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    IMAGE ANALYSIS BASED DOCUMENT PROCESSING FOR INFERENCE OF KEY-VALUE PAIRS IN NON-FIXED DIGITAL DOCUMENTS
    81.
    发明申请
    IMAGE ANALYSIS BASED DOCUMENT PROCESSING FOR INFERENCE OF KEY-VALUE PAIRS IN NON-FIXED DIGITAL DOCUMENTS 有权

    公开(公告)号：US20220215195A1

    公开(公告)日：2022-07-07

    申请号：US17140987

    申请日：2021-01-04

    Applicant: salesforce.com, inc.

    Inventor： Mingfei Gao ,  Zeyuan Chen ,  Le Xue ,  Ran Xu ,  Caiming Xiong

    IPC: G06K9/00 ,  G06F40/289 ,  G06F40/186

    Abstract: An online system extracts information from non-fixed form documents. The online system receives an image of a form document and obtains a set of phrases and locations of the set of phrases on the form image. For at least one field, the online system determines key scores for the set of phrases. The online system identifies a set of candidate values for the field from the set of identified phrases and identifies a set of neighbors for each candidate value from the set of identified phrases. The online system determines neighbor scores, where a neighbor score for a candidate value and a respective neighbor is determined based on the key score for the neighbor and a spatial relationship of the neighbor to the candidate value. The online system selects a candidate value and a respective neighbor based on the neighbor score as the value and key for the field.

    SYSTEMS AND METHODS FOR MUTUAL INFORMATION BASED SELF-SUPERVISED LEARNING
    82.
    发明申请
    SYSTEMS AND METHODS FOR MUTUAL INFORMATION BASED SELF-SUPERVISED LEARNING 有权

    公开(公告)号：US20220067534A1

    公开(公告)日：2022-03-03

    申请号：US17006570

    申请日：2020-08-28

    Applicant: salesforce.com, inc.

    Inventor： Junwen Bai ,  Weiran Wang ,  Yingbo Zhou ,  Caiming Xiong

    IPC: G06N3/08 ,  G06K9/62 ,  G06N3/04

    Abstract: Embodiments described herein combine both masked reconstruction and predictive coding. Specifically, unlike contrastive learning, the mutual information between past states and future states are directly estimated. The context information can also be directly captured via shifted masked reconstruction—unlike standard masked reconstruction, the target reconstructed observations are shifted slightly towards the future to incorporate more predictability. The estimated mutual information and shifted masked reconstruction loss can then be combined as the loss function to update the neural model.

    Systems and Methods for Out-of-Distribution Detection
    84.
    发明申请
    Systems and Methods for Out-of-Distribution Detection 有权

    公开(公告)号：US20210374524A1

    公开(公告)日：2021-12-02

    申请号：US17098007

    申请日：2020-11-13

    Applicant: salesforce.com, inc.

    Inventor： Yihao Feng ,  Caiming Xiong

    IPC: G06N3/08 ,  G06K9/62 ,  G06N3/063

    Abstract: Some embodiments of the current disclosure disclose methods and systems for detecting out-of-distribution (ODD) data. For example, a method for detecting ODD data includes obtaining, at a neural network composed of a plurality of layers, a set of training data generated according to a distribution. Further, the method comprises generating, via a processor, a feature map by combining mapping functions corresponding to the plurality of layers into a vector of mapping function elements and mapping, by the feature map, the set of training data to a set of feature space training data in a feature space. Further, the method comprises identifying, via the processor, a hyper-ellipsoid in the feature space enclosing the feature space training data based on the generated feature map. In addition, the method comprises determining, via the processor, the first test data sample is OOD data when a mapped first test data sample in the feature space is outside the hyper-ellipsoid.

    EFFICIENT DETERMINATION OF USER INTENT FOR NATURAL LANGUAGE EXPRESSIONS BASED ON MACHINE LEARNING
    85.
    发明申请
    EFFICIENT DETERMINATION OF USER INTENT FOR NATURAL LANGUAGE EXPRESSIONS BASED ON MACHINE LEARNING 有权

    公开(公告)号：US20210374353A1

    公开(公告)日：2021-12-02

    申请号：US17005316

    申请日：2020-08-28

    Applicant: salesforce.com, inc.

    Inventor： Jianguo Zhang ,  Kazuma Hashimoto ,  Chien-Sheng Wu ,  Wenhao Liu ,  Richard Socher ,  Caiming Xiong

    IPC: G06F40/30 ,  G10L15/16

    Abstract: An online system allows user interactions using natural language expressions. The online system uses a machine learning based model to infer an intent represented by a user expression. The machine learning based model takes as input a user expression and an example expression to compute a score indicating whether the user expression matches the example expression. Based on the scores, the intent inference module determines a most applicable intent for the expression. The online system determines a confidence threshold such that user expressions indicating a high confidence are assigned the most applicable intent and user expressions indicating a low confidence are assigned an out-of-scope intent. The online system encodes the example expressions using the machine learning based model. The online system may compare an encoded user expression with encoded example expressions to identify a subset of example expressions used to determine the most applicable intent.

