the digital resource may be of higher quality), and / or based on the degree to which the token data conforms to one or more uniform standards for non-fungible tokens. The quality 1010 may be used to generate identification data 1014. In some implementations, the quality 1014 can be used to determine whether to index 1016 the token data and / or how to index the token data 1016 (e.g., fully index, partially index, index using quality flags or tags, and / or not index).

[0207] In some implementations, the blockchain data 1004 can be processed to determine other characteristics 1012 that can be used to generate identification data 1014. The other characteristics 1014 can include data related to lazy minting, empty payloads, non-fungible token communities, relevance of non-fungible tokens, duplicate non-fungible tokens, types of digital resources (e.g., images, videos, augmented reality rendering assets, virtual reality environments, audio, physical objects, domains, and / or authentication certificates for virtual property), and / or recency of minting.

[0208] The identification data 1014 may be provided to an index computing system, which may determine which subsets, if any, of the blockchain data 1004 to index 1016. For example, identified high-quality token data (e.g., token data associated with non-fungible tokens from verified issuers, including high-resolution digital assets as payloads without vulgar content) may be indexed 1016, while data sets that are less likely to be associated with non-fungible tokens may not be indexed 1016.

[0209] The index data can then be stored in an index database which can be used to enable a non-fungible token search platform.

[0210] Fig.15A block diagram depicts an example index filtering system 1100 according to an example embodiment of the present disclosure. Specifically, the example index filtering system 1100 can process blockchain data 1102, determine token data 1106 in the blockchain data 1102, and determine whether to index the token data 1106 1110. In some implementations, the index filtering system 1100 can include multiple machine learning models (e.g., an identification model 1104 for determining a subset of blockchain data 1102 associated with a non-fungible token and / or a quality model 1108 for determining the quality of the identified data subset) to determine what and how to index 1110.

[0211] For example, blockchain data 1102 can be obtained from a blockchain node and / or repository. Blockchain data 1102 can be associated with a blockchain. Blockchain data can describe byte code.

[0212] The blockchain data 1102 may be processed by the recognition model 1104 to determine and / or generate token data 1106. The token data 1106 may be associated with a non-fungible token. In some implementations, the token data 1106 may be generated by identifying a token ID and / or a contract address. The recognition model 1104 may detect the token ID and / or the contract address, and then may obtain a portion of the blockchain data associated with the token ID and / or the contract address, and then may process the portion (e.g., the data may be processed to aggregate and / or connect the data) to generate the token data 1106. Alternatively and / or in addition, the recognition model 1104 may determine and / or generate the token data 1106 by processing the blockchain data 1104 to determine a subset of the blockchain data 1102 associated with at least one of a blockchain event, one or more unified non-fungible token standards, and / or a given non-fungible token intent.

[0213] The token data 1106 may then be processed with a quality model 1108, which may then be utilized to determine whether and / or how to index the token data 1110. The quality model 1108 may process the token data 1106 to determine whether the token data 1106 includes suspicious characteristics, may include vulnerabilities, is associated with a high-quality digital resource, and / or is vulgar or offensive. The quality model 1108 may generate a quality output (e.g., a quality metric), which may then be utilized to determine whether to index the token data 1108 1110. In some implementations, a token data set having a threshold quality metric may be indexed 1110, while a token data set that does not meet the threshold may not be indexed and / or may be partially indexed. Alternatively and / or in addition, the quality metric and / or one or more flags / tags associated with the quality metric may be indexed with the token data 1106.

[0214] In some implementations, the determination of whether to index 1110 the token data 1106 can be based on a weighted score, which can be based on the recognition data from the recognition model 1104 and the quality data from the quality model 1108 .

[0215] One or more machine learning models can be trained on labeled training examples and / or can be trained based on labeled features. One or more machine learning models can include one or more classification models, one or more feature extractors, one or more detection models, one or more segmentation models, and / or one or more enhancement models. One or more machine learning models can be trained to perform natural language processing, computer vision, image processing, video processing, latent coding processing, audio processing, and / or encoding / decoding.

[0216] Fig.16 A block diagram depicts an example index determination system 1200 according to an example embodiment of the present disclosure. Specifically, the example index determination system 1200 can obtain blockchain data 1202, identify candidate token data 1204, generate identification data 1206 based on the identified candidate token data 1204, and generate an index determination 1212 based on the identification data. The index determination 1212 can include indexing the token data 1214, partially indexing the token data 1216, or not indexing the token data 1218.

[0217] For example, blockchain data 1202 may be obtained from a blockchain computing system. The blockchain data 1202 may include data describing a plurality of non-fungible tokens associated with a plurality of digital resources. The blockchain data 1202 may be processed to identify candidate token data 1204 in the blockchain data. The identification may be based on a determined blockchain triggering event, based on one or more unified non-fungible token standards, based on a determined issuer intent, and / or based on a determined association with a digital resource (e.g., a digital asset).

[0218] The candidate token data may then be processed to generate identification data 1206. The identification data may describe the candidate token data associated with a non-fungible token. The identification data may include an identification metric describing the likelihood that the candidate token data is associated with a non-fungible token.

[0219] The identification data may then be processed to generate an index determination. The index determination 1212 may include data that may be transmitted to the index calculation system to instruct the index calculation system to index the token data 1214, partially index the token data 1216, or not index the token data 1218. The index determination 1212 may be based on the identification data such that candidate token data having a first threshold likelihood may be indexed 1214, candidate token data having a likelihood between the first threshold and a second threshold may be partially indexed 1216, and candidate token data having a likelihood below both thresholds may not be indexed 1218.

[0220] Alternatively and / or in addition, candidate token datasets determined to be associated with non-fungible tokens may be indexed; however, the level of indexing may be based on a quality metric such that high-quality token datasets may be fully indexed 1214, while low-quality token datasets may be partially indexed 1216.

[0221] Fig.17 A block diagram depicts an example quality determination system 1300 according to an example embodiment of the present disclosure. Specifically, the quality determination system 1300 can obtain blockchain data 1302, determine token data 1304 in the blockchain data 1302, generate token data quality data 1306, and determine whether to index the token data 1310 based on the quality data 1306.

[0222] For example, blockchain data 1302 may be obtained from a blockchain repository (e.g., blockchain data pre-filtered in a sandbox after being obtained from a blockchain node). The blockchain data 1302 may be processed to determine token data 1304 in the blockchain data 1302 (e.g., the blockchain data may be processed to determine that a subset of the blockchain data includes data associated with a non-fungible token).

[0223] The token data may then be processed to generate quality data 1306 associated with the token data. The quality data 1306 may be determined based on the resolution of the digital resource associated with the non-fungible token of the token data. Additionally and / or alternatively, the quality data 1306 may be generated based on the level of difference between the digital resources of the associated non-fungible tokens. The level of difference may be based on whether the digital resource is more than just white noise, a blank image, and / or non-descriptive data. In some implementations, the level of difference may be based on whether the digital resource is the same or substantially similar to other digital resources associated with other non-fungible tokens. The minting date and / or generation date of the digital resource may be considered. Additionally and / or alternatively, the quality data 1306 may be based on whether the non-fungible token includes vulgar and / or offensive content (e.g., the digital resource depicts nudity and / or the title includes hate speech).

[0224] The quality data 1306 can be utilized to generate one or more flags 1308 for the processed token data. Additionally and / or alternatively, the quality data 1306 can be utilized to determine whether to index 1310 the token data.

[0225] The token data may be indexed 1312 based on the determination whether to index 1310. Indexing 1312 may include indexing the token data using one or more generated flags 1308.

[0226] Fig.18 A block diagram depicting an example token index deduplication 2000 according to an example embodiment of the present disclosure. In some implementations, the example token index deduplication 2000 may be configured to receive a plurality of index data sets 2002 describing token data for a plurality of non-fungible tokens, and provide an updated index database 2014 adjusted based on determined duplications as a result of receiving the plurality of index data sets 2002. Thus, in some implementations, the token index deduplication 2000 may include a duplication determination block 2004 operable to determine which index data sets are associated with each other.

[0227] Specifically, Fig.18 Depicting processing of multiple index data sets 2002 to determine multiple duplicate index data sets. For example, multiple index data sets 2002 may be processed with a duplicate determination block 2004 to determine multiple duplicates, including a first duplicate 2006, a second duplicate 2008, and an nth duplicate 2010. The determination may be based on the digital resources (e.g., digital assets) of different identified tokens being the same (e.g., matching and / or within a threshold similarity).

[0228] Multiple repetitions (e.g., first repetition 2006, second repetition 2008, and / or nth repetition 2010) may be processed with authenticity determination block 2012 to generate verification data. The verification data may include the determined authenticity of each repetition index data set. The authenticity may include analyzing the data source, minting date, quality of the associated digital resource, and / or publisher.

[0229] The index database may be adjusted using the verification data to generate an updated index database 2014. Adjusting the index database may include removing specific index data sets. For example, index data sets generated based on data obtained directly from the blockchain may be retained, while index data sets generated based on data obtained from web pages may be removed. Alternatively and / or in addition, index data sets determined to be fraudulent may be removed (e.g., non-fungible token data may be obtained from a fraudulent website or be a reference to a fraudulent website).

[0230] Alternatively and / or in addition, adjusting the index database may include merging duplicate index data sets. In some implementations, adjusting the index database may include adding data to an existing index data set. The added data may include references to other duplicates, one or more flags indicating the nature of the duplicate, and / or one or more weights associated with the determined authenticity.

[0231] Fig.19 A block diagram depicting an example index validity determination 2100 according to an example embodiment of the present disclosure. Specifically, Fig.19 The index validity determination 2100 includes duplicate data 2102, which can be processed to generate verification data 2122. The duplicate data 2102 can describe one or more index data sets associated with one or more non-fungible tokens. The duplicate data 2102 can describe that two or more indexed non-fungible tokens are the same (e.g., match or are substantially similar). For example, a first index data set can describe a first non-fungible token associated with a first digital resource (e.g., a first digital asset that is a payload of the first non-fungible token). A second index data set can describe a second non-fungible token associated with a second digital resource (e.g., a second digital asset that is a payload of the second non-fungible token). In some implementations, the first non-fungible token and the second non-fungible token can be the same non-fungible token that is indexed twice because the data is obtained from multiple sources containing data associated with the non-fungible token. Alternatively and / or in addition, the first non-fungible token and the second non-fungible token may be different and the first digital resource and the second digital resource may be the same (e.g., the digital assets may match and / or may include similar data with subtle differences (e.g., depicting the same scene at different resolutions and / or different color saturation)).

