Abstract: A system may include a generative language model platform facilitating generation of novel text via a generative language model. The system may include a communication gateway communicating with client systems located outside of the computing services environment and within a first geographic region. The system may include a feedback service configured to write generative language model logging input data to a storage location within the geographic region. The system may include a data service instance configured to store database records determined based on generative language model logging input data retrieved from the storage location.

Abstract: A computing services environment may include application servers providing computing services including access to a database system, a unified metadata framework including autonomous agent definitions referencing action definitions defining a plurality of actions capable of being performed within the computing services environment, an agent service configured to instantiate an autonomous agent instance based on an autonomous agent definition, and an orchestration layer configured to determine an orchestration plan based on novel planning text generated by a generative language model. The orchestration plan may include a subset of the plurality of actions identified in the novel planning text. The computing services environment may execute the subset of the plurality of actions within the computing services environment.

Abstract: A method may include obtaining a generative artificial intelligence (AI) model that includes a set of weights and that is associated with an explicit reward model and an implicit reward model. The method may include zeroing a partition function of the implicit reward model. The method may include obtaining feedback data associated with the explicit reward model that includes preference feedback data, binary feedback data, score feedback data, or any combination thereof. The method may include generating the explicit reward model based on the feedback data. The method may include fine-tuning the set of weights of the generative AI model based on a comparison of the explicit reward model and the implicit reward model and further based on the feedback data. The method may include receiving a query and generating, based on the query and the fine-tuned set of weights, a response that is responsive to the query.

Abstract: An application server may receive, from a client and at an interface for accessing a large language model, a prompt for a response from the large language model. The application server may receive, via a model interface, a streaming output of the large language model, where the streaming output includes a first portion of the response and a threshold number of tokens. The application server may then provide the first portion of the response to a scoring model that determines a first incremental score indicating a first probability that the first portion of the response includes content from one or more content categories. The application server may transmit, to the client and based on the first probability, the first portion of the response, an indication of the first incremental score, or both.

Abstract: In some systems, a set of sentences of a relatively large document may be vectorized into a set of vectors via an embedding model for summarization. Further, a subset of vectors of the set of vectors may be selected via a farthest point sampling (FPS) procedure based on a vector-space distance between respective vectors of the subset of vectors. Moreover, the subset of vectors that are associated with a subset of sentences may be ordered based on the order of the subset of sentences within the set of sentences of the document. Further, to generate a summary of the document, a query may be transmitted to a large language model (LLM) that includes a summarization prompt and the subset of sentences that correspond with the selected subset of vectors. A summary of the document may then be received from the LLM based on transmitting the query.

Abstract: Embodiments described herein provide a structured conversation summarization framework. A user interface may be provided which allows an agent to perform a conversation with a customer, for example regarding resolving a customer support issue. Utterances by both the agent and customer may be stored, and at the end of the conversation, the utterances may be used to generate a structured summary. The structured summary may include components such as a general summary, an issue summary, and a resolution summary. Using neural network models and heuristics, each component of the summary may be automatically generated.

Abstract: In some embodiments, a method stores a total number of generative credits for a generative artificial intelligence (AI) solution that is integrated with a software application in a database system. Usage data is tracked for a request to the generative artificial intelligence (AI) solution in the database system. The method determines a context from the usage data and retrieves a contextual pricing model for the generative AI solution using the context. The contextual pricing model translates a model specific charging policy to generative credits. The method applies the usage data to the contextual pricing model to translate the usage data to a number of generative credits. The number of generative credits for the generative AI solution is applied to an available number of generative credits of the total number of generative credits to generate a new available number of generative credits.

Abstract: Embodiments described herein provide systems and methods for document recommendation. A system receives a set of training data including a plurality of documents. The system determines whether the set of training data includes annotated contextual information corresponding to the plurality of documents. The system trains supervised and/or unsupervised models based on the availability of data. The models are used to generate vectors representing the documents. During a live text conversation, text from the conversation may be vectorized using the models and the vectors compared to those representing the documents in order to find the most relevant documents. The system may generate an indication of a recommended document.

Abstract: Described herein are systems, apparatus, methods and computer program products for machine learning intent classification. In various embodiments, historical utterances provided by users may be utilized for bot training. Context and personally identifiable information may be removed from the utterances. The utterances may be associated with vectors. The utterances and vectors may be used to determine recommendations.

Abstract: Embodiments described herein provide A method for training a neural network based model. The methods include receiving a training dataset with a plurality of training samples, and those samples are encoded into representations in feature space. A positive sample is determined from the raining dataset based on a relationship between the given query and the positive sample in feature space. For a given query, a positive sample from the training dataset is selected based on a relationship between the given query and the positive sample in a feature space. One or more negative samples from the training dataset that are within a reconfigurable distance to the positive sample in the feature space are selected, and a loss is computed based on the positive sample and the one or more negative samples. The neural network is trained based on the loss.

