# US 11,298,820 B2 · Corpus curation for action manifestation for cognitive robots > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 11,298,820 B2 - Document ID: US11298820B2 - Status: Granted - Kind: B2 - Application number: 16/214,300 - Filed: 2018-12-10 - Published: 2019-04-11 - Granted: 2022-04-12 - Earliest claimed: 2016-06-29 - Assignees: International Business Machines Corporation - Applicants: International Business Machines Corporation - Inventors: Laura Irina Rusu; Guy Sharon; Gandhi Sivakumar - Web page: https://patentgrep.com/patent/US11298820B2 ## CPC - B25J 9/163: {learning, adaptive, model based, rule based expert control} - B25J 11/0005: {Manipulators having means for high-level communication with users, e.g. speech generator, face recognition means} - G06N 3/008: based on physical entities controlled by simulated intelligence so as to replicate intelligent life forms, e.g. based on robots replicating pets or humans in their appearance or behaviour - G06N 5/022: Knowledge engineering; Knowledge acquisition - G06N 5/046: Forward inferencing; Production systems - G06N 20/00: Machine learning - Y10S 901/03: Teaching system - B25J: MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES ({manipulators specially adapted for use in surgery A61B34/70; manipulators used in cleaning hollow articles B08B9/04}; manipulators associated with rolling mills B21B39/20; manipulators associated with forging machines B21J13/10; {manipulators associated with picking-up and placing mechanisms B23P19/007}; means for holding wheels or parts thereof B60B30/00; {vehicles with ground-engaging propulsion means, e.g. walking members B62D57/02, B62D57/032; devices for picking-up and depositing articles or materials between conveyors B65G47/90, B65G47/91; manipulators with gripping or holding means for transferring packages B65H67/065}; cranes B66C; {manipulators used in the protection or supervision of pipe-line installations F17D5/00; walking equipment adapted for nuclear steam-generators F22B37/006}; manipulators specially adapted for, or associated with, nuclear reactors G21C; {apparatus used for handling wafers during manufacture or treatment of semiconductor H10P72/50}) - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS - Y10S: TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS ## Abstract A corpus curation method, system, and non-transitory computer readable medium, include mapping a kinematic motion of a robot to a granular feature of an item in the corpus and answering a user question using the mapped kinematic motion embedded in an answer by the robot. ## Claims ### Claim 1 A corpus curation method including a corpus, the method comprising: mapping a kinematic motion of a robot to a granular feature of an item in the corpus; performing corpus processing to add modifications to the kinematic motion based on a lexical term associated of the corpus; and answering a user question using the mapped kinematic motion embedded in an answer by the robot that is stored in an onboard memory, wherein the mapping is decoupled from corpus processing to eliminate a need to perform the mapping for each additional user question, and wherein the kinematic motion is maintained offline. ### Claim 2 (depends on 1) The method of claim 1, wherein the granular feature comprises a value of a feature of the item in the corpus, wherein the modifications to the kinematic motion includes modifying a velocity of the kinematic motion, and wherein the velocity is modified by inferring the velocity from the lexical term associated of the corpus. ### Claim 3 (depends on 1) The method of claim 1, wherein the mapping maps a plurality of kinematic motions to the granular feature, and wherein the modifications to the kinematic motion includes a change to the kinematic motion such that an inference is drawn from the change to the kinematic motion. ### Claim 4 (depends on 1) The method of claim 1, wherein the kinematic motion comprises a visual representation of a value of the granular feature such that the robot performs the kinematic motion during the answering as the visual representation of the item in the answer. ### Claim 5 (depends on 1) The method of claim 1, further comprising determining an audience type of the answer and a delivery capability of the robot and causing the answering to change a context of the answer according to the audience and the delivery capability. ### Claim 6 (depends on 1) The method of claim 1, wherein a delivery type of the answer by the robot includes at least one of: a textual display interface for a text output; a kinematic motion output by the robot; and a speech output by the robot, and wherein an audience type comprises any of: a blind user; a hearing-impaired user; and a non-impaired user, and the method further comprising determining the audience type and the delivery capability and causing the robot to change the answer based on the delivery capability and the audience type. ### Claim 7 (depends on 6) The method of claim 6, wherein, if the determining determines the audience type as the hearing-impaired user, the answering only causes the robot to deliver the answer with the kinematic motion, and wherein, if the determining determines the audience type as the blind