# US 10,945,888 B2 · Intelligent blind guide method and apparatus > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 10,945,888 B2 - Document ID: US10945888B2 - Status: Granted - Kind: B2 - Application number: 16/435,255 - Filed: 2019-06-07 - Published: 2019-09-26 - Granted: 2021-03-16 - Earliest claimed: 2016-12-07 - Assignees: Cloudminds (Shenzhen) Robotics Systems Co., Ltd. - Applicants: Cloudminds (Shenzhen) Robotics Systems Co., Ltd. - Inventors: Yimin Lin; Shiguo Lian - Web page: https://patentgrep.com/patent/US10945888B2 ## CPC - A61F 9/08: Devices or methods enabling eye-patients to replace direct visual perception by another kind of perception - A61H 3/06: Walking aids for blind persons (replacing direct visual perception by another kind of perception A61F9/08) - G01S 15/89: for mapping or imaging - G06F 3/011: {Arrangements for interaction with the human body, e.g. for user immersion in virtual reality (blind teaching G09B21/00)} - G09B 21/006: {using audible presentation of the information} - G01S 15/88: Sonar systems specially adapted for specific applications (seismic or acoustic prospecting or detecting G01V1/00) - A61F: FILTERS IMPLANTABLE INTO BLOOD VESSELS; PROSTHESES; DEVICES PROVIDING PATENCY TO, OR PREVENTING COLLAPSING OF, TUBULAR STRUCTURES OF THE BODY, e.g. STENTS; ORTHOPAEDIC, NURSING OR CONTRACEPTIVE DEVICES; FOMENTATION; TREATMENT OR PROTECTION OF EYES OR EARS; BANDAGES, DRESSINGS OR ABSORBENT PADS; FIRST-AID KITS (dental prosthetics A61C) - A61H: PHYSICAL THERAPY APPARATUS, e.g. DEVICES FOR LOCATING OR STIMULATING REFLEX POINTS IN THE BODY; ARTIFICIAL RESPIRATION; MASSAGE; BATHING DEVICES FOR SPECIAL THERAPEUTIC OR HYGIENIC PURPOSES OR SPECIFIC PARTS OF THE BODY (electrotherapy, magnetotherapy, radiation therapy or ultrasound therapy A61N) - G01S: RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) - G09B: EDUCATIONAL OR DEMONSTRATION APPLIANCES; APPLIANCES FOR TEACHING, OR COMMUNICATING WITH, THE BLIND, DEAF OR MUTE; MODELS; PLANETARIA; GLOBES; MAPS; DIAGRAMS (devices for psychotechnics or for testing reaction times A61B5/16; games, sports, amusements A63; projectors, projector screens G03B) ## Abstract The embodiment of the present disclosure discloses an intelligent blind guide method and apparatus, and relates to the field of artificial intelligence, so as to solve the problem that an intelligent blind guide system requires the whole-process intervention of an artificial customer service staff, which results in high work intensity. The intelligent blind guide method includes: obtaining a confidence of an intelligent blind guide according to sensor information, wherein the confidence indicates the reliability of blind guide information generated according to the sensor information without artificial decision; generating the blind guide information according to the sensor information if the confidence is greater than or equal to a preset threshold, and importing artificial decision to generate the blind guide information if the confidence is less than the preset threshold. The embodiment of the present disclosure is applied to a blind guide helmet. ## Claims ### Claim 1 An intelligent blind guide method, comprising: obtaining, through a sensor, sensed information about an environment surrounding an intelligent blind guide; obtaining a confidence value of the intelligent blind guide by evaluating the sensed information, wherein the confidence value of the intelligent blind guide indicates the reliability of blind guide information generated by processing the sensed information; and generating blind guide information by processing the sensed information using an artificial intelligence algorithm in a case that the confidence value of the intelligent blind guide is greater than or equal to a threshold, and triggering artificial blind guide in a case that the confidence value of the intelligent blind guide is less than the threshold; wherein the obtaining the sensed information includes obtaining sensed information on at least two different categories of information about the environment; wherein the obtaining the confidence value of the intelligence blind guide includes analyzing the sensed information on the at least two different categories of information about the environment; and wherein the analyzing the sensed information on the at least two different categories of information about the environment includes: obtaining confidence sub-values on one or more of: positioning accuracy confidence, obstacle avoidance success confidence and object identification confidence; and fusing at least two of the obtained confidence sub-values to obtain the confidence value of the intelligent blind guide. ### Claim 2 (depends on 1) The method according to claim 1, wherein the sensed information comprises ultrasonic sensed information and visual sensed information. ### Claim 3 (depends on 1) The method according to claim 1, wherein the generating the blind guide information by processing the sensed information using the artificial intelligence algorithm includes: generating one or more of the following information according to the sensed information: current position coordinates, current travelable direction and object identification tag. ### Claim 4 (depends on 1) The method of claim 1, wherein the at least two different categories of information include positioning information, obstacle information or object identification information. ### Claim 5 (depends on 1) The method according to claim 1, wherein the obtaining the positioning accuracy confidence includes obtaining the positioning accuracy confidence according to one or more of the following parameters: texture quality, number of tracking or quality of motion. ### Claim 6 (depends on 1) The method according to claim 1, wherein the obtaining the obstacle avoidance success confidence includes: analyzing a size ratio of a passable area in a scene view based on a depth reconstruction result; and determining the obstacle avoidance success confidence according to the obtained size ratio. ### Claim 7 (depends on 1) The method according to claim 1, wherein the obtaining the object identification confidence includes: extracting features in an image; matching the extracted