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公开(公告)号：US20210117773A1
公开(公告)日：2021-04-22
申请号：US16658327
申请日：2019-10-21
Applicant: salesforce.com, inc.
Inventor： Michael Sollami , Yang Zhang
Abstract: Systems, device and techniques are disclosed for training data generation for visual search model training. A catalog including catalog entries which may include images of an item and data about the item may be received. Labels may be applied to the images of the items based on the data about the items. The images of the items may be sorted into clusters using cluster analysis on the labels. Each cluster may include labels as categories of the cluster. Additional images may be received based on searching for the categories. Generative adversarial network (GAN) training data sets may be generated from the images of the items, the additional images, and the categories. GANs may be trained with the GAN training data sets. The GANs may generate images including images of generated items, which may be replaced with images of items from the catalog entries to create feature model training images.
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公开(公告)号：US20230177269A1
公开(公告)日：2023-06-08
申请号：US17545168
申请日：2021-12-08
Applicant: salesforce.com, inc.
Inventor： Jessica Lundin , Sönke Rohde , Owen Winne Schoppe , Michael Sollami , David Woodward , Brian Lonsdorf , Alan Martin Ross , Scott Bokma
IPC: G06F40/289 , G06N20/00
CPC classification number: G06F40/289 , G06N20/00
Abstract: Systems, devices, and techniques are disclosed for conversation topic extraction. Text of a communication channel may be received. The text of the communication channel may be divided into conversation documents based on conversation threads of the communication channel. Phrases of the text of the conversation documents may be tokenizes. Topic phrases for the conversation documents may be determined by assigning importance scores to the tokenized phrases using unsupervised topic extraction. The topic phrases may be the tokenized phrases with the highest importance scores.
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公开(公告)号：US11531885B2
公开(公告)日：2022-12-20
申请号：US16658327
申请日：2019-10-21
Applicant: salesforce.com, inc.
Inventor： Michael Sollami , Yang Zhang
Abstract: Systems, device and techniques are disclosed for training data generation for visual search model training. A catalog including catalog entries which may include images of an item and data about the item may be received. Labels may be applied to the images of the items based on the data about the items. The images of the items may be sorted into clusters using cluster analysis on the labels. Each cluster may include labels as categories of the cluster. Additional images may be received based on searching for the categories. Generative adversarial network (GAN) training data sets may be generated from the images of the items, the additional images, and the categories. GANs may be trained with the GAN training data sets. The GANs may generate images including images of generated items, which may be replaced with images of items from the catalog entries to create feature model training images.
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公开(公告)号：US11507989B2
公开(公告)日：2022-11-22
申请号：US16658315
申请日：2019-10-21
Applicant: salesforce.com, inc.
Inventor： Michael Sollami , Yang Zhang
Abstract: Systems, device and techniques are disclosed for multi-label product categorization. A catalog entry and a list of categories may be received. The catalog entry may be associated with an item. A textual description may be generated by comparing words in the catalog entry to existing vocabularies of words and applying part-of-speech tagging to the catalog entry. A feature vector may be generated from the textual description by applying any of token frequency feature creation, term frequency-inverse document frequency feature creation, and pre-trained word embeddings to the textual description. A set of probabilities may be determined by inputting the feature vector into a machine learning model. The set of probabilities may include a probability for each category in the list of categories.
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公开(公告)号：US20220114349A1
公开(公告)日：2022-04-14
申请号：US17067000
申请日：2020-10-09
Applicant: salesforce.com, inc.
Inventor： Michael Sollami , Aashish Jain
IPC: G06F40/56 , G06F40/51 , G06F16/35 , G06F40/284
Abstract: Systems and method are provided for selecting product corpus data. Natural language processing may be used to cluster and filter the dataset for valid descriptions of the product having a predetermined sentence length and normal natural language structure. A transformer based a multi-modal conditioned natural language generator may be instantiated based on the clustered and filtered dataset. The instantiated multi-modal conditioned natural language generator may be trained. An evaluation of an output of the multi-modal conditioned natural language generator may be performed. A product description may be generated based on the trained multi-modal conditioned natural language generator, and the product description may be output for an electronic product catalog.
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Patent Agency Ranking
公开(公告)号：US20210117484A1
公开(公告)日：2021-04-22
申请号：US16658322
申请日：2019-10-21
Applicant: salesforce.com, inc.
