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1. 发明申请

WO2018060450A1 QUESTION GENERATION 审中-公开
标题翻译：问题产生
公开(公告)号：WO2018060450A1
公开(公告)日：2018-04-05
申请号：PCT/EP2017/074818
申请日：2017-09-29
申请人： KONINKLIJKE PHILIPS N.V.
发明人： GHAEINI, Reza , AL HASAN, Sheikh, Sadid , FARRI, Oladimeji, Feyisetan , LEE, Kathy, Mi, Young , DATLA, Vivek, Varma , QADIR, Ashequl , LIU, Junyi , PRAKASH, Adi
IPC分类号： G06F17/30
CPC分类号： G06F16/3329
摘要： Methods and systems for generating a question from free text. The system is trained on a corpus of data and receives a tuple consisting of a paragraph (free text), a focused fact, and a question type. The system implements a language model to find the most optimal combination of words to return a question for the paragraph about the focused fact.
摘要翻译：
从自由文本生成问题的方法和系统。系统在数据语料库上接受培训，并接收由段落（自由文本），焦点事实和问题类型组成的元组。该系统实现了一种语言模型，以找到单词的最佳组合，从而为有关聚焦事实的段落返回一个问题。

2. 发明申请

WO2019215109A1 CONVOLUTIONAL LOCALIZATION NETWORKS FOR INTELLIGENT CAPTIONING OF MEDICAL IMAGES 审中-公开
公开(公告)号：WO2019215109A1
公开(公告)日：2019-11-14
申请号：PCT/EP2019/061602
申请日：2019-05-07
申请人： KONINKLIJKE PHILIPS N.V.
发明人： SWISHER, Christine, Menking , AL HASAN, Sheikh, Sadid , RUBIN, Jonathan , POTES BLANDON, Cristhian, Mauricio , LING, Yuan , FARRI, Oladimeji, Feyisetan , SREENIVASAN, Rithesh
IPC分类号： G06K9/00 , G06K9/46
摘要： A method (100) for generating a textual description of a medical image, comprising: receiving (130) a medical image of an anatomical region, the image comprising one or more abnormalities; segmenting (140) the anatomical region in the received medical image from a remainder of the image; identifying (150) at least one of the one or more abnormalities in the segmented anatomical region; extracting (160) one or more features from the identified abnormality; generating (170), using the extracted features and a trained text generation model, a textual description of the identified abnormality; and reporting (180), via a user interface of the system, the generated textual description of the identified abnormality.

3. 发明申请

WO2018069026A1 PATIENT-CENTRIC CLINICAL KNOWLEDGE DISCOVERY SYSTEM 审中-公开
标题翻译：以病人为中心的临床知识发现系统
公开(公告)号：WO2018069026A1
公开(公告)日：2018-04-19
申请号：PCT/EP2017/074155
申请日：2017-09-25
申请人： KONINKLIJKE PHILIPS N.V.
发明人： FARRI, Oladimeji, Feyisetan , AL HASAN, Sheikh, Sadid , LIU, Junyi , LEE, Kathy, Mi, Young , DATLA, Vivek, Varma
IPC分类号： G06F19/00 , G06F17/30 , G06F17/27
CPC分类号： G06N5/04 , G06F16/35 , G06F17/2785 , G06N3/08 , G16H15/00 , G16H50/20 , G16H50/70
摘要： A medical information retrieval system comprises a natural language processing system that processes a vocal user query to identify key words and phrases. These key words and phrases are provided to an inferencing engine that provides a set of knowledge-based inferences from medical knowledge sources, based on these key words and phrases. Thereafter, these knowledge-based inferences are provided to an information retrieval engine that retrieves a corresponding plurality of medical articles based on these knowledge-based inferences, and ranks each with respect to the knowledge-based inferences. A summary engine receives the ranked articles and creates a model based on the topical keywords and candidate sentences found in the highly ranked articles. A paraphrase engine processes the candidate sentences to provide a summary response based on a knowledge-based paraphrase model. An audio output device renders the summary report as the response to the user's original vocal query.
摘要翻译：医疗信息检索系统包括处理声音用户查询以识别关键词和短语的自然语言处理系统。这些关键词和短语被提供给推理引擎，其基于这些关键词和短语提供来自医学知识源的一组基于知识的推论。此后，将这些基于知识的推论提供给信息检索引擎，该信息检索引擎基于这些基于知识的推论来检索相应的多个医学文章，并且将每个关于基于知识的推论进行排名。摘要引擎接收排名的文章，并基于在高度排名的文章中找到的主题关键词和候选句子创建模型。释义引擎处理候选句子以基于基于知识的释义模型提供总结响应。音频输出设备将摘要报告呈现为对用户原始语音查询的响应。

