公开(公告)号：US20240378207A1

公开(公告)日：2024-11-14

申请号：US18780357

申请日：2024-07-22

Applicant: Salesforce, Inc.

Inventor： Zachary Alexander ,  Yixin Mao

IPC: G06F16/2457 ,  G06F16/242 ,  G06F16/28

Abstract: Database systems and methods are provided for assigning structural metadata to records and creating automations using the structural metadata. One method of assigning structural metadata involves determining a candidate group of semantically similar conversations based on unassigned conversations, determining a clustering performance metric associated with the candidate group based on a relationship between the candidate group and a plurality of existing groups of semantically similar conversations, and when the clustering performance metric is greater than a threshold, automatically assigning one or more unassigned conversations to the candidate group based on the representative utterances associated therewith and automatically updating one or more associated records at a database system to include metadata identifying the candidate group.

公开(公告)号：US20230401387A1

公开(公告)日：2023-12-14

申请号：US17836591

申请日：2022-06-09

Applicant: Salesforce, Inc.

Inventor： Zachary Alexander ,  Shubham Mehrotra

IPC: G06F40/35 ,  G06N5/02

CPC classification number: G06F40/35 ,  G06N5/022

Abstract: The system receives a base machine learning model trained using a generic dataset. For example, the base machine learning model may be an off-the-shelf machine learning based model. The base machine learning model is trained to receive an input and generate a feature vector representing the input. The input may be a natural language expression, an image, or any other type of input. The system receives a domain specific training dataset based on known categories for input values. The system determines an orthogonal transformation for reducing the dimensions of the base machine learning model using on the domain specific training dataset. The system applies the orthogonal transformation to the base machine learning model to obtain a domain specific machine learning model. The system uses the domain specific machine learning model for processing inputs, for example, in a production environment.

公开(公告)号：US20230090924A1

公开(公告)日：2023-03-23

申请号：US17933385

申请日：2022-09-19

Applicant: Salesforce, Inc.

Inventor： Yixin Mao ,  Zachary Alexander ,  Tian Xie ,  Wenhao Liu

IPC: G10L15/18 ,  G10L15/08

Abstract: Database systems and methods are provided for assigning structural metadata to records and creating automations using the structural metadata. One method of assigning structural metadata to a record associated with a conversation involves obtaining a plurality of utterances associated with the conversation, identifying, from among the plurality of utterances, a representative utterance for semantic content of the conversation, assigning the conversation to a group of semantically similar conversations based on the representative utterance, and automatically updating the record associated with the conversation at a database system to include metadata identifying the group of semantically similar conversations.

公开(公告)号：US12111858B1

公开(公告)日：2024-10-08

申请号：US18481036

申请日：2023-10-04

Applicant: Salesforce, Inc.

Inventor： Regunathan Radhakrishnan ,  Zachary Alexander ,  Yuanxin Wang ,  Sitaram Asur ,  Aron Kale

IPC: G06F16/33 ,  G06F16/31 ,  G06N3/0455

CPC classification number: G06F16/3347 ,  G06F16/31 ,  G06N3/0455

Abstract: A text interaction record including interaction text from one or more messages between a client machine and a service provider is received at a database system. A search vector including a text embedding representing the interaction text in a multi-dimensional vector space may be determined based on the interaction text via a processor at the database system. A reference interaction record including reference interaction text may be retrieved from the database system based on the search vector. The reference interaction record may include a reference vector representing the reference interaction text in the multi-dimensional vector space. Recommended reply text is determined based on the interaction text and the reference interaction text by a large language model configured to generate the recommended reply text in response to a prompt that includes one or more natural language instructions.

公开(公告)号：US20240303443A1

公开(公告)日：2024-09-12

申请号：US18496523

申请日：2023-10-27

Applicant: Salesforce, Inc.

Inventor： Na (Claire) Cheng ,  Jayesh Govindarajan ,  Zachary Alexander ,  Shashank Harinath ,  Atul Kshirsagar ,  Fermin Ordaz

IPC: G06F40/40 ,  G06F16/33 ,  G06F40/295

CPC classification number: G06F40/40 ,  G06F16/3347 ,  G06F40/295

Abstract: Embodiments provide a generative AI creation framework to a customized generative AI stack using a foundational model (such as GPT) based on user-defined prompts, a natural language description of the task to be accomplished, and domain adaptation. In one embodiment, organization-specific knowledge may be injected into either the prompt and/or the foundational model. In this way, the customized generative AI stack thus supports a full spectrum of domain-adaptive prompts to enable a full spectrum of personalized and adaptive AI chat applications.

