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1. 发明申请

US20220129834A1 SYSTEMS AND METHODS FOR IMPROVING RECOMMENDATION SYSTEMS 有权
公开(公告)号：US20220129834A1
公开(公告)日：2022-04-28
申请号：US17388332
申请日：2021-07-29
申请人： Stitch Fix, Inc.
发明人： Jason B. Martin , Katherine A. Livins , Bradley J. Klingenberg , Tarek Rached
IPC分类号： G06Q10/08 , G06N20/00 , G06F16/332
摘要： Data about clients, items included in an item inventory, executable code for recommendation processes, and executable code for feature selection process(es), are stored. The recommendation processes are used to identify items in the item inventory that are recommended for specific clients, and the feature selection process(es) is/are used to select features that are displayed to entities tasked with selecting items from the item inventory for clients. Selection feedback and client feedback are received. One or more feature selection process(es) is/are modified based on the selection feedback and/or the client feedback, to thereby change which, where or/or how certain portion of the data about clients is displayed to the entities tasked with selecting items from the item inventory for the clients, and/or change which, where and/or how certain portion of the data about items is displayed to the entities tasked with selecting items from the item inventory for the clients.

2. 发明授权

US11232506B1 Contextual set selection 有权
公开(公告)号：US11232506B1
公开(公告)日：2022-01-25
申请号：US16715867
申请日：2019-12-16
申请人： Stitch Fix, Inc.
发明人： Kevin J. Zielnicki
IPC分类号： G06Q30/00 , G06Q30/06 , G06N20/00 , G06Q10/08
摘要： An initial list of candidate items automatically evaluated and chosen for an end-user is provided. A selection of one or more items in the initial list of candidate items is received from an expert user different than the end-user to include in an item group set for the end-user. Eligible items are evaluated to identify an additional item to include in the item group set based at least in part on an expert judgment prediction machine learning model trained to predict based at least in part on the one or more items already in the item group set a likelihood of a certain item being evaluated would be selected for inclusion in the item group set. Based on the evaluation, the additional item is included in the item group set. Member items in the item group set are indicated.

3. 发明授权

US10984342B2 Using artificial intelligence to determine a value for a variable size component 有权
公开(公告)号：US10984342B2
公开(公告)日：2021-04-20
申请号：US15729275
申请日：2017-10-10
申请人： Stitch Fix, Inc.
发明人： Zhou Yu , Ian Andrew Hepworth , Daragh Edgar Sibley
IPC分类号： G06N20/00 , G06N7/00 , A41H43/00 , G06Q30/06 , A41H3/00 , G06Q50/04
摘要： A machine learning model for predicting a size fit satisfaction for a variable size component is trained using at least sizing profiles of a plurality of items and feedbacks of subjects regarding sizing of the plurality of items. The machine learning model is used to determine a value for the variable size component that corresponds to an optimal predicted size fit satisfaction. The determined value of the variable size component is provided for use in creating a new item with a sizing variation based on the determined value.

4. 发明授权

US10956960B2 Method, medium, and system for batch-processing and on-demand processing to provide recommendations 有权
公开(公告)号：US10956960B2
公开(公告)日：2021-03-23
申请号：US16508072
申请日：2019-07-10
申请人： Stitch Fix, Inc.
发明人： Jeffrey S. Magnusson , LiKuan Alex Chen , Akshay Wadia
IPC分类号： G06Q30/00 , G06Q30/06
摘要： Systems and methods described herein, which utilize a combination of batch-processing and on-demand processing to provide recommendations, can include database(s) that store client data, item data, and executable code for composable algorithms that utilize the client data and/or the item data to provide recommendations. The system also includes a batch-process results data store that stores results of composable algorithms executed using batch-processing. Additionally, the system includes an algorithm engine that accepts calls to composable algorithms and output results thereof. The algorithm engine determines which called composable algorithms are designated as being executable using batch-processing, and which are designated as being executable using on-demand processing. The algorithm engine returns results of called composable algorithms designated as being executable using on-demand processing by executing the executable code thereof, and returns results of called composable algorithms designated as being executable using batch-demand-processing by obtaining the results from the batch-process results data store.

