Generating comprehensive non-homogenized token search index

Abstract

Systems and methods for non-homogenized token data indexing may include processing blockchain data and / or web page data to identify token data associated with non-homogenized tokens. The identified token data may then be used to generate index data associated with the non-homogenized tokens. The index data may be used to generate an index database. The index database may then be used for non-homogenized token search and / or to generate derivative data associated with non-homogenized tokens. The revealed non-homogenized token search results may be provided in a specific search result 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 particularly, the present disclosure relates to crawling blockchains and / or non-fungible token markets to generate index data for a non-fungible token index database. Background Art

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

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

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

[0005] An example aspect of the present disclosure relates to a computing system. The system may include one or more processors and one or more non-transitory computer-readable media, which together store instructions that, when executed by one or more processors, cause the computing system to perform operations. The operations may include obtaining blockchain data from a blockchain computing system. The blockchain data may include one or more function signatures. The operations may include determining that a specific portion of the blockchain data includes token data. In some implementations, the token data may describe a non-fungible token associated with a digital resource. The operations may include generating index data based on the token data. The index data may include information obtained from the blockchain data. In some implementations, the index data may be associated with a digital resource. The operations may include storing the index data in an index database.

[0006] In some implementations, determining that a particular portion of blockchain data includes token data may include determining that a particular portion of blockchain data includes token data based on one or more function signatures. The one or more function signatures may be associated with a non-fungible token standard. In some implementations, generating index data based on token data may include: obtaining a digital resource associated with a non-fungible token; processing the digital resource to determine one or more features in the digital resource; and generating feature data describing the one or more features. The index data may include feature data. In some implementations, the digital resource may include image data, and processing the digital resource may include processing the digital resource with a machine-learned model to determine one or more image features. In some implementations, generating feature data describing the one or more features may include determining one or more feature descriptor terms associated with the one or more features. The feature data may describe the one or more feature descriptor terms.

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

[0008] Another example aspect of the present disclosure relates to a computer-implemented method. The method may include obtaining blockchain data from a blockchain computing system by a computing system including one or more processors. The blockchain data may include a script associated with a digital resource. The method may include determining by the computing system that a subset of the blockchain data includes token data based on that the subset has a structure associated with one or more standards. The token data may describe a non-fungible token associated with a digital resource. The method may include generating index data based on the token data by the computing system. The index data may include information obtained from the blockchain data. In some implementations, the index data may be associated with the digital resource. The method may include storing the index data in an index database by the computing system.

[0009] In some implementations, the method may include: determining, by a computing system, a web content item associated with a digital resource; obtaining, by a computing system, a release time associated with the web content item; determining, by a computing system, a mint time associated with a non-fungible token based on blockchain data; and generating, by a computing system, time difference data based on the mint time and the release time. The index data may include the time difference data. The structure may include a format of a code in the blockchain data, the format of the code in the blockchain data being associated with a standard format of a non-fungible token code.

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

[0011] Another example aspect of the present disclosure relates to one or more non-transitory computer-readable media that collectively store instructions that, when executed by one or more computing devices, cause one or more computing devices to perform operations. The operations may include obtaining blockchain data. The operations may 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 implementations, the index data may include reference data associated with the digital resource. The operations may include storing the index data in a search database. In some implementations, the operations may include receiving a search query from a user computing system, and determining that the search query is associated with the index data. The operations may include providing search results associated with the digital resource to the user computing system.

[0012] In some implementations, the search results may include a preview of the digital resource. The search results may include an indicator indicating that the search results are associated with a non-fungible token. In some implementations, determining that the search query is associated with the index data may include 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. In some implementations, providing the search results associated with the digital resource to the user computing system may include: determining that one or more web pages are associated with the search query; generating one or more general web results based on the one or more web pages; and providing the search results page to the user computing system. The search results page may include the search results and the one or more general web results.

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

[0014] These and other features, aspects and advantages of various embodiments of the present disclosure will be better understood with reference to the following description and appended claims.The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate example embodiments of the present disclosure and, together with the description, serve to explain the relevant principles. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] A detailed discussion of embodiments for those of ordinary skill in the art is set forth in this specification with reference to the accompanying drawings, in which:

[0016] Figure 1A Depicted is a block diagram of an example computing system that performs non-fungible token indexing according to example embodiments of the present disclosure.

[0017] Figure 1B A block diagram of an example computing device that performs non-fungible token indexing according to an example embodiment of the present disclosure is depicted.

[0018] Figure 2 Depicted is a block diagram of an example indexing system according to an example embodiment of the present disclosure.

[0019] Figure 3 Depicted is a block diagram of an example indexed data set according to an example embodiment of the present disclosure.

[0020] Figure 4 Depicted is a block diagram of an example search according to an example embodiment of the present disclosure.

[0021] Figure 5A Depicted is a diagram of an example search results page according to an example embodiment of the present disclosure.

[0022] Figure 5B Depicted is a diagram of an example search results page according to an example embodiment of the present disclosure.

[0023] Figure 5C Depicted is a diagram of an example search results page according to an example embodiment of the present disclosure.

[0024] Figure 6 Depicted is a flow diagram of an example method of performing non-fungible token indexing according to an example embodiment of the present disclosure.

[0025] Figure 7 Depicted is a flow diagram of an example method of performing non-fungible token indexing according to an example embodiment of the present disclosure.

[0026] Figure 8 Depicted is a flow diagram of an example method of performing a non-fungible token index search according to an example embodiment of the present disclosure.

[0027] Fig.9A Depicted is a block diagram of an example computing system that performs non-fungible token indexing according to example embodiments of the present disclosure.

[0028] Fig. 9B A block diagram of an example computing device that performs non-fungible token indexing according to an example embodiment of the present disclosure is depicted.

[0029] Fig. 9C Depicted is a block diagram of an example computing system that performs non-fungible token indexing according to example embodiments of the present disclosure.

[0030] Reference numerals that are repeated across multiple drawings are intended to identify like features in the various implementations. DETAILED DESCRIPTION

[0031] Overview

[0032] In general, the present disclosure relates to systems and methods for indexing non-fungible tokens. In particular, the systems and methods disclosed herein can identify data associated with non-fungible tokens in a blockchain (e.g., in the code of the blockchain). For example, multiple non-fungible tokens can be identified and indexed based on a variety of techniques. One technique can include analyzing (e.g., crawling) a blockchain to identify data associated with a non-fungible token. The data can then be parsed to determine identifiable data that can be indexed. Another technique can include analyzing (e.g., crawling) a web platform (e.g., a non-fungible token market) to identify non-fungible tokens for sale. Data related to non-fungible tokens can then be obtained and indexed. In some implementations, a variety of techniques can be utilized to generate a more inclusive database for searching.

[0033] The systems and methods disclosed herein may include obtaining blockchain data from a blockchain computing system. The blockchain data may include one or more function signatures. In some implementations, the blockchain data may include script data associated with a digital resource (e.g., a digital asset). A specific portion (e.g., a subset) of the blockchain data may be determined to include token data. The token data may describe a non-fungible token associated with a digital resource (e.g., a digital asset). Index data may be generated based on the token data. The index data may include information obtained from the blockchain data. In some implementations, the index data may be associated with a digital resource (e.g., a digital asset). The index data may then be stored in an index database. The index database may then be used for various purposes (e.g., non-fungible token search, data aggregation and analysis, training data for a machine-learned model, and / or generating statistical representations).

[0034] The systems and methods disclosed herein can be used to implement non-fungible token searches via a search engine. For example, a search query can be received. The search engine can process the search query and can determine a plurality of search results associated with the search query. In some implementations, one or more search results can be associated with one or more 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 in a search result interface (e.g., a search result page). One or more non-fungible token search results can be provided for display in 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 indicating the nature of the non-fungible token can be provided to one or more non-fungible token search results (e.g., providing one or more labels, logos, and / or marks). In some implementations, a generated preview of a digital resource based on the index data can be provided to one or more non-fungible token search results.

[0035] In some implementations, the systems and methods disclosed herein may be used for Web3 profiles, Web3 transactions, and / or Web3 identification.

[0036] The systems and methods can improve the search user interface and user experience by identifying and indexing relevant data that can be used to determine whether a non-fungible token is responsive 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 search result can be provided with a non-fungible token tag and / or whether a ranking boost can be given based on an association with an authenticated non-fungible token.

[0037] The systems and methods may obtain blockchain data (e.g., code from a blockchain) from a blockchain computing system (e.g., a decentralized computing system that stores distributed data). The blockchain data may include one or more function signatures. In some implementations, the blockchain data may include scripts associated with digital resources (e.g., digital assets). The blockchain data may be obtained via a blockchain node. The blockchain data may include code and / or records stored on the blockchain. The code may include scripts and may describe multiple smart contracts. The blockchain data may include transaction data associated with the acquisition of digital resources (e.g., digital assets) and / or the exchange of digital currencies (e.g., cryptocurrencies). The blockchain data may include metadata associated with one or more non-fungible tokens.

[0038] The systems and methods disclosed herein may include determining that a particular portion of blockchain data includes token data. The token data may describe a non-fungible token associated with a digital resource (e.g., a digital asset). Alternatively and / or additionally, the systems and methods may determine that a subset of blockchain data includes token data based on that the subset has a structure associated with one or more standards. The token data may describe a non-fungible token associated with a digital resource (e.g., a digital asset). In some implementations, the structure may include a format of a code in the blockchain data, the format of the code in the blockchain data being associated with a standard format for non-fungible token code.

[0039] In some implementations, blockchain data may be parsed, and each parsed segment of the blockchain data may be processed to classify whether the parsed segment includes token data describing a non-fungible token. The classification may be generated using a machine-learned 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, satisfies one or more criteria, and / or describes a smart contract). In some implementations, blockchain data may be parsed using a machine-learned segmentation model that is trained to parse blockchain data based on one or more factors (e.g., syntax, semantics, structure, code length, payload characteristics, code features, potential encoding markers, and / or another machine-learned characteristic).

[0040] In some implementations, determining that the specific portion of the blockchain data includes the token data may include determining that the specific portion of the blockchain data includes the token data based on one or more function signatures. The one or more function signatures may be associated with a non-fungible token standard.

[0041] Alternatively and / or additionally, determining that the specific portion of the blockchain data includes token data may include determining that the specific portion includes a smart contract associated with a digital media item. The digital media item may be a payload of the smart contract. In some implementations, the digital media item may be a digital asset.

