Abstract: Methods and systems are disclosed that include receiving problem information from a user interface at a resolution identification system, receiving product information at the resolution identification system, and performing machine learning analysis of the problem information and the product information. The machine learning analysis produces one or more model outputs, and is performed by a machine learning system of the resolution identification system, using one or more machine learning models. Each of the one or more machine learning models produces a corresponding one of the one or more model outputs. Such a method can further include generating resolution information by performing an action identification operation using the one or more model outputs, and outputting the resolution information from the resolution identification system. The resolution information is output to the user interface.

Abstract: A system of one or more computers can be configured to facilitate the design of a service. The disclosed system may operate to add a process block to a service design structure for the service. The process block is provided to a trained AI/ML process prediction model. The trained AI/ML process prediction model suggests one or more further process blocks for addition to the service design structure based, at least in part, on the addition of the process block to the service design structure. In certain embodiments, a process block is selected from the suggested one or more further process blocks and added to the service design structure. Other embodiments of this aspect of the disclosure include corresponding computer systems, apparatus, and computer programs recorded on one or more computer storage devices, each configured to perform the actions of the methods.

Abstract: The described technology is generally directed towards processing various customer input data to extract frequently recurring customer experience themes, including positive and negative sentiment regarding customer experiences. Natural language processing, image processing, speech recognition and/or computer vision techniques can be used on customer-related data to determine themes, tests and scenarios, as well as discover insights that can be used to improve customer experiences. The technology can be used to recreate a customer engagement, journey and overall experience by designing test scenarios around failure themes.

Abstract: Systems and methods for assessing the skills of a customer support agent using one or more Artificial Intelligence/Machine Learning (AI/ML) models are disclosed. In at least one embodiment, one or more benchmarks against which the performance of the customer support agent is to be measured are established. The one or more benchmarks may be derived through direct and/or indirect analysis of historical customer service data by an AI/ML benchmark model. In at least one embodiment, data relating to performance of the customer support agent during a customer call is monitored. In at least one embodiment, the AI/ML benchmark model is used to determine one or more benchmark scores identifying whether the customer support agent is meeting the one or more benchmarks.

Abstract: A sample rack includes a housing that has multiple spaces or compartments each for receiving and retaining sample containers of various sizes. The sample rack includes dual hooks on the ends for engaging a sample rack handling system. Chamfers formed in the housing of the sample rack assist in placing and removing the sample rack from a sample rack handling system. The sample tube rack also includes a handle that extends upward from one end and includes gripping features. A groove and bar, incorporated into each sample rack, are able to selectively interlock with adjacent racks to assist in lifting multiple sample racks together.

Abstract: Systems and methods are provided for recognizing various sample containers carried in a rack. The systems and methods are performed to identify sample containers in the rack and detect various characteristics associated with the containers and/or the rack, which are evaluated to determine the validity and/or types of the containers in the rack.

Abstract: In some examples, a server may determine that a case, created to address an issue of a computing device, is closed and perform an analysis of a communication session between a user and a technician and the steps taken by the technician to resolve the issue. Machine learning may be used on results of the analysis to predict potential pain points. For example, steps that take longer than average and during which particular words spoken by the user increase in pitch and/or volume may be predicted to be potential pain points. The machine learning may create questions for inclusion in a custom survey based on the potential pain points. The custom survey may be presented to the user. The answers may be correlated with the potential pain points to determine actual pain points in the steps taken to resolve the issue.

Abstract: The described technology is generally directed towards processing various customer input data to extract frequently recurring customer experience themes, including positive and negative sentiment regarding customer experiences. Natural language processing, image processing, speech recognition and/or computer vision techniques can be used on customer-related data to determine themes, tests and scenarios, as well as discover insights that can be used to improve customer experiences. The technology can be used to recreate a customer engagement, journey and overall experience by designing test scenarios around failure themes.

Abstract: Systems and methods are provided for automatically tailoring treatment of samples in sample containers carried in a rack. The systems and methods may identify sample containers in the rack and/or detect various characteristics associated with the containers and/or the rack. This information may then be used to tailor their treatment, such as by aspirating and dispensing fluid from the sample containers in a way that accounts for the types of the samples/containers carrying them.

Abstract: A system to intelligently guide a customer along a service engagement path is disclosed. In certain embodiments, a customer persona for the customer is determined as well as the current location of the customer in a process interaction along the service engagement path. The customer persona of the customer and current location of the customer along the service engagement path may be provided to an Artificial Intelligence/Machine Learning (AI/ML) path guidance model. Intelligent guidance data is received from the AI/ML path guidance model, where the intelligent guidance data corresponds to a suggested location along the service engagement path based on the customer persona and current location of the customer along the service engagement path. The customer is directed to the suggested location in the service engagement path.

