Generating a comprehensive non-fungible token search index

Abstract

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

Description

Technical Field

[0005] ,

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

Background Art

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

[0003] However, it can be difficult to search for and identify non-fungible token search results. Searching for non-fungible tokens can be limited to finding general web pages associated with various non-fungible tokens, but the search engine may not be able to appropriately identify and verify non-fungible token search results as non-fungible tokens. <​​​​​​​​​One exemplary aspect of this disclosure relates to a computing system. The system may include one or more processors and one or more non-temporary computer-readable media that, when executed by one or more processors, collectively store instructions causing the computing system to perform an action. An action may include retrieving blockchain data from a blockchain computing system. The blockchain data may include one or more function signatures. An action may include determining that a particular portion of the blockchain data includes token data. In some embodiments, the token data may be a description of a non-fungible token associated with a digital resource. An action may include generating index data based on the token data. The index data may include information retrieved from the blockchain data. In some embodiments, the index data may be associated with a digital resource. An action may include storing the index data in an index database.

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

[0007] In some embodiments, the operation may include receiving requests for digital resources from a user computing system and providing token data to the user computing system. Determining that a particular portion of blockchain data contains token data may include determining that a particular portion contains a smart contract associated with a digital media item. In some embodiments, the digital media item may be the payload of a smart contract, and the digital media item may be a digital resource. Index data may include transaction data associated with non-fungible tokens. In some embodiments, index data may include data describing metadata associated with non-fungible tokens.

[0008] Other exemplary embodiments of this disclosure relate to methods implemented on a computer. The method may include retrieving blockchain data from a blockchain computing system by a computing system comprising one or more processors. The blockchain data may include scripts associated with digital resources. The method may include the computing system determining that a subset of the blockchain data includes token data based on a subset having a structure associated with one or more standards. The token data may be descriptions of non-fungible tokens associated with digital resources. The method may include the computing system generating index data based on the token data. The index data may include information retrieved from the blockchain data. In some embodiments, the index data may be associated with digital resources. The method may include the computing system storing the index data in an index database.

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

[0010] In some embodiments, generating index data based on token data by a computing system may include determining reference data associated with a digital resource based on token data and determining the issuer of a non-fungible token based on token data by the computing system. The index data may include reference data, issuer, and data describing a specific blockchain associated with the blockchain data. In some embodiments, 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] Other exemplary aspects of this disclosure relate to one or more non-temporary computer-readable media that, when executed by one or more computing devices, collectively store instructions causing one or more computing devices to perform an action. An action may include retrieving blockchain data. An action may include determining that a subset of blockchain data describes non-fungible tokens associated with digital resources and generating index data based at least in part on the subset of blockchain data. In some embodiments, the index data may include reference data associated with the digital resources. An action may include storing the index data in a search database. In some embodiments, an action may include receiving a search query from a user computing system and determining that the search query is associated with the index data. An action may include providing the user computing system with search results associated with the digital resources.

[0012] In some embodiments, search results may include previews of digital resources. Search results may include indicators that the search results are associated with a non-fungible token. In some embodiments, determining that a search query is associated with index data may include determining that one or more search terms in the search query are descriptions of at least one of the following: a digital resource, an author of a digital resource, or non-fungible token metadata. In some embodiments, providing search results associated with digital resources to a user computing system may include determining that one or more web pages are associated with the search query, generating one or more generic web results based on 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 one or more generic web results.

[0013] Other aspects of this disclosure cover a variety of systems, apparatus, non-temporary computer-readable media, user interfaces, and electronic devices.

[0014] These and other features, aspects and advantages of the various embodiments of this disclosure will be better understood by referring to the embodiments for carrying out the invention and the appended claims below. The appended drawings incorporated herein and constituting part thereof illustrate exemplary embodiments of this disclosure and, together with the embodiments for carrying out the invention, illustrate the relevant principles.

[0015] A detailed description of embodiments intended for those skilled in the art is given herein with reference to the accompanying drawings. [Brief explanation of the drawing]

[0016] [Figure 1A] A block diagram of an exemplary computing system for indexing non-fungible tokens according to an exemplary embodiment of the present disclosure is shown. [Figure 1B] A block diagram of an exemplary computing device for indexing non-fungible tokens according to an exemplary embodiment of the present disclosure is shown. [Figure 2] A block diagram of an exemplary indexing system according to an exemplary embodiment of the present disclosure is shown. [Figure 3] A block diagram of an exemplary index dataset according to an exemplary embodiment of the present disclosure is shown. [Figure 4] A block diagram of an exemplary search according to an exemplary embodiment of the present disclosure is shown. [Figure 5A] A diagram of an exemplary search results page according to an exemplary embodiment of the present disclosure is shown. [Figure 5B] A diagram of an exemplary search results page according to an exemplary embodiment of the present disclosure is shown. [Figure 5C] A diagram of an exemplary search results page according to an exemplary embodiment of the present disclosure is shown. [Figure 6] A flowchart illustrating an exemplary method for indexing non-fungible tokens according to an exemplary embodiment of the present disclosure is shown. [Figure 7] A flowchart illustrating an exemplary method for indexing non-fungible tokens according to an exemplary embodiment of the present disclosure is shown. [Figure 8] A flowchart illustrating an exemplary method for performing a non-fungible token index search according to an exemplary embodiment of the present disclosure is shown. [Figure 9A] A block diagram of an exemplary computing system for indexing non-fungible tokens according to an exemplary embodiment of the present disclosure is shown. [Figure 9B] A block diagram of an exemplary computing device for indexing non-fungible tokens according to an exemplary embodiment of the present disclosure is shown. [Figure 9C] A block diagram of an exemplary computing system for indexing non-fungible tokens according to an exemplary embodiment of the present disclosure is shown.

Best Mode for Carrying Out the Invention

[0017] Reference numerals repeated throughout multiple drawings are intended to identify the same features in various embodiments.

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

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

[0020] The systems and methods disclosed herein can be used to enable a search engine to search for non-fungible tokens. For example, a search query can be received. The search engine can process the search query and determine a number of search results associated with the search query. In some embodiments, one or more search results can be associated with a non-fungible token. A particular search result can be associated with index data. One or more non-fungible token search results can be provided for display within a search results interface (e.g., a search results 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 non-fungible token search results can be provided with one or more indicators that show the properties of the non-fungible token (e.g., one or more labels, flags, and / or tags can be provided). In some embodiments, one or more non-fungible token search results can be provided with a generated preview of a digital resource based on index data.

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

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

[0023] The system and method can retrieve blockchain data (e.g., code from the blockchain) from a blockchain computing system (e.g., a decentralized computing system that stores distributed data). Blockchain data may include one or more function signatures. In some embodiments, blockchain data may include scripts associated with digital resources (e.g., digital assets). Blockchain data may be retrieved via blockchain nodes. Blockchain data may include code and / or records stored on the blockchain. The code may include scripts and may be descriptions of multiple smart contracts. 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). Blockchain data may include metadata associated with one or more non-fungible tokens.

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

[0025] In some embodiments, blockchain data can be parsed, and each parsed segment of the blockchain data can be processed to classify whether the parsed segment contains token data describing a non-fungible token. The classification can be generated by a machine learning classification model, which is trained to classify whether the parsed segment contains data associated with a non-fungible token (e.g., whether the data contains a reference to a payload, whether it meets one or more standards, and / or whether it describes a smart contract). In some embodiments, blockchain data can be parsed by a machine learning segmentation model, which is trained to parsing blockchain data based on one or more factors (e.g., syntax, semantics, structure, code length, payload characteristics, code features, latent coding markers, and / or other machine learning-trained characteristics).

