De-duplication of non-homogenized tokens in search indices

Abstract

Systems and methods for non-homogenized token indexing may include obtaining and identifying token data associated with a plurality of non-homogenized tokens. In generating an index database, the systems and methods may index tokens multiple times and / or may index tokens having the same or similar payloads. The systems and methods may identify a duplicate token index and may adjust an index database based on the identification of the duplicate token index data. The adjustment may affect a determination of which search results are to appear in response to the search query.

Description

Technical Field

[0001] The present disclosure generally relates to identifying duplicate token index data and adjusting an index database based on the identification. More specifically, the present disclosure relates to identifying multiple token index data sets having the same or similar payloads and adjusting an index database based on an analysis of the duplicate token index data sets. 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. The search engine can crawl the web to determine web pages with certain terms. The search results can typically be indicated using a caption and / or a text title. The 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 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 storing instructions together, which, when executed by one or more processors, cause the computing system to perform operations. The operation may include obtaining blockchain data from a blockchain computing system. The operation may include determining 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. The operation may include generating first index data based on the first token data. In some implementations, the first index data may be stored in an index database. The operation may include obtaining web page data from a digital web platform user interface, and determining 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. The operation may include generating second index data based on the second token data. In some implementations, the second index data may be stored in an index database. The operation may include determining that the first digital resource and the second digital resource are the same digital resource, and adjusting the index database based on that the first digital resource and the second digital resource are the same digital resource.

[0006] In some implementations, the blockchain computing system may include a distributed computing system including a plurality of computing devices. The blockchain data may include embedded code associated with a plurality of non-fungible tokens. In some implementations, the operation may include receiving a search request, determining one or more search results based on an index database, and providing one or more search results for display. The one or more search results may include a specific search result associated with a first digital resource. In some implementations, adjusting the index database based on the first digital resource and the second digital resource being the same digital resource may include removing the second index data from the index database. Adjusting the index database based on the first digital resource and the second digital resource being the same digital resource may include merging the first index data and the second index data to generate enhanced index data.

[0007] In some implementations, adjusting the index database based on the first digital resource and the second digital resource being the same digital resource may include adjusting a first text entry associated with the first index data based on the first digital resource and the second digital resource being the same digital resource. Adjusting the index database based on the first digital resource and the second digital resource being the same digital resource may include adjusting a second text entry associated with the second index data based on the first digital resource and the second digital resource being the same digital resource. The second index entry may be stored in the index database.

[0008] In some implementations, determining that the blockchain data includes the first token data may include crawling embedded code. The blockchain data may include embedded code. In some implementations, the web page data may include a snapshot of a digital resource listing web page associated with the second non-fungible token. The second digital resource may depict an enhanced version of a scene depicted in the first digital resource.

[0009] Another example aspect of the present disclosure relates to a computer-implemented method. The method may include generating, by a computing system including one or more processors, a plurality of first index entries of an index database based on crawling a blockchain. The method may include generating, by the computing system, a plurality of second index entries of an index database based on crawling a digital web platform. The method may include determining, by the computing system, 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. The method may include adjusting, by the computing system, the index database in response to determining that the particular first index entry is associated with the particular second index entry.

[0010] In some implementations, determining that a particular first index entry among the plurality of first index entries is associated with a particular second index entry among the plurality of second index entries may include determining that a first payload associated with a first non-fungible token and a second payload associated with a second non-fungible token are the same. The first non-fungible token may be associated with the particular first index entry, and the second non-fungible token may be associated with the particular second index entry. In some implementations, the first payload may include a first digital resource, the second payload may include a second digital resource, and the first digital resource and the second digital resource may match.

[0011] In some implementations, the first payload may include a first digital resource, the second payload may include a second digital resource, and the first digital resource and the second digital resource may depict the same scene with different resolutions. The digital web page may include a user interface for providing a plurality of non-fungible tokens for sale.

[0012] 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 a specific index entry and multiple index entries. The multiple index entries may be obtained from an index database. The operations may include processing the specific index entry and multiple index entries to generate duplicate data. In some implementations, the duplicate data may describe whether one or more different index entries in the multiple index entries are associated with a digital resource associated with the specific index entry. The operations may include generating verification data based at least in part on the duplicate data. The verification data may be associated with the authenticity of a non-fungible token associated with a specific index entry. The operations may include providing the verification data as an output.

[0013] 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. In some implementations, generating verification data based at least in part on the duplicate data may include determining that the particular index entry is authentic by determining that the particular index entry is associated with a first minting of a digital resource. 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 resource.

[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 non-fungible token indexing according to an example embodiment of the present disclosure.

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

[0019] Figure 2 A block diagram depicting example token index deduplication according to example embodiments of the present disclosure.

[0020] Figure 3 A block diagram depicts an example index validity determination according to an example embodiment of the present disclosure.

[0021] Figure 4 A block diagram depicting example token index deduplication according to example embodiments of the present disclosure.

[0022] Figure 5 A block diagram depicting example search results page generation according to an example embodiment of the present disclosure.

[0023] Figure 6 A flowchart depicting an example method for performing non-fungible token index deduplication according to an example embodiment of the present disclosure.

[0024] Figure 7 A flowchart depicting an example method for performing non-fungible token index deduplication according to an example embodiment of the present disclosure.

[0025] Figure 8 A flowchart depicting an example method for performing non-fungible token index validation according to an example embodiment of the present disclosure.

[0026] Fig.9A A block diagram depicting an example computing system that performs non-fungible token indexing according to an example embodiment of the present disclosure.

[0027] Fig. 9B A block diagram depicting an example computing device that performs non-fungible token indexing according to an example embodiment of the present disclosure.