公开(公告)号：US20240242022A1

公开(公告)日：2024-07-18

申请号：US18156043

申请日：2023-01-18

Applicant: Salesforce, Inc.

Inventor： Victor Yee ,  Chien-Sheng Wu ,  Na Cheng ,  Alexander R. Fabbri ,  Zachary Alexander ,  Nicholas Feinig ,  Sameer Abhinkar ,  Shashank Harinath ,  Sitaram Asur ,  Jacob Nathaniel Huffman ,  Wojciech Kryscinski ,  Caiming Xiong

IPC: G06F40/174 ,  G06F16/34

CPC classification number: G06F40/174 ,  G06F16/345

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.

公开(公告)号：US20240143945A1

公开(公告)日：2024-05-02

申请号：US18161767

申请日：2023-01-30

Applicant: Salesforce, Inc.

Inventor： Shubham Mehrotra ,  Zachary Alexander ,  Shilpa Bhagavath ,  Gurkirat Singh ,  Shashank Harinath ,  Anuprit Kale

IPC: G06F40/47 ,  G06F40/30 ,  G06F40/51

CPC classification number: G06F40/47 ,  G06F40/30 ,  G06F40/51

Abstract: Embodiments described herein provide a cross-lingual intent classification model that predicts in multiple languages without the need of training data in all the multiple languages. For example, data requirement for training can be reduced to just one utterance per intent label. Specifically, when an utterance is fed to the intent classification model, the model checks whether the utterance is similar to any of the example utterances provided for each intent. If any such utterance(s) are found, the model returns the specified intent, otherwise, it returns out of domain (OOD).

公开(公告)号：US20240411992A1

公开(公告)日：2024-12-12

申请号：US18335898

申请日：2023-06-15

Applicant: Salesforce, Inc.

Inventor： Shiva Kumar Pentyala ,  Prafulla Kumar Choubey ,  Shashank Harinath ,  Sitaram Asur ,  Chien-Sheng Jason Wu ,  Zachary Alexander ,  Caiming Xiong

IPC: G06F40/284 ,  G06N3/08

Abstract: Embodiments described herein provide a training framework for generative NLP models. Specifically, the training input, e.g., in the form of a sequence of tokens representing a user-agent dialogue, may be randomly masked for a few spans, which can be one or more tokens, one or more words, one or more sentences, or one or more paragraphs. These masked spans are replaced with their embeddings generated from pre-trained large language models are then used for training the NLP model.

公开(公告)号：US20240411991A1

公开(公告)日：2024-12-12

申请号：US18330216

申请日：2023-06-06

Applicant: Salesforce, Inc.

Inventor： Shiva Kumar Pentyala ,  Prafulla Kumar Choubey ,  Shashank Harinath ,  Sitaram Asur ,  Chien-Sheng Jason Wu ,  Zachary Alexander ,  Caiming Xiong

IPC: G06F40/284

Abstract: Embodiments described herein provide a training framework for generative NLP models that operate on previously learnt knowledge from pretrained large language models. Specifically, to train an NLP model to generate a response to a user utterance (e.g., “resolve login issue”), document embeddings of support IT documents encoded by a pretrained LLM are fed to an NLP decoder together with a training dialogue (e.g., a dialogue between the chat agent on how to “resolve login issue”). The NLP decoder can thus be trained by a causal language modeling loss computed based on the predicted next token and the ground-truth token from the training dialogue.

公开(公告)号：US20240303473A1

公开(公告)日：2024-09-12

申请号：US18496513

申请日：2023-10-27

Applicant: Salesforce, Inc.

Inventor： Na (Claire) Cheng ,  Jayesh Govindarajan ,  Zachary Alexander ,  Shashank Harinath ,  Atul Kshirsagar ,  Fermin Ordaz

IPC: G06N3/0475

CPC classification number: G06N3/0475

Abstract: Embodiments provide a generative AI creation framework to a customized generative AI stack using a foundational model (such as GPT) based on user-defined prompts, a natural language description of the task to be accomplished, and domain adaptation. In one embodiment, organization-specific knowledge may be injected into either the prompt and/or the foundational model. In this way, the customized generative AI stack thus supports a full spectrum of domain-adaptive prompts to enable a full spectrum of personalized and adaptive AI chat applications.