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

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

[0028] Next, index data can be generated based on token data. The index data may include information obtained from blockchain data. In some embodiments, 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 a non-fungible token. In some embodiments, the index data may include a digital resource type (e.g., a digital asset) associated with a 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 the URI (Uniform Resource Identifier) ​​has changed, and if the URI has changed, whether the payload data (e.g., image pixels and / or text of a text string) has changed. Furthermore and / or alternatively, the index data may include information describing the URI change. The index data may include data that may describe factors that can be used to determine whether a non-fungible token is fraudulent.

[0029] In some embodiments, generating index data based on token data may include retrieving 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 within the digital resource (e.g., one or more features within the digital asset), and generating feature data that describes one or more features. The index data may include feature data.

[0030] In some embodiments, generating index data may include determining the digital resources associated with the nonfungible tokens and processing the nonfungible tokens by machine learning models (e.g., classification models, detection models, feature extractor models, and / or semantic models) to determine one or more classifications, features, and / or attributes associated with the digital resources (e.g., attributes associated with digital assets). One or more classifications, features, and / or attributes may be included in the index data. The index data may include names associated with the nonfungible tokens and / or digital resources (e.g., digital assets), names of the creators / issuers of the digital resources (e.g., creators of digital assets and / or minters of nonfungible tokens), transaction data (e.g., current and / or past owners, purchase price, transaction trends, trends of related nonfungible tokens, gas fees, etc.), topics of the digital resources (topics of digital assets), nonfungible token metadata, pixel labels, descriptions of the nonfungible tokens, freeform text associated with the nonfungible tokens, and / or reputation associated with the nonfungible tokens, issuers, or creators.

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

[0032] Alternatively, and / or further, generating feature data that describes one or more features may include determining one or more feature descriptor terms associated with one or more features. The feature data may be descriptions of one or more feature descriptor terms.

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

[0034] Alternatively, and / or further, generating index data based on token data may include determining reference data associated with digital resources (e.g., digital assets) based on token data, and determining the issuer of non-fungible tokens based on token data. The index data may include data describing the specific blockchain associated with the reference data, issuer, and blockchain data.

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

[0036] An index database can be used for several different purposes. For example, an index database can be used as a database for storing non-fungible token data for quick retrieval during requests or searches. The system and method may include receiving requests for digital resources (e.g., requests for digital assets) from a user computing system and providing token data to the user computing system.

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

[0038] Alternatively, and / or further, the systems and methods disclosed herein can obtain webpage data (e.g., marketplace data) from a webpage. The webpage data may be a description of listing information for non-fungible tokens. In some embodiments, the webpage data may include data associated with digital resources. Furthermore, and / or alternatively, obtaining webpage data may include generating a snapshot of a webpage listing non-fungible tokens for sale. In some embodiments, the webpage data may be a description of a leaf page of a digital marketplace website that hosts and / or facilitates the purchase and sale of non-fungible tokens. The marketplace data may include a snapshot of a webpage and may include image data, text data, and / or latent encoded data.

[0039] A specific portion (e.g., a subset) of webpage data can be processed to determine if the webpage data contains token data. Token data may be a description of a non-fungible token associated with a digital resource (e.g., a digital asset). Alternatively, and / or further, a digital marketplace website can be crawled to determine multiple leaf pages associated with multiple non-fungible tokens, and each leaf page can be processed to generate index data for each of the non-fungible tokens.

[0040] Next, index data can be generated based on token data. In some embodiments, the index data may include information obtained from web page data. The index data may be associated with digital resources (e.g., digital assets). Generating index data may involve processing leaf page image data, text data, and / or latent encoded data to determine the data associated with multiple index fields contained in the index data.

[0041] The system and method may include storing index data in an index database. The index database may be stored in a server computing system. In some embodiments, the index database can be used to reveal non-fungible token search results for a search engine. Furthermore, and / or alternatively, the index database can be used to determine statistics associated with non-fungible tokens. For example, trends in non-fungible tokens of a particular type and / or creator can be determined and then used for ranking non-fungible tokens in a marketplace, within search results pages, and / or to inform users to make informed purchases.

[0042] An index database can contain index data from multiple different sources (e.g., multiple different blockchains associated with multiple different blockchain computing systems, and / or multiple different web pages associated with multiple different marketplaces). An index database can be used for multiple different tasks (e.g., searching, statistical generation, and / or model training). For example, a system and method can retrieve blockchain data. The system and method may determine that a subset of the blockchain data is a description of non-fungible tokens associated with digital resources (e.g., digital assets). Index data can be generated based at least partially on a subset of the blockchain data. Index data may include reference data associated with digital resources (e.g., digital assets). In some embodiments, index data can be stored in a search database. The system and method may receive search queries from a user computing system. The system and method may determine that a search query is associated with index data. Search results associated with digital resources (e.g., digital assets) can then be provided to the user computing system.

[0043] The system and method may include acquiring blockchain data (e.g., blockchain data obtained from blockchain nodes associated with a blockchain computing system). Alternatively, and / or further, the system and method may acquire webpage data (e.g., marketplace data obtained by taking snapshots of webpages associated with a non-fungible token marketplace). In some embodiments, the system and method may include acquiring both blockchain data and webpage data. The acquired data may be accessed via an application programming interface. In some embodiments, the acquired data may be updated at intervals. Updates may occur at set intervals and / or at a frequency based on transaction trends, types of digital resources (e.g., types of digital assets), a particular blockchain, and / or cost.

[0044] Next, the system and method may determine that a subset of the retrieved data (e.g., blockchain data and / or web page data) is a description of a non-fungible token associated with a digital resource (e.g., a digital asset). The determination may be based on the search for a specific structure, specific terms, and / or specific actors. This determination may be based on a known digital resource creator / issuer, a known digital resource attribute (e.g., digital asset attribute), a known digital resource type (e.g., image, video, augmented reality rendering asset, and / or domain), a known digital resource name, a known digital resource description, metadata, and / or labels. The determination may be based on the fact that the subset of the retrieved data conforms to the EIP ("Ethereum Improvement Proposals", Ethereum (January 2018), https: / / eips.ethereum.org / EIPS / eip-721). In some embodiments, strict compliance may not be required. For example, the data type may deviate from the specifications of the standard. The functional nature and / or purpose of the data may be determined to be associated with a non-functional token.

[0045] Index data can be generated based at least partially on a subset of retrieved data (e.g., blockchain data and / or web page data). In some embodiments, index data may include reference data associated with the digital resource (e.g., script data referencing a URL (Uniform Resource Locator) or URI). Index data may include the digital resource creator / issuer, digital resource attributes, digital resource type, digital resource name, digital resource description, metadata, payload information, smart contract information, freeform text, transaction data, blockchain information (e.g., information associated with the specific blockchain to which the non-fungible token is minted), mint time, initial issuance time of the digital resource, modification of the digital resource, specific marketplace, and / or labels (e.g., pixel labels). In some embodiments, retrieved data may be processed to determine a non-fungible token community associated with a particular non-fungible token, and the non-fungible token community may be indexed in the index data.

[0046] In some embodiments, index data can be generated by processing data obtained using one or more machine learning models (e.g., segmentation models, detection models, classification models, and / or feature extractor models). Transaction history can be processed to determine the price history of non-fungible tokens, and the price history can be processed to index trend data and / or stability data. Furthermore, and / or alternatively, whether or not a non-fungible token has been lazy-minted may be indexed. Index data may include whether or not a non-fungible token has been auctioned.

[0047] The system and method can then store the index data in a search database. The search database may contain multiple index datasets associated with multiple non-fungible tokens. In some implementations, multiple non-fungible tokens can be identified by processing data from multiple sources.

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

[0049] Search queries can be processed to determine whether they are associated with index data. Search queries may be received and processed by a search engine. A search engine can be configured to crawl blockchains, web pages, and / or index databases or search databases.

