公开(公告)号：US12288032B2

公开(公告)日：2025-04-29

申请号：US18499077

申请日：2023-10-31

Applicant: Salesforce, Inc.

Inventor： Anuprit Kale ,  Weiping Peng ,  Na Cheng ,  Rick Lindstrom ,  Zachary Alexander

IPC: G06F40/289 ,  G06F16/31 ,  G06F16/3329 ,  G06F16/334 ,  G06F40/30

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.

公开(公告)号：US12019984B2

公开(公告)日：2024-06-25

申请号：US17479748

申请日：2021-09-20

Applicant: Salesforce, Inc.

Inventor： Shilpa Bhagavath ,  Shubham Mehrotra ,  Abhishek Sharma ,  Shashank Harinath ,  Na Cheng ,  Zineb Laraki

IPC: G06F40/263 ,  G06F18/2415 ,  G06F18/2431 ,  G06F40/35 ,  G06F40/58 ,  G10L15/22

CPC classification number: G06F40/263 ,  G06F18/2415 ,  G06F18/2431 ,  G06F40/35 ,  G06F40/58 ,  G10L15/22

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.

公开(公告)号：US20250166060A1

公开(公告)日：2025-05-22

申请号：US18514287

申请日：2023-11-20

Applicant: Salesforce, Inc.

Inventor： Oleksandr Minaiev ,  Fermin Ordaz ,  Khoa Le ,  Na Cheng

IPC: G06Q40/03

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.

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：US20240256581A1

公开(公告)日：2024-08-01

申请号：US18160449

申请日：2023-01-27

Applicant: Salesforce, Inc.

Inventor： Feifei Jiang ,  Aron Kale ,  Anuprit Kale ,  Sitaram Asur ,  Na Cheng ,  Zachary Alexander ,  Victor Yee ,  Fermin Ordaz

IPC: G06F16/332 ,  G06F16/33 ,  G06F40/263

CPC classification number: G06F16/3329 ,  G06F16/3347 ,  G06F40/263

Abstract: Embodiments described herein provide ______.

公开(公告)号：US12292906B2

公开(公告)日：2025-05-06

申请号：US18160449

申请日：2023-01-27

Applicant: Salesforce, Inc.

Inventor： Feifei Jiang ,  Aron Kale ,  Anuprit Kale ,  Sitaram Asur ,  Na Cheng ,  Zachary Alexander ,  Victor Yee ,  Fermin Ordaz

IPC: G06F16/332 ,  G06F16/33 ,  G06F16/3329 ,  G06F16/334 ,  G06F40/263

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.

公开(公告)号：US20240412059A1

公开(公告)日：2024-12-12

申请号：US18330488

申请日：2023-06-07

Applicant: Salesforce, Inc.

Inventor： Regunathan Radhakrishnan ,  Zachary Alexander ,  Sitaram Asur ,  Shashank Harinath ,  Na Cheng ,  Shiva Kumar Pentyala

IPC: G06N3/08

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.

公开(公告)号：US11836450B2

公开(公告)日：2023-12-05

申请号：US17099083

申请日：2020-11-16

Applicant: Salesforce, Inc.

Inventor： Anuprit Kale ,  Weiping Peng ,  Na Cheng ,  Rick Lindstrom ,  Zachary Alexander

IPC: G06F40/289 ,  G06F16/33 ,  G06F16/31 ,  G06F16/332 ,  G06F40/30

CPC classification number: G06F40/289 ,  G06F16/31 ,  G06F16/3329 ,  G06F16/3344 ,  G06F16/3347 ,  G06F40/30

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.