[0028] Fig. 9C A block diagram depicting an example computing system that performs non-fungible token indexing according to an example embodiment 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] Reference numerals repeated across multiple drawings are intended to identify like features in the various implementations. DETAILED DESCRIPTION

[0036] Overview

[0037] In general, the present disclosure relates to systems and methods for deduplication of non-fungible token index data in an index database (e.g., a search index). Specifically, 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 can 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 can 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 obtained and indexed. In some implementations, a variety of techniques can 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 an identified market non-fungible token with an identified blockchain non-fungible token to determine whether a particular non-fungible token has been indexed multiple times. The comparison may include text processing, image processing, and / or latent coding processing. 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 indexed events may be merged and / or all indexed events except one for the particular non-fungible token may be deleted.

[0038] 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. The 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). The 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 additionally, 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.

[0039] 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 additionally, 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, flags, and / or tags). 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.

[0040] In some implementations, the systems and methods disclosed herein may be used for Web3 profiles, Web3 transactions, and / or Web3 identification.

[0041] The systems and methods may improve search user interfaces and user experiences by identifying and indexing relevant data that may be used to determine that a non-fungible token is responsive to a search query. Additionally and / or alternatively, the indexed data may be used to determine the ranking of non-fungible token search results relative to other search results (e.g., certain indexed data sets may be ranked higher or lower based on duplication determinations). The systems and methods may be used to determine whether search results may be provided with a non-fungible token logo and / or may be given a ranking boost based on association with an authenticated non-fungible token. Additionally and / or alternatively, the systems and methods may improve the quality of search results by removing and / or hiding lower quality duplicate non-fungible tokens that may be associated with scam tokens and / or diluted tokens.

[0042] 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 additionally, 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.

[0043] 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 techniques, which may include the use of heuristics. The system and method may analyze transaction data to determine tokens associated with the original digital resource creator (e.g., the original digital asset creator). Alternatively and / or additionally, the system and method may process transaction data to determine the token with the earliest minting date. Alternatively and / or additionally, the system and method may process transaction data to determine whether one of the tokens has a transaction trend that appears to be more fraudulent than another token. For example, a set of transactions 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 a trending period of the particular digital resource series (e.g., the particular digital asset series) may be considered a more authentic chain of transactions.

[0044] In some implementations, the 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).

[0045] Alternatively and / or additionally, 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.

[0046] Alternatively and / or additionally, on-chain sources may take precedence over off-chain sources. In some implementations, digital resources directly embedded in 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.

[0047] When suspicious tokens are retrieved, a warning may be surfaced using a flag or alert for suspicious or potentially fraudulent tokens (e.g., a warning provided along with search results for suspicious tokens). Although digital assets may be discussed in detail below, the system and method may be implemented for any digital resource (e.g., any digital resource associated with a non-fungible token). Alternatively and / or additionally, the system or method 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.)).

[0048] Specifically, the system and method 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.

[0049] 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 including 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 additionally, the blockchain data may be processed to identify one or more code features indicating a non-fungible token (e.g., a structure that complies with an EIP). 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.

[0050] 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.

[0051] 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 the 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 additionally, 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.

[0052] 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.

[0053] 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 digital asset type, a determined fraud likelihood score, and / or one or more determined flags.

[0054] 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., image classification models, audio classification models, video classification models, object detection models, feature extractor models, and / or one or more recognition models).

[0055] 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.

[0056] 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.

[0057] 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.

[0058] 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.

[0059] 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 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.

[0060] In some implementations, the system and method 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.

[0061] 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).

[0062] 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).

[0063] The system and method may generate a plurality of first index entries of an index database based on crawling a blockchain. In some implementations, the system and method 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.

[0064] 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.

[0065] Additionally and / or alternatively, the system and method 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.

[0066] 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).

[0067] 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.

[0068] 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.

[0069] 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.

[0070] Alternatively and / or additionally, the system and method 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.

[0071] The system and method 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.

[0072] 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.

[0073] 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.

[0074] 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.

[0075] 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.

[0076] The systems and methods of the present disclosure provide a number of technical effects and benefits. As an example, the systems and methods may provide systems and methods for indexing data associated with one or more non-fungible tokens. For example, the systems and methods disclosed herein may generate index data based on identified token data, which may then be used for a number of different tasks (e.g., searching, trend determination, and / or catalog generation).

[0077] Another technical benefit of the systems and methods of the present disclosure is the ability to utilize the generated verification data or authentication data to notify users of non-fungible tokens with potentially fraudulent data. For example, the systems and methods disclosed herein can process multiple index data sets to identify duplicate token index data, which can then be analyzed to determine whether a particular token has fraudulent characteristics, which can then be provided to the user for informed interaction. Alternatively and / or additionally, the identification of fraudulent characteristics can allow the system to take steps to remove duplicate or fraudulent tokens or prevent the addition of duplicate or fraudulent tokens so that they are not included on the index.

[0078] Another example of technical effects and benefits relates to improved computing efficiency and improvements in the operation of computing systems. For example, the systems and methods disclosed herein can utilize deduplication to significantly reduce the computing power required to search and visualize non-fungible token data. Additionally, the systems and methods disclosed herein can reduce the computing power used to manage and store index databases.

[0079] Referring now to the drawings, example embodiments of the present disclosure will be discussed in further detail.

[0080] Example Apparatus and Systems

[0081] Figure 1A A block diagram depicts an example computing system 100 that performs index deduplication according to an example embodiment of the present disclosure. System 100 includes a user computing system 130, a server computing system 110, a creator computing system 150, and a blockchain computing system 170 that are communicatively coupled via a network 180.

[0082] User computing system 130 can 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), a game console or controller, a wearable computing device, an embedded computing device, or any other type of computing device.

[0083] 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.

[0084] 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 components through which a user can provide user input.

[0085] 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 that are executed by the processor 112 to cause the server computing system 110 to perform operations.

[0086] In some implementations, the server computing system 110 includes or is otherwise implemented by one or more server computing devices. In the case 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.

[0087] 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 one 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 cause 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.

[0088] Network 180 may be any type of communications 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 network 180 may be carried via any type of wired and / or wireless connection using a wide variety of communications 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).

[0089] 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 common API across all applications).