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

[0051] Next, search results associated with digital resources (e.g., digital assets) can 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 further, the search results may include an indicator showing that the search result is associated with a non-fungible token.

[0052] In some embodiments, 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 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 one or more general web results.

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

[0054] In some embodiments, index data may be updated at intervals. Updates may occur at set intervals and / or be retrieved at a frequency based on transaction trends, performance data, type of digital resource, specific blockchain, mint time (for example, the older a non-fungible token is, the greater the computational cost associated with crawling, so the update frequency may be lower), and / or be retrieved at a frequency based on cost.

[0055] The index database may include an index item field associated with the digital resource type so as to distinguish between non-fungible tokens associated with augmented reality rendering assets and non-fungible tokens associated with images. In some embodiments, the index data may include the launch, execution, and / or display method of the payload (e.g., digital asset). For example, video player labels, image preview labels, specific augmented reality applications, and / or specific devices may be indexed.

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

[0057] The systems and methods disclosed herein offer several technical effects and advantages. For example, the systems and methods may provide a system and method for indexing data associated with one or more non-fungible tokens. For instance, the systems and methods disclosed herein can generate index data based on identified token data, which can then be used for several different tasks (e.g., retrieval, trend determination, and / or catalog generation).

[0058] Another technical advantage of the systems and methods disclosed herein is their ability to leverage one or more machine learning models to generate labels and descriptors for one or more digital resources. For example, the systems and methods disclosed herein can process digital resources with one or more machine learning models to generate labels and descriptors for one or more digital resources, and then add these labels and descriptors to index data for each nonfungible token. The labels and descriptors can then be used for searching (e.g., keyword searching) and / or correlating nonfungible tokens to classify nonfungible token families and / or communities.

[0059] Other examples of technical effects and benefits relate to improvements in computational efficiency and the functionality of computing systems. For example, the systems and methods disclosed herein can leverage generated index data and significantly reduce the computational power required to navigate through multiple web pages to identify and purchase relevant non-fungible tokens. Furthermore, the systems and methods disclosed herein can reduce the computational power required when a user retrieves data about digital resources (e.g., digital assets) and then attempts to purchase those digital resources (e.g., digital assets). For example, a user retrieving data about digital resources can use generated index data to retrieve the data, which may be stored locally, retrieved more efficiently, and / or cached or partially cached to improve retrieval.

[0060] Exemplary embodiments of this disclosure are discussed in further detail here with reference to the drawings.

[0061] Exemplary devices and systems Figure 1A shows a block diagram of an exemplary computing system 100 for indexing token data according to an exemplary embodiment of the present disclosure. The system 100 includes a user computing system 130, a server computing system 110, a creator computing system 150, and a blockchain computing system 170, all of which are communicably coupled via a network 180.

[0062] The user computing system 130 can be any type of computing device, such as a personal computing device (e.g., a laptop or desktop), a mobile computing device (e.g., a smartphone or tablet), a game console or controller, a wearable computing device, an embedded computing device, or any other type of computing device.

[0063] The user computing system 130 includes one or more processors 132 and a memory 134. The one or more processors 132 may be any suitable processing device (e.g., a processor core, microprocessor, ASIC, FPGA, controller, microcontroller, etc.) and may be one processor or multiple processors connected in an operable manner. The memory 134 may include one or more non-temporary computer-readable storage media, such as RAM, ROM, EEPROM, EPROM, flash memory devices, magnetic disks, and combinations thereof. The memory 134 can store data 136 and instructions 138 executed by the processors 132 to cause the user computing system 130 to perform operations.

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

[0065] 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, microprocessor, ASIC, FPGA, controller, microcontroller, etc.) and may be a single processor or multiple processors connected in an operable manner. The memory 114 may include one or more non-temporary computer-readable storage media, such as RAM, ROM, EEPROM, EPROM, flash memory devices, magnetic disks, and combinations thereof. The memory 114 can store data 118 and instructions 116 executed by the processors 112 to cause the server computing system 110 to perform operations.

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

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

[0068] Network 180 can be any type of communication network, such as a local area network (e.g., an intranet), a wide area network (e.g., the Internet), or any combination thereof, and may include any number of wired or wireless links. Generally, communication over Network 180 can be conducted by any type of wired and / or wireless connection using a wide variety of communication protocols (e.g., TCP / IP, HTTP, SMTP, FTP), encoding or formatting (e.g., HTML, XML), and / or protection schemes (e.g., VPN, Secure HTTP, SSL).

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

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

[0071] Furthermore, and / or alternatively, Figure 1A shows an exemplary computing system 100 that can be used to implement token data indexing according to an aspect of the present disclosure. System 100 has a user-server architecture that includes a server 110 that communicates with one or more user computing systems 130 over a network 180. However, the present disclosure can be implemented using other suitable architectures that may include any number of computing systems communicating over the network 180.

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

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

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

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

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

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

[0078] The server 110 can exchange data with one or more user computing systems 130 via the network 180. Although two user computing systems 130 are shown in Figure 1A, any number of user computing systems 130 can connect to the server 110 via the network 180. The user computing system 130 can be any suitable type of computing device, such as a general-purpose computer, a dedicated computer, a navigation device, a laptop, a desktop, an integrated circuit, a mobile device, a smartphone, a tablet, a wearable computing device, a display with one or more processors combined and / or incorporated, or other suitable computing device. Furthermore, the user computing system 130 can be multiple computing devices that work together to perform operations or computing actions.

[0079] Similar to the server 110, the user computing system 130 may include a processor(s) 132 and memory 134. The memory 134 can store data and information accessible by the processor(s) 132, including instructions that can be executed by the processor(s) 132. For example, the memory 134 may store data 136 and instructions 138.

[0080] Instruction 138 may provide instructions for implementing a browser, purchasing non-fungible tokens, and / or several other functions. Specifically, a user of the user computing system 130 may exchange data with the server 110 by using a browser to visit a website accessible at a specific web address. The identification and indexing of token data of this disclosure may be provided as an element of the user interface of a website and / or application.

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

[0082] The user computing system 130 may include various user input devices for receiving information from the user, such as a touchscreen, touchpad, data entry keys, speaker, mouse, motion sensor, and / or microphone suitable for voice recognition. Furthermore, the user computing system 130 may have a display for presenting information such as a user interface, displaying digital resources, displaying pop-ups or application elements displayed on the interface, and / or for other forms of information.

[0083] The user computing system 130 may also include a user profile 140 that can be used to identify a user of the user computing system 130. The user profile 140 may be optionally used by the user to perform one or more transactions, which may then be recorded in one or more ledgers 172 of the blockchain computing system 170. The user profile 140 may be a description of 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 by application extension and / or embedding.

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

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

[0086] Network 180 can be any type of communication network, such as a local area network (e.g., an intranet), a wide area network (e.g., the Internet), or any combination thereof. Network 180 can also include a direct connection between client devices 130 and server 110. Generally, communication between server 110 and client devices 130 can take place via a network interface using any type of wired and / or wireless connection, employing various communication protocols (e.g., TCP / IP, HTTP), encoding or formatting (e.g., HTML, XML), and / or protection schemes (e.g., VPN, Secure HTTP, SSL).

[0087] In some embodiments, the exemplary computing system 100 may include one or more creator computing systems 150. 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. One or more creator computing systems 150 may include one or more processors 152, which may be used to perform one or more operations for implementing the systems and methods disclosed herein. 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 relating to one or more applications, one or more media datasets, etc. The instructions 158 may include one or more operations for implementing the systems and methods disclosed herein.

