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31. 发明申请

US20170075958A1 Visual Interactive Search 审中-公开
标题翻译：视觉互动搜索
公开(公告)号：US20170075958A1
公开(公告)日：2017-03-16
申请号：US15311163
申请日：2015-05-04
申请人： SENTIENT TECHNOLOGIES (BARBADOS) LIMITED
发明人： Nigel Duffy
IPC分类号： G06F17/30
CPC分类号： G06F16/3328 , G06F16/2455 , G06F16/25 , G06F16/904 , G06F16/93
摘要： Roughly described, a system for user identification of a desired document. A database is provided which identifies a catalog of documents in an embedding space, the database identifying a distance in the embedding space between each pair of documents corresponding to a predetermined measure of dissimilarity between the pair of documents. The system presents an initial collection of the documents toward the user, from an initial candidate space which is part of the embedding space. The system then iteratively refines the candidate space using geometric constraints on the embedding space determined in response to relative feedback by the user. At each iteration the system identifies to the user a subset of documents from the then-current candidate space, based on which the user provides the relative feedback. In an embodiment, these subsets of documents are more discriminative than the average discriminativeness of similar sets of documents in the then-current candidate space.
摘要翻译：粗略地描述了用于识别所需文件的系统。提供了一种数据库，其标识嵌入空间中的文档目录，数据库标识对应于一对文档之间的预定的不相似度量度的每对文档之间的嵌入空间中的距离。该系统从作为嵌入空间的一部分的初始候选空间向用户呈现文档的初始集合。然后，系统使用对用户的相对反馈确定的嵌入空间上的几何约束来迭代地优化候选空间。在每次迭代时，系统向用户标识来自当前候选空间的文档的子集，用户基于该子集提供相对反馈。在一个实施例中，这些文档子集比当前候选空间中的类似文档集合的平均歧视性更具歧视性。

32. 发明授权

US10503765B2 Visual interactive search 有权
公开(公告)号：US10503765B2
公开(公告)日：2019-12-10
申请号：US15311163
申请日：2015-05-04
申请人： SENTIENT TECHNOLOGIES (BARBADOS) LIMITED
发明人： Nigel Duffy
IPC分类号： G06F16/25 , G06F16/93 , G06F16/332 , G06F16/904 , G06F16/2455
CPC分类号： G06F16/3328 , G06F16/2455 , G06F16/904 , G06F16/93 , G06F16/25
摘要： Roughly described, a system for user identification of a desired document. A database is provided which identifies a catalog of documents in an embedding space, the database identifying a distance in the embedding space between each pair of documents corresponding to a predetermined measure of dissimilarity between the pair of documents. The system presents an initial collection of the documents toward the user, from an initial candidate space which is part of the embedding space. The system then iteratively refines the candidate space using geometric constraints on the embedding space determined in response to relative feedback by the user. At each iteration the system identifies to the user a subset of documents from the then-current candidate space, based on which the user provides the relative feedback. In an embodiment, these subsets of documents are more discriminative than the average discriminativeness of similar sets of documents in the then-current candidate space.

33. 发明授权

US10268953B1 Data mining technique with maintenance of ancestry counts 有权
公开(公告)号：US10268953B1
公开(公告)日：2019-04-23
申请号：US14595991
申请日：2015-01-13
申请人： SENTIENT TECHNOLOGIES (BARBADOS) LIMITED
发明人： Daniel E. Fink , Hormoz Shahrzad
IPC分类号： G06N3/12 , G06N5/02
摘要： Roughly described, a computer-implemented evolutionary data mining system includes a memory storing a candidate gene database in which each candidate individual has a respective fitness estimate; a gene pool processor which tests individuals from the candidate gene pool on training data and updates the fitness estimate associated with the individuals in dependence upon the tests; and a gene harvesting module for deploying selected individuals from the gene pool, wherein the gene pool processor includes a competition module which selects individuals for discarding in dependence upon their updated fitness estimate. The system maintains the ancestry count for each of the candidate individuals, and may use this information to adjust the competition among the individuals, to adjust the selection of individuals for further procreation, and/or for other purposes.