[0232] The duplicate data 2102 may be processed 2110 to generate verification data 2122. Processing 2110 the duplicate data 2102 may include obtaining and processing transaction data 2112 associated with each of the duplicate index data sets. The transaction data 2112 may be processed to determine whether any of the duplicates are associated with fraudulent transactions and / or fraudulent transaction trends. In some implementations, the transaction data 2112 may be processed to determine more popular and / or more popular duplicates. Additionally and / or alternatively, the transaction data 2112 may be processed to determine different participants in the transaction that may indicate the validity of the non-fungible token. Price data may also be derived from the transaction data 2112 and may be used to index data validity. In some implementations, the price data may be obtained from a web page (e.g., a market).

[0233] Additionally and / or alternatively, the source data 2114, URL and / or URI data 2116, and / or minting date data 2120 may be processed to determine the validity of each duplication. For example, the source data 2114 may be processed to determine which duplications originate from data from a trusted source. Specifically, index data generated based on data directly obtained from the blockchain may be prioritized over index data generated based on data from an untrusted website (e.g., a website marked as spam by one or more spam filters). In some implementations, the URL and / or URI data 2116 referencing a digital resource (e.g., a digital asset) may be processed to determine whether the digital resource is available, has changed, has been upgraded, and / or has been moved. If the link is invalid, the duplication may be considered invalid. If the address has changed, the validity may be questioned. If the digital resource has not undergone a change that changes the nature of the digital resource, upgrades and moves may be a positive factor. Additionally and / or alternatively, the minting date data 2120 may be used to determine which duplication is published first, which may provide insight into the validity of possible non-fungible tokens.

[0234] In some implementations, the verification data 2122 may be generated using one or more other fraud detection 2118 techniques.

[0235] The verification data 2122 may include data describing the determined validity (and / or authenticity) of each of the respective duplicate index data sets. The verification data 2122 may include one or more flags, labels, and / or annotations describing the determined factors describing the index data set validity.

[0236] Fig. 20 A block diagram depicting an example token index deduplication 2200 according to an example embodiment of the present disclosure. In some implementations, the example token index deduplication 2200 may be configured to receive a plurality of index data sets 2202 describing token data for a plurality of non-fungible tokens, and provide an updated index database 2214 adjusted based on determined duplications as a result of receiving the plurality of index data sets 2202. Thus, in some implementations, the token index deduplication 2200 may include a duplication determination block 2204 operable to determine which index data sets are associated with each other.

[0237] Specifically, Fig. 20 Depicting processing of multiple index data sets 2202 to determine multiple duplicate index data sets. For example, multiple index data sets 2202 can be processed with a duplicate determination block 2204 to determine multiple duplicates, including a first duplicate 2206, a second duplicate 2208, and an nth duplicate 2210. The determination can be based on the digital resources of different identified tokens being the same (e.g., matching and / or within a threshold similarity).

[0238] Multiple repetitions (e.g., first repetition 2206, second repetition 2208, and / or nth repetition 2210) may be processed with authenticity determination block 2212 to generate verification data. The verification data may include the determined authenticity of each repetition index data set. The authenticity may include analyzing the data source, minting date, quality of the associated digital resource, and / or publisher.

[0239] The verification data may be utilized to adjust the index database to generate an updated index database. Adjusting the index database may include retaining 2214 one or more of the duplicate index data sets, merging 2216 one or more of the duplicate index data sets, and / or removing 2218 one or more of the duplicate data sets.

[0240] One or more of the duplicate index data sets may be retained 2214 to ensure that at least one of the non-fungible tokens associated with a particular digital resource is maintained for search purposes. The particular index data set retained 2214 may be determined based on: the source of the data, the determined authenticity (or validity), transaction data for one or more of the duplicates, the hosting location of the digital resource, the quality of the digital resource, the minting time, and / or metadata for one or more of the duplicates.

[0241] Alternatively and / or in addition, adjusting the index database may include merging 2216 duplicate index data sets. In some implementations, certain duplicate index data sets may be selected for merging 2216. Merging 2216 duplicate index data sets may include adding any non-duplicate data to one of the duplicate data sets and deleting the other duplicate index data set. For example, an index data set generated based on data directly obtained from a blockchain may be supplemented with data in an index data set generated based on a web page, which may include descriptions of digital resources, authors, and / or other data not directly stored on the blockchain. In some implementations, adjusting the index database may include adding data to an existing index data set. The added data may include references to other duplicates, one or more flags indicating the nature of the duplicate, and / or one or more weights associated with the determined authenticity.

[0242] Adjusting the index database may include removing 2218 specific index data sets. For example, index data sets generated based on data obtained directly from the blockchain may be retained 2214, while index data sets generated based on data obtained from web pages may be removed 2218. Alternatively and / or in addition, index data sets determined to be fraudulent may be removed 2218 (e.g., non-fungible token data may be obtained from a fraudulent website or be a reference to a fraudulent website).

[0243] Fig.21A block diagram depicting an example search results page generation 2300 according to an example embodiment of the present disclosure. In some implementations, systems and methods may receive a search query 2302. The search query 2302 may include one or more search terms, one or more input images, and / or one or more other inputs. In some implementations, the search query 2302 may be received from a user computing system associated with a user.

[0244] The search query 2302 may be processed by a search engine to determine a plurality of search results 2304. The plurality of search results may be determined based on the determined responsiveness to the search query 2302. The plurality of search results may be processed by a ranking block 2306 to determine an order of display of the plurality of search results. Additionally and / or alternatively, the plurality of search results may be processed to determine whether to provide a warning or badge 2308 to one or more of the search results. For example, if one or more of the search results are non-fungible token search results, a badge indicating the type of search result may be provided. Additionally and / or alternatively, verification data associated with each of the corresponding non-fungible token search results may be provided via a warning or via a badge. In some implementations, a warning may be provided if the search result indicates a duplicate non-fungible token.

[0245] The plurality of search results may be ranked based on the determined relevance, the determined truthfulness, and / or the determined context of each of the plurality of search results by ranking block 2306. Then, a search results page 2310 may be generated and provided based on the ranked search results and the determined warnings and badges.

[0246] Example Method

[0247] Figure 6 A flowchart is depicted for an example method performed according to an example embodiment of the present disclosure. Figure 6 The steps performed in a specific order are depicted for the purpose of illustration and discussion, but the method of the present disclosure is not limited to the order or arrangement specifically shown. The various steps of method 600 may be omitted, rearranged in various ways, combined and / or adjusted without departing from the scope of the present disclosure.

[0248] At 602, a computing system may obtain blockchain data from a blockchain computing system. The blockchain data may include code data (e.g., byte code) associated with a plurality of non-fungible tokens. The blockchain data may describe blockchain transactions that may be associated with a blockchain ledger. In some implementations, the code of the blockchain data may be associated with one or more standards for token code.

[0249] At 604, the computing system may store the blockchain data in a blockchain log. In some implementations, storing the blockchain data in the blockchain log may include preprocessing the blockchain data in a sandbox to determine whether the blockchain data includes vulnerabilities, viruses, and / or suspicious data. The preprocessing may utilize the sandbox environment to mitigate the spread of any potentially malicious content. Alternatively and / or in addition, a sandbox blockchain node may be utilized to provide information about addresses and / or other data in an "offline mode."

[0250] At 606, the computing system may process the blockchain data to determine token data associated with a non-fungible token in the plurality of non-fungible tokens. The determination may be based on detection of a blockchain triggering event. Alternatively and / or in addition, the determination may be based on determining that the token data is associated with a digital resource (e.g., a digital asset).

[0251] In some implementations, the computing system may obtain standard data. The standard data may describe one or more standards for the token code. The token data may be determined based at least in part on the one or more standards.

[0252] At 608, the computing system may determine that the portion of the token data includes descriptive data. The descriptive data may be associated with the non-fungible token. In some implementations, the descriptive data may include information determined to be associated with a specific identifying characteristic of the non-fungible token. The determination may be based on one or more criteria associated with the structure of the non-fungible token byte code. In some implementations, the descriptive data may include at least one of: a digital asset associated with the non-fungible token, transaction data associated with the non-fungible token, a name associated with the non-fungible token, a description associated with the non-fungible token, or a creator identification associated with a creator of the non-fungible token.

[0253] Alternatively and / or in addition, determining that a portion of the token data includes descriptive data may include: parsing the token data to generate a plurality of fragment data sets; processing each of the plurality of fragment data sets with a machine learning natural language processing model to generate a plurality of fragment classifications; and determining, based on the plurality of fragment classifications, that a particular fragment data set in the plurality of fragment data sets includes descriptive data.

[0254] Alternatively and / or in addition, determining that a portion of the token data includes descriptive data may include: determining token identification data associated with the non-fungible token; determining a plurality of transactions in the blockchain data based on the token identification data; and determining the portion of the token data based on the plurality of transactions.

[0255] In some implementations, the descriptive data may include transaction data (e.g., data describing the transaction history of a non-fungible token). The transaction data may be associated with an electronic ledger. The blockchain data may include embedded images, pointers, executable code, and other data structures that may be identified as descriptive data, which may be segmented out of the blockchain data and stored.

[0256] At 610, the computing system may segment the descriptive data from the blockchain data. In some implementations, multiple data strings associated with a particular non-fungible token may be identified and segmented from the blockchain data. The multiple data strings may then be aggregated and / or concatenated.

[0257] At 612, the computing system may store the descriptive data. The descriptive data may be stored in a database for retrieval and processing for future tasks. The storage may be local and / or may be stored on a server computing system accessible to multiple users.

[0258] Figure 7 A flowchart is depicted for an example method performed according to an example embodiment of the present disclosure. Figure 7 The steps performed in a specific order are depicted for the purpose of illustration and discussion, but the method of the present disclosure is not limited to the order or arrangement specifically shown. The various steps of method 700 may be omitted, rearranged in various ways, combined and / or adjusted without departing from the scope of the present disclosure.