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

[0089] Furthermore, and / or alternatively, the exemplary computing system 100 may include one or more blockchain computing systems 170. One or more blockchain computing systems 170 may include multiple computing devices used for decentralized data storage so that multiple “blocks” can be distributed across a network of computing devices to provide a secure system for data storage that may include one or more ledgers 172 and one or more tokens 174. In some embodiments, each of the one or more tokens 174 may be associated with at least one or more ledgers 172.

[0090] A blockchain can refer to a system configured to securely record information. A blockchain can include a decentralized system that makes it extremely difficult to alter information. A blockchain can include a digital ledger of transactions that can be replicated and distributed across a network of computing systems. Each block in the chain can contain several transactions. When a new transaction occurs on the blockchain, a record of that transaction can be added to the ledger of all computing devices. A blockchain can be used to track the exchange of currencies and / or digital resources by recording transactions in a digital ledger that can propagate across a decentralized system. Currencies exchanged and tracked via a blockchain computing system can be called cryptocurrencies.

[0091] Token 174 may contain one or more non-fungible tokens. Non-fungible tokens can be minted on a blockchain associated with the blockchain computing system 170. Non-fungible tokens (NFTs) can be certificates of authenticity for digital resources (e.g., certificates of authenticity for digital assets). Because NFTs may be non-tradable, their value will depend on the price that anyone is willing to pay for the asset. NFTs can be minted on a blockchain so that their scarcity and authenticity can be maintained. Digital resources can be defined as things stored digitally and can be uniquely identified as things that an organization can use to realize value. Examples of digital resources can include tweets, social media comments, documents, audio, images, videos, logos, website domains, slide presentations, spreadsheets, CSS files and formats, executable code, and / or websites.

[0092] Figure 1B shows a block diagram of an exemplary blockchain 50 that may be utilized by the blockchain computing system 170 of the exemplary computing system 100 in Figure 1A. The exemplary blockchain 50 may contain multiple blocks and may be used to store data having one or more cryptographic features. Blockchain 50 may be stored in a decentralized computing system that includes multiple computing devices. Blockchain 50 may be a public blockchain (e.g., an open blockchain with no access restrictions, allowing anyone with internet access to send or validate 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 and restricted blocks). Blockchain 50 may contain proof-of-work features, which may include proofs in one or more cryptographic forms. Proof-of-work may be provided in response to a request to update Blockchain 50 (e.g., a request to update the ledger based on a new transaction). Proof-of-work can indicate that a particular device or group of devices has performed a certain amount of computation, which can then be validated by other parties. Once validated, blockchain 50 can be updated, or it may remain unchanged in response to a validation failure. The proof-of-work function can be used to reduce the computational cost of all devices in the system, as they would need to perform the same computational functions and checks to determine that a request is valid to update blockchain 50.

[0093] Each block may contain a hash, a previous hash associated with the hash of the previous block, and data. In some embodiments, each block may contain a nonce. A hash can be a fixed-length hash value that can be a fingerprint of a particular block. A hash value can be generated based on a hash function and may change whenever changes are made to the data of that particular block. A previous hash may contain the hash value of the block immediately preceding a particular block. Previous hashes can be used to ensure that downstream ground truth remains unchanged unless appropriate validation is performed. Data may include transaction data (e.g., transaction ledger), timestamps, values ​​associated with cryptocurrency value, and non-fungible tokens (e.g., non-fungible tokens containing scripts that reference digital resources, nonce data, and / or general blockchain data). A nonce (i.e., a one-time number) can be a number added to a block in the blockchain so that it can satisfy a difficulty level limit when the block is rehashed. A nonce can be a number that blockchain miners are resolving to receive incentives (e.g., cryptocurrency).

[0094] Blockchain 50 may include one or more security protocols and / or functions. Blockchain 50 may include a cryptographic system. For example, Blockchain 50 can be validated for validity by ensuring that a stored previous hash stored in a block matches the hash value of a previous block, returning from the last block to the first block (e.g., the genesis block). In some embodiments, Blockchain 50 may include proof-of-work validation, which can rely on verifying a proof of computation before implementing changes to the stored data (e.g., the stored ledger). Proof-of-work validation can take seconds, minutes, and / or hours, depending in part on the number of blocks in Blockchain 50. Furthermore, and / or alternatively, Blockchain 50 can be implemented on a distributed, decentralized computing system. In some embodiments, each computing device in the distributed, decentralized computing system may store a portion (e.g., one of several blocks) or all of the blocks in Blockchain 50. Thus, the system can validate the data by ensuring that the data is uniform across, but not all, of the distributed system. Each node in a distributed system can be checked for tampering before adding new data.

[0095] The data may include data associated with cryptocurrency values ​​(e.g., ledgers associated with specific cryptocurrency values), data associated with digital resources (e.g., non-fungible tokens minted on Blockchain 50, which may include scripts associated with digital assets), data associated with smart contracts (e.g., smart contracts containing 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.).

[0096] In particular, Figure 1B shows the first block 10, the second block 20, the third block 30, the fourth block 40, and n blocks 60. Although five blocks are shown, any number of blocks can be used. The first block 10 can be the genesis block (e.g., the first global block in the blockchain). The first block 10 can contain each first hash 12 (e.g., the hash value associated with the first block 10). The first block 10 can contain a first previous hash 14 (e.g., if the first block 10 has a block before it in the blockchain 50, the hash of the previous block can be stored in the first block 10). Furthermore, and / or alternatively, the first block 10 can contain data 16 and a nonce 18.

[0097] A second block 20 may follow the first block 10. The second block 20 may contain each second hash 22 (for example, the hash value associated with the second block 20). The second block 20 may contain a second previous hash 24 (for example, the second previous hash 24 is the same as, or can refer to, the first hash 12). In addition and / or instead, the second block 20 may contain data 26 and a nonce 28.

[0098] A third block 30 may follow a second block 20. A third block 30 may contain each third hash 32 (for example, the hash value associated with the third block 30). A third block 30 may contain a third prior hash 34 (for example, the third prior hash 34 is the same as, or can refer to, the second hash 22). In addition and / or instead, a third block 30 may contain data 36 and a nonce 38.

[0099] Furthermore, and / or alternatively, the fourth block 40, the nth block 60, and other potential blocks may contain their respective hashes, their respective previous hashes, and data. The data of the first block 16, the second block 26, the third block 36, and the data of the other blocks may contain duplicate data, may be different, and / or may be the same so that the data is reproducible to all blocks. In some embodiments, each block may be associated with a different transaction (e.g., different mints, different sales, etc.). The nonce of the first block 18, the second block 28, the third block 38, and the nonce of the other blocks may be different and may be resolved during mining.

[0100] The data within each block may include ledger data, timestamps, exchanged assets and / or cryptocurrencies, actors involved in the transaction, and / or various other information.

[0101] In some embodiments, multiple different blockchains may be used in the systems and methods disclosed herein. Different blockchains may include different configurations. Different blockchains may include parallel chains, side chains, shared blocks, different chains, different permissions, different purposes, different number of blocks, and / or different hash functions and / or different hash value lengths.

[0102] In some embodiments, the system and method may include one or more machine learning model computing systems 900. One or more machine learning models can be used for a variety of tasks to enable identification and indexing of token data.

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

[0104] The user computing device 902 can be any type of computing device, such as a personal computing device (e.g., a laptop or desktop), a mobile computing device (e.g., a smartphone or tablet), a game console or controller, a wearable computing device, an embedded computing device, or any other type of computing device.

[0105] The user computing device 902 includes one or more processors 912 and a memory 914. The one or more processors 912 can be any suitable processing device (e.g., a processor core, microprocessor, ASIC, FPGA, controller, microcontroller, etc.) and can be one processor or multiple processors connected in an operable manner. The memory 914 can include one or more non-temporary computer-readable storage media, such as RAM, ROM, EEPROM, EPROM, flash memory devices, magnetic disks, and combinations thereof. The memory 914 can store data 916 and instructions 918 executed by the processors 912 to cause the user computing device 902 to perform operations.

