DETAILED ACTION
This Office Action is in response to Applicant's response to application filed on 11 February 2025. Currently, claims 1-9 are pending. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Information Disclosure Statement
The information disclosure statements (IDS) submitted are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements are being considered by the examiner.
Specification
The title of the invention is not descriptive. A new title is required that is clearly indicative of the invention to which the claims are directed.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-9 are clearly drawn to at least one of the four categories of patent eligible subject matter recited in 35 U.S.C. 101 (apparatuses and method). Claims 1-9 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. Claims 1, 6 and 9 recite the abstract idea of a workability information acquisition unit configured to acquire, in association with a robot work service provider, workability information related to a space in which the robot work service provider can provide work and a first spatial information generation unit configured to generate first spatial information related to a space based on the workability information acquired by the workability information acquisition unit and a work request information acquisition unit configured to acquire, in association with a robot work service client, work request information related to a space in which the robot work service client desires work to be provided and a second spatial information generation unit configured to generate second and spatial information related to a space based on the work request information acquired by the work request information acquisition unit and a degree of coincidence determination unit configured to determine a degree of coincidence based on the first spatial information and the second spatial information. Claim 6 further recites the abstract idea of first spatial information related to a space that is an object placement characteristic database representing positional relationships of a plurality of objects in a space in which the robot work service provider can provide work and a third spatial information generation unit configured to search the object placement characteristic database using the object type information and the position information included in the second spatial information as a key, and generate third spatial information related to a space comprising the object type information and the position information and a degree of coincidence determination unit configured to determine a degree of coincidence based on the object type information and the position information included in the third spatial information and the object type information and the position information included in the second spatial information. The claims are directed to a type of acquiring information related to a robot work service provider workability related to spatial information and a work request including a second spatial information and determining a degree of coincidence between the spatial information. Under prong 1 of Step 2A, these claims are considered abstract because the claims are certain method of organizing human activity including commercial interactions (including agreements or business relations) and mental processes performed in the human including an evaluation or judgment. Applicant’s claims are organizing human activity because the claims show determining a coincidence (organizing) between human/business activity (the work request and the workability information of the robot service provider) and because the coincidence determination can be considered a type of evaluation. Under prong 2 of Step 2A, the judicial exception is not integrated into a practical application because the claims (the judicial exception and any additional elements individually or in combination such as an information processing apparatus comprising: at least one processor and a memory holding a program that makes the processor function) are not an improvement to a computer or a technology, the claims do not apply the judicial exception with a particular machine, the claims do not effect a transformation or reduction of a particular article to a different state or thing nor do the claims apply the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment such that the claims as a whole is more than a drafting effort designed to monopolize the exception. These limitations at best are merely implementing an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f). Under Step 2B, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements individually or in combination such as an information processing apparatus comprising: at least one processor and a memory holding a program that makes the processor function (as evidenced by para [0015]-[0016], [0021]-[0022], [0104] of applicant’s own specification) are well understood, routine and conventional in the field. Dependent claims 2-5, 7-8 also do not include additional elements that integrate the judicial exception into a practical application because the additional elements either individually or in combination are merely an extension of the abstract idea itself by further showing wherein, based on the degree of coincidence determined by the degree of coincidence determination unit, information associating the robot work service provider and the degree of coincidence is presented to the robot work service client and wherein, based on the degree of coincidence determined by the degree of coincidence determination unit, information associating the robot work service client and the degree of coincidence is presented to the robot work service provider and wherein the first spatial information and the second spatial information include spatial information parameters related to a difficulty level of robot work, and wherein the degree of coincidence determination unit determines the degree of coincidence based on a value of the spatial information parameters and wherein the spatial information parameter is defined in advance for each work type of robot work, and a value of the spatial information parameter is determined for each work type of the robot work and wherein the object placement characteristic database includes spatial information parameters related to a difficulty level of robot work, and wherein the degree of coincidence determination unit determines the degree of coincidence based on a value of the spatial information parameters and wherein the spatial information parameters are defined in advance for each work type of robot work, and the value of the spatial information parameters is generated for each work type of the robot work.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
Claims 1-5, 9 are rejected under 35 U.S.C. 103 as being unpatentable over Walsh et al. (US 2020/0394584 A1) (hereinafter Walsh) in view of Michalowski et al. (US 11,209,798 B1) (hereinafter Michalowski).
