Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-15 and 17-18 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Stone (US 2023/0007451).
As per claims 1, 13, and 15 Stone teaches, a method and a robot configured to be located in on interior of a building for the automatic production of a digital twin of at least one section of an interior of a building using at least one robot which is located in the interior of the building (Stone, ¶[0020] “Building management platform 140 may create digital representations, referred to as “digital twins,” of physical spaces, equipment, people, and/or events based on the collected information.” And ¶[0018] “the DTWRs may be associated with non-building entities such as traffic signs, vehicles, robots, drones, and/or people.” And “he implementation of on-premises digital twin wireless routers (DTWRs) can allow the generation and transmission of real-time, artificial intelligence (AI) based responses as incidents begin to develop.” Therefore robots are then used ), wherein the at least one robot has at least one sensor for scanning of a robot environment in the interior of the building, wherein in the robot environment there is at least one object (Stone, ¶[0018] “the DTWRs may be associated with non-building entities such as traffic signs, vehicles, robots, drones, and/or people.” And “he implementation of on-premises digital twin wireless routers (DTWRs) can allow the generation and transmission of real-time, artificial intelligence (AI) based responses as incidents begin to develop.” At least one robot is used this shows), and wherein the at least one object is classified in at least one object type from a number of predetermined object types (Stone, fig.7 “devices…” are part of the digital twin therefore there is classification of the object type, ¶[0036] “As described above, an entity may be any person, place, space, physical object, equipment, or the like. Further, an entity may be any event, data point, record structure, or the like. Entities and entity graph 170 are described in detail below with reference to FIGS. 3A-3B.” ), the method comprising: capturing of the robot environment by the at least one sensor of the at least one robot to receive captured environment data (Stone, ¶[0016] “In this case, the digital twin incident response system may send sensors on drones into the vicinity of a nuclear reactor after an incident to map a radiation cloud and collaborate with the digital twins in nearby building to implement a response to the incident (e.g., evacuating a building if the radiation cloud gets to close).” The drone would be the robot and would capture that environment as well as having the sensor); associating the at least one object type with the at least one object in the robot environment by use of a neural network and based on the captured environment data (Stone, ¶[0021] “In brief overview, an entity graph is a data structure representing entities (e.g., spaces, equipment, people, events, etc.) and relationships between the entities. In various embodiments, the entity graph data structure facilitates advanced artificial intelligence and machine learning associated with the entities. In various embodiments, entities within the entity graph data structure include or are associated with “agents,” or software entities configured to take actions with respect to the digital twins/real world entities with which they are associated. In some implementations, the agents may be configured to implement artificial intelligence/machine learning methodologies.” The artificial intelligence/machine learning methodologies), wherein the neural network is configured to recognize object types and is executed by a processor (Stone, ¶[0021] “ In some implementations, the agents may be configured to implement artificial intelligence/machine learning methodologies. The agents may be configured to facilitate communication and collection of information between a variety of different data sources. Each of the data sources may be implemented as, include, or otherwise use respective agents for facilitating communication amongst or between the data sources and building management platform 140. The agents of building management platform 140 and data sources may be configured to communicate using defined channels across which the agents may exchange information, messages, data, etc. amongst each other. In some examples, channels may be defined for particular spaces, subspaces, control loops, groups of equipment, people, buildings or groups of buildings, etc.” the different things represent recognizing object types and artificial intelligence/machine learning represent neural network ); and linking the at least one object type with position information that indicates a position of the at least one object to create the digital twin (Stone, ¶[0021] “In some examples, channels may be defined for particular spaces, subspaces, control loops, groups of equipment, people, buildings or groups of buildings, etc.” groups of equipment represent the location which is the position of those equipment of that digital twin ).
As per claim 2, Stone teaches, the method according to claim 1, wherein the processor is implemented, and wherein, when linking the at least one object type with the position information, the at least one object type is sent to a data processing device via a communication network, and the data processing device links the at least one object type with the position information (Stone, fig.4, 450 access network represents a data processing device via a communication network for that data ).
As per claim 3, Stone teaches, the method according to claim 1, wherein the processor is implemented in a data processing device, and wherein, when linking the at least one object type with the position information, the robot sends the captured environment data via a communication network to the data processing device, and the data processing device links the at least one object type with the position information (Stone, fig.4 450 network connection represents robot sends the captured environment data via a communication network to the data processing device as 435 one 445 would then be a robot to transmit that).
As per claim 4, Stone teaches, the method according to claim 1, wherein an output of the neural network represents a digital feature set that represents the at least one object type (Stone, ¶[0021] “In some implementations, the agents may be configured to implement artificial intelligence/machine learning methodologies. The agents may be configured to facilitate communication and collection of information between a variety of different data sources. Each of the data sources may be implemented as, include, or otherwise use respective agents for facilitating communication amongst or between the data sources and building management platform 140. The agents of building management platform 140 and data sources may be configured to communicate using defined channels across which the agents may exchange information, messages, data, etc. amongst each other. In some examples, channels may be defined for particular spaces, subspaces, control loops, groups of equipment, people, buildings or groups of buildings, etc.” people would represent at least one object type along with the equipment would then represent the other object type).
