231 results about "Alphanumeric" patented technology

The systems and methods disclosed herein receive an output generation request from that includes input for generating an output using a language model. The input includes a set of alphanumeric characters associated with operative standards for a first set of actions. The system divides the set of alphanumeric characters into text subsets. For each text subset, a vector representation is determined. Prompts are created for each vector representation including the set of alphanumeric characters, query contexts, keywords, and / or the text subset. Each vector representation's prompt is input into the language model, which generates a second set of actions of related actions, where subsequently generated actions are based on prior generated actions. The system aggregates the second set of actions into a third set of actions and displays a graphical layout. The graphical layout displays a representation of the set of alphanumeric characters and the corresponding actions.

The technology evaluates the compliance of an AI application with predefined vector constraints. The technology employs multiple specialized models trained to identify specific types of non-compliance with the vector constraints within AI-generated responses. One or more models evaluate the existence of certain patterns within responses generated by an AI model by analyzing the representation of the attributes within the responses. Additionally, one or more models can identify vector representations of alphanumeric characters in the AI model's response by assessing the alphanumeric character's proximate locations, frequency, and / or associations with other alphanumeric characters. Moreover, one or more models can determine indicators of vector alignment between the vector representations of the AI model's response and the vector representations of the predetermined characters by measuring differences in the direction or magnitude of the vector representations.

Toggling between different arrangements of discrete graphical elements conveying representations of alphanumeric data in a non-alphanumeric manner includes: causing an initial arrangement of tiles on a display. The tiles are associated with items including first and second characteristics having first and second alphanumeric values, and each item is associated with a common objective. Each tile adopts first and second visual characteristics. The tiles are grouped based on the second visual characteristics, and are arranged to visualize progress toward the common objective. In response to a trigger, underlying first alphanumeric values of each item are aggregated on a grouping-by-grouping basis, and the groupings are merged into an associated merged tile expressing the second visual characteristic. The merged tiles have different sizes based on the aggregating of the underlying associated first alphanumeric values. The merged tiles are presented on the display by juxtaposing them using the second set of visual characteristics.

The systems and methods disclosed herein obtain a set of alphanumeric characters defining constraints for agents and the agents' operational data. Each agent uses an output from a first set of artificial intelligence (AI) models and predefined objectives to autonomously generate proposed actions for execution on software application(s). For each agent, a second set of AI models evaluates the agent by identifying gaps in the proposed actions by comparing them with the expected actions. Using a third set of AI models and the identified gaps, the systems modify the proposed actions by adding, altering, or removing actions from the proposed actions.

Various embodiments of the technology described herein cause an LLM to intelligently process data based on a user query and a schema determined for a data set. Certain embodiments programmatically leverage an LLM and utilize its output based on a user query. In this manner, data is processed without the LLM having to access an entire data set, and instead only utilizes information associated with the user query and the schema. The schema comprises a textual description, such as a string of alphanumeric characters, that describes the data, data types, and / or data structure of the data set. Embodiments of the technology described herein are performed by an LLM interface layer separate from a user device layer and an LLM layer. The LLM interface layer is positioned between an LLM abstraction layer and an application layer by which a user can interface with the LLM interface layer.

Presented herein are interactive graphical user interfaces (GUIs) for providing automated, real-time interactive assistance to a human user creating electronic documents. In certain embodiments, the GUI includes an alphanumeric text processing region, an AI-generated text region, and a reference list region. The AI-generated text region contains one or more alternative blocks of text responsive to a user query and based on specific references (e.g., articles, books, or other written works). The references are listed in the reference list region of the GUI.

The systems and methods disclosed herein receives, from a computing device, operational data indicating software or hardware assets used on informational assets, and obtains set of alphanumeric characters defining operative boundaries for expected system assets, which include a set of common attributes. Using the set of attributes, a first set of AI models determines observed system assets from the operational data, each with specific features. A second set of AI models associates each information asset with the corresponding observed system assets. For each observed system asset, a third set of AI models identifies criteria within the alphanumeric characters, compares the criteria with the asset's features to identify gaps, and generates actions to ensure the observed system asset meets the identified criteria.

Described herein are technologies related to tokenizing alphanumeric text through use of a tokenization algorithm that is at least partially implemented as a finite state machine. The tokenization algorithm is configured to output numeric identifiers that represent tokens or sub-tokens in the alphanumeric text.

