Abstract: The present embodiments relate to providing near real-time communications from a public network to a private network. A first computing device in a public network can obtain data packets to be provided to the private network from an application executing on the first computing device. A trust module executed by the first computing device can authenticate the user, application, and the data packets to be provided to the private network and add metadata relating to the sending user, recipient user, etc. The data packets can be forwarded to the private network via a cross-domain system (CDS). The metadata and the digital signature on the data packets can be verified by a trust module executing on a second computing device in the private network. The second computing device can receive the data packets and store the data packets for subsequent actions to be performed in the private network.

Abstract: A cloud-based security solution that provides a robust and secure framework for managing and enforcing security policies related to various resources managed in the cloud is disclosed. The cloud-based security solution is implemented by a centralized application programming Interface (API) system and a security zone policy enforcement system in a cloud service provider infrastructure. The centralized API system receives an API request that identifies an operation to be performed on a resource in the CSPI. The system determines, from the API request, compartment information and context information associated with the resource. Responsive to determining the compartment information and the context information associated with the resource, the system determines that the resource resides in a compartment that is associated with a security zone. The system then processes the API request and transmits a result of processing of the API request to a user of the centralized API processing system.

Abstract: A method for performing a location and velocity check at an SEPP to protect against a spoofing attack includes receiving an SBI request message relating to authentication of UE. The method further includes querying a database NF to obtain previous authentication information for the UE, the previous authentication information including a previous network identifier and a previous authentication time for the UE. The method further includes receiving a response from the database NF, the response including the previous network identifier and the previous authentication time.

Abstract: The present disclosure relates to a framework that provides execution of serverless functions in a cloud environment based on occurrence of events/notifications from services in an entirely different cloud environment. A target agent obtains a notification from a source agent, where the target agent is deployed in a target cloud environment and the source agent is deployed in a source cloud environment that is different than the target cloud environment. The target agent determines a function that is to be invoked based on the notification. Upon successfully verifying whether the target agent is permitted to invoke the function that is deployed in a target customer tenancy of the target cloud environment, the target agent invokes the function in the target customer tenancy of the target cloud environment.

Abstract: Techniques are disclosed for training a machine learning model to identify and rank entities relative to a set of requirements. The trained machine learning model may present an array of interface elements (e.g., icons) in a graphical user interface (GUI), where the interface elements represent corresponding entities. These interface elements are arranged in the GUI based on their corresponding ranks. The ranks of entities, and therefore the locations of corresponding interface elements are based, at least in part, on a degree of match between values of a subset of entity attributes and a corresponding subset of the set of requirements. The machine learning model may be further trained by receiving a user input that changes a location of a particular user interface element within the graphical user interface displaying the ranked user interface elements.

Abstract: The present embodiments relate to systems and methods for automatic sign in upon account signup. Particularly, the present embodiments can utilize a federated login approach for automatic sign in upon account signup for a cloud infrastructure. Specifically, the signup and sign in service (also known as SOUP) and an identity provider portal can be configured such that the nodes are aware of each other as Security Assertion Markup Language (SAML) partners. After new account registration, the signup service can redirect the user browser to a cloud infrastructure console to start with a federated login flow, where a sign in service can issue a SAML authentication request, and redirects it to signup service. Responsive to validating the browser using a SAML authentication process, the browser can be automatically signed into the new account and allowed access the account relating to the cloud infrastructure service.

Abstract: Techniques are disclosed herein for configuring agents for use by digital assistants that use generative artificial intelligence. An agent may be in the form of a container that is configured to have one or more actions that can be executed by a digital assistant. The agent may be configured by initially defining specification parameters for the agent based on natural language input from a user. Configuration information for the one or more assets can be imported into the agent. One or more actions may then be defined for the agent based on importing of the configuration information, the natural language input from the user, or both. A specification document can be generated for the agent and can comprise various description metadata, such as agent, asset, or action metadata, or combinations thereof. The specification document may be stored in a data store that is communicatively coupled to the digital assistant.