[0090] The central intelligence layer can communicate with a central device data layer. The central device data layer can be a centralized data repository for the computing system 100. In some implementations, the central device data layer can communicate with multiple other components of the computing device, such as, for example, one or more sensors, context managers, device state components, and / or additional components. In some implementations, the central device data layer can use an API (e.g., a private API) to communicate with each device component.

[0091] Additionally and / or alternatively, Figure 1AAn exemplary computing system 100 is depicted that can be used to implement index adjustment (e.g., index deduplication) 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 may be implemented using other suitable architectures that may include any number of computing systems that communicate via a network 180.

[0092] 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.

[0093] 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 device. The memory 114 may store information accessible to 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.

[0094] 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 utilizing one or more user computing systems 130. In some implementations, the user profile database 120 may be configured to facilitate one or more interactions. The facilitation of one or more interactions may involve using a blockchain application programming interface (API) 122 to send data to and receive data from the blockchain computing system 170. 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 that is 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 interaction in the environments), smart contracts, physical item 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.

[0095] 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, the element and / 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 (e.g., computer executable instructions) stored in a tangible computer-readable storage medium such as RAM, a hard disk, or an optical or magnetic medium.

[0096] 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, parcels, 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.

[0097] 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.

[0098] Server 110 may exchange data with one or more user computing systems 130 via network 180. Figure 1A 10 , two user computing systems 130 are illustrated, 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, a desktop computer, an integrated circuit, a mobile device, a smart phone, a tablet computer, a wearable computing device, a display having one or more processors coupled thereto and / or embedded therein, or other suitable computing devices. Further, the user computing system 130 may be a plurality of computing devices acting together to perform an operation or computing action.

[0099] Similar to server 110, user computing system 130 may include processor 132 and memory 134. Memory 134 may store information accessible to 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.

[0100] 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 visit a website accessible at a particular URL. The index adjustment of the present disclosure may be provided as an element of a user interface of a website and / or application.

[0101] 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.

[0102] 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 voice recognition. Further, the user computing system 130 may have a display for presenting information (such as a user interface), displaying digital assets, displaying pop-ups or application elements displayed in an interface, and / or other forms of information.

[0103] The user computing system 130 may also include a user profile 140 that may be used to identify a user of the user computing system 130. The user profile 140 may optionally be used by the user 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.

[0104] User computing system 130 may further include a graphics processing unit. The graphics processing unit may be used by processor 132 for index adjustments. In some embodiments, user computing system 130 performs any and all index adjustments.

[0105] 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.

[0106] 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 client device 130 and the server 110. In general, communications between the server 110 and the client device 130 may be carried 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).

[0107] 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.

[0108] 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 a variety of 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.

[0109] 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 across a network of computing devices to provide a secure system for data storage, and 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.

[0110] Blockchain may refer to a system configured to record information securely. Blockchain may include a decentralized system that can make it extremely difficult to change information. Blockchain may include a digital ledger of transactions that may be repeated 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 records of transactions on a digital ledger that may be propagated in a decentralized system. Currencies exchanged and tracked via the blockchain computing system 170 may be referred to as cryptocurrencies.

[0111] 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. Non-fungible tokens (NFTs) may be certificates of authenticity of digital assets. NFTs may be non-interchangeable, thus making their value dependent on the price anyone may be willing to pay for the asset. NFTs may be printed on the blockchain so that their scarcity and authenticity may be maintained. Digital assets may be defined as anything that is stored digitally and may be uniquely identifiable and that 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.

[0112] Figure 1B Describing can be done by Figure 1ABlock diagram of an example blockchain 50 utilized by the blockchain computing system 170 of the 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 rights 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, which may include one or more cryptographic forms of proof. Proof of work may be provided at a request to update the blockchain 50 (e.g., a request to update the ledger based on a new transaction). The proof of work may convey that a certain device or 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 failure of verification. 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 functions and checks to determine that a request is valid for updating the blockchain 50.

[0113] 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 nonce. A hash may be a fixed-length hash value that can be used as a fingerprint of a particular block. A hash value may be generated based on a hash function and may be changed 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 the downstream true value remains unchanged unless appropriate verification occurs. 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 nonce (i.e., a number that is used only once) may be a number that can satisfy a difficulty level limit when a block is rehashed for a block added to the blockchain. A nonce may be a number that a blockchain miner is solving in order to receive an incentive (e.g., cryptocurrency).

[0114] 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 proof of computation before implementing changes to the stored data (e.g., the stored ledger). Based in part on the number of blocks in the blockchain 50, the proof-of-work verification may take seconds, minutes, and / or hours. 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 unified across most, if not all, of the distributed system. Before adding new data, each node of the distributed system may be checked for tampering.

[0115] 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 the 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.).

[0116] 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 utilized. 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 value 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.

[0117] The second block 20 may follow 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.

[0118] The third block 30 may follow 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.

[0119] 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, which may be different and / or the same so that the data is repeated for all blocks. In some implementations, each block may be associated with a different transaction (e.g., different minting, different selling, 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 mining.

[0120] The data in each block may include ledger data that may include a timestamp, the asset and / or cryptocurrency exchanged, the actors involved in the transaction, and / or a variety of other information.

[0121] 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, varying permissions, varying purposes, varying numbers of blocks, and / or varying hash functions and / or varying hash value lengths.

[0122] In some implementations, the system and method may include one or more machine learning model computing systems 900. One or more machine learning models may be used for a variety of tasks to implement token data identification, indexing, and deduplication.

[0123] Fig.9A A block diagram depicts an example computing system 900 that performs token data identification, 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.

[0124] The user computing device 902 can 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), a game console or controller, a wearable computing device, an embedded computing device, or any other type of computing device.

[0125] 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.

[0126] 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 3 and Fig.11 Discuss example token indexing model 920.

[0127] 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).

[0128] More specifically, the token index model may include one or more detection models, one or more segmentation models, one or more classification models, and / or one or more feature extractor models. The token index 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.

[0129] 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.

[0130] 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 means by which a user can provide user input.