As per claim 5, Stone teaches, the method according to claim 1, wherein the position information is: information obtained from a global positioning system regarding a position of the robot, a position of the at least one object, or determined from the captured environment data (Stone, ¶[0094] Moreover, a computer may be embedded in another device (e.g., a mobile telephone, a personal digital assistant (PDA), a mobile audio or video player, a game console, a Global Positioning System (GPS) receiver, or a portable storage device (e.g., a universal serial bus (USB) flash drive), etc.).” this represents having Global Positioning System on the robot).
As per claims 6 and 14, Stone teaches, the method according to claim 1, wherein: (i) the at least object has an attribute that is captured by the at least one sensor in the captured environment data, or (ii) the robot has an additional sensor for the capture of the attribute, and that outputs attribute data; and wherein the processor is configured to detect the attribute in (i) the captured environment data or (ii) the attribute data (Stone, ¶[0063] “The plurality of on-premises devices 435 shows various DTWRs 445a-445n associated with various building entities 440a-440n. Each of the DTWRs 445a-445n store a digital twin that is digital representation of each building entity 440a-440b. For example, a building entity could be an entire building such as daycare center and it would have an associated daycare center DTWR. Another building entity could be the HVAC system of a campus or building and it would have an associated HVAC DTWR. In yet another example, a building entity could be a room or space within a building such as a conference room, lobby, or parking lot and it would have an associated room DTWR.” The HVAC system differences would represent attribute data and by being part of the digital two this is captured environmental data).
As per claim 7, Stone teaches, the method according to claim 6, wherein the attribute is a geometric characteristic of the at least object, the attribute comprising at least one of: a geometric shape, a geometric expanse, an object label or an object orientation (Stone, ¶[0088] “Although only a few embodiments have been described in detail in this disclosure, many modifications are possible (e.g., variations in sizes, dimensions, structures, shapes and proportions of the various elements, values of parameters, mounting arrangements, use of materials, colors, orientations, etc.).” this represents orientation).
As per claim 8, Stone teaches, a method according to claim 6, wherein the processor is configured to recognize the attribute on a basis of pattern recognition, the neural network, or an additional neural network that is configured to recognize attributes (Stone, ¶[0021] “In some implementations, the agents may be configured to implement artificial intelligence/machine learning methodologies. The agents may be configured to facilitate communication and collection of information between a variety of different data sources. Each of the data sources may be implemented as, include, or otherwise use respective agents for facilitating communication amongst or between the data sources and building management platform 140. The agents of building management platform 140 and data sources may be configured to communicate using defined channels across which the agents may exchange information, messages, data, etc. amongst each other. In some examples, channels may be defined for particular spaces, subspaces, control loops, groups of equipment, people, buildings or groups of buildings, etc. In some implementations, agents may communicate by publishing messages to particular channels and subscribing to messages on particular channels and/or published by particular other agents/types of agents. In various embodiments, the data sources include buildings. “ The type of agent and the type of spaces and what they do represent the attributes).
As per claim 9, Stone teaches, the method according to claim 6, wherein the attribute, with the at least one object type, is linked with the position information (Stone, ¶[0094] “ Moreover, a computer may be embedded in another device (e.g., a mobile telephone, a personal digital assistant (PDA), a mobile audio or video player, a game console, a Global Positioning System (GPS) receiver, or a portable storage device (e.g., a universal serial bus (USB) flash drive), etc.).” this represents position information ).
As per claim 10, Stone teaches, the method according to claim 9, wherein the linking of the at least one object type with the position information is performed by: (i) an association of the position information with the at least one object type, or (ii) the by an entry of the position information in a digital map (Stone, fig.7 The devices have some sort of location and position and this is stored in step 708).
As per claim 11, Stone teaches, the method according to claim 1, wherein at least one additional object is located in the robot environment, wherein at least one additional object type of the at least one additional object is captured by the execution of the neural network on the processor, and wherein the position information or additional low information is linked with the at least one additional object type (Stone, fig.4 440a-n different building entities would have different object types and the digital twin would produce this and ¶[0021] “In some implementations, the agents may be configured to implement artificial intelligence/machine learning methodologies.” Represents execution of the neural network on the processor ).
As per claim 12, Stone teaches, the method according to claim 1, wherein the at least one object type is a pallet, a robot, a wall, an aisle, a shelf, a door, or an information sign (Stone, ¶[0016] “In some embodiments, based on the received data, the digital twin incident response system may be configured to determine one or more potential incidents relating to the on-premises building devices or non-building devices (e.g., wireless routers on traffic lights, wireless routers in vehicles such as cars, trucks, boats, missiles, robots, drones, etc.)” this represents the objects being robots).
As per claim 17, Stone teaches, the method according to claim 6, wherein the additional sensor is a laser scanner that outputs the attribute data (Stone, fig.7 710-720 in order to have distances to be able to have a digital twin the additional sensors would be laser scanners to be able to measure the distances).
As per claim 18, Stone teaches, the robot according to claim 14, wherein the additional sensor is a laser scanner that outputs the attribute data (Stone, (Stone, fig.7 710-720 in order to have distances to be able to have a digital twin the additional sensors would be laser scanners to be able to measure the distances).
Conclusion
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/SANTIAGO GARCIA/Primary Examiner, Art Unit 2673
/SG/
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