[0042] Index data may then be generated based on the token data. The index data may include information obtained from the blockchain data. In some implementations, the index data may be associated with a digital resource (e.g., associated with a digital asset). The index data may include transaction data associated with a non-fungible token. The index data may include data describing metadata associated with the non-fungible token. In some implementations, the index data may include a digital resource (e.g., a digital asset) type associated with the digital resource. The digital resource type may be an augmented reality rendering asset type, and the digital resource (e.g., a digital asset) may be an augmented reality rendering asset. The index data may include whether a URI (Uniform Resource Identifier) ​​changes, and whether the data of the payload (e.g., pixels of an image and / or text of a text string) changes when the URI changes. Additionally and / or alternatively, the index data may include information describing changes to the URI. The index data may include data that may describe factors that may be used to determine whether a non-fungible token involves fraud.

[0043] In some implementations, generating index data based on token data may include: obtaining a digital resource (e.g., a digital asset) associated with a non-fungible token; processing the digital resource (e.g., processing the digital asset) to determine one or more features in the digital resource (e.g., one or more features in the digital asset); and generating feature data describing the one or more features. The index data may include the feature data.

[0044] In some implementations, generating index data may include determining a digital resource associated with a non-fungible token, and processing the non-fungible token with a machine-learned model (e.g., a classification model, a detection model, a feature extractor model, and / or a semantic model) to determine one or more classifications, features, and / or attributes associated with the digital resource (e.g., attributes associated with a digital asset). The one or more classifications, features, and / or attributes may be included in the index data. The index data may include a name associated with the non-fungible token and / or the digital resource (e.g., the digital asset), the name of the digital resource creator / publisher (e.g., the creator of the digital asset and / or the minter of the non-fungible token), transaction data (e.g., current and / or past owners, purchase price, transaction trends, trends of related non-fungible tokens, gas prices, etc.), the subject of the digital resource (e.g., the subject of the digital asset), non-fungible token metadata, pixel tags, non-fungible token descriptions, free-form text associated with the non-fungible token, and / or popularity associated with the non-fungible token, publisher, or creator.

[0045] A digital resource (e.g., a digital asset) may include image data, video data, text data, audio data, a domain, an augmented reality asset, a virtual reality experience, and / or potentially encoded data. In some implementations, a digital resource (e.g., a digital asset) may include image data, and processing the digital resource (e.g., a digital asset) may include processing the digital resource (e.g., a digital asset) with a machine-learned model to determine one or more image features.

[0046] Alternatively and / or additionally, generating feature data describing the one or more features may include determining one or more feature descriptor terms associated with the one or more features. The feature data may describe the one or more feature descriptor terms.

[0047] In some implementations, the systems and methods may include determining that a web content item is associated with a digital resource (e.g., a digital asset). A release time associated with the web content item may be obtained. The systems and methods may determine a minting time associated with a non-fungible token based on blockchain data. Time difference data may be generated based on the minting time and the release time. In some implementations, the index data may include the time difference data.

[0048] Alternatively and / or additionally, generating index data based on the token data may 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 may include data describing the reference data, the issuer, and the specific blockchain associated with the blockchain data.

[0049] The systems and methods may store index data in an index database. The index database may be stored on a server computing system. The index database may include multiple index data sets associated with multiple non-fungible tokens. For example, first token data may be identified to generate a first index data set, second token data may be identified to generate a second index data set, and third token data may be identified to generate a third index data set. The first token data may be associated with a first non-fungible token, the second token data may be associated with a second non-fungible token, and the third token data may be associated with a third non-fungible token.

[0050] The index database can be used for a variety of different purposes. For example, the index database can be used as a database for storing non-fungible token data for rapid retrieval upon request or search. The systems and methods may include receiving a request for a digital resource (e.g., a request for a digital asset) from a user computing system and providing the token data to the user computing system.

[0051] The index database may include an index data set generated based on blockchain data, web page data (e.g., market data), and / or data obtained from other sources.

[0052] Alternatively and / or additionally, the systems and methods disclosed herein may obtain web data (e.g., market data) from a web page. The web data may describe listing information for non-fungible tokens. In some implementations, the web data may include data associated with a digital resource. Additionally and / or alternatively, obtaining the web data may include generating a snapshot of a web page listing non-fungible tokens for sale. In some implementations, the web data may describe a leaf page of a digital market website that hosts and / or facilitates the purchase and sale of non-fungible tokens. The market data may include a snapshot of a web page, which may include image data, text data, and / or potentially encoded data.

[0053] A specific portion (e.g., a subset) of the web page data may be processed to determine that the web page data includes token data. The token data may describe a non-fungible token associated with a digital resource (e.g., a digital asset). Alternatively and / or additionally, a digital marketplace website may be crawled to determine a plurality of leaf pages associated with a plurality of non-fungible tokens, and each leaf page may be processed to generate index data for each of the non-fungible tokens.

[0054] Index data may then be generated based on the token data. In some implementations, the index data may include information obtained from the web page data. The index data may be associated with a digital resource (e.g., associated with a digital asset). Generating the index data may include processing image data, text data, and / or potentially encoded data of a leaf page to determine data associated with a plurality of index fields to be included in the index data.

[0055] The systems and methods may include storing index data in an index database. The index database may be stored on a server computing system. In some implementations, the index database may be used to surface non-fungible token search results for a search engine. Additionally and / or alternatively, the index database may be used to determine statistics associated with non-fungible tokens. For example, trending non-fungible tokens of a certain type and / or creator may be determined and then used for non-fungible token rankings in a marketplace, search results pages, and / or for notifying users to make informed purchases.

[0056] The index database may include index data from multiple different sources (e.g., multiple different blockchains associated with multiple different blockchain computing systems and / or multiple different web pages associated with multiple different markets). The index database may be used for multiple different tasks (e.g., search, statistics generation, and / or model training). For example, the system and method may obtain blockchain data. The system and method may include determining that a subset of blockchain data describes a non-fungible token associated with a digital resource (e.g., a digital asset). The index data may be generated based at least in part on a subset of blockchain data. The index data may include reference data associated with a digital resource (e.g., a digital asset). In some implementations, the index data may be stored in a search database. The system and method may include receiving a search query from a user computing system. The system and method may determine that the search query is associated with the index data. Search results associated with the digital resource (e.g., associated with the digital asset) may then be provided to the user computing system.

[0057] The systems and methods may include obtaining blockchain data (e.g., blockchain data obtained from a blockchain node associated with a blockchain computing system). Alternatively and / or additionally, the systems and methods may obtain web page data (e.g., market data obtained by taking a snapshot of a web page associated with a non-fungible token market). In some implementations, the systems and methods may include obtaining blockchain data and web page data. The obtained data may be obtained via an application programming interface. In some implementations, the obtained data may be updated at intervals. Updates may occur at set intervals and / or may be obtained at a certain frequency based on transaction trends, digital resource types (e.g., digital asset types), specific blockchains, and / or based on cost.

[0058] The system and method may then determine that a subset of the obtained data (e.g., blockchain data and / or web page data) describes a non-fungible token associated with a digital resource (e.g., a digital asset). The determination may be based on a search for a specific structure, a specific term, and / or a specific actor. The determination may be based on a known digital resource creator / publisher, a known digital resource attribute (e.g., a digital asset attribute), a known digital resource type (e.g., an image, video, augmented reality rendering asset, and / or domain), a known digital resource name, a known digital resource description, metadata, and / or tags. The determination may be based on whether the subset of the obtained data conforms to an EIP (“Ethereum Improvement Proposals,” Ethereum (January 2018), https: / / eips.ethereum.org / EIPS / eip-721). In some implementations, strict conformance may not be required. For example, a data type may deviate from the specification of a standard. The functional nature and / or spirit of the data may be determined to be associated with a non-functional token.

[0059] The index data may be generated based at least in part on a subset of the obtained data (e.g., blockchain data and / or web page data). In some implementations, the index data may include reference data associated with a digital resource (e.g., script data referencing a URL (Uniform Resource Locator) or URI). The index data may 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 specific blockchain for minting non-fungible tokens), minting time, first release time of a digital resource, digital resource changes, specific markets, and / or tags (e.g., pixel tags). In some implementations, the obtained data may be processed to determine a non-fungible token community associated with a specific non-fungible token, and the non-fungible token community may be indexed in the index data.

[0060] In some implementations, the index data may be generated by processing the obtained data with one or more machine-learned models (e.g., a segmentation model, a detection model, a classification model, and / or a feature extractor model). The transaction history may be processed to determine the price history of the non-fungible token, and the price history may be processed to index the trend data and / or stability data. Additionally and / or alternatively, whether the non-fungible token is lazy minted may be indexed. The index data may include whether the non-fungible token is auctioned.

[0061] The systems and methods may then store the index data in a search database. The search database may include multiple index data sets associated with multiple non-fungible tokens. In some implementations, multiple non-fungible tokens may be identified by processing data from multiple sources.

[0062] In some implementations, the systems and methods may receive a search query from a user computing system. The search query may include one or more search terms. Alternatively and / or additionally, the search query may include one or more images, audio data, latently encoded data, and / or multimodal data.

[0063] The search query may be processed to determine that the search query is associated with the index data. The search query may be received and processed by a search engine. The search engine may be configured to crawl a blockchain, a web page, and / or an index database or a search database.

[0064] In some implementations, determining that a 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 (e.g., a digital asset), an author of a digital resource (e.g., a creator of the digital asset), or non-fungible token metadata.

[0065] Search results associated with the digital resource (e.g., digital asset) may then be provided to the user computing system. The search results may include a preview of the digital resource (e.g., a preview of the digital asset). Alternatively and / or additionally, the search results may include an indicator indicating that the search result is associated with a non-fungible token.

[0066] In some implementations, providing search results associated with a digital resource (e.g., a digital asset) to a user computing system may 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 may include the search results and the one or more general web results.

[0067] In some implementations, index data generated based on blockchain data and index data generated based on web page data can be coordinated. For example, in the event of a conflict between blockchain data and web page data, the system and method can process the data, determine which data is more trustworthy, determine whether to merge the data, and / or determine whether to delete one or both sets of data.

[0068] In some implementations, index data may be updated at intervals. Updates may occur at set intervals and / or may be based on transaction trends, performance data, digital resource type, specific blockchain, minting time (e.g., older non-fungible tokens may be updated less frequently due to greater computational costs associated with crawling), and / or based on cost.

[0069] The index database may include index entry fields associated with digital asset types, such that non-fungible tokens associated with augmented reality rendering assets may be distinguished from non-fungible tokens associated with images. In some implementations, the index data may include how to launch, run, and / or view a payload (e.g., a digital asset). For example, a video player tag, an image preview tag, a specific augmented reality application, and / or a specific device may be indexed.