[0259] At 702, the computing system may obtain blockchain data from the blockchain computing system. The blockchain data may include code data (e.g., byte code) associated with a plurality of non-fungible tokens. The blockchain data may be obtained via a blockchain node. In some implementations, the blockchain data may be obtained from a repository separate from the blockchain computing system.

[0260] In some implementations, obtaining blockchain data may include accessing a blockchain node. A blockchain node may be associated with a particular blockchain. Obtaining blockchain data may include recursively crawling a particular blockchain. Recursive crawling may occur at set intervals, trigger-based intervals (e.g., based on an event that occurs (e.g., a social media post describing a non-fungible token launch post)), and / or user-specified intervals.

[0261] At 704, the computing system may process the blockchain data to determine a link in the blockchain data. The link may include at least one of a uniform resource identifier, a uniform resource locator, and / or an IPFS address.

[0262] At 706, the computing system may obtain a login page (and / or IPFS content) associated with the link. In some implementations, the login page may be obtained based on the link. The login page may include a digital asset. The digital asset may be the payload of a smart contract associated with a non-fungible token. The IPFS content may point to other (on|off)-IPFS content.

[0263] At 708, the computing system may determine content data associated with the login page. The content data may be associated with a non-fungible token in the plurality of non-fungible tokens. In some implementations, the content data may include a preview of a digital asset associated with the non-fungible token.

[0264] In some implementations, the computing system can include determining a second link based on the landing page and obtaining a second landing page based on the second link. The second landing page can include content data.

[0265] At 710, the computing system may store content data. The content data may include a preview of a digital resource (e.g., a digital asset) associated with a non-fungible token.

[0266] In some implementations, acquiring and / or generating image data based on a digital resource of a non-fungible token may include nested crawling to follow links and navigate to different landing pages. Additionally and / or alternatively, the content data may include a synthetic representation associated with the digital resource (e.g., a synthetic representation of the appearance and / or sound of the digital resource). The synthetic representation may be similar to (e.g., similar to) a preview of the digital resource. The synthetic representation may be indexed to be provided as a search result (e.g., an image search result, a video search result, and / or an audio search result), which may be displayed and / or provided adjacent to and / or with the web2.0 search results.

[0267] In some implementations, the digital resources may be accessed and processed to generate a composite representation. In some implementations, the composite representation may include a representative frame of a video digital asset. Alternatively and / or in addition, the composite representation may include compressed video, a graphics interchange format, and / or enhanced video.

[0268] Figure 8 A flowchart is depicted for an example method performed according to an example embodiment of the present disclosure. Figure 8 The steps performed in a specific order are depicted for the purpose of illustration and discussion, but the method of the present disclosure is not limited to the order or arrangement specifically shown. The various steps of method 800 may be omitted, rearranged in various ways, combined and / or adjusted without departing from the scope of the present disclosure.

[0269] At 802, the computing system may obtain blockchain data from a blockchain computing system. The blockchain data may include code associated with a plurality of non-fungible tokens.

[0270] At 804, the computing system may process the blockchain data to determine a first set of data associated with a particular non-fungible token in the plurality of non-fungible tokens. The first set of data may include free-format text data.

[0271] In some implementations, processing the blockchain data to determine a first set of data associated with a particular non-fungible token in the plurality of non-fungible tokens may include crawling the blockchain data to identify link data and obtaining content data associated with the link data.

[0272] At 806, the computing system may process the blockchain data to determine a second set of data associated with a particular non-fungible token in the plurality of non-fungible tokens. The second set of data may include transaction data from the electronic ledger.

[0273] In some implementations, processing the blockchain data to determine the second set of data associated with a particular non-fungible token in the plurality of non-fungible tokens may include determining a plurality of data sets associated with non-fungible token transactions. The non-fungible token transactions may be associated with the particular non-fungible token. Processing the blockchain data to determine the second set of data associated with the particular non-fungible token in the plurality of non-fungible tokens may include generating the second set of data based on the plurality of data sets.

[0274] Additionally and / or alternatively, the computing system may segment the first set of data and the second set of data from the blockchain data. Segmentation may involve utilizing a machine learning segmentation model.

[0275] At 808, the computing system may store the first set of data and the second set of data. In some implementations, the first set of data and the second set of data may be stored in a database. Alternatively and / or in addition, the first set of data and the second set of data may be provided to an index computing system to index data associated with a particular non-fungible token.

[0276] Index data for a particular non-fungible token may then be generated based on the first set of data and the second set of data. The index data may include a preview of a digital resource associated with the non-fungible token. Additionally and / or alternatively, the index data may include a title, description, transaction history, and / or publisher information associated with the non-fungible token. Generating the index data may involve processing the content data using one or more machine learning models.

[0277] Example index and usage

[0278] The systems and methods disclosed herein can identify data associated with non-fungible tokens in a blockchain (e.g., in the code of the blockchain). For example, multiple non-fungible tokens can be identified and indexed based on a variety of techniques. One technique can include analyzing (e.g., crawling) a blockchain to identify data associated with a non-fungible token. The data can then be parsed to determine identifiable data that can be indexed. Another technique can include analyzing (e.g., crawling) a non-fungible token market to identify non-fungible tokens to be sold. Data related to non-fungible tokens can then be obtained and indexed. In some implementations, a variety of techniques can be utilized to generate a more comprehensive database for searching.

[0279] The systems and methods disclosed herein may include obtaining blockchain data from a blockchain computing system. The blockchain data may include one or more function signatures. In some implementations, the blockchain data may include script data associated with a digital asset. A specific portion (e.g., a subset) of the blockchain data may be determined to include token data. The token data may describe a non-fungible token associated with a digital asset. Index data may be generated based on the token data. The index data may include information obtained from the blockchain data. In some implementations, the index data may be associated with a digital asset. The index data may then be stored in an index database. The index database may then be used for a variety of purposes (e.g., non-fungible token search, data aggregation and analysis, training data for a machine learning model, and / or for generating statistical representations).

[0280] Systems and methods can obtain blockchain data (e.g., code from a blockchain) from a blockchain computing system (e.g., a decentralized computing system that stores distributed data). The blockchain data may include one or more function signatures. In some implementations, the blockchain data may include scripts associated with digital assets. The blockchain data may be obtained via a blockchain node. The blockchain data may include code and / or records stored on the blockchain. The code may include scripts and may describe multiple smart contracts. The blockchain data may include transaction data associated with the acquisition of digital assets and / or the exchange of digital currencies (e.g., cryptocurrency). In some implementations, the transaction data may describe one or more mutations of a contract (e.g., "set URI", etc.). The blockchain data may include metadata associated with one or more non-fungible tokens.

[0281] The systems and methods disclosed herein may include determining that a particular portion of blockchain data includes token data. The token data may describe a non-fungible token associated with a digital asset. Alternatively and / or in addition, the systems and methods may determine that a subset of blockchain data includes token data based on that the subset has a structure associated with one or more standards. The token data may describe a non-fungible token associated with a digital asset. In some implementations, the structure may include a format of a code in the blockchain data associated with a standard format for non-fungible token code.

[0282] In some implementations, blockchain data may be parsed, and each parsed segment of the blockchain data may be processed to classify whether the parsed segment includes token data describing a non-fungible token. The classification may be generated using a machine learning classification model that has been trained to classify whether the parsed segment includes data associated with a non-fungible token (e.g., whether the data includes a reference to a payload, satisfies one or more criteria, and / or describes a smart contract). In some implementations, the blockchain data may be parsed using a machine learning segmentation model trained to parse blockchain data based on one or more factors (e.g., syntax, semantics, structure, code length, payload characteristics, code features, potential encoding markers, and / or another machine learning characteristic).

[0283] In some implementations, determining that the specific portion of the blockchain data includes the token data may include determining that the specific portion of the blockchain data includes the token data based on one or more function signatures. The one or more function signatures may be associated with a non-fungible token standard.

[0284] Alternatively and / or in addition, determining that a particular portion of the blockchain data includes token data may include determining that the particular portion includes a smart contract associated with a digital media item. The digital media item may be a payload of the smart contract. In some implementations, the digital media item may be a digital asset.

[0285] Then, index data may be generated based on the token data. The index data may include information obtained from the blockchain data. In some implementations, the index data may be associated with the digital asset. The index data may include transaction data associated with the non-fungible token. The index data may include data describing metadata associated with the non-fungible token. In some implementations, the index data may include a digital asset type associated with the digital asset. The digital asset type may be an augmented reality rendering asset type, and the digital asset may be an augmented reality rendering asset. The index data may include whether the URI (Uniform Resource Identifier) ​​has changed and whether the data of the payload (e.g., pixels of an image and / or text of a text string) has changed when the URI has changed. Additionally and / or alternatively, the index data may include information describing changes to the URI. The index data may include data that may describe factors that may be used to determine whether a non-fungible token involves fraud.

[0286] In some implementations, generating index data based on token data may include obtaining a digital asset associated with a non-fungible token, processing the digital asset to determine one or more characteristics of the digital asset, and generating characteristic data describing the one or more characteristics. The index data may include the characteristic data.

[0287] In some implementations, generating the index data may include determining a digital asset associated with a non-fungible token, and processing the non-fungible token using a machine learning model (e.g., a classification model, a detection model, a feature extractor model, and / or a semantic model) to determine one or more classifications, features, and / or attributes associated with the digital asset. The one or more classifications, features, and / or attributes may be included in the index data. The index data may include a name associated with the non-fungible token and / or digital asset, a name of a digital asset creator / issuer, transaction data (e.g., current and / or past owners, purchase price, transaction trends, trends of related non-fungible tokens, fuel prices, etc.), a subject of the digital asset, non-fungible token metadata, pixel tags, a non-fungible token description, free-form text associated with the non-fungible token, and / or a notoriety associated with the non-fungible token, issuer, or creator.

[0288] The digital asset may include image data, video data, text data, audio data, domains, augmented reality assets, virtual reality experiences, and / or potentially encoded data. In some implementations, the digital asset may include image data, and processing the digital asset may include processing the digital asset using a machine learning model to determine one or more image features.