Abstract: Embodiments described herein provide ______.

Abstract: Embodiments described herein provide a structured conversation summarization framework. A user interface may be provided which allows an agent to perform a conversation with a customer, for example regarding resolving a customer support issue. Utterances by both the agent and customer may be stored, and at the end of the conversation, the utterances may be used to generate a structured summary. The structured summary may include components such as a general summary, an issue summary, and a resolution summary. Using neural network models and heuristics, each component of the summary may be automatically generated.

Abstract: A method that includes receiving an input at an interactive conversation service that uses an intent classification model. The method may further include generating, using an encoder model of the intent classification model, a set of output vectors corresponding to the input, where the encoder model is configured to determine a set of metrics corresponding to intent classifications. The method may further include determining, using an outlier detection model of the intent classification model, whether the input is in-domain or out-of-domain (OOD) based on a first vector of the set of output vectors satisfying a domain threshold relative to one or more of the intent classifications. The method may further include outputting, by the intent classification model, a second vector of the set of output vectors that indicates the set of metrics corresponding to the intent classifications or an indication that the input is OOD.

Abstract: Disclosed are some implementations of systems, apparatus, methods and computer program products for integrating question generation and answer retrieval in a question answer system. The system generates a question using a set of documents and determines whether it is semantically distinct from questions in a question-answer repository. After determining that the question is semantically distinct from questions in the question-answer repository, the system adds the question to the question-answer repository. Upon receipt of a user-submitted question, the system uses the question-answer repository to identify a semantically similar question. The system retrieves an answer corresponding to the identified question from the question-answer repository and provides the answer in response to the user-submitted question.

Abstract: Described herein are systems, apparatus, methods and computer program products for machine learning intent classification. In various embodiments, historical utterances provided by users may be utilized for bot training. Context and personally identifiable information may be removed from the utterances. The utterances may be associated with vectors. The utterances and vectors may be used to determine recommendations.

Abstract: An chip for integrated tumor cell behavior experiments, which comprises a functional area I, a functional area II, a functional area III, a functional area IV and a functional area V, wherein the functional area I comprises a cell invasion 3D co-culture plate (400) for cell invasion experiments; the functional area II comprises a cell migration culture hole (500) for cell migration experiments; the functional area III comprises a cell proliferation single-cell culture hole (600) for tumor single-cell culture; the functional area IV comprises an angiogenesis 3D co-culture plate (700) for tumor-related angiogenesis experiments; and the functional area V comprises a tumor single-cell culture hole (803), a matrix glue groove (805) and a tumor cell attraction factor hole (801) connected by matrix glue for tumor single-cell migration or invasion experiments.

Abstract: Described herein are systems, apparatus, methods and computer program products for machine learning intent classification. In various embodiments, historical utterances provided by users may be utilized for bot training. Context and personally identifiable information may be removed from the utterances. The utterances may be associated with vectors. The utterances and vectors may be used to determine recommendations.

Abstract: A system uses conversation engines to process natural language requests and conduct automatic conversations with users. The system generates responses to users in an online conversation. The system ranks generated user responses for the online conversation. The system generates a context vector based on a sequence of utterances of the conversation and generates response vectors for generated user responses. The system ranks the user responses based on a comparison of the context vectors and user response vectors. The system uses a machine learning based model that uses a pretrained neural network that supports multiple languages. The system determines a context of an utterance based on utterances in the conversation. The system generates responses and ranks them based on the context. The ranked responses are used to respond to the user.

Abstract: A method that includes receiving an input at an interactive conversation service that uses an intent classification model. The method may further include generating, using an encoder model of the intent classification model, a set of output vectors corresponding to the input, where the encoder model is configured to determine a set of metrics corresponding to intent classifications. The method may further include determining, using an outlier detection model of the intent classification model, whether the input is in-domain or out-of-domain (OOD) based on a first vector of the set of output vectors satisfying a domain threshold relative to one or more of the intent classifications. The method may further include outputting, by the intent classification model, a second vector of the set of output vectors that indicates the set of metrics corresponding to the intent classifications or an indication that the input is OOD.

Abstract: A method and system for recommending articles including: receiving a customer request from the customer during the session; generating case data for a case, by an article recommender app; configuring a training set based on the subject and description data of the customer request; identifying, by an artificial intelligence (AI) app, a first pool of articles from a knowledge database; identifying by at least one query, a second pool of articles from a case article database to into a merged pool of articles; assigning, by the AI app, an implicit label to one of the first pool and the second pool of the articles; applying a model derived by the AI app based on customer behavior and a set of features related to the case to classify each article of the merged pool of articles based at least in part on the predicted relevance of the article.