Reliable Fact-Based Services for Blockchain Smart Contracts

Abstract

To provide a system and method for determining a trigger event, which can include processing block-chain data in order to determine a trigger event.SOLUTION: A computer-implemented method for determining a trigger event may include: processing data associated with a trigger event in order to determine a query; then, utilizing the query in order to retrieve data describing a trigger event to take place by recursively searching a database; and, then, providing a notification in order to instruct a resulting action to be taken place.SELECTED DRAWING: Figure 2

Description

Technical Field

[0001] Related Applications This application claims the benefit and priority of U.S. Provisional Patent Application No. 63 / 396,723, filed on August 10, 2022. U.S. Provisional Patent Application No. 63 / 396,723 is hereby incorporated by reference in its entirety.

[0002] The present disclosure generally relates to providing reliable fact-based services for blockchain smart contracts. More specifically, the present disclosure relates to obtaining blockchain data, determining smart contract trigger events based on the blockchain data, determining that a trigger event has occurred, and providing instructions for executing a resulting action based on the determination of the trigger event.

Background Art

[0003] Blockchains can include smart contracts that can be conditioned on specific occurrences. The operation of these smart contracts may require a reliable and trustworthy source of facts. Reliable sources may be limited and / or may lack reliability. Further, smart contracts can be directed at multiple different trigger events associated with multiple different topics.

Summary of the Invention

Means for Solving the Problems

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

[0005] One exemplary aspect of the present disclosure is directed to a computing system for determining a trigger event. The system can include one or more processors and one or more non-transitory computer-readable media that collectively store instructions that, when executed by the one or more processors, cause the computing system to perform an operation. The operation can include obtaining blockchain data from a blockchain. The blockchain data can be assumed to describe a smart contract. In some implementations, the smart contract can be associated with a trigger event and a resulting action. The operation can include processing the blockchain data to determine a trigger event. The trigger event can be associated with one or more specific knowledge graphs among a plurality of knowledge graphs. The operation can include generating a query based on the trigger event. The query can be associated with one or more specific knowledge graphs. The operation can include determining that the trigger event has occurred based on the query. The operation can include transmitting a notification to a blockchain computing system. In some implementations, the blockchain computing system can be associated with the blockchain. The notification can be assumed to describe the trigger event that has occurred. The notification can instruct the blockchain computing system to cause a resulting action to occur.

[0006] Another exemplary aspect of the present disclosure is directed to a computer-implemented method for determining a trigger event. The method can include obtaining blockchain data from a blockchain by a computing system including one or more processors. The blockchain data can be assumed to describe a smart contract. In some implementations, the smart contract can be associated with a trigger event and a resulting action. The method can include processing the blockchain data by the computing system to determine a trigger event. The method can include generating a query by the computing system based on the trigger event. The method can include recursively querying a knowledge database using the query by the computing system. The method can include determining by the computing system that a trigger event has occurred based on the query and the knowledge database. The method can include transmitting a notification by the computing system to a blockchain computing system. In some implementations, the blockchain computing system can be associated with the blockchain. The notification can be assumed to describe the occurred trigger event. The notification can instruct the blockchain computing system to cause a resulting action to occur.

[0007] Another exemplary aspect of the present disclosure is directed to one or more non-transitory computer-readable media that, when executed by one or more computing devices, collectively store instructions that cause the one or more computing devices to perform an operation. The operation can include obtaining blockchain data from a blockchain. The blockchain data can be assumed to describe a smart contract. In some implementations, the smart contract can be associated with a trigger event and a resulting action. The operation can include processing the blockchain data to determine a trigger event. The trigger event can be associated with one or more entities. The operation can include generating a query based on the trigger event. In some implementations, the query can be associated with one or more entities. The operation can include determining that the trigger event has occurred based on recursively executing the query against a search engine using the query. The operation can include transmitting a notification to a blockchain computing system. The blockchain computing system can be associated with the blockchain. In some implementations, the notification can be assumed to describe the trigger event that has occurred. The notification can instruct the blockchain computing system to cause a resulting action to occur.

[0008] Other aspects of the present disclosure are directed to various systems, devices, non-transitory computer-readable media, user interfaces, and electronic devices.

[0009] These and other features, aspects, and advantages of the various embodiments of the present disclosure will be better understood with reference to the following description and the appended claims. The accompanying drawings, which are incorporated herein and form a part hereof, illustrate exemplary embodiments of the present disclosure and, together with the description, serve to explain the related principles.

[0010] A detailed description of the embodiments directed to those skilled in the art is described herein with reference to the accompanying drawings.

Brief Description of the Drawings

[0011]

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[0012] Reference numerals repeated across multiple figures are intended to identify the same features in various implementations.

[0013] Generally, the present disclosure is directed to systems and methods for smart contract trigger event determination. In particular, the systems and methods disclosed herein can utilize query generation and recursive search to determine that a trigger event has occurred. The systems and methods disclosed herein can utilize a knowledge database, a search engine, and / or a plurality of knowledge graphs to determine that a smart contract trigger event has occurred and / or is occurring. For example, a search system can be utilized to enable an oracle system for blockchain smart contracts and to search for information regarding one or more elements associated with a smart contract trigger event. In some implementations, the search system can prioritize certain sources (e.g., the search system can weight search results based on the determined reliability of the source). The systems and methods can process word definitions, web answers, geographic data, live feeds of movies, and / or live feeds of sports events to determine that a trigger event has occurred.

[0014] For example, the present system and method can include obtaining blockchain data from a blockchain. The blockchain data can be assumed to describe a smart contract. The smart contract can be associated with a trigger event and a resulting action. The blockchain data can be processed to determine the trigger event. The trigger event can be associated with one or more specific knowledge graphs among a plurality of knowledge graphs. The present system and method can include generating a query based on the trigger event. In some implementations, the query can be associated with one or more specific knowledge graphs. The present system and method can determine that a trigger event has occurred based on the query. A notification can be transmitted to a blockchain computing system. The blockchain computing system can be associated with the blockchain. In some implementations, the notification can be assumed to describe the trigger event that has occurred. The notification can instruct the blockchain computing system to cause a resulting action to occur.

[0015] This system and method can obtain blockchain data from a blockchain. The blockchain data can be assumed to describe a smart contract. In some implementations, the blockchain data can be obtained via a blockchain node. A smart contract can be associated with a trigger event and a resulting action. In some implementations, the resulting action can include providing a payload to a specific user. The payload can include non-fungible tokens (e.g., non-fungible tokens associated with digital resources) and / or cryptocurrencies. The resulting action can include implementing an application programming interface to execute a specific interaction (e.g., an update action, a transaction action, and / or a message interaction). The trigger event can include a specific result of a sports event (e.g., the victory of a specific team, the statistical line of a specific player, a specific play, and / or a specific total score). Alternatively and / or additionally, the trigger event can include a specific weather phenomenon that occurs (e.g., the occurrence of rain, high temperature, low temperature, the occurrence of a storm, and / or the type of precipitation). In some implementations, the trigger event can include a specific location-based event that occurs (e.g., a traffic event, a population event, a voting result event, an employment event, a law-making event, and / or a local social event). The trigger event can include that a query threshold is met. For example, the threshold can be the total amount of a query, the total amount of queries in a given period, the location-based amount of time, and / or the amount of a specific user query.

[0016] To determine a trigger event, blockchain data can be processed. The trigger event can be associated with one or more specific knowledge graphs among a plurality of knowledge graphs (e.g., a knowledge graph associated with one or more entities (e.g., objects, people, events, tasks, situations, and / or concepts)). In some implementations, one or more specific knowledge graphs can include a sports knowledge graph associated with a specific sport. A sports event can be associated with a specific sport. Alternatively and / or additionally, one or more specific knowledge graphs can include a weather knowledge graph associated with a specific weather type. One or more specific knowledge graphs can include a location knowledge graph associated with a specific location. Determining a trigger event can include identifying a subset of blockchain data associated with a smart contract. To identify trigger event data and resulting action data, a subset of blockchain data associated with a smart contract can be processed. Then, the trigger event data can be processed by one or more machine learning models to determine the semantic intent of the trigger event data. The trigger event can be an if clause associated with a smart contract. The resulting action can be a then clause associated with the smart contract. The structure of the data can be utilized to determine trigger event data.

[0017] Queries can be generated based on trigger events. The queries can be associated with one or more specific knowledge graphs. The queries can include one or more terms associated with one or more specific knowledge graphs. Additionally and / or alternatively, the queries can include one or more terms associated with the semantic intent of the trigger event. The queries can include text data, image data, latent encoding data, audio data, and / or video data. The queries can be generated based on a deterministic function and / or based on heuristics. Alternatively and / or additionally, the queries can be generated using one or more machine learning models. The one or more machine learning models can include natural language processing models (e.g., large pre-trained language models), segmentation models, augmentation models, image processing models, audio processing models, video processing models, and / or latent encoding processing models. In some implementations, query generation can include determining one or more labels associated with the trigger event and utilizing the one or more labels.

[0018] The system and method can determine that a trigger event has occurred based on a query. In some implementations, the queries can be provided to the search engine recursively at predetermined intervals. Alternatively and / or additionally, the intervals can be machine-learned and / or can vary based on one or more variables. In some implementations, the queries can be utilized to search a database. The database can be determined based on a knowledge graph, can be pre-associated with the trigger event, can be determined based on the determined semantic intent, and / or can be a database of reliable sources associated with a particular topic.