[0289] Alternatively and / or additionally, generating feature data describing the one or more features may include determining one or more feature descriptor terms associated with the one or more features.The feature data may describe the one or more feature descriptor terms.

[0290] In some implementations, the systems and methods may include determining that a web content item is associated with a digital asset. A publishing time associated with the web content item may be obtained. The systems and methods may determine a minting time associated with a non-fungible token based on the blockchain data. Time difference data may be generated based on the minting time and the publishing time. In some implementations, the index data may include the time difference data.

[0291] Alternatively and / or in addition, generating index data based on the token data may include determining reference data associated with the digital asset based on the token data, and determining an issuer of the non-fungible token based on the token data. The index data may include data describing the reference data, the issuer, and the specific blockchain associated with the blockchain data.

[0292] The systems and methods may store index data in an index database. The index database may be stored on a server computing system. The index database may include multiple index data sets associated with multiple non-fungible tokens. For example, first token data may be identified to generate a first index data set, second token data may be identified to generate a second index data set, and third token data may be identified to generate a third index data set. The first token data may be associated with a first non-fungible token, the second token data may be associated with a second non-fungible token, and the third token data may be associated with a third non-fungible token.

[0293] The index database can be used for a variety of different purposes. For example, the index database can be used as a database for storing non-fungible token data for quick retrieval upon request or search. The system and method can include receiving a request for a digital asset from a user computing system, and providing the token data to the user computing system.

[0294] The index database may include an index data set generated based on blockchain data, web page data, and / or data obtained from other sources.

[0295] Alternatively and / or in addition, the systems and methods disclosed herein may obtain web page data from a web page. The web page data may describe list information of non-fungible tokens. In some implementations, the web page data may include data associated with digital assets. Additionally and / or alternatively, obtaining web page data may include generating a snapshot of a web page listing non-fungible tokens to be sold. In some implementations, the web page data may describe a leaf page of a digital web platform website (e.g., a digital marketplace website) that hosts and / or facilitates the purchase and sale of non-fungible tokens. The web page data may include a snapshot of a web page, which may include image data, text data, and / or potentially encoded data.

[0296] A specific portion (e.g., a subset) of the web page data may be processed to determine that the web page data includes token data. The token data may describe a non-fungible token associated with a digital asset. Alternatively and / or in addition, a digital marketplace website may be crawled to determine a plurality of leaf pages associated with a plurality of non-fungible tokens, and each leaf page may be processed to generate index data for each of the non-fungible tokens.

[0297] Then, index data may be generated based on the token data. In some implementations, the index data may include information obtained from the web page data. The index data may be associated with the digital asset. Generating the index data may include processing the image data, text data, and / or potentially encoded data of the leaf page to determine data associated with a plurality of index fields to be included in the index data.

[0298] The systems and methods may include storing the index data in an index database. The index database may be stored on a server computing system. In some implementations, the index database may be used to visualize non-fungible token search results of a search engine. Additionally and / or alternatively, the index database may be used to determine statistics associated with non-fungible tokens. For example, trending non-fungible tokens of a certain type and / or creator may be determined and then used to rank non-fungible tokens in a marketplace, search results page, and / or to inform users to make smart purchases.

[0299] The index database may include index data from multiple different sources (e.g., multiple different blockchains associated with multiple different blockchain computing systems and / or multiple different web pages associated with multiple different markets). The index database can be used for multiple different tasks (e.g., search, statistics generation, and / or model training). For example, the system and method may obtain blockchain data. The system and method may include determining that a subset of the blockchain data describes a non-fungible token associated with a digital asset. The index data may be generated based at least in part on the subset of the blockchain data. The index data may include reference data associated with the digital asset. In some implementations, the index data may be stored in a search database. The system and method may include receiving a search query from a user computing system. The system and method may determine that the search query is associated with the index data. Then, search results associated with the digital asset may be provided to the user computing system.

[0300] The systems and methods may include obtaining blockchain data (e.g., blockchain data obtained from a blockchain node associated with a blockchain computing system). Alternatively and / or in addition, the systems and methods may obtain web page data (e.g., market data obtained by taking a snapshot of a web page associated with a non-fungible token market). In some implementations, the systems and methods may include obtaining both blockchain data and web page data. The obtained data may be obtained via an application programming interface. In some implementations, the obtained data may be updated from time to time. Updates may occur at set intervals, and / or may be obtained at a certain frequency based on transaction trends, the type of digital asset, a specific blockchain, and / or based on cost.

[0301] Then, the system and method can determine that a subset of the acquired data (e.g., blockchain data and / or web page data) describes a non-fungible token associated with a digital asset. The determination can be based on a search for a specific structure, a specific term, and / or a specific participant. The determination can be based on a known digital asset creator / publisher, a known digital asset attribute, a known digital asset type, a known digital asset name, a known digital asset description, metadata, and / or tags. The determination can be based on whether the subset of the acquired data conforms to the EIP (EIP-721: Non-Fungible Token Standard, Ethereum Improvement Proposal (January 24, 2018), https: / / eips.ethereum.org / EIPS / eip-721.). In some implementations, strict compliance may not be required. For example, a data type may deviate from the specification of a standard. The functional nature and / or spirit of the data may be determined to be associated with a non-functional token.

[0302] The index data may be generated based at least in part on a subset of the acquired data (e.g., blockchain data and / or web page data). In some implementations, the index data may include reference data associated with a digital asset (e.g., script data that references a URL (Uniform Resource Locator) or URI). The index data may include digital asset creator / publisher, digital asset attributes, digital asset type, digital asset name, digital asset description, metadata, payload information, smart contract information, free-form text, transaction data, blockchain information (e.g., information associated with a specific blockchain on which a non-fungible token was minted), minting time, first release time of a digital asset, digital asset changes, specific web pages (e.g., specific marketplaces), and / or tags (e.g., pixel tags). In some implementations, the acquired data may be processed to determine a non-fungible token community associated with a specific non-fungible token, and the non-fungible token community may be indexed in the index data.

[0303] In some implementations, index data may be generated by processing the acquired data using one or more machine learning models (e.g., a segmentation model, a detection model, a classification model, and / or a feature extractor model). Transaction history may be processed to determine a price history of a non-fungible token, and the price history may be processed to index trend data and / or stability data. Additionally and / or alternatively, whether a non-fungible token is lazily minted may be indexed. Index data may include whether a non-fungible token is auctioned.

[0304] The systems and methods may then store the index data in a search database. The search database may include multiple index data sets associated with multiple non-fungible tokens. In some implementations, multiple non-fungible tokens may be identified by processing data from multiple sources.

[0305] In some implementations, the systems and methods may receive a search query from a user computing system. The search query may include one or more search terms. Alternatively and / or in addition, the search query may include one or more images, audio data, latently encoded data, and / or multimodal data.

[0306] The search query may be processed to determine that the search query is associated with the index data. A search engine may be utilized to receive and process the search query. The search engine may be configured to crawl a blockchain, a web page, and / or an index database or a search database.

[0307] In some implementations, determining that the search query is associated with the index data can include determining that one or more search terms of the search query describe at least one of the digital asset, the author of the digital asset, or non-fungible token metadata.

[0308] Search results associated with the digital asset may then be provided to the user computing system. The search results may include a preview of the digital asset. Alternatively and / or in addition, the search results may include an indicator indicating that the search result is associated with a non-fungible token.

[0309] In some implementations, providing search results associated with the digital asset to a user computing system may include determining that one or more web pages are associated with the search query, generating one or more general web results based on the one or more web pages, and providing a search results page to the user computing system. The search results page may include the search results and the one or more general web results.

[0310] In some implementations, index data generated based on blockchain data and index data generated based on web page data can be reconciled. For example, in the event of a conflict between blockchain data and web data, the systems and methods can process the data, determine which data is more credible, determine whether to merge the data, and / or determine whether to delete one or both data sets.

[0311] In some implementations, index data may be updated periodically. Updates may occur at set intervals, and / or may be acquired at a frequency based on trading trends, performance data, type of digital asset, specific blockchain, minting time (e.g., older non-fungible tokens may be updated less frequently due to greater computational costs associated with crawling), and / or based on cost.

[0312] The index database may include index entry fields associated with digital asset types, such that non-fungible tokens associated with augmented reality rendering assets may be distinguished from non-fungible tokens associated with images. In some implementations, the index data may include how to launch, run, and / or view a payload (e.g., a digital asset). For example, a video player tag, an image preview tag, a specific augmented reality application, and / or a specific device may be indexed.

[0313] Data extraction for index data generation may be machine learning based, heuristic based, and / or may be deterministic.

[0314] The systems and methods disclosed herein may be used for searching to allow a user to make a conscious decision whether to invest or not invest in a particular non-fungible token.

[0315] Specifically, systems and methods can be used to make Web3 technologies easily accessible to a significant portion of our users (not just Web3-savvy early adopters or crypto enthusiasts). The way Web3 can work can deviate significantly from the average user mental model. For example, systems and methods can focus on simplifying technology, jargon, applications, and / or settings.

[0316] The blockchain computing system may include a decentralized system; however, the systems and methods disclosed herein may be implemented in a decentralized system, a closed ecosystem, and / or a hybrid system including portions performed by a decentralized system and portions performed by a centralized system.

[0317] In some implementations, the systems and methods disclosed herein may be used for identification purposes. For example, the systems and methods disclosed herein may involve and / or interact with a user's crypto wallet to store user identity data to be utilized across Web3. A user may log into a website, share personal data, prove identity, and / or transfer cryptocurrency based at least in part on the identity data.

[0318] Wallet apps can be built into browsers. In Web3, there can be a single mechanism for logging in while keeping your data for yourself.

[0319] The blockchain computing system can store data describing a variety of information, which can include non-fungible token content IDs and / or ownership information of non-fungible tokens. Non-fungible tokens can introduce scarcity and authenticity to digital items.

[0320] The systems and methods disclosed herein can be implemented into search engines to enhance image, video, and audio searches. For example, the systems and methods implemented into search engines can signal to users who owns an image or video on the web and where the digital asset came from, can enforce copyright rules for digital assets, can detect whether a digital asset has been tampered with or has trust issues, and / or can enable the sale or transfer of digital content on the web (e.g., a non-fungible token market for images, music, and / or another form of digital asset).