[0070] Data extraction for index data generation may be machine-learned, heuristic-based, and / or may be deterministic.

[0071] The systems and methods of the present disclosure provide a number of technical effects and benefits. As an example, the systems and methods may provide systems and methods for indexing data associated with one or more non-fungible tokens. For example, the systems and methods disclosed herein may generate index data based on the identified token data, which may then be used for a number of different tasks (e.g., searching, trend determination, and / or catalog generation).

[0072] Another technical benefit of the systems and methods of the present disclosure is the ability to utilize one or more machine-learned models to generate tags and descriptors for one or more digital resources. For example, the systems and methods disclosed herein can process digital resources with one or more machine-learned models to generate tags and descriptors for one or more digital resources, which can then be added to the index data of the corresponding non-fungible tokens. The tags and descriptors can then be used for searching (e.g., keyword searching) and / or for non-fungible token associations to classify non-fungible token families and / or communities.

[0073] Another example of technical effects and benefits relates to improved computing efficiency and improvements in the functionality of computing systems. For example, the systems and methods disclosed herein can utilize the generated index data and greatly reduce the computing power of navigating multiple web pages to identify and purchase related non-fungible tokens. Additionally, the systems and methods disclosed herein can reduce the computing power used by users when trying to obtain data about digital resources (e.g., digital assets) and then purchasing digital resources (e.g., digital assets). For example, a user who obtains data about 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 retrieval.

[0074] Referring now to the accompanying drawings, example embodiments of the present disclosure will be discussed in greater detail.

[0075] Example Apparatus and Systems

[0076] Figure 1A A block diagram of an example computing system 100 that performs token data indexing according to an example embodiment of the present disclosure is depicted. System 100 includes a user computing system 130, a server computing system 110, a creator computing system 150, and a blockchain computing system 170 that are communicatively coupled via a network 180.

[0077] 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.

[0078] The user computing system 130 includes one or more processors 132 and memory 134. The one or more processors 132 may be any suitable processing device (e.g., a processor core, a microprocessor, an ASIC, an FPGA, a controller, a microcontroller, etc.) and may be one processor or a plurality of processors operatively connected. The memory 134 may include one or more non-transitory computer-readable storage media, such as RAM, ROM, EEPROM, EPROM, flash memory devices, disks, etc., and combinations thereof. The memory 134 may store data 136 and instructions 138, which are executed by the processor 132 to cause the user computing system 130 to perform operations.

[0079] The user computing system 130 may also include one or more user input components that receive user input. For example, the user input component may be a touch-sensitive component (e.g., a touch-sensitive display screen or a touchpad) that is sensitive to the touch of a user input object (e.g., a finger or a stylus). The touch-sensitive component may be used to implement a virtual keyboard. Other example user input components include a microphone, a traditional keyboard, or other components through which a user can provide user input.

[0080] The server computing system 110 includes one or more processors 112 and memory 114. The one or more processors 112 may be any suitable processing device (e.g., a processor core, a microprocessor, an ASIC, an FPGA, a controller, a microcontroller, etc.) and may be one processor or a plurality of processors operatively connected. The memory 114 may include one or more non-transitory computer-readable storage media, such as RAM, ROM, EEPROM, EPROM, flash memory devices, magnetic disks, etc., and combinations thereof. The memory 114 may store data 118 and instructions 116 that are executed by the processor 112 to cause the server computing system 110 to perform operations.

[0081] In some implementations, the server computing system 110 includes or is otherwise implemented by one or more server computing devices. Where the server computing system 110 includes multiple server computing devices, such server computing devices may operate according to a sequential computing architecture, a parallel computing architecture, or some combination thereof.

[0082] The blockchain computing system 170 includes one or more processors and memory. The one or more processors may be any suitable processing device (e.g., a processor core, a microprocessor, an ASIC, an FPGA, a controller, a microcontroller, etc.) and may be one processor or multiple processors operatively connected. The memory may include one or more non-transitory computer-readable storage media, such as RAM, ROM, EEPROM, EPROM, flash memory devices, disks, etc., and combinations thereof. The memory may store data and instructions that are executed by the processor to cause the blockchain computing system 170 to perform operations. In some implementations, the blockchain computing system 170 includes one or more server computing devices or is otherwise implemented by one or more server computing devices.

[0083] The network 180 may be any type of communication network, such as a local area network (e.g., an intranet), a wide area network (e.g., the Internet), or some combination thereof, and may include any number of wired or wireless links. In general, communications over the network 180 may be conducted via any type of wired and / or wireless connection using a wide variety of communication protocols (e.g., TCP / IP, HTTP, SMTP, FTP), encodings or formats (e.g., HTML, XML), and / or protection schemes (e.g., VPN, secure HTTP, SSL).

[0084] The computing system 100 may include multiple applications (e.g., applications 1 to N). Each application may communicate with a central intelligence layer. Example applications may include a text messaging application, an email application, a dictation application, a virtual keyboard application, a browser application, etc. In some implementations, each application may communicate with the central intelligence layer (and the models stored therein) using an API (e.g., a public API across all applications).

[0085] The central intelligence layer can communicate with a central device data layer. The central device data layer can be a centralized data repository for the computing system 100. In some implementations, the central device data layer can communicate with multiple other components of the computing device (e.g., such as one or more sensors, context managers, device state components, and / or additional components). In some implementations, the central device data layer can communicate with each device component using an API (e.g., a private API).

[0086] Additionally and / or alternatively, Figure 1A An exemplary computing system 100 that can be used to implement token data indexing according to aspects of the present disclosure is depicted. The system 100 has a user-server architecture that includes a server 110 that communicates with one or more user computing systems 130 via a network 180. However, the present disclosure may be implemented using other suitable architectures that may include any number of computing systems that communicate via a network 180.

[0087] System 100 includes a server 110, such as a web server, for example. Server 110 may be one or more computing devices implemented as a parallel computing system and / or a distributed computing system. In particular, multiple computing devices may act together as a single server 110. Server 110 may have one or more processors 112 and a memory 114. Server 110 may also include a network interface for communicating with one or more computing devices (e.g., user devices) 130 via a network 180.

[0088] The processor 112 may be any suitable processing device, such as a microprocessor, a microcontroller, an integrated circuit, or other suitable processing device. The memory 114 may include any suitable computing system or medium, including but not limited to non-transitory computer-readable media, RAM, ROM, a hard drive, a flash drive, or other memory devices. The memory 114 may store information accessible by the processor 112, including instructions 116 that may be executed by the processor 112. The instructions 116 may be any set of instructions that, when executed by the processor 112, cause the processor 112 to provide the desired functionality.

[0089] In particular, the instructions 116 may be executed by the processor 112 to implement token data identification and indexing. The user profile database 120 may be configured to store a plurality of user profiles associated with a plurality of users utilizing one or more user computing systems 130. In some implementations, the user profile database 120 may be configured to facilitate one or more interactions. The facilitation of one or more interactions may involve using a blockchain application programming interface (API) 122 to send data to and receive data from the blockchain computing system 170. For example, the server computing system 110 may utilize the blockchain API 122 to update one or more ledgers 172 of the blockchain computing system 170. The one or more ledgers 172 may be associated with one or more tokens 174. The one or more tokens 174 may include one or more non-fungible tokens, which may include scripts associated with digital resources (e.g., image data, video data, text data, latently encoded data, domain data, audio data, augmented reality asset rendering data, and / or virtual reality asset rendering data). In particular, the script may reference a specific digital resource (e.g., a digital asset) that is offered for sale. Digital resources (e.g., digital assets) may include image data, text data, video data, potentially encoded data, domain names, virtual property, augmented reality assets, virtual reality assets (e.g., virtual reality environments and / or virtual reality objects for interacting in the environments), smart contracts, physical item authentication, etc. In some implementations, one or more ledgers 172 may be associated with a cryptocurrency that may be used to conduct transactions in a physical marketplace and / or a virtual marketplace.

[0090] It should be understood that the term "element" may refer to computer logic for providing desired functionality. Thus, any element, function, and / or instruction may be implemented in hardware, dedicated circuits, firmware, and / or software that controls a general-purpose processor. In one implementation, an element or function is a program code file stored on a storage device, loaded into a memory, and executed by a processor, or may be provided from a computer program product (e.g., computer executable instructions) stored in a tangible computer-readable storage medium (such as RAM, a hard disk, or an optical or magnetic medium).

[0091] The memory 114 may also include data 118 that may be retrieved, manipulated, created, or stored by the processor 112. The data 118 may include search result data, ranking data, image data (e.g., digital maps, satellite images, aerial photographs, street-level photographs, synthetic models, paintings, personal images, portraits, etc.), video data, audio data, text data (e.g., books, articles, blogs, poems, etc.), potentially encoded data, blockchain address data, tables, vector data (e.g., vector representations of roads, plots of land, buildings, etc.), points of interest data (e.g., places such as islands, cities, restaurants, hospitals, parks, hotels, and schools), or other data or related information. As an example, the data 118 may be used to access information and data associated with a specific digital resource (e.g., a specific digital asset), a website, a search result, a blockchain, etc.

[0092] The data 118 may be stored in one or more databases. The one or more databases may be connected to the server 110 via a high bandwidth LAN or WAN, or may also be connected to the server 110 via a network 180. The one or more databases may be split so that they are located at multiple locations.

[0093] Server 110 may exchange data with one or more user computing systems 130 via network 180. Figure 1A 1, two user computing systems 130 are shown, but any number of user computing systems 130 may be connected to the server 110 via the network 180. The user computing system 130 may be any suitable type of computing device, such as a general purpose computer, a special purpose computer, a navigation device, a laptop, a desktop, an integrated circuit, a mobile device, a smart phone, a tablet computer, a wearable computing device, a display coupled with and / or embedded with one or more processors, or other suitable computing devices. In addition, the user computing system 130 may be a plurality of computing devices that are used together to perform operations or computing actions.

[0094] Similar to server 110, user computing system 130 may include processor 132 and memory 134. Memory 134 may store information accessible by processor 132, including instructions and data that may be executed by the processor. As an example, memory 134 may store data 136 and instructions 138.

[0095] Instructions 138 may provide instructions for implementing a browser, non-fungible token purchases, and / or a number of other functions. In particular, a user of user computing system 130 may exchange data with server 110 by using a browser to access a website accessible at a particular URL. The token data identification and indexing of the present disclosure may be provided as an element of a user interface of a website and / or application.

[0096] Data 136 may include data related to running a specialized application on user computing system 130. In particular, the specialized application may be used to exchange data with server 110 via network 160. Data 136 may include user device readable code for providing and implementing various aspects of the present disclosure. Additionally and / or alternatively, data 136 may include data related to previously input or received data. For example, data 136 may include data related to past occurrences of a specialized application.

