Abstract: Generation of digital content by an artificial intelligence (AI) generator is controlled through the use of non-fungible tokens (NFTs) for prompts to the AI generator. When a request is received for the AI generator to generate digital content using a prompt as input, ownership of an NFT for the prompt is verified. If ownership is verified, the prompt is provided as input to the AI generator to generate digital content. However, if ownership is not verified, the prompt is not provided as input to the AI generator, thereby preventing the AI generator from generating digital content using the prompt.

Abstract: The disclosed technologies include receiving a request from a second computing device to verify ownership of a blockchain address. A challenge content is generated and sent to the requestor. A signature is received comprising a hash of the challenge content generated using a private key. A public key corresponding to the private key is obtained, and the signature is validated using the public key. In response to validating the signature, a characteristic is associated with a user associated with the blockchain address.

Abstract: The disclosed technologies include receiving a request from a second computing device to verify ownership of a blockchain address. A challenge content is generated and sent to the requestor. A signature is received comprising a hash of the challenge content generated using a private key. A public key corresponding to the private key is obtained, and the signature is validated using the public key. In response to validating the signature, a characteristic is associated with a user associated with the blockchain address.

Abstract: A method to automatically classify emails may include generating multiple entity data objects using entities identified in fields of emails and categorizing the multiple entity data objects into a first set of data objects and a second set of data objects. The method may also include extracting all tokens from each email and searching the extracted tokens for tokens associated with the data objects of the first set of data objects. The method may further include identifying the emails that include the extracted tokens that are associated with the data objects of the first set of data objects, identifying a particular data object of the first set of data objects to which an identified email corresponds, and automatically classifying the identified email in the first category in response to identifying the particular data object of the first set of data objects to which an identified email corresponds.

Abstract: A method to automatically classify emails may include generating multiple entity data objects using entities identified in receiver and sender fields of emails and categorizing the multiple entity data objects into a first set of data objects and a second set of data objects. The method may also include extracting all tokens from each email and searching the extracted tokens for tokens associated with the data objects of the first set of data objects. The method may further include identifying the emails that include the extracted tokens that are associated with the data objects of the first set of data objects, identifying a particular data object of the first set of data objects to which an identified email corresponds, and automatically classifying the identified email in the first category in response to identifying the particular data object of the first set of data objects to which an identified email corresponds.

Abstract: Methods, systems, and computer storage media are provided for minting non-fungible tokens (NFTs) using one or more location verification devices. For example, a request to mint an NFT may be received from a user device. In embodiments, the request includes an event identifier associated with an event (e.g., a sporting event) and an event location. A public blockchain address can be identified based on the request to mint the NFT. Using location verification data associated with the location verification device, a location of the user device can be verified. For example, location verification data can be associated with at least one signal received from the user device. In some embodiments, the location verification data also corresponds to a user device communication with a base station. Accordingly, the NFT can be minted based on the public blockchain address and verifying the location of the user device.

Abstract: Identifying documents that contain potential code words using a machine learning model. In some embodiments, a method may include receiving documents, identifying a first corpus and a second corpus in the documents, extracting a first set of word embeddings from the first corpus and a second set of word embeddings from the second corpus, generating a first vector space for the first set of word embeddings and a second vector space for the second set of word embeddings using a machine learning model, performing a vector rotation to improve alignment of the first set of word embeddings with the second set of word embeddings, identifying a word embedding in the first vector space that is not aligned with a corresponding word embedding in the second vector space as a potential code word, and identifying one or more documents that contain the potential code word in the first corpus.

Abstract: To determine the authenticity between an identity, such as a person, business, or social media handle, and a blockchain address, a verification index is generated. The verification index is generated by determining that the identity controls transaction of the blockchain address using a message encrypted by the identity using the private key. Once the authenticity between the identity and a blockchain address is verified, the identity is mapped to the blockchain address in the verification index. The verification index can be used provide an indication of the authenticity between the identity and the blockchain address by querying the verification index, such as in response to an API (application programming interface) call.

Abstract: Generation of digital content by an artificial intelligence (AI) generator is controlled through the use of non-fungible tokens (NFTs) for prompts to the AI generator. When a request is received for the AI generator to generate digital content using a prompt as input, ownership of an NFT for the prompt is verified. If ownership is verified, the prompt is provided as input to the AI generator to generate digital content. However, if ownership is not verified, the prompt is not provided as input to the AI generator, thereby preventing the AI generator from generating digital content using the prompt.

Abstract: Techniques for resource allocation with content creators are described that overcome the challenges of conventional techniques by supporting customized digital content generation and resource allocation specified by content creators of digital content. In one example, a content provider system, based on user input received via a control, specifies the portions of a resource allocation to be shared. A first portion is configured to be shared with a content creator of digital content, the digital content including service access data configured to access a digital service. A second portion is also configured to be shared with a client device that utilizes the service access data to access the digital service. Digital content is generated based on the specified portions and is transmitted. Upon access by the client device to the digital service, an indication of receipt of the first portion of the resource allocation is displayed by the content provider system.