features with a pre-trained feature library; and determining the object identification confidence according to a result of the matching. ### Claim 8 A non-transitory computer storage medium, storing computer executable instructions used with an intelligent blind guide apparatus, the computer executable instructions, when executed by a processor, configures the processor to implement actions comprising: causing a sensor to obtain sensor information about an environment surrounding an intelligent blind guide; obtaining a confidence value of the intelligent blind guide by evaluating the sensor information; and generating blind guide information by processing the sensor information using an artificial intelligence algorithm in a case that the confidence value of the intelligent blind guide is greater than or equal to a threshold, and triggering artificial blind guide in a case that the confidence value of the intelligent blind guide is less than the threshold; wherein the obtaining the sensor information includes obtaining sensor information on at least two different categories of information about the environment; wherein the obtaining the confidence value of the intelligence blind guide includes analyzing the sensor information on the at least two different categories of information about the environment; and wherein the analyzing the sensor information on the at least two different categories of information about the environment includes: obtaining confidence sub-values on one or more of: positioning accuracy confidence, obstacle avoidance success confidence and object identification confidence; and fusing at least two of the obtained confidence sub-values to obtain the confidence value of the intelligent blind guide. ### Claim 9 (depends on 8) The computer storage medium according to claim 8, wherein the sensor information comprises ultrasonic sensor information and visual sensor information. ### Claim 10 (depends on 8) The computer storage medium according to claim 8, wherein the generating the blind guide information by processing the sensor information using the artificial intelligence algorithm includes: generating one or more of the following information according to the sensor information: current position coordinates, current travelable direction and object identification tag. ### Claim 11 (depends on 8) The computer storage medium according to claim 8, wherein the obtaining the confidence sub-value on the positioning accuracy confidence includes obtaining the positioning accuracy confidence according to one or more of the following parameters: texture quality, number of tracking or quality of motion. ### Claim 12 (depends on 8) The computer storage medium according to claim 8, wherein the obtaining the confidence sub-value on the obstacle avoidance success confidence includes: analyzing a size ratio of a passable area in a scene view based on a depth reconstruction result; and determining the obstacle avoidance success confidence according to the obtained size ratio. ### Claim 13 (depends on 8) The computer storage medium according to claim 8, wherein the obtaining the confidence sub-value on the object identification confidence includes: extracting features in an image; matching the extracted features with a pre-trained feature library; and determining the object identification confidence according to a result of the matching. ### Claim 14 A system, comprising: a processor; a communication interface configured to perform data transmission between the server and an external device; and a memory storing computer executable instructions, which when executed by the processor, configures the processor to conduct actions including: causing a sensor of the external device to obtain sensor information about an environment surrounding the external device; receiving data on the sensor information from the external device; evaluating the sensor information to obtain an evaluation value; and generating a feedback information about the environment by processing the sensor information using an artificial intelligence algorithm in a case that the evaluation value is greater than or equal to a threshold, and triggering human intervention in a case that the evaluation value is less than the threshold; wherein the obtaining the sensor information includes obtaining sensor information on at least two different categories of information about the environment; wherein the evaluating the sensor information includes analyzing the sensor information on the at least two different categories of information about the environment; and wherein the analyzing the sensor information on the at least two different categories of information about the environment includes: obtaining confidence sub-values on one or more of: positioning accuracy confidence, obstacle avoidance success confidence and object identification confidence; and fusing at least two of the obtained confidence sub-values to obtain the confidence value of the intelligent blind guide. ### Claim 15 (depends on 14) The system of claim 14, wherein the at least two different categories of information include positioning information, obstacle information or object identification information. ### Claim 16 (depends on 14) The system according to claim 14, wherein the obtaining the confidence sub-value on the positioning accuracy confidence includes obtaining the positioning accuracy confidence according to one or more of the following parameters: texture quality, number of tracking or quality of motion. ### Claim 17 (depends on 14) The system according to claim 14, wherein the obtaining the confidence sub-value on the obstacle avoidance success confidence includes: analyzing a size ratio of a passable area in a scene view based on a depth reconstruction result; and determining the obstacle avoidance success confidence according to the obtained size ratio. ### Claim 18 (depends on 14) The system according to claim 14, wherein the obtaining the confidence sub-value on the object identification confidence includes: extracting features in an image; matching the extracted features with a pre-trained feature library; and determining the object identification confidence according to a result of the matching.