Abstract: Methods and systems are disclosed that include the identification of one or more actions in an action flow that is intended to resolve a problem, and to guide a user through the one or more actions of such an action flow, dynamically adjusting the action flow during such guidance and/or subsequent thereto, using machine learning techniques. In some embodiments, such a method can include. for example, receiving outcome information at a machine learning system (where the outcome information is associated with an action of an action flow and the action flow comprises a plurality of actions), generating update information (where the update information is generated by the machine learning system based, at least in part, on the outcome information), and updating action information of the action (where the action information is updated based, at least in part, on the update information).

Abstract: In some examples, a server may use machine learning to determine, based on service requests associated with multiple computing devices, that a component included in the multiple computing devices is predicted to fail at a particular date. The server may use the machine learning to determine, based on the particular date and an expiration date of a warranty associated with the computing devices, that a customer may initiate a service request on a predicted service date. The machine learning may determine recommended solutions including purchasing an extended warranty, purchasing extended services (e.g., on-site service), purchasing a depot clinic service, or trading in the multiple computing devices for newer computing devices. In response to receiving a purchase order to purchase at least one of the recommended solutions, the server may initiate at least one of the recommended solutions.

Abstract: Systems and methods for assessing the skills of a customer support agent using one or more Artificial Intelligence/Machine Learning (AI/ML) models are disclosed. In at least one embodiment, one or more benchmarks against which the performance of the customer support agent is to be measured are established. The one or more benchmarks may be derived through direct and/or indirect analysis of historical customer service data by an AI/ML benchmark model. In at least one embodiment, data relating to performance of the customer support agent during a customer call is monitored. In at least one embodiment, the AI/ML benchmark model is used to determine one or more benchmark scores identifying whether the customer support agent is meeting the one or more benchmarks.

Abstract: In some examples, a server may determine that a case, created to address an issue of a computing device, is closed and perform an analysis of a communication session between a user and a technician and the steps taken by the technician to resolve the issue. Machine learning may be used on results of the analysis to predict potential pain points. For example, steps that take longer than average and during which particular words spoken by the user increase in pitch and/or volume may be predicted to be potential pain points. The machine learning may create questions for inclusion in a custom survey based on the potential pain points. The custom survey may be presented to the user. The answers may be correlated with the potential pain points to determine actual pain points in the steps taken to resolve the issue.

Abstract: A system of one or more computers can be configured to facilitate the design of a service. The disclosed system may operate to add a process block to a service design structure for the service. The process block is provided to a trained AI/ML process prediction model. The trained AI/ML process prediction model suggests one or more further process blocks for addition to the service design structure based, at least in part, on the addition of the process block to the service design structure. In certain embodiments, a process block is selected from the suggested one or more further process blocks and added to the service design structure. Other embodiments of this aspect of the disclosure include corresponding computer systems, apparatus, and computer programs recorded on one or more computer storage devices, each configured to perform the actions of the methods.

Abstract: In some examples, a server may receive a user communication describing an issue with a computing device and assign a case to the computing device. The server may determine previously provided telemetry data (e.g., logs and usage data sent by the computing device) as well as previous cases associated with the computing device. Machine learning may be used to predict, based on the user communication, the telemetry data, and the previous cases, a predicted cause of the issue, a predicted time to close the case, and a predicted set of steps to resolve the issue. The machine learning may predict a bottleneck in at least one step of the set of steps that causes the predicted time to close to exceed a threshold and predict one or more actions to address the bottleneck. The server may automatically perform at least one action of the one or more actions.

Abstract: Methods and systems are disclosed that include receiving problem information from a user interface at a resolution identification system, receiving product information at the resolution identification system, and performing machine learning analysis of the problem information and the product information. The machine learning analysis produces one or more model outputs, and is performed by a machine learning system of the resolution identification system, using one or more machine learning models. Each of the one or more machine learning models produces a corresponding one of the one or more model outputs. Such a method can further include generating resolution information by performing an action identification operation using the one or more model outputs, and outputting the resolution information from the resolution identification system. The resolution information is output to the user interface.

Abstract: Systems and methods are provided for recognizing various sample containers carried in a rack. The systems and methods are performed to identify sample containers in the rack and detect various characteristics associated with the containers and/or the rack, which are evaluated to determine the validity and/or types of the containers in the rack.

Abstract: A sample rack includes a housing that has multiple spaces or compartments each for receiving and retaining sample containers of various sizes. The sample rack includes dual hooks on the ends for engaging a sample rack handling system. Chamfers formed in the housing of the sample rack assist in placing and removing the sample rack from a sample rack handling system. The sample tube rack also includes a handle that extends upward from one end and includes gripping features. groove and bar, incorporated into each sample rack, are able to selectively interlock with adjacent racks to assist in lifting multiple sample racks together.