[0131] The server computing system 930 includes one or more processors 932 and memory 934. The one or more processors 932 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 934 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 934 may store data 936 and instructions 938 executed by the processor 932 to cause the server computing system 930 to perform operations.

[0132] In some implementations, the server computing system 930 includes or is otherwise implemented by one or more server computing devices. In the case 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.

[0133] 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 various 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 3 and Fig.11 Example model 940 is discussed.

[0134] 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.

[0135] 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.

[0136] The training computing system 950 may include a model trainer 960 that trains machine-learned 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 parameters over multiple training iterations.

[0137] 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, dropout, etc.) to improve the generalization capabilities of the model being trained.

[0138] 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, true value labels, true value index information, and / or true value segmentation masks.

[0139] 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 on user-specific data received from the user computing device 902. In some cases, this process may be referred to as personalizing the model.

[0140] 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 computer executable instruction sets stored in a tangible computer readable storage medium such as RAM, a hard disk, or an optical or magnetic medium.

[0141] The network 980 may be any type of communications 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 980 may be carried via any type of wired and / or wireless connection using a wide variety of communications 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).

[0142] The machine learning models described in this specification can be used in a variety of tasks, applications and / or use cases.

[0143] In some implementations, the input to 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 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.

[0144] In some implementations, the input to the machine learning model of the present disclosure may be text or natural language data. The machine learning model may process text or natural language data to generate an output. As an example, the machine learning model may process natural language data to generate a language encoding output. As another example, the machine learning model may process text or natural language data to generate a potential text embedding output. As another example, the machine learning model may process text or natural language data to generate a classification output. As another example, the machine learning model may process text or natural language data to generate a text segmentation output. As another example, the machine learning model may process text or natural language data to generate a semantic intent output. As another example, the machine learning model may process text or natural language data to generate an upgraded (upscale) text or natural language output (e.g., text or natural language data of higher quality than the input text or natural language, etc.). As another example, the machine learning model may process text or natural language data to generate a prediction output.

[0145] In some implementations, the input to the machine learning model of the present disclosure may be speech data. The machine learning model may process speech data to generate an output. As an example, the machine learning model may process speech data to generate a speech recognition output. As another example, the machine learning model may process speech data to generate a speech translation output. As another example, the machine learning model may process speech data to generate a potential embedding output. As another example, the machine learning model may process speech data to generate an encoded speech output (e.g., an encoded and / or compressed representation of speech data, etc.). As another example, the machine learning model may process speech data to generate a text representation output (e.g., a text representation of input speech data, etc.). As another example, the machine learning model may process speech data to generate a predicted output.

[0146] In some implementations, the input to the machine-learned model of the present disclosure may be latent coded data (e.g., a latent space representation of an input, etc.). The machine-learned model may process the latent coded data to generate an output. As an example, the machine-learned model may process the latent coded data to generate a recognition output. As another example, the machine-learned model may process the latent coded data to generate a reconstruction output. As another example, the machine-learned model may process the latent coded data to generate a search output. As another example, the machine-learned model may process the latent coded data to generate a re-clustering output. As another example, the machine-learned model may process the latent coded data to generate a prediction output.

[0147] 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.

[0148] In some cases, the machine learning model can be configured to perform a task including 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).

[0149] 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 the 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 identifies the possibility that the region depicts an object of interest for each region. 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 predetermined category set for each pixel in one or more images. For example, the category set may be a foreground and a background. As another example, the category set may be an object class. 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 in one of the input images.

[0150] 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 performance task, such as branch prediction or memory address translation.

[0151] Fig.9A An example computing system that can be used to implement the present disclosure is illustrated. 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 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.

[0152] Fig. 9B A block diagram depicts an example computing device 970 that performs 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.

[0153] 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.

[0154] like Fig. 9B As illustrated, each application can communicate with multiple other components of the computing device (such as, for example, one or more sensors, context managers, device state components, 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.

[0155] Fig. 9C A block diagram depicts an example computing device 990 that performs 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.

[0156] 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 common API across all applications).

[0157] The central intelligence layer includes multiple machine learning models. For example, Fig. 9C As illustrated, a corresponding machine learning model (e.g., model) can be provided for each application, and the machine learning model is 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.

[0158] 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 illustrated, the central device data layer can communicate with multiple other components of the computing device (such as, for example, one or more sensors, context managers, device state components, and / or additional components). In some implementations, the central device data layer can use an API (e.g., a private API) to communicate with each device component.

[0159] Example system layout

[0160] Figure 2 A block diagram depicting an example token index deduplication 1000 according to an example embodiment of the present disclosure. In some implementations, the example token index deduplication 1000 may be configured to receive a plurality of index data sets 1002 describing token data for a plurality of non-fungible tokens, and provide an updated index database 1014 adjusted based on determined duplications as a result of receiving the plurality of index data sets 1002. Thus, in some implementations, the token index deduplication 1000 may include a duplication determination block 1004 operable to determine which index data sets are associated with each other.

[0161] Specifically, Figure 2 Depicting that multiple index data sets 1002 are processed to determine multiple duplicate index data sets. For example, multiple index data sets 1002 may be processed using duplicate determination block 1004 to determine multiple duplicates, the multiple duplicates including a first duplicate 1006, a second duplicate 1008, and an nth duplicate 1010. The determination may be based on digital resources (e.g., digital assets) of different identified tokens being the same (e.g., matching and / or within a threshold similarity).

[0162] Multiple repetitions (e.g., first repetition 1006, second repetition 1008, and / or nth repetition 1010) may be processed using authenticity determination block 1012 to generate verification data. The verification data may include a determined authenticity of each of the repetition index data sets. The authenticity may include the source of the analysis data, the minting date, the quality of the associated digital resource, and / or the publisher.

[0163] The verification data may be used to adjust the index database to generate an updated index database 1014. 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 additionally, 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).

[0164] Alternatively and / or additionally, 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 authenticity of the determination.