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: The techniques described herein provide a novel clinical digital assistant (CDA) processing pipeline enabling medical entity detection and resolution that works against various EHRs and with different ontologies (e.g., medical coding systems). In some embodiments, the processing pipeline may involve two machine-learning models that can perform named entity recognition on the natural language utterance to identify medical entities that are associated with different medical entity types, and link the medical entities to medical codes of standard medical coding systems. A FHIR-compliance data structure may be generated using the identified medical codes, their associated medical coding systems, the identified medical entities, and their associated medical entity types.

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 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: 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 are disclosed for returning references associated with an answer to a query. The techniques include accessing a text portion and identifying a plurality of sentences in the text portion. Each of the sentences is embedded to generate a respective plurality of text sentence embeddings. The text portion or a derivative thereof and a query are provided to a language model and a response to the query based on the text portion is received from the language model. A plurality of sentences are identified in the response. The plurality of sentences in the response is embedded to generate a plurality of response embeddings. The response embeddings are compared to the sentence embeddings to generate a similarity score for each sentence embedding-response embedding pair. Based on the similarity scores, an indication of a subset of the plurality of sentences is output with the response to the query.

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 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 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 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: 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 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: 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: 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 managing the implementation of application-code scanning processes are disclosed. A system scans application code by analyzing metadata associated with the application code to identify a set of data needed to scan the application code with a scanning application. Based on the information obtained from the application metadata, the system identifies extraction processes that are needed to obtain the set of data. The system applies a set of one or more application-code scanners by implementing the extraction processes. The system presents in a graphical user interface (GUI) a set of results from scanning operations.

Abstract: A summary generation system is disclosed that is configured to generate a summary for content to be summarized by identifying relevant chunks of information from the content to be summarized using a large language model (LLM) and a set of questions. The set of questions enable the system to identify and retrieve relevant chunks of information. Each question undergoes a translation or transformation process to generate multiple question variants for each question. The multiple question variants are used by the system to optimize the search to obtain relevant chunks of information. Then, using the multiple question variants and an LLM, the system extracts information (i.e., answers) from the relevant chunks of information. The summary generation system then collates the answers to create an accurate and comprehensive summary for the content to be summarized.

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.

Abstract: Techniques for providing cross-cluster transaction risk assessment are disclosed herein.

Abstract: A system receives a configuration request comprising an infrastructure definition that defines a set of resources, to be selected from a set of tenant-managed resources implemented on a tenant's premises, for implementing the compute target entity. The system generates a compute target entity associated with an addressable identifier. The compute target entity corresponds to the set of resources selected from the set of tenant-managed resources. The system receives an execution request for execution of a set of operations, where the execution request specifies the addressable identifier associated with the compute target entity for execution of the set of operations. The system maps the addressable identifier of the compute target entity to the set of resources. The system causes execution of the set of operations on the set of resources on the tenant's premises via the compute target entity.

Abstract: A system and computer-implemented method include receiving a request for allocating graphical processing unit (GPU) resources for performing an operation. The request includes metadata identifying a client identifier (ID) associated with a client, throughput, and latency of the operation. A resource limit is determined for performing the operation based on the metadata. Attributes associated with each GPU resource of a plurality of GPU resources available for assignment are obtained. The attribute is analyzed that is associated with each GPU resource with respect to the resource limit. A set of GPU resources is identified from the plurality of GPU resources based on the analysis. A dedicated AI cluster is generated by patching the set of GPU resources within a single cluster. The dedicated AI cluster reserves a portion of a computation capacity of a computing system for a period of time and the dedicated AI cluster is allocated to the client associated with the client ID.

Abstract: Techniques are disclosed herein for routing an utterance to action for a digital assistant with generative artificial intelligence. An input query comprising particular data can be received from a user. An action and a set of input argument slots within a schema associated with the action can be identified based on the input query. The input argument slots can be filled by determining whether one or more parameters are derivable from the particular data and filling the input argument slot with a version of the parameters that conforms to the schema. An execution plan that comprises the action that includes the set of filled input argument sots can be sent to an execution engine configured to execute the action for generating a response to the input query.