[0019] In some implementations, determining that a trigger event has occurred based on a query can include providing the query to a search engine, obtaining search result data from the search engine, and determining that a trigger event has occurred based on the search result data. The search result data can be processed to determine whether the search result data describes a particular result. The particular result can be processed to determine whether the particular result is a trigger event. In some implementations, the search result data can be processed to determine a confidence level of the particular result determination. The search result data can be assumed to describe information obtained from multiple information sources. Each piece of information from a particular source may be weighted differently based on its relevance to a topic, the ranking of the search result, and / or the determined reliability of the source.

[0020] Alternatively and / or additionally, determining that a trigger event has occurred based on a query can include determining that a trend topic is associated with the query and determining that the trend topic describes a trigger event in which the trend topic occurs. The trend topic can be associated with a disaster topic (e.g., hurricane or wildfire), a topic of a media content item (e.g., movie, song, album, image, and / or GIF), a stock topic, a topic of a sports team, and / or a topic of a particular entity.

[0021] The notification can be transmitted to a blockchain computing system. The notification can be generated based on a determined trigger event. The blockchain computing system can be associated with a blockchain. In some implementations, the notification can describe the occurred trigger event. The notification can instruct the blockchain computing system to generate a resulting action. In some implementations, the notification can be transmitted to the blockchain computing system via an application programming interface. The notification can be associated with one or more keys for verifying an oracle system using the blockchain. The notification can include one or more lines of executable code, proof of work, a hash function, and / or evidentiary data.

[0022] In some implementations, the system and method can include encoding data associated with the occurrence of a trigger event into a blockchain. For example, one or more pointers can be embedded into the blockchain. The one or more pointers can lead a user to evidence of the trigger event occurrence (e.g., a uniform resource locator address of a reliable source).

[0023] Queries can be utilized to recursively search a knowledge database to determine that a trigger event has occurred. The system and method can include obtaining blockchain data from a blockchain. The blockchain data can be assumed to describe a smart contract. The smart contract can be associated with a trigger event and a resulting action. The blockchain data can be processed to determine a trigger event. A query can be generated based on the trigger event. The system and method can include recursively searching the knowledge database using the query. The trigger event can be determined to have occurred based on the query and the knowledge database. The system and method can include transmitting a notification to a blockchain computing system. The blockchain computing system can be associated with the blockchain. The notification can be assumed to describe the trigger event that has occurred. In some implementations, the notification can instruct the blockchain computing system to cause a resulting action to occur.

[0024] This system and method can obtain blockchain data from a blockchain. The blockchain data can be assumed to describe a smart contract. In some implementations, the smart contract can be associated with a trigger event and a resulting action. The smart contract can include causing an action that results from the trigger event to be executed in response to determining that the trigger event has occurred. The smart contract can be assumed to describe an if-then function. The if clause can be the trigger event, and the resulting action can be the then clause. The trigger event can include the result of the event. The resulting action can include the transfer of digital resources and / or cryptocurrency. In some implementations, the resulting action can include causing an application programming interface to interact with a specific web platform. Alternatively and / or additionally, the resulting action can include generating an additional smart contract and embedding that smart contract in the blockchain.

[0025] To determine the trigger event, the blockchain data can be processed. Determining the trigger event can include determining that a subset of the blockchain data contains data associated with a smart contract. This determination can be made based on one or more standards and / or one or more protocols for the smart contract. In some implementations, the determination may be made based on a data structure. Additionally and / or alternatively, the blockchain data can be decrypted and processed to generate a plain English translation of the data, and the plain English translation can be processed by a natural language processing model to identify the trigger event.

[0026] Queries can be generated based on trigger events. The queries can be generated based on the identification of one or more keywords within the decoded trigger event. Then, the one or more keywords can be used in the query. In some implementations, one or more labels can be determined based on the one or more keywords, and the one or more labels can be utilized to generate the query.

[0027] The present system and method can recursively search a knowledge database using the queries. The recursive search can be performed in a hybrid set of intervals such that the equal intervals can be complemented by equal intervals, variable intervals, and / or additional search instances. The knowledge database can be associated with one or more sources. The one or more sources can be topic-specific reliable sources and can have various levels of authority.

[0028] In some implementations, the present system and method can determine that the trigger event is associated with a particular topic. The knowledge database can include data associated with the particular topic. The knowledge database can be determined based on one or more particular knowledge graphs associated with the trigger event.

[0029] Additionally and / or alternatively, multiple search result data sets can be generated by recursively searching the knowledge database using the queries. Each search result data set can be associated with a respective different instance of the search. In some implementations, the present system and method can determine that the trigger event has occurred based on one or more of the multiple search result data sets.

[0030] Next, the trigger event can be determined to have occurred based on the query and the knowledge database. This determination can include obtaining search result data associated with a recursive search of the knowledge database. The search result data can be processed to determine whether it describes a trigger event that occurred.

[0031] The notification can be transmitted to the blockchain computing system. The blockchain computing system can be associated with the blockchain. In some implementations, the notification can be assumed to describe the trigger event that occurred. The notification can instruct the blockchain computing system to generate a resulting action.

[0032] Additionally and / or alternatively, the query can be generated based on entities associated with the trigger event. For example, the system and method can obtain blockchain data from a blockchain. The blockchain data can be assumed to describe a smart contract. In some implementations, the smart contract can be associated with a trigger event and the resulting action. The system and method can process the blockchain data to determine the trigger event. The trigger event can be associated with one or more entities. A query can be generated based on the trigger event. The query can be associated with one or more entities. Based on recursively executing the query on a search engine using the query, it can be determined that the trigger event has occurred. In some implementations, the system and method can transmit a notification to a blockchain computing system. The blockchain computing system can be associated with the blockchain. In some implementations, the notification can be assumed to describe the trigger event that has occurred. The notification can instruct the blockchain computing system to cause the resulting action to occur.

[0033] Blockchain data can be obtained from a blockchain. The blockchain data can be assumed to describe a smart contract. In some implementations, the smart contract can be associated with a trigger event and a resulting action. The trigger event can include a specific blockchain action. The specific blockchain action can be associated with a specific user and a specific blockchain transaction. In some implementations, the resulting action can include transferring digital resources to a specific user. The trigger event can include a transaction action associated with a specific non-fungible token. The resulting action can include a transaction action associated with another non-fungible token.

[0034] The system and method can process blockchain data to determine a trigger event. The trigger event can be associated with one or more entities. In some implementations, the one or more entities can include a sports team, a performer, a politician, an athlete, and / or a production company. The one or more entities can be determined by processing blockchain data.

[0035] A query can be generated based on the trigger event. The query can be associated with one or more entities. The query can include one or more descriptors associated with the one or more entities. The query can include boolean terms and / or one or more words related to a specific event type.

[0036] In some implementations, generating a query can include processing blockchain data using a machine learning language model to determine the semantic intent of a trigger event (e.g., the meaning of the trigger event can be associated with the result of a sports event, the type of a weather phenomenon, and / or a blockchain transaction), and generating a query based on the semantic intent.

[0037] The system and method can determine that a trigger event has occurred based on recursively executing a query on a search engine using the query. The search engine can be a general search engine, a topic-specific search engine, an academic paper search engine, an image search engine, and / or a reliable source search engine (e.g., a search engine that identifies reliable sources and their respective contents). The search engine can utilize one or more knowledge graphs.

[0038] The system and method can transmit a notification to a blockchain computing system. The blockchain computing system can be associated with a blockchain. The notification can be assumed to describe the trigger event that has occurred. In some implementations, the notification can instruct the blockchain computing system to generate a resulting action.

[0039] The present system and method can be utilized as an oracle for smart contracts (e.g., it can determine trigger events, thereby enabling the execution of resulting actions). For example, the trigger event can be a disaster event determined based on geographical data, and this can be used to instruct notifications provided to multiple users. Alternatively and / or additionally, the trigger event can include sports results associated with sports bets entered by a user, which can be determined by querying a sports database and / or based on a live stream or feed. In some implementations, the trigger event can be associated with a meteorological phenomenon, which can be determined based on data obtained from reliable meteorological resources to trigger the payment of a meteorological bet. The trigger event can be associated with a query threshold being met. In some implementations, the trigger event can be associated with a particular topic trend.

[0040] In some implementations, the systems and methods disclosed herein can utilize world triggers and / or one or more application programming interfaces associated with one or more world triggers. The present system and method can utilize knowledge graph mapping to determine that a trigger event has occurred. The determination of the trigger event can include checking multiple websites via a web crawl. The check can be decisive based on a predefined interval search for a particular web resource. Alternatively and / or additionally, the check may be performed based on one or more machine learning parameters.

[0041] The source of the data can be determined based on the reliability of the source based on a high confidence score. In some implementations, the source may be special information obtained by a search engine, a browser application, a virtual assistant application, a social media platform, and / or a platform as a service.

[0042] In some implementations, the system and method can include storing advertising data on a blockchain. The advertising data can include text data, image data, video data, latent encoding data, and / or audio data. The advertising data can include content items used in the advertisement. Additionally and / or alternatively, the advertising data can include parameters for providing the advertisement, data associated with the advertiser, and / or metadata of the advertisement. The system and method can determine that an impression has occurred and store data describing the impression on the blockchain. The impression can be embedded in the blockchain. The impression can then be utilized to determine whether a trigger event of a smart contract has been satisfied.

[0043] The systems and methods of the present disclosure provide many technical effects and advantages. As an example, the systems and methods can provide a system and method for providing a reliable fact-based service for blockchain smart contracts. For example, the systems and methods disclosed herein can utilize multiple knowledge graphs and / or search engines to determine that a trigger event has occurred, and the smart contract indicates that an action resulting from the occurrence of the trigger event is to be executed.