[0165] Figure 3 A block diagram depicts an example token validity determination 1100 according to an example embodiment of the present disclosure. Specifically, Figure 3 The index validity determination 1100 includes duplicate data 1102, which can be processed to generate verification data 1122. The duplicate data 1102 can describe one or more index data sets associated with one or more non-fungible tokens. The duplicate data 1102 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 additionally, 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., the same scene depicted with different resolutions and / or different color saturation)).

[0166] The duplicate data 1102 may be processed 1110 to generate verification data 1122. Processing 1110 the duplicate data 1102 may include obtaining and processing transaction data 1112 associated with each of the duplicate index data sets. The transaction data 1112 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 1112 may be processed to determine more trending and / or more popular duplicates. Additionally and / or alternatively, the transaction data 1112 may be processed to determine different actors in the transaction that may indicate the validity of the non-fungible token. Price data may also be derived from the transaction data 1112 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).

[0167] Additionally and / or alternatively, the source data 1114, the URL and / or URI data 1116, and / or the minting date data 1120 may be processed to determine the validity of each of the repetitions. For example, the source data 1114 may be processed to determine which repetitions are derived from data from a trusted source. Specifically, index data generated based on data obtained directly 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 1116 that references a digital resource (e.g., a digital asset) may be processed to determine whether the digital resource is available, has been changed, has been upgraded, and / or has been moved. If the link is dead, the repetition may be considered invalid. If the address has been 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 positive factors. Additionally and / or alternatively, the minting date data 1120 may be used to determine which repetition was published first, which may provide insight into the validity of possible non-fungible tokens.

[0168] In some implementations, the verification data 1122 may be generated using one or more other fraud detection 1118 techniques.

[0169] The verification data 1122 may include data describing the determined validity (and / or authenticity) of each of the corresponding duplicate index data sets. The verification data 1122 may include one or more flags, tags, and / or annotations describing the determined factors that describe the index data set validity.

[0170] Figure 4A block diagram depicting an example token index deduplication 1200 according to an example embodiment of the present disclosure. In some implementations, the example token index deduplication 1200 may be configured to receive a plurality of index data sets 1202 describing token data for a plurality of non-fungible tokens, and provide an updated index database 1214 adjusted based on determined duplications as a result of receiving the plurality of index data sets 1202. Thus, in some implementations, the token index deduplication 1200 may include a duplication determination block 1204 operable to determine which index data sets are associated with each other.

[0171] Specifically, Figure 4 Depicting that the plurality of index data sets 1202 are processed to determine a plurality of duplicate index data sets. For example, the plurality of index data sets 1202 may be processed using a duplicate determination block 1204 to determine a plurality of duplicates, the plurality of duplicates including a first duplicate 1206, a second duplicate 1208, and an nth duplicate 1210. The determination may be based on the digital resources of the different identified tokens being the same (e.g., matching and / or within a threshold similarity).

[0172] Multiple repetitions (e.g., first repetition 1206, second repetition 1208, and / or nth repetition 1210) may be processed using authenticity determination block 1212 to generate verification data. The verification data may include a determined authenticity of each of the repetition index data sets. The authenticity may include the source of the analysis data, the minting date, the quality of the associated digital resource, and / or the publisher.

[0173] The verification data may be used to adjust the index database to generate an updated index database. Adjusting the index database may include retaining 1214 one or more of the duplicate index data sets, merging 1216 one or more of the duplicate index data sets, and / or removing 1218 one or more of the duplicate data sets.

[0174] One or more of the duplicate index data sets may be retained 1214 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 1214 may be determined based on: the source of the data, the authenticity (or validity) of the determination, transaction data of 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 of one or more of the duplicates.

[0175] Alternatively and / or additionally, adjusting the index database may include merging 1216 duplicate index data sets. In some implementations, certain duplicate index data sets may be selected for merging 1216. Merging 1216 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 obtained directly from a blockchain may be supplemented with data from an index data set generated based on a web page, which data from the index data set 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 authenticity of the determination.

[0176] Adjusting the index database may include removing 1218 specific index data sets. For example, index data sets generated based on data obtained directly from the blockchain may be retained 1214, while index data sets generated based on data obtained from web pages may be removed 1218. Alternatively and / or additionally, index data sets determined to be fraudulent may be removed 1218 (e.g., non-fungible token data may be obtained from a fraudulent website or be a reference to a fraudulent website).

[0177] Figure 5 A block diagram depicting an example search results page generation 1300 according to an example embodiment of the present disclosure. In some implementations, the systems and methods can receive a search query 1302. The search query 1302 can include one or more search terms, one or more input images, and / or one or more other inputs. In some implementations, the search query 1302 can be received from a user computing system associated with a user.

[0178] The search query 1302 may be processed by a search engine to determine a plurality of search results 1304. The plurality of search results may be determined based on a determined responsiveness to the search query 1302. The plurality of search results may be processed by a ranking block 1306 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 1308 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.

[0179] Multiple search results may be ranked based on the determined relevance, determined truthfulness, and / or determined context of each of the multiple search results by ranking block 1306. Then, a search result page 1310 may be generated and provided based on the ranked search results and the determined warnings and badges.

[0180] Fig.10 A block diagram depicting an example indexing system 200 according to an example embodiment of the present disclosure is depicted. In some implementations, the indexing system 200 is configured to receive blockchain data 202 describing a blockchain storing 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 for generating index data.

[0181] 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 a script that may be deployed to interact with the smart contract code of the blockchain data 202).

[0182] 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 additionally, identifying the token data may include crawling the blockchain data to search for specific characteristics, structures, and / or features associated with the token data.

[0183] 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.

[0184] 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 a plurality of other index terms for annotating and / or characterizing various aspects of the non-fungible token that may be searched.

[0185] 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, web page data 326 associated with a non-fungible token that is the same as or related to the non-fungible token associated with the token data 302 may be used to determine one or more additional index terms for one or more index term fields.