34. 发明申请

US20180329990A1 INTELLIGENTLY DRIVEN VISUAL INTERFACE ON MOBILE DEVICES AND TABLETS BASED ON IMPLICIT AND EXPLICIT USER ACTIONS 审中-公开
公开(公告)号：US20180329990A1
公开(公告)日：2018-11-15
申请号：US15977931
申请日：2018-05-11
申请人： SENTIENT TECHNOLOGIES (BARBADOS) LIMITED
发明人： Robert SEVERN , Matthew J. STROM , Diego Guy M. LEGRAND , James O'NEILL , Scott HENNING
IPC分类号： G06F17/30 , G06K9/62
CPC分类号： G06F17/30713 , G06F17/30011 , G06F17/30699 , G06K9/6223
摘要： A method for identifying a desired document is provided to include forming K clusters of documents and, for each cluster: for each respective document of the cluster determining a sum of distances between (i) the respective document and (ii) each of the other documents of the cluster; and identifying a medoid document of the cluster as the document of the cluster having the smallest sum of determined distances of all of the documents of the cluster. The method also includes selecting M representative documents for each cluster, identifying for dynamic display toward the user K groupings of documents, wherein each of the K groupings of documents identifies the selected M representative documents of a corresponding cluster, and, in response to user selection of one of the K groupings of documents, identifying for dynamic display toward the user P documents of the cluster that corresponds to the selected grouping.

35. 发明申请

US20180322395A1 Training and Control System for Evolving Solutions to Data-Intensive Problems Using Nested Experience-Layered Individual Pool 审中-公开
公开(公告)号：US20180322395A1
公开(公告)日：2018-11-08
申请号：US16038074
申请日：2018-07-17
申请人： SENTIENT TECHNOLOGIES (BARBADOS) LIMITED
发明人： Hormoz Shahrzad , Babak Hodjat
IPC分类号： G06N3/12 , G06N5/02
CPC分类号： G06N3/126 , G06N5/025
摘要： Roughly described, in an evolutionary technique for finding optimal solutions to a provided problem, a computer system uses a grouping algorithm that is better able to find diverse and optimum solutions in data mining environment with multiple solution landscapes and a plurality of candidate individuals. Each candidate individual identifies with a potential solution, and is associated with a testing experience level and one or more partition tags. Each candidate individual is assigned into one of a plurality of competition groups in dependence upon the individual's testing experience level and partition tag. During competition among candidate individuals, a candidate individual can only replace another candidate individual if both the candidate individuals have a common partition tag and are in the same competition group. A candidate individual cannot replace another candidate individual if they have different partition tags or are in different competition groups.

36. 发明授权

US10102277B2 Bayesian visual interactive search 有权
公开(公告)号：US10102277B2
公开(公告)日：2018-10-16
申请号：US15373897
申请日：2016-12-09
申请人： SENTIENT TECHNOLOGIES (BARBADOS) LIMITED
发明人： Diego Guy M. Legrand , Philip M. Long , Nigel Duffy , Olivier Francon
IPC分类号： G06F17/30 , G06N5/02
摘要： A method for identifying a desired document is provided to include calculating a Prior probability score for each document of a candidate list including a portion of documents of an embedding space, the Prior probability score indicating a preliminary probability, for each document of the candidate list, that the document is the desired document, and identifying an initial (i=0) collection of N0>1 candidate documents from the candidate list in dependence on the calculated Prior probability scores, the initial collection of candidate documents having fewer documents than the candidate list. The method further includes, for each i'th iteration in a plurality of iterations, beginning with a first iteration (i=1) and in response to user selection of an i'th selected document from the (i−1)'th collection of candidate documents, identifying an i'th collection of Ni>1 candidate documents from the candidate list in dependence on Posterior probability scores.