[0044] Another technical advantage of the systems and methods of the present disclosure is that a machine learning model can be utilized to generate a query based on a trigger event and recursively retrieve data associated with the trigger event. For example, the systems and methods disclosed herein can process blockchain data using a machine learning model to determine the semantic intent of a trigger event. The semantic intent can then be utilized to generate a query associated with the trigger event. The query can then be utilized to recursively search for the occurrence of the trigger event.

[0045] Another example of a technical effect and advantage relates to improved computational efficiency and enhanced functionality of a computing system. For example, the systems and methods disclosed herein can utilize a search system to determine that a trigger event has occurred without the need to generate a new database.

[0046] Reference is now made to the drawings, in which exemplary embodiments of the disclosure are shown in more detail.

[0047] Exemplary Devices and Systems FIG. 1A shows a block diagram of an exemplary computing system 100 that performs trigger event determination, 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 communicatively coupled via a network 180.

[0048] The user computing system 130 can be any type of computing device, such as, for example, a personal computing device (e.g., a laptop or desktop), a mobile computing device (e.g., a smartphone or tablet), a game console or controller, a wearable computing device, an embedded computing device, or any other type of computing device.

[0049] The user computing system 130 includes one or more processors 132 and a memory 134. The one or more processors 132 can be any suitable processing device (e.g., a processor core, a microprocessor, an ASIC, an FPGA, a controller, a microcontroller, etc.), or one or more processors operably connected. The memory 134 can include one or more non-transitory computer-readable storage media, such as RAM, ROM, EEPROM, EPROM, flash memory devices, magnetic disks, etc., and combinations thereof. The memory 134 can store data 136 and instructions 138 that are executed by the processor 132 to cause the user computing system 130 to perform operations.

[0050] The user computing system 130 can also include one or more user input components that receive user input. For example, the user input component can be a touch sensing component (e.g., a touch sensing display screen or a touch pad) that senses the touch of a user input object (e.g., a finger or a stylus). The touch sensing component can function to implement a virtual keyboard. Examples of other user input components include a microphone, a conventional keyboard, or any other means by which a user can provide user input.

[0051] Server computing system 110 includes one or more processors 112 and a memory 114. The one or more processors 112 may be any suitable processing device (e.g., a processor core, a microprocessor, an ASIC, an FPGA, a controller, a microcontroller, etc.), or may be one or more processors operably connected. The memory 114 can include one or more non-transitory computer-readable storage media such as RAM, ROM, EEPROM, EPROM, flash memory devices, magnetic disks, and combinations thereof. The memory 114 can store data 118 and instructions 116 that are executed by the processor 112 to cause the server computing system 110 to perform operations.

[0052] In some implementations, the server computing system 110 includes or is implemented by one or more server computing devices. When 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 a combination thereof.

[0053] 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.) or one or more processors operably connected. The memory can include one or more non-transitory computer-readable storage media such as RAM, ROM, EEPROM, EPROM, flash memory devices, magnetic disks, etc., and combinations thereof. The memory can store data and instructions that are executed by the processors to cause the blockchain computing system 170 to perform operations. In some implementations, the blockchain computing system 170 includes or is implemented by one or more server computing devices.

[0054] The network 180 can be any type of communication network, such as a local area network (e.g., an intranet), a wide area network (e.g., the Internet), or some combination thereof, and can include any number of wired or wireless links. Generally, communication over the network 180 can be carried over any type of wired and / or wireless connection using various communication protocols (e.g., TCP / IP, HTTP, SMTP, FTP), encodings or formats (e.g., HTML, XML), and / or security schemes (e.g., VPN, secure HTTP, SSL).

[0055] Computing system 100 can include a number of applications (e.g., applications 1 through N). Each application can communicate with a central intelligence layer. Examples of applications can include a text messaging application, an email application, a dictation application, a virtual keyboard application, a browser application, and the like. In some implementations, each application can use an API (e.g., a common API across all applications) to communicate with the central intelligence layer (and the models stored therein).

[0056] The central intelligence layer can communicate with a central device data layer. The central device data layer can be a centralized repository of data for computing system 100. In some implementations, the central device data layer can communicate with many other components of the computing device, such as, for example, one or more sensors, a context manager, a device state component, and / or additional components. In some implementations, the central device data layer can use an API (e.g., a private API) to communicate with each device component.

[0057] Additionally and / or alternatively, FIG. 1A shows an exemplary computing system 100 that can be used to implement a reliable fact-based service for a blockchain smart contract according to aspects of the present disclosure. System 100 has a user server architecture that includes a server 110 that communicates with one or more user computing systems 130 via a network 180. However, the present disclosure can be implemented using other suitable architectures that can include any number of computing systems that communicate via network 180.

[0058] 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. In particular, multiple computing devices can operate together as a single server 110. Server 110 can have one or more processors 112 and a memory 114. Server 110 can also include a network interface used to communicate with one or more remote computing devices (e.g., user devices) 130 via a network 180.

[0059] Processor 112 can be any suitable processing device, such as a microprocessor, a microcontroller, an integrated circuit, or other suitable processing device. Memory 114 can include any suitable computer system or medium, including but not limited to non-transitory computer-readable media, RAM, ROM, hard drives, flash drives, or other memory devices. Memory 114 can store information accessible by processor 112, including instructions 116 that can be executed by processor 112. Instructions 116 can be any set of instructions that, when executed by processor 112, cause processor 112 to provide a desired function.

[0060] In particular, instruction 116 can be executed by processor 112 to implement trigger event determination. User profile database 120 can be configured to store a plurality of user profiles associated with a plurality of users who utilize one or more user computing systems 130. In some implementations, user profile database 120 can be configured to be utilized to facilitate one or more interactions. Facilitation of one or more interactions can include the use of blockchain application programming interface (API) 122 to send and receive data with blockchain computing system 170. For example, server computing system 110 can utilize blockchain API 122 to update one or more ledgers 172 of blockchain computing system 170. One or more ledgers 172 can be associated with one or more tokens 174. One or more tokens 174 can include one or more non-fungible tokens that can include a script associated with a digital asset (e.g., image data, video data, text data, potential encoding data, domain data, audio data, augmented reality asset rendering data, and / or virtual reality asset rendering data). In particular, the script can reference a particular digital asset being offered for sale. Digital assets can include image data, text data, video data, potential encoding data, domain names, virtual properties, augmented reality assets, virtual reality assets (e.g., virtual reality environments and / or virtual reality objects for interacting within the environment), smart contracts, authentication of physical items, and the like. In some implementations, one or more ledgers 172 can be associated with a cryptocurrency that can be utilized to conduct transactions in physical and / or virtual markets.

[0061] It will be understood that the term "element" can refer to computer logic utilized to provide a desired function. Thus, any element, function, and / or instruction can be implemented in hardware, application-specific circuitry, firmware, and / or software controlling a general-purpose processor. In one implementation, an element or function may be a program code file stored in a storage device, loaded into memory, and executed by a processor, and may be provided from a computer program product such as computer-executable instructions stored in a tangible computer-readable storage medium such as RAM, a hard disk, an optical medium, or a magnetic medium.

[0062] Memory 114 can also include data 118 that can be retrieved, manipulated, created, or stored by processor 112. Data 118 can include search result data, ranking data, image data (e.g., digital maps, satellite images, aerial photos, street photos, synthetic models, paintings, personal images, portraits, etc.), video data, audio data, text data (e.g., books, articles, blogs, poems, etc.), potential encoding data, blockchain address data, tables, vector data (e.g., vector representations of roads, plots, buildings, etc.), place data (e.g., places such as islands, cities, restaurants, hospitals, parks, hotels, and schools), or other data or related information. As an example, data 118 can be used to access information and data associated with a particular digital asset, website, search result, blockchain, etc.

[0063] Data 118 can be stored in one or more databases. The one or more databases can also be connected to server 110 by a high-bandwidth LAN or WAN, or can be connected to server 110 through network 180. The one or more databases can be split and placed in multiple locations.

[0064] Server 110 can exchange data with one or more user computing systems 130 via network 180. Although two user computing systems 130 are shown in FIG. 1A, any number of user computing systems 130 can be connected to server 110 via network 180. User computing system 130 can be any suitable type of computing device such as a general-purpose computer, a dedicated computer, a navigation device, a laptop, a desktop, an integrated circuit, a mobile device, a smartphone, a tablet, a wearable computing device, a display connected to and / or embedded with one or more processors, or other suitable computing device. Further, user computing system 130 may be a plurality of computing devices that cooperate to perform operations or computing actions.

[0065] Similar to server 110, user computing system 130 can include a processor 132 and a memory 134. Memory 134 can store information accessible by processor 132, including instructions and data that can be executed by the processor. As an example, memory 134 can store data 136 and instructions 138.

[0066] Instructions 138 can provide instructions for implementing a browser, the purchase of non-replaceable tokens, and / or other multiple functions. In particular, a user of user computing system 130 can exchange data with server 110 by using a browser to access a website accessible at a specific web address. The trigger event determination of the present disclosure can be provided as an element of the user interface of a website and / or application.

[0067] Data 136 can include data related to the execution of a special application on user computing system 130. In particular, the special application can be used to exchange data with server 110 via network 160. Data 136 can include user device-readable code for providing and implementing aspects of the present disclosure. Additionally and / or alternatively, data 136 can include data related to previously input or received data. For example, data 136 can include data related to past occurrences of a special application.

[0068] User computing system 130 can include various user input devices for receiving information from a user, such as a touch screen, touch pad, data input keys, speakers, mouse, motion sensors, and / or a microphone suitable for voice recognition. Further, user computing system 130 can have a display for presenting information such as a user interface, for displaying digital assets, for pop-ups or application elements displayed on the interface, and / or for displaying other forms of information.