[0106] In some embodiments, the user computing device 902 may store or include one or more token indexing models 920. For example, the token indexing models 920 may be various machine learning models such as neural networks (e.g., deep neural networks), or other types of machine learning models including nonlinear and / or linear models, or may otherwise include them. The neural networks may include feedforward neural networks, recurrent neural networks (e.g., long-short-term memory recurrent neural networks), convolutional neural networks, or other forms of neural networks. An exemplary token indexing model 920 is illustrated with reference to Figure 3.

[0107] In some embodiments, 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 by one or more processors 912, or otherwise implemented. In some embodiments, a user computing device 902 may implement multiple parallel instances of a single token indexing model 920 (for example, to perform parallel token data indexing across multiple instances of token data describing non-fungible tokens).

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

[0109] In addition, or alternatively, one or more token indexing models 940 may be contained in, or otherwise stored in, a server computing system 930 that communicates with a user computing device 902 according to a client-server relationship, and thereby implemented. For example, a token indexing model 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 in the user computing device 902, and / or one or more models 940 may be stored and implemented in the server computing system 930.

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

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

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

[0113] As described above, the server computing system 930 may store, or otherwise include, one or more machine learning token indexing models 940. For example, the model 940 may be a variety of machine learning models, or may include a variety of machine learning models. Exemplary machine learning models include neural networks or other multilayer nonlinear models. Exemplary neural networks include feedforward neural networks, deep neural networks, recurrent neural networks, and convolutional neural networks. Exemplary model 940 is illustrated with reference to Figure 3.

[0114] The user computing device 902 and / or the server computing system 930 can train models 920 and / or 940 by interacting with a training computing system 950, which is connected to it communicatively via a network 980. The training computing system 950 may be separate from the server computing system 930 or may be part of the server computing system 930.

[0115] The training computing system 950 includes one or more processors 952 and memory 954. The one or more processors 952 may be any suitable processing device (e.g., a processor core, microprocessor, ASIC, FPGA, controller, microcontroller, etc.) and may be a single processor or multiple processors operably connected. The memory 954 may include one or more non-temporary computer-readable storage media, such as RAM, ROM, EEPROM, EPROM, flash memory devices, magnetic disks, and combinations thereof. The memory 954 can store data 956 and instructions 958 executed by the processors 952 to cause the training computing system 950 to perform operations. In some embodiments, the training computing system 950 includes one or more server computing devices, or is otherwise implemented by one or more server computing devices.

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

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

[0118] In particular, the model trainer 960 can train the token indexing models 920 and / or 940 based on the set of training data 962. The training data 962 may include, for example, training blockchain data, training webpage data, Grand Truth labels, Grand Truth index information, and / or Grand Truth segmentation masks.

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

[0120] The model trainer 960 includes computer logic used to provide desired functionality. The model trainer 960 can be implemented in hardware, firmware, and / or software that controls a general-purpose processor. For example, in some embodiments, the model trainer 960 includes a program file stored in a storage device, loaded into memory, and executed by one or more processors. In other embodiments, the model trainer 960 includes one or more sets of computer executable instructions stored in a RAM hard disk or a tangible computer-readable storage medium such as an optical or magnetic medium.

[0121] 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 any combination thereof, and may include any number of wired or wireless links. Generally, communication over Network 980 can be conducted by any type of wired and / or wireless connection using a wide variety of communication protocols (e.g., TCP / IP, HTTP, SMTP, FTP), encoding or formatting (e.g., HTML, XML), and / or protection schemes (e.g., VPN, Secure HTTP, SSL).

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

[0123] In some embodiments, the input to the machine learning model(s) of this disclosure may be image data. The machine learning model(s) may process the image data to produce an output. For example, the machine learning model(s) may process the image data to produce an image recognition output (e.g., recognition of image data, latent embedding of image data, encoded representation of image data, hash of image data, etc.). As another example, the machine learning model(s) may process the image data to produce an image segmentation output. As yet another example, the machine learning model(s) may process the image data to produce an image classification output. As yet another example, the machine learning model(s) may process the image data to produce an image data modification output (e.g., modification of image data, etc.). As yet another example, the machine learning model(s) may process the image data to produce an encoded image data output (e.g., encoded and / or compressed representation of image data, etc.). As yet another example, the machine learning model(s) may process the image data to produce a prediction output.

[0124] In some embodiments, the input to the machine learning model(s) of this disclosure may be text or natural language data. The machine learning model(s) may process the text or natural language data to produce an output. For example, the machine learning model(s) may process natural language data to produce a language coding output. As another example, the machine learning model(s) may process text or natural language data to produce a latent text embedding output. As yet another example, the machine learning model(s) may process text or natural language data to produce a classification output. As yet another example, the machine learning model(s) may process text or natural language data to produce a text segmentation output. As yet another example, the machine learning model(s) may process text or natural language data to produce a semantic intent output. As yet another example, the machine learning model(s) may process text or natural language data to produce an upscaled text or natural language output (e.g., text or natural language data of higher quality than the input text or natural language). As yet another example, the machine learning model(s) may process text or natural language data to produce a predictive output.

[0125] In some embodiments, the input to the machine learning model(s) of this disclosure may be audio data. The machine learning model(s) may process the audio data to produce an output. For example, the machine learning model(s) may process the audio data to produce a speech recognition output. As another example, the machine learning model(s) may process the audio data to produce a speech translation output. As yet another example, the machine learning model(s) may process the audio data to produce a latent embedding output. As yet another example, the machine learning model(s) may process the audio data to produce an encoded audio output (e.g., an encoded and / or compressed representation of the audio data). As yet another example, the machine learning model(s) may process the audio data to produce a text representation output (e.g., a text representation of the input audio data). As yet another example, the machine learning model(s) may process the audio data to produce a prediction output.

[0126] In some embodiments, the input to the machine learning model(s) of this disclosure may be latent coded data (e.g., a latent spatial representation of the input). The machine learning model(s) may process the latent coded data to produce an output. For example, the machine learning model(s) may process the latent coded data to produce a recognition output. As another example, the machine learning model(s) may process the latent coded data to produce a reconstruction output. As yet another example, the machine learning model(s) may process the latent coded data to produce a search output. As yet another example, the machine learning model(s) may process the latent coded data to produce a reclustered output. As yet another example, the machine learning model(s) may process the latent coded data to produce a prediction output.

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

[0128] In some cases, a machine learning model(s) may be configured to perform a task that involves encoding input data for reliable and / or efficient transmission or storage (and / or corresponding decryption). For example, the task may be an audio compression task. The input may contain audio data, and the output may contain compressed audio data. In another example, the input may contain visual data (e.g., one or more images or videos), and the output may contain compressed visual data, and the task is a visual data compression task. In yet another example, the task may involve generating embeddings for input data (e.g., input audio or visual data).

[0129] In some cases, the input includes visual data, and the task is a computer vision task. In other cases, the input includes pixel data from one or more images, and the task is an image processing task. For example, an image processing task could be image classification, with the output being a set of scores, each corresponding to a different object class, representing the likelihood that one or more images depict objects belonging to that object class. An image processing task could be object detection, with the image processing output identifying one or more regions within one or more images, and for each region, the likelihood that the region depicts an object of interest. Another example is image segmentation, with the image processing output defining the likelihood for each category within a given set of categories for each pixel in one or more images. For example, the set of categories could be foreground and background. Another example is that the set of categories could be object classes. Another example is depth estimation, with the image processing output defining the depth value for each pixel in one or more images. As another example, the image processing task could be motion estimation, where the network input includes multiple images, and the image processing output defines the motion of the scene depicted in the pixels between the images in the network input, for each pixel in the input images.