5. 发明申请

US20210342917A1 EXTENDING MACHINE LEARNING TRAINING DATA TO GENERATE AN ARTIFICIAL INTELLIGENCE RECOMMENDATION ENGINE 有权
公开(公告)号：US20210342917A1
公开(公告)日：2021-11-04
申请号：US17379071
申请日：2021-07-19
申请人： Stitch Fix, Inc.
发明人： Hilary S. Parker , Allison M. Barros
IPC分类号： G06Q30/06 , G06N20/00
摘要： A catalog of physical items associated with a target user is accessed. At least a portion of the catalog is at least in part automatically generated based on a retention of one or more of the physical items provided to the target user. A machine learning model trained using outfit combination information gathered from other users is used to automatically determine for the target user, at least a portion of one or more recommended outfit combinations of a plurality of physical items among the physical items within the catalog. An indication of a selected one of the one or more recommended outfit combinations is provided to the target user.

6. 发明授权

US11144845B2 Using artificial intelligence to design a product 有权
公开(公告)号：US11144845B2
公开(公告)日：2021-10-12
申请号：US15612080
申请日：2017-06-02
申请人： Stitch Fix, Inc.
发明人： Erin S. Boyle , Daragh Sibley
IPC分类号： G06N20/00 , G06N5/04 , G06F16/2457 , G06Q10/04 , G06Q10/06 , G06F16/9535 , G06Q30/02 , G06F30/00 , G06F113/12
摘要： In an embodiment, a method for optimizing computer machine learning includes receiving an optimization goal. The optimization goal is used to search a database of base option candidates (BOC) to identify matching BOCs that at least in part matches the goal. A selection of a selected base option among the matching BOCs is received. Machine learning prediction model(s) are selected based at least in part on the goal to determine prediction values associated with alternative features for the selected base option, where the model(s) were trained using training data to at least identify weight values associated with the alternative features for models. Based on the prediction values, at least a portion of the alternative features is sorted to generate an ordered list. The ordered list is provided for use in manufacturing an alternative version of the selected base option with the alternative feature(s) in the ordered list.

7. 发明授权

US11138648B2 Systems, apparatuses, and methods for generating inventory recommendations 有权
公开(公告)号：US11138648B2
公开(公告)日：2021-10-05
申请号：US16047280
申请日：2018-07-27
申请人： Stitch Fix, Inc.
发明人： Gillis Baxter , Whitney Casey
IPC分类号： G06Q30/06 , H04L12/58 , H04L29/08 , G06F30/20 , G06Q10/08 , G06Q10/10 , G06Q30/08
摘要： Disclosed are systems, methods, and devices for managing a personal user inventory. In one embodiment, the method comprises retrieving a message associated with a user identifier, the message including a body content portion; identifying a parser associated with the message; parsing the body content portion using the parser to identify an item of merchandise present within the body content portion, wherein the item of merchandise is associated with an item identifier and a set of item details; associating the item of merchandise with the user identifier; and generating a display listing merchandise associated with the user identifier, wherein the listing of merchandise includes the item of merchandise.

8. 发明授权

US11113659B2 Systems and methods for improving recommendation systems 有权
公开(公告)号：US11113659B2
公开(公告)日：2021-09-07
申请号：US15241899
申请日：2016-08-19
申请人： Stitch Fix, Inc.
发明人： Jason B. Martin , Katherine A. Livins , Bradley J. Klingenberg , Tarek Rached
IPC分类号： G06F17/00 , G06Q10/08 , G06N20/00 , G06F16/332
摘要： Data about clients, items included in an item inventory, executable code for recommendation processes, and executable code for feature selection process(es), are stored. The recommendation processes are used to identify items in the item inventory that are recommended for specific clients, and the feature selection process(es) is/are used to select features that are displayed to entities tasked with selecting items from the item inventory for clients. Selection feedback and client feedback are received. One or more feature selection process(es) is/are modified based on the selection feedback and/or the client feedback, to thereby change which, where or/or how certain portion of the data about clients is displayed to the entities tasked with selecting items from the item inventory for the clients, and/or change which, where and/or how certain portion of the data about items is displayed to the entities tasked with selecting items from the item inventory for the clients.