[0069] User computing system 130 can also include a user profile 140 that can be used to identify a user of user computing system 130. User profile 140 can be optionally used by a user to perform one or more transactions and can then be recorded in one or more ledgers 172 of blockchain computing system 170. User profile 140 can be assumed to describe user information that can include an identification number and / or payment account information. For example, user profile 140 can include data associated with a cryptographic wallet that can be linked to a browser application via application extensions and / or embeds.

[0070] The user computing system 130 can further include a graphics processing unit. The graphics processing unit can be used by the processor 132 to determine that a trigger event associated with the blockchain smart contract has occurred. In some embodiments, the user computing system 130 performs any determination and / or generation processing.

[0071] The user computing system 130 can include a network interface for communicating with the server 110 via the network 180. The network interface can include any component or configuration suitable for communicating with the server 110 via the network 180, which can include, 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.

[0072] The network 180 can be any type of communication network, such as a local area network (e.g., intranet), wide area network (e.g., Internet), or some combination thereof. The network 180 can also include a direct connection between the client device 130 and the server 110. Generally, communication between the server 110 and the client device 130 can be carried via a network interface using any type of wired and / or wireless connection using various communication protocols (e.g., TCP / IP, HTTP), encodings, or formats (e.g., HTML, XML), and / or protection schemes (e.g., VPN, secure HTTP, SSL).

[0073] In some implementations, the exemplary computing system 100 can include one or more creator computing systems 150. The one or more creator computing systems 150 can be utilized to generate images, videos, essays, poems, audio, etc., and then provide them for sale. The one or more creator computing systems 150 can include one or more processors 152 that can be utilized to perform one or more operations for implementing the systems and methods disclosed herein. The one or more creator computing systems 150 can include one or more memory components 154 that can be utilized to store data 156 and one or more instructions 158. The data 156 can include data related to one or more applications, one or more media datasets, etc. The instructions 158 can include one or more operations for implementing the systems and methods disclosed herein.

[0074] The one or more creator computing systems 150 can store data associated with one or more digital assets 160 and / or one or more creator profiles 162. The one or more digital assets 160 can include text data, image data, video data, audio data, potential encoding data, domain data, or various other data formats. The one or more creator profiles 162 can include information associated with one or more "creators" of the one or more digital assets 160. The one or more creator profiles 162 can include identification data, transaction data, and / or cryptocurrency wallet data.

[0075] Additionally and / or alternatively, the exemplary computing system 100 can include one or more blockchain computing systems 170. The one or more blockchain computing systems 170 can include a plurality of computing devices utilized for decentralized data storage, and as a result, a plurality of "blocks" can be distributed across the network of computing devices to provide a secure system for data storage, which includes one or more ledgers 172 and one or more tokens 174. In some implementations, each of the one or more tokens 174 can be associated with at least a portion of the one or more ledgers 172.

[0076] A blockchain can refer to a system configured to securely record information. A blockchain can include a decentralized system that can make it very difficult to change information. A blockchain can include a digital ledger of transactions that can be replicated and distributed across a network of computing systems. Each block in the chain can include a number of transactions. When a new transaction occurs on the blockchain, a record of that transaction can be added to the ledgers of all computing devices. A blockchain can be utilized to track the exchange of currency and / or digital assets through the recording of transactions on the digital ledger, which can be propagated across the entire decentralized system. The currency exchanged and tracked via the blockchain computing system 170 can sometimes be referred to as cryptocurrency.

[0077] Token 174 can include one or more non-fungible tokens. Non-fungible tokens can be minted on a blockchain associated with the blockchain computing system 170. Non-fungible tokens (NFTs) can be certificates of authenticity for digital assets. Since NFTs are non-exchangeable, their value depends on the price that anyone may pay for the asset. NFTs can be minted on a blockchain to maintain scarcity and authenticity. Digital assets can be defined as uniquely identifiable things that are stored digitally and can be used by an organization to realize value. Examples of digital assets 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.

[0078] FIG. 1B shows a block diagram of an exemplary blockchain 50 that can be utilized by the blockchain computing system 170 of the exemplary computing system 100 of FIG. 1A. The exemplary blockchain 50 can include a plurality of blocks that can be utilized to store data with one or more cryptographic functions. The blockchain 50 can be stored on a decentralized computing system comprising a plurality of computing devices. The blockchain 50 can be a public blockchain (e.g., an open blockchain without access restrictions where anyone with access to the Internet can send or verify transactions as part of a decentralized distributed system), a private blockchain (e.g., a blockchain that provides access based on permissions set by a network administrator), or a hybrid blockchain (e.g., a blockchain having a combination of unrestricted blocks and restricted blocks). The blockchain 50 can include a proof-of-work function that can include one or more cryptographic proof formats. The proof of work can be provided in response to a request to update the blockchain 50 (e.g., a request to update a ledger based on a new transaction). The proof of work can communicate that a particular device or group of devices has performed a certain amount of computation, which can be verified by other parties. Once the verification is complete, the blockchain 50 can be updated, or it may remain unchanged in response to a failed verification. The proof-of-work function can be utilized to reduce the computational cost for the same computing function and for all devices within the system that must perform checks to determine if a request to update the blockchain 50 is valid.

[0079] Each block can include a hash, a previous hash associated with the hash of the previous block, and data. In some implementations, each block can include a nonce. The hash can be a fixed-length hash value that can serve as a fingerprint of a particular block. The hash value can be generated based on a hash function and can change each time a change is made to the data of that particular block. The previous hash can include the hash value of the block immediately preceding a particular block. The previous hash can be utilized to confirm that the downstream ground truth is not changed unless appropriate verification is performed. The data can include transaction data (e.g., a transaction ledger), a timestamp, a value associated with the value of a cryptocurrency, non-fungible tokens (e.g., non-fungible tokens including scripts referencing digital assets, nonce data, and / or general blockchain data). A nonce (i.e., a numerical value used only once) can be a numerical value added to a block in a blockchain that can satisfy a difficulty level limit when the block is rehashed. The nonce can be a numerical value that a blockchain miner solves in order to receive an incentive (e.g., a cryptocurrency).

[0080] The blockchain 50 can include one or more security protocols and / or functions. The blockchain 50 can include an encryption system. For example, the blockchain 50 can verify that it is valid by confirming that the previous hash stored in a block matches the hash value of the previous block from the last block to the first block (e.g., the genesis block). In some implementations, the blockchain 50 can include a proof-of-work verification that can depend on the verification of a proof of computation before implementing a change to the stored data (e.g., the stored ledger). The proof-of-work verification may take seconds, minutes, and / or hours, depending in part on the number of blocks within the blockchain 50. Additionally and / or alternatively, the blockchain 50 can be implemented on a distributed and decentralized computing system. In some implementations, each computing device within the distributed and decentralized computing system can store a part (e.g., a block out of a plurality of blocks) or all of the blocks within the blockchain 50. Thus, the system can verify the data by confirming that most, if not all, of the data is uniform. Before adding new data, it is possible to check for tampering of each node in the distributed system.

[0081] The data can include data associated with the value of a cryptocurrency (e.g., a ledger associated with the value of a specific cryptocurrency), data associated with a digital asset (e.g., a non-fungible token minted on a blockchain 50 that can include a script associated with the digital asset), data associated with a smart contract (e.g., a smart contract that includes conditions to automatically initiate an action when conditions are met), and / or timestamp data (e.g., timestamp data for block creation, minting, transactions, etc.).

[0082] In particular, FIG. 1B shows a first block 10, a second block 20, a third block 30, a fourth block 40, and an nth block 60. Although five blocks are shown, any number of blocks can be utilized. The first block 10 can be a genesis block (e.g., the first overall block in a blockchain). The first block 10 can include respective first hashes 12 (e.g., hash values associated with the first block 10). The first block 10 can include a first previous hash 14 (e.g., if the first block 10 has a previous block within the blockchain 50, the hash of the previous block can be stored in the first block 10). Additionally and / or alternatively, the first block 10 can include data 16 and nonces 18.

[0083] The second block 20 can follow the first block 10. The second block 20 can include respective second hashes 22 (e.g., hash values associated with the second block 20). The second block 20 can include a second previous hash 24 (e.g., the second previous hash 24 can be the same as the first hash 12 or can reference the first hash 12). Additionally and / or alternatively, the second block 20 can include data 26 and nonces 28.

[0084] The third block 30 can follow the second block 20. The third block 30 can include respective third hashes 32 (e.g., hash values associated with the third block 30). The third block 30 can include a third previous hash 34 (e.g., the third previous hash 34 can be the same as the second hash 22 or can reference the second hash 22). Additionally and / or alternatively, the third block 30 can include data 36 and nonces 38.

[0085] Additionally and / or alternatively, the fourth block 40, the nth block 60, and other potential blocks can each include respective hashes, respective previous hashes, and data. The first data 16, the second data 26, the third data 36, and the data of other blocks can include duplicate data, and the data can be different and / or the same such that the data duplicates across all blocks. In some implementations, each block can be associated with different transactions (e.g., different minting, different sales, etc.). The first nonce 18, the second nonce 28, the third nonce 38, and the nonces of other blocks can be different and can be solved during mining.

[0086] The data within each block can include ledger data, and the ledger data can include a timestamp, the assets and / or cryptocurrency exchanged, the parties involved in the transaction, and / or other various information.