[0097] The user computing system 130 may include various user input devices for receiving information from the user, such as a touch screen, a touch pad, data input keys, a speaker, a mouse, a motion sensor, and / or a microphone suitable for speech recognition. In addition, the user computing system 130 may have a display for presenting information (such as a user interface), displaying digital resources, displaying pop-up windows or application elements displayed in an interface, and / or other forms of information.

[0098] The user computing system 130 may also include a user profile 140 that may be used to identify a user of the user computing system 130. The user profile 140 may optionally be used by the user to conduct one or more transactions, which may then be recorded on one or more ledgers 172 of the blockchain computing system 170. The user profile 140 may describe user information, which may include an identification number and / or payment account information. For example, the user profile 140 may include data associated with a crypto wallet, which may be linked to a browser application via an application extension and / or embedding.

[0099] The user computing system 130 may also include a graphics processing unit. The graphics processing unit may be used by the processor 132 for token data indexing. In some embodiments, the user computing system 130 performs any and all token data identification and indexing.

[0100] User computing system 130 may include a network interface for communicating with server 110 over network 180. Network interface may include any component or configuration suitable for communicating with server 110 over network 180, including, for example, one or more ports, transmitters, wireless cards, controllers, physical layer components, or other items for communicating according to any currently known or later developed communication protocol or technology.

[0101] The network 180 may be any type of communication network, such as a local area network (e.g., an intranet), a wide area network (e.g., the Internet), or some combination thereof. The network 180 may also include a direct connection between the client device 130 and the server 110. In general, the communication between the server 110 and the client device 130 may be conducted via any type of wired and / or wireless connection using a variety of communication protocols (e.g., TCP / IP, HTTP), encodings or formats (e.g., HTML, XML), and / or protection schemes (e.g., VPN, secure HTTP, SSL).

[0102] In some implementations, the exemplary computing system 100 may include one or more creator computing systems 150. The one or more creator computing systems 150 may be used to generate images, videos, prose, poetry, audio, etc., which may then be offered for sale. The one or more creator computing systems 150 may include one or more processors 152, which may be used to perform one or more operations to implement the systems and methods disclosed herein. The one or more creator computing systems 150 may include one or more memory components 154, which may be used to store data 156 and one or more instructions 158. The data 156 may include data related to one or more applications, one or more media data sets, etc. The instructions 158 may include one or more operations for implementing the systems and methods disclosed herein.

[0103] One or more creator computing systems 150 may store data associated with one or more digital resources 160 and / or one or more creator profiles 162. One or more digital resources 160 may include text data, image data, video data, audio data, potentially encoded data, domain data, or a variety of other data formats. One or more creator profiles 162 may include information associated with one or more "creators" of one or more digital resources 160. One or more creator profiles 162 may include identification data, transaction data, and / or crypto wallet data.

[0104] Additionally and / or alternatively, the exemplary computing system 100 may include one or more blockchain computing systems 170. The one or more blockchain computing systems 170 may include multiple computing devices for decentralized data storage, such that multiple "blocks" may be distributed throughout a network of computing devices to provide a secure system for data storage, and the blockchain computing system may include one or more ledgers 172 and one or more tokens 174. In some implementations, each of the one or more tokens 174 may be associated with at least a portion of the one or more ledgers 172.

[0105] Blockchain may refer to a system configured to securely record information. Blockchain may include a decentralized system that can make it extremely difficult to change information. Blockchain may include a digital ledger of transactions that can be replicated and distributed on a network of computing systems. Each block in the chain may include multiple transactions. When a new transaction occurs on the blockchain, a record of the transaction may be added to the ledger of each computing device. Blockchain may be used to track the exchange of currency and / or digital resources by recording transactions on a digital ledger, which may be propagated throughout the decentralized system. Currencies exchanged and tracked via the blockchain computing system 170 may be referred to as cryptocurrencies.

[0106] Tokens 174 may include one or more non-fungible tokens. Non-fungible tokens may be minted on a blockchain associated with the blockchain computing system 170. Non-fungible tokens (NFTs) may be certificates of authenticity for digital resources (e.g., certificates of authenticity for digital assets). NFTs may not be interchangeable, thus making their value dependent on the price anyone may be willing to pay for the asset. NFTs may be minted on a blockchain so that their scarcity and authenticity may be maintained. Digital resources may be defined as anything that is stored digitally and may be uniquely identifiable, which an organization may use to realize value. Examples of digital resources may include tweets, social media comments, documents, audio, images, videos, logos, website domains, slide presentations, spreadsheets, CSS files and formats, executable code, and / or websites.

[0107] Figure 1B Describes the Figure 1ABlock diagram of an example blockchain 50 utilized by a blockchain computing system 170 of an exemplary computing system 100 of FIG. The example blockchain 50 may include a plurality of blocks that may be used to store data having one or more cryptographic features. The blockchain 50 may be stored on a decentralized computing system including a plurality of computing devices. The blockchain 50 may be a public blockchain (e.g., an open blockchain with no access restrictions, such that anyone with access to the Internet may send transactions or verify transactions as part of a decentralized distributed system), a private blockchain (e.g., a blockchain that provides access based on permissions set by a network administrator), or a hybrid blockchain (e.g., a blockchain with a combination of unrestricted blocks and restricted blocks). The blockchain 50 may include a proof of work feature, which may include one or more forms of cryptographic proof. Proof of work may be provided in response to a request to update the blockchain 50 (e.g., a request to update a ledger based on a new transaction). Proof of work may convey that a particular device or a group of devices has performed a particular amount of computation, which may then be verified by other parties. Once verified, the blockchain 50 may be updated, or may remain unchanged in response to a verification failure. The proof-of-work feature may be used to mitigate the computational cost of each device in the system having to perform the same computational functions and checks to determine that a request to update the blockchain 50 is valid.

[0108] Each block may include a hash, a previous hash associated with the hash of the previous block, and data. In some implementations, each block may include a random number. The hash may be a fixed-length hash value that may be a fingerprint of a particular block. The hash value may be generated based on a hash function and may change each time the data of the particular block is changed. The previous hash may include the hash value of the block immediately preceding the particular block. The previous hash may be used to ensure that the downstream benchmark remains unchanged unless appropriate verification occurs. The data may include transaction data (e.g., a transaction ledger), a timestamp, a value associated with a cryptocurrency value, a non-fungible token (e.g., a non-fungible token including a script that references a digital resource, random number data, and / or general blockchain data). A random number (i.e., a number that is used only once) may be a number added to a block in a blockchain that may satisfy a difficulty level limit when the block is rehashed. A random number may be a number that a blockchain miner is solving in order to obtain an incentive (e.g., a cryptocurrency).

[0109] The blockchain 50 may include one or more security protocols and / or features. The blockchain 50 may include a cryptographic system. For example, the blockchain 50 may verify that the blockchain 50 is valid by ensuring that the stored previous hash stored in the block matches the hash of the previous block from the last block back to the first block (e.g., the genesis block). In some implementations, the blockchain 50 may include proof-of-work verification, which may rely on verifying a computational proof before implementing changes to stored data (e.g., a stored ledger). Based in part on the number of blocks in the blockchain 50, proof-of-work verification may take seconds, minutes, and / or hours. Additionally and / or alternatively, the blockchain 50 may be implemented on a distributed decentralized computing system. In some implementations, each computing device in the distributed decentralized computing system may store a portion (e.g., one of the multiple blocks) or all of the blocks in the blockchain 50. Therefore, the system can verify the data by ensuring that the data is uniform across most, if not all, of the distributed system. Before adding new data, each node of the distributed system may be checked for tampering.

[0110] The data may include data associated with a cryptocurrency value (e.g., a ledger associated with a specific cryptocurrency value), data associated with a digital resource (e.g., a non-fungible token minted on the blockchain 50 that may include a script associated with a digital asset), data associated with a smart contract (e.g., a smart contract that includes conditions that automatically initiate actions in response to criteria being met), and / or timestamp data (e.g., timestamp data for block creation, minting, transactions, etc.).

[0111] In particular, Figure 1B A first block 10, a second block 20, a third block 30, a fourth block 40, and an nth block 60 are depicted. Although five blocks are depicted, any number of blocks may be utilized. The first block 10 may be a genesis block (e.g., the first total block in a blockchain). The first block 10 may include a corresponding first hash 12 (e.g., a hash value associated with the first block 10). The first block 10 may include a first previous hash 14 (e.g., if there is a block before the first block 10 in the blockchain 50, the hash of the previous block may be stored on the first block 10). Additionally and / or alternatively, the first block 10 may include data 16 and a random number 18.

[0112] The second block 20 may follow the first block 10. The second block 20 may include a corresponding second hash 22 (e.g., a hash value associated with the second block 20). The second block 20 may include a second previous hash 24 (e.g., the second previous hash 24 may be the same as or reference the first hash 12). Additionally and / or alternatively, the second block 20 may include data 26 and a random number 28.

[0113] The third block 30 may follow the second block 20. The third block 30 may include a corresponding third hash 32 (e.g., a hash value associated with the third block 30). The third block 30 may include a third previous hash 34 (e.g., the third previous hash 34 may be the same as or reference the second hash 22). Additionally and / or alternatively, the third block 30 may include data 36 and a random number 38.

[0114] Additionally and / or alternatively, the fourth block 40, the nth block 60, and other potential blocks may include corresponding hashes, corresponding previous hashes, and data. The first data 16, the second data 26, the third data 36, ​​and the data of the other blocks may include overlapping data, which may be different and / or the same, such that the data is repeated for all blocks. In some implementations, each block may be associated with a different transaction (e.g., a different mint, a different sale, etc.). The first random number 18, the second random number 28, the third random number 38, and the random numbers of the other blocks may be different and may be solved during mining.

[0115] The data in each block may include ledger data that may include a timestamp, the assets and / or cryptocurrencies exchanged, the actors involved in the transaction, and / or a variety of other information.

[0116] In some implementations, multiple different blockchains can be used for the systems and methods disclosed herein. Different blockchains can include different configurations. Different blockchains can include parallel chains, side chains, shared blocks, different chains, different permissions, different purposes, different numbers of blocks and / or different hash functions and / or different hash value lengths.

[0117] In some implementations, the systems and methods may include one or more machine-learned model computation systems 900. One or more machine-learned models may be used for a variety of tasks to achieve token data identification and indexing.