9. 发明授权

US10395301B2 Method, medium, and system for using a combination of batch-processing and on-demand processing to provide recommendations 有权
公开(公告)号：US10395301B2
公开(公告)日：2019-08-27
申请号：US15142541
申请日：2016-04-29
申请人： Stitch Fix, Inc.
发明人： Jeffrey S. Magnusson , LiKuan Alex Chen , Akshay Wadia
IPC分类号： G06Q30/00 , G06Q30/06
摘要： Systems and methods described herein, which utilize a combination of batch-processing and on-demand processing to provide recommendations, can include database(s) that store client data, item data, and executable code for composable algorithms that utilize the client data and/or the item data to provide recommendations. The system also includes a batch-process resultsP data store that stores results of composable algorithms executed using batch-processing. Additionally, the system includes an algorithm engine that accepts calls to composable algorithms and output results thereof. The algorithm engine determines which called composable algorithms are designated as being executable using batch-processing, and which are designated as being executable using on-demand processing. The algorithm engine returns results of called composable algorithms designated as being executable using on-demand processing by executing the executable code thereof, and returns results of called composable algorithms designated as being executable using batch-demand-processing by obtaining the results from the batch-process results data store.

10. 发明申请

US20160292769A1 SYSTEMS AND METHODS THAT EMPLOY ADAPTIVE MACHINE LEARNING TO PROVIDE RECOMMENDATIONS 审中-公开
标题翻译：使用自适应机器学习提供建议的系统和方法
公开(公告)号：US20160292769A1
公开(公告)日：2016-10-06
申请号：US14674641
申请日：2015-03-31
申请人： Stitch Fix, Inc.
发明人： Eric C. Colson , Bradley J. Klingenberg , Jeffrey S. Magnusson , W. Joel Strait
IPC分类号： G06Q30/06 , G06N99/00 , G06N5/04 , G06Q10/08
CPC分类号： G06Q30/0631 , G06N5/046 , G06N20/00 , G06Q10/087
摘要： Systems and methods described herein employ adaptive machine learning to provide recommendations to an entity that selects one or more items for a client from an item inventory. Client information, item information, and recommendation algorithms are stored and are accessible by a recommendation engine. The recommendation algorithms utilize the client information and the item information in different manners to identify different subsets of items recommended for a client. Information about two or more of the subsets of the items in the item inventory that are identified are selected for display to the entity tasked with selecting items for the client. Feedback information, including client, selection and/or coverage feedback information, is obtained and adaptive machine learning is used to modify the stored client information, the stored item information and/or stored recommendation algorithm(s), in dependence on the client feedback information, the selection feedback information and/or the coverage feedback information.
摘要翻译：本文描述的系统和方法使用自适应机器学习来向从物料库存中为客户机选择一个或多个物品的实体提供建议。客户信息，项目信息和推荐算法被存储并可由推荐引擎访问。推荐算法以不同的方式利用客户信息和项目信息来识别为客户推荐的不同子项。选择用于标识的项目清单中的项目的两个或多个子集的信息以显示给被选择用于客户端的项目的实体。获得反馈信息，包括客户端，选择和/或覆盖反馈信息，并且使用自适应机器学习来根据客户端反馈信息修改所存储的客户端信息，存储的项目信息和/或存储的推荐算法，选择反馈信息和/或覆盖反馈信息。

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