[0087] In some implementations, multiple different blockchains can be utilized for the systems and methods disclosed herein. The different blockchains can include different configurations. The different blockchains can include parallel chains, side chains, shared blocks, different chains, various permissions, various purposes, various numbers of blocks, and / or various hash functions and / or various lengths of hash values.

[0088] In some implementations, the system and method can include one or more machine learning model computing systems 900. The one or more machine learning models can be utilized for various tasks to enable identification, acquisition, indexing, and deduplication of token data.

[0089] FIG. 9A shows a block diagram of an exemplary computing system 900 that performs trigger event determination, according to an exemplary embodiment of the present disclosure. System 900 includes a user computing device 902, a server computing system 930, and a training computing system 950 communicatively coupled via a network 980.

[0090] The user computing device 902 can be any type of computing device, such as, for example, a personal computing device (e.g., a laptop or desktop), a mobile computing device (e.g., a smartphone or tablet), a game console or controller, a wearable computing device, an embedded computing device, or any other type of computing device.

[0091] The user computing device 902 includes one or more processors 912 and a memory 914. The one or more processors 912 may be any suitable processing device (e.g., a processor core, a microprocessor, an ASIC, an FPGA, a controller, a microcontroller, etc.), or may be one or more processors operably connected. The memory 914 can include one or more non-transitory computer-readable storage media such as RAM, ROM, EEPROM, EPROM, flash memory devices, magnetic disks, etc., and combinations thereof. The memory 914 can store data 916 and instructions 918 that are executed by the processor 912 to cause the user computing device 902 to perform operations.

[0092] In some implementations, the user computing device 902 can store or include one or more trigger event determination models 920. For example, the trigger event determination model 920 can be various machine learning models such as a neural network (e.g., a deep neural network), or other types of machine learning models including non-linear models and / or linear models, or can include it. The neural network can include a feedforward neural network, a recurrent neural network (e.g., a long short-term memory recurrent neural network), a convolutional neural network, or other forms of neural networks. Examples of the trigger event determination model 920 will be described with reference to FIGS. 2-5.

[0093] In some implementations, one or more trigger event determination models 920 are received from a server computing system 930 via a network 980, stored in a user computing device memory 914, and can then be used or implemented by one or more processors 912. In some implementations, a user computing device 902 can implement multiple parallel instances of a single trigger event determination model 920 (e.g., to perform parallel trigger event determination across multiple instances of a smart blockchain smart contract trigger event).

[0094] More specifically, computing system 900 can obtain blockchain data from a blockchain via a blockchain node. The blockchain data can be accessed based on one or more specific keys associated with server computing system 930. The blockchain data can be processed by one or more trigger event determination models 940 to determine semantic intent, determine keywords, determine trigger events, and / or generate queries. The semantic intent, keywords, queries, and / or plain English translations of trigger events can be utilized to query one or more databases for data associated with the occurring trigger events.

[0095] Additionally or alternatively, one or more trigger event determination models 940 may be included in or stored and implemented by a server computing system 930 that communicates with user computing device 902 according to a client-server relationship. For example, trigger event determination model 940 may be implemented by server computing system 940 as part of a web service (e.g., a service based on reliable facts). Thus, one or more models 920 may be stored and implemented on user computing device 902, and / or one or more models 940 may be stored and implemented on server computing system 930.

[0096] User computing device 902 may also include one or more user input components 922 that receive user input. For example, user input component 922 may be a touch sensing component (e.g., a touch sensing display screen or touch pad) that senses the touch of a user input object (e.g., a finger or stylus). The touch sensing component may function to implement a virtual keyboard. Examples of other user input components include a microphone, a conventional keyboard, or other means by which a user may provide user input.

[0097] The server computing system 930 includes one or more processors 932 and a memory 934. The one or more processors 932 may be any suitable processing device (e.g., a processor core, a microprocessor, an ASIC, an FPGA, a controller, a microcontroller, etc.), or may be one or more processors operably connected. The memory 934 can include one or more non-transitory computer-readable storage media such as RAM, ROM, EEPROM, EPROM, flash memory devices, magnetic disks, and combinations thereof. The memory 934 can store data 936 and instructions 938 that are executed by the processor 932 to cause the server computing system 930 to perform operations.

[0098] In some implementations, the server computing system 930 includes or is implemented by one or more server computing devices. When the server computing system 930 includes multiple server computing devices, such server computing devices can operate according to a sequential computing architecture, a parallel computing architecture, or a combination thereof.

[0099] As described above, the server computing system 930 can store or otherwise include one or more machine learning trigger event determination models 940. For example, the model 140 can be or include various machine learning models. Examples of machine learning models can include neural networks or other multi-layer non-linear models. Examples of neural networks can include feed-forward neural networks, deep neural networks, recurrent neural networks, and convolutional neural networks. Examples of the model 940 will be described with reference to FIGS. 2-5.

[0100] User computing device 902 and / or server computing system 930 can train model 920 and / or 940 through interaction with a training computing system 950 communicatively coupled via 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.

[0101] 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, a microprocessor, an ASIC, an FPGA, a controller, a microcontroller, etc.) or may be one or more processors operably connected. The memory 954 can include one or more non-transitory computer-readable storage media such as RAM, ROM, EEPROM, EPROM, flash memory devices, magnetic disks, and combinations thereof. The memory 954 can store data 956 and instructions 958 that are executed by the processor 952 to cause the training computing system 950 to perform operations. In some implementations, the training computing system 950 includes or is implemented by one or more server computing devices.

[0102] The training computing system 950 can include a model trainer 960 that trains the machine learning models 920 and / or 940 stored in the user computing device 902 and / or the server computing system 930 using various training or learning techniques such as, for example, backpropagation of errors. For example, a loss function can be backpropagated through the model to update one or more parameters of the model (e.g., based on the gradient of the loss function). Various loss functions can be used, such as mean squared error, likelihood loss, cross-entropy loss, hinge loss, and / or other various loss functions. Gradient descent can be used to repeatedly update the parameters by repeating the training multiple times.

[0103] In some implementations, performing backpropagation of errors can include performing truncated backpropagation over time. The model trainer 960 can perform some generalization techniques (e.g., weight decay, dropout, etc.) to improve the generalization ability of the model during training.

[0104] In particular, the model trainer 960 can train the trigger event determination models 920 and / or 940 based on a set of training data 962. The training data 962 can include, for example, a labeled training dataset, ground truth training data, blockchain data, semantic intent labels, trigger event labels, labeled samples of smart contracts, and / or a query generation training dataset.

[0105] In some implementations, if the user consents, the training examples can be provided by the user computing device 902. Thus, in such implementations, the model 920 provided to the user computing device 902 can be trained by the training computing system 950 based on user-specific data received from the user computing device 902. In some cases, this process can be referred to as personalization of the model.

[0106] The model trainer 960 includes computer logic utilized to provide the desired functionality. The model trainer 960 can be implemented in hardware, firmware, and / or software controlling a general-purpose processor. For example, in some implementations, the model trainer 960 includes program files stored on a storage device, loaded into memory, and executed by one or more processors. In other implementations, the model trainer 960 includes one or more sets of computer-executable instructions stored on a tangible computer-readable storage medium such as a RAM hard disk or optical or magnetic media.

[0107] The network 980 can be any type of communication network such as a local area network (e.g., an intranet), a wide area network (e.g., the Internet), or some combination thereof, and can include any number of wired or wireless links. Generally, communication via the network 980 can be carried via any type of wired and / or wireless connection using various communication protocols (e.g., TCP / IP, HTTP, SMTP, FTP), encodings or formats (e.g., HTML, XML), and / or security schemes (e.g., VPN, secure HTTP, SSL).

[0108] The machine learning models described in this specification can be used in various tasks, applications, and / or use cases.

[0109] In some implementations, the input to the machine learning models of the present disclosure can be image data. The machine learning models can process the image data to generate an output. As an example, the machine learning models can process the image data to generate an image recognition output (e.g., recognition of the image data, potential embedding of the image data, encoded representation of the image data, hash of the image data, etc.). As another example, the machine learning models can process the image data to generate an image segmentation output. As another example, the machine learning models can process the image data to generate an image classification output. As another example, the machine learning models can process the image data to generate an image data modification output (e.g., modification of the image data, etc.). As another example, the machine learning models can process the image data to generate an encoded image data output (e.g., encoded representation and / or compressed representation of the image data, etc.). As another example, the machine learning models can process the image data to generate an upscaled image data output. As another example, the machine learning models can process the image data to generate a prediction output.

[0110] In some implementations, the input to the machine learning model of the present disclosure can be text or natural language data. The machine learning model can process the text or natural language data to generate an output. As an example, the machine learning model can process natural language data to generate a language encoding output. As another example, the machine learning model can process text or natural language data to generate a latent text embedding output. As another example, the machine learning model can process text or natural language data to generate a translation output. As another example, the machine learning model can process text or natural language data to generate a classification output. As another example, the machine learning model can process text or natural language data to generate a text segmentation output. As another example, the machine learning model can process text or natural language data to generate a semantic intent output. As another example, the machine learning model can process text or natural language data to generate an upscaled text or natural language output (e.g., text or natural language data of higher quality than the input text or natural language). As another example, the machine learning model can process text or natural language data to generate a prediction output.

[0111] In some implementations, the input to the machine learning model of the present disclosure can be audio data. The machine learning model can process the audio data to generate an output. As an example, the machine learning model can process the audio data to generate an audio recognition output. As another example, the machine learning model can process the audio data to generate an audio translation output. As another example, the machine learning model can process the audio data to generate a potential embedding output. As another example, the machine learning model can process the audio data to generate an encoded audio output (e.g., an encoded representation and / or a compressed representation of the audio data, etc.). As another example, the machine learning model can process the audio data to generate an upscaled audio output (e.g., audio data of higher quality than the input audio data, etc.). As another example, the machine learning model can process the audio data to generate a text representation output (e.g., a text representation of the input audio data, etc.). As another example, the machine learning model can process the audio data to generate a prediction output.

