Generation of an inclusive non-fungible token search index

Abstract

Systems and methods for indexing non-fungible token data can include processing blockchain data and / or web page data to identify token data associated with non-fungible tokens. Next, the identified token data can be utilized to generate index data associated with the non-fungible tokens. The index data can be utilized to generate an index database. Next, the index database can be utilized to search for non-fungible tokens and / or generate derivative data associated with the non-fungible tokens. The disclosed non-fungible token search results can be provided within a specific search results panel and / or adjacent to general search results.

Description

Technical Field

[0001] The present disclosure generally relates to indexing data associated with non-fungible tokens. More specifically, the present disclosure relates to crawling a blockchain and / or non-fungible token marketplace to generate index data for a non-fungible token index database.

Background Art

[0002] A search engine can receive a search query and provide search results that list web pages determined to be responsive to the search query. The search engine can crawl the web to determine web pages having specific terms. The search results can generally be indicated by a caption and / or a text title. The search results can include text results, video results, and image results.

[0003] However, it can be difficult to search for and identify non-fungible token search results. Searching for non-fungible tokens can be limited to finding general web pages associated with various non-fungible tokens, but there may be cases where the search engine cannot properly 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 are shown in part in the following description, or can be learned from the description, or can be learned through the practice of the embodiments.

[0005] One exemplary aspect of the present disclosure is directed to a computing system. The system can include one or more processors and one or more non-transitory computer-readable media that collectively store instructions that, when executed by the one or more processors, cause the computing system to perform operations. The operations can include obtaining blockchain data from a blockchain computing system. The blockchain data can include one or more function signatures. The operations can include determining that a particular portion of the blockchain data includes token data. In some embodiments, the token data can be a description of non-fungible tokens associated with digital resources. The operations can include generating index data based on the token data. The index data can include information obtained from the blockchain data. In some embodiments, the index data can be associated with digital resources. The operations can include storing the index data in an index database.

[0006] In some embodiments, determining that a particular portion of blockchain data includes token data can include determining that the particular portion of blockchain data includes token data based on one or more function signatures. The one or more function signatures can be associated with a non-fungible token standard. In some embodiments, generating index data based on token data can include obtaining a digital resource associated with a non-fungible token, processing the digital resource to determine one or more features within the digital resource, and generating feature data that describes the one or more features. The index data can include the feature data. In some embodiments, the digital resource can include image data, and processing the digital resource can include processing the digital resource by a machine learning model to determine one or more image features. In some embodiments, generating feature data that describes one or more features can include determining one or more feature descriptor terms associated with the one or more features. The feature data can be a description of the one or more feature descriptor terms.

[0007] In some embodiments, the operations can include receiving, from a user computing system, a request for a digital resource and providing token data to the user computing system. Determining that a particular portion of blockchain data includes token data can include determining that the particular portion includes a smart contract associated with a digital media item. In some embodiments, the digital media item can be the payload of the smart contract, and the digital media item can be a digital resource. The index data can include transaction data associated with a non-fungible token. In some embodiments, the index data can include data that describes metadata associated with a non-fungible token.

[0008] Other exemplary aspects of the present disclosure are directed to computer-implemented methods. The method can include obtaining blockchain data from a blockchain computing system by a computing system including one or more processors. The blockchain data can include a script associated with a digital resource. The method can include determining by the computing system that a subset of the blockchain data includes token data based on a subset having a structure associated with one or more specifications. The token data can be a description of a non-fungible token associated with a digital resource. The method can include generating by the computing system index data based on the token data. The index data can include information obtained from the blockchain data. In some embodiments, the index data can be associated with a digital resource. The method can include storing by the computing system the index data in an index database.

[0009] In some embodiments, the method can include determining by the computing system that a web content item is associated with a digital resource, obtaining by the computing system an issuance time associated with the web content item, determining by the computing system a mint time associated with a non-fungible token based on the blockchain data, and generating by the computing system time difference data based on the mint time and the issuance time. The index data can include the time difference data. This structure can include a format for a code within the blockchain data associated with a standard format for a non-fungible token code.

[0010] In some embodiments, generating index data based on token data by a computing system can include determining, by the computing system, reference data associated with a digital resource based on the token data, and determining, by the computing system, an issuer of a non-fungible token based on the token data. The index data can include reference data, an issuer, and data describing a particular blockchain associated with the blockchain data. In some embodiments, the index data can include a digital resource type associated with the digital resource. The digital resource type can be an augmented reality rendering asset type, and the digital resource can be an augmented reality rendering asset.

[0011] Other exemplary aspects of the present disclosure are directed to 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. The operations can include obtaining blockchain data. The operations can include determining that a subset of the blockchain data describes a non-fungible token associated with a digital resource, and generating index data based at least in part on the subset of the blockchain data. In some embodiments, the index data can include reference data associated with the digital resource. The operations can include storing the index data in a search database. In some embodiments, the operations can include receiving a search query from a user computing system and determining that the search query is associated with the index data. The operations can include providing search results associated with the digital resource to the user computing system.

[0012] In some embodiments, the search results can include a preview of the digital resource. The search results can include an indicator indicating that the search results are associated with a non-fungible token. In some embodiments, determining that a search query is associated with index data can include determining that one or more search terms of the search query are a description of at least one of a digital resource, an author of the digital resource, or non-fungible token metadata. In some embodiments, providing search results associated with a digital resource to a user computing system can 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 can include the search results and one or more general web results.

[0013] Other aspects of the present disclosure are directed to various systems, devices, non-transitory computer-readable media, user interfaces, and electronic devices.

[0014] These and other features, aspects, and advantages of the various embodiments of the present disclosure will become better understood with reference to the following description of the invention and the accompanying claims. The accompanying drawings, which are incorporated herein and form a part of this specification, illustrate exemplary embodiments of the present disclosure and, together with the description, serve to explain the relevant principles.

[0015] A detailed description of embodiments directed to those of ordinary skill in the art is set forth in this specification with reference to the accompanying drawings.

Brief Description of the Drawings

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DETAILED DESCRIPTION OF THE INVENTION

[0017] Reference numerals repeated throughout the several views are intended to identify like features in the various embodiments.

[0018] Overview In general, the present disclosure is directed to systems and methods for indexing non-fungible tokens. More specifically, the systems and methods disclosed herein can identify data associated with non-fungible tokens within a blockchain (e.g., within the code of the blockchain). For example, multiple non-fungible tokens can be identified and indexed based on multiple techniques. One technique can include analyzing (e.g., crawling) the blockchain to identify data associated with the non-fungible tokens. The data can then be parsed to determine identifiable data that can be indexed. Another technique can include analyzing (e.g., crawling) a web platform (e.g., a non-fungible token marketplace) to identify non-fungible tokens on sale. Data regarding the non-fungible tokens can then be obtained and indexed. In some embodiments, multiple techniques can be utilized to generate more databases to include for searching.

[0019] The systems and methods disclosed herein can include obtaining blockchain data from a blockchain computing system. The blockchain data can include one or more function signatures. In some embodiments, the blockchain data can include script data associated with a digital resource (e.g., a digital asset). A particular portion (e.g., a subset) of the blockchain data can be determined to include token data. The token data can be a description of a non-fungible token associated with a digital resource (e.g., a digital asset). Index data can be generated based on the token data. The index data can include information obtained from the blockchain data. In some embodiments, the index data can be associated with a digital resource (e.g., a digital asset). The index data can then be stored in an index database. The index database can then be utilized for various purposes (e.g., searching for non-fungible tokens, data aggregation and analysis, training data for machine learning models, and / or training data for generating statistical representations).

[0020] The systems and methods disclosed herein can be utilized to enable the search of non-fungible tokens by a search engine. For example, a search query can be received. The search engine can process the search query and determine a plurality of search results associated with the search query. In some embodiments, one or more of the search results can be associated with non-fungible tokens. A particular search result can be associated with index data. One or more non-fungible token search results can be provided for display within a search result interface (e.g., a search result page). One or more non-fungible token search results can be provided for display within a separate panel. Alternatively and / or additionally, one or more non-fungible token search results can be provided for display adjacent to general search results. One or more indicators can be provided for one or more non-fungible token search results to indicate the nature of the non-fungible tokens (e.g., one or more labels, flags, and / or tags can be provided). In some embodiments, one or more generated previews of digital resources based on index data can be provided for one or more non-fungible token search results.

[0021] In some embodiments, the systems and methods disclosed herein can be utilized for Web3 profiles, Web3 transactions, and / or Web3 identification.

[0022] Systems and methods can improve the search user interface and user experience by identifying and indexing relevant data that can be used to determine that a non-fungible token is responding to a search query. Additionally and / or alternatively, the indexed data can be utilized to determine the ranking of non-fungible token search results relative to other search results. The systems and methods can be used to determine whether a non-fungible token tag can be provided in search results and / or whether a ranking boost can be given based on an association with an authenticated non-fungible token.

[0023] Systems and methods can obtain blockchain data (e.g., code from a blockchain) from a blockchain computing system (e.g., a decentralized computing system that stores distributed data). The blockchain data can include one or more function signatures. In some embodiments, the blockchain data can include a script associated with a digital resource (e.g., a digital asset). The blockchain data may be obtained via a blockchain node. The blockchain data can include code and / or records stored on the blockchain. The code can include a script and can be a description of multiple smart contracts. The blockchain data can include transaction data associated with the acquisition of a digital resource (e.g., a digital asset) and / or the exchange of a digital currency (e.g., a cryptocurrency). The blockchain data can include metadata associated with one or more non-fungible tokens.

[0024] The systems and methods disclosed herein can include determining that a particular portion of blockchain data includes token data. The token data can be a description of a non-fungible token associated with a digital resource (e.g., a digital asset). Alternatively and / or additionally, the systems and methods can determine that a subset of the blockchain data includes token data based on a subset having a structure associated with one or more standards. The token data can be a description of a non-fungible token associated with a digital resource (e.g., a digital asset). In some embodiments, this structure can include a format for code within the blockchain data associated with a standard format for non-fungible token code.

[0025] In some embodiments, the blockchain data can be parsed, and each parsed segment of the blockchain data can be processed to classify whether the parsed segment includes token data that describes a non-fungible token. The classification can be generated by a machine learning classification model that is 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, meets one or more standards, and / or describes a smart contract). In some embodiments, the blockchain data can be parsed by a machine learning segmentation model that is trained to parse the blockchain data based on one or more factors (e.g., syntax, semantics, structure, code length, payload characteristics, code features, potential encoding markers, and / or other machine-learned characteristics).

