Abstract: Techniques for recording submissions of a user actions in relation to interface elements of a GUI for replay are disclosed. Data arguments generated in response to the user actions required for executing a command associated with the user actions are recorded. A system monitors execution of an application. The system detects a command or action that corresponds to submission of a user action in relation to target interface elements displayed by a GUI. When the system detects the command, the system records the user action in relation to the target interface element and data arguments selected for executing the command. When the system receives a request to replay the submission of the user action, the system retrieves the data arguments for executing the command and causes execution of the command by submitting the user action to the GUI along with the data arguments for executing the command.

Abstract: Techniques for securely accessing a computer network are described. An access provider sends network access credentials to an access management device. Upon receiving the credentials, the access management device generates an image key that embeds the credentials. The access management device then presents the image key to a client device. The client device receives the image key and extracts the credentials from within the image key. The client device transmits the credentials to the access provider with an authentication request. Based on the credentials included with the authentication request, the access provider attempts to authenticate the client device. If authentication is successful, the access provider grants the client device access to the wireless network and resources accessible via the wireless network.

Abstract: Techniques are disclosed herein for onboarding users from a single tenant cloud environment to a multi-tenant cloud environment. In one aspect, a method is provided that includes in response to an eligibility status check indicating that a first cloud service instance running a first version of a cloud service in a first cloud environment is eligible for the upgrade, exporting a copy of data from the first cloud service instance to a common storage device, provisioning a second cloud service instance running on the second version of the cloud service in a second cloud environment, importing the first copy of the data from the common storage device to the second cloud service instance, and activating the second cloud service instance to run the second version of the cloud service. During the exporting, provisioning, and importing the first cloud service instance continues to run the first version of the cloud service.

Abstract: Techniques for using logit values for classifying utterances and messages input to chatbot systems in natural language processing. A method can include a chatbot system receiving an utterance generated by a user interacting with the chatbot system. The chatbot system can input the utterance into a machine-learning model including a set of binary classifiers. Each binary classifier of the set of binary classifiers can be associated with a modified logit function. The method can also include the machine-learning model using the modified logit function to generate a set of distance-based logit values for the utterance. The method can also include the machine-learning model applying an enhanced activation function to the set of distance-based logit values to generate a predicted output. The method can also include the chatbot system classifying, based on the predicted output, the utterance as being associated with the particular class.

Abstract: Techniques are disclosed for automatically generating Subjective, Objective, Assessment and Plan (SOAP) notes. Particularly, techniques are disclosed for training data collection and evaluation for automatic SOAP note generation. Training data is accessed, and evaluation process is performed on the training data to result in evaluated training data. A fine-tuned machine-learning model is generated using the evaluated training data. The fine-tuned machine-learning model can be used to perform a task associated with generating a SOAP note.

Abstract: In some aspects, a computing device may receive, at a data processing system, a set of utterances for training or inferencing with a named entity recognizer to assign a label to each token piece from the set of utterances. The computing device may determine a length of each utterance in the set and when the length of the utterance exceeds a pre-determined threshold of token pieces: dividing the utterance into a plurality of overlapping chunks of token pieces; assigning a label together with a confidence score for each token piece in a chunk; determining a final label and an associated confidence score for each chunk of token pieces by merging two confidence scores; determining a final annotated label for the utterance based at least on the merging the two confidence scores; and storing the final annotated label in a memory.

Abstract: A method includes disassembling a reference binary of a library to generate a control flow graph of the referenced binary, normalizing the control flow graph to generate a normalized graph, traversing the normalized graph to generate execution traces from the normalized graph, and generating library vector embeddings. Generating library vector embeddings includes, for each execution trace of at least a subset of the execution traces, processing the execution trace by a vector embedding model to generate a library vector embedding of the execution trace. The method further includes relating, in storage, a library identifier of the library to the plurality of library vector embeddings as a fingerprint of the library.