[0130] In some cases, the input may include audio data representing spoken utterances, and the task may be a speech recognition task. The output may include text output mapped to the spoken utterances. In some cases, the task may include encrypting or decrypting the input data. In some cases, the task may include a microprocessor performance task, such as branch prediction or memory address translation.

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

[0132] Figure 9B shows a block diagram of an exemplary computing device 970 that operates according to an exemplary embodiment of the present disclosure. The computing device 970 may be a user computing device or a server computing device.

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

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

[0135] Figure 9C shows a block diagram of an exemplary computing device 990 that operates according to an exemplary embodiment of the present disclosure. The computing device 990 may be a user computing device or a server computing device.

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

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

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

[0139] System Configuration Example Figure 2 shows a block diagram of an exemplary indexing system 200 according to an exemplary embodiment of the present disclosure. In some embodiments, the indexing system 200 is configured to receive blockchain data 202 describing a blockchain that stores data associated with a plurality of non-fungible tokens, and, as a result of receiving the input data 202, to provide output data 220 describing a plurality of index datasets associated with a plurality of non-fungible tokens. Thus, in some embodiments, the indexing system 200 may include one or more models and / or one or more functions to identify token data and extract relevant information for generating index data.

[0140] In particular, the exemplary indexing system 200 may include retrieving blockchain data 202 associated with a blockchain. Blockchain data 202 may include the blockchain code. Furthermore, and / or alternatively, blockchain data 202 may include script data associated with multiple non-fungible tokens (for example, data describing scripts that can be deployed to interact with the smart contract code of blockchain data 202).

[0141] Blockchain data 202 can be processed to identify the first token data of the first nonfungible token 204, the second token data of the second nonfungible token 206, the third token data of the third nonfungible token 208, and the nth token data of the nth nonfungible token 210. Identifying token datasets may involve parsing blockchain data and determining whether each parsed segment is associated with one or more nonfungible token properties. Alternatively, and / or additionally, identifying token data may include crawling blockchain data to find specific properties, structures, and / or features associated with the token data.

[0142] The first token data associated with the first non-fungible token 204 can be processed to generate the first index data 212 associated with the first non-fungible token 204. The second token data associated with the second non-fungible token 206 can be processed to generate the second index data 214 associated with the second non-fungible token 206. The third token data associated with the third non-fungible token 208 can be processed to generate the third index data 216 associated with the third non-fungible token 208. The nth token data associated with the nth non-fungible token 210 can be processed to generate the nth index data 218 associated with the nth non-fungible token 210.

[0143] The first index data 212, the second index data 214, the third index data 216, and the nth index data 218 can be stored in the index database 220 and then used for various tasks (e.g., non-fungible token lookup). An index dataset can contain index information associated with multiple index item fields. An index dataset can contain various other index items to annotate and / or characterize the title of a non-fungible token, a reference to a digital resource payload, a descriptor, and / or the form of the non-fungible token that can be looked up.

[0144] Figure 3 shows a block diagram of an exemplary index dataset 300 according to an exemplary embodiment of the present disclosure. In some embodiments, the index data 310 can be generated based on blockchain data and / or webpage data 326. The blockchain data can be processed to identify a subset of blockchain data describing token data 302. The token data 302 can be processed to determine one or more index items for the generation of the index data 310. Furthermore, and / or alternatively, one or more additional index items can be determined for one or more index item fields by utilizing webpage data 326 associated with non-fungible tokens that are the same as or related to the non-fungible token associated with the token data 302.

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

[0146] Figure 4 shows a block diagram of an exemplary search 400 according to an exemplary embodiment of the present disclosure. The exemplary 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 input formats. The search engine 404 may process the search query 402 and then access the non-fungible token index database 406 to determine one or more non-fungible token search results 410 in response to the search query 402. Alternatively, and / or instead, the search engine 404 may process the search query 402 and then access the web database 408 to determine one or more general web results 412 in response to the search query 402. The one or more non-fungible token search results 410 and / or 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.

[0147] Figures 5A to 5C show different examples of search results page configurations. Three configurations are shown, but other variations may be used to provide search results for display. 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 the responsiveness determined to the input search query. In addition, and / or alternatively, the knowledge panel 506 may include structured data associated with topics determined to respond to the input query.

[0148] Figure 5A shows an exemplary search results page 500 according to an exemplary embodiment of the present disclosure. The exemplary search results page 500 includes a search query input box 504 displayed above the search interface 502, and a knowledge panel 506 is displayed on the side panel of the search interface 502. Furthermore, the exemplary 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 exemplary search results page 500 may include a separate general web results section that can display one or more general web results (e.g., a first general web result 520, a second general web result 522, a third general web result 524, and / or a fourth general web result 526). Furthermore, and / or alternatively, one or more non-fungible token search results and one or more general web results may be presented in different formats (for example, non-fungible token search results may be presented by image thumbnails, while general web results may be displayed as text only).

[0149] Figure 5B shows an exemplary search results page 540 according to an exemplary embodiment of the present disclosure. The exemplary search results page 540 includes a search query input box 504 displayed above the search interface 502, and a knowledge panel 506 is displayed on the side panel of the search interface 502. The exemplary 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 so 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 other factors. The ordering may be based purely on scores that do not have preferences for the type of result.

[0150] Figure 5C shows an exemplary search results page 580 according to an exemplary embodiment of the present disclosure. The exemplary search results page 580 may include both 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 provided for display by media content item previews.

[0151] Exemplary Method Figure 6 shows a flowchart of an exemplary method to be carried out according to an exemplary embodiment of the present disclosure. While Figure 6 shows steps performed in a specific order for illustrative and explanatory purposes, the methods of the present disclosure are not limited to the order or arrangement shown. Various steps of Method 600 can be omitted, rearranged, combined, and / or adapted in various ways without departing from the scope of the present disclosure.

[0152] In 602, a computing system can retrieve blockchain data from a blockchain computing system. Blockchain data may include one or more function signatures. In some embodiments, blockchain data may include scripts associated with digital resources (e.g., scripts associated with digital assets). Blockchain data may be retrieved via blockchain nodes. Blockchain data may include code and / or records stored on the blockchain. The code may include scripts and may be descriptions of multiple smart contracts. Blockchain data may include transaction data associated with the retrieval of digital resources and / or the exchange of digital currencies (e.g., cryptocurrencies). Blockchain data may include metadata associated with one or more non-fungible tokens.

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

[0154] In some embodiments, blockchain data can be parsed, and each parsed segment of the blockchain data can be processed to classify whether the parsed segment contains token data describing a non-fungible token. The classification can be generated by a machine learning classification model, which is trained to classify whether the parsed segment contains data associated with a non-fungible token (e.g., whether the data contains a reference to a payload, whether it meets one or more standards, and / or whether it is a description of a smart contract). In some embodiments, blockchain data can be parsed by a machine learning segmentation model, which is trained to parsing blockchain data based on one or more factors (e.g., syntax, semantics, structure, code length, payload characteristics, code features, latent coding markers, and / or other machine learning-trained characteristics).

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

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

[0157] In 606, a computing system can generate index data based on token data. The index data may include information obtained from blockchain data. In some embodiments, 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 a non-fungible token. In some embodiments, 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 has changed, and if the URI has changed, whether the pixels of the payload have changed. The index data may include data that may describe factors that can be used to determine whether a non-fungible token is fraudulent.

[0158] In some embodiments, generating index data based on token data may include retrieving 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 within the digital resource (e.g., one or more features within a digital asset), and generating feature data that describes one or more features. The index data may include feature data.