37. 发明申请

US20180260713A1 Asynchronous Evaluation Strategy For Evolution Of Deep Neural Networks 审中-公开
公开(公告)号：US20180260713A1
公开(公告)日：2018-09-13
申请号：US15915028
申请日：2018-03-07
申请人： SENTIENT TECHNOLOGIES (BARBADOS) LIMITED
发明人： Jason Zhi LIANG , Hormoz SHAHRZAD , Babak HODJAT , Risto MIIKKULAINEN
IPC分类号： G06N3/08 , G06N3/04
CPC分类号： G06N3/086 , G06N3/04 , G06N3/0454 , G06T2207/20084
摘要： The technology disclosed proposes a novel asynchronous evaluation strategy (AES) that increases throughput of evolutionary algorithms by continuously maintaining a queue of K individuals ready to be sent to the worker nodes for evaluation and evolving the next generation once a fraction Mi of the K individuals have been evaluated by the worker nodes, where Mi

38. 发明申请

US20180137390A1 DATA OBJECT CREATION AND RECOMMENDATION USING MACHINE LEARNING BASED ONLINE EVOLUTION 审中-公开
公开(公告)号：US20180137390A1
公开(公告)日：2018-05-17
申请号：US15813041
申请日：2017-11-14
申请人： Sentient Technologies (Barbados) Limited
发明人： Myles BRUNDAGE , Risto MIIKKULAINEN
IPC分类号： G06K9/62 , G06N3/08 , G06N3/04
CPC分类号： G06F16/5838 , G06F16/583 , G06F16/5854 , G06K9/00624 , G06K9/4628 , G06K9/6215 , G06K9/6253 , G06K9/6255 , G06K9/6257 , G06K9/626 , G06K9/6262 , G06K9/6271 , G06N3/0454 , G06N3/084 , G06N3/086 , G06N20/00
摘要： The technology disclosed relates to providing expanded data object correlations for user-generated web customizations. Roughly described, it relates to creating an initial plurality of candidate data object image representations by procreation, storing a candidate population having the plurality of candidate data object image representations, scoring each of the candidate data object image representations for conformity with a predefined goal using a trained neural network system and/or a rule-based percept filter, discarding candidate data object image representations from the candidate population, adding new data object image representations to the candidate population by procreation, iterating the scoring, the discarding, and the adding until the discarding yields a candidate pool of candidate data object image representations not yet discarded but which satisfy a convergence condition, and providing as recommendation for display to a user at least one of the data object images represented in the candidate pool.

39. 发明申请

US20180114116A1 COOPERATIVE EVOLUTION OF DEEP NEURAL NETWORK STRUCTURES 审中-公开
公开(公告)号：US20180114116A1
公开(公告)日：2018-04-26
申请号：US15794913
申请日：2017-10-26
申请人： Sentient Technologies (Barbados) Limited
发明人： Jason Zhi LIANG , Risto MIIKKULAINEN
IPC分类号： G06N3/08 , G06N3/04
CPC分类号： G06N3/086 , G06N3/04 , G06N3/0454 , G06N3/084
摘要： The technology disclosed relates to evolving a deep neural network based solution to a provided problem. In particular, it relates to providing an improved cooperative evolution technique for deep neural network structures. It includes creating blueprint structures that include a plurality of supermodule structures. The supermodule structures include a plurality of modules. The modules are neural networks. A first loop of evolution executes at the blueprint level. A second loop of evolution executes at the supermodule level. Further, multiple mini-loops of evolution execute at each of the subpopulations of the supermodules. The first loop, the second loop, and the mini-loops execute in parallel.

40. 发明申请

US20170323219A1 DATA MINING TECHNIQUE WITH DISTRIBUTED NOVELTY SEARCH 审中-公开
公开(公告)号：US20170323219A1
公开(公告)日：2017-11-09
申请号：US15146846
申请日：2016-05-04
申请人： SENTIENT TECHNOLOGIES (BARBADOS) LIMITED
发明人： Hormoz Shahrzad , Babak Hodjat , Risto Miikkulainen
IPC分类号： G06N99/00 , G06N3/12
CPC分类号： G06N3/126
摘要： Roughly described, an evolutionary data mining system includes at least two processing units, each having a pool of candidate individuals in which each candidate individual has a fitness estimate and experience level. A first processing unit tests candidate individuals against training data, updates an individual's experience level, and assigns each candidate to one of multiple layers of the candidate pool based on the individual's experience level. Individuals within the same layer of the same pool compete with each other to remain candidates. The first processing unit selects a set of candidates to retain based on the relative novelty of their responses to the training data. The first processing unit reports successful individuals to the second processing unit, and receives individuals for further testing from the second processing unit. The second processing unit selects individuals to retain based on their fitness estimate.

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