Abstract: A method may include sending a query to one of a cobot or an LLM, where the query may be formed to trigger the cobot to perform a target objective, and where the LLM may be constrained to respond to requests to cause the cobot to perform a given objective with a set of human-readable discrete tasks to accomplish the given objective. The method may also include receiving a target set of human-readable discrete tasks from the LLM, where the target set of human-readable discrete tasks may correspond to target computer-readable code that is configured to cause the cobot to perform the target objective. The method may additionally include auditing the target set of human-readable discrete tasks, and, based on the target set of human-readable discrete tasks passing the audit, authorizing the cobot to implement the target computer-readable code to perform the target objective.

Abstract: A robot may include a body, a control system, and a control interface. The control system may be configured to cause the robot to transition between an autonomous mode and an intervention mode. In the autonomous mode, the control system may be configured to autonomously control the body within an environment, In the intervention mode, the control system may be configured to control the body within the environment based on operator input. The control interface may be configured to receive the operator input.

Abstract: A method may include obtaining an artificial intelligence (AI) model configured to identify series of tasks to be performed by a robot in accordance with general parameters. The method may include obtaining data indicating a particular parameter corresponding to a particular environment. The method may include identifying, using the AI model, the particular parameter as corresponding to the particular environment. The particular parameter may be used by the AI model to identify the series of tasks to be performed by the robot such that the series of tasks are performed in accordance with the general and the particular parameters. The method may include identifying, using the AI model and the particular parameter, a series of tasks to be performed by the robot to complete an operation in the particular environment. The method may include causing the robot to autonomously perform the series of tasks in the particular environment.

Abstract: A method may include receiving input data identifying an operation to be completed in an environment. The method may also include identifying, using an artificial intelligence (AI) model, a series of tasks to be performed by robots to complete the operation based on the input data. In addition, the method may include identifying a subset of the robots to perform the series of tasks based on capabilities to be used to perform the series of tasks. Further, the method may include causing the subset of the robots to autonomously perform the series of tasks to complete the operation.

Abstract: A method may include obtaining input data corresponding to a robot. The method may also include generating, using an artificial intelligence (AI) model, output data based on the input data. The output data may be representative of a state of the robot. In addition, the method may include identifying, using an AI policy model, a set of tasks to be performed by the robot based on the output data. The set of tasks may involve movement of the robot associated with the state of the robot to perform an operation. The method may include causing the robot to autonomously perform the set of tasks to complete the operation.

Abstract: One or more embodiments of the present disclosure may include a method. The method may include receiving an instruction identifying an operation to be completed by a robot. The method may also include identifying, using an artificial intelligence (AI) model, a task to be performed by the robot to complete the operation. Additionally, the method may include identifying, using the AI model, a series of movements to be made by the robot to perform the task. Further, the method may include identifying, using the AI model, a series of raw electrical signals configured to cause actuators to move the robot in accordance with the series of movements. The method may include generating the series of raw electrical signals to cause the actuators to move the robot in accordance with the series of movements and cause the robot to perform the task.

Abstract: A method may include sending a query to one of a cobot or an LLM, where the query may be formed to trigger the cobot to perform a target objective, and where the LLM may be constrained to respond to requests to cause the cobot to perform a given objective with a set of human-readable discrete tasks to accomplish the given objective. The method may also include receiving a target set of human-readable discrete tasks from the LLM, where the target set of human-readable discrete tasks may correspond to target computer-readable code that is configured to cause the cobot to perform the target objective. The method may additionally include auditing the target set of human-readable discrete tasks, and, based on the target set of human-readable discrete tasks passing the audit, authorizing the cobot to implement the target computer-readable code to perform the target objective.

Abstract: A method may include sending a query to one of a cobot or an LLM, where the query may be formed to trigger the cobot to perform a target objective, and where the LLM may be constrained to respond to requests to cause the cobot to perform a given objective with a set of human-readable discrete tasks to accomplish the given objective. The method may also include receiving a target set of human-readable discrete tasks from the LLM, where the target set of human-readable discrete tasks may correspond to target computer-readable code that is configured to cause the cobot to perform the target objective. The method may additionally include auditing the target set of human-readable discrete tasks, and, based on the target set of human-readable discrete tasks passing the audit, authorizing the cobot to implement the target computer-readable code to perform the target objective.

Abstract: A phone application state management mechanism is described. The mechanism adopts the state storage format widely known as “Cookies” to store information in a cross-application standard format. Cookies for multiple users are stored on a single machine that retrieves phone applications using a hypertext transfer protocol (HTTP). Suitable state information for a particular user is automatically retrieved in connection with HTTP requests. Further, by phone based applications can set the values of suitable cookies. An enforced access policy can be used to prevent an application provided by a first legal entity from accessing state information stored by an application provided by another legal entity. The approach can also be used to reduce the passing of telephone identifying information between applications for user identification purposes.

Abstract: A system and method are provided for an intermediate party to facilitate an interactive telephony session between a customer and a sender of a request for the session. The sender can provide the request to the intermediate party via electronic mail. The request includes the information to initiate the interactive telephony session with the customer as well as the information to conduct that session. In one embodiment, the information to conduct the interactive telephony session includes a URL that provides an application in VoiceXML (Voice Extensible Markup Language). The intermediate party can initiate and conduct the session at the appropriate time and provide the sender with a status regarding the session. During the session, the customer can respond with voice or DTMF inputs to various prompts, thereby proceeding with a transaction and/or providing valuable feedback to the sender.

Abstract: A method for providing a gray water reclamation system includes connecting one or more gray water sources and one or more black water sources to a main drain line. A collection valve is also connected to the main drain line, the collection valve having (i) an inlet connected to the main drain line down stream from at least one of the black water sources, and (ii) an outlet connectable to either a gray water drain line or a black water drain line. A black water flow sensor is configured to detect water flow from a black water source, wherein the black water flow sensor is operable to transmit a black water event signal upon detecting water flow from the black water source, and wherein the collection valve is operable to connect the outlet to the black water drain line if the black water event signal is received thereby.

Abstract: Occluder nodes, which are planes with given width and height in object space that block the rendering of objects behind them in world space, are used to compute a conservative set of visible objects from a current viewpoint. Occluder nodes can be incorporated by scene designers and authoring tools to perform many high performance rendering techniques, including culling geometry in densely occluded models, region based scene division, and using images to represent far distant geometry. Using this simple primitive, generalized scene viewers are provided that allow interactive rendering of complex worlds, including multi-user environments, architectural models, three dimensional games, and large engineering models.

Abstract: A method of and apparatus for supporting intelligent call routing (ICR) systems multiple vendors, in a vendor neutral fashion using a computer is described. One embodiment has a voice program send a call routing request using an HTTP format to a call routing program. The call routing program decodes the HTTP request and identifies the appropriate vendor-specific communication format and communications method for talking to the ICR system specified in the HTTP request. The call routing program sends the request and receives the answers from the ICR system in the vendor specific formats. The call routing program provides the ICR system response back to the voice program in a vendor neutral fashion. This approach allows voice programs to easily be written that work with multiple ICR systems and allow component reuse of call routing code amongst programs that end up working with multiple systems.