[0159] In some embodiments, generating index data may include determining the digital resources (e.g., digital assets) associated with the non-fungible tokens and processing the non-fungible tokens by machine learning models (e.g., classification models, detection models, feature extractor models, and / or semantic models) to determine one or more classifications, features, and / or attributes (e.g., one or more attributes of the digital assets) associated with the digital resources. One or more classifications, features, and / or attributes may be included in the index data. The index data may include the name associated with the non-fungible tokens and / or digital resources, the name of the creator / issuer of the digital resources, transaction data (e.g., current and / or past owners, purchase price, transaction trends, trends of related non-fungible tokens, gas fees, etc.), the topic of the digital resources, non-fungible token metadata, pixel labels, descriptions of the non-fungible tokens, freeform text associated with the non-fungible tokens, and / or bad reputation associated with the non-fungible tokens, issuers, or creators.

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

[0161] Alternatively, and / or further, generating feature data that describes one or more features may include determining one or more feature descriptor terms associated with one or more features. The feature data may be descriptions of one or more feature descriptor terms.

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

[0163] Alternatively, and / or further, generating index data based on token data may include determining reference data associated with digital resources (e.g., digital assets) based on token data, and determining the issuer of non-fungible tokens based on token data. The index data may include data describing a particular blockchain associated with the reference data, issuer, and blockchain data and / or other derived data.

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

[0165] An index database can be used for several different purposes. For example, an index database can be used as a database for storing non-fungible token data for quick retrieval during requests or searches. The system and method may include receiving requests for digital resources (e.g., digital assets) from a user computing system and providing token data to the user computing system.

[0166] Figure 7 shows a flowchart of an exemplary method to be carried out according to an exemplary embodiment of the present disclosure. While Figure 7 shows the steps to be performed in a particular order for illustrative and explanatory purposes, the methods of the present disclosure are not limited to the order or arrangement shown. Various steps of Method 700 may be omitted, rearranged, combined, and / or adapted in various ways without departing from the scope of the present disclosure.

[0167] In 702, a computing system can retrieve webpage data from a webpage. Webpage data may be a description of list information for non-fungible tokens. In some embodiments, webpage data may include data associated with digital resources (e.g., digital assets). Furthermore, and / or alternatively, retrieving webpage data may include generating a snapshot of a webpage listing non-fungible tokens for sale. In some embodiments, webpage data may be a description of a leaf page of a digital marketplace website that hosts and / or facilitates the purchase and sale of non-fungible tokens. Webpage data may include a snapshot of a webpage and may include image data, text data, and / or latent encoded data.

[0168] In 704, a computing system may determine that a subset of webpage data contains token data. Token data may be a description of a non-fungible token associated with a digital resource. Alternatively, and / or further, a digital marketplace website may be crawled to determine multiple leaf pages associated with multiple non-fungible tokens, and each leaf page may be processed to generate index data for each of the non-fungible tokens.

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

[0170] In 708, the computing system can store index data in an index database. The index database may be stored in a server computing system. In some embodiments, the index database can be used to reveal non-fungible token search results for a search engine. Furthermore, and / or alternatively, the index database can be used to determine statistics associated with non-fungible tokens. For example, trends in non-fungible tokens of a particular type and / or creator can be determined and then used to inform users for ranking non-fungible tokens in a marketplace, within search results pages, and / or for informed purchasing.

[0171] Figure 8 shows a flowchart of an exemplary method to be carried out according to an exemplary embodiment of the present disclosure. While Figure 8 shows steps performed in a specific order for illustrative and explanatory purposes, the methods of the present disclosure are not limited to the order or arrangement shown. Various steps of Method 800 can be omitted, rearranged, combined, and / or adapted in various ways without departing from the scope of the present disclosure.

[0172] In 802, the computing system can acquire blockchain data. For example, the computing system may acquire blockchain data (e.g., blockchain data acquired from blockchain nodes associated with the blockchain computing system). Alternatively, and / or further, the computing system may acquire web page data (e.g., marketplace data acquired by taking snapshots of web pages associated with a non-fungible token marketplace). In some embodiments, the computing system may acquire both blockchain data and web page data. In addition, and / or instead, the computing system may further acquire peer-to-peer network data. Acquired data may be accessed via an application programming interface. In some embodiments, the acquired data may be updated at intervals. Updates may occur at set intervals and / or at frequencies based on transaction trends, types of digital resources, a particular blockchain, and / or cost. Updates may be provided to one or more users via push-based updates, notifications, and / or other user interface update elements.

[0173] In 804, a computing system may determine that a subset of blockchain data describes non-fungible tokens associated with digital resources. This determination may be based on a search for specific structures, terms, and / or actors. This determination may be based on known digital resource creators / issuers, known digital resource attributes, known digital resource types, known digital resource names, known digital resource descriptions, metadata, and / or labels. The determination may also be based on the fact that the retrieved subset of data conforms to the EIP.

[0174] In 806, a computing system can generate index data based at least partially on a subset of blockchain data. In some embodiments, the index data may include reference data associated with a digital resource (e.g., script data that references a URL or URI). The index data may include the digital resource creator / issuer, digital resource attributes, digital resource type, digital resource name, digital resource description, metadata, payload information, smart contract information, freeform text, transaction data, blockchain information (e.g., information associated with the particular blockchain to which the non-fungible token is minted), mint time, initial issuance time of the digital resource, modification of the digital resource, specific marketplace, and / or labels (e.g., pixel labels). In some embodiments, the retrieved data may be processed to determine a non-fungible token community associated with a particular non-fungible token, and the non-fungible token community may be indexed in the index data.

[0175] In some embodiments, index data can be generated by processing data obtained using one or more machine learning models (e.g., segmentation models, detection models, classification models, and / or feature extractor models). Transaction history can be processed to determine the price history of non-fungible tokens, and the price history can be processed to index trend data and / or stability data. Furthermore, and / or alternatively, whether or not a non-fungible token has been lazy-minted may be indexed. Index data may include whether or not a non-fungible token has been auctioned.

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

[0177] In 810, the computing system can receive a search query from a user computing system and determine that the search query is associated with index data. The search query may include one or more search terms. Alternatively, and / or further, the search query may include one or more images, audio data, latent encoded data, and / or multimodal data. The search query may also be received and processed by a search engine. The search engine may be configured to crawl blockchains, web pages, and / or index databases, or search databases.

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

[0179] In 812, a computing system may provide a user computing system with search results associated with a digital resource. The search results may include a preview of the digital resource. Alternatively, and / or further, the search results may include an indicator that the search result is associated with a non-fungible token.

[0180] In some embodiments, providing search results associated with digital resources 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 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 one or more general web results.

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

[0182] Exemplary embodiments and uses The systems and methods disclosed herein can be used for searching, enabling users to carefully consider and decide whether or not to invest in a particular non-fungible token.

[0183] In particular, systems and methods can be used to make Web3 technology easily accessible to a significant number of users (and not just early adopters or cryptography enthusiasts familiar with Web3). The way Web3 can function may deviate significantly from the mental model of the average user. For example, systems and methods may focus on simplifying the technology, terminology, applications, and / or setup.

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

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

[0186] Wallet apps can be embedded in browsers. In Web3, there may be a single mechanism for signing in while keeping your own data.

[0187] Blockchain computing systems can store data describing various types of information, including non-fungible token content IDs and / or ownership information of non-fungible tokens. Non-fungible tokens can bring scarcity and authenticity to digital items.

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

[0189] The systems and methods disclosed herein may include creator tokens. The Web3 model allows creators to own their content and have direct relationships with their followers / subscribers / fans, bypassing current platforms. This can be highly disruptive to video platforms but could be an opportunity for search engines, which creators may not have considered. Creator tokens can be a way for fans to "invest" in their favorite creators, while at the same time allowing creators to form communities centered around their fans.

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

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

[0192] The system and method may include a decentralized autonomous organization that can be used to automate decision-making. In some embodiments, information about the decentralized autonomous organization of the search engine may be provided, making decision-making more transparent.