The systems and methods disclosed herein receive an output generation request from that includes input for generating an output using a language model. The input includes a set of alphanumeric characters associated with operative standards for a first set of actions. The system divides the set of alphanumeric characters into text subsets. For each text subset, a vector representation is determined. Prompts are created for each vector representation including the set of alphanumeric characters, query contexts, keywords, and / or the text subset. Each vector representation's prompt is input into the language model, which generates a second set of actions of related actions, where subsequently generated actions are based on prior generated actions. The system aggregates the second set of actions into a third set of actions and displays a graphical layout. The graphical layout displays a representation of the set of alphanumeric characters and the corresponding actions.

A method including transmitting, by a device to a storage server, a request to receive encrypted content and one or more encrypted keys stored in association with the encrypted content; receiving, by the device from the storage server, the encrypted content and the one or more encrypted keys; receiving, by the device, a master string of alphanumeric characters; determining, by the device, a master key based at least in part on the master string; decrypting, by the device based at least in part on utilizing the master key, an encrypted assigned private key associated with the device to determine an assigned private key, the encrypted assigned private key being included in the one or more encrypted keys; and decrypting, by the device, the encrypted content based at least in part on utilizing the assigned private key is disclosed. Various other aspects are contemplated.

The systems and methods disclosed herein obtain a set of alphanumeric characters defining constraints for agents and the agents' operational data. Each agent uses an output from a first set of artificial intelligence (AI) models and predefined objectives to autonomously generate proposed actions for execution on software application(s). For each agent, a second set of AI models evaluates the agent by identifying gaps in the proposed actions by comparing them with the expected actions. Using a third set of AI models and the identified gaps, the systems modify the proposed actions by adding, altering, or removing actions from the proposed actions.

The systems and methods disclosed herein receive alphanumeric characters defining operative boundaries for expected model use cases, along with operational data. The expected model use cases share common attributes, which are used by a first AI model to construct observed model use cases from the operational data. Each observed model use case includes features such as a text-based description, expected input and output,? AI model(s) generating the expected output from the input, and / or data supporting the AI models. For each observed model use case, a second AI model maps the alphanumeric characters and features to a risk category, selecting from multiple risk categories based on the level of risk associated with the features. The system identifies criteria for the observed model use case within the alphanumeric characters and generates gaps by comparing the criteria with the features of the observed model use case.

The systems and methods disclosed herein receive alphanumeric characters defining operative boundaries for expected model use cases, along with operational data. The expected model use cases share common attributes, which are used by a first AI model to construct observed model use cases from the operational data. Each observed model use case includes features such as a text-based description, expected input and output, AI model(s) generating the expected output from the input, and / or data supporting the AI models. For each observed model use case, a second AI model maps the alphanumeric characters and features to a risk category, selecting from multiple risk categories based on the level of risk associated with the features. The system identifies criteria for the observed model use case within the alphanumeric characters and generates gaps by comparing the criteria with the features of the observed model use case.

Systems and methods are disclosed comprising techniques for record-to-event conversion, such as retrieving at least one alphanumeric record associated with a monitored digital communication transmitted among two or more users, generating a time-enumerated data structure that stores an event entry set for the monitored digital communication, selectively identifying at least one discrete event for the monitored digital communication, generating one or more relevance scores for the at least one discrete event, identifying at least one valid discrete event from the at least one discrete event, generating an event attribute set for the at least one valid discrete event, updating the normalized event attribute set for a new event entry within the event entry set of the time-enumerated data structure, and transmitting the updated time-enumerated data structure within an elapsed duration after retrieving the at least one alphanumeric record.

The systems and methods disclosed herein receive an output generation request from that includes input for generating an output using a language model. The input includes a set of alphanumeric characters associated with operative standards for a first set of actions. The system divides the set of alphanumeric characters into text subsets. For each text subset, a vector representation is determined. Prompts are created for each vector representation including the set of alphanumeric characters, query contexts, keywords, and / or the text subset. Each vector representation's prompt is input into the language model, which generates a second set of actions of related actions, where subsequently generated actions are based on prior generated actions. The system aggregates the second set of actions into a third set of actions and displays a graphical layout. The graphical layout displays a representation of the set of alphanumeric characters and the corresponding actions.