Abstract: Techniques for perspective-preserving seamless application switching are disclosed. A system may display a first interface using a first application. The first interface includes interface elements representing a plurality of objects. The system may detect a zoom-in command, received by the first application, requesting a particular zoom level for a first interface element, corresponding to a first object in the first plurality of objects. The system may determine that the requested zoom level exceeds a threshold. Responsive to determining that the requested zoom level exceeds the threshold, the system may display, using a second application, a second interface corresponding to the first object. The second interface may include one or more of: (a) characteristics associated with the first object that were not displayed by the first application, or (b) user input elements for executing operations associated with the first object that were not displayed by the first application.

Abstract: The present disclosure relates to intelligent network encryption of traffic between a source and a destination. In an example, a network element receives, during a session between the source and the destination, first traffic exchanged between the source and the destination. The network element determines whether a traffic exchange between the source and the destination is expected to be secured by at least one of the source or the destination at any of a network layer, a transport layer, or an application layer. The network element generates a decision whether to secure the first session at the network layer based on whether the traffic exchange is expected to be secured or unsecured. The network element implements the decision on at least one of the first traffic or second traffic exchanged between the source and the destination during the first session.

Abstract: Systems, methods, and machine-readable media to migrate data from source databases to target databases are disclosed. Data may be received, relating to the source databases and the target databases. For each source database, a migration assessment may be generated based on analyzing the data, and a migration method may be selected. A migration plan that specifies a parallel migration of a set of databases to the target databases may be created, with a first migration method to migrate a first subset of the set of databases and a second migration method to migrate a second subset of the set of databases. The parallel migration may be executed according to the migration plan may be caused so that the first subset of the set of databases is migrated with the first migration method while the second subset of the set of databases is migrated with the second migration method.

Abstract: Techniques for defining and using reusable modules to generate form control code are disclosed, including: displaying a form control implementation interface for applying form control functions to forms; receiving via the form control implementation interface: a first user input selecting a form control function of the form control functions; a second user input selecting one or more input parameters, for the form control function, that are to be extracted from the target form; a third user input selecting a target field of a target form, one or more attributes of the target field to be modified via execution of the form control function; generating form control code that extracts the one or more input parameters from form data received for the target form and applies the form control function to the one or more input parameters to modify the one or more attributes of the target field.

Abstract: Embodiments predict errors using database validation rules. Validation rules can be defined that include business logic for validating transactions performed on a database with a data model. Transactions can be performed using the database, where the database is in a post-transaction state after performance of the transactions. The database can be validated in the post-transaction state by performing the defined business logic for a subset of validation rules, where at least one validation rule fails to validate. Using a trained machine learning model, one or more errors for one or more future transactions can be predicted, the predicted errors being based on the at least one failed validation rule.

Abstract: A system and computer-implemented method for a log analytics system that can configure, collect, parse, and analyze log records in an efficient manner. Log records are accessed, each of the log records is associated with a log source. A base parser is identified for parsing a log record based on a type of the log record indicated in the log source. The log record is parsed using the base parser to extract base field values corresponding to base fields. A base-parsed log record is generated on parsing. Sub-parsers are identified using field mappings. The field mappings include base field values mapped to corresponding sub-parsers. The base-parsed log record is parsed using the sub-parsers to extract sub-fields. The sub-fields are merged to the base fields to generate and present an output that includes the parsed log record, the base fields, base field values, the sub-fields and the sub-field values.

Abstract: Techniques are disclosed to establish trust in a cluster of edge devices. An edge device cloud service can associate a first cloud-computing edge device with a fleet of cloud-computing edge devices and provision the first cloud-computing edge device with a master encryption key. The edge device cloud service can associate a second cloud-computing edge device with the fleet and provision the second cloud-computing edge device with the master encryption key and the first public encryption key. The first cloud-computing edge device can receive from the second cloud-computing edge device encrypted message data comprising the second public encryption key. The first cloud-computing edge device can decrypt the encrypted message data using the master encryption key stored in the first key store and update the first key store with the second public encryption key.