[0118] Fig.9AA block diagram of an example computing system 900 that performs token data identification and indexing according to an example embodiment of the present disclosure is depicted. The system 900 includes a user computing device 902, a server computing system 930, and a training computing system 950 that are communicatively coupled via a network 980.

[0119] 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.

[0120] The user computing device 902 includes one or more processors 912 and memory 914. The one or more processors 912 can be any suitable processing device (e.g., a processor core, a microprocessor, an ASIC, an FPGA, a controller, a microcontroller, etc.) and can be one processor or multiple processors operatively connected. The memory 914 can include one or more non-transitory computer-readable storage media, such as RAM, ROM, EEPROM, EPROM, flash memory devices, disks, etc. and combinations thereof. The memory 914 can store data 916 and instructions 918, which are executed by the processor 912 to cause the user computing device 902 to perform operations.

[0121] In some implementations, the user computing device 902 may store or include one or more token indexing models 920. For example, the token indexing model 920 may be or may otherwise include various machine-learned models, such as a neural network (e.g., a deep neural network) or other types of machine-learned models, including nonlinear models and / or linear models. The neural network may include a feedforward neural network, a recurrent neural network (e.g., a long short-term memory recurrent neural network), a convolutional neural network, or other forms of neural networks. Reference Figure 3 An example token indexing model 920 is discussed.

[0122] In some implementations, one or more token indexing models 920 may be received from a server computing system 930 via a network 980, stored in a user computing device memory 914, and then used or otherwise implemented by one or more processors 912. In some implementations, a user computing device 902 may implement multiple parallel instances of a single token indexing model 920 (e.g., to perform parallel token data indexing across multiple instances of token data describing non-fungible tokens).

[0123] More specifically, the token indexing model may include one or more detection models, one or more segmentation models, one or more classification models, and / or one or more feature extractor models. The token indexing model may process blockchain data and / or web page data to generate index data describing index information associated with one or more corresponding non-fungible tokens.

[0124] Additionally or alternatively, one or more token indexing models 940 may be included in or otherwise stored and implemented by a server computing system 930 that communicates with the user computing device 902 according to a client-server relationship. For example, the token indexing models 940 may 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 may be stored and implemented at the user computing device 902, and / or one or more models 940 may be stored and implemented at the server computing system 930.

[0125] The user computing device 902 may also include one or more user input components 922 that receive user input. For example, the user input component 922 may be a touch-sensitive component (e.g., a touch-sensitive display screen or a touchpad) that is sensitive to the touch of a user input object (e.g., a finger or a stylus). The touch-sensitive component may be used to implement a virtual keyboard. Other example user input components include a microphone, a traditional keyboard, or other means by which a user can provide user input.

[0126] The server computing system 930 includes one or more processors 932 and memory 934. The one or more processors 932 may be any suitable processing device (e.g., a processor core, a microprocessor, an ASIC, an FPGA, a controller, a microcontroller, etc.) and may be one processor or a plurality of processors operatively connected. The memory 934 may include one or more non-transitory computer-readable storage media, such as RAM, ROM, EEPROM, EPROM, flash memory devices, disks, etc., and combinations thereof. The memory 934 may store data 936 and instructions 938 that are executed by the processor 932 to cause the server computing system 930 to perform operations.

[0127] In some implementations, the server computing system 930 includes or is otherwise implemented by one or more server computing devices. Where the server computing system 930 includes multiple server computing devices, such server computing devices may operate according to a sequential computing architecture, a parallel computing architecture, or some combination thereof.

[0128] As described above, the server computing system 930 may store or otherwise include one or more machine-learned token indexing models 940. For example, the model 940 may be or may otherwise include a variety of machine-learned models. Example machine-learned models include neural networks or other multi-layer nonlinear models. Example neural networks include feedforward neural networks, deep neural networks, recurrent neural networks, and convolutional neural networks. Figure 3 Example model 940 is discussed.

[0129] User computing device 902 and / or server computing system 930 may train models 920 and / or 940 via interaction with training computing system 950 communicatively coupled via network 980. Training computing system 950 may be separate from server computing system 930 or may be part of server computing system 930.

[0130] The training computing system 950 includes one or more processors 952 and a memory 954. The one or more processors 952 may be any suitable processing device (e.g., a processor core, a microprocessor, an ASIC, an FPGA, a controller, a microcontroller, etc.) and may be one processor or a plurality of processors operatively connected. The memory 954 may include one or more non-transitory computer-readable storage media, such as RAM, ROM, EEPROM, EPROM, flash memory devices, disks, etc., and combinations thereof. The memory 954 may store data 956 and instructions 958, which are executed by the processor 952 to cause the training computing system 950 to perform operations. In some implementations, the training computing system 950 includes or is otherwise implemented by one or more server computing devices.

[0131] The training computing system 950 may include a model trainer 960 that trains the machine-learned models 920 and / or 940 stored at the user computing device 902 and / or the server computing system 930 using various training or learning techniques (e.g., such as error back propagation). For example, a loss function may be back propagated through the model to update one or more parameters of the model (e.g., based on the gradient of the loss function). Various loss functions may be used, such as mean squared error, likelihood loss, cross entropy loss, hinge loss, and / or various other loss functions. Gradient descent techniques may be used to iteratively update parameters over multiple training iterations.

[0132] In some implementations, performing error back propagation may include performing truncated back propagation through time. The model trainer 960 may perform a variety of generalization techniques (eg, weight decay, dropout, etc.) to improve the generalization ability of the model being trained.

[0133] In particular, the model trainer 960 can train the token indexing model 920 and / or 940 based on a set of training data 962. The training data 962 can include, for example, training blockchain data, training web page data, ground truth labels, ground truth index information, and / or ground truth segmentation masks.

[0134] In some implementations, if the user has provided consent, the training examples may be provided by the user computing device 902. Thus, in such implementations, the model 920 provided to the user computing device 902 may be trained by the training computing system 950 based on user-specific data received from the user computing device 902. In some cases, this process may be referred to as personalizing the model.

[0135] The model trainer 960 includes computer logic for providing the desired functionality. The model trainer 960 can be implemented in hardware, firmware, and / or software that controls a general purpose processor. For example, in some implementations, the model trainer 960 includes a program file stored on a storage device, loaded into a memory, and executed by one or more processors. In other implementations, the model trainer 960 includes one or more sets of computer executable instructions stored in a tangible computer readable storage medium such as RAM, a hard disk, or an optical or magnetic medium.

[0136] The network 980 can be any type of communication network, such as a local area network (e.g., an intranet), a wide area network (e.g., the Internet), or some combination thereof, and can include any number of wired or wireless links. In general, communications over the network 980 can be conducted via any type of wired and / or wireless connection using a wide variety of communication protocols (e.g., TCP / IP, HTTP, SMTP, FTP), encodings or formats (e.g., HTML, XML), and / or protection schemes (e.g., VPN, secure HTTP, SSL).

[0137] The machine-learned models described in this specification can be used for a variety of tasks, applications, and / or use cases.

[0138] In some implementations, the input of the machine-learned model of the present disclosure may be image data. The machine-learned model may process the image data to generate an output. As an example, the machine-learned model may process the image data to generate an image recognition output (e.g., recognition of image data, potential embedding of image data, encoded representation of image data, hash of image data, etc.). As another example, the machine-learned model may process the image data to generate an image segmentation output. As another example, the machine-learned model may process the image data to generate an image classification output. As another example, the machine-learned model may process the image data to generate an image data modification output (e.g., a change in image data, etc.). As another example, the machine-learned model may process the image data to generate an encoded image data output (e.g., an encoded representation and / or a compressed representation of image data, etc.). As another example, the machine-learned model may process the image data to generate a prediction output.

[0139] In some implementations, the input of the machine-learned model of the present disclosure may be text or natural language data. The machine-learned model may process text or natural language data to generate an output. As an example, the machine-learned model may process natural language data to generate a language encoding output. As another example, the machine-learned model may process text or natural language data to generate a potential text embedding output. As another example, the machine-learned model may process text or natural language data to generate a classification output. As another example, the machine-learned model may process text or natural language data to generate a text segmentation output. As another example, the machine-learned model may process text or natural language data to generate a semantic intent output. As another example, the machine-learned model may process text or natural language data to generate an amplified text or natural language output (e.g., text or natural language data with higher quality than the input text or natural language, etc.). As another example, the machine-learned model may process text or natural language data to generate a predicted output.

[0140] In some implementations, the input of the machine-learned model of the present disclosure may be speech data. The machine-learned model may process speech data to generate an output. As an example, the machine-learned model may process speech data to generate a speech recognition output. As another example, the machine-learned model may process speech data to generate a speech translation output. As another example, the machine-learned model may process speech data to generate a potential embedding output. As another example, the machine-learned model may process speech data to generate an encoded speech output (e.g., an encoded representation and / or a compressed representation of speech data, etc.). As another example, the machine-learned model may process speech data to generate a text representation output (e.g., a text representation of input speech data, etc.). As another example, the machine-learned model may process speech data to generate a predicted output.

[0141] In some implementations, the input of the machine-learned model of the present disclosure may be potentially coded data (e.g., a latent space representation of an input, etc.). The machine-learned model may process the potentially coded data to generate an output. As an example, the machine-learned model may process the potentially coded data to generate a recognition output. As another example, the machine-learned model may process the potentially coded data to generate a reconstruction output. As another example, the machine-learned model may process the potentially coded data to generate a search output. As another example, the machine-learned model may process the potentially coded data to generate a re-clustering output. As another example, the machine-learned model may process the potentially coded data to generate a prediction output.

[0142] In some implementations, the input of the machine-learned model of the present disclosure may be statistical data. The machine-learned model may process statistical data to generate an output. As an example, the machine-learned model may process statistical data to generate an identification output. As another example, the machine-learned model may process statistical data to generate a prediction output. As another example, the machine-learned model may process statistical data to generate a classification output. As another example, the machine-learned model may process statistical data to generate a segmentation output. As another example, the machine-learned model may process statistical data to generate a segmentation output. As another example, the machine-learned model may process statistical data to generate a visualization output. As another example, the machine-learned model may process statistical data to generate a diagnostic output.

[0143] In some cases, the machine-learned model can be configured to perform a task that includes encoding input data for reliable and / or efficient transmission or storage (and / or corresponding decoding). For example, the task can be an audio compression task. The input can include audio data, and the output can include compressed audio data. In another example, the input includes visual data (e.g., one or more images or videos), the output includes compressed visual data, and the task is a visual data compression task. In another example, the task can include generating an embedding for the input data (e.g., input audio or visual data).