[0321] The systems and methods disclosed herein may include creator tokens. The Web3 model may enable creators to own their content and have a direct relationship with their followers / subscribers / fans, which may bypass today's platforms. This may be very disruptive to video platforms, but may be an opportunity for search engines, which may not be the creator's primary concern. Creator tokens may be a way for fans to "invest" in their favorite creators while also enabling creators to create communities around their fans.

[0322] In some implementations, the systems and methods disclosed herein can be used to provide an oracle system (e.g., a system that can provide a service provider system for a smart contract to check whether something is true or has occurred).

[0323] The systems and methods disclosed herein may include general information retrieval. The systems and methods may enable searches to index important portions of the blockchain and make them accessible and useful.

[0324] Systems and methods may include a decentralized autonomous organization that can be used for automated decision making. In some implementations, information about the decentralized autonomous organization of the search engine can be provided, and this information can make the decision making more transparent.

[0325] In some implementations, the systems and methods can enable ownership of any digital asset on the web by empowering creators and publishers to claim ownership of their digital content and set usage rules for the digital content in a scalable and high-speed manner. Additionally and / or alternatively, the systems and methods can enable ownership of any digital asset on the web by empowering search users to understand the provenance and history of digital assets while also being able to take actions on digital assets in a manner permitted by the original creator (e.g., buying non-fungible tokens and / or using non-fungible tokens as tickets).

[0326] In some implementations, systems and methods can simultaneously create a carrot for creators and publishers to publish non-fungible content on the web, while also giving them the tools to create this content and set usage rules for the content.

[0327] For example, rewards could be created to show who owns an image on the web or to show items published by a creator / publisher, copies of "owned" digital assets could be blocked or at least any revenue generated by them transferred to the original owner, users could be enabled to sell and monetize generated content, and royalties could be realized.

[0328] Additionally and / or alternatively, the systems and methods may include tools for creators / publishers to create non-fungible content. For example, the systems and methods may give creators / publishers easy tools to publish non-fungible content and set rules for content usage that can be accomplished by both casual creators and mature large players.

[0329] In some implementations, systems and methods may include a smart contract blockchain that may be open, closed, and / or a hybrid of both.

[0330] Systems and methods can be implemented to implement a non-fungible token marketplace across various digital asset platforms (e.g., image platforms, video hosting platforms, and / or music hosting platforms) and / or across various service platforms (e.g., search engines, social media platforms, and / or blogging platforms). Creators and / or publishers can generate digital assets, mint digital assets, sell digital assets, and / or receive royalties on future sales of digital assets.

[0331] Non-fungible tokens can be used to access private chat groups or blogs within different platforms and communities, can be used to set up avatars that are verified to be owned by a specific user, can enable quick communication between creators and owners, and can provide transaction history that can be used to provide insightful recommendations for future issuance and purchases.

[0332] Example deduplication system and method

[0333] The systems and methods disclosed herein can identify that a non-fungible token has been identified twice by comparing a non-fungible token identified from a leaf page and a non-fungible token identified by crawling a blockchain. For example, multiple non-fungible tokens can be identified and indexed based on a variety of techniques. One technique may include analyzing a blockchain to identify data associated with a non-fungible token. The data can then be parsed to determine identifiable data that can be indexed. Another technique may include analyzing a non-fungible token leaf page (e.g., a web page associated with a non-fungible token market) to identify a non-fungible token to be sold. Data related to the non-fungible token can then be acquired and indexed. In some implementations, a variety of techniques may be utilized to generate a more comprehensive database for searching; however, the use of a variety of techniques may result in non-fungible tokens being indexed multiple times. For deduplication, the systems and methods disclosed herein may compare the identified market non-fungible tokens with the identified blockchain non-fungible tokens to determine whether a particular non-fungible token has been indexed multiple times. The comparison may include text processing, image processing, and / or potential coding processing. The metadata, blockchain address data, and / or image data may be compared to determine that a non-fungible token has been duplicated. In response to identifying a duplication, the data of the two index events may be merged and / or all index events except one for that particular non-fungible token may be deleted.

[0334] The system and method may include obtaining blockchain data from a blockchain computing system. The system and method may determine that the blockchain data includes first token data. The first token data may describe a first non-fungible token associated with a first digital resource (e.g., a first digital asset). In some implementations, first index data may be generated based on the first token data. The first index data may be stored in an index database. Web page data may be obtained from a digital web platform user interface (e.g., a digital marketplace user interface). The system and method may determine that the web page data includes second token data. The second token data may describe a second non-fungible token associated with a second digital resource (e.g., a second digital asset). Second index data may be generated based on the second token data. The second index data may be stored in an index database. In some implementations, the system and method may determine that the first digital resource (e.g., the first digital asset) and the second digital resource (e.g., the second digital asset) are the same digital resource (e.g., the same digital asset). The index database may be adjusted based on the first digital resource (e.g., the first digital asset) and the second digital resource (e.g., the second digital asset) being the same digital resource (e.g., the same digital asset). Alternatively and / or in addition, the determination of duplication can be based on other data. For example, matching contract addresses, matching token IDs, and / or matching blockchains. The index database can then be adjusted based on this determination.

[0335] For example, the systems and methods disclosed herein can deduplicate and / or mark duplicate instances of tokens being indexed. The systems and methods can compare the payloads of various tokens and contracts. The payloads (e.g., digital assets) of different tokens can be compared to determine whether two or more tokens share the same payload (e.g., digital assets with slight variations (e.g., images with different saturation or cropping)). The determination can be generated by processing the payloads of different tokens using one or more machine learning models. The determination can include image analysis (e.g., pixel analysis), video analysis, audio analysis, text analysis, and / or latent coding analysis. By determining that the digital resources (e.g., digital assets) match and / or are substantially similar (e.g., within a threshold similarity), the digital resources can be determined to be the same resource (e.g., the same asset). In response to determining that the payload matches, one or more actions can be determined. The one or more actions can include marking in the index data of each associated token to indicate that the corresponding tokens have the same payload. Alternatively and / or in addition, the one or more actions can include merging the index data of the tokens, removing the index data of at least one of the tokens, and / or generating data linking the index data of the related tokens.

[0336] In some implementations, one or more actions may be based at least in part on processing transaction data for each of the tokens with duplicate payloads. For example, the system and method may include processing a first transaction data set and a second transaction data set to determine whether the first token or the second token is more likely to be authentic (e.g., a valid token that was first minted and / or minted by the creator). In some implementations, the system and method may include fraud detection technology, which may include the use of heuristics. The system and method may analyze the transaction data to determine the token associated with the original digital resource creator (e.g., the original digital asset creator). Alternatively and / or in addition, the system and method may process the transaction data to determine the token with the earliest minting date. Alternatively and / or in addition, the system and method may process the transaction data to determine whether one of the tokens has a more fraudulent transaction trend than another token. For example, a transaction set for a single token that is always between two parties may be a sign of fraudulent activity, while a chain of transactions between the parties during the trending period of the particular digital resource series (e.g., the particular digital asset series) may be considered a more authentic transaction chain.

[0337] In some implementations, systems and methods may defer to index data generated based on blockchain data obtained from blockchain nodes, rather than index data generated based on web page data (e.g., market data).

[0338] Alternatively and / or in addition, the systems and methods may utilize prior data generated based on other validation analyses to determine the authenticity and / or validation of the current analysis. In some implementations, the URL, URI, and / or IPFS address of the payload may be processed to determine valid tokens. For example, URLs, URIs, and / or IPFS addresses with invalid addresses, expired addresses, and / or severely altered addresses may be determined to be invalid. URLs and / or URIs that reference suspicious websites may cause the associated tokens to be downgraded or discarded in the index database. In some implementations, different blockchains and / or different web platforms (e.g., different marketplaces, different posts, or different other content platforms) may take precedence over each other.

[0339] Alternatively and / or in addition, on-chain sources may take precedence over off-chain sources. In some implementations, digital resources embedded directly on blockchain data may take precedence over digital resources referenced only on-chain. Metadata indexed in an index database may be used to determine the authenticity of a particular token.

[0340] When a suspicious token is retrieved, a warning may be presented with a flag or alert for the suspicious or potentially fraudulent token (e.g., a warning provided with the search results for the suspicious token). Although digital assets may be discussed in detail below, the systems and methods may be implemented for any digital resource (e.g., any digital resource associated with a non-fungible token). Alternatively and / or in addition, the systems or methods may flag common or non-scarce digital resources (e.g., the same artist may intentionally create 100 copies of the same piece of digital art (e.g., in a "limited series," etc.)).

[0341] Specifically, the systems and methods can obtain blockchain data from a blockchain computing system. The blockchain data can be associated with a particular blockchain and / or from multiple blockchains. Additionally and / or alternatively, the blockchain data can be obtained from a blockchain node. In some implementations, the blockchain computing system can include a decentralized distributed computing system that stores data associated with multiple non-fungible tokens and multiple transactions associated with the non-fungible tokens.

[0342] The blockchain data may be determined to include first token data. The first token data may describe a first non-fungible token associated with a first digital asset. In some implementations, the blockchain computing system may include a distributed computing system comprising a plurality of computing devices. The blockchain data may include embedded code associated with a plurality of non-fungible tokens. In some implementations, determining that the blockchain data includes the first token data may include crawling the embedded code. The blockchain data may include embedded code. Alternatively and / or in addition, the blockchain data may be processed to identify one or more code features (e.g., a structure that complies with an EIP) that indicate a non-fungible token. In some implementations, the blockchain data may be parsed into fragments that may be processed separately to determine whether the fragments include non-fungible token data.

[0343] The first index data may be generated based on the first token data. The first index data may be stored in an index database. The first token data may include data describing a payload (e.g., a digital asset associated with a non-fungible token). In some implementations, the first token data may include data describing a creator / issuer of the non-fungible token, a description of the digital asset, a minting time, transaction data associated with the non-fungible token, a specific blockchain associated with the non-fungible token, a type of digital asset, a determined fraud likelihood score, and / or one or more determined flags.