[0026] In some embodiments, determining that a particular portion of blockchain data includes token data can include determining that the particular portion of blockchain data includes token data based on one or more function signatures. The one or more function signatures can be associated with a non-fungible token standard.

[0027] Alternatively, and / or additionally, determining that a particular portion of blockchain data includes token data can include determining that the particular portion includes a smart contract associated with a digital media item. The digital media item can be the payload of the smart contract. In some embodiments, the digital media item can be a digital resource.

[0028] Next, index data can be generated based on the token data. The index data can include information obtained from the blockchain data. In some embodiments, the index data can be associated with a digital resource (e.g., associated with a digital asset). The index data can include transaction data associated with a non-fungible token. The index data can include data that describes metadata associated with a non-fungible token. In some embodiments, the index data can include the type of digital resource (e.g., digital asset) associated with the digital resource. The digital resource type can be an augmented reality rendering asset type, and the digital resource (e.g., digital asset) can be an augmented reality rendering asset. The index data can include whether the URI (Uniform Resource Identifier) has been changed and whether the payload data (e.g., the pixels of an image and / or the text of a text string) has been changed if the URI has been changed. Additionally and / or alternatively, the index data can include information that describes the change of the URI. The index data can include data that can be a description of factors used to determine whether a non-fungible token is associated with fraud.

[0029] In some embodiments, generating index data based on the token data can include obtaining a digital resource (e.g., digital asset) associated with a non-fungible token, processing the digital resource (e.g., processing the digital asset) to determine one or more features within the digital resource (e.g., one or more features within the digital asset), and generating feature data that describes the one or more features. The index data can include the feature data.

[0030] In some embodiments, generating index data can include determining digital resources associated with non-fungible tokens and processing the non-fungible tokens with a machine learning model (e.g., a classification model, a detection model, a feature extraction model, and / or a semantic model) to determine one or more classifications, features, and / or attributes associated with the digital resources (e.g., attributes associated with a digital asset). The one or more classifications, features, and / or attributes can be included in the index data. The index data can include names associated with non-fungible tokens and / or digital resources (e.g., digital assets), names of creators / issuers of the digital resources (e.g., creators of digital assets and / or mintors of non-fungible tokens), transaction data (e.g., current and / or past owners, purchase price, transaction trends, trends of associated non-fungible tokens, gas fees, etc.), topics of the digital resources (topics of digital assets), non-fungible token metadata, pixel labels, descriptions of non-fungible tokens, free-form text associated with non-fungible tokens, and / or negative reviews associated with non-fungible tokens, issuers, or creators.

[0031] Digital resources (e.g., digital assets) can include image data, video data, text data, audio data, domains, augmented reality assets, virtual reality experiences, and / or latent encoded data. In some embodiments, the digital resources (e.g., digital assets) can include image data, and processing the digital resources (e.g., digital assets) can include processing the digital resources (e.g., digital assets) with a machine learning model to determine one or more image features.

[0032] Instead, and / or further, generating feature data that describes one or more features can include determining one or more feature descriptor terms associated with the one or more features. The feature data can be a description of one or more feature descriptor terms.

[0033] In some embodiments, the system and method can include determining that a web content item is associated with a digital resource (e.g., a digital asset). An issuance time associated with the web content item can be obtained. The system and method can determine a mint time associated with a non-fungible token based on blockchain data. The time difference data can be generated based on the mint time and the issuance time. In some embodiments, the index data can include the time difference data.

[0034] Instead, and / or further, generating index data based on token data can include determining reference data associated with a digital resource (e.g., a digital asset) based on the token data and determining an issuer of a non-fungible token based on the token data. The index data can include data that describes the reference data, the issuer, and a particular blockchain associated with the blockchain data.

[0035] The system and method can store index data in an index database. The index database can be stored in a server computing system. The index database can include a plurality of index data sets associated with a plurality of non-fungible tokens. For example, first token data can be identified to generate a first index data set, second token data can be identified to generate a second index data set, and third token data can be identified to generate a third index data set. The first token data can be associated with a first non-fungible token, the second token data can be associated with a second non-fungible token, and the third token data can be associated with a third non-fungible token.

[0036] The index database can be utilized for a plurality of different purposes. For example, the index database can be utilized as a database for storing non-fungible token data for quick retrieval upon request or search. The system and method can include receiving a request regarding a digital resource (e.g., a request for a digital asset) from a user computing system and providing token data to the user computing system.

[0037] The index database can include index data sets generated based on data obtained from blockchain data, web page data (e.g., marketplace data), and / or other sources.

[0038] Instead, and / or additionally, the systems and methods disclosed herein can obtain web page data (e.g., marketplace data) from a web page. The web page data can be a description of a list of non-fungible tokens. In some embodiments, the web page data can include data associated with a digital resource. Additionally, and / or instead, obtaining the web page data can include generating a snapshot of a web page listing non-fungible tokens for sale. In some embodiments, the web page data can be a description of a landing page of a digital marketplace website that hosts and / or facilitates the purchase and sale of non-fungible tokens. The marketplace data can include a snapshot of the web page and can include image data, text data, and / or latent encoded data.

[0039] Certain portions (e.g., subsets) of the web page data can be processed to determine that the web page data includes token data. The token data can be a description of non-fungible tokens associated with a digital resource (e.g., a digital asset). Instead, and / or additionally, a digital marketplace website can be crawled to determine a plurality of landing pages associated with a plurality of non-fungible tokens, and each landing page can be processed to generate index data for each of the non-fungible tokens.

[0040] Next, index data can be generated based on the token data. In some embodiments, the index data can include information obtained from web page data. The index data can be associated with a digital resource (e.g., associated with a digital asset). Generating the index data can include processing the image data, text data, and / or latent encoded data of the leaf page to determine data associated with a plurality of index fields included in the index data.

[0041] The system and method can include storing the index data in an index database. The index database can be stored in a server computing system. In some embodiments, the index database can be used to clarify non-fungible token search results of a search engine. Further, and / or alternatively, the index database can be utilized to determine statistical values associated with non-fungible tokens. For example, determining the trend of non-fungible tokens of a particular type and / or creator, and then utilizing it for ranking non-fungible tokens within a marketplace, search result page, and / or for notifying a user to purchase based on the information.

[0042] The index database can 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 marketplaces). The index database can be utilized for multiple different tasks (e.g., searching, statistic generation, and / or model training). For example, the system and method can obtain blockchain data. The system and method can include determining that a subset of the blockchain data is a description of non-fungible tokens associated with digital resources (e.g., digital assets). The index data can be generated at least partially based on the subset of the blockchain data. The index data can include reference data associated with digital resources (e.g., digital assets). In some embodiments, the index data can be stored in a search database. The system and method can include receiving a search query from a user computing system. The system and method can determine that the search query is associated with the index data. Next, search results associated with digital resources (e.g., digital assets) can be provided to the user computing system.

[0043] The system and method can include obtaining blockchain data (e.g., blockchain data obtained from a blockchain node associated with a blockchain computing system). Alternatively and / or additionally, the system and method can obtain web page data (e.g., marketplace data obtained by taking a snapshot of a web page associated with a non-fungible token marketplace). In some embodiments, the system and method can include obtaining both blockchain data and web page data. The obtained data can be obtained via an application programming interface. In some embodiments, the obtained data may be updated at intervals. The updates can occur at set intervals and / or may be obtained at a frequency based on transaction trends, types of digital resources (e.g., types of digital assets), a particular blockchain, and / or cost.

[0044] Next, the system and method can determine that a subset of the acquired data (e.g., blockchain data and / or web page data) is a description of a non-fungible token associated with a digital resource (e.g., a digital asset). The determination can be based on a search for specific structures, specific terms, and / or specific actors. This determination can be based on known digital resource creators / issuers, known digital resource attributes (e.g., digital asset attributes), known digital resource types (e.g., images, videos, augmented reality rendering assets, and / or domains), known digital resource names, known descriptions of digital resources, metadata, and / or labels. The determination can be based on the subset of the acquired data conforming to the EIP (“Ethereum Improvement Proposals”, Ethereum (January 201). In some embodiments, strict compliance may not be required. For example, the data type may deviate from the specification of the standard. The functional nature and / or gist of the data may be determined to be associated with a non-functional token.

[0045] Index data can be generated based at least in part on a subset of the acquired data (e.g., blockchain data and / or web page data). In some embodiments, the index data can include reference data associated with the digital resource (e.g., script data referring to a URL (Uniform Resource Locator) or URI). The index data can include digital resource creators / publishers, digital resource attributes, digital resource types, digital resource names, digital resource descriptions, metadata, payload information, smart contract information, free-form text, transaction data, blockchain information (e.g., information associated with a particular blockchain on which a non-fungible token is minted), mint time, the first issuance time of the digital resource, changes to the digital resource, a particular marketplace, and / or labels (e.g., pixel labels). In some embodiments, the acquired data can be processed to determine a non-fungible token community associated with a particular non-fungible token, and the non-fungible token community can be indexed in the index data.

[0046] In some embodiments, the index data can be generated by processing the acquired data using one or more machine learning models (e.g., a segmentation model, a detection model, a classification model, and / or a feature extractor model). The transaction history can be processed to determine the price history of the non-fungible token, and the price history can be processed to index trend data and / or stability data. Additionally and / or alternatively, whether the non-fungible token was lazily minted can be indexed. The index data can include whether the non-fungible token was put up for auction.

[0047] Next, the system and method can store the index data in a search database. The search database can include a plurality of index data sets associated with a plurality of non-fungible tokens. In some implementations, the plurality of non-fungible tokens can be identified by processing data from a plurality of sources.

[0048] In some embodiments, the system and method can receive a search query from a user computing system. The search query can include one or more search terms. Alternatively, and / or additionally, the search query can include one or more images, audio data, latent encoded data, and / or multimodal data.

[0049] The search query can be processed to determine that the search query is associated with index data. The search query can be received and processed by a search engine. The search engine can be configured to crawl a blockchain, web pages, and / or an index database, or a search database.

[0050] In some embodiments, determining that a search query is associated with index data can include determining that one or more search terms of the search query are a description of at least one of a digital resource (e.g., a digital asset), an author of the digital resource (e.g., a creator of the digital asset), or non-fungible token metadata.

[0051] Next, search results associated with a digital resource (e.g., a digital asset) can be provided to the user computing system. The search results can include a preview of the digital resource (e.g., a preview of the digital asset). Alternatively, and / or additionally, the search results can include an indicator indicating that the search results are associated with non-fungible tokens.

[0052] In some embodiments, providing search results associated with digital resources (e.g., digital assets) to a user computing system can include determining that one or more web pages are associated with a 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 can include the search results and the one or more general web results.