Abstract: Identifying documents that contain potential code words using a machine learning model. In some embodiments, a method may include receiving documents, identifying a first corpus and a second corpus in the documents, extracting a first set of word embeddings from the first corpus and a second set of word embeddings from the second corpus, generating a first vector space for the first set of word embeddings and a second vector space for the second set of word embeddings using a machine learning model, performing a vector rotation to improve alignment of the first set of word embeddings with the second set of word embeddings, identifying a word embedding in the first vector space that is not aligned with a corresponding word embedding in the second vector space as a potential code word, and identifying one or more documents that contain the potential code word in the first corpus.

Abstract: The disclosed technologies include receiving a request from a second computing device to verify ownership of a blockchain address. A challenge content is generated and sent to the requestor. A signature is received comprising a hash of the challenge content generated using a private key. A public key corresponding to the private key is obtained, and the signature is validated using the public key. In response to validating the signature, a characteristic is associated with a user associated with the blockchain address.

Abstract: Technologies are shown for controlling a transaction on a blockchain. A first block is created on a blockchain for a transaction involving a seller entity associated with a first key, a buyer entity associated with a second key, and an intermediary entity associated with a third key. Funds for the transaction are associated with the first block. A second block storing refund code is created and linked to the first block. The refund code is executable to refund the funds when a seller digital signature using the first key and/or an intermediary digital signature using the third key are received. A third block storing payment code is created and linked to the first block. The payment code is executable to transfer the funds when a buyer digital signature using the second key and/or an intermediary digital signature using the third key are received.

Abstract: The disclosed technologies include receiving a request from a second computing device to verify ownership of a blockchain address. A challenge content is generated and sent to the requestor. A signature is received comprising a hash of the challenge content generated using a private key. A public key corresponding to the private key is obtained, and the signature is validated using the public key. In response to validating the signature, a characteristic is associated with a user associated with the blockchain address.

Abstract: Technologies are shown for controlling a transaction on a digitally stored blockchain by creating a contract data block for a transaction involving entities and by event code executed after receipt of digital signatures from the entities. A refund transaction may be digitally signed by entities and linked to the contract block within the blockchain. A payment transaction block may also be digitally signed by entities and linked to the contract block. Code authorized to execute by the blocks executes based on receipt of digital signature events.

Abstract: Techniques for resource allocation with content creators are described that overcome the challenges of conventional techniques by supporting customized digital content generation and resource allocation specified by content creators of digital content. In one example, a content provider system, based on user input received via a control, specifies the portions of a resource allocation to be shared. A first portion is configured to be shared with a content creator of digital content, the digital content including service access data configured to access a digital service. A second portion is also configured to be shared with a client device that utilizes the service access data to access the digital service. Digital content is generated based on the specified portions and is transmitted. Upon access by the client device to the digital service, an indication of receipt of the first portion of the resource allocation is displayed by the content provider system.

Abstract: The present invention relates to architected stamps having beneficial printing properties. In particular, negative Poisson ratio structures and/or porous reservoirs can be employed to control fluid dynamics and to provide metered ink transfer.

Abstract: Identifying documents that contain potential code words using a machine learning model. In some embodiments, a method may include receiving documents, identifying a first corpus and a second corpus in the documents, extracting a first set of word embeddings from the first corpus and a second set of word embeddings from the second corpus, generating a first vector space for the first set of word embeddings and a second vector space for the second set of word embeddings using a machine learning model, performing a vector rotation to improve alignment of the first set of word embeddings with the second set of word embeddings, identifying a word embedding in the first vector space that is not aligned with a corresponding word embedding in the second vector space as a potential code word, and identifying one or more documents that contain the potential code word in the first corpus.

Abstract: Techniques for resource allocation with content creators are described that overcome the challenges of conventional techniques by supporting customized digital content generation and resource allocation specified by content creators of digital content. In one example, a content provider system, based on user input received via a control, specifies the portions of a resource allocation to be shared. A first portion is configured to be shared with a content creator of digital content, the digital content including service access data configured to access a digital service. A second portion is also configured to be shared with a client device that utilizes the service access data to access the digital service. Digital content is generated based on the specified portions and is transmitted. Upon access by the client device to the digital service, an indication of receipt of the first portion of the resource allocation is displayed by the content provider system.

Abstract: A method for identifying and ranking potentially privileged documents using a machine learning topic model may include receiving a set of documents. The method may also include, for each of two or more documents in the set of documents, extracting a set of spans from the document, generating, using a machine learning topic model, a set of topics and a subset of legal topics for the set of spans, generating a vector of probabilities for each span with a probability being assigned to each topic in the set of topics for the span, assigning a score to one or more spans in the set of spans by summing the probabilities in the vector that are assigned to a topic in the subset of legal topics, and assigning a score to the document. The method may further include ranking the two or more documents by their assigned scores.