Abstract: Techniques for incremental stack walking are disclosed, including: performing a stack walk of a runtime stack, at least by traversing the runtime stack from a current frame to a root frame, to obtain a set of stack walking results; storing a cache of the set of stack walking results; and installing, on the runtime stack, a marker frame that marks a boundary of stack frames represented by the set of stack walking results.

Abstract: A technique may include receiving, by a management service a plurality of instance configurations from a client device. The technique may then include receiving, by the management service, information identifying a launch request for a compute instance. The technique may include determining, by the management service, one or more candidate shapes for the compute instance based at least in part on the plurality of instance configurations. The technique may include selecting, by the management service and from the one or more candidate shapes, a launch shape for the compute instance and launching the compute instance using the launch shape. The technique may then include providing, the client device access to the compute instance, launched based on the launch shape.

Abstract: Techniques for script-based runtime assembly of object graphs using native instructions compiled by an ahead-of-time compiler are disclosed, including: generating, based on a data structure that defines a business process, a script including instructions for assembling an object graph that represents relationships between objects used by the business process; obtaining, at runtime by a business process execution engine compiled to native instructions by an ahead-of-time compiler, the script; assembling, at runtime by the business process execution engine, the object graph based at least on the instructions in the script.

Abstract: Techniques are disclosed for fine-tuning a pre-trained machine learning model to be used by a digital assistant for supporting a user's interactions. In one aspect, a method includes accessing a set of training examples, generating a set of synthesized training examples using an iterative process including accessing a dialog script and corresponding prompt template and response template for a predefined scenario, generating one or more prompts based on the dialog script and corresponding prompt template, generating one or more responses associated with each of the one or more prompts based on the dialog script and the response template, and linking each of the responses with the associated prompts to generate one or more synthesized training examples in the set of synthesized training examples. The pre-trained machine learning model is then fine-tuned using the set of training examples and the set of synthesized training examples.

Abstract: Techniques for metadata-driven rapid adapter building (RAB) are disclosed, including: receiving, by an RAB framework, a function call from a third-party application; obtaining, by the RAB framework, a metadata document that defines an adapter between a server-side runtime and the third-party application; determining that the metadata document includes one or more metadata fields that map the function call to one or more software development kit (SDK) functions exposed by the server-side runtime; responsive to receiving the function call and based on the one or more metadata fields, executing the one or more SDK functions exposed by the server-side runtime.

Abstract: Techniques for maintaining state and context of conversations between a user and digital assistant using threads. In one aspect, a method includes receiving a natural language utterance from a user during a session, obtaining a topic context instance for the natural language utterance, and generating, by a GenAI model, a list comprising an executable action based on candidate actions associated with the topic context instance. The executable action is then executed to produce an output. The executing includes determining there is no thread running within the session that is associated with the topic context instance, the executable action, or both, and responsive to determining there is no thread running, creating a thread associated with the topic context instance, the executable action, or both, and executing, using the thread, the executable action to obtain the output. The output or a communication derived from the output is then sent to the user.

Abstract: Techniques are disclosed for storage and retrieval mechanisms for knowledge artifacts acquired and applicable across conversations to enrich user interactions with a digital assistant. In one aspect, a method includes receiving a natural language utterance form a user during a session between the user and the digital assistant and obtaining a topic context instance for the utterance. The obtaining includes executing a search, determining whether the utterance satisfies a threshold of similarity with one or more topics, identifying the topic context instance associated with the topics, and associating the utterance with the topic context instance. A first generative artificial intelligence model can then be used to generate a list of executable actions. An execution plan is then created, and the topic context instances is updated with the execution plan. The execution plan is then executed, and an output or communication derived from the output is sent to the user.

Abstract: Techniques are disclosed for automatically generating Subjective, Objective, Assessment and Plan (SOAP) notes. Particularly, techniques are disclosed for identifying entities for automatic SOAP note generation. A text transcript is accessed and segmented into portions. The text transcript can correspond to an interaction between a first entity and a second entity. Entities for the respective portions are identified using machine-learning models. A SOAP note is generated using the one or more machine-learning models and facts are derived from the text transcript based at least in-part on the entities. The SOAP note can be stored in a database in association with at least one of the first entity and the second entity.