[0053] In some embodiments, index data generated based on blockchain data can be collated with index data generated based on web page data. For example, in cases where the blockchain data and the web data conflict, the system and method can process the data, determine which data is more trustworthy, determine whether the data needs to be merged, and / or determine whether one or both sets of the data need to be deleted.

[0054] In some embodiments, the index data may be updated at intervals. The updates can occur at set intervals and / or may be obtained at a frequency based on transaction trends, performance data, types of digital resources, specific blockchains, mint times (e.g., as non-fungible tokens get older, the computational cost associated with crawling may increase, resulting in a lower update frequency), and / or may be obtained at a cost-based frequency.

[0055] The index database can include an index item field associated with a digital resource type so that it can distinguish between non-fungible tokens associated with augmented reality rendering assets and non-fungible tokens associated with images. In some embodiments, the index data can include how a payload (e.g., a digital asset) is launched, executed, and / or displayed. For example, a video player label, an image previewer label, a particular augmented reality application, and / or a particular device can be indexed.

[0056] Data extraction for index data generation can be machine-learned based on heuristics and / or can be deterministic.

[0057] The systems and methods of the present disclosure provide several technical effects and advantages. As an example, the systems and methods can provide a system and method for indexing data associated with one or more non-fungible tokens. For example, the systems and methods disclosed herein can generate index data based on identified token data and then utilize the index data for a plurality of different tasks (e.g., searching, trend determination, and / or catalog generation).

[0058] Another technical advantage of the systems and methods of the present disclosure is the ability to utilize one or more machine learning models to generate labels and descriptors for one or more digital resources. For example, the systems and methods disclosed herein can process digital resources by one or more machine learning models to generate labels and descriptors for the one or more digital resources and then add these labels and descriptors to the index data of their respective non-fungible tokens. The labels and descriptors can then be used in searches (e.g., keyword searches) and / or to correlate non-fungible tokens to classify non-fungible token families and / or communities.

[0059] Other examples of technical effects and benefits relate to improvements in computational efficiency and improvements in the functionality of computing systems. For example, the systems and methods disclosed herein can utilize the generated index data and can significantly reduce the computational power required to navigate through multiple web pages to identify and purchase relevant non-fungible tokens. Further, the systems and methods disclosed herein can obtain data regarding digital resources (e.g., digital assets) and can reduce the computational power utilized by a user when attempting to purchase a digital resource (e.g., digital asset). For example, a user obtaining data regarding a digital resource can use the generated index data to obtain the data, which can be stored locally, searched more efficiently, and / or cached or partially cached to improve the acquisition.

[0060] Reference is now made to the drawings, in which exemplary embodiments of the disclosure are discussed in more detail.

[0061] Exemplary Devices and Systems FIG. 1A shows a block diagram of an exemplary computing system 100 for indexing token data, according to an exemplary embodiment of the 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 communicatively coupled via a network 180.

[0062] The 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), 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.

[0063] The user computing system 130 includes one or more processors 132 and a memory 134. The one or more processors 132 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 a single processor or multiple processors operably connected. The memory 134 can include one or more non-transitory computer-readable storage media, such as RAM, ROM, EEPROM, EPROM, flash memory devices, magnetic disks, and combinations thereof. The memory 134 can store data 136 and instructions 138 that are executed by the processor 132 to cause the user computing system 130 to perform operations.

[0064] The user computing system 130 can also include one or more user input components that receive user input. For example, the user input component can be a touch-sensitive component (e.g., a touch-sensitive display screen or a touch pad) that is sensitive to the touch of a user input object (e.g., a finger or a stylus). The touch-sensitive component can serve to implement a virtual keyboard. Other exemplary user input components include a microphone, a conventional keyboard, or any other means by which a user can provide user input.

[0065] Server computing system 110 includes one or more processors 112 and a 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 a single processor or multiple processors operably connected. The memory 114 can include one or more non-transitory computer-readable storage media such as RAM, ROM, EEPROM, EPROM, flash memory devices, magnetic disks, and combinations thereof. The memory 114 can store data 118 and instructions 116 that are executed by the processor 112 to cause the server computing system 110 to perform operations.

[0066] In some embodiments, the server computing system 110 includes or is otherwise implemented by one or more server computing devices. If the server computing system 110 includes multiple server computing devices, such server computing devices can operate according to a sequential computing architecture, a parallel computing architecture, or some combination thereof.

[0067] The blockchain computing system 170 includes one or more processors and a memory. The one or more processors may be any suitable processing device (e.g., a processor core, a microprocessor, an ASIC, an FPGA, a controller, a microcontroller, etc.), and may be a single processor or multiple processors operably connected. The memory can include one or more non-transitory computer-readable storage media such as RAM, ROM, EEPROM, EPROM, flash memory devices, magnetic disks, and combinations thereof. The memory can store data and instructions that are executed by the processors to cause the blockchain computing system 170 to perform operations. In some embodiments, the blockchain computing system 170 includes or is implemented by one or more server computing devices.

[0068] The network 180 can be any type of communication network, such as a local area network (e.g., an intranet), a wide area network (e.g., the Internet), or some combination thereof, and can include any number of wired or wireless links. In general, communication via the network 180 can be performed by any type of wired and / or wireless connection using a variety of communication protocols (e.g., TCP / IP, HTTP, SMTP, FTP), encodings or formats (e.g., HTML, XML), and / or security schemes (e.g., VPN, secure HTTP, SSL).

[0069] Computing system 100 may include several applications (e.g., applications 1 to N). Each application may communicate with the central intelligence layer. Exemplary applications may include a text messaging application, an email application, a dictation application, a virtual keyboard application, a browser application, and the like. In some embodiments, each application may communicate with the central intelligence layer (and the model(s) stored therein) using an API (e.g., a common API across all applications).

[0070] The central intelligence layer may communicate with the central device data layer. The central device data layer may be a centralized repository of data for the computing system 100. In some embodiments, the central device data layer may communicate with several other components of the computing device, such as, for example, one or more sensors, a context manager, a device state component, and / or additional components. In some embodiments, the central device data layer may communicate with each device component using an API (e.g., a private API).

[0071] Further, and / or alternatively, FIG. 1A shows an exemplary computing system 100 that may be used to implement token data indexing, according to aspects of the present disclosure. 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 network 180.

[0072] System 100 includes a server 110, such as a web server. Server 110 can be implemented as one or more computing devices that are a parallel computing system and / or a distributed computing system. Specifically, the plurality of computing devices can function together as a single server 110. Server 110 can have one or more processors 112 and a memory 114. Server 110 can also include a network interface used to communicate with one or more remote computing devices (e.g., user devices) 130 via network 180.

[0073] Processor(s) 112 can be any suitable processing device, such as a microprocessor, a microcontroller, an integrated circuit, or other suitable processing device. Memory 114 can include any suitable computing system or medium, including but not limited to non-transitory computer-readable media, RAM, ROM, hard drives, flash drives, or other memory devices. Memory 114 can store information accessible by processor(s) 112, including instructions 116 that can be executed by processor(s) 112. Instructions 116 can be any set of instructions that, when executed by processor(s) 112, cause processor(s) 112 to provide a desired function.

[0074] Specifically, instruction 116 can be executed by processor(s) 112 to implement token data identification and indexing. User profile database 120 can be configured to store multiple user profiles associated with multiple users who utilize one or more user computing systems 130. In some embodiments, user profile database 120 can be configured to be utilized to facilitate one or more interactions. Facilitation of one or more interactions can involve using blockchain application programming interface (API) 122 to send data to and receive data from blockchain computing system 170. For example, server computing system 110 can utilize blockchain API 122 to update one or more ledgers 172 of blockchain computing system 170. One or more ledgers 172 can be associated with one or more tokens 174. One or more tokens 174 can include one or more non-fungible tokens and can include scripts associated with digital resources (e.g., image data, video data, text data, latent encoded data, domain data, audio data, augmented reality asset rendering data, and / or virtual reality asset rendering data). In particular, the script can reference specific digital resources (e.g., digital assets) provided for sale. Digital resources (e.g., digital assets) can include image data, text data, video data, latent encoded data, domain names, virtual characteristics, augmented reality assets, virtual reality assets (e.g., virtual reality environments and / or virtual reality objects for interactions within the environment), smart contracts, authentication of physical items, and the like. In some embodiments, one or more ledgers 172 can be associated with cryptocurrencies that can be utilized to conduct transactions within a physical marketplace and / or a virtual marketplace.

[0075] It will be appreciated that the term "element" can refer to computer logic utilized to provide a desired function. Thus, any element, function, and / or instruction can be implemented in hardware, application-specific circuitry, firmware, and / or software controlling a general-purpose processor. In one embodiment, an element or function is a program code file stored in a storage device, loaded into memory, and executed by a processor, or a computer program product stored in a tangible computer-readable storage medium such as RAM, a hard disk, or an optical or magnetic medium, for example, provided from computer-executable instructions.

[0076] Memory 114 may also include data 118 that can be obtained, manipulated, created, or stored by processor(s) 112. The data 118 can include search result data, ranking data, image data (e.g., digital maps, satellite images, aerial photos, street photos, synthetic models, paintings, personal images, portraits, etc.), video data, audio data, text data (e.g., books, articles, blogs, poems, etc.), latent encoded data, blockchain address data, tables, vector data (e.g., vector representations of roads, plots, buildings, etc.), target location data (e.g., locations such as islands, cities, restaurants, hospitals, parks, hotels, and schools), or other data or related information. By way of example, the data 118 can be used to access information and data associated with specific digital resources (e.g., specific digital assets), websites, search results, blockchains, etc.

[0077] Data 118 can be stored in one or more databases. The one or more databases can be connected to server 110 by a high-bandwidth LAN or WAN, or can also be connected to server 110 by network 180. The one or more databases can be divided such that they are located in multiple locations.

[0078] Server 110 can exchange data with one or more user computing systems 130 via network 180. Two user computing systems 130 are shown in FIG. 1A, but any number of user computing systems 130 can be connected to server 110 via network 180. User computing system 130 can be any suitable type of computing device, such as a general-purpose computer, a dedicated computer, a navigation device, a laptop, a desktop, an integrated circuit, a mobile device, a smartphone, a tablet, a wearable computing device, a display in which one or more processors are coupled and / or incorporated, or other suitable computing device. Further, user computing system 130 can be a plurality of computing devices that function together to perform an operation or a computing action.