[0144] In some cases, the input includes visual data, and the task is a computer vision task. In some cases, the input includes pixel data of one or more images, and the task is an image processing task. For example, the image processing task may be image classification, where the output is a set of scores, each score corresponding to a different object class and representing the possibility that one or more images depict an object belonging to an object class. The image processing task may be object detection, where the image processing output identifies one or more regions in one or more images, and for each region, identifies the possibility that the region depicts an object of interest. As another example, the image processing task may be image segmentation, where the image processing output defines, for each pixel in one or more images, a corresponding possibility for each category in a predetermined set of categories. For example, the set of categories may be foreground and background. As another example, the set of categories may be an object class. As another example, the image processing task may be depth estimation, where the image processing output defines a corresponding depth value for each pixel in one or more images. As another example, the image processing task may be motion estimation, where the network input includes multiple images, and the image processing output defines, for each pixel in one of the input images, the motion of the scene depicted at the pixel between the images in the network input.

[0145] In some cases, the input includes audio data representing a spoken utterance, and the task is a speech recognition task. The output may include a text output mapped to the spoken utterance. In some cases, the task includes encrypting or decrypting the input data. In some cases, the task includes a microprocessor performance task, such as branch prediction or memory address translation.

[0146] Fig.9AAn example computing system that can be used to implement the present disclosure is shown. Other computing systems can also be used. For example, in some implementations, the user computing device 902 can include a model trainer 960 and a training data set 962. In such implementations, the model 920 can be trained and used locally at the user computing device 902. In some of such implementations, the user computing device 902 can implement the model trainer 960 to personalize the model 920 based on user-specific data.

[0147] Fig. 9B Depicted is a block diagram of an example computing device 970 performing in accordance with an example embodiment of the present disclosure. Computing device 970 may be a user computing device or a server computing device.

[0148] Computing device 970 includes multiple applications (e.g., Application 1 to Application N). Each application contains its own machine learning library and machine-learned model. For example, each application can include a machine-learned model. Example applications include text messaging applications, email applications, dictation applications, virtual keyboard applications, browser applications, etc.

[0149] like Fig. 9B As shown, each application can communicate with multiple other components of the computing device (e.g., such as one or more sensors, a context manager, a device state component, and / or additional components). In some implementations, each application can communicate with each device component using an API (e.g., a public API). In some implementations, the API used by each application is specific to the application.

[0150] Fig. 9C Depicted is a block diagram of an example computing device 990 performing in accordance with an example embodiment of the present disclosure. The computing device 990 may be a user computing device or a server computing device.

[0151] The computing device 990 includes a plurality of applications (e.g., Application 1 to Application N). Each application communicates with a central intelligence layer. Example applications include a text messaging application, an email application, a dictation application, a virtual keyboard application, a browser application, etc. In some implementations, each application can communicate with the central intelligence layer (and the models stored therein) using an API (e.g., a public API across all applications).

[0152] The central intelligence layer includes multiple machine-learned models. For example, Fig. 9CAs shown, a corresponding machine-learned model (e.g., model) can be provided for each application, and the machine-learned model is managed by the central intelligence layer. In other implementations, two or more applications can share a single machine-learned model. For example, in some implementations, the central intelligence layer can provide a single model (e.g., a single model) for all applications. In some implementations, the central intelligence layer is included in the operating system of the computing device 990 or is otherwise implemented by the operating system.

[0153] The central intelligence layer can communicate with the central device data layer. The central device data layer can be a centralized data repository for computing devices 990. Fig. 9C As shown, the central device data layer can communicate with multiple other components of the computing device (e.g., such as one or more sensors, context managers, device state components, and / or additional components). In some implementations, the central device data layer can communicate with each device component using an API (e.g., a private API).

[0154] Example system layout

[0155] Figure 2 A block diagram of an example indexing system 200 according to an example embodiment of the present disclosure is depicted. In some implementations, the indexing system 200 is configured to receive blockchain data 202 describing a blockchain storing data associated with a plurality of non-fungible tokens, and provide output data 220 describing a plurality of index data sets associated with the plurality of non-fungible tokens as a result of receiving the input data 202. Thus, in some implementations, the indexing system 200 may include one or more models and / or one or more functions for identifying token data and extracting relevant information to generate index data.

[0156] In particular, the example indexing system 200 can include obtaining blockchain data 202 associated with a blockchain. The blockchain data 202 can include the code of the blockchain. Additionally and / or alternatively, the blockchain data 202 can include script data associated with a plurality of non-fungible tokens (e.g., data describing scripts that can be deployed to interact with the smart contract code of the blockchain data 202).

[0157] The blockchain data 202 may be processed to identify first token data of a first non-fungible token 204, second token data of a second non-fungible token 206, third token data of a third non-fungible token 208, and nth token data of an nth non-fungible token 210. Identifying the token data sets may involve parsing the blockchain data and determining whether each parsed segment is associated with one or more non-fungible token characteristics. Alternatively and / or additionally, identifying the token data may include crawling the blockchain data to search for specific characteristics, structures, and / or features associated with the token data.

[0158] The first token data associated with the first non-fungible token 204 may be processed to generate first index data 212 associated with the first non-fungible token 204. The second token data associated with the second non-fungible token 206 may be processed to generate second index data 214 associated with the second non-fungible token 206. The third token data associated with the third non-fungible token 208 may be processed to generate third index data 216 associated with the third non-fungible token 208. The nth token data associated with the nth non-fungible token 210 may be processed to generate nth index data 218 associated with the nth non-fungible token 210.

[0159] The first index data 212, the second index data 214, the third index data 216, and the nth index data 218 may be stored in an index database 220 and may then be used for various tasks (e.g., non-fungible token searches). The index data set may include index information associated with a plurality of index term fields. The index data set may include a title of a non-fungible token, a reference to a digital resource payload, a descriptor, and / or a variety of other index terms for annotating and / or characterizing various aspects of a non-fungible token that may be searched.

[0160] Figure 3 A block diagram of an example index data set 300 is depicted according to an example embodiment of the present disclosure. In some implementations, index data 310 may be generated based on blockchain data and / or web page data 326. The blockchain data may be processed to identify a subset of the blockchain data describing token data 302. The token data 302 may be processed to determine one or more index terms for index data 310 generation. Additionally and / or alternatively, web page data 326 associated with the same or related non-fungible token of the non-fungible token associated with the token data 302 may be used to determine one or more additional index terms for one or more index term fields.

[0161] Additionally and / or alternatively, the token data 302 and / or the web page data 326 may be processed with one or more machine-learned models 324 to generate one or more outputs that may be used as index items for the index data 310. Alternatively and / or additionally, the input data for the one or more machine-learned models 324 may be obtained from other data sources. In some implementations, the index data 310 may include data describing a blockchain 312 on which a non-fungible token was minted. The index data 310 may include references 314 (e.g., URIs) to digital resources, time data 316 (e.g., minting time, first digital resource release time, and / or a time difference between two times), transaction data 318 (e.g., purchase time, acquisition amount, acquisition frequency, identity of buyers and bidders, and / or auction event data), metadata 320 (e.g., stored by a blockchain, a marketplace, and / or another data source), other machine-learned data 322, and / or other derived data.

[0162] Figure 4 A block diagram of an example search 400 is depicted according to an example embodiment of the present disclosure. The example search 400 may include receiving a search query 402. The search query 402 may include one or more words, one or more images, and / or one or more other forms of input. The search query 402 may be processed by a search engine 404, which may then access a non-fungible token index database 406 in response to the search query 402 to determine one or more non-fungible token search results 410. Additionally and / or alternatively, the search query 402 may be processed by the search engine 404, which may then access a web database 408 in response to the search query 402 to determine one or more general web results 412. The one or more non-fungible token search results 410 and / or the one or more general web results 412 may be used to generate a search results page 414. The search results page 414 may then be provided for display.

[0163] FIG. 5A to FIG. 5CDifferent example search results page configurations are depicted. Although three configurations are depicted, other variations may be utilized to provide search results for display. The different variations depicted may include a search interface 502, which may include a search query input box 504, one or more non-fungible token search results (e.g., a first non-fungible token search result 510, a second non-fungible token search result 512, a third non-fungible token search result 514, and / or a fourth non-fungible token search result 516), one or more general web results (e.g., a first general web result 520, a second general web result 522, a third general web result 524, and / or a fourth general web result 526), ​​and / or a knowledge panel 506. The search query input box 504 may be configured to receive and / or display one or more search queries. One or more non-fungible token search results and one or more general web results may be determined and provided based on a determined responsiveness to the input search query. Additionally and / or alternatively, the knowledge panel 506 may include structured data associated with a topic determined to be responsive to the input query.

[0164] Figure 5A An illustration of an example search results page 500 is depicted in accordance with an example embodiment of the present disclosure. The example search results page 500 includes a search query input box 504 displayed at the top of a search interface 502 and a knowledge panel 506 displayed in a side panel of the search interface 502. Additionally, the example search results page 500 includes a non-fungible token search results section that displays one or more non-fungible token search results (e.g., a first non-fungible token search result 510, a second non-fungible token search result 512, a third non-fungible token search result 514, and / or a fourth non-fungible token search result 516). The example search results page 500 may include a separate general web results section that may display one or more general web results (e.g., a first general web result 520, a second general web result 522, a third general web result 524, and / or a fourth general web result 526). Additionally and / or alternatively, one or more non-fungible token search results and one or more general web results may be provided in different formats (e.g., non-fungible token search results may be provided with image thumbnails, while general web results may be provided only with text).

[0165] Figure 5BAn illustration of an example search results page 540 according to an example embodiment of the present disclosure is depicted. The example search results page 540 includes a search query input box 504 displayed at the top of the search interface 502 and a knowledge panel 506 displayed in a side panel of the search interface 502. The example search results page 540 may include one or more non-fungible token search results (e.g., a first non-fungible token search result 510, a second non-fungible token search result 512, a third non-fungible token search result 514, and / or a fourth non-fungible token search result 516), and one or more general web results (e.g., a first general web result 520, a second general web result 522, a third general web result 524, and / or a fourth general web result 526) may be displayed in a mixed format such that different types of search results may be adjacent to each other. The order may be determined based on a score determined based on responsiveness to the search query and / or based on other factors. The sorting may be purely based on the score without bias towards the type of result.

[0166] Figure 5C An illustration of an example search results page 580 is depicted, according to an example embodiment of the present disclosure. The example search results page 580 may include one or more non-fungible token search results (e.g., first non-fungible token search result 510, second non-fungible token search result 512, third non-fungible token search result 514, and / or fourth non-fungible token search result 516), and one or more general web results (e.g., first general web result 520, second general web result 522, third general web result 524, and / or fourth general web result 526) may be provided for display with a media content item preview.