The present invention relates to an item organisation system (100) that includes: one or more storage labels (200), each including a two-dimensional label region (202) containing: a storage attribute code (203) including visible machine-readable indicia (204) encoding data; and a receptacle identifier (210), the receptacle identifier comprising a string of one or more human readable alphanumeric characters, wherein the data encoded by the visible machine-readable indicia includes the receptacle identifier; and a computer program including instructions, which when executed by one or more processors of an electronic device, cause the electronic device to capture an image of the storage attribute code (203) and to decode the data encoded therein to generate decoded data.

Upon an attempt to access a service of a third-party server, full-duplex password-less authentication provides a one-time password to the user displayed at the client device and at a mobile device associated with the user. The user verifies the access by comparing the one-time password displayed at the mobile device and the one-time password displayed at the client device. In some embodiments, the one-time password is displayed as a picture while in other embodiments, a combination of a picture the one-time password is displayed as a picture and a set of alphanumeric characters for ease in making the comparison. The user determines whether to accept or deny the authentication sequence after a simple visual comparison.

The technology evaluates the compliance of an AI application with predefined vector constraints. The technology employs multiple specialized models trained to identify specific types of non-compliance with the vector constraints within AI-generated responses. One or more models evaluate the existence of certain patterns within responses generated by an AI model by analyzing the representation of the attributes within the responses. Additionally, one or more models can identify vector representations of alphanumeric characters in the AI model's response by assessing the alphanumeric character's proximate locations, frequency, and / or associations with other alphanumeric characters. Moreover, one or more models can determine indicators of vector alignment between the vector representations of the AI model's response and the vector representations of the predetermined characters by measuring differences in the direction or magnitude of the vector representations.

Devices and techniques are generally described for privacy preservation for computer vision models. In some examples, a first field of text and a second field of text may be detected in a first image. A first alpha-numeric text string may be detected in the first field and a second alpha-numeric text string may be detected in the second field. A first sub-image including the first alpha-numeric text string may be generated and a second sub-image including the second alpha-numeric text string may be generated. The first sub-image may be sent to a first computing device for annotation and the second sub-image may be sent to a second computing device for annotation.

Exhibiting alphanumeric data as organized and segmented as graphical distinctions includes accessing a data structure including common objective items including a first and second characteristics, requesting that items from the data structure be graphically grouped for visualizing progress toward the common objective, analyzing the items to segment items in a first partitioning and a second partitioning, generating differently sized tiles associated with the items conveying magnitudes of the first and second characteristics, the second magnitude being greater than the first magnitude, wherein the second partitioning causes an organizational structure dividing tiles into distinct collections, wherein the tiles of the distinct collections visually differ based on the different sizes, and the distinct collections are presented on a common display.

The technology evaluates the compliance of an AI application with predefined vector constraints. The technology employs multiple specialized models trained to identify specific types of non-compliance with the vector constraints within AI-generated responses. One or more models evaluate the existence of certain patterns within responses generated by an AI model by analyzing the representation of the attributes within the responses. Additionally, one or more models can identify vector representations of alphanumeric characters in the AI model's response by assessing the alphanumeric character's proximate locations, frequency, and / or associations with other alphanumeric characters. Moreover, one or more models can determine indicators of vector alignment between the vector representations of the AI model's response and the vector representations of the predetermined characters by measuring differences in the direction or magnitude of the vector representations.

The systems and methods disclosed herein obtain a set of alphanumeric characters defining constraints for agents and the agents' operational data. Each agent uses an output from a first set of artificial intelligence (AI) models and predefined objectives to autonomously generate proposed actions for execution on software application(s). For each agent, a second set of AI models evaluates the agent by identifying gaps in the proposed actions by comparing them with the expected actions. Using a third set of AI models and the identified gaps, the systems modify the proposed actions by adding, altering, or removing actions from the proposed actions.

The invention belongs to the technical field of natural language processing, and discloses a large language model interface calling method based on privacy protection, which specifically comprises the following steps of: 1, privacy entity content identification: identifying privacy entity content in a text through regular expression matching, named entity identification and self-defined dictionary matching technologies, the information is sensitive information in the text. According to the method, sensitive data is changed through a method of calling a large language model interface by utilizing a privacy protection technology and modes of replacing random values, changing letters into random letters, changing numbers into random numbers and randomly replacing characters with characters, so that the format of original data can be reserved to a certain extent; meanwhile, safe transmission and accuracy of the data (especially when sensitive information is involved) on the internet can be guaranteed, the privacy leakage problem existing in the large language model calling process can be effectively solved, and the safety privacy of the data is protected.