Abstract: Techniques are disclosed herein for contextual query rewriting. The techniques include inputting a first user utterance and a conversation history to a first language model. The first language model identifies an ambiguity in the first user utterance and one or more terms in the conversation history to resolve the ambiguity, modifies the first user utterance to include the one or more terms identified to resolve the ambiguity to generate a modified utterance, and outputs the modified utterance. The computing system provides the modified utterance as input to a second language model. The second language model performs a natural language processing task based on the input modified utterance and outputs a result. The computing system outputs a response to the first user utterance based on the result.

Abstract: The present disclosure relates to resource allocation among a plurality of clients, for using a cloud-based service, e.g., a generative artificial intelligence (GenAI) service. A first target amount of resource and a second target amount of resource can be allocated to a first client and a second client (respectively). A first and a second client, a first target amount of resource can be allocated to a first client, and a second target amount of resource can be allocated to a second client for using the service. A request can be received from a third client for allocating resources; estimating that (i) the first client is using a first subset of the first target amount and not using a second subset of first target amount, and (ii) the second client is using a third subset of the second target amount and not using a fourth subset of second target amount. It can be determined that the second subset is greater than the fourth subset.

Abstract: Techniques are disclosed herein for managing date-time intervals in transforming natural language utterances to logical forms by providing an enhanced grammar, a natural language utterance comprising a date-time interval, and database schema information to a machine learning model that has been trained to convert natural language utterances to logical forms; and using the machine learning model to convert the natural language utterance to an output logical form, wherein the output logical form comprises at least one of the date-time interval and an extraction function for extracting date-time information corresponding to the date-time interval from at least one date-time attribute of the database schema information.

Abstract: A method for detecting a deserialization attack may include identifying, in a byte stream, a class name corresponding to a class, generating, for the class, a feature vector, generating, by applying a benign deserialization model to the feature vector, a benign probability window, generating, by applying a malicious deserialization model to the feature vector, a malicious probability window, comparing the benign probability window and the malicious probability window to obtain a comparison result, and determining, based on the comparison result, that the class is malicious.

Abstract: A system accesses transaction data associated with a plurality of transactions, and based on characteristics of the transaction data, determines a set of functions to be applied to the transaction data at different corresponding levels of granularity. Determining the set of functions includes determining parallel processing requirements corresponding to the set of functions and determining an execution order corresponding to the set of functions based on the parallel processing requirements. The system schedules parallel execution of (a) a first function on the transaction data at a first level of granularity to generate a first dataset having the first level of granularity, and (b) a second function on the transaction data at a second level of granularity to generate a second dataset having the second level of granularity.

Abstract: A summary generation and summary selection system is disclosed that is capable of automatically evaluating multiple summaries generated for content and selecting a single summary that is deemed to be the “best” among the multiple generated summaries. The system includes capabilities to use multiple different selection techniques to select the best summary from multiple generated summaries. A first selection technique involves identifying entities and entity relationships from the content to be summarized and selecting a summary from multiple summaries generated for the content based on the entities and entity relationships identified in the content. A second selection technique involves determining a set of questions that are answered by each summary. The technique then selects a summary based upon the set of questions answered by each summary. The system then outputs the selected summary as the summary for the content.

Abstract: Techniques for managing secure virtual card number (VCN) transactions are disclosed. A POS terminal that processes payments receives an instruction in a secure digital communication over a network to process a payment from a customer to a supplier. Based on receiving a payment request via a network, the POS terminal identifies a VCN associated with the request. The POS terminal validates the VCN and processes the payment request. The POS terminal communicates the VCN to the supplier's bank to initiate a funds transfer between the supplier's bank and the customer's bank that issued the VCN. Upon completion of the transaction, the banks confirm the transaction to the customer and the POS terminal.