Claims 1 and 9:
Walsh, as shown, discloses the following limitations of claims 1 and 9:
An information processing apparatus (and corresponding method) comprising: at least one processor and a memory holding a program that makes the processor function (see para [0039], "FIG. 1 depicts an example of a system 100 for dynamically updating a virtual queue and summoning agents to a consumable event. System 100 comprises memory 101 and one or more queue managers 102 implemented by program code running on one or more processors. In this respect, system 100 is typically implemented on a server but may also be implemented in other ways, for example, using cloud-based service hosting.") as: a workability information acquisition unit configured to acquire, in association with a robot work service provider, workability information related to a space in which the robot work service provider can provide work (see para [0031], "A second type or class of agent is a human or a non-human entity that performs actions related to the consumable event or events as directed by the system. For example, these actions may be to support the participation of the first class of agent in the consumable event or to support the continued running of the consumable event. This second class of agent may be, for example, a cleaning personnel, a bus driver, or one or more automated vehicles or robots." and see para [0037], "The system may determine an Agent Score of an agent as a method to dynamically determine when to summon the agent to a consumable event. Agent Scores are calculated values determined by the system based on a variety of different factors to determine either or both the capacity of an agent to or how likely it is that an agent will arrive at the physical location of the consumable event within a target time window. An agent can have multiple Agent Scores at any time if an agent is in multiple virtual queues. In some examples, separate Agent Scores can also be maintained for an agent's progress through a consumable event relative to an estimated target time for Agents to consume the consumable event. Such Agent Scores can be used as factors in determining summon times for other agents in the virtual queue for the respective consumable event. Agent Scores are discussed in further detail below." and see para [0087], " In some embodiments of the present invention, the queue manager 102 receives physical space capacity limit data, the average time data indicating how long it has taken agents to consume the activity or event 105, a calculated estimated completion time for agents consuming the event 105, and actual dynamic time data of current agents attending the consumable event 105 from the consumable event 105, e.g., sensors, terminals, kiosks, or other local data sources of the consumable event 105, or venue 104. As agents currently attending or consuming a consumable event 105 make their way through the event and ultimately exit the event, the queue manager 102 tracks this agent flow rate data by calculating agent location and movement data received by the system 100 from the agent mobile devices 103 over the physical space data of the consumable event 105. Queue manager 102 may utilise this agent flow rate data and physical space capacity limit data, average agent consumption time data, estimated agent consumption completion time data, and dynamic agent consumption time data to calculate a further Agent Score for the agent which is used as an input for summoning further agents to the virtual queue for the consumable event 105. In one example, such an Agent Score indicates the likelihood that the agent will finish consuming the event within a target time (e.g., 5 minutes) which is then used in the determination of when to summon the next agent or agents in the virtual queue to the consumable event 105. For example, when agents are making their way through the consumable event 105 particularly quickly or slowly, the queue manager 102 will adjust the timing of when to summon the next agent or agents. As the system 100 may receive data defining the size of the physical queue from a computer or a staff device at the consumable event 105, the queue manager 102 may revise the queue by summoning additional agents if agents move through the physical queue (and consumable event) faster than anticipated or ramping down the summoning of agents when the physical queue becomes larger than ideal.”);
a first spatial information generation unit configured to generate first spatial information related to a space based on the workability information acquired by the workability information acquisition unit (see para [0087], "In some embodiments of the present invention, the queue manager 102 receives physical space capacity limit data, the average time data indicating how long it has taken agents to consume the activity or event 105, a calculated estimated completion time for agents consuming the event 105, and actual dynamic time data of current agents attending the consumable event 105 from the consumable event 105, e.g., sensors, terminals, kiosks, or other local data sources of the consumable event 105, or venue 104. As agents currently attending or consuming a consumable event 105 make their way through the event and ultimately exit the event, the queue manager 102 tracks this agent flow rate data by calculating agent location and movement data received by the system 100 from the agent mobile devices 103 over the physical space data of the consumable event 105. Queue manager 102 may utilise this agent flow rate data and physical space capacity limit data, average agent consumption time data, estimated agent consumption completion time data, and dynamic agent consumption time data to calculate a further Agent Score for the agent which is used as an input for summoning further agents to the virtual queue for the consumable event 105. In one example, such an Agent Score indicates the likelihood that the agent will finish consuming the event within a target time (e.g., 5 minutes) which is then used in the determination of when to summon the next agent or agents in the virtual queue to the consumable event 105. For example, when agents are making their way through the consumable event 105 particularly quickly or slowly, the queue manager 102 will adjust the timing of when to summon the next agent or agents. As the system 100 may receive data defining the size of the physical queue from a computer or a staff device at the consumable event 105, the queue manager 102 may revise the queue by summoning additional agents if agents move through the physical queue (and consumable event) faster than anticipated or ramping down the summoning of agents when the physical queue becomes larger than ideal." and see para [0073]-[0074], [0092], [0114]);
a second spatial information generation unit configured to generate second spatial information related to a space based on the work request information acquired by the work request information acquisition unit (see para [0051], showing physical layout and floorplan data provided by the venue); and
a degree of coincidence determination unit configured to determine a degree of coincidence based on the first spatial information and the second spatial information (see para [0067], where the agent score can be considered to show a degree of coincidence given broadest reasonable interpretation).