[0344] The web page data may be obtained from a digital web platform user interface (e.g., a digital marketplace user interface). In some implementations, the web page data may include a snapshot of a digital resource listing web page associated with a second non-fungible token. The digital web platform user interface (e.g., a digital marketplace user interface) may be provided by a web platform computing system (e.g., a marketplace computing system) that hosts a non-fungible token marketplace for selling and displaying non-fungible tokens. Alternatively and / or in addition, a leaf page may be obtained from a website associated with the non-fungible token. The leaf page may be processed to generate the web page data.

[0345] The web page data may be determined to include second token data. The second token data may describe a second non-fungible token associated with a second digital asset. The second token data may include reference data associated with the second digital asset. In some implementations, the second digital asset may depict an enhanced version of a scene depicted in the first digital asset.

[0346] Second index data may be generated based on the second token data. The second index data may be stored in an index database. The second index data may include data describing a creator / issuer of the non-fungible token, a description of the digital asset, a minting time, transaction data associated with the non-fungible token, a specific blockchain associated with the non-fungible token, a type of digital asset, a determined fraud likelihood score, and / or one or more determined flags.

[0347] The first digital asset and the second digital asset can be determined to be the same digital asset. The determination can be based on determining the first digital asset of the first non-fungible token and determining the second digital asset of the second non-fungible token. The first digital asset and the second digital asset can be compared to determine similar and / or overlapping data. The determination can include image processing, text processing, audio processing, video processing, and / or potential coding processing. In some implementations, the determination can include processing the first digital asset and the second digital asset using one or more machine learning models (e.g., an image classification model, an audio classification model, a video classification model, an object detection model, a feature extractor model, and / or one or more recognition models).

[0348] The index database may be adjusted based on the first digital asset and the second digital asset being the same digital asset. The specific type of adjustment may be based on the transaction data of the non-fungible token, the source of the token data, and / or a variety of other factors.

[0349] In some implementations, adjusting the index database based on the first digital asset and the second digital asset being the same digital asset may include removing the second index data from the index database.

[0350] Alternatively and / or additionally, adjusting the index database based on the first digital asset and the second digital asset being the same digital asset may include merging the first index data and the second index data to generate enhanced index data.

[0351] Alternatively and / or additionally, adjusting the index database based on the first digital asset and the second digital asset being the same digital asset may include adjusting a first text entry associated with the first index database based on the first digital asset and the second digital asset being the same digital asset.

[0352] Alternatively and / or in addition, adjusting the index database based on the first digital asset and the second digital asset being the same digital asset may include adjusting a second text entry associated with the second index data based on the first digital asset and the second digital asset being the same digital asset. In some implementations, the second index entry may be stored in the index database.

[0353] In some implementations, the systems and methods may include receiving a search request. The search request may be received from a user computing system. The search request may include one or more search terms, one or more input images, and / or one or more other forms of input data.

[0354] One or more search results may be determined based on an index database. One or more search results may be determined based on responsiveness to a search request. Additionally and / or alternatively, one or more search results may be determined based on context data (e.g., a user computing system context, a global context, and / or a non-fungible token context).

[0355] Then, one or more search results may be provided for display. The one or more search results may include a specific search result associated with the first digital asset. In some implementations, the one or more search results may be provided for display with one or more tags (e.g., a non-fungible token tag, a fraudulent website tag, a suspicious transaction tag, a verification tag, and / or a duplicate non-fungible token tag).

[0356] The systems and methods may generate a plurality of first index entries of an index database based on crawling a blockchain. In some implementations, the systems and methods may generate a plurality of second index entries of an index database based on crawling a digital web platform (e.g., a digital marketplace). A particular first index entry in the plurality of first index entries may be determined to be associated with a particular second index entry in the plurality of second index entries. In response to determining that the particular first index entry is associated with the particular second index entry, the index database may be adjusted.

[0357] The system and method can generate a plurality of first index entries of an index database based on crawling a blockchain. The plurality of first index entries can include a plurality of first index data sets associated with a plurality of first non-fungible tokens.

[0358] Additionally and / or alternatively, the systems and methods can generate a plurality of second index entries of the index database based on crawling a digital web platform (e.g., a digital marketplace). The digital web platform can include a user interface for providing a plurality of non-fungible tokens for sale. The plurality of second index entries can include a plurality of second index data sets associated with the plurality of second non-fungible tokens.

[0359] A particular first index entry in a plurality of first index entries may be determined to be associated with a particular second index entry in a plurality of second index entries. The determination may include comparing each of the plurality of first index entries with each of the plurality of second index entries. The comparison may include obtaining a plurality of first digital assets associated with the plurality of first index entries. Additionally and / or alternatively, the comparison may include obtaining a plurality of second digital assets associated with the plurality of second index entries. Each of the plurality of first digital assets may be compared with each of the plurality of second digital assets. The comparison may include processing the digital assets using one or more machine learning models to determine whether the digital assets are the same and / or which digital assets are the same (e.g., the digital assets match and / or are within a threshold similarity).

[0360] In some implementations, the first payload can include a first digital asset. The second payload can include a second digital asset. In some implementations, the first digital asset and the second digital asset can match and / or be similar.

[0361] In some implementations, determining that a particular first index entry in the plurality of first index entries is associated with a particular second index entry in the plurality of second index entries can include determining that a first payload associated with the first non-fungible token and a second payload associated with the second non-fungible token are the same. The first non-fungible token can be associated with the particular first index entry. In some implementations, the second non-fungible token can be associated with the particular second index entry.

[0362] In response to determining that the particular first index entry is associated with the particular second index entry, the index database may be adjusted. Adjusting the index database may include merging index entries, removing index entries, and / or adding a flag to each of the index entries determined to be duplicates.

[0363] Alternatively and / or in addition, the systems and methods may include obtaining a particular index entry and a plurality of index entries. The plurality of index entries may be obtained from an index database. The particular index entry and the plurality of index entries may be processed to generate duplicate data. The duplicate data may describe whether one or more different index entries in the plurality of index entries are associated with a digital asset associated with the particular index entry. Verification data may be generated based at least in part on the duplicate data. In some implementations, the verification data may be associated with the authenticity of a non-fungible token associated with the particular index entry. The verification data may be provided as an output.

[0364] The systems and methods may include obtaining a specific index entry and a plurality of index entries. The plurality of index entries may be obtained from an index database. In some implementations, the plurality of index entries may include one or more index entries obtained by extracting data from a blockchain. The plurality of index entries may include one or more index entries obtained by extracting data from a non-fungible token market.

[0365] The specific index entry and the plurality of index entries may be processed to generate duplicate data. The duplicate data may describe whether one or more different index entries in the plurality of index entries are associated with the digital asset associated with the specific index entry.

[0366] Verification data may be generated based at least in part on the duplicate data. The verification data may be associated with the authenticity of the non-fungible token associated with the particular index entry. In some implementations, the verification data may be based at least in part on a timestamp associated with the particular index entry. The verification data may describe whether the non-fungible token was minted prior to another issuance of the digital asset.

[0367] In some implementations, generating verification data based at least in part on the duplicate data can include determining that a particular index entry is authentic by determining that the particular index entry is associated with a first minting of a digital asset. The determination can be based on transaction data, minting time, issuance time, a particular blockchain, a source of token data, payload URI data, and / or digital asset quality. One or more machine learning models can be utilized to generate the verification.

[0368] The verification data may be provided as an output. The verification data may be provided via one or more user interface elements (e.g., a banner, a pop-up window, a user interface label, and / or a user interface annotation). In some implementations, the verification data may be provided for display adjacent to a preview of the digital asset.

[0369] Example Identification Systems and Methods

[0370] Systems and methods may utilize blockchain trigger event determination and processing to identify data describing a non-fungible token. Systems and methods may include obtaining input data. The blockchain data may include byte code associated with a blockchain. The blockchain data may be processed to determine that a subset of the blockchain data includes candidate token data. In some implementations, the candidate token data may be associated with a blockchain trigger event. An output action associated with the blockchain trigger event may be determined. The output action may be associated with a digital resource. The output action may be processed to determine that the candidate token data describes a non-fungible token. Identification data may be provided as an output. The identification data may describe that the candidate token data is associated with a non-fungible token.

[0371] Systems and methods may acquire blockchain data. The blockchain data may be associated with a blockchain that stores data in a decentralized and distributed system. For example, the blockchain data may include byte code associated with the blockchain. The blockchain data may be associated with a plurality of non-fungible tokens. The plurality of non-fungible tokens may be associated with a plurality of digital resources (e.g., a plurality of digital assets). In some implementations, the blockchain data may be acquired from a blockchain node associated with a particular blockchain. The blockchain data may include pre-filtered blockchain data that has been processed to filter out data that may not have been utilized during identification and / or indexing.

[0372] Systems and methods can process blockchain data to determine that a subset of the blockchain data includes candidate token data. The candidate token data can be associated with a blockchain trigger event (e.g., an "if-then" event that can result in the transfer of a digital resource (e.g., a digital asset)). In some implementations, the candidate token data can be determined based on one or more criteria associated with a non-fungible token code standard. Additionally and / or alternatively, the candidate token data can include reference data associated with the digital resource. The blockchain trigger event can be associated with a smart contract. In some implementations, the candidate token data can be determined by parsing the blockchain data into fragments that can be processed separately. The candidate token data can be determined based on a determination that a subset of the blockchain data is associated with a smart contract. The smart contract can include a blockchain trigger event, and the blockchain trigger event can include an event in which a payload (e.g., a digital resource) is transferred in response to a transfer request (e.g., purchasing a digital resource with a cryptocurrency).

[0373] An output action associated with the blockchain triggering event may be determined. The output action may be associated with a digital resource. In some implementations, the output action may be a result action occurring in response to the blockchain triggering event. The output action may be associated with an embedded uniform resource identifier and / or an embedded uniform resource locator.

[0374] In some implementations, processing the output action to determine that the candidate token data describes the non-fungible token may include determining an entity associated with the output action, and determining that the entity is a digital resource (e.g., a digital asset) being transferred in response to a blockchain triggering event. Alternatively and / or in addition, the entity may be processed to determine that the smart contract is an empty contract. In response to the smart contract being an empty contract, the identification data may describe that the candidate token data is not data to be indexed.