[0112] In some implementations, the input to the machine learning model of the present disclosure can be potential encoding data (e.g., a potential spatial representation of the input, etc.). The machine learning model can process the potential encoding data to generate an output. As an example, the machine learning model can process the potential encoding data to generate a recognition output. As another example, the machine learning model can process the potential encoding data to generate a reconstruction output. As another example, the machine learning model can process the potential encoding data to generate a search output. As another example, the machine learning model can process the potential encoding data to generate a reclustering output. As another example, the machine learning model can process the potential encoding data to generate a prediction output.

[0113] In some implementations, the input to the machine learning model of the present disclosure can be statistical data. The machine learning model can process the statistical data to generate an output. As an example, the machine learning model can process the statistical data to generate a recognition output. As another example, the machine learning model can process the statistical data to generate a prediction output. As another example, the machine learning model can process the statistical data to generate a classification output. As another example, the machine learning model can process the statistical data to generate a segmentation output. As another example, the machine learning model can process the statistical data to generate a visualization output. As another example, the machine learning model can process the statistical data to generate a diagnostic output.

[0114] In some cases, the machine learning model can be configured to perform tasks that include encoding the input data for reliable and / or efficient transmission or storage (and / or corresponding decoding). For example, the task can be an audio compression task. The input can include audio data, and the output can include compressed audio data. In another example, the input includes visual data (e.g., one or more images or videos), the output includes compressed visual data, and the task is a visual data compression task. In another example, the task can include generating an embedding of the input data (e.g., input audio or visual data).

[0115] In some cases, the input includes visual data and the task is a computer vision task. In some cases, the input includes pixel data of one or more images and the task is an image processing task. For example, the image processing task can be image classification, and the output is a set of scores, where each score corresponds to a different object class and represents the likelihood that one or more images depict an object belonging to that object class. The image processing task can also be object detection, and the image processing output identifies one or more regions in one or more images and, for each region, the likelihood that the region represents the object of interest. As another example, the image processing task can be image segmentation, and the image processing output defines, for each pixel in one or more images, the likelihood of each category within a given set of categories. For example, the set of categories can be foreground and background. As another example, the set of categories can be object classes. As another example, the image processing task is depth estimation, and the image processing output defines, for each pixel in one or more images, a respective depth value. As another example, the image processing task can be motion estimation, the network input includes multiple images, and the image processing output defines, for each pixel in one of the input images, the motion of the scene depicted at the pixel between the images in the network input.

[0116] In some cases, the input includes audio data representing spoken utterances and the task is a speech recognition task. The output can include a text output mapped to the spoken utterance. In some cases, the task includes encrypting or decrypting the input data. In some cases, the task includes microprocessor performance tasks such as branch prediction or memory address translation.

[0117] FIG. 9A shows an example of a computing system that can be used to implement the present disclosure. Other computing systems can be used as well. For example, in some implementations, the user computing device 902 can include a model trainer 960 and a training dataset 962. In such implementations, the model 920 can be trained and used locally on the user computing device 902. In some of such implementations, the user computing device 902 can implement the model trainer 960 to personalize the model 920 based on user-specific data.

[0118] FIG. 9B shows a block diagram of an exemplary computing device 970 that executes in accordance with an exemplary embodiment of the present disclosure. The computing device 970 can be a user computing device or a server computing device.

[0119] The computing device 970 includes a number of applications (e.g., applications 1 through N). Each application includes its own machine learning library and machine learning model. For example, each application can include a machine learning model. Examples of applications include a text messaging application, an email application, a dictation application, a virtual keyboard application, a browser application, and the like.

[0120] As shown in FIG. 9B, each application can communicate with many other components of a computing device, such as, for example, one or more sensors, a context manager, a device state component, and / or additional components. In some implementations, each application can communicate with each device component using an API (e.g., a public API). In some implementations, the API used by each application is specific to that application.

[0121] FIG. 9C shows a block diagram of an exemplary computing device 990 that operates in accordance with an exemplary embodiment of the present disclosure. The computing device 990 can be a user computing device or a server computing device.

[0122] The computing device 990 includes a number of applications (e.g., applications 1 through N). Each application communicates with a central intelligence layer. Examples of applications include a text messaging application, an email application, a dictation application, a virtual keyboard application, a browser application, and the like. In some implementations, each application can communicate with the central intelligence layer (and the models stored therein) using an API (e.g., a common API across all applications).

[0123] The central intelligence layer includes a number of machine learning models. For example, as shown in FIG. 9C, each machine learning model (e.g., a model) can be provided for each application and managed by the central intelligence layer. In other implementations, two or more applications can share a single machine learning model. For example, in some implementations, the central intelligence layer can provide a single model (e.g., a single model) for all applications. In some implementations, the central intelligence layer is included in or implemented by the operating system of the computing device 990.

[0124] 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 shown in FIG. 9C, the central device data layer can communicate with many other components of the computing device, such as, for example, one or more sensors, a context manager, a device state component, and / or additional components. In some implementations, the central device data layer can communicate with each device component using an API (e.g., a private API).

[0125] Exemplary system configuration Figure 2 shows a block diagram of an exemplary trigger event determination system 200 according to an exemplary embodiment of the present disclosure. In some implementations, the trigger event determination system 200 is trained to receive a set of blockchain data 202 that describes a smart contract, and as a result of receiving the blockchain data 202, determines that a trigger event has occurred (210), and then causes data to be sent to a blockchain computing system to execute a resulting action 206. Thus, in some implementations, the trigger event determination system 200 can include a decision action block 208 operable to check the status of the trigger event 204 at one or more intervals.

[0126] In particular, the blockchain data 202 can be obtained from a blockchain and / or from a repository or blockchain log. The blockchain data can be processed to identify a subset of data that describes a smart contract. A trigger event 204 and / or a resulting action 206 can be determined. The identification of the subset of data and / or the determination of the trigger event 204 can be performed by one or more machine learning models.

[0127] Next, in order to determine and / or generate the decision action 208, the trigger event 204 can be processed. The decision action 208 can include generating a query based on the trigger event and querying a database based on the query. Alternatively and / or additionally, the decision action 208 can continuously process an RSS feed, a video feed, a social media feed, and / or an audio feed. The decision action 208 can be executed recursively until the trigger event occurs (210). When the trigger event determination system 200 determines that the trigger event has occurred (210), the data associated with the determination can be transmitted to the blockchain computing system, thereby enabling the resulting action 206 to be executed.

[0128] FIG. 3 shows a block diagram of an exemplary query generation system 300 according to an exemplary embodiment of the present disclosure. In particular, the query generation system 300 can include obtaining blockchain data 302. The blockchain data 302 can be processed using one or more machine learning models 304 (e.g., natural language processing models, detection models, decision models, classification models, segmentation models, extension models, image processing models, latent encoding models, decoder models, encoder models, self-attention models, and / or decoding models) to determine the semantic intent 306. The semantic intent 306 can be assumed to describe the result associated with the trigger event.

[0129] Next, the semantic intent 306 can be processed by the generation block 308 to generate a query 314. The generation block 308 can generate a query based on heuristics and / or based on one or more learning parameters. The query 314 can be generated based on one or more knowledge graphs.

[0130] In some implementations, blockchain data 302 can be processed to determine a topic 310 associated with a trigger event. Alternatively and / or additionally, blockchain data 302 can be processed to determine one or more entities 312 associated with a trigger event. The one or more topics 310 and / or the one or more entities 312 can be utilized to generate and / or augment a query 314. In some implementations, the one or more topics 310 and / or the one or more entities 312 can be utilized to determine a particular database to search using query 314.

[0131] Figure 4 shows a block diagram of an exemplary trigger event determination system 400 according to an exemplary embodiment of the present disclosure. In particular, the trigger event determination system 400 can include obtaining and / or determining a query 402 based on a determined trigger event. The query 402 can then be provided to a search engine 404 that can search one or more databases. The one or more databases can include a first database 406, a second database 408, and / or a third database 410. Each database can be associated with one or more respective sources. Different respective resources can have different levels of authority. The first database 406 can be associated with a government agency, the second database 408 can be associated with a private agency, and the third database 410 can be associated with a general database. Utilizing the search engine 404 to search the one or more databases can generate one or more search results 412. The search results 412 can be processed by a decision block to determine whether a trigger event has occurred. The decision block 414 can include one or more machine-generated models. The decision block 414 can process the search results 412 to determine whether a particular result has occurred, and the decision block 414 can determine whether the particular result is associated with a trigger event. One or more search results associated with the first database 406 may be weighted differently than one or more search results associated with the second database 408. A confidence level can be output by the decision block 414.

[0132] The decision block 414 can generate a notification 416 for transmission to a blockchain computing system in response to the occurrence of a trigger event. Alternatively and / or additionally, the decision block 414 can repeat a decision loop in response to data that describes that a trigger event has not occurred. The notification can include instructions for performing a resulting action.