Abstract: A system and computer-implemented method include accessing a request for allocating graphical processing unit (GPU) resources for performing an operation. The request includes metadata identifying a client identifier associated with a client, throughput, and a latency of the operation. A predicted resource limit for performing the operation is determined based on the metadata. A parameter of GPU resources is obtained. The parameter includes a status indicating whether a GPU resource is occupied for performing another operation. A GPU resource utilization value is determined for each node based on the status. The GPU resource utilization value indicates the amount of utilization of GPU resources of the corresponding node. The GPU resource utilization value of each node is compared with a pre-defined resource utilization threshold value. The GPU resources are re-scheduled based on the predicted resource limit. Further, a set of GPU resources from the re-scheduled GPU resources for performing the operation.

Abstract: Techniques for generating recommendations based on the predicted performance of an execution plan are disclosed. A system predicts the future characteristics of a set of data objects associated with a set of structured query language (SQL) statements. The system predicts how the changes to the set of data objects will result in changes to a query execution plan associated with the SQL statements. The system predicts a set of performance metrics for the changed query execution plan. Based on the predicted performance, the system generates recommendations for modifying data, applications, or database server operations to improve performance.

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 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: 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 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 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: Techniques are disclosed herein for generating and using a knowledge base of information extracted from documents. The techniques include accessing a document comprising text and dividing the document into a plurality of chunks of text. The chunks are indexed by storing each chunk mapped to respective identifying metadata including a chunk index for each chunk. A query is received and a chunk relevant to the query is identified. A prompt is formulated including the query, the identified relevant chunk, and a subsequent chunk. The prompt is provided to a language model and output is received from the language model based on the prompt. An answer to the query is returned based on the received output.

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 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 are described for performing packet level data centric protection enforcement. Instead of being restricted to perimeter-based security and defining and creating rules that are difficult to maintain, techniques described herein allow users to create data-centric, intent-based policies that are enforced at different enforcement points within one or more networks. In some examples, a method comprises receiving a packet at an enforcement point (EP) within one or more networks that include a plurality of enforcement points (EPs); accessing enforcement data that indicates allowed communications between the EP and one or more other EPs, wherein the data are generated from a policy that specifies how traffic flows the one or more networks and a determination of possible data movements between at least two of EPs in the plurality of EPs; and enforcing the flow of the packet at the EP based on the data.

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: Techniques are disclosed herein for implementing digital assistants using generative artificial intelligence. An input prompt comprising a natural language utterance and candidate agents and associated actions can be constructed. An execution plan can be generated using a first generative artificial model based on the input prompt. The execution plan can be executed to perform actions included in the execution plan using agents indicated by the execution plan. A response to the natural language utterance can be generated by a second generative artificial intelligence model using one or more outputs from executing the execution plan.

Abstract: Techniques are disclosed herein for managing ambiguous date mentions in natural language utterances in transforming natural language utterances to logical forms by encoding the uncertainties of the ambiguous date mentions and including the encoded uncertainties in the logical forms. In a training phase, training examples including natural language utterances, logical forms, and database schema information are automatically augmented and used to train a machine learning model to convert natural language utterances to logical form. In an inference phase, input database schema information is augmented and used by the trained machine learning model to convert an input natural language utterance to logical form.

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 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 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: 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 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: The present disclosure relates to secure deployment of model weights from a generative artificial intelligence (GenAI) platform to a cloud service. The method includes accessing the model metadata and a set of weights of a GenAI model associated with a GenAI platform. These model weights may be encrypted using a first encryption key that may be provided in the model metadata. These encrypted model weights may be decrypted based on the model metadata by utilizing the first encryption key from the model metadata. Each key may be associated with the specific type of GenAI model. Before storing the model weights from the GenAI platform cloud tenancy to a cloud storage in GenAI home region, the model weights may be encrypted again by utilizing a second encryption key. This encryption by the cloud may enable independent control over the sensitive information during transit and storing.

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