[0167] Example Method

[0168] Figure 6 Depicted is a flow chart of an example method performed according to an example embodiment of the present disclosure. Figure 6 The steps performed in a specific order are depicted for the purpose of illustration and discussion, but the method of the present disclosure is not limited to the specific order or arrangement shown. The various steps of method 600 may be omitted, rearranged in various ways, combined and / or adjusted without departing from the scope of the present disclosure.

[0169] At 602, the computing system may obtain blockchain data from the blockchain computing system. The blockchain data may include one or more function signatures. In some implementations, the blockchain data may 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 may include code and / or records stored on the blockchain. The code may include a script and may describe multiple smart contracts. The blockchain data may include transaction data associated with the acquisition of a digital resource and / or the exchange of a digital currency (e.g., a cryptographic currency). The blockchain data may include metadata associated with one or more non-fungible tokens.

[0170] At 604, the computing system may determine that a particular portion of the blockchain data includes token data. The token data may describe a non-fungible token associated with a digital resource. Alternatively and / or additionally, the computing system may determine that a subset of the blockchain data includes token data based on the subset having a structure associated with one or more standards. The token data may describe a non-fungible token associated with a digital resource (e.g., a digital asset). In some implementations, the structure may include a format of a code in the blockchain data, the format of the code in the blockchain data being associated with a standard format for non-fungible token code.

[0171] In some implementations, blockchain data may be parsed, and each parsed segment of the blockchain data may be processed to classify whether the parsed segment includes token data describing a non-fungible token. The classification may be generated using a machine-learned 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, satisfies one or more criteria, and / or describes a smart contract). In some implementations, blockchain data may be parsed using a machine-learned segmentation model that is trained to parse blockchain data based on one or more factors (e.g., syntax, semantics, structure, code length, payload characteristics, code features, potential encoding markers, and / or another machine-learned characteristic).

[0172] In some implementations, determining that the specific portion of the blockchain data includes the token data may include determining that the specific portion of the blockchain data includes the token data based on one or more function signatures. The one or more function signatures may be associated with a non-fungible token standard.

[0173] Alternatively and / or additionally, determining that the particular portion of the blockchain data includes token data may include determining that the particular portion includes a smart contract associated with a digital media item. The digital media item may be a payload of the smart contract. In some implementations, the digital media item may be a digital resource (e.g., a digital asset).

[0174] At 606, the computing system may generate index data based on the token data. The index data may include information obtained from the blockchain data. In some implementations, the index data may be associated with a digital resource (e.g., a digital asset). The index data may include transaction data associated with a non-fungible token. The index data may include data describing metadata associated with the non-fungible token. In some implementations, the index data may include a digital resource type associated with a digital resource (e.g., a digital asset). The digital resource type may be an augmented reality rendering asset type, and the digital resource (e.g., a digital asset) may be an augmented reality rendering asset. The index data may include whether the URI changes and whether the pixels of the payload change when the URI changes. The index data may include data that may describe factors that may be used to determine whether a non-fungible token involves fraud.

[0175] In some implementations, generating index data based on token data may include: obtaining a digital resource (e.g., a digital asset) associated with a non-fungible token; processing the digital resource (e.g., a digital asset) to determine one or more features in the digital resource (e.g., one or more features in the digital asset); and generating feature data describing the one or more features. The index data may include the feature data.

[0176] In some implementations, generating index data may include determining a digital resource (e.g., a digital asset) associated with a non-fungible token, and processing the non-fungible token with a machine-learned model (e.g., a classification model, a detection model, a feature extractor model, and / or a semantic model) to determine one or more classifications, features, and / or attributes associated with the digital resource (e.g., one or more attributes of the digital asset). The one or more classifications, features, and / or attributes may be included in the index data. The index data may include a name associated with the non-fungible token and / or the digital resource, a name of a creator / publisher of the digital resource, transaction data (e.g., current and / or past owners, purchase price, transaction trends, trends of related non-fungible tokens, gas prices, etc.), a subject of the digital resource, non-fungible token metadata, pixel tags, a non-fungible token description, free-form text associated with the non-fungible token, and / or a popularity associated with the non-fungible token, publisher, or creator.

[0177] The digital resource may include image data, video data, text data, audio data, domains, augmented reality assets, virtual reality experiences, and / or potentially encoded data. In some implementations, the digital resource may include image data, and processing the digital resource may include processing the digital resource (e.g., digital asset) with a machine-learned model to determine one or more image features.

[0178] Alternatively and / or additionally, generating feature data describing the one or more features may include determining one or more feature descriptor terms associated with the one or more features. The feature data may describe the one or more feature descriptor terms.

[0179] In some implementations, the systems and methods may include determining that a web content item is associated with a digital resource (e.g., a digital asset). A release time associated with the web content item may be obtained. The systems and methods may determine a minting time associated with a non-fungible token based on blockchain data. Time difference data may be generated based on the minting time and the release time. In some implementations, the index data may include the time difference data.

[0180] Alternatively and / or additionally, generating index data based on the token data may 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 may include data describing the reference data, the issuer, and the specific blockchain associated with the blockchain data and / or other derived data.

[0181] At 608, the computing system may store the index data in an index database. The index database may be stored on a server computing system. The index database may include multiple index data sets associated with multiple non-fungible tokens. For example, first token data may be identified to generate a first index data set, second token data may be identified to generate a second index data set, and third token data may be identified to generate a third index data set. The first token data may be associated with a first non-fungible token, the second token data may be associated with a second non-fungible token, and the third token data may be associated with a third non-fungible token.

[0182] The index database can be used for a variety of different purposes. For example, the index database can be used as a database for storing non-fungible token data for rapid retrieval upon request or search. The systems and methods may include receiving a request for a digital resource (e.g., a digital asset) from a user computing system and providing the token data to the user computing system.

[0183] Figure 7 Depicted is a flow chart of an example method performed according to an example embodiment of the present disclosure. Figure 7 The steps performed in a specific order are depicted for the purpose of illustration and discussion, but the method of the present disclosure is not limited to the specific order or arrangement shown. The various steps of method 700 may be omitted, rearranged in various ways, combined and / or adjusted without departing from the scope of the present disclosure.

[0184] At 702, the computing system may obtain web page data from a web page. The web page data may describe listing information of non-fungible tokens. In some implementations, the web page data may include data associated with a digital resource (e.g., a digital asset). Additionally and / or alternatively, obtaining the web page data may include generating a snapshot of a web page listing non-fungible tokens for sale. In some implementations, the web page data may describe a leaf page of a digital market website that hosts and / or facilitates the purchase and sale of non-fungible tokens. The web page data may include a snapshot of a web page, which may include image data, text data, and / or potentially encoded data.

[0185] At 704, the computing system may determine that the subset of the web page data includes token data. The token data may describe a non-fungible token associated with a digital resource. Alternatively and / or additionally, the digital marketplace website may be crawled to determine a plurality of leaf pages associated with a plurality of non-fungible tokens, and each leaf page may be processed to generate index data for each of the non-fungible tokens.

[0186] At 706, the computing system may generate index data based on the token data, wherein the index data includes information obtained from the web page data. In some implementations, the index data may include information obtained from the web page data. The index data may be associated with the digital resource. Generating the index data may include processing image data, text data, and / or potentially encoded data of the leaf page to determine data associated with a plurality of index fields to be included in the index data.

[0187] At 708, the computing system may store the index data in an index database. The index database may be stored on a server computing system. In some implementations, the index database may be used to surface non-fungible token search results for a search engine. Additionally and / or alternatively, the index database may be used to determine statistics associated with non-fungible tokens. For example, trending non-fungible tokens of a certain type and / or creator may be determined and then used for non-fungible token rankings in a marketplace, search results page, and / or for notifying users to make informed purchases.

[0188] Figure 8 Depicted is a flow chart of an example method performed according to an example embodiment of the present disclosure. Figure 8The steps performed in a specific order are depicted for the purpose of illustration and discussion, but the method of the present disclosure is not limited to the specific order or arrangement shown. The various steps of method 800 may be omitted, rearranged in various ways, combined and / or adjusted without departing from the scope of the present disclosure.

[0189] At 802, the computing system may obtain blockchain data. For example, the computing system may 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 may obtain web page data (e.g., market data obtained by taking a snapshot of a web page associated with a non-fungible token market). In some implementations, the computing system may obtain both blockchain data and web page data. Additionally and / or alternatively, the computing system may also obtain peer-to-peer network data. The obtained data may be obtained via an application programming interface. In some implementations, the obtained data may be updated at intervals. Updates may occur at set intervals and / or may be obtained at a certain frequency based on transaction trends, digital resource types, specific blockchains, and / or based on costs. Updates may be provided to one or more users via push-based updates, notifications, and / or via other user interface update elements.

[0190] At 804, the computing system may determine that a subset of the blockchain data describes a non-fungible token associated with a digital resource. The determination may be based on a search for a particular structure, a particular term, and / or a particular actor. The determination may be based on a known digital resource creator / publisher, a known digital resource attribute, a known digital resource type, a known digital resource name, a known digital resource description, metadata, and / or tags. The determination may be based on the subset of the obtained data conforming to the EIP.

[0191] At 806, the computing system may generate index data based at least in part on a subset of the blockchain data. In some implementations, the index data may include reference data associated with the digital resource (e.g., script data referencing a URL or URI). The index data may 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 specific blockchain for casting non-fungible tokens), casting time, first release time of the digital resource, digital resource changes, specific markets, and / or tags (e.g., pixel tags). In some implementations, the obtained data may be processed to determine a non-fungible token community associated with a specific non-fungible token, and the non-fungible token community may be indexed in the index data.

[0192] In some implementations, the index data may be generated by processing the obtained data with one or more machine-learned models (e.g., a segmentation model, a detection model, a classification model, and / or a feature extractor model). The transaction history may be processed to determine the price history of the non-fungible token, and the price history may be processed to index the trend data and / or stability data. Additionally and / or alternatively, whether the non-fungible token is lazy minted may be indexed. The index data may include whether the non-fungible token is auctioned.

[0193] At 808, the computing system may store the index data in a search database. The search database may include multiple index data sets associated with multiple non-fungible tokens. In some implementations, multiple non-fungible tokens may be identified by processing data from multiple sources.

[0194] At 810, a computing system may receive a search query from a user computing system and determine that the search query is associated with index data. The search query may include one or more search terms. Alternatively and / or additionally, the search query may include one or more images, audio data, potentially encoded data, and / or multimodal data. The search query may be received and processed by a search engine. The search engine may be configured to crawl a blockchain, a web page, and / or an index database or search a database.