    Generating dual sequence inferences using a neural network model
    86.
    发明授权
    Generating dual sequence inferences using a neural network model 有权

    公开(公告)号：US11170287B2

    公开(公告)日：2021-11-09

    申请号：US15881582

    申请日：2018-01-26

    Applicant: salesforce.com, inc.

    Inventor： Victor Zhong ,  Caiming Xiong ,  Richard Socher

    IPC: G06N3/04 ,  G06N3/08 ,  G06N5/04 ,  G06F16/93 ,  G06F16/2458 ,  G06N3/00

    Abstract: A computer-implemented method for dual sequence inference using a neural network model includes generating a codependent representation based on a first input representation of a first sequence and a second input representation of a second sequence using an encoder of the neural network model and generating an inference based on the codependent representation using a decoder of the neural network model. The neural network model includes a plurality of model parameters learned according to a machine learning process. The encoder includes a plurality of coattention layers arranged sequentially, each coattention layer being configured to receive a pair of layer input representations and generate one or more summary representations, and an output layer configured to receive the one or more summary representations from a last layer among the plurality of coattention layers and generate the codependent representation.

    SELF-AWARE VISUAL-TEXTUAL CO-GROUNDED NAVIGATION AGENT
    87.
    发明申请
    SELF-AWARE VISUAL-TEXTUAL CO-GROUNDED NAVIGATION AGENT 有权

    公开(公告)号：US20210286369A1

    公开(公告)日：2021-09-16

    申请号：US17332756

    申请日：2021-05-27

    Applicant: salesforce.com, inc.

    Inventor： Chih-Yao Ma ,  Caiming Xiong

    IPC: G05D1/02 ,  B25J9/00 ,  B25J9/16 ,  G06F3/16 ,  G06N3/08 ,  G06N20/00 ,  G06N3/02

    Abstract: An agent for navigating a mobile automated system is disclosed herein. The navigation agent receives a navigation instruction and visual information for one or more observed images. The navigation agent is provided or equipped with self-awareness, which provides or supports the following abilities: identifying which direction to go or proceed by determining the part of the instruction that corresponds to the observed images (visual grounding), and identifying which part of the instruction has been completed or ongoing and which part is potentially needed for the next action selection (textual grounding). In some embodiments, the navigation agent applies regularization to ensures that the grounded instruction can correctly be used to estimate the progress made towards the navigation goal (progress monitoring).

    Systems and Methods for Out-of-Distribution Classification
    88.
    发明申请
    Systems and Methods for Out-of-Distribution Classification 有权

    公开(公告)号：US20210150365A1

    公开(公告)日：2021-05-20

    申请号：US16877325

    申请日：2020-05-18

    Applicant: salesforce.com, inc.

    Inventor： Govardana Sachithanandam Ramachandran ,  Ka Chun Au ,  Shashank Harinath ,  Wenhao Liu ,  Alexis Roos ,  Caiming Xiong

    IPC: G06N3/08 ,  G06N20/10 ,  G06K9/62 ,  G06F17/18

    Abstract: An embodiment provided herein preprocesses the input samples to the classification neural network, e.g., by adding Gaussian noise to word/sentence representations to make the function of the neural network satisfy Lipschitz property such that a small change in the input does not cause much change to the output if the input sample is in-distribution. Method to induce properties in the feature representation of neural network such that for out-of-distribution examples the feature representation magnitude is either close to zero or the feature representation is orthogonal to all class representations. Method to generate examples that are structurally similar to in-domain and semantically out-of domain for use in out-of-domain classification training. Method to prune feature representation dimension to mitigate long tail error of unused dimension in out-of-domain classification. Using these techniques, the accuracy of both in-domain and out-of-distribution identification can be improved.

    Structured text translation
    90.
    发明授权
    Structured text translation 有权

    公开(公告)号：US10963652B2

    公开(公告)日：2021-03-30

    申请号：US16264392

    申请日：2019-01-31

    Applicant: salesforce.com, inc.

    Inventor： Kazuma Hashimoto ,  Raffaella Buschiazzo ,  James Bradbury ,  Teresa Marshall ,  Caiming Xiong ,  Richard Socher

    IPC: G06F40/58 ,  G06N3/08

    Abstract: Approaches for the translation of structured text include an embedding module for encoding and embedding source text in a first language, an encoder for encoding output of the embedding module, a decoder for iteratively decoding output of the encoder based on generated tokens in translated text from previous iterations, a beam module for constraining output of the decoder with respect to possible embedded tags to include in the translated text for a current iteration using a beam search, and a layer for selecting a token to be included in the translated text for the current iteration. The translated text is in a second language different from the first language. In some embodiments, the approach further includes scoring and pointer modules for selecting the token based on the output of the beam module or copied from the source text or reference text from a training pair best matching the source text.

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