Abstract: A system that generates an explicit plan to separately guide multiple mechanisms to collectively generate a response to an event. The multiple mechanisms can include a program generation mechanism and a response generation mechanism, each of which may be provided with the generated plan. Each mechanism can utilize a prediction engine, such as for example a machine learning model or large language model, to perform a task based on the generated plan. The event can include an event triggered based on an interaction between an automated agent and a customer. The plan may be generated using a state machine, a machine learning model, such as for example a large language model, or some other mechanism. The plan can be structured such that it can be interpreted and audited for conformance with predefined constraints.

Abstract: A system automatically imputes automated agent decision logic. An interaction record is used with other data to determine an optimal and efficient logical path from a starting point in an interaction record to the end of the interaction record between an automated agent and a client. The logical path can be determined using a process that works backward, from the end point to the starting point, to determine the optimal path. Once complete, the resulting interaction record is supplemented with additional information regarding what states and/or steps were taken in the agent architecture specification to navigate through the interaction record.

Abstract: A system provides a language model that utilizes an optimized parser and operates at the byte- level in a context free grammar (CFG) and a tokenizer vocabulary. The CFG is constrained and automatically transformed to a byte-level grammar. A prediction mechanism is used to predict the allowed sequences of bytes that can appear after a given prefix. In some instances, only the next token is constrained so that candidates for the next token can be intersected with tokens in the tokenizer vocabulary. To achieve this, lattice parsing is performed using a finite state automaton (FSA) that is generated and then minimized.

Abstract: A system generates a response to a change of state by an automated agent using structure-based constraints. The system receives input regarding an interaction with a client, external data, and current operations data. The received input is used to generate a content sketch. The content sketch can include a plan for how to generate the response. The program language can be constrained to a set of known atoms, such as for example particular specified function, values, and flow control. Relevant instructions for generating a response are generated from the content sketch and an instruction bank. The relevant instructions are then used to generate a response. The generated response may then be executed by the automated agent system.

Abstract: The present technology, roughly described, generates grammars, such as context free grammars, dynamically and by induction, that can specify constraints for a decoding system. The grammar can be created to support an automated agent involved in an interaction (e.g., a conversation) with a client. In some instances, grammars can be generated dynamically based on copying portions of an interaction history and based on business logic rules. Grammars can also be induced automatically from API specifications and collections of strings. The newly generated context free grammars can be used by a language model to perform constrained decoding.

Abstract: The system provides improved controls for an automated agent by generating a declarative specification for an automated agent. The declarative specification is generated based at least in part from previous conversation data associated with interactions between an automated agent and a customer. The policies may include general policies and specific policies. A general policy is one that is applied to all automated agents. After creating the declarative specification, an automated agent can interact with a customer in an interaction based on the specification. The automated agent responses are evaluated based on checklists associated with the policies to determine if a response was proper.

Abstract: A system that generates relevant and vetted training data using intelligent simulated users and evaluation of conversation data. A simulated user and an automated agent engage in a conversation to generate conversation and/or interaction data. The simulated user is guided by scenarios which are generated based on one or more controls to be followed by the automated agent. Using a simulated user driven by control-derived scenarios ensures the ensuing conversation data is relevant to the desired scope of operation for the automated agent. The conversation data is evaluated based on the controls to confirm the automated agent actions and responses followed the controls properly. Evaluating the conversation data based on the controls ensures that conversation data associated with properly followed controls is used as subsequent training data.

Abstract: A system providing a state-driven automated agent. The state machine can include multiple nested state machines to receive, process, and generate responses to a user inquiry received through a chat application. The state machine has a plurality of states, and navigates between states based on one or more machine learning (ML) models. In some instances, each state is associated with a machine learning model that can predict what state the state machine should transition to from its current state. The machine learning model(s) can be implemented using one or more large language models (LLM). The state-driven automated agent includes an auditing state that analyzes a predicted response to a user inquiry and identifies errors with the response. The errors may be identified and automatically self-corrected.

Abstract: A system for providing and managing an automated agent. The automated agent may interact with a customer and utilize rules and instructions to determine a response to the customer and actions to perform. The customer interactions by the automated agent are driven by a set and/or subset of instructions that can be analyzed before each automated agent response or action. The subset of instructions, customer conversational input, and other content can be processed by a machine learning model, which may be implemented as a large language model (LLM).

Abstract: A system for generating instructions based on part from human training materials, wherein the generated instructions are used to train automated agents. The training materials may include training manuals, knowledge-base documents, articles, and other content. The training materials are processed and converted to a format that is digestible by a machine learning model. The processed training materials can be submitted as a prompt, along with role information and prompt instruction information. The prompt instructions provided with the role and training materials may direct the machine learning model to find relevant generated instructions from the provided training materials. The machine learning model processes the prompt and outputs extracted instructions from the content. The instruction document may be stored in a vector database for later use.

Abstract: A label applicator device for use with a printer or combination printer/cutting device for applying a printed label to an object. The device comprises a mounting plate for mechanically connecting the device to the printer or combination printer/cutting device, a positioning arm, an adjusting element, and a tamping plate attached to the adjusting element. The positioning arm movably retains the adjusting element to control the depth that the tamping plate moves when applying a label exiting the printer or combination printer/cutting device to a product. The device further comprises an electronic interface port for electrically communicating with the printer or combination printer/cutting device. Alternatively, a printer or a combination printer/cutting device may be directly integrated into the label applicator device. The label applicator device is adaptable for use with both new and existing printers and cutting devices.

Abstract: There is disclosed a portable printer and a method of selectively printing on any one of a plurality of label rolls housed in the printer, or on a wide roll housed in the printer.