Abstract: Techniques for time-bound hyperparameter tuning are disclosed. The techniques enable the determination of optimized hyperparameters for a machine learning (ML) model given a specified time bound using a three-stage approach. A series of trials are executed, during each of which the ML model is trained using a distinct set of hyperparameters. In the first stage, a small number of trials are executed to initialize the algorithm. In the second and third stages, a certain number of trials are executed in each stage. The number of trials to run in each stage are determined using one or more computer-implemented techniques. The computer-implemented techniques can also be used to narrow the hyperparameter search space and the feature space. Following the third stage, a set of optimized hyperparameters is adopted based a predefined optimization criterion like minimization of an error function.

Abstract: Techniques for configuring autosave triggers in a computing environment based on environment and data conditions are disclosed. A system trains a machine learning model based on data attributes and environmental attributes to generate autosave value triggers for a computing environment. The autosave value triggers are triggered by different conditions. For example, one autosave trigger may be triggered when an error condition is detected. Another may be triggered when a certain number of operations are performed. The machine learning model generates autosave trigger values scores for one or more autosave triggers. The system may implement the autosave triggers in the computing environment based on the autosave trigger values.

Abstract: Techniques are disclosed for stream orchestration for variable-length message streams, including routes specified using an implementation-independent stream orchestration language (SOL). In an example method, a computing system receives a variable-length message, the variable-length message including context information and a payload. The computing system determines, from the context information, routing information that identifies at least one consumer of the variable-length message. The computing system outputs the variable-length message to the consumer.

Abstract: Techniques for managing events that record occurrences in a computing environment are disclosed. The system identifies events, and the system applies event processing mechanisms to the events. The event processing mechanisms generate incidents to represent the events. The system presents an interface that demonstrates how the events are mapped to the incidents. A user may interact with the interface to modify the event processing mechanisms and/or define new event processing mechanisms. Furthermore, the system may identify a group of uncompressed events, and the system may determine a candidate compression policy that would generate a single incident to represent the group of uncompressed events. The system may generate the candidate compression policy by applying a trained machine learning model to the group of uncompressed events. The system may simulate applying the candidate compression policy, and the system may present the results of the simulated application to the user on the interface.

Abstract: Techniques are disclosed herein for selecting document chunks that are most relevant to a query. The techniques include receiving a query and comparing a plurality of stored text passages to the query using a first similarity metric. Based on the comparison, a subset of the plurality of stored text passages that are most similar to the query are selected. A plurality of sentences from the subset of the plurality of stored text passages are identified. The identified sentences are ranked based on the query and a second similarity metric. A subset of the sentences are selected based on the ranking. The subset of the sentences or a derivative thereof are output in response to the query.

Abstract: A system is disclosed that provides the ability for a resource residing in one tenancy of a cloud service provider infrastructure (CSPI) to use the identity of a higher-level resource upon which the resource is built to access other resources residing in another tenancy of the CSPI. The system obtains a first identity associated with the first resource that is provisioned in a first tenancy of the CSPI and obtains a first token for the first resource. The system executes instructions to obtain a second identity associated with a second resource upon which the first resource is built. The second resource resides in a second tenancy of the CSPI. The system obtains a second identity associated with the second resource and obtains a second token for the first resource. The first resource uses the second token to access resources that reside in the second tenancy of the CSPI.

Abstract: Techniques for generating terms to replace an initial set of search terms for a query are disclosed. A system generates a training data set for training a machine learning model. Generating the training data set includes generating search value vectors for each of a set of labels based on sets of search values associated respectively with the labels in the set of labels. The system trains a machine learning model to predict a target label for a target search vector based on the set of labels and the respectively associated search value vectors. The system generates a target search value vector based on an initial set of search values. The system then applies the trained machine learning model to the target search value vector to predict the target label. The target label is used as a search term, that replaces the initial set of search values, for executing the query.

Abstract: Techniques for standardizing text data are disclosed. The system may identify, within a content item, a target phrase that is to be standardized. A subset of characters of a verb in the target phrase may be selected for comparison to a list of nouns. The subset of characters may be compared to a list of nouns identified in a data corpus. A noun in the list of nouns may be added to a candidate subset of nouns to replace the verb if the noun includes a sequence of characters that matches the subset of characters. A particular noun to replace the verb may be selected from the candidate subset of nouns based on a frequency associated with the particular noun occurring within the data corpus. The system may convert the target phrase to generate a standard phrase at least by replacing the verb with the particular noun.