[0079] Similar to server 110, user computing system 130 can include a processor(s) 132 and a memory 134. Memory 134 can store information accessible by processor(s) 132, including instructions executable by processor(s) 132, and data. By way of example, memory 134 can store data 136 and instructions 138.

[0080] Command 138 can provide commands for implementing a browser, purchasing non-fungible tokens, and / or a plurality of other functions. Specifically, a user of the user computing system 130 can exchange data with the server 110 by visiting a website accessible at a specific web address using a browser. The identification and indexing of the token data of the present disclosure can be provided as elements of the user interface of a website and / or application.

[0081] Data 136 can include data regarding the execution of a special application on the user computing system 130. In particular, this special application can be used to exchange data with the server 110 via the network 160. Data 136 can include user device-readable code for providing and implementing aspects of the present disclosure. Further, and / or alternatively, data 136 can include data regarding previously input or received data. For example, data 136 can include data regarding past occurrences of a special application.

[0082] The user computing system 130 can include various user input devices for receiving information from a 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 can have a display for presenting information such as a user interface, displaying digital resources, displaying pop-ups or application elements displayed in the interface, and / or other forms of information.

[0083] In addition, the user computing system 130 can include a user profile 140 that can be used to identify the user of the user computing system 130. The user profile 140 can be optionally used by the user to perform one or more transactions, and then these one or more transactions can be recorded in one or more ledgers 172 of the blockchain computing system 170. The user profile 140 can be a description of user information that can include an identification number and / or payment account information. For example, the user profile 140 can include data associated with a cryptographic wallet, and this data may be linked to a browser application by extension and / or incorporation of the application.

[0084] Furthermore, the user computing system 130 can include a graphics processing unit. The graphics processing unit can be used by the processor 132 to tokenize a data index. In some embodiments, the user computing system 130 performs the identification and indexing of all token data.

[0085] The user computing system 130 can include a network interface for communicating with the server 110 via the network 180. The network interface can include, for example, one or more ports, transmitters, wireless cards, controllers, physical layer components, or any other item for communication according to any currently known or future-developed communication protocol or technology, and can include any component or configuration suitable for communication with the server 110 via the network 180.

[0086] Network 180 can be any type of communication network, such as a local area network (e.g., intranet), a wide area network (e.g., Internet), or some combination thereof. Network 180 can also include a direct connection between client device 130 and server 110. Generally, communication between server 110 and client device 130 can be performed via a network interface that uses any type of wired and / or wireless connection, using various communication protocols (e.g., TCP / IP, HTTP), encodings or formats (e.g., HTML, XML), and / or protection schemes (e.g., VPN, secure HTTP, SSL).

[0087] In some embodiments, exemplary computing system 100 can include one or more creator computing systems 150. One or more creator computing systems 150 can be utilized to generate images, videos, prose, poetry, audio, etc., which can then be provided for sale. One or more creator computing systems 150 can include one or more processors 152, which can be utilized to execute one or more operations for implementing the systems and methods disclosed herein. One or more creator computing systems 150 can include one or more memory components 154, which can be utilized to store data 156 and one or more instructions 158. Data 156 can include data regarding one or more applications, one or more media datasets, etc. Instructions 158 can include one or more operations for implementing the systems and methods disclosed herein.

[0088] One or more creator computing systems 150 can store data associated with one or more digital resources 160 and / or one or more creator profiles 162. The one or more digital resources 160 can include text data, image data, video data, audio data, latent encoded data, domain data, or other various data formats. The one or more creator profiles 162 can include information associated with one or more "creators" of the one or more digital resources 160. The one or more creator profiles 162 can include identification data, transaction data, and / or cryptocurrency wallet data.

[0089] Further, and / or alternatively, exemplary computing system 100 can include one or more blockchain computing systems 170. The one or more blockchain computing systems 170 can include a plurality of computing devices for use in decentralized data storage such that a plurality of "blocks" can be distributed across a network of computing devices to provide a secure system that can include one or more ledgers 172 and one or more tokens 174 for data storage. In some embodiments, each of the one or more tokens 174 can be associated with at least a portion of the one or more ledgers 172.

[0090] A blockchain can refer to a system configured to securely record information. A blockchain can include a decentralized system that can make it extremely difficult to change information. A blockchain can include a digital ledger of transactions that can be replicated and distributed across a network of computing systems. Each block within the chain can include several transactions. When a new transaction occurs on the blockchain, the record of that transaction can be added to the ledgers of all computing devices. A blockchain can be utilized to track the exchange of currency and / or digital resources by the record of transactions in a digital ledger that can be propagated throughout the decentralized system. The currency exchanged and tracked via the blockchain computing system 170 can be referred to as cryptocurrency.

[0091] Token 174 can include one or more non-fungible tokens. Non-fungible tokens can be minted on a blockchain associated with the blockchain computing system 170. A non-fungible token (NFT) can be a certificate of authenticity of a digital resource (e.g., a certificate of authenticity of a digital asset). Since NFTs may be non-exchangeable, their value comes to depend on the price that anyone may consider paying for the asset. NFTs can be minted on a blockchain so that their scarcity and authenticity can be maintained. A digital resource can be defined as being digitally stored and can be uniquely identifiable as something that an organization can use to realize value. Examples of digital resources can include tweets, social media comments, documents, audio, images, videos, logos, website domains, slide presentations, spreadsheets, CSS files and formats, executable code, and / or websites.

[0092] Figure 1B shows a block diagram of an exemplary blockchain 50 that can be utilized by the blockchain computing system 170 of the exemplary computing system 100 of Figure 1A. The exemplary blockchain 50 can include a plurality of blocks and can be utilized to store data having one or more cryptographic features. The blockchain 50 can be stored in a decentralized computing system including a plurality of computing devices. The blockchain 50 can be a public blockchain (e.g., an open blockchain without access restrictions such that anyone with Internet access can send or validate a transaction as part of the decentralized distributed system), a private blockchain (e.g., a blockchain that provides access based on permissions set by a network administrator), or a hybrid blockchain (e.g., a blockchain having a combination of unrestricted blocks and restricted blocks). The blockchain 50 can include a proof-of-work feature and can include a proof in one or more cryptographic formats. The proof of work can be provided in connection with a request to update the blockchain 50 (e.g., a request to update the ledger based on a new transaction). The proof of work can communicate that a particular device or group of devices has performed a certain amount of computation, which can then be verified for validity by other parties. Once verified, the blockchain 50 can be updated or can remain unchanged in response to a verification failure. The proof-of-work function can be utilized to reduce the computational cost for all devices within the system that need to perform the same computational functions and checks to determine if a request is valid to update the blockchain 50.

[0093] Each block can include a hash, a previous hash associated with the hash of the previous block, and data. In some embodiments, each block can include a nonce. The hash can be a fixed-length hash value that can be a fingerprint of a particular block. The hash value can be generated based on a hash function and can change each time a change is made to the data of that particular block. The previous hash can include the hash value of the block immediately preceding a particular block. The previous hash can be utilized to ensure that the downstream grand truth remains unchanged unless appropriate validation is performed. The data can 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 referring to digital resources, nonce data, and / or general blockchain data). The nonce (i.e., a number used only once) can be a number added to a block within a blockchain that can satisfy a difficulty level limit when the block is rehashed. The nonce can be a number that a blockchain miner is solving for in order to receive an incentive (e.g., a cryptocurrency).

[0094] The blockchain 50 can include one or more security protocols and / or functions. The blockchain 50 can include a cryptographic system. For example, the blockchain 50 can verify the validity that the blockchain 50 is valid by ensuring that the stored previous hash stored in a block matches the hash value of the previous block that goes back from the last block to the first block (e.g., the genesis block). In some embodiments, the blockchain 50 can include a proof-of-work validation that can depend on verifying a proof of computation before implementing a change to the stored data (e.g., the stored ledger). The proof-of-work validation can take seconds, minutes, and / or hours, depending in part on the number of blocks within the blockchain 50. Further, and / or alternatively, the blockchain 50 can be implemented on a distributed and decentralized computing system. In some embodiments, each computing device within the distributed and decentralized computing system can store a portion (e.g., one block out of a plurality of blocks) or all of the blocks within the blockchain 50. Thus, the system can verify the data by ensuring that the data is uniform across most, if not all, of the distributed system. Each node of the distributed system can have tampering checked before new data is added.

[0095] The data can include data associated with cryptocurrency values (e.g., a ledger associated with a specific cryptocurrency value), data associated with digital resources (e.g., a non-fungible token minted on the blockchain 50 that can include a script associated with a digital asset), data associated with smart contracts (e.g., a smart contract that includes conditions that automatically initiate an action in response to criteria being met), and / or timestamp data (e.g., timestamp data for block creation, minting, transactions, etc.).

[0096] In particular, FIG. 1B shows a first block 10, a second block 20, a third block 30, a fourth block 40, and an nth block 60. Although five blocks are shown, any number of blocks can be utilized. The first block 10 can be a genesis block (e.g., the first overall block within a blockchain). The first block 10 can include respective first hashes 12 (e.g., hash values associated with the first block 10). The first block 10 can include a first previous hash 14 (e.g., if the first block 10 has a block before it within the blockchain 50, the hash of the previous block can be stored in the first block 10). Further, and / or alternatively, the first block 10 can include data 16 and nonces 18.

[0097] The second block 20 can follow the first block 10. The second block 20 can include respective second hashes 22 (e.g., hash values associated with the second block 20). The second block 20 can include a second previous hash 24 (e.g., the second previous hash 24 can be the same as, or can reference, the first hash 12). Further, and / or alternatively, the second block 20 can include data 26 and nonces 28.

[0098] The third block 30 can follow the second block 20. The third block 30 can include respective third hashes 32 (e.g., hash values associated with the third block 30). The third block 30 can include a third previous hash 34 (e.g., the third previous hash 34 can be the same as, or can reference, the second hash 22). Further, and / or alternatively, the third block 30 can include data 36 and nonces 38.

[0099] Further, and / or alternatively, the fourth block 40, the nth block 60, and other potential blocks can each include a respective hash, a respective previous hash, and data. The first data 16, the second data 26, the third data 36, and the data of other blocks can include duplicate data, can be different, and / or can be the same such that the data is replicable across all blocks. In some embodiments, each block can be associated with a different transaction (e.g., different mints, different sales, etc.). The first nonce 18, the second nonce 28, the third nonce 38, and the nonces of other blocks can be different and may be solved during mining.

[0100] The data within each block can include ledger data and can include a timestamp, the assets and / or cryptocurrency exchanged, the actors involved in the transaction, and / or other various information.