[0133] FIG. 5 shows a block diagram of an exemplary smart contract oracle system 500 according to an exemplary embodiment of the present disclosure. In particular, the smart contract oracle system 500 can include obtaining data associated with the smart contract 510. The data associated with the smart contract 510 can be processed to determine a trigger event 512 and a resulting action 514 associated with the smart contract 510. One or more knowledge graphs 520 can be utilized to determine whether a trigger event has occurred. In some implementations, one or more knowledge graphs 520 can be utilized to determine one or more resources associated with the subject of the trigger event 512. For example, the trigger event 512 can include sports results of a particular team. One or more knowledge graphs 520 can include determining and / or identifying a first resource 522, a second resource 524, and / or a knowledge panel 526 associated with the trigger event 512. The first resource 522 can be a sports resource associated with the authority of a particular sport. The second resource 524 can be a team-specific sports website (e.g., a team-specific blog, an official team website, and / or a local news source for the team). The knowledge panel 526 can be a knowledge panel associated with a particular sport, a particular team, and / or the sports results for a particular day.

[0134] The first resource 522, the second resource 524, and / or the knowledge panel 526 can be utilized to generate search result data 530. The search result data 530 can be processed to generate an event determination 532. The event determination 532 can be described as indicating whether a trigger event has occurred. The determination loop can be iteratively repeated.

[0135] Exemplary Method FIG. 6 shows a flowchart of an exemplary method of operating in accordance with an exemplary embodiment of the present disclosure. FIG. 6 shows steps that are performed in a particular order for purposes of illustration and discussion, but the methods of the present disclosure are not limited to the particular order or arrangement shown. The various steps of method 600 can be omitted, rearranged, combined, and / or adapted in various ways without departing from the scope of the present disclosure.

[0136] In 602, the computing system can obtain blockchain data from a blockchain. The blockchain data can be assumed to describe a smart contract. In some implementations, the blockchain data can be obtained via a blockchain node. A smart contract can be associated with a trigger event and a resulting action. In some implementations, the resulting action can include providing a payload to a specific user. The payload can include non-fungible tokens (e.g., non-fungible tokens associated with digital resources) and / or cryptocurrencies. The resulting action can include implementing an application programming interface to execute a specific interaction (e.g., an update action, a transaction action, and / or a message interaction). The trigger event can include a specific result of a sports event (e.g., the victory of a specific team, the statistical line of a specific player, a specific play, and / or a specific total score). Alternatively and / or additionally, the trigger event can include a specific weather phenomenon that occurs (e.g., the occurrence of rain, high temperature, low temperature, the occurrence of a storm, and / or the type of precipitation). In some implementations, the trigger event can include a specific location-based event that occurs (e.g., a traffic event, a population event, a voting result event, an employment event, a law-making event, and / or a social event in a region). The trigger event can include that a query threshold is met. For example, the threshold can be the total amount of a query, the total amount of queries in a given period, the location-based amount of time, and / or the amount of a specific user query.

[0137] In 604, the computing system can process blockchain data to determine a trigger event. The trigger event can be associated with one or more specific knowledge graphs among a plurality of knowledge graphs (e.g., a knowledge graph associated with one or more entities (e.g., objects, people, events, tasks, situations, and / or concepts)). In some implementations, one or more specific knowledge graphs can include a sports knowledge graph associated with a specific sport. A sports event can be associated with a specific sport. Alternatively and / or additionally, one or more specific knowledge graphs can include a weather knowledge graph associated with a specific weather type. One or more specific knowledge graphs can include a location knowledge graph associated with a specific location. Determining the trigger event can include identifying a subset of the blockchain data associated with the smart contract. A subset of the blockchain data associated with the smart contract can be processed to identify trigger event data and resulting action data. Then, the trigger event data can be processed by one or more machine learning models to determine the semantic intent of the trigger event data. The trigger event can be an if clause associated with the smart contract. The resulting action can be a then clause associated with the smart contract. The structure of the data can be utilized to determine the trigger event data.

[0138] In 606, a computing system can generate a query based on a trigger event. The query can be associated with one or more specific knowledge graphs. The query can include one or more terms associated with one or more specific knowledge graphs. Additionally and / or alternatively, the query can include one or more terms associated with the semantic intent of the trigger event. The query can include text data, image data, latent encoding data, audio data, and / or video data. The query can be generated based on a deterministic function and / or based on heuristics. Alternatively and / or additionally, the query can be generated using one or more machine learning models. The one or more machine learning models can include a natural language processing model (e.g., a large pre-trained language model), a segmentation model, an augmentation model, an image processing model, an audio processing model, a video processing model, and / or a latent encoding processing model. In some implementations, query generation can include determining one or more labels associated with the trigger event and utilizing the one or more labels.

[0139] In 608, a computing system can determine that a trigger event has occurred based on a query. In some implementations, the query can be provided to a search engine recursively at predetermined intervals. Alternatively and / or additionally, the intervals can be machine-learned and / or can vary based on one or more variables. In some implementations, a query can be utilized to search a database. The database can be determined based on a knowledge graph, can be pre-associated with the trigger event, can be determined based on the determined semantic intent, and / or can be a reliable source database associated with a particular topic.

[0140] In some implementations, determining that a trigger event has occurred based on a query can include providing the query to a search engine, obtaining search result data from the search engine, and determining that a trigger event has occurred based on the search result data. The search result data can be processed to determine whether the search result data describes a particular result. The particular result can be processed to determine whether the particular result is a trigger event. In some implementations, the search result data can be processed to determine a confidence level of the particular result determination. The search result data can be assumed to describe information obtained from multiple information sources. Each piece of information from a particular source may be weighted differently based on its relevance to a topic, the ranking of the search results, and / or the determined reliability of the source.

[0141] Alternatively and / or additionally, determining that a trigger event has occurred based on a query can include determining that a trend topic is associated with the query and determining that the trend topic describes a trigger event in which the trend topic occurs. The trend topic can be associated with a disaster topic (e.g., hurricane or wildfire), a topic of a media content item (e.g., movie, song, album, image, and / or GIF), a stock topic, a topic of a sports team, and / or a topic of a particular entity.

[0142] At 610, the computing system can transmit a notification to the blockchain computing system. The notification can be generated based on a determined trigger event. The blockchain computing system can be associated with the blockchain. In some implementations, the notification can describe the occurred trigger event. The notification can instruct the blockchain computing system to generate a resulting action. In some implementations, the notification can be transmitted to the blockchain computing system via an application programming interface. The notification can be associated with one or more keys for verifying the oracle system using the blockchain. The notification can include one or more lines of executable code, proof of work, a hash function, and / or evidentiary data.

[0143] FIG. 7 shows a flowchart diagram of an exemplary method executed in accordance with an exemplary embodiment of the present disclosure. FIG. 7 shows steps executed in a particular order for purposes of illustration and discussion, but the method of the present disclosure is not limited to the specifically illustrated order or arrangement. The various steps of method 700 can be omitted, rearranged, combined, and / or adapted in various ways without departing from the scope of the present disclosure.

[0144] In 702, the computing system can obtain blockchain data from the blockchain. The blockchain data can be assumed to describe a smart contract. In some implementations, the smart contract can be associated with a trigger event and a resulting action. The smart contract can include causing an action that results from the trigger event to be executed in response to determining that the trigger event has occurred. The smart contract can be assumed to describe an if-then function. The if clause can be the trigger event, and the resulting action can be the then clause. The trigger event can include the result of the event. The resulting action can include the transfer of digital resources and / or cryptocurrency. In some implementations, the resulting action can include causing an application programming interface to interact with a specific web platform. Alternatively and / or additionally, the resulting action can include generating an additional smart contract and embedding that smart contract in the blockchain.

[0145] In 704, the computing system can process blockchain data to determine a trigger event. Determining the trigger event can include determining that a subset of the blockchain data includes data associated with a smart contract. This determination can be made based on one or more standards and / or one or more protocols for the smart contract. In some implementations, the determination may be made based on a data structure. Additionally and / or alternatively, the blockchain data can be decrypted and processed to generate a plain English translation of the data, and the plain English translation can be processed by a natural language processing model to identify the trigger event.

[0146] In 706, the computing system can generate a query based on a trigger event. The query can be generated based on the identification of one or more keywords within the decoded trigger event. Then, one or more keywords can be used in the query. In some implementations, one or more labels can be determined based on one or more keywords, and one or more labels can be utilized to generate the query.

[0147] In 708, the computing system can recursively search a knowledge database using the query. The recursive search can be performed in a hybrid set of intervals where the equal intervals can be complemented by equal intervals, variable intervals, and / or additional search instances. The knowledge database can be associated with one or more sources. The one or more sources can be topic-specific reliable sources and can have various levels of authority.

[0148] In some implementations, the computing system can determine that the trigger event is associated with a specific topic. The knowledge database can include data associated with the specific topic. The knowledge database can be determined based on one or more specific knowledge graphs associated with the trigger event.

[0149] Additionally and / or alternatively, by recursively searching the knowledge database using the query, a plurality of search result data sets can be generated. Each search result data set can be associated with a respective different instance of the search. In some implementations, the computing system can determine that the trigger event has occurred based on one or more of the plurality of search result data sets.

[0150] At 710, the computing system can determine that a trigger event has occurred based on a query and a knowledge database. This determination can include obtaining search result data associated with a recursive search of the knowledge database. The search result data can be processed to determine whether it describes a trigger event that has occurred.

[0151] At 712, the computing system can transmit a notification to a blockchain computing system. The blockchain computing system can be associated with a blockchain. In some implementations, the notification can be assumed to describe the trigger event that has occurred. The notification can instruct the blockchain computing system to cause a resulting action to occur.

[0152] FIG. 8 shows a flowchart diagram of an exemplary method executed in accordance with an exemplary embodiment of the present disclosure. FIG. 8 shows steps that are executed in a particular order for purposes of illustration and discussion, but the method of the present disclosure is not limited to the particular order or arrangement shown. The various steps of method 800 can be omitted, rearranged, combined, and / or adapted in various ways without departing from the scope of the present disclosure.