[0186] Additionally and / or alternatively, the token data 302 and / or the web page data 326 may be processed using one or more machine-learned models 324 to generate one or more outputs that may be used as index items for the index data 310. Alternatively and / or additionally, input data for the one or more machine-learned 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 was 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, acquisition amount, acquisition frequency, identity of buyers and bidders, and / or auction event data), metadata 320 (e.g., stored by the blockchain, the marketplace, and / or another data source), other machine-learned data 322, and / or other derived data.

[0187] 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. The one or more non-fungible token search results 410 and / or the one or more general web results 412 may be used to generate a search results page 414. The search results page 414 may then be provided for display.

[0188] 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, which 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.

[0189] Fig.13AAn illustration of an example search results page 500 is depicted according to 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 as image thumbnails, while general web results may be displayed only as text).

[0190] 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), ​​which may be displayed in an intermingle format such that different types of search results may be adjacent to each other. The order may be determined based on a score based on a determination of responsiveness to the search query and / or based on other factors. The sorting may be based entirely on the score without preference for the type of result.

[0191] 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 both one or more non-fungible token search results (e.g., first non-fungible token search result 510, second non-fungible token search result 512, third non-fungible token search result 514, and / or fourth non-fungible token search result 516) and 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), ​​both of which may be provided for display with a media content item preview.

[0192] Example Method

[0193] Figure 6 A flowchart depicting an example method to be performed according to an example embodiment of the present disclosure. Figure 6 The steps are depicted as being performed in a particular order for purposes of illustration and discussion, but the method of the present disclosure is not limited to the particular illustrated order or arrangement. The various steps of method 600 may be omitted, rearranged, combined and / or adapted in various ways without departing from the scope of the present disclosure.

[0194] At 602, the computing system may obtain blockchain data from a blockchain computing system, and may determine that the blockchain data includes first token data. The blockchain data may be associated with a particular blockchain and / or from multiple blockchains. Additionally and / or alternatively, the blockchain data may be obtained from a blockchain node. In some implementations, the blockchain computing system may include a decentralized distributed computing system that stores data associated with multiple non-fungible tokens and multiple transactions associated with the non-fungible tokens.

[0195] The first token data may describe a first non-fungible token associated with a first digital resource. In some implementations, the blockchain computing system may include a distributed computing system including 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 additionally, the blockchain data may be processed to identify one or more code features indicating a non-fungible token (e.g., a structure that complies with an EIP). 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.

[0196] At 604, the computing system may generate first index data 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 resource 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 resource, a minting time, transaction data associated with the non-fungible token, a specific blockchain associated with the non-fungible token, a type of digital resource, a determined fraud likelihood score, and / or one or more determined flags.

[0197] At 606, the computing system may obtain web page data from the digital web platform user interface, and may determine that the web page data includes second token data. In some implementations, the web page data may include a snapshot of a market webpage associated with the second non-fungible token. The digital web platform user interface may be provided by a web platform computing system (e.g., a market computing system) that hosts a non-fungible token marketplace for selling and displaying non-fungible tokens. Alternatively and / or additionally, a leaf page may be obtained from a website associated with the non-fungible token. The leaf page may be processed to generate web page data.

[0198] The second token data may describe a second non-fungible token associated with the second digital resource. In some implementations, the second digital resource may depict an enhanced version of a scene depicted in the first digital resource. In some implementations, the second digital resource may depict an enhanced version of a scene depicted in the first digital resource.

[0199] At 608, the computing system may generate second index data 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 resource, a minting time, transaction data associated with the non-fungible token, a specific blockchain associated with the non-fungible token, a type of digital resource, a determined fraud likelihood score, and / or one or more determined flags.

[0200] At 610, the computing system may determine that the first digital resource and the second digital resource are the same digital resource. The determination may be based on determining the first digital resource of the first non-fungible token and determining the second digital resource of the second non-fungible token. The first digital resource and the second digital resource may be compared to determine similar and / or overlapping data. The determination may include image processing, text processing, audio processing, video processing, and / or potential coding processing. In some implementations, the determination may include processing the first digital resource and the second digital resource 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).

[0201] At 612, the computing system can adjust the index database based on the first digital resource and the second digital resource being the same digital resource. The specific type of adjustment can be based on the transaction data of the non-fungible token, the source of the token data, and / or a variety of other factors.

[0202] In some implementations, adjusting the index database based on the first digital resource and the second digital resource being the same digital resource may include removing the second index data from the index database.

[0203] Alternatively and / or additionally, adjusting the index database based on the first digital resource and the second digital resource being the same digital resource may include merging the first index data and the second index data to generate enhanced index data.

[0204] Alternatively and / or additionally, adjusting the index database based on the first digital resource and the second digital resource being the same digital resource may include adjusting a first text entry associated with the first index database based on the first digital resource and the second digital resource being the same digital resource.

[0205] Alternatively and / or additionally, adjusting the index database based on the first digital resource and the second digital resource being the same digital resource may include adjusting a second text entry associated with the second index data based on the first digital resource and the second digital resource being the same digital resource. In some implementations, the second index entry may be stored in the index database.

[0206] Figure 7 A flowchart depicting an example method to be performed according to an example embodiment of the present disclosure. Figure 7 The steps are depicted as being performed in a specific order for purposes of illustration and discussion, but the method of the present disclosure is not limited to the specific illustrated order or arrangement. The various steps of method 700 may be omitted, rearranged, combined and / or adapted in various ways without departing from the scope of the present disclosure.

[0207] At 702, the computing system may generate a plurality of first index entries of an index database based on crawling a blockchain. The plurality of first index entries may include a plurality of first index data sets associated with a plurality of first non-fungible tokens.

[0208] At 704, the computing system may 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 (e.g., a digital marketplace) may include a user interface for providing a plurality of non-fungible tokens for sale. The plurality of second index entries may include a plurality of second index data sets associated with the plurality of second non-fungible tokens.