[0101] In some embodiments, multiple different blockchains can be utilized in the systems and methods disclosed herein. The different blockchains can include different configurations. The different blockchains can include parallel chains, side chains, shared blocks, different chains, different permissions, different purposes, different numbers of blocks, and / or different hash functions and / or different lengths of hash values.

[0102] In some embodiments, the systems and methods can include one or more machine learning model computing systems 900. The one or more machine learning models can be utilized for various tasks to enable the identification and indexing of token data.

[0103] FIG. 9A shows a block diagram of an exemplary computing system 900 that performs token data identification and indexing according to an exemplary embodiment of the present disclosure. The system 900 includes a user computing device 902, a server computing system 930, and a training computing system 950 communicatively coupled via a network 980.

[0104] 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), 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.

[0105] The user computing device 902 includes one or more processors 912 and a 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 operably connected. The memory 914 can include one or more non-transitory computer-readable storage media, such as RAM, ROM, EEPROM, EPROM, flash memory devices, magnetic disks, and combinations thereof. The memory 914 can store data 916 and instructions 918 that are executed by the processor 912 to cause the user computing device 902 to perform operations.

[0106] In some embodiments, the user computing device 902 can store or include one or more token indexing models 920. For example, the token indexing model 920 can be various machine learning models such as neural networks (e.g., deep neural networks), or other types of machine learning models including non-linear models and / or linear models, or can include them in other ways. The neural network can 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. An exemplary token indexing model 920 is described with reference to FIG. 3.

[0107] In some embodiments, one or more token indexing models 920 are received from the server computing system 930 via the network 980, stored in the user computing device memory 914, and then used by one or more processors 912 or can be implemented in other ways. In some embodiments, the user computing device 902 can 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 that describe non-fungible tokens).

[0108] More specifically, the token indexing model can 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 indexing model can process blockchain data and / or web page data to generate index data that describes index information associated with one or more respective non-fungible tokens.

[0109] Additionally, or alternatively, one or more token indexing models 940 can be included in, or otherwise stored and thereby implemented by, a server computing system 930 that communicates with a user computing device 902 according to a client-server relationship. For example, the token indexing model 940 can be implemented by the server computing system 940 as part of a web service (e.g., a token indexing service). Thus, one or more models 920 can be stored and implemented on the user computing device 902, and / or one or more models 940 can be stored and implemented on the server computing system 930.

[0110] Also, the user computing device 902 can include one or more user input components 922 that receive user input. For example, the user input component 922 can be a touch-sensitive component (e.g., a touch-sensitive display screen or a touch pad) that is sensitive to the touch of a user input object (e.g., a finger or a stylus). The touch-sensitive component can serve to implement a virtual keyboard. Other exemplary user input components include a microphone, a conventional keyboard, or other means by which a user can provide user input.

[0111] The server computing system 930 includes one or more processors 932 and a memory 934. The one or more processors 932 can be any suitable processing device (e.g., a processor core, a microprocessor, an ASIC, an FPGA, a controller, a microcontroller, etc.) and can be a single processor or multiple processors operably connected. The memory 934 can include one or more non-transitory computer-readable storage media such as RAM, ROM, EEPROM, EPROM, flash memory devices, magnetic disks, and combinations thereof. The memory 934 can store data 936 and instructions 938 that are executed by the processor 932 to cause the server computing system 930 to perform operations.

[0112] In some embodiments, the server computing system 930 includes or is otherwise implemented by one or more server computing devices. When the server computing system 930 includes multiple server computing devices, such server computing devices can operate according to a sequential computing architecture, a parallel computing architecture, or some combination thereof.

[0113] As described above, the server computing system 930 can store or otherwise include one or more machine learning token indexing models 940. For example, the model 940 can be or can include various machine learning models. Exemplary machine learning models include neural networks or other multi-layer non-linear models. Exemplary neural networks include feed-forward neural networks, deep neural networks, recurrent neural networks, and convolutional neural networks. The exemplary model 940 is described with reference to FIG. 3.

[0114] User computing device 902 and / or server computing system 930 can train models 920 and / or 940 through interaction with a training computing system 950 communicatively coupled via network 980. The training computing system 950 can be separate from the server computing system 930 or can be part of the server computing system 930.

[0115] The training computing system 950 includes one or more processors 952 and a memory 954. The one or more processors 952 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 operably connected. The memory 954 can include one or more non-transitory computer-readable storage media such as RAM, ROM, EEPROM, EPROM, flash memory devices, magnetic disks, and combinations thereof. The memory 954 can store data 956 and instructions 958 to be executed by the processor 952 to cause the training computing system 950 to perform operations. In some embodiments, the training computing system 950 includes or is otherwise implemented by one or more server computing devices.

[0116] The training computing system 950 can include a model trainer 960 that trains machine learning models 920 and / or 940 stored in user computing device 902 and / or server computing system 930 using various training or learning techniques such as, for example, backpropagation of error. For example, a loss function can be backpropagated through the model(s) to update one or more parameters of the model(s) (e.g., based on the gradient of the loss function). Various loss functions can be used, such as mean squared error, likelihood loss, cross entropy loss, hinge loss, and / or various other loss functions. Gradient descent can be used to iteratively update the parameters over several training iterations.

[0117] In some embodiments, performing backpropagation of error can include performing truncated backpropagation over time. The model trainer 960 can perform several generalization techniques (e.g., weight decay, dropout, etc.) to improve the generalization ability of the model being trained.

[0118] In particular, the model trainer 960 can train the token indexing models 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, ground truth labels, ground truth index information, and / or ground truth segmentation masks.

[0119] In some embodiments, if the user provides consent, the user computing device 902 can provide training examples. Thus, in such embodiments, the model 920 provided to the user computing device 902 can be trained by the training computing system 950 on user-specific data received from the user computing device 902. In some instances, this process can be referred to as model customization.

[0120] The model trainer 960 includes computer logic utilized to provide 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 embodiments, the model trainer 960 includes a program file stored on a storage device, loaded into memory, and executed by one or more processors. In other embodiments, the model trainer 960 includes one or more sets of computer-executable instructions stored on a tangible computer-readable storage medium such as a RAM hard disk, or optical or magnetic media.

[0121] The network 980 can be any type of communication network, such as a local area network (e.g., intranet), wide area network (e.g., Internet), or some combination thereof, and can include any number of wired or wireless links. Generally, communication via the network 980 can be performed using any type of wired and / or wireless connection, using a variety of communication protocols (e.g., TCP / IP, HTTP, SMTP, FTP), encodings or formats (e.g., HTML, XML), and / or security schemes (e.g., VPN, secure HTTP, SSL).

[0122] The machine learning models described herein can be used for a variety of tasks, applications, and / or use cases.

[0123] In some embodiments, the input to the machine learning model(s) of the present disclosure may be image data. The machine learning model(s) can process the image data to generate an output. By way of example, the machine learning model(s) can process the image data to generate an image recognition output (e.g., recognition of the image data, potential embedding of the image data, encoded representation of the image data, hash of the image data, etc.). As another example, the machine learning model(s) can process the image data to generate an image segmentation output. As another example, the machine learning model(s) can process the image data to generate an image classification output. As another example, the machine learning model(s) can process the image data to generate an image data modification output (e.g., alteration of the image data, etc.). As another example, the machine learning model(s) can process the image data to generate an encoded image data output (e.g., encoded and / or compressed representation of the image data, etc.). As another example, the machine learning model(s) can process the image data to generate a prediction output.

[0124] In some embodiments, the input to the machine learning model(s) of the present disclosure may be text or natural language data. The machine learning model(s) can process the text or natural language data to generate an output. By way of example, the machine learning model(s) can process natural language data to generate a language encoding output. As another example, the machine learning model(s) can process text or natural language data to generate a latent text embedding output. As another example, the machine learning model(s) can process text or natural language data to generate a classification output. As another example, the machine learning model(s) can process text or natural language data to generate a text segmentation output. As another example, the machine learning model(s) can process text or natural language data to generate a semantic intent output. As another example, the machine learning model(s) can process text or natural language data to generate an upscaled text or natural language output (e.g., text or natural language data of higher quality than the input text or natural language). As another example, the machine learning model(s) can process text or natural language data to generate a prediction output.

[0125] In some embodiments, the input to the machine learning model(s) of the present disclosure can be audio data. The machine learning model(s) can process the audio data to generate an output. By way of example, the machine learning model(s) can process the audio data to generate an audio recognition output. As another example, the machine learning model(s) can process the audio data to generate an audio translation output. As another example, the machine learning model(s) can process the audio data to generate a latent embedding output. As another example, the machine learning model(s) can process the audio data to generate an encoded audio output (e.g., an encoded and / or compressed representation of the audio data, etc.). As another example, the machine learning model(s) can process the audio data to generate a text representation output (e.g., a text representation of the input audio data, etc.). As another example, the machine learning model(s) can process the audio data to generate a prediction output.

[0126] In some embodiments, the input to the machine learning model(s) of the present disclosure can be latent encoded data (e.g., a latent space representation of the input, etc.). The machine learning model(s) can process the latent encoded data to generate an output. By way of example, the machine learning model(s) can process the latent encoded data to generate a recognition output. As another example, the machine learning model(s) can process the latent encoded data to generate a reconstruction output. As another example, the machine learning model(s) can process the latent encoded data to generate a search output. As another example, the machine learning model(s) can process the latent encoded data to generate a reclustering output. As another example, the machine learning model(s) can process the latent encoded data to generate a prediction output.

[0127] In some implementations, input to the machine learning model(s) of the present disclosure can be statistical data. The machine learning model(s) can process the statistical data to generate an output. As an example, the machine learning model(s) can process the statistical data to generate a recognition output. As another example, the machine learning model(s) can process the statistical data to generate a prediction output. As another example, the machine learning model(s) can process the statistical data to generate a classification output. As another example, the machine learning model(s) can process the statistical data to generate a segmentation output. As another example, the machine learning model(s) can process the statistical data to generate a segmentation output. As another example, the machine learning model(s) can process the statistical data to generate a visualization output. As another example, the machine learning model(s) can process the statistical data to generate a diagnostic output.

[0128] In some cases, the machine learning model(s) can be configured to perform a task that includes encoding input data for reliable and / or efficient transmission or storage (and / or corresponding decoding). For example, the task can be an audio compression task. The input can include audio data, and the output can include compressed audio data. In another example, the input can include visual data (e.g., one or more images or videos), and the output can include 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).