A method of operating an apparatus for receiving one or more mail items is described. The method includes activating the communication between a user interface device and a remote device; generating, by an electronic control unit, at least one random alphanumeric code; sending, via a network interface device, the random alphanumeric code to the remote device; sending, by the remote device and via the network interface device, an unlock signal based on the random alphanumeric code to the electronic control unit. An unlock signal is processed by the electronic control unit to unlock the movable closure element and allow one or more mail items to be introduced in or withdrawn from the internal cavity.

A method comprising receiving a first control signal indicating that a first key on a keyboard is being pressed, the first key functionally assigned as a (logical) modifier; sending a first make command indicating that the modifier is active; receiving a second control signal indicating that a second key on the keyboard is contemporaneously being pressed, the second key functionally assigned as a non-modifier, alphanumeric character; accessing a key remapping database stored on the keyboard that includes key triggers and remapped key targets; in response to determining that the combination of the first key and second keys are included as a key trigger in the key remapping database: sending a first break command indicating that the modifier is inactive; and sending a second make command corresponding to a key target of the remapping database that is associated with the combination of the first and second keys being pressed.

Disclosed herein are system, method, and computer program product embodiments for vaultless tokenization. Alphanumeric values may be determined based on numeric values generated from a hash of numeric user information shuffled through a plurality of randomly generated alphanumeric tables. The numeric user information and the alphanumeric values may be used to generate a table index. Shuffled numeric user information may be generated based on the table index and a plurality of randomly generated numeric tables, and transformed to alphanumeric user information (e.g., via format-preserving encryption, additive cipher, etc.). Each character of the alphanumeric user information may be shuffled through a different alphanumeric table of the plurality of alphanumeric tables identified for the character based on the table index. Moreover, an alphanumeric token may be generated based on the shuffled characters of the alphanumeric user information.

The systems and methods disclosed herein receive an output generation request from that includes input for generating an output using a language model. The input includes a set of alphanumeric characters associated with operative standards for a first set of actions. The system divides the set of alphanumeric characters into text subsets. For each text subset, a vector representation is determined. Prompts are created for each vector representation including the set of alphanumeric characters, query contexts, keywords, and / or the text subset. Each vector representation's prompt is input into the language model, which generates a second set of actions of related actions, where subsequently generated actions are based on prior generated actions. The system aggregates the second set of actions into a third set of actions and displays a graphical layout. The graphical layout displays a representation of the set of alphanumeric characters and the corresponding actions.

The systems and methods disclosed herein receive alphanumeric characters defining operative boundaries for expected model use cases, along with operational data. The expected model use cases share common attributes, which are used by a first AI model to construct observed model use cases from the operational data. Each observed model use case includes features such as a text-based description, expected input and output,? AI model(s) generating the expected output from the input, and / or data supporting the AI models. For each observed model use case, a second AI model maps the alphanumeric characters and features to a risk category, selecting from multiple risk categories based on the level of risk associated with the features. The system identifies criteria for the observed model use case within the alphanumeric characters and generates gaps by comparing the criteria with the features of the observed model use case.

Training images mimicking handwritten text including one or more non-alphanumeric characters are used at least for training an optical character recognition (OCR) machine learning model. The train images are generated as follows. A character sequence format and the non-alphanumeric characters are specified. Character sequences in the specified character sequence format with the specified non-alphanumeric characters are generated using a regular expression pattern for the specified character sequence format. Synthetic handwritten images are generated for each character sequence. A handwritten text image-generating machine learning model for generating the training images is trained using at least the generated synthetic handwritten images. The training images mimicking the handwritten text including the non-alphanumeric characters are generated using the trained handwritten text image-generating machine learning model.

Methods and systems for parallel processing of batch communications during data validation using a plurality of independent processing streams. For example, the system may receive a plurality of communications for batch processing during a predetermined time period. The system may process, with a batch configuration file, a first alphanumeric data string of a first communication of the plurality of communications. The system may process, with the batch configuration file, a second alphanumeric data string of a second communication of the plurality of communications. The system may direct the first communication to a first micro-batch for processing within the predetermined time period based on the first metadata tag, wherein the first micro-batch is processed using a first validation and enrichment protocol and a first micro-batch configuration file, wherein the first validation and enrichment protocol and the first micro-batch configuration file are specific to the first source.