4. 发明申请

WO2021156491A1 METHOD AND DEVICE FOR PRESERVING CONTEXT IN CONVERSATIONS 审中-公开
公开(公告)号：WO2021156491A1
公开(公告)日：2021-08-12
申请号：PCT/EP2021/052886
申请日：2021-02-05
申请人： KONINKLIJKE PHILIPS N.V.
发明人： ANAND, Shreya , SREENIVASAN, Rithesh , AL HASAN, Sheikh, Sadid , FARRI, Oladimeji, Feyisetan
IPC分类号： G06F16/332 , G06F16/335
摘要： The present disclosure relates to preserving context in a conversation between a user (101) and a digital assistant device (102). During training, the digital assistant device (102) is provided with a plurality of conversations having a plurality of dialogues. Each of the plurality of dialogue is assigned an ID based on a context. Further, two or more test queries having a same context is provided as input and the two are more queries are assigned an ID based on the context. Thereafter, the digital assistant device (102) is configured to retrieve one or more dialogues from the plurality of dialogues where the ID of the one or more dialogues match the ID of the two or more queries. In real-time, one or more queries are received and based on a context of the one or more queries, one or more dialogues are retrieved and are provided to the user.

5. 发明申请

WO2018141622A1 EXTRACTED CONCEPT NORMALIZATION USING EXTERNAL EVIDENCE 审中-公开
公开(公告)号：WO2018141622A1
公开(公告)日：2018-08-09
申请号：PCT/EP2018/051820
申请日：2018-01-25
申请人： KONINKLIJKE PHILIPS N.V.
发明人： AL HASAN, Sheikh, Sadid , LING, Yuan , FARRI, Oladimeji, Feyisetan , DATLA, Vivek, Varma
IPC分类号： G16H50/20
摘要： Various embodiments described herein relate to a method, system, and non-transitory machine- readable medium including one or more of the following: extracting a first concept from input data presented for processing by a downstream function; identifying external data from an external resource based on the first concept; extracting a second concept from the external data; revising the first concept based on the second concept to produce a revised concept, wherein revising includes: applying a machine learning agent to determine whether to keep the first concept or adopt the second concept, and adopting the second concept in place of the first concept for use as the revised concept based on a decision by the machine learning agent to adopt the second concept; and further processing the revised concept according to the downstream function to generate an output.

6. 发明申请

WO2018077906A1 KNOWLEDGE GRAPH-BASED CLINICAL DIAGNOSIS ASSISTANT 审中-公开
标题翻译：基于知识图的临床诊断助手
公开(公告)号：WO2018077906A1
公开(公告)日：2018-05-03
申请号：PCT/EP2017/077208
申请日：2017-10-24
申请人： KONINKLIJKE PHILIPS N.V.
发明人： DATLA, Vivek, Varma , AL HASAN, Sheikh, Sadid , FARRI, Oladimeji, Feyisetan , LIU, Junyi , LEE, Kathy, Mi, Young , QADIR, Ashequl , PRAKASH, Adi
IPC分类号： G16H50/20
CPC分类号： G16H50/20 , G06F16/90324 , G06F16/907 , G06F17/10 , G16H50/70
摘要： A system (500) for automated clinical diagnosis includes: a knowledge graph (310, 510) generated using a curated corpus of medical information (520) and comprising a plurality of nodes; a user interface (512) configured to receive input comprising information about at least one patient symptom (316) and at least one patient demographic parameter (318); and a processor (530) configured to extract the at least one patient symptom and demographic parameter, and further configured to: (i) weight the extracted patient symptom; (ii) query the knowledge graph to generate a diagnosis graph as a subset of the knowledge graph; (iii) identify a ranked list of medical conditions for the patient from the diagnosis graph; and (iv) adjust, based on the extracted at least one demographic parameter about the patient, the ranking of the ranked list; wherein the identified medical conditions are provided to the user via the user interface.
摘要翻译：一种用于自动临床诊断的系统（500），包括：使用策划的医疗信息集（520）生成并包括多个节点的知识图（310,510）用户接口（512），其被配置为接收包括关于至少一个患者症状（316）和至少一个患者人口统计参数（318）的信息的输入; 以及处理器（530），被配置为提取所述至少一个患者症状和人口统计参数，并且还被配置为：（i）对所提取的患者症状加权; （ii）查询知识图以生成作为知识图的子集的诊断图; （iii）从诊断图中识别患者的医疗状况的排序列表; 以及（iv）基于提取的关于患者的至少一个人口统计参数来调整排名列表的排名; 其中识别的医疗状况通过用户界面提供给用户。

7. 发明申请

WO2020148355A1 A SYSTEM FOR MULTI-PERSPECTIVE DISCOURSE WITHIN A DIALOG 审中-公开
公开(公告)号：WO2020148355A1
公开(公告)日：2020-07-23
申请号：PCT/EP2020/050954
申请日：2020-01-16
申请人： KONINKLIJKE PHILIPS N.V.
发明人： DATLA, Vivek, Varma , AL HASAN, Sheikh, Sadid , PRAKASH, Aaditya , FARRI, Oladimeji, Feyisetan , ARORA, Tilak, Raj , LIU, Junyi , QADIR, Ashequl
IPC分类号： G06N3/04 , G06N3/08 , G06N5/04
摘要： Techniques are described for training and/or utilizing sub-agent machine learning models to generate candidate dialog responses. In various implementations, a user-facing dialog agent (202, 302), or another component on its behalf, selects one of the candidate responses which is closest to user defined global priority objectives (318). Global priority objectives can include values (306) for a variety of dialog features such as emotion, confusion, objective-relatedness, personality, verbosity, etc. In various implementations, each machine learning model includes an encoder portion and a decoder portion. Each encoder portion and decoder portion can be a recurrent neural network (RNN) model, such as a RNN model that includes at least one memory layer, such as a long short-term memory (LSTM) layer.