Walsh, however, do not specifically disclose a work request information acquisition unit configured to acquire, in association with a robot work service client, work request information related to a space in which the robot work service client desires work to be provided. In analogous art, Michalowski discloses the following limitations:
a work request information acquisition unit configured to acquire, in association with a robot work service client, work request information related to a space in which the robot work service client desires work to be provided (col 9, line 4 to col 10, line 54, "The planner subsystem 302 processes the layout parameters 304 to determine an optimized workspace layout and to generate a workspace layout specification 314 that that describes the optimized workspace layout. To do this, the planner subsystem 302 can include a layout constraint generator 322, a layout evaluation engine 324, a layout performance simulator 326, and a layout selection engine 328. Further, the planner subsystem 302 interacts with a workspace layout engine 306, an optimization manager 308, and, optionally, a user terminal 312 in the course of determining the optimized workspace layout and generating the workspace layout specification 314. In general, the planner subsystem 302 iteratively evaluates candidate workspace layouts generated by the workspace layout engine 306 with respect to an objective function 320 provided by the optimization manager 308. The planner subsystem 302 can provide feedback to the workspace layout engine 306 to guide its generation or selection of candidate workspace layouts, and the top candidate workspace layout(s) can be provided as an output of the planner subsystem 302 as the top “optimized” workspace layouts. The layout constraint generator 322 processes the layout parameters 304 and, optionally, inputs from user terminal 312 and simulation and evaluation results from previously simulated and evaluated candidate workspace layouts, to generate a set of layout constraints 316. The layout constraints 316 describe constraints for candidate workspace layout generation by the workspace layout engine 306. In some implementations, the layout constraints 316 include parameters that have been directly imported from the layout parameters 304, such as product design specification parameters 304a, fabrication process parameters 304b, environmental specification parameters 304c, and/or resource catalog parameters 304d. In some implementations, the layout constraint generator 322 derives at least some of the layout constraints 316 from the layout parameters 304, where such constraints are not directly specified in the layout parameters 304 themselves. For example, the layout constraint generator 322 can process a blueprint or map of the workspace, as indicated by the environmental specification parameters 304c, to determine relevant features of the workspace (e.g., dimensions or a size of the workspace) that can be used by the workspace layout engine 306 to generate a candidate workspace layout. The layout constraint generator 322 can structure, format, and/or filter information from the layout parameters 304 into data that can be processed by the workspace layout engine 306. Additionally, the layout constraint generator 322 can define additional constraints that are not specified in the layout parameters 304 but that further guide the generation or selection of candidate workspace layouts, such as instructions to maintain a portion of the layout without change from a previously evaluated workspace layout or instructions to change a portion of the layout from a previously evaluated workspace layout. The workspace layout engine 306 provides to the planner subsystem 302 candidate layout data 318 that describes a particular candidate workspace layout. The layout evaluation engine 324 then evaluates the candidate workspace layout to assess how well it optimizes one or more specified workspace layout criteria. Each workspace layout criterion can represent a measure of quality of the candidate workspace layout, such as the layout's compliance with resource, process, and/or design constraints, a speed at which tasks can be executed in view of the layout, and a cost of implementing the layout (e.g., a cost of the robots and/or other equipment or resources necessary to implement the layout). The workspace layout criteria to be applied by the system 300 can be set by default or may be specified by user input through the user terminal 312. The optimization manager 308 is configured to maintain the applicable set of workspace layout criteria and to configure the planner subsystem 302 to utilize the applicable set of workspace layout criteria to evaluate candidate workspace layouts. In some implementations, the optimization manager 308 generates an objective function 320 based on multiple workspace layout criteria. For example, the objective function 320 may be a weighted sum of workspace criteria that, when evaluated, provides an overall quality score for a candidate workspace layout. The optimization manager 308 provides the objective function 320 to the layout evaluation engine 324, and the layout evaluation engine 324 uses the objective function 320 to assess the quality of each candidate workspace layout.")
a degree of coincidence determination unit configured to determine a degree of coincidence based on the first spatial information and the second spatial information (col 9, line 4 to col 10, line 54, where it would be obvious to one of ordinary sill in the art that "the optimization manager 308 generates an objective function 320 based on multiple workspace layout criteria. For example, the objective function 320 may be a weighted sum of workspace criteria that, when evaluated, provides an overall quality score for a candidate workspace layout. The optimization manager 308 provides the objective function 320 to the layout evaluation engine 324, and the layout evaluation engine 324 uses the objective function 320 to assess the quality of each candidate workspace layout." would also consider the work robot provider when optimizing the workspace).
It would have been obvious to one or ordinary skill in the art at the time of the invention to combine the teachings of Michalowski with Walsh because including acquiring work information related to a space in which the work robot work is to be provided enables workspaces that are more suitable to automated processes (see Michalowski, col 1, line 27-65).
Moreover, it would have been obvious to one of ordinary skill in the art at the time of the invention to include the system for robotic workspace layout planning as taught by Michalowski in the queue management system of Walsh