[0129] 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 can be image classification, and the output is a set of scores, where each score corresponds to a different object class and represents the likelihood that one or more images depict an object belonging to that object class. The image processing task can be object detection, and the image processing output identifies one or more regions within one or more images and, for each region, the likelihood that the region depicts an object of interest. As another example, the image processing task can be image segmentation, and the image processing output defines, for each pixel within one or more images, the respective likelihoods for each category within a predetermined set of categories. For example, the set of categories can be foreground and background. As another example, the set of categories can be object classes. As another example, the image processing task can be depth estimation, and the image processing output defines, for each pixel within one or more images, the respective depth values. As another example, the image processing task can be motion estimation, the network input includes multiple images, and the image processing output defines, for each pixel of one of the input images, the motion of the scene depicted by the pixels between the images within the network input.

[0130] In some cases, the input includes audio data representing a spoken utterance and the task is a speech recognition task. The output can 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 microprocessor performance tasks such as branch prediction or memory address translation.

[0131] FIG. 9A shows one exemplary computing system that can be used to implement the present disclosure. Other computing systems can be used as well. For example, in some embodiments, user computing device 902 can include model trainer 960 and training dataset 962. In such embodiments, model 920 can be both trained locally and used in user computing device 902. In some of such embodiments, user computing device 902 can implement model trainer 960 that individualizes model 920 based on user-specific data.

[0132] FIG. 9B represents a block diagram of an exemplary computing device 970 executed in accordance with an exemplary embodiment of the present disclosure. Computing device 970 can be a user computing device or a server computing device.

[0133] Computing device 970 includes several applications (e.g., applications 1 - N). Each application includes its own machine learning library and machine learning model(s). For example, each application can include a machine learning model. Exemplary applications include text messaging applications, email applications, dictation applications, virtual keyboard applications, browser applications, and the like.

[0134] As illustrated in FIG. 9B, each application can communicate with some other components of the computing device, such as, for example, one or more sensors, a context manager, a device state component, and / or additional components. In some embodiments, each application can communicate with each device component using an API (e.g., a public API). In some embodiments, the API used by each application is specific to that application.

[0135] FIG. 9C represents a block diagram of an exemplary computing device 990 implemented in accordance with an exemplary embodiment of the present disclosure. The computing device 990 can be a user computing device or a server computing device.

[0136] The computing device 990 includes a number of applications (e.g., applications 1 - N). Each application communicates with a central intelligence layer. Exemplary applications include a text messaging application, an email application, a dictation application, a virtual keyboard application, a browser application, and the like. In some embodiments, each application can communicate with the central intelligence layer (and the model(s) stored therein) using an API (e.g., a common API across all applications).

[0137] The central intelligence layer includes several machine learning models. For example, as shown in FIG. 9C, each machine learning model (e.g., the model) can be provided for each application and managed by the central intelligence layer. In other embodiments, two or more applications can share a single machine learning model. For example, in some embodiments, the central intelligence layer can provide a single model (e.g., a single model) for all of the applications. In some embodiments, the central intelligence layer is included within or otherwise implemented by the operating system of computing device 990.

[0138] The central intelligence layer can communicate with the central device data layer. The central device data layer can be a centralized repository of data for computing device 990. As illustrated in FIG. 9C, the central device data layer can communicate with several other components of the computing device, such as, for example, one or more sensors, a context manager, a device state component, and / or additional components. In some embodiments, the central device data layer can communicate with each device component using an API (e.g., a private API).

[0139] System configuration example Figure 2 shows a block diagram of an exemplary indexing system 200 according to an exemplary embodiment of the present disclosure. In some embodiments, the indexing system 200 is configured to receive blockchain data 202 that describes a blockchain storing data associated with a plurality of non-fungible tokens, and provide output data 220 that describes 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 embodiments, the indexing system 200 can include one or more models and / or one or more functions to identify token data and extract relevant information for generating index data.

[0140] In particular, the exemplary indexing system 200 can include obtaining blockchain data 202 associated with the blockchain. The blockchain data 202 can include the code of the blockchain. Further, and / or alternatively, the blockchain data 202 can include script data associated with a plurality of non-fungible tokens (e.g., data that describes a script that can be deployed to interact with the smart contract code of the blockchain data 202).

[0141] The blockchain data 202 can be processed to identify the first token data of the first non-fungible token 204, the second token data of the second non-fungible token 206, the third token data of the third non-fungible token 208, and the nth token data of the nth non-fungible token 210. Identifying the token data set can 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 can include crawling the blockchain data to search for specific characteristics, structures, and / or features associated with the token data.

[0142] The first token data associated with the first non-fungible token 204 can 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 can 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 can 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 can be processed to generate nth index data 218 associated with the nth non-fungible token 210.

[0143] The first index data 212, the second index data 214, the third index data 216, and the nth index data 218 can be stored in an index database 220 and then utilized for various tasks (e.g., non-fungible token search). The index data set can include index information associated with a plurality of index item fields. The index data set can include the title of the non-fungible token, references to digital resource payloads, descriptors, and / or various other index items for annotating and / or characterizing aspects of the non-fungible token that can be searched.

[0144] Figure 3 shows a block diagram of an exemplary index data set 300 according to an exemplary embodiment of the present disclosure. In some embodiments, the index data 310 can be generated based on blockchain data and / or web page data 326. The blockchain data can be processed to identify a subset of the blockchain data that describes the token data 302. The token data 302 can be processed to determine one or more index items for the generation of the index data 310. Additionally and / or alternatively, web page data 326 associated with the non-fungible tokens or with the same or related non-fungible tokens associated with the token data 302 can be utilized to determine one or more additional index items in one or more index item fields.

[0145] Additionally and / or alternatively, the token data 302 and / or the web page data 326 can be processed by one or more machine learning models 324 to generate one or more outputs that can be utilized as index items of the index data 310. Instead and / or additionally, the input data for the one or more machine learning models 324 can be obtained from other data sources. In some embodiments, the index data 310 can include data that describes the blockchain 312 on which the non-fungible tokens are minted. The index data 310 can include references 314 to digital resources (e.g., URIs), time data 316 (e.g., mint time, first digital resource issuance time, and / or time difference between two times), transaction data 318 (e.g., purchase time, acquisition quantity, acquisition frequency, identities of purchasers and bidders, and / or action event data), metadata 320 (e.g., stored by the blockchain, marketplace, and / or other data sources), other machine learning data 322, and / or other derived data.

[0146] FIG. 4 shows a block diagram of an exemplary search 400 according to an exemplary embodiment of the present disclosure. The exemplary search 400 can include receiving a search query 402. The search query 402 can include one or more words, one or more images, and / or one or more other input formats. The search engine 404 can process the search query 402 and then access a non-substitutable token index database 406 to determine one or more non-substitutable token search results 410 in response to the search query 402. Further and / or alternatively, the search engine 404 can process the search query 402 and then access a web database 408 to determine one or more general web results 412 in response to the search query 402. One or more non-substitutable token search results 410 and / or one or more general web results 412 can be utilized to generate a search result page 414. Next, the search result page 414 can be provided for display.

[0147] Figures 5A-5C illustrate different examples of configurations of a search results page. Although three configurations are shown, other variations may be utilized to provide search results for display. The different variations depicted may include a search interface 502, which can include a search query input box 504, one or more non-substitutable token search results (e.g., a first non-substitutable token search result 510, a second non-substitutable token search result 512, a third non-substitutable token search result 514, and / or a fourth non-substitutable 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 can be configured to receive and / or display one or more search queries. The one or more non-substitutable token search results and the one or more general web results can be determined and provided based on a responsiveness determined for the input search query. Further, and / or alternatively, the knowledge panel 506 can include structured data associated with a topic determined to respond to the input query.

[0148] FIG. 5A shows a diagram of an exemplary search results page 500 according to an exemplary embodiment of the present disclosure. The exemplary search results page 500 includes a search query input box 504 displayed on top of a search interface 502, and a knowledge panel 506 is displayed in a side panel of the search interface 502. Further, the exemplary search results page 500 includes a non-substitutable token search results portion that displays search results for one or more non-substitutable tokens (e.g., a first non-substitutable token search result 510, a second non-substitutable token search result 512, a third non-substitutable token search result 514, and / or a fourth non-substitutable token search result 516). The exemplary search results page 500 can include a separate general web results portion that can 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). Further, and / or alternatively, the one or more non-substitutable token search results and the one or more general web results can be provided in different formats (e.g., the non-substitutable token search results can be provided by image thumbnails, while the general web results can be displayed in text only).

[0149] FIG. 5B shows a diagram of an exemplary search results page 540 according to an exemplary embodiment of the present disclosure. The exemplary search results page 540 includes a search query input box 504 displayed on top of the search interface 502, and a knowledge panel 506 is displayed in the side panel of the search interface 502. The exemplary search results page 540 can include one or more non-substitutable token search results (e.g., a first non-substitutable token search result 510, a second non-substitutable token search result 512, a third non-substitutable token search result 514, and / or a fourth non-substitutable 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) can be displayed in a mixed format such that different types of search results can be adjacent to each other. The order may be determined based on a score determined based on responsiveness to the search query and / or based on other factors. The ordering may be purely based on a score that has no preference for the type of result.

[0150] FIG. 5C shows a diagram of an exemplary search results page 580 according to an exemplary embodiment of the present disclosure. The exemplary search results page 580 can include both one or more non-substitutable token search results (e.g., a first non-substitutable token search result 510, a second non-substitutable token search result 512, a third non-substitutable token search result 514, and / or a fourth non-substitutable 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) can be provided by a media content item preview for display.

[0151] Exemplary method FIG. 6 shows a flowchart of an exemplary method for performing in accordance with an exemplary embodiment of the present disclosure. FIG. 6 shows steps performed in a particular order for purposes of illustration and explanation, but the method of the present disclosure is not limited to the particular order or arrangement shown. The various steps of method 600 can be omitted, rearranged, combined, and / or adapted in various ways without departing from the scope of the present disclosure.

[0152] At 602, the computing system can obtain blockchain data from a blockchain computing system. The blockchain data can include one or more function signatures. In some embodiments, the blockchain data can include a script associated with a digital resource (e.g., a script associated with a digital asset). The blockchain data may be obtained via a blockchain node. The blockchain data can include code and / or records stored on the blockchain. The code can include a script and can be a description of multiple smart contracts. The blockchain data can include transaction data associated with the acquisition of digital resources and / or the exchange of digital currency (e.g., cryptocurrency). The blockchain data can include metadata associated with one or more non-fungible tokens.