Inventor： Michael Sollami
IPC: G06F16/958 , G06F17/24 , G06F16/957 , G06N3/08 , G06K9/62
Abstract: Systems, device and techniques are disclosed for webpage template generation. Scores may be generated for images of webpages. A generative adversarial network may be trained using the images of the webpages and the scores generated for the images of the webpages. Images may be generated using the trained generative adversarial network. A webpage template may be generated from an image generated using the trained generative adversarial network. The webpage template may include one or both of HTML code and a wireframe template.
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公开(公告)号：US11727463B2
公开(公告)日：2023-08-15
申请号：US16594257
申请日：2019-10-07
Applicant: salesforce.com, inc.
Inventor： Michael Sollami , Yang Zhang
IPC: G06Q30/00 , G06Q30/0601 , G06T7/11 , G06T7/70
CPC classification number: G06Q30/0631 , G06Q30/0643 , G06T7/11 , G06T7/70
Abstract: Systems and methods are provided for receiving an image that includes a clothed person, determining a pose of the person in the image, and segmenting the image into one or more first fashion items. One or more second fashion items may be determined using a similarity search that searches at least one storage device communicatively coupled to the server based on the one or more first fashion items. At least one outfit proposal may be generated based on the one or more second fashion items. Image re-stylization of corresponding portions of the image may be performed, including the clothed person to generate recommended outfit images based on the at least one outfit proposal. The generated outfit images may be transmitted for display.
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公开(公告)号：US11663642B2
公开(公告)日：2023-05-30
申请号：US16594241
申请日：2019-10-07
Applicant: salesforce.com, inc.
Inventor： Michael Sollami , Yang Zhang
IPC: G06Q30/06 , G06K9/62 , G06T7/90 , G06Q30/0601 , G06N3/04 , G06N3/08 , G06F16/535 , G06T7/194 , G06F18/22
CPC classification number: G06Q30/0625 , G06F16/535 , G06F18/22 , G06N3/04 , G06N3/08 , G06T7/194 , G06T7/90 , G06T2207/20081 , G06T2207/20084
Abstract: Systems and methods are provided for receiving at least a first query color, and searching an electronic catalog including a plurality of product images for the first query color to determine a similarity measure between the first query color and a product image of a plurality of product images. The similarity measure may be determined by determining a Euclidean distance between values in a three-dimensional color space for the first query color and a target color of the product image, and determining the similarity measure between the query color and the product image by determining a sum of the similarity measures from all target colors on the product image, weighted by the coverage of each target color. The search results may be transmitted based on the searching of the electronic catalog including the plurality of product images for the first query color.
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公开(公告)号：US20230129431A1
公开(公告)日：2023-04-27
申请号：US17649016
申请日：2022-01-26
Applicant: salesforce.com, inc.
Inventor： Michael Sollami , Sönke Rohde , Alan Martin Ross , David James Woodward , Jessica Lundin , Owen Winne Schoppe , Brian J. Lonsdorf , Aashish Jain
IPC: G06F3/04845 , G06N3/04 , G06V10/771 , G06V10/762 , G06V10/82
Abstract: Techniques are disclosed for automatically generating new content using a trained 1-to-N generative adversarial network (GAN) model. In disclosed techniques, a computer system receives, from a computing device, a request for newly-generated content, where the request includes current content. The computer system automatically generates, using the trained 1-to-N GAN model, N different versions of new content, where a given version of new content is automatically generated based on the current content and one of N different style codes, where the value of N is at least two. After generating the N different versions of new content, the computer system transmits them to the computing device. The disclosed techniques may advantageously automate a content generation process, thereby saving time and computing resources via execution of the 1-to-N GAN machine learning model.
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公开(公告)号：US20230128686A1
公开(公告)日：2023-04-27
申请号：US17509024
申请日：2021-10-24
Applicant: salesforce.com, inc.
Inventor： Michael Sollami , Xiangyu Peng
Abstract: Systems, devices, and techniques are disclosed for automatic product description generation. A first set of features including labels including words may be generated from an image using a first feature extraction model. A second set of features including labels including words may be generated from the image using a second feature extraction model. A text description of a product depicted in the image may be generated by inputting the image and metadata for the image to a description generating model. The text description may include words. Each of the words may be generated by assigning probabilities to candidate words, boosting the assigned probabilities of candidate words that are similar to words of labels of the first set of features or words of labels of the second set of features, and selecting one of the candidate words based on the assigned probabilities after the boosting as a word of the text description.
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