[0209] At 706, the computing system may determine that a particular first index entry in a plurality of first index entries is 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 resources associated with the plurality of first index entries. Additionally and / or alternatively, the comparison may include obtaining a plurality of second digital resources associated with the plurality of second index entries. Each of the plurality of first digital resources may be compared with each of the plurality of second digital resources. The comparison may include processing the digital resources using one or more machine learning models to determine whether the digital resources are the same and / or which digital resources are the same (e.g., match and / or are within a threshold similarity).

[0210] In some implementations, the first payload may include a first digital resource. The second payload may include a second digital resource. In some implementations, the first digital resource and the second digital resource may match and / or be similar.

[0211] 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.

[0212] At 708, the computing system can adjust the index database in response to determining that the particular first index entry is associated with the particular second index entry. Adjusting the index database can include merging index entries, removing index entries, and / or adding a flag to each of the index entries determined to be duplicates.

[0213] Figure 8 A flowchart depicting an example method to be performed according to an example embodiment of the present disclosure. Figure 8 The steps are depicted as being performed in a specific order for purposes of illustration and discussion, but the method of the present disclosure is not limited to the specific illustrated order or arrangement. The various steps of method 800 may be omitted, rearranged, combined and / or adapted in various ways without departing from the scope of the present disclosure.

[0214] At 802, a computing system may obtain a particular 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.

[0215] At 804, the computing system may process the particular index entry and the plurality of index entries 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 resource associated with the particular index entry.

[0216] At 806, the computing system may generate verification data 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 release of the digital resource.

[0217] 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 resource. The determination can be based on transaction data, minting time, release time, a particular blockchain, a source of token data, payload URI data, and / or digital resource quality. One or more machine-learned models can be utilized to generate the verification.

[0218] At 808, the computing system may provide verification data as 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 resource.

[0219] Example index and usage

[0220] 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.

[0221] 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 model for machine learning, and / or for generating statistical representations).

[0222] The system and method 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). The blockchain data may include metadata associated with one or more non-fungible tokens.

[0223] 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 additionally, 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.

[0224] 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).

[0225] 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.

[0226] Alternatively and / or additionally, determining that the 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.

[0227] 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 a URI (Uniform Resource Identifier) ​​has been changed and whether the data of the payload (e.g., pixels of an image and / or text of a text string) has been changed when the URI has been 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.

[0228] 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.

[0229] In some implementations, generating 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 / publisher, transaction data (e.g., current and / or past owners, purchase price, transaction trends, trends of related non-fungible tokens, gas price, etc.), a topic of the digital asset, non-fungible token metadata, pixel tags, non-fungible token descriptions, free-form text associated with the non-fungible token, and / or notoriety associated with the non-fungible token, publisher, or creator.

[0230] 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.

[0231] 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.

[0232] 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.

[0233] Alternatively and / or additionally, 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.

[0234] The system and method 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.

[0235] 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 rapid retrieval upon request or search. The system and method may include receiving a request for a digital asset from a user computing system, and providing the token data to the user computing system.

[0236] The index database may include an index data set generated based on blockchain data, web page data, and / or data obtained from other sources.

[0237] Alternatively and / or additionally, 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.

[0238] 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 additionally, 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.

[0239] 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.

[0240] The system and method 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.

[0241] 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 may 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.

[0242] 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 additionally, the systems and methods may obtain web page data (e.g., market data obtained via 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 particular blockchain, and / or based on cost.

[0243] The system and method may then determine that a subset of the obtained data (e.g., blockchain data and / or web page data) describes a non-fungible token associated with a digital asset. The determination may be based on a search for a specific structure, a specific term, and / or a specific actor. The determination may 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 may be based on whether the subset of the obtained data complies with an EIP (“Ethereum Improvement Proposals”, ETHEREUM (January 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.

[0244] Index data may be generated based at least in part on a subset of the obtained 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 obtained 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.

[0245] In some implementations, the index data may be generated by processing the obtained 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). The transaction history may be processed to determine the price history of the non-fungible token, and the price history may be processed to index the trend data and / or stability data. Additionally and / or alternatively, whether the non-fungible token is lazy minted may be indexed. The index data may include whether the non-fungible token is auctioned.

[0246] The system and method 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.

[0247] 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 additionally, the search query may include one or more images, audio data, latently encoded data, and / or multimodal data.

[0248] 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.

[0249] 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.

[0250] 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 additionally, the search results may include an indicator indicating that the search results are associated with a non-fungible token.

[0251] 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.

[0252] 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 system and method 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 sets of data.

[0253] In some implementations, index data may be updated periodically. Updates may occur at set intervals, and / or may be obtained at a certain 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 often due to greater computational costs associated with crawling), and / or based on cost.

[0254] 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.

[0255] Data extraction for index data generation may be machine learning based, heuristic based, and / or may be deterministic.

[0256] 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.

[0257] Specifically, the system and method can be used to make Web3 technology 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, the system and method can focus on simplifying technology, jargon, applications, and / or settings.

[0258] 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.

[0259] In some implementations, the systems and methods disclosed herein can be used for identity purposes. For example, the systems and methods disclosed herein can involve and / or interface with a user's crypto wallet to store user identity data to be utilized across Web3. A user can log into a website, share personal data, prove identity, and / or transfer cryptocurrency based at least in part on the identity data.

[0260] Wallet apps can be built into browsers. In Web3, there can be a single mechanism to log in while keeping your data for yourself.

[0261] 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.

[0262] 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).

[0263] 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.

[0264] 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).

[0265] The systems and methods disclosed herein may include general information retrieval. The systems and methods may enable searches to index important parts of the blockchain and make them accessible and useful.

[0266] The system and method may include a decentralized autonomous organization that can be used to automate decision making. In some implementations, information about the decentralized autonomous organization of the search engine may be provided, and the information may make decision making more transparent.

[0267] In some implementations, the system and method can enable ownership of any digital asset on the web by empowering creators and publishers to claim ownership of their digital content and set rules for the use of digital content in a scalable and high-speed manner. Additionally and / or alternatively, the system and method 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).

[0268] In some implementations, the system and method 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.

[0269] 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.