Abstract: Techniques for enhanced chatbot interaction using various large language model providers are provided. In one aspect, a method may include generating a request payload having a common request body specification based on an utterance such that the common request body specification may be a standardized data input format used by a generative artificial intelligence (GenAI) interface for interacting with GenAI model providers. In various embodiments, the method may include converting the common request body specification into a custom request body specification having a data input format associated with a GenAI model provider selected from the plurality of GenAI model providers, communicating, by the GenAI interface, the request payload with the custom request body specification to the GenAI provider for processing by a GenAI model, receiving, at the GenAI interface from the GenAI model provider, a response payload associated with: (i) an error, (ii) processing the request payload, or (iii) both.

Abstract: Techniques are disclosed for assisting healthcare providers with common clinical tasks by way of a clinical software application that can be installed on and utilized from various client computing devices. The clinical software application(s) can enable a healthcare provider to record conversations with patients, dictate in natural language, generate patient notes, populate patient records, schedule tasks and generate task notifications, and perform numerous other clinical functions. Applications executing on a mobile computing device and a desktop computing device and concurrently associated with a same user session with a cloud service provider platform, can be paired with one another so that the mobile client application and the desktop client application can operate in concert, under the control of the cloud service provider platform, to provide an end user with a single seamless experience when the end user switches between client devices while performing a task.

Abstract: A system is disclosed that includes capabilities by which a nested sub-resource residing in a service tenancy can access a customer-owned resource residing in a customer tenancy without the use of a cross-tenant policy. The disclosed system provides the ability for a nested sub-resource residing in a service tenancy to obtain the resource principal identity of a higher-level resource residing in the customer tenancy and use the identity of the higher-level resource to access a customer-owned resource residing in the customer tenancy. Using the resource principal identity of its higher-level resource, the sub-resource can access a customer-owned resource that resides in a customer tenancy in a seamless way without having to write a cross-tenancy policy statement that provides permission to the sub-resource to access the customer-owned resource.

Abstract: Techniques for implementing a digital assistant with copilot support to enhance application usage. In one aspect, a method includes receiving a message payload, invoking, using a thread, a flow based on the message payload, generating a context variable data structure associated with the thread, responsive to invoking the flow, determining, using a machine learning model, an intent of the user, accessing, based on the intent, a prompt and an object schema, and revising the prompt based on the message payload, data in the context variable data structure, and the object schema. A generative artificial intelligence model then generates a list comprising one or more executable actions based on the prompt. The one or more executable actions are executed based on one or more parameters to obtain an output, and the output or a communication derived from the output are then sent to a user.

Abstract: Techniques are disclosed herein for executing an execution plan for a digital assistant with generative artificial intelligence (genAI). A first genAI model can generate a list of executable actions based on an utterance provided by a user. An execution plan can be generated to include the executable actions. The execution plan can be executed by performing an iterative process for each of the executable actions. The iterative process can include identifying an action type, invoking one or more states, and executing, by the one or more states, the executable action using an asset to obtain an output. A second prompt can be generated based on the output obtained from executing each of the executable actions. A second genAI model can generate a response to the utterance based on the second prompt.

Abstract: Techniques are disclosed for session management for variable-length message streams. In an example method, a computing system establishes a first session by receiving, from a first computer system, registration information including a first session identifier and a specification of a channel; determining a stream orchestration instance for the channel; and joining the first computer system to the first session for the stream orchestration instance based on the first session identifier. The computing system receives, from a second computer system, a message including context information, the context information including the first session identifier. The computing system identifies the first session based on the first session identifier, the first session having one or more member computer systems. The computing system outputs the message to at least one of the one or more member computer systems of the first session.