[0375] Alternatively and / or in addition, processing the output action to determine that the candidate token data describes the non-fungible token may include: determining that the output action includes a link to a login address (e.g., a login web page and / or a login storage file). Login data associated with the login address may be obtained. The login data may then be processed to determine that the login data includes a digital resource (e.g., a digital asset). The digital resource may be determined to be associated with the non-fungible token.

[0376] Alternatively and / or in addition, processing the output action to determine candidate token data describing the non-fungible token may include obtaining a uniform resource identifier via an application programming interface.

[0377] The output action may be processed to determine that the candidate token data describes a non-fungible token. The determination may be determined based on the output action being associated with a digital resource (e.g., a digital asset). In some implementations, the determination may be determined based on the output action describing an intent to associate a smart contract with a non-fungible token.

[0378] The identification data may be provided as an output. The identification data may describe that the candidate token data is associated with a non-fungible token. The identification data may be generated based on a determination that the candidate token data describes a non-fungible token. The identification data may describe that the candidate token data includes token data for indexing.

[0379] In some implementations, the systems and methods may include determining the validity of the candidate token data. The validity may be determined based on whether the candidate token data includes the determined problem characteristic. The identification data may be generated based at least in part on the validity.

[0380] In some implementations, the systems and methods may include processing the output action to determine a digital resource (e.g., a digital asset) associated with the non-fungible token. The digital resource may be processed to determine a quality of the digital resource (e.g., a quality of the digital asset). The systems and methods may determine whether to index the candidate token data based at least in part on the identification data and the quality of the digital resource. The quality of the digital resource may be determined based on a resolution of the digital resource, a size of the digital resource, a machine learning determination (e.g., an output of a machine learning model that has processed data associated with the digital resource), and / or a determined uniqueness of the digital resource.

[0381] Alternatively and / or in addition, the systems and methods may utilize standard data to identify token data in blockchain code. For example, the systems and methods may include obtaining blockchain data. The blockchain data may include byte code associated with the blockchain. The blockchain data may be processed to determine that a subset of the blockchain data includes candidate token data. The candidate token data may be associated with a blockchain trigger event. The systems and methods may include obtaining standard data. In some implementations, the standard data may include one or more standards associated with a standard for a non-fungible token. The candidate token data may be processed to determine that the candidate token data is associated with one or more standards. Identification data may be generated based on the candidate token data being associated with the one or more standards. In some implementations, the identification data may describe that the candidate token data is associated with a non-fungible token. The identification data may be provided as an output.

[0382] The systems and methods may acquire blockchain data. The blockchain data may include bytecode associated with the blockchain. The blockchain data may be associated with a plurality of smart contracts. The plurality of smart contracts may include a plurality of sets of if-then logic.

[0383] In some implementations, obtaining blockchain data may include obtaining first node data from a blockchain node. The blockchain node may be associated with a particular blockchain. Obtaining blockchain data may further include processing the first node data in a sandbox (e.g., an isolated computer environment for mitigating the spread of any potential unintended consequences (e.g., computer vulnerabilities)) to generate a first portion of blockchain data, and storing the first portion of blockchain data.

[0384] Additionally and / or alternatively, obtaining the blockchain data may include obtaining second node data from the blockchain node. The blockchain node may be associated with a particular blockchain. In some implementations, obtaining the blockchain data may further include processing the second node data in the sandbox to generate a second portion of the blockchain data, and storing the second portion of the blockchain data.

[0385] The blockchain data may be processed to determine that a subset of the blockchain data includes candidate token data. In some implementations, the candidate token data may be associated with a blockchain triggering event. The blockchain triggering event may include an "if-then" event such that if a certain event occurs, then ownership of a digital resource may be transferred. The candidate token data may be determined based on detection of the blockchain triggering event in the byte code of the blockchain.

[0386] The systems and methods may acquire standard data. The standard data may include one or more standards associated with standards for non-fungible tokens. The standard may be associated with a predetermined interface for uniformly interfacing with different non-fungible tokens. The smart contract structure may conform to the standard for implementing a standard application programming interface to facilitate interaction with different non-fungible tokens (e.g., regardless of a particular issuer).

[0387] The candidate token data may be processed to determine whether the candidate token data is associated with one or more criteria. The determination may include searching the candidate token data to determine whether one of the predetermined interfaces is included in the candidate token data. In some implementations, the candidate token data may be processed to identify whether a particular blockchain trigger event language (e.g., a function signature that may include a particular string that matches one of the predefined string templates for non-fungible tokens as indicated by the one or more criteria) is included in the candidate token data.

[0388] The identification data may be generated based on associating the candidate token data with one or more criteria. The identification data may describe that the candidate token data is associated with a non-fungible token.

[0389] The identification data may be provided as an output. The identification data may be provided to a user computing system. In some implementations, the identification data may be provided to an index computing system to instruct the index computing system whether to index the candidate token data. For example, if the identification data describes that the candidate token data is associated with a non-fungible token, the candidate token data may be indexed. Alternatively and / or in addition, if the identification data describes that the candidate token data is not associated with a non-fungible token, the candidate token data may not be indexed. Candidate token data determined to be associated with a non-fungible token may be identified as token data that may be stored for indexing and / or for other purposes (e.g., for machine learning training to train one or more models to detect and identify token data associated with a non-fungible token).

[0390] Index data may then be generated based on the candidate token data and the identification data. The identification data may be used to determine whether and to what extent the candidate token data is to be indexed. In some implementations, data describing the identification data may be indexed using other data associated with the candidate token data.

[0391] In some implementations, a blockchain trigger event can be processed to determine a digital asset associated with a non-fungible token. The digital asset can be processed to determine that the digital asset is vulgar, and the systems and methods can determine whether to index candidate token data based at least in part on the identification data and the digital asset being vulgar.

[0392] Alternatively and / or in addition, token data may be identified based on the determined intent. For example, the systems and methods may include obtaining blockchain data. The blockchain data may include byte code associated with the blockchain. The blockchain data may be processed to determine that a subset of the blockchain data includes candidate token data. In some implementations, the candidate token data may be associated with a blockchain trigger event. The candidate token data may be processed to determine an intent associated with the candidate token data. The candidate token data may include reference data associated with a digital resource. The intent may be processed to determine that the candidate token data describes a non-fungible token. In some implementations, identification data may be provided as an output. The identification data may describe that the candidate token data is associated with a non-fungible token.

[0393] The systems and methods can obtain blockchain data. The blockchain data can include byte code associated with the blockchain. The blockchain data can be obtained from a blockchain node. Alternatively and / or in addition, the blockchain data can be obtained from a non-blockchain repository.

[0394] The blockchain data may be processed to determine that a subset of the blockchain data includes candidate token data. The candidate token data may be associated with a blockchain triggering event. In some implementations, the candidate token data may include data describing a smart contract. The blockchain triggering event may be an event that triggers the transfer of a payload of a smart contract.

[0395] The candidate token data may be processed to determine an intent associated with the candidate token data. The candidate token data may include reference data associated with a digital resource. In some implementations, the intent may be determined based at least in part on a blockchain trigger event. Alternatively and / or in addition, the intent may be determined based at least in part on free-form text in the candidate blockchain data. The intent may be a minting intent to issue a non-fungible token on a blockchain. In some implementations, the intent may be determined by processing the candidate token data with a machine learning model. The machine learning model may be trained to determine whether the candidate token data describes script data structured to provide non-fungible token functionality.

[0396] The intent may be processed to determine whether candidate token data describes a non-fungible token. For example, the intent may be processed to determine whether the intent meets a threshold likelihood of being a non-fungible token intent.

[0397] The systems and methods may provide identification data as an output. The identification data may describe that the candidate token data is associated with a non-fungible token. The identification data may be generated based at least in part on a determination that the intent is a non-fungible token intent.

[0398] In some implementations, the systems and methods disclosed herein may identify non-fungible token data based on whether the actions associated with the smart contract match (e.g., are the same or similar) to the actions of how the non-fungible token will act. For example, the smart contract may include a "get URI" that may include an application programming interface for pulling a uniform resource identifier. Additionally and / or alternatively, the smart contract may include a structure similar to one or more EIP-721 standards (EIP-721: Non-fungible Token Standard, Ethereum Improvement Proposal (January 24, 2018), https: / / eips.ethereum.org / EIPS / eip-721.). Alternatively and / or in addition, the blockchain may be crawled to identify payloads that may match (e.g., are the same or substantially similar) to digital assets of non-fungible tokens. The systems and methods may identify a token dataset even if the dataset includes deviations from one or more standards.

[0399] Additionally and / or alternatively, the systems and methods may include one or more pre-filtering techniques. For example, the systems and methods may include filtering out any data sets associated with suspicious addresses, suspicious embeddings, and / or suspicious actors (e.g., unknown issuers or owners / issuers with associations with past scam-related non-fungible tokens).

[0400] In some implementations, the identified token data may be further processed to determine whether and / or how to index the identified token data. For example, the systems and methods may process candidate token data to determine whether the token data is suspicious, potentially includes vulnerabilities, and / or includes low-quality digital assets.

[0401] The token data may be determined to be suspicious based on an embedded uniform resource identifier and / or an embedded uniform resource locator. Alternatively and / or in addition, the token data may be determined to be suspicious based on transaction data of the non-fungible token (e.g., a suspicious string of transactions, suspicious participants in transactions, suspicious circumstances surrounding minting, etc.).

[0402] Determining the potential for a vulnerability can be based on heuristics and / or machine learning techniques (e.g., utilizing a machine learning model to process token data).

[0403] A low-quality digital resource (e.g., a low-quality digital asset) may be a digital asset that has few distinguishing features (e.g., a white noise audio clip or a digital asset that is identical or substantially similar to a plurality of other digital assets or media files that are older than the date of minting). Alternatively and / or in addition, a low-quality digital asset may be a digital asset that has poor resolution, heavy pixelation, heavy noise, file errors, and / or is relatively small in size compared to other non-fungible token digital assets of the same type or class. In some implementations, a low-quality digital asset may be a digital asset with vulgar and / or offensive content. For example, the system and method may obtain a digital asset associated with token data. The digital asset may be processed to determine whether the digital asset includes vulgar and / or offensive material. If it is determined that the digital asset includes vulgar and / or offensive content, the corresponding token data may be marked and / or flagged. The system and method may determine not to index the token data based on the flag, and / or may index the token data using a flag indicating the vulgar and / or offensive nature of the digital asset. Flags may then be utilized to limit the presentation of the indexed data to only a subset of users (eg, users who are permitted to present vulgar and / or offensive content).