user, the answering only causes the robot to deliver the answer with a speech output. ### Claim 8 (depends on 1) The method of claim 1, further comprising inferring the kinematic motion of a mapped item to a related item in the corpus when the related item is not mapped by the mapping such that answering causes the robot to perform the kinematic motion of the mapped item for the related item. ### Claim 9 (depends on 8) The method of claim 8, wherein the inferring uses an association rule by data mining the corpus to infer the related item to a mapped item, and wherein the modifications to the kinematic motion includes a change to the kinematic motion such that an inference is drawn from the change to the kinematic motion. ### Claim 10 A non-transitory computer-readable recording medium recording a corpus curation program including a corpus, the program causing a computer to perform: mapping a kinematic motion of a robot to a granular feature of an item in the corpus; performing corpus processing to add modifications to the kinematic motion based on a lexical term associated of the corpus; and answering a user question using the mapped kinematic motion embedded in an answer by the robot that is stored in an onboard memory, wherein the mapping is decoupled from corpus processing to eliminate a need to perform the mapping for each additional user question, and wherein the kinematic motion is maintained offline. ### Claim 11 (depends on 10) The non-transitory computer-readable recording medium of claim 10, wherein the granular feature comprises a value of a feature of the item in the corpus, wherein the modifications to the kinematic motion includes modifying a velocity of the kinematic motion, and wherein the velocity is modified by inferring the velocity from the lexical term associated of the corpus. ### Claim 12 (depends on 10) The non-transitory computer-readable recording medium of claim 10, wherein the mapping maps a plurality of kinematic motions to the granular feature. ### Claim 13 (depends on 10) The non-transitory computer-readable recording medium of claim 10, wherein the kinematic motion comprises a visual representation of a value of the granular feature such that the robot performs the kinematic motion during the answering as the visual representation of the item in the answer. ### Claim 14 (depends on 10) The non-transitory computer-readable recording medium of claim 10, further comprising determining an audience type of the answer and a delivery capability of the robot and causing the answering to change a context of the answer according to the audience and the delivery capability. ### Claim 15 (depends on 10) The non-transitory computer-readable recording medium of claim 10, wherein a delivery type of the answer by the robot includes at least one of: a textual display interface for a text output; a kinematic motion output by the robot; and a speech output by the robot, and wherein an audience type comprises any of: a blind user; a hearing-impaired user; and a non-impaired user, and the method further comprising determining the audience type and the delivery capability and causing the robot to change the answer based on the delivery capability and the audience type. ### Claim 16 (depends on 15) The non-transitory computer-readable recording medium of claim 15, wherein, if the determining determines the audience type as the hearing-impaired user, the answering only causes the robot to deliver the answer with the kinematic motion, and wherein, if the determining determines the audience type as the blind user, the answering only causes the robot to deliver the answer with a speech output. ### Claim 17 (depends on 10) The non-transitory computer-readable recording medium of claim 10, further comprising inferring the kinematic motion of a mapped item to a related item in the corpus when the related item is not mapped by the mapping such that the answering causes the robot to perform the kinematic motion of the mapped item for the related item. ### Claim 18 A corpus curation system, said system comprising: a corpus; a processor; and a memory, the memory storing instructions to cause the processor to: map a kinematic motion of a robot to a granular feature of an item in the corpus; perform corpus processing to add modifications to the kinematic motion based on a lexical term associated of the corpus; and answer a user question using the mapped kinematic motion embedded in an answer by the robot that is stored in an onboard memory, wherein the mapping is decoupled from corpus processing to eliminate a need to perform the mapping for each additional user question, and wherein the kinematic motion is maintained offline. ### Claim 19 (depends on 18) The system of claim 18, wherein during a run-time when the answering answers the user question, a context of the audience is selected and the kinematic motion of the robot align dynamically, wherein the modifications to the kinematic motion includes modifying a velocity of the kinematic motion, wherein the velocity is modified by inferring the velocity from the context of the corpus, and wherein a degree of a first lexical term is compared to a degree of a second lexical term to determine the modification to the kinematic motion.