[0193] In some embodiments, the system and method can enable ownership of any digital asset on the web by granting creators and publishers the authority to claim ownership of digital content and to set usage rules in a scalable and rapid manner. Furthermore, and / or alternatively, the system and method can enable ownership of any digital asset on the web by granting search users the authority to understand the origin and history of digital assets while simultaneously taking action on them in a manner permitted by the original creator (e.g., purchasing non-fungible tokens and / or using non-fungible tokens as tickets).

[0194] In some embodiments, the system and method can simultaneously create a reward for creators and publishers as they publish non-fungible content on the web, while also providing them with tools to create this content and set rules for its use.

[0195] For example, compensation can be created to identify the owner of an image on the web or to identify an item issued by a creator / publisher, it can block copies of “owned” digital assets or at least transfer some of the revenue generated therefrom to the original owner, it can enable users to sell and monetize the generated content, and it can enable royalties to be paid.

[0196] Furthermore, and / or alternatively, the system and method may include tools for creators / publishers to create non-fungible content. For example, the system and method may provide creators / publishers with tools that facilitate the publication of non-fungible content and may set content usage rules that can be used by both casual creators and advanced, large-scale players.

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

[0198] The system and methods can be implemented to enable a non-fungible token marketplace across various digital asset platforms (e.g., image platforms, video hosting platforms, and / or music hosting platforms) and / or various service platforms (e.g., search engines, social media platforms, and / or blogging platforms). Creators and / or issuers can generate digital assets, mint digital assets, sell digital assets, and / or receive royalties for future sales of digital assets.

[0199] Non-fungible tokens can be used to access private chat groups or blogs within different platforms and communities, to set up avatars that are verified to be owned by specific users, to enable rapid communication between creators and owners, and to provide a transaction history that can be used for future launches and purchases by offering insightful suggestions.

[0200] Additional disclosures The technologies described herein refer to servers, databases, software applications, and other computer-based systems, as well as actions performed and information transmitted to and from such systems. The inherent flexibility of computer-based systems allows for a wide variety of feasible configurations, combinations, and divisions of tasks and functions between 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.

[0201] The subject matter of this disclosure has been described in detail in various specific and exemplary embodiments, each example provided for illustrative purposes only and not limiting the disclosure. Those skilled in the art, having attained the foregoing understanding, will readily be able to produce modifications, variations, and equivalents to such embodiments. Therefore, the disclosure of the subject matter does not preclude the inclusion of such modifications, variations, and / or additions to the subject matter as would be readily apparent to those skilled in the art. For example, features illustrated or described as part of one embodiment may be used in other embodiments to produce yet another embodiment. Thus, this disclosure is intended to cover such modifications, variations, and equivalents.

Claims

1. A computing system, One or more processors, One or more computer-readable storage media that, when executed by the one or more processors, collectively store instructions that cause the computing system to perform an operation, Includes, The aforementioned operation is, The acquisition of blockchain data from a blockchain computing system, wherein the blockchain data includes one or more function signatures. Determining that a specific portion of the blockchain data includes token data, and that the token data describes a non-fungible token associated with a digital resource, Generating index data based on the token data, comprising: obtaining the digital resource associated with the non-fungible token; processing the digital resource to determine one or more features within the digital resource; and generating feature data describing the one or more features, wherein the index data includes information obtained from the blockchain data and the feature data, and the index data is associated with the digital resource. The aforementioned index data is stored in an index database, A system that includes this.

2. Determining that the aforementioned specific portion of the blockchain data includes the aforementioned token data means that The system according to claim 1, comprising determining that a particular portion of the blockchain data includes token data based on one or more functional signatures, wherein the one or more functional signatures are associated with a non-fungible token standard.

3. The said digital resources include image data, and processing the said digital resources is The system according to claim 1, comprising processing the digital resources by a machine learning model to determine one or more image features.

4. Generating the feature data that describes one or more of the above features is, This includes determining one or more feature descriptor terms associated with the one or more features, The system according to claim 1, wherein the feature data describes one or more feature descriptor terms.

5. The aforementioned operation is, Receiving requests for the aforementioned digital resources from the user computing system, Providing the aforementioned token data to the user computing system, The system according to claim 1, further comprising:

6. Determining that the aforementioned specific portion of the blockchain data includes the aforementioned token data means that The system according to claim 1, comprising determining that the particular portion includes a smart contract associated with a digital media item.

7. The aforementioned digital media item is the payload of the smart contract, The system according to claim 6, wherein the digital media item is the digital resource.

8. The system according to claim 1, wherein the index data includes transaction data associated with the non-fungible token.

9. The system according to claim 1, wherein the index data includes data describing metadata associated with the non-fungible token.

10. A method implemented in a computer, The acquisition of blockchain data from a blockchain computing system by a computing system including one or more processors, wherein the blockchain data includes scripts associated with digital resources. The computing system determines that a subset of the blockchain data includes token data based on the subset having a structure associated with one or more standards, wherein the token data describes non-fungible tokens associated with the digital resources. The computing system generates index data based on the token data, which includes: acquiring the digital resource associated with the non-fungible token; processing the digital resource to determine one or more features within the digital resource; and generating feature data describing one or more features, wherein the index data includes information acquired from the blockchain data and the feature data, and the index data is associated with the digital resource. The computing system stores the index data in an index database, Methods that include...

11. The computing system determines that a web content item is associated with the digital resource, The computing system obtains the publication time associated with the web content item, The computing system determines the mint time associated with the non-fungible token based on the blockchain data, The computing system generates time difference data based on the mint time and the issuance time, It further includes, The method according to claim 10, wherein the index data includes the time difference data.

12. The method according to claim 10, wherein the structure includes a format for the code in the blockchain data associated with a standard format for non-fungible token codes.

13. The computing system generates the index data based on the token data, The computing system determines the reference data associated with the digital resource based on the token data, The computing system determines the issuer of the non-fungible token based on the token data, Includes The method according to claim 10, wherein the index data includes data describing a specific blockchain associated with the reference data, the issuer, and the blockchain data.

14. The index data includes the digital resource type associated with the digital resource, The aforementioned digital resource type is an augmented reality rendering asset type, The method according to claim 10, wherein the digital resource is an augmented reality rendering asset.

15. One or more computer-readable storage media that, when executed by one or more computing devices, collectively store instructions that cause the one or more computing devices to perform an action, The aforementioned operation is, Obtaining blockchain data and The determination that the subset of blockchain data describes non-fungible tokens associated with digital resources, Generating index data based at least partially on a subset of the blockchain data, comprising: acquiring the digital resource; processing the digital resource to determine one or more features within the digital resource; and generating feature data describing the one or more features, wherein the index data includes reference data and feature data associated with the digital resource. The aforementioned index data is stored in a search database, Receiving search queries from user computing systems, The aforementioned search query is determined to be associated with index data, To provide the search results associated with the digital resources to the user computing system, One or more computer-readable storage media, including [the specified element].

16. The search results include a preview of the digital resource in one or more computer-readable storage media according to claim 15.

17. The computer-readable storage medium according to claim 15, wherein the search result includes an indicator indicating that the search result is associated with a non-fungible token.

18. Determining that the aforementioned search query is associated with index data means that One or more computer-readable storage media according to claim 15, wherein one or more search terms of the search query determine that they describe at least one of the following: the digital resource, the author of the digital resource, or non-fungible token metadata.

19. Providing the search results associated with the digital resources to the user computing system is, It is determined that one or more web pages are associated with the search query, To generate one or more general web results based on the one or more web pages, To provide a search results page to the user computing system, wherein the search results page includes the search results and one or more general web results. One or more computer-readable storage media according to claim 15, including the following:

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