[0404] Alternatively and / or in addition, the quality of a non-fungible token can be determined based on the degree of compliance of the token data with the EIP and ERC standards for non-fungible tokens. Compliance can be weighted based on which standards are met and which standards are lacking in the token data. In some implementations, quality can be based on whether the uniform resource identifier can be used to navigate to the digital asset without editing.

[0405] In some implementations, the name and / or description associated with the token data can be processed to determine whether the name and / or description satisfies certain naming patterns or description patterns.

[0406] In some implementations, token data can be identified as being associated with a particular non-fungible token grouping (e.g., a particular community) based on a determined artist name, a determined publisher, and / or through metadata associated with the non-fungible token. Groupings can be determined based on minting date, association with an event, and / or based on a purchaser's transaction history.

[0407] Alternatively and / or in addition, the token data can be identified by processing the blockchain data and identifying the method signature. The method signature can be associated with the first four bytes of the hash. In some implementations, the method signature can be associated with a uniform resource identifier having a set of two parameters. The first parameter can include the token ID of the specific non-fungible token, and the second parameter can include a string of data.

[0408] The systems and methods disclosed herein can crawl a blockchain by utilizing a peer node running on one or more computing devices. The systems and methods can passively "listen" and then dump any new data into a log for later processing. Alternatively and / or in addition, the systems and methods can involve "live monitoring" of nodes at certain intervals (e.g., manually set intervals, predetermined intervals, and / or automatically determined intervals). The systems and methods can crawl the blockchain to determine whether any changes have occurred, and any changes can be identified and added to the log. In some implementations, the systems and methods can utilize an application programming interface to identify and obtain new data.

[0409] In some implementations, the systems and methods may utilize a non-blockchain repository to obtain blockchain data. For example, a third-party application programming interface may obtain blockchain data and store it in a non-blockchain repository that can be accessed by the systems and methods to obtain blockchain data for processing.

[0410] When crawling the blockchain and / or when crawling the web, the systems and methods may search for contract addresses and token IDs to identify token data. Additionally and / or alternatively, the systems and methods may crawl the web to identify non-fungible token drop events and / or lazy minting events. The systems and methods may monitor messaging applications and / or social media platforms to obtain data about potential minting events.

[0411] The acquisition and processing of blockchain data from blockchain nodes may include one or more security techniques for mitigating the possibility of data being corrupted by opening or processing vulnerabilities or computer viruses. For example, blockchain data may be acquired and stored in a sandbox. The data may be processed in the sandbox prior to indexing to ensure that vulnerabilities and / or viruses do not affect the index database.

[0412] In some implementations, the systems and methods may include customized blockchain nodes for different tasks. For example, multiple blockchain nodes may be configured to obtain and filter blockchain data for multiple different purposes. One node may filter blockchain data to obtain and process transaction data, while another node may filter blockchain data to obtain and process newly minted non-fungible tokens. In addition and / or alternatively, different nodes may be configured to identify and obtain data associated with different non-fungible token types. For example, one node may be configured to identify and obtain token data for a non-fungible token associated with an image, while another node may be configured to identify and obtain token data for a non-fungible token associated with a domain. Multiple nodes may be associated with different blockchains and / or may be associated with filtering based on different identification techniques. For example, a first node may obtain blockchain data for identifying token data based on identification of a digital asset payload, a second node may obtain data to be processed to determine token data based on a naming pattern, and a third node may obtain blockchain data for processing to determine token data based on one or more non-fungible token standards.

[0413] In some implementations, the systems and methods may probe the blockchain by sending beacon transactions to measure and / or track network health, along with potentially tracking other data characteristics. This probing may be used to update "data extraction" strategies or methods. For example, if beacon transactions are taking a long time to process for a given amount of fuel, the systems and methods may utilize a more complex, dynamic extraction strategy that may back off until the transaction rate picks up. This adjustment may help to potentially reduce the cost of obtaining "latest" data from the blockchain.

[0414] Additional public content

[0415] The technology discussed herein relates to servers, databases, software applications, and other computer-based systems, as well as the actions taken and the information sent to and from such systems. The inherent flexibility of computer-based systems allows for multiple possible configurations, combinations, and partitioning of tasks and functions between and among components. For example, the processes discussed herein can be implemented using a single device or component or multiple devices or components working in combination. Databases and applications can be implemented on a single system, or distributed across multiple systems. Distributed components can operate sequentially or in parallel.

[0416] Although the present subject matter has been described in detail with respect to various specific example embodiments of the present subject matter, each example is provided by way of explanation rather than limitation of the present disclosure. Those skilled in the art may easily produce changes, modifications, and equivalents to such embodiments after understanding the foregoing. Therefore, the present disclosure does not exclude such modifications, variations, and / or additions to the present subject matter that would be readily understood by those of ordinary skill in the art. For example, a feature shown or described as part of an embodiment may be used together with another embodiment to produce yet further embodiments. Therefore, the present disclosure is intended to encompass such changes, variations, and equivalents.

Claims

1. A computing system, the system comprising: one or more processors; as well as one or more non-transitory computer-readable media that collectively store instructions that, when executed by the one or more processors, cause the computing system to perform operations comprising: Obtaining blockchain data from a blockchain computing system, wherein the blockchain data includes code associated with a plurality of non-fungible tokens; storing the blockchain data in a blockchain log; processing the blockchain data to determine token data associated with a non-fungible token of the plurality of non-fungible tokens; determining that a portion of the token data includes descriptive data, wherein the descriptive data is associated with the non-fungible token; Segmenting the descriptive data from the blockchain data; and The descriptive data is stored.

2. A system as described in any preceding claim, wherein the descriptive data includes information determined to be associated with a specific identifying characteristic of a non-fungible token, and wherein the determination is based on one or more criteria associated with the structure of the non-fungible token byte code.

3. The system of any preceding claim, wherein determining that the portion of the token data includes the descriptive data comprises: parsing the token data to generate a plurality of segment data sets; processing each of the plurality of segment data sets using a machine learning natural language processing model to generate a plurality of segment classifications; as well as A determination is made based on the plurality of segment classifications that a particular segment data set among the plurality of segment data sets includes descriptive data.

4. The system of any preceding claim, wherein the operations further comprise: obtaining standards data, wherein the standards data describes one or more standards for a token code; and Wherein the token data is determined based at least in part on the one or more criteria.

5. The system of claim 4, wherein the code of the blockchain data is associated with the one or more standards of token code.

6. The system of any preceding claim, wherein determining that the portion of the token data includes the descriptive data comprises: determining token identification data associated with the non-fungible token; determining a plurality of transactions in the blockchain data based on the token identification data; as well as The portion of the token data is determined based on the plurality of transactions.

7. A system as described in any preceding claim, wherein the descriptive data includes at least one of: a digital asset associated with the non-fungible token, transaction data associated with the non-fungible token, a name associated with the non-fungible token, a description associated with the non-fungible token, or a creator identifier associated with a creator of the non-fungible token.

8. A computer-implemented method, the method comprising: Retrieving, by a computing system including one or more processors, blockchain data from a blockchain computing system, wherein the blockchain data includes code associated with a plurality of non-fungible tokens; processing the blockchain data by the computing system to determine links in the blockchain data; acquiring, by the computing system, a landing page associated with the link, wherein the landing page is acquired based on the link; determining, by the computing system, content data associated with the login page, wherein the content data is associated with a non-fungible token from the plurality of non-fungible tokens; as well as The content data is stored by the computing system.

9. A method as claimed in any preceding claim, wherein the link comprises at least one of a uniform resource identifier or a uniform resource locator.

10. A method as claimed in any preceding claim, wherein the login page includes a digital asset, wherein the digital asset is the payload of a smart contract associated with the non-fungible token.

11. A method as claimed in any preceding claim, wherein the content data comprises a preview of a digital asset associated with the non-fungible token.

12. The method of any preceding claim, further comprising: determining a second link based on the login page; acquiring a second login page based on the second link; and The second login page includes the content data.

13. The method of any preceding claim, wherein obtaining the blockchain data comprises: accessing a blockchain node, wherein the blockchain node is associated with a particular blockchain; as well as Recursively crawl the specific blockchain.

14. One or more non-transitory computer-readable media collectively storing instructions that, when executed by one or more computing devices, cause the one or more computing devices to perform operations comprising: Obtaining blockchain data from a blockchain computing system, wherein the blockchain data includes code associated with a plurality of non-fungible tokens; processing the blockchain data to determine a first set of data associated with a particular non-fungible token of the plurality of non-fungible tokens; processing the blockchain data to determine a second set of data associated with the particular non-fungible token of the plurality of non-fungible tokens; as well as The first set of data and the second set of data are stored.

15. The one or more non-transitory computer-readable media of any preceding claim, wherein the operations further comprise: The first set of data and the second set of data are split from the blockchain data.

16. One or more non-transitory computer-readable media as claimed in any preceding claim, wherein the first set of data comprises free-format text data.

17. The one or more non-transitory computer-readable media of any preceding claim, wherein the second set of data comprises transaction data from an electronic ledger.

18. The one or more non-transitory computer-readable media of any preceding claim, wherein the operations further comprise: Generate index data of the specific non-fungible token based on the first set of data and the second set of data.

19. The one or more non-transitory computer-readable media of any preceding claim, wherein processing the blockchain data to determine the first set of data associated with the particular non-fungible token in the plurality of non-fungible tokens comprises: crawling the blockchain data to identify link data; as well as The content data related to the link data is acquired.

20. The one or more non-transitory computer-readable media of any preceding claim, wherein processing the blockchain data to determine the second set of data associated with the particular non-fungible token in the plurality of non-fungible tokens comprises: determining a plurality of data sets associated with non-fungible token transactions, wherein the non-fungible token transactions are associated with the particular non-fungible token; as well as The second set of data is generated based on the plurality of data sets.

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