[0270] Additionally and / or alternatively, the system and method may include tools for creators / publishers to create non-fungible content. For example, the system and method may give creators / publishers easy tools to publish non-fungible content and set rules for content usage that can be done by both casual creators and mature large players.

[0271] In some implementations, the systems and methods may include a smart contract blockchain that may be open, closed, and / or a hybrid of both.

[0272] The systems and methods can be implemented to implement a non-fungible token market 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 for future sales of digital assets.

[0273] 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.

[0274] Additional disclosure

[0275] 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 partitions of tasks and functionality 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.

[0276] 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, variations, and equivalents of such embodiments when obtaining an understanding of the foregoing. Therefore, the present disclosure does not exclude such modifications, variations, and / or additions to the present subject matter that would be readily apparent to those of ordinary skill in the art. For example, a feature illustrated or described as part of an embodiment may be used together with another embodiment to produce still further embodiments. Therefore, the present disclosure is intended to cover 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 collectively storing instructions that, when executed by the one or more processors, cause the computing system to perform operations comprising: Obtain blockchain data from a blockchain computing system; determining that the blockchain data includes first token data, wherein the first token data describes a first non-fungible token associated with a first digital resource; generating first index data based on the first token data, wherein the first index data is stored in an index database; Obtain web page data from the digital web platform user interface; determining that the web page data includes second token data, wherein the second token data describes a second non-fungible token associated with a second digital resource; generating second index data based on the second token data, wherein the second index data is stored in the index database; determining that the first digital resource and the second digital resource are the same digital resource; and The index database is adjusted based on the first digital resource and the second digital resource being the same digital resource.

2. A system as described in any preceding claim, wherein the blockchain computing system comprises a distributed computing system, the distributed computing system comprising a plurality of computing devices, and wherein the blockchain data comprises embedded code associated with a plurality of non-fungible tokens.

3. The system of any preceding claim, wherein the operations further comprise: receiving a search request; determining one or more search results based on the index database; as well as The one or more search results are provided for display, wherein the one or more search results include a particular search result associated with the first digital resource.

4. The system of any preceding claim, wherein adjusting the index database based on the first digital resource and the second digital resource being the same digital resource comprises: The second index data is removed from the index database.

5. The system of any preceding claim, wherein adjusting the index database based on the first digital resource and the second digital resource being the same digital resource comprises: The first index data and the second index data are merged to generate enhanced index data.

6. The system of any preceding claim, wherein adjusting the index database based on the first digital resource and the second digital resource being the same digital resource comprises: A first text entry associated with the first index data is adjusted based on the first digital resource and the second digital resource being the same digital resource.

7. The system of any preceding claim, wherein adjusting the index database based on the first digital resource and the second digital resource being the same digital resource comprises: A second text entry associated with the second index data is adjusted based on the first digital resource and the second digital resource being the same digital resource, wherein the second index entry is stored in the index database.

8. The system of any preceding claim, wherein determining that the blockchain data includes first token data comprises: Crawl embedded code, wherein the blockchain data packet includes the embedded code.

9. A system as claimed in any preceding claim, wherein the web page data comprises a snapshot of a digital resource listing web page associated with the second non-fungible token.

10. A system as claimed in any preceding claim, wherein the second digital asset depicts an enhanced version of a scene depicted in the first digital asset.

11. A computer-implemented method, the method comprising: Generating, by a computing system including one or more processors, a plurality of first index entries of an index database based on crawling a blockchain; Generating, by the computing system, a plurality of second index entries of the index database based on crawling a digital web platform; determining, by the computing system, that a particular first index entry among the plurality of first index entries is associated with a particular second index entry among the plurality of second index entries; as well as In response to determining that the particular first index entry is associated with the particular second index entry, the index database is adjusted by the computing system.

12. The method of any preceding claim, wherein determining that the particular first index entry of the plurality of first index entries is associated with the particular second index entry of the plurality of second index entries comprises: Determining that a first payload associated with a first non-fungible token and a second payload associated with a second non-fungible token are the same, wherein the first non-fungible token is associated with the particular first index entry, and wherein the second non-fungible token is associated with the particular second index entry.

13. A method as claimed in any preceding claim, wherein the first payload comprises a first digital resource, wherein the second payload comprises a second digital resource, and wherein the first digital resource and the second digital resource match.

14. A method as claimed in any preceding claim, wherein the first payload comprises a first digital asset, wherein the second payload comprises a second digital asset, and wherein the first digital asset and the second digital asset depict the same scene having different resolutions.

15. A method as claimed in any preceding claim, wherein the digital web page comprises a user interface for offering a plurality of non-fungible tokens for sale.

16. One or more non-transitory computer-readable media that collectively store instructions that, when executed by one or more computing devices, cause the one or more computing devices to perform operations comprising: obtaining a specific index entry and a plurality of index entries, wherein the plurality of index entries are obtained from an index database; processing the particular index entry and the plurality of index entries to generate duplicate data, wherein the duplicate data describes whether one or more different index entries in the plurality of index entries are associated with a digital resource associated with the particular index entry; generating verification data based at least in part on the duplicate data, wherein the verification data is associated with the authenticity of the non-fungible token associated with the particular index entry; and The verification data is provided as output.

17. The one or more non-transitory computer-readable media of any preceding claim, wherein the plurality of index entries comprises one or more index entries obtained by extracting data from a blockchain.

18. One or more non-transitory computer-readable media as described in any preceding claim, wherein the plurality of index entries includes one or more index entries obtained by extracting data from a non-fungible token market.

19. The one or more non-transitory computer-readable media of any preceding claim, wherein generating the verification data based at least in part on the repetitive data comprises: The particular index entry is determined to be authentic by determining that the particular index entry is associated with a first minting of the digital resource.

20. One or more non-transitory computer-readable media as described in any preceding claim, wherein the verification data is based at least in part on a timestamp associated with the particular index entry, wherein the verification data describes whether the non-fungible token was minted prior to another release of the digital resource.

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