[0153] In 604, the computing system can determine that a particular portion of the blockchain data includes token data. The token data can be a description of a non-fungible token associated with a digital resource. Alternatively and / or additionally, the computing system can determine that a subset of the blockchain data includes token data based on a subset having a structure associated with one or more standards. The token data can be a description of a non-fungible token associated with a digital resource (e.g., a digital asset). In some embodiments, this structure can include a format for code within the blockchain data associated with a standard format of non-fungible token code.

[0154] In some embodiments, the blockchain data can be parsed, and each parsed segment of the blockchain data can be processed to classify whether the parsed segment includes token data that describes a non-fungible token. The classification can be generated by a machine learning classification model that is 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, meets one or more standards, and / or is a description of a smart contract). In some embodiments, the blockchain data can be parsed by a machine learning segmentation model that is trained to parse the blockchain data based on one or more factors (e.g., syntax, semantics, structure, code length, payload characteristics, code features, potential encoding markers, and / or other machine-learned characteristics).

[0155] In some embodiments, determining that a particular portion of blockchain data includes token data can include determining that the particular portion of blockchain data includes token data based on one or more function signatures. The one or more function signatures can be associated with a non-fungible token standard.

[0156] Alternatively, and / or additionally, determining that a particular portion of blockchain data includes token data can include determining that the particular portion includes a smart contract associated with a digital media item. The digital media item can be the payload of the smart contract. In some embodiments, the digital media item can be a digital resource (e.g., a digital asset).

[0157] At 606, the computing system can generate index data based on the token data. The index data can include information obtained from the blockchain data. In some embodiments, the index data can be associated with a digital resource (e.g., a digital asset). The index data can include transaction data associated with a non-fungible token. The index data can include data describing metadata associated with a non-fungible token. In some embodiments, the index data can include a digital resource type associated with a digital resource (e.g., a digital asset). The digital resource type can be an augmented reality rendering asset type, and the digital resource (e.g., a digital asset) can be an augmented reality rendering asset. The index data can include whether the URI has changed and whether the pixels of the payload have changed if the URI has changed. The index data can include data that can be a description of factors utilized to determine whether a non-fungible token is involved in fraud.

[0158] In some embodiments, generating index data based on token data can include obtaining digital resources (e.g., digital assets) associated with non-fungible tokens, processing the digital resources (e.g., digital assets) to determine one or more features within the digital resources (e.g., one or more features within the digital assets), and generating feature data that describes the one or more features. The index data can include the feature data.

[0159] In some embodiments, generating index data can include determining digital resources (e.g., digital assets) associated with non-fungible tokens, and processing the non-fungible tokens by a machine learning model (e.g., a classification model, a detection model, a feature extraction model, and / or a semantic model) to determine one or more classifications, features, and / or attributes associated with the digital resources (e.g., one or more attributes of the digital assets). The one or more classifications, features, and / or attributes can be included in the index data. The index data can include names associated with the non-fungible tokens and / or digital resources, names of the creators / issuers of the digital resources, transaction data (e.g., current and / or past owners, purchase price, transaction trends, trends of related non-fungible tokens, gas fees, etc.), topics of the digital resources, non-fungible token metadata, pixel labels, descriptions of the non-fungible tokens, free-form text associated with the non-fungible tokens, and / or negative reviews associated with the non-fungible tokens, issuers, or creators.

[0160] Digital resources can include image data, video data, text data, audio data, domains, augmented reality assets, virtual reality experiences, and / or latent encoded data. In some embodiments, the digital resources can include image data, and processing the digital resources can include processing the digital resources (e.g., digital assets) by a machine learning model to determine one or more image features.

[0161] Alternatively, and / or additionally, generating feature data that describes one or more features can include determining one or more feature descriptor terms associated with the one or more features. The feature data can be a description of one or more feature descriptor terms.

[0162] In some embodiments, the system and method can include determining that a web content item is associated with a digital resource (e.g., digital asset). An issuance time associated with the web content item can be obtained. The system and method can determine a mint time associated with a non-fungible token based on blockchain data. The time difference data can be generated based on the mint time and the issuance time. In some embodiments, the index data can include the time difference data.

[0163] Alternatively, and / or additionally, generating index data based on token data can include determining reference data associated with a digital resource (e.g., digital asset) based on the token data and determining an issuer of a non-fungible token based on the token data. The index data can include reference data, the issuer, and data that describes a particular blockchain associated with the blockchain data and / or other derived data.

[0164] In 608, the computing system can store index data in an index database. The index database can be stored in a server computing system. The index database can include a plurality of index data sets associated with a plurality of non-fungible tokens. For example, the first token data can be identified to generate a first index data set, the second token data can be identified to generate a second index data set, and the third token data can be identified to generate a third index data set. The first token data can be associated with a first non-fungible token, the second token data can be associated with a second non-fungible token, and the third token data can be associated with a third non-fungible token.

[0165] The index database can be utilized for a plurality of different purposes. For example, the index database can be utilized as a database for storing non-fungible token data for quick retrieval upon request or search. The system and method can include receiving a request for a digital resource (e.g., a digital asset) from a user computing system and providing token data to the user computing system.

[0166] FIG. 7 shows a flowchart of an exemplary method for performing in accordance with an exemplary embodiment of the present disclosure. FIG. 7 shows steps performed in a particular order for purposes of illustration and description, but the method of the present disclosure is not limited to the particular order or arrangement shown. The various steps of method 700 can be omitted, rearranged, combined, and / or adapted in various ways without departing from the scope of the present disclosure.

[0167] At 702, the computing system can obtain web page data from a web page. The web page data can be a description of a list of non-fungible tokens. In some embodiments, the web page data can include data associated with a digital resource (e.g., a digital asset). Additionally and / or alternatively, obtaining the web page data can include generating a snapshot of a web page listing non-fungible tokens for sale. In some embodiments, the web page data can be a description of a leaf page of a digital marketplace website that hosts and / or facilitates the purchase and sale of non-fungible tokens. The web page data can include a snapshot of the web page and can include image data, text data, and / or latent encoded data.

[0168] At 704, the computing system can determine that a subset of the web page data includes token data. The token data can be a description of non-fungible tokens associated with a digital resource. Alternatively and / or additionally, the digital marketplace website can be crawled to determine a plurality of leaf pages associated with a plurality of non-fungible tokens, and each leaf page can be processed to generate index data for each of the non-fungible tokens.

[0169] At 706, the computing system can generate index data based on the token data, and the index data includes information obtained from the web page data. In some embodiments, the index data can include information obtained from the web page data. The index data can be associated with a digital resource. Generating the index data can include processing the image data, text data, and / or latent encoded data of the leaf page to determine data associated with a plurality of index fields included in the index data.

[0170] In 708, the computing system can store index data in an index database. The index database can be stored in a server computing system. In some embodiments, the index database can be used to clarify non-substitutable token search results of a search engine. Further, and / or alternatively, the index database can be utilized to determine statistical values associated with non-substitutable tokens. For example, trends of non-substitutable tokens of a particular type and / or creator can be determined and then utilized for ranking non-substitutable tokens within a marketplace, search result page, and / or for notifying users for information-based purchases.

[0171] FIG. 8 shows a flowchart diagram of an exemplary method for performing in accordance with an exemplary embodiment of the present disclosure. FIG. 8 shows steps executed in a particular order for purposes of illustration and description, but the methods of the present disclosure are not limited to the particular order or arrangement shown. The various steps of method 800 can be omitted, rearranged, combined, and / or adapted in various ways without departing from the scope of the present disclosure.

[0172] In 802, the computing system can obtain blockchain data. For example, the computing system can include obtaining blockchain data (e.g., blockchain data obtained from a blockchain node associated with the blockchain computing system). Alternatively, and / or additionally, the computing system can obtain web page data (e.g., marketplace data obtained by taking a snapshot of a web page associated with a non-fungible token marketplace). In some embodiments, the computing system can include obtaining both blockchain data and web page data. Additionally, and / or alternatively, the computing system can further obtain peer-to-peer network data. The obtained data can be obtained via an application programming interface. In some embodiments, the obtained data can be updated at intervals. The updates can occur at set intervals and / or be obtained at a frequency based on transaction trends, types of digital resources, frequency based on a particular blockchain, and / or cost. The updates can be provided to one or more users via push-based updates, via notifications, and / or via update elements of other user interfaces.

[0173] In 804, the computing system can determine that a subset of the blockchain data describes a non-fungible token associated with a digital resource. The determination can be based on a search for a particular structure, particular terms, and / or a particular actor. This determination can be based on known digital resource creators / issuers, known digital resource attributes, known digital resource types, known digital resource names, known descriptions of digital resources, metadata, and / or labels. The determination can be based on the subset of the obtained data conforming to an EIP.

[0174] In 806, the computing system can generate index data based at least in part on a subset of blockchain data. In some embodiments, the index data can include reference data associated with a digital resource (e.g., script data referring to a URL or URI). The index data can include digital resource creators / issuers, digital resource attributes, digital resource types, digital resource names, digital resource descriptions, metadata, payload information, smart contract information, free-form text, transaction data, blockchain information (e.g., information associated with a particular blockchain on which a non-fungible token is minted), mint time, the first issuance time of the digital resource, changes to the digital resource, a particular marketplace, and / or labels (e.g., pixel labels). In some embodiments, the acquired data can be processed to determine a non-fungible token community associated with a particular non-fungible token, and the non-fungible token community can be indexed in the index data.

[0175] In some embodiments, the index data can be generated by processing data obtained 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 can be processed to determine the price history of a non-fungible token, and the price history can be processed to index trend data and / or stability data. Further, and / or alternatively, whether a non-fungible token was lazily minted can be indexed. The index data can include whether the non-fungible token was put up for auction.

[0176] At 808, the computing system can store index data in a search database. The search database can include a plurality of index data sets associated with a plurality of non-fungible tokens. In some implementations, the plurality of non-fungible tokens can be identified by processing data from a plurality of sources.

[0177] At 810, the computing system can receive a search query from a user computing system and determine that the search query is associated with index data. The search query can include one or more search terms. Alternatively, and / or additionally, the search query can include one or more images, audio data, latent encoded data, and / or multimodal data. The search query can be received and / or processed by a search engine. The search engine can be configured to crawl a blockchain, web page, and / or index database, or search database.

[0178] In some embodiments, determining that the search query is associated with index data can include determining that one or more search terms of the search query describe at least one of a digital resource, an author of the digital resource, or non-fungible token metadata.

[0179] At 812, the computing system can provide search results associated with the digital resource to the user computing system. The search results can include a preview of the digital resource. Alternatively, and / or additionally, the search results can include an indicator indicating that the search results are associated with non-fungible tokens.

[0180] In some embodiments, providing search results associated with digital resources to a user computing system can include determining that one or more web pages are associated with a 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 can include the search results and one or more general web results.