[0195] In some implementations, determining that the search query is associated with the index data can include determining that one or more search terms of the search query describe at least one of the digital resource, the author of the digital resource, or non-fungible token metadata.

[0196] At 812, the computing system may provide search results associated with the digital resource to the user computing system. The search results may include a preview of the digital resource. Alternatively and / or additionally, the search results may include an indicator indicating that the search results are associated with a non-fungible token.

[0197] In some implementations, providing search results associated with a digital resource to a user computing system may 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 may include the search results and the one or more general web results.

[0198] In some implementations, index data generated based on blockchain data and index data generated based on web page data can be coordinated. For example, in the event of a conflict between blockchain data and web page data, the system and method can process the data, determine which data is more trustworthy, determine whether to merge the data, and / or determine whether to delete one or both sets of data.

[0199] Example implementation and usage

[0200] The systems and methods disclosed herein may be used for searching to allow a user to make a conscious decision to invest or not invest in a particular non-fungible token.

[0201] In particular, the systems and methods may be used to make Web3 technologies easily accessible to a significant portion of our users (not just early adopters or Web3-savvy crypto enthusiasts). The way Web3 may work may be very different from the average user's mental model. For example, the systems and methods may focus on simplifying technology, jargon, applications, and / or setup.

[0202] The blockchain computing system may include a decentralized system; however, the systems and methods disclosed herein may be implemented in a decentralized system, a closed ecosystem, and / or a hybrid system including portions performed by a decentralized system and portions performed by a centralized system.

[0203] In some implementations, the systems and methods disclosed herein can be used for identity purposes. For example, the systems and methods disclosed herein can be related to and / or interfaced with a user's crypto wallet to store user identity data to be utilized on Web3. A user can log into a website, share personal data, prove identity, and / or transfer cryptocurrency based in part on identity data.

[0204] Wallet apps can be built into browsers. In Web3, there may be a single mechanism for logging in while keeping the data for yourself.

[0205] The blockchain computing system can store data describing a variety of information, which may include non-fungible token content IDs and / or ownership information of non-fungible tokens. Non-fungible tokens can introduce scarcity and authenticity to digital items.

[0206] The systems and methods disclosed herein can be implemented into search engines to enhance image, video, and audio searches. For example, the systems and methods implemented into search engines can signal to users who owns an image or video on the web and where a digital resource (e.g., a digital asset) is from, can enforce copyright rules for digital resources (e.g., copyright rules for digital assets), can detect whether a digital resource has been tampered with or has trust issues, and / or can enable digital content on the web to be sold or transferred (e.g., a non-fungible token market for images, music, and / or another form of digital asset).

[0207] The systems and methods disclosed herein may include creator tokens. The Web3 model may enable creators to own their own content and have a direct relationship with their followers / subscribers / fans, which may bypass today's platforms. This could be very disruptive to video platforms, but could be an opportunity for search engines, which may not be the creator's primary concern. Creator tokens could be a way for fans to "invest" in their favorite creators, while also enabling creators to create communities around their fans.

[0208] In some implementations, the systems and methods disclosed herein can be used to provide an oracle system (e.g., a system that can provide a service provider system for a smart contract to check whether something is real or has happened).

[0209] The systems and methods disclosed herein may include general information retrieval. The systems and methods may enable searches to index important parts of the blockchain and make them accessible and useful.

[0210] The system and method may include a decentralized autonomous organization that can be used to automate decision making. In some implementations, information about the decentralized autonomous organization of the search engine may be provided, and the information may make decision making more transparent.

[0211] In some implementations, the systems and methods may enable ownership of any digital asset on the web by giving creators and publishers the power to claim ownership of their digital content and set rules for its use in a scalable and high-speed manner. Additionally and / or alternatively, the systems and methods may enable ownership of any digital asset on the web by giving searching users the power to understand the provenance and history of a digital asset while also being able to take action on the digital asset in a manner permitted by the original creator (e.g., purchase a non-fungible token and / or use a non-fungible token as a ticket).

[0212] In some implementations, the systems and methods can simultaneously create rewards (carrots) for creators and publishers to publish non-fungible content on the web, while also giving them the tools to create this content and set the rules for its use.

[0213] For example, rewards could be created to show who owns an image on the web or to show items published by a creator / publisher, copying of “owned” digital assets could be discouraged or at least any revenue generated by them transferred to the original owner, users could be enabled to sell and monetize generated content, and copyright fees could be enabled.

[0214] Additionally and / or alternatively, the systems and methods may include tools for creators / publishers to create non-fungible content. For example, the systems and methods may give creators / publishers easy tools to publish non-fungible content and set rules for content usage, which can be done by both casual creators and sophisticated large-scale players.

[0215] In some implementations, the systems and methods may include a smart contract blockchain that may be open, closed, and / or a hybrid of both.

[0216] The systems and methods can be implemented to implement non-fungible token markets on various digital asset platforms (e.g., image platforms, video hosting platforms, and / or music hosting platforms) and / or on various service platforms (e.g., search engines, social media platforms, and / or blogging platforms). Creators and / or publishers can generate digital assets, mint digital assets, sell digital assets, and / or receive royalties for future sales of digital assets.

[0217] Non-fungible tokens can be used to access private chat groups or blogs within different platforms and communities, can be used to set up avatars that are verified as owned by a specific user, can enable quick communication between creators and owners, and can provide transaction history that can be used for future publishing and purchases by providing insightful recommendations.

[0218] Additional public content

[0219] The technology discussed herein refers to servers, databases, software applications, and other computer-based systems, as well as the actions taken and the information sent to and from such systems. The inherent flexibility of computer-based systems allows for a variety of possible configurations, combinations, and partitioning of tasks and functions between and among components. For example, the processes discussed herein can be implemented using a single device or component or multiple devices or components working in combination. Databases and applications can be implemented on a single system or distributed across multiple systems. Distributed components can operate sequentially or in parallel.

[0220] Although the subject matter has been described in detail with respect to various specific example embodiments of the subject matter, each example is provided by way of explanation, rather than limitation of the present disclosure. Those skilled in the art, after understanding the foregoing, can easily produce changes, modifications, and equivalents of such embodiments. Therefore, the present disclosure does not exclude such modifications, changes, and / or additions to the subject matter that will be readily understood by those of ordinary skill in the art. For example, a feature shown or described as part of one embodiment can be used together with another embodiment to produce yet another embodiment. Therefore, the present disclosure is intended to cover such changes, changes, and equivalents.

Claims

1. A computing system, the system comprising: one or more processors; as well as one or more non-transitory computer-readable media that collectively store instructions that, when executed by the one or more processors, cause the computing system to perform operations comprising: Obtaining blockchain data from a blockchain computing system, wherein the blockchain data includes one or more function signatures; determining that the specific portion of the blockchain data includes token data, wherein the token data describes a non-fungible token associated with a digital resource; generating index data based on the token data, wherein the index data includes information obtained from the blockchain data, and wherein the index data is associated with the digital resource; and The index data is stored in an index database.

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

3. The system of any preceding claim, wherein generating index data based on the token data comprises: Obtaining the digital resource associated with the non-fungible token; processing the digital resource to determine one or more characteristics of the digital resource; generating feature data describing the one or more features; and The index data includes the feature data.

4. The system of claim 3, wherein the digital resource comprises image data, and wherein processing the digital resource comprises: The digital resource is processed using a machine-learned model to determine one or more image features.

5. The system of claim 3, wherein generating the feature data describing the one or more features comprises: determining one or more feature descriptor terms associated with the one or more features; and The feature data describes the one or more feature descriptor terms.

6. The system of any preceding claim, wherein the operations further comprise: receiving a request for the digital resource from a user computing system; as well as The token data is provided to the user computing system.

7. The system of any preceding claim, wherein determining that the particular portion of the blockchain data includes the token data comprises: Determining the particular portion includes a smart contract associated with the digital media item.

8. The system of claim 7, wherein the digital media item is a payload of the smart contract, and wherein the digital media item is the digital asset.

9. A system as claimed in any preceding claim, wherein the index data includes transaction data associated with the non-fungible token.

10. A system as claimed in any preceding claim, wherein the index data comprises data describing metadata associated with the non-fungible token.

11. A computer-implemented method, the method comprising: Obtaining, by a computing system including one or more processors, blockchain data from a blockchain computing system, wherein the blockchain data includes a script associated with a digital resource; determining, by the computing system, that the subset of the blockchain data includes token data based on the subset having a structure associated with one or more criteria, wherein the token data describes a non-fungible token associated with the digital resource; generating, by the computing system, index data based on the token data, wherein the index data includes information obtained from the blockchain data, and wherein the index data is associated with the digital resource; and The index data is stored in an index database by the computing system.

12. The method of any preceding claim, further comprising: determining, by the computing system, that a web content item is associated with the digital resource; obtaining, by the computing system, a publishing time associated with the web content item; determining, by the computing system, a minting time associated with the non-fungible token based on the blockchain data; generating, by the computing system, time difference data based on the casting time and the release time; and The index data includes the time difference data.

13. A method as claimed in any preceding claim, wherein the structure comprises a format of code in the blockchain data, the format of the code in the blockchain data being associated with a standard format for non-fungible token code.

14. The method of any preceding claim, wherein generating, by the computing system, the index data based on the token data comprises: 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; and The index data includes data describing the reference data, the publisher, and a specific blockchain associated with the blockchain data.

15. A method as claimed in any preceding claim, wherein the index data comprises a digital asset type associated with the digital asset, wherein the digital asset type is an augmented reality rendering asset type, and wherein the digital asset is an augmented reality rendering asset.

16. One or more non-transitory computer-readable media collectively storing instructions that, when executed by one or more computing devices, cause the one or more computing devices to perform operations comprising: Obtain 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 blockchain data, wherein the index data includes 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 index data; as well as Search results associated with the digital resource are provided to the user computing system.

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

18. One or more non-transitory computer-readable media as described in any preceding claim, wherein the search result includes an indicator indicating that the search result is associated with a non-fungible token.

19. The one or more non-transitory computer-readable media of any preceding claim, wherein determining that the search query is associated with index data comprises: Determining that one or more search terms of the search query describe at least one of the digital resource, an author of the digital resource, or non-fungible token metadata.

20. The one or more non-transitory computer-readable media of any preceding claim, wherein providing the search results associated with the digital resource to the user computing system comprises: determining one or more web pages associated with the search query; generating one or more general web results based on the one or more web pages; as well as A search results page is provided to the user computing system, wherein the search results page includes the search results and one or more general web results.

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