Abstract: Techniques for using a LLM to detect OOS and OOD utterances. In one aspect, a method includes routing an utterance to a skill bot. The skill bot is configured to execute an action for completing a task associated with the utterance, and a workflow associated with the action includes a GenAI component state configured to facilitate completion of at least part of the task. The method further includes inputting a prompt into a GenAI model for processing. The prompt includes the utterance and scope-related elements that teach the GenAI model to output an invalid input variable when the utterance is OOS or OOD. When the GenAI model determines the utterance is OOS or OOD as part of the processing, the response is generated to include the invalid input variable, and the GenAI component state is caused to transition to a different state or workflow based on the response.

Abstract: Techniques are disclosed herein for improving the performance of an end-to-end (E2E) Automatic Speech Recognition (ASR) model in a target domain. A set of test examples are generated. The set of test examples comprise multiple subsets of test examples and each subset of test examples corresponds to a particular test category. A machine language model is then used to convert audio samples of the subset of test examples to text transcripts. A word error rate is determined for the subset of test examples. A test category is then selected based on the word error rates and a set of training examples is generated for training the ASR model in a particular target domain from a selected subset of test examples The training examples are used to fine-tune the model in the target domain. The trained model is then deployed in a cloud infrastructure of a cloud service provider.

Abstract: Techniques are disclosed for automatically evaluating SOAP notes. A method comprises accessing a Subjective, Objective, Assessment and Plan (SOAP) note and a checklist that includes checklist facts; using a first machine-learning model prompt to extract SOAP note facts from the SOAP note; using one or more second machine-learning model prompts to generate feedback for the SOAP note, the feedback indicating whether individual checklist facts are supported by at least one of the SOAP note facts, and whether individual SOAP note facts are supported by at least one of the checklist facts; and generating a score for the SOAP note based on the feedback.

Abstract: Techniques are disclosed for automatically generating Subjective, Objective, Assessment and Plan (SOAP) notes. Particularly, techniques are disclosed for identifying entities for automatic SOAP note generation. A text transcript is accessed and segmented into portions. The text transcript can correspond to an interaction between a first entity and a second entity. One or more entities for the respective portions are identified using one or more machine-learning models. Facts are from the respective portions using the one or more machine-learning models based at least in-part on the context of the respective portions. A SOAP note is generated using the one or more machine-learning models and based at least in-part on the facts. The SOAP note can be stored in a database in association with at least one of the first entity and the second entity.

Abstract: Operations of a certificate bundle distribution service may include: detecting a trigger condition to distribute a certificate bundle that includes a set of certificate authority certificates; determining, for each of a plurality of network entities associated with a computer network, a fault domain representing at least one single point of failure; partitioning the plurality of network entities into a plurality of certificate distribution groups, based on a set of partitioning criteria that includes a fault domain of each particular network entity, in which each particular certificate distribution group includes a particular subset of network entities, and the particular subset of network entities are associated with a particular fault domain; selecting a particular certificate distribution group, of the plurality of certificate distribution groups, for distribution of the certificate bundle; and transmitting the certificate bundle to the particular subset of network entities in the particular certificate di

Abstract: Techniques are disclosed for providing an authenticated model customization for a machine-learning model. A cloud service provider platform accesses a message including, at least, timestamp data and user identification data. A training group of data entities is identified based on the data in the message. A training dataset is determined based on the training group of data entities. A machine-learning model is modified based on the training dataset. The modified machine-learning model is provided during an authenticated network session associated with the user identification data. In some embodiments, the modification of the machine-learning model is removed based on a determination that the authenticated network session had ended.

Abstract: Techniques are disclosed for automatically generating Subjective, Objective, Assessment and Plan (SOAP) notes. Particularly, techniques are disclosed for automatic SOAP note generation using task decomposition. A text transcript is accessed and segmented into portions. The text transcript can correspond to an interaction between a first entity and a second entity. Machine-learning model prompts are used to extract entities and facts for the respective portions and generate SOAP note sections based at least in-part on the facts. A SOAP note is generated by combining the SOAP note sections. The SOAP note can be stored in a database in association with at least one of the first entity and the second entity.