8. 发明申请

WO2019211250A1 VISUAL QUESTION ANSWERING USING ON-IMAGE ANNOTATIONS 审中-公开
公开(公告)号：WO2019211250A1
公开(公告)日：2019-11-07
申请号：PCT/EP2019/060977
申请日：2019-04-29
申请人： KONINKLIJKE PHILIPS N.V.
发明人： FARRI, Oladimeji, Feyisetan , AL HASAN, Sheikh, Sadid , LING, Yuan
IPC分类号： G16H30/40 , G16H80/00 , G16H50/70
摘要： Techniques described herein relate to visual question answering ("VQA") using trained machine learning models. In various embodiments, a VQA machine learning model may be trained using the follow operations: obtaining (302) a corpus of digital images, each respective digital image (232) including on-image annotation(s) (234) that identify pixel coordinate(s) on the respective digital image; obtaining (304) question-answer pair(s) associated with each of the digital images; generating (306) training examples, each including a respective digital image of the corpus, including the associated on-image annotations, and the associated question-answer pair(s); and for each respective training example of the plurality of training examples: applying (312) the respective training example as input across a machine learning model to generate a respective output; and training (314) the machine learning model based on comparison of the respective output with an answer of the question-answer pair(s) of the respective training example.

9. 发明申请

WO2019202136A1 MACHINE LEARNING MODEL WITH EVOLVING DOMAIN-SPECIFIC LEXICON FEATURES FOR TEXT ANNOTATION 审中-公开
公开(公告)号：WO2019202136A1
公开(公告)日：2019-10-24
申请号：PCT/EP2019/060212
申请日：2019-04-18
申请人： KONINKLIJKE PHILIPS N.V.
发明人： LING, Yuan , AL HASAN, Sheikh, Sadid , FARRI, Oladimeji, Feyisetan , LIU, Junyi
IPC分类号： G16B40/30 , G16B50/10
摘要： A method of generating embeddings for a machine learning model, including: extracting a character embedding and a word embedding from a first textual data; generating a domain knowledge embedding from a domain knowledge dataset; combining the character embedding, the word embedding, and the domain knowledge embedding into a combined embedding; and providing the combined embedding to a layer of the machine learning model.

10. 发明申请

WO2018077655A1 MULTI DOMAIN REAL-TIME QUESTION ANSWERING SYSTEM 审中-公开
标题翻译：多领域实时问题回答系统
公开(公告)号：WO2018077655A1
公开(公告)日：2018-05-03
申请号：PCT/EP2017/076390
申请日：2017-10-17
申请人： KONINKLIJKE PHILIPS N.V.
发明人： DATLA, Vivek, Varma , AL HASAN, Sheikh, Sadid , FARRI, Oladimeji, Feyisetan , LIU, Junyi , LEE, Kathy, Mi, Young , QADIR, Ashequl , PRAKASH, Adi
IPC分类号： G06F17/30
摘要： A system (1000) for automated question answering, including: semantic space (210) generated from a corpus of questions and answers; a user interface (1030) configured to receive a question; and a processor (1100) comprising: (i) a question decomposition engine (1050) configured to decompose the question into a domain, a keyword, and a focus word; (ii) a question similarity generator (1060) configured to identify one or more questions in a semantic space using the decomposed question; (iii) an answer extraction and ranking engine (1080) configured to: extract, from the semantic space, answers associated with the one or more identified questions; and identify one or more of the extracted answers as a best answer; and (iv) an answer tuning engine (1090) configured to fine-tune the identified best answer using one or more of the domain, keyword, and focus word; wherein the fine-tuned answer is provided to the user via the user interface.
摘要翻译：一种用于自动问答的系统（1000），包括：从问题和答案的语料库生成的语义空间（210）用户界面（1030），其被配置为接收问题; 以及处理器（1100），其包括：（i）问题分解引擎（1050），其被配置为将所述问题分解成域，关键词和焦点词; （ii）问题相似度生成器（1060），被配置为使用分解的问题来识别语义空间中的一个或多个问题; （iii）答案提取和排名引擎（1080），其被配置为：从语义空间提取与一个或多个识别的问题相关联的答案; 并将一个或多个提取的答案标识为最佳答案; （iv）回答调整引擎（1090），被配置为使用领域，关键词和焦点词中的一个或多个对所识别的最佳答案进行微调; 其中微调的答案通过用户界面提供给用户。

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