[0153] At 802, the computing system can obtain blockchain data from the blockchain. The blockchain data can be assumed to describe a smart contract. In some implementations, the smart contract can be associated with a trigger event and a resulting action. The trigger event can include a specific blockchain action. The specific blockchain action can be associated with a specific user and a specific blockchain transaction. In some implementations, the resulting action can include transferring digital resources to a specific user. The trigger event can include a transaction action associated with a specific non-fungible token. The resulting action can include a transaction action associated with another non-fungible token.

[0154] At 804, the computing system can process the blockchain data to determine a trigger event. The trigger event can be associated with one or more entities. In some implementations, the one or more entities can include a sports team, a performer, a politician, an athlete, and / or a production company. The one or more entities can be determined by processing the blockchain data.

[0155] At 806, the computing system can generate a query based on the trigger event. The query can be associated with one or more entities. The query can include one or more descriptors associated with the one or more entities. The query can include boolean terms and / or one or more words related to a specific event type.

[0156] In some implementations, generating a query can include processing blockchain data using a machine learning language model to determine the semantic intent of a trigger event (e.g., the meaning of the trigger event can be associated with the result of a sports event, the type of a weather phenomenon, and / or a blockchain transaction), and generating a query based on the semantic intent.

[0157] At 808, the computing system can determine that a trigger event has occurred based on recursively executing a query against a search engine using the query. The search engine can be a general search engine, a topic-specific search engine, an academic paper search engine, an image search engine, and / or a reliable source search engine (e.g., a search engine that identifies reliable sources and their respective contents). The search engine can utilize one or more knowledge graphs.

[0158] At 810, the computing system can transmit a notification to a blockchain computing system. The blockchain computing system can be associated with a blockchain. The notification can be assumed to describe the trigger event that has occurred. In some implementations, the notification can instruct the blockchain computing system to cause a resulting action to occur.

[0159] Additional Disclosure The technology described in this specification refers to servers, databases, software applications, and other computer-based systems, as well as the actions performed and the information transmitted and received between such systems. Due to the inherent flexibility of computer-based systems, a wide variety of configurations, combinations, and divisions of tasks and functions between components are possible. For example, the processes described in this specification can be implemented using a single device or component, or multiple devices or components operating in combination. The database and application can be implemented in a single system or distributed across multiple systems. The distributed components can operate sequentially or in parallel.

[0160] Although the subject matter of the present invention has been described in detail with respect to its various specific exemplary embodiments, each example is provided for illustrative purposes only and does not limit the present disclosure. Those skilled in the art, upon understanding the foregoing, can readily generate modifications, variations, and equivalents to such embodiments. Accordingly, the present disclosure is not intended to preclude such modifications, variations, and / or additions to the subject matter that would be readily apparent to those skilled in the art. For example, for obtaining yet another embodiment, features illustrated or described as part of one embodiment can be used in conjunction with another embodiment. Accordingly, the present disclosure is intended to cover such modifications, variations, and equivalents.

Description of Reference Numerals

[0161] 10 First block 14 First previous hash 16 Data 18 Nonces 20 Second block 22 Second hash 24 Second previous hash 26 Data 28 Nonces 30 Third block 32 Third hash 34 The third previous hash 36 Data 38 Nonce 40 The fourth block 50 Blockchain 60 The nth block 100 Computing system 110 Server computing system 112 Processor 114 Memory 116 Instructions 118 Data 120 User profile database 122 Blockchain application programming interface (API) 130 User computing system 132 Processor 134 Memory 136 Data 138 Instructions 140 User profile 150 Creator computing system 152 Processor 154 Memory component 156 Data 158 Instructions 160 Digital asset 162 Creator profile 170 Blockchain computing system 172 Ledger 174 Token 180 Network 200 Trigger event determination system 202 Blockchain data 204 Trigger event 206 Resulting action 208 Decision action block 210 The trigger event occurs 300 Query generation system 302 Blockchain data 304 Machine learning model 306 Semantic intention 308 Generation block 310 Topic 312 Entity 314 Query 400 Trigger event determination system 402 Query 404 Search engine 406 First database 408 Second database 410 Third database 412 Search results 414 Decision block 416 Notification 500 Smart contract oracle system 510 Smart contract 512 Trigger event 514 Resulting action 520 Knowledge graph 522 First resource 524 Second resource 526 Knowledge panel 530 Search result data 532 Event decision 600 Method 700 Method 800 Method 900 Machine learning model computing system 902 User computing device 912 Processor 914 User computing device memory 916 Data 918 Instruction 920 Trigger event determination model 922 User input component 930 Server computing system 932 Processor 934 Memory 936 Data 938 Instruction 940 Trigger Event Determination Model 950 Training Computing System 952 Processor 954 Memory 956 Data 958 Instruction 960 Model Trainer 962 Training Data 970 Computing Device 980 Network 990 Computing Device

Claims

1. A computing system for determining a trigger event, comprising: one or more processors; one or more non-transitory computer-readable media collectively storing instructions that, when executed by the one or more processors, cause the computing system to perform operations, the operations including: obtaining blockchain data from a blockchain, the blockchain data describing a smart contract, the smart contract being associated with a trigger event and a resulting action; processing the blockchain data to determine the trigger event, the trigger event being associated with one or more specific knowledge graphs among a plurality of knowledge graphs; processing the blockchain data using a machine learning language model to determine a semantic intent of the trigger event; generating a query based on the semantic intent of the trigger event, the query being associated with the one or more specific knowledge graphs; determining that the trigger event has occurred based on the query, the determining that the trigger event has occurred based on the query including: providing the query to a search engine; obtaining search result data from the search engine; determining that the trigger event has occurred based on the search result data; and transmitting a notification to a blockchain computing system, the blockchain computing system being associated with the blockchain, the notification describing the occurrence of the trigger event, and the notification instructing the blockchain computing system to cause the resulting action. A system comprising

2. The system according to claim 1, wherein the query is recursively provided to the search engine at predetermined intervals.

3. The system according to claim 1, wherein the resulting action includes providing a payload to a specific user.

4. The system according to claim 1, wherein the trigger event comprises a specific result of a sports event, the one or more specific knowledge graphs comprise a sports knowledge graph associated with a specific sport, and the sports event is associated with the specific sport.

5. The system according to claim 1, wherein the trigger event comprises a specific weather phenomenon that occurs, and the one or more specific knowledge graphs comprise a weather knowledge graph associated with a specific weather type.

6. The system according to claim 1, wherein the trigger event comprises a specific location-based event that occurs, and the one or more specific knowledge graphs comprise a location knowledge graph associated with a specific location.

7. The system according to claim 1, wherein the trigger event is an event triggered when a query threshold is met.

8. The system according to claim 1, wherein the notification is transmitted to the blockchain computing system via an application programming interface.

9. The system according to claim 1, wherein the blockchain data is obtained via a blockchain node.

10. Determining that the trigger event has occurred based on the query is Determining that the query is associated with a specific trending topic, and determining that the specific trend topic describes the trigger event The system according to claim 1, comprising: **Claim 11** A computer-implemented method for determining a trigger event, comprising: obtaining blockchain data from a blockchain by a computing system comprising one or more processors, wherein the blockchain data describes a smart contract, and the smart contract is associated with a trigger event and an action resulting therefrom; processing the blockchain data by the computing system to determine the trigger event; processing the blockchain data by the computing system using a machine learning language model to determine the semantic intent of the trigger event; generating a query by the computing system based on the semantic intent of the trigger event; recursively querying a knowledge database using the query by the computing system; determining by the computing system that the trigger event has occurred based on the query and the knowledge database; transmitting a notification by the computing system to a blockchain computing system, wherein the blockchain computing system is associated with the blockchain, the notification describes the occurrence of the trigger event, and the notification instructs the blockchain computing system to generate the action resulting therefrom; A method comprising: **Claim 12** The method according to claim 11, comprising the step of causing the smart contract to execute an action resulting as the result in response to determining that the trigger event has occurred.

13. The method further comprising the step of determining, by the computing system, that the trigger event is associated with a particular topic, The method according to claim 11, wherein the knowledge database comprises data associated with a particular topic.

14. Generating, by the computing system, a plurality of search result datasets by recursively searching the knowledge database using the query, each search result dataset being associated with a respective different instance of the search, The method according to claim 11, wherein the computing system determines that the trigger event has occurred based on one or more of the plurality of search result datasets.

15. One or more non-transitory computer-readable media that, when executed by one or more computing devices, collectively store instructions that cause the one or more computing devices to perform operations, the operations being Obtaining blockchain data from a blockchain, the blockchain data describing a smart contract, the smart contract being associated with a trigger event and an action resulting as a result, Processing the blockchain data to determine the trigger event, the trigger event being associated with one or more entities, Processing the blockchain data using a machine learning language model to determine the semantic intent of the trigger event, Generating a query based on the semantic intent of the trigger event, wherein the query is associated with the one or more entities; Determining that the trigger event has occurred based on recursively executing the query on a search engine using the query; Transmitting a notification to a blockchain computing system, wherein the blockchain computing system is associated with the blockchain, the notification describes the occurrence of the trigger event, and the notification instructs the blockchain computing system to cause an action resulting therefrom; One or more non-transitory computer-readable media comprising. Claim 16 The one or more non-transitory computer-readable media of claim 15, wherein the one or more entities comprise at least one of a sports team, a performer, a politician, an athlete, or a production company. Claim 17 The one or more non-transitory computer-readable media of claim 15, wherein the action resulting therefrom includes transmitting a digital resource to a specific user.

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