公开(公告)号：US11566809B2

公开(公告)日：2023-01-31

申请号：US16681131

申请日：2019-11-12

申请人： Robert Bosch GmbH

发明人： Jonathan Francis ,  Sirajum Munir ,  Matias Alberto Quintana Rosales

IPC分类号： F24F11/64 ,  F24F11/49 ,  F24F120/10

摘要： Occupant thermal comfort may be inferred and improved using body shape information. Height, weight, and shoulder circumference of an occupant of a room may be obtained using a depth sensor. A model may be utilized that is trained on a dataset including information reflecting of occupant comfort within the room versus temperature, the model receiving, as inputs, the height, the weight, and the shoulder circumference of the occupant and environmental information and outputting a comfort class. A temperature set-point for is identified which the room occupant is identified by the model as having the comfort class being indicative of user comfort. Heating, ventilation, and air conditioning (HVAC) controls are adjusted for the room to the identified temperature set-point.

公开(公告)号：US20200074228A1

公开(公告)日：2020-03-05

申请号：US16468164

申请日：2017-12-12

申请人： Robert Bosch GmbH

发明人： Jonathan Francis ,  Sirajum Munir ,  Charles Shelton

IPC分类号： G06K9/62 ,  G06T7/254 ,  G06T11/60 ,  G06K9/00

摘要： An RGBD sensing based system for detecting, tracking, classifying, and reporting objects in real-time includes a processor, a computer readable media, and a communication interface communicatively coupled to each other via a system bus is illustrated. An object detection module is integrated into the system that detects and tracks any objects that are moved under its field of view.

公开(公告)号：US20220147861A1

公开(公告)日：2022-05-12

申请号：US17091499

申请日：2020-11-06

申请人： Robert Bosch GmbH

发明人： Alessandro Oltramari ,  Jonathan Francis ,  Kaixin Ma ,  Filip llievski

IPC分类号： G06N20/00 ,  G06N5/02 ,  G06F16/9536

摘要： A computer-implemented system and method relates to natural language processing. The computer-implemented system and method are configured to obtain a current data structure from a global knowledge graph, which comprises various knowledge graphs. The current data structure includes a current head element, a current relationship element, and a current tail element. A sentence is obtained based on the current data structure. A question is generated by removing the current tail element from the sentence. A correct answer is generated for the question. The correct answer includes the current tail element. A pool of data structures is extracted from the global knowledge graph based on a set of distractor criteria. The set of distractor criteria ensures that each extracted data structure includes the current relationship element. Tail elements from the pool of data structures are extracted to create a pool of distractor candidates. A set of distractors are selected from the pool of distractor candidates. A query task is created that includes the question and a set of response options. The set of response options include the correct answer and the set of distractors. The query task is included in a training set. A machine learning system is trained with the training set. The machine learning system is configured to receive the query task and respond to the question with a predicted answer that is selected from among the set of response options.