[0181] In some embodiments, index data generated based on blockchain data can be collated with index data generated based on web page data. For example, in cases where the blockchain data and the web data conflict, the system and method can process the data, determine which data is more trustworthy, determine whether the data needs to be merged, and / or determine whether one or both sets of the data need to be deleted.

[0182] Exemplary Embodiments and Uses By being able to utilize the systems and methods disclosed herein for searching, a user can make a decision after considering whether to invest in a particular non-fungible token.

[0183] In particular, the systems and methods can be utilized to enable a significant number of users (not just early adopters or crypto enthusiasts well-versed in Web3) to easily access Web3 technology. The ways in which Web3 can function can deviate significantly from the mental models of general users. For example, the systems and methods can focus on simplifying the technology, jargon, applications, and / or setup.

[0184] A blockchain computing system can include a decentralized system, but the systems and methods disclosed herein can be implemented in a hybrid system that includes a decentralized system, a closed-off ecosystem, and / or parts completed by a decentralized system and parts completed by a centralized system.

[0185] In some embodiments, the systems and methods disclosed herein can be utilized for identity purposes. For example, the systems and methods disclosed herein can be involved in and / or interface with a user's crypto wallet to store user identity data utilized throughout Web3. A user may sign into a website, share personal data, prove an identity, and / or transfer cryptocurrency based in part on identity data.

[0186] The wallet app can be embedded in a browser. In Web3, there may be a single mechanism for signing in while retaining one's own data.

[0187] A blockchain computing system can store data that describes various information, which can include non-fungible token content IDs and / or ownership information of non-fungible tokens. Non-fungible tokens can bring scarcity and authenticity to digital items.

[0188] The systems and methods disclosed herein can be implemented in a search engine to enhance the search of images, videos, and audio. For example, the systems and methods implemented in a search engine can send signals to users who own images or videos on the web and to users of the origin of digital resources (e.g., digital assets), enforce copyright rules of digital resources (e.g., copyright rules of digital assets), detect whether a digital resource has been tampered with or has credibility issues, and / or enable the sale or transfer of digital content on the web (e.g., a non-fungible token marketplace for images, music, and / or other forms of digital assets).

[0189] The systems and methods disclosed herein can include creator tokens. The Web3 model enables creators to own their content and have a direct relationship with their followers / subscribers / fans, bypassing the current platforms. This can be very disruptive to video platforms but can be an opportunity for search engines, which may not be in the minds of creators. Creator tokens can be a way for fans to "invest" in their favorite creators while enabling the creators to form a community centered around their fans.

[0190] In some embodiments, the systems and methods disclosed herein can be utilized to provide an oracle system (e.g., a system that can provide a service provider system for smart contracts to check whether something is real or has occurred).

[0191] The systems and methods disclosed herein can include general information acquisition. The systems and methods can enable indexing to important parts of a blockchain by search, making those important parts accessible and useful.

[0192] The systems and methods can include decentralized autonomous organizations that can be used to automate decision-making. In some embodiments, information regarding the decentralized autonomous organization of a search engine can be provided, which can make decision-making more transparent.

[0193] In some embodiments, the systems and methods can enable the ownership of any digital asset on the web by giving creators and issuers the right to claim ownership of digital content and set its usage rules in a scalable and fast way. Additionally and / or alternatively, the systems and methods can enable the ownership of any digital asset on the web by giving search users the right to understand the origin and history of a digital asset and at the same time take actions on the digital asset (e.g., purchase non-fungible tokens and / or use non-fungible tokens as tickets) in a way permitted by the original creator.

[0194] In some embodiments, the systems and methods can simultaneously create a reward for creators and issuers by giving them tools to create and set usage rules for non-fungible content while issuing non-fungible content on the web.

[0195] For example, rewards can be created to indicate the owner of an image on the web or to indicate an item issued by a creator / publisher, block copies of "owned" digital assets, or at least remit some of the proceeds generated therefrom to the original owner, enable the monetization of user-generated content, and enable the payment of royalties.

[0196] Furthermore, and / or alternatively, the system and method can include tools for a creator / publisher to create non-fungible content. For example, the system and method can provide the creator / publisher with easy tools to issue non-fungible content and set content usage rules that can be performed by both casual creators and sophisticated large-scale players.

[0197] In some embodiments, the system and method can include a smart contract blockchain that can be open, closed, and / or both hybrid.

[0198] The system and method can be implemented to enable a non-fungible token marketplace across various digital asset platforms (e.g., image platforms, video hosting platforms, and / or music hosting platforms) and / or across various service platforms (e.g., search engines, social media platforms, and / or blog 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.

[0199] 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 verified to be owned by specific users, can enable rapid communication between creators and owners, and can provide a transaction history that can be used for future launches and purchases by providing insightful proposals.

[0200] Additional Disclosure The technology described herein refers to servers, databases, software applications, and other computer-based systems, as well as the actions performed and the information sent to and from such systems. The flexibility inherent in computer-based systems allows for a wide variety of possible configurations, combinations, and divisions of tasks and functions among components. For example, the processes discussed herein can be implemented using a single device or component, or multiple devices or components operating in combination. Databases and applications can be implemented in a single system or distributed across multiple systems. Distributed components can operate sequentially or in parallel.

[0201] Although the subject matter of the present disclosure has been described in detail with respect to its various specific and exemplary embodiments, each example is provided for illustrative purposes and is not intended to limit the present disclosure. Those skilled in the art will, upon reaching the foregoing understanding, be able to readily create modifications, variations, and equivalents to such embodiments. Accordingly, the disclosure of the subject matter does not exclude including such modifications, variations, and / or additions to the present subject matter as would be apparent to those skilled in the art. For example, features illustrated or described as part of one embodiment may be used in other embodiments to create still other embodiments. Accordingly, the present disclosure is intended to cover such changes, variations, and equivalents.

Claims

1. A computing system comprising: one or more processors; and one or more non-transitory computer-readable media that collectively store instructions which, when executed by the one or more processors, cause the computing system to perform operations, wherein the operations include: obtaining blockchain data from a blockchain computing system, the blockchain data including one or more function signatures; determining that a particular portion of the blockchain data includes token data, the token data describing non-fungible tokens associated with digital resources; generating index data based on the token data, the index data including information obtained from the blockchain data and being associated with the digital resources; and storing the index data in an index database.

2. The determining that the particular portion of the blockchain data includes the token data comprises: determining that the particular portion of the blockchain data includes token data based on the one or more function signatures, the one or more function signatures being associated with a non-fungible token standard, The system of claim 1.

3. The generating of the index data based on the token data comprises: obtaining the digital resources associated with the non-fungible tokens; processing the digital resources to determine one or more features within the digital resources; and generating feature data describing the one or more features, wherein the index data includes the feature data, The system of any of claims 1 or 2.

4. The digital resources include image data, and the processing of the digital resources comprises: processing the digital resources by a machine learning model to determine one or more image features, The system of claim 3.

5. The generating of the feature data describing the one or more features comprises: Determining one or more feature descriptor terms associated with the one or more features, The feature data describes the one or more feature descriptor terms, the system according to claim 3. **Claim 6** The operation is Receiving a request for the digital resource from a user computing system, Providing the token data to the user computing system, The system according to any one of claims 1 to 5, further comprising. **Claim 7** Determining that the particular portion of the blockchain data includes the token data is Determining that the particular portion includes a smart contract associated with a digital media item, the system according to any one of claims 1 to 6. **Claim 8** The digital media item is the payload of the smart contract, The digital media item is the digital resource, the system according to claim 7. **Claim 9** The index data includes transaction data associated with the non-fungible token, the system according to any one of claims 1 to 8. **Claim 10** The index data includes data describing metadata associated with the non-fungible token, the system according to any one of claims 1 to 9. **Claim 11** A method implemented on a computer, Obtaining blockchain data from a blockchain computing system by a computing system including one or more processors, the blockchain data including a script associated with a digital resource, obtaining, Determining by the computing system that a subset of the blockchain data includes token data based on the subset having a structure associated with one or more specifications, the token data describing a non-fungible token associated with the digital resource, determining Generating, by the computing system, index data based on the token data, wherein the index data includes information obtained from the blockchain data and is associated with the digital resource; Storing, by the computing system, the index data in an index database; A method comprising the above.

12. Determining, by the computing system, that a web content item is associated with the digital resource; Obtaining, by the computing system, an issuance time associated with the web content item; Determining, by the computing system, a mint time associated with the non-fungible token based on the blockchain data; Generating, by the computing system, time difference data based on the mint time and the issuance time; Further comprising: The method according to claim 11, wherein the index data includes the time difference data.

13. The method according to any one of claims 11 to 12, wherein the structure includes a format for the code in the blockchain data associated with the standard format of the non-fungible token code.

14. Generating, by the computing system, the index data based on the token data includes: Determining, by the computing system, reference data associated with the digital resource based on the token data; Determining, by the computing system, an issuer of the non-fungible token based on the token data; Including: The method according to any one of claims 11 to 13, wherein the index data includes the reference data, the issuer, and data describing a specific blockchain associated with the blockchain data.

15. The index data includes a digital resource type associated with the digital resource, The digital resource type is an augmented reality rendering asset type. The method according to any one of claims 11 to 14, wherein the digital resource is an augmented reality rendering asset.

16. One or more non-transitory computer-readable media that, when executed by one or more computing devices, collectively store instructions that cause the one or more computing devices to perform operations, wherein the operations include: obtaining blockchain data; determining that a subset of the blockchain data describes a non-fungible token associated with a digital resource; generating index data based at least in part on the subset of the blockchain data, the index data including reference data associated with the digital resource; storing the index data in a search database; receiving a search query from a user computing system; determining that the search query is associated with the index data; providing search results associated with the digital resource to the user computing system. One or more non-transitory computer-readable media including the above.

17. The one or more non-transitory computer-readable media according to claim 16, wherein the search results include a preview of the digital resource.

18. The one or more non-transitory computer-readable media according to any one of claims 16 to 17, wherein the search results include an indicator indicating that the search results are associated with a non-fungible token.

19. The determination that the search query is associated with the index data includes: determining that one or more search terms of the search query describe at least one of the digital resource, the author of the digital resource, or non-fungible token metadata. The one or more non-transitory computer-readable media according to any one of claims 16 to 18.

20. The providing of the search results associated with the digital resource to the user computing system includes: 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; providing a search results page to the user computing system, the search results page including the search results and one or more general web results; A non-transitory computer-readable medium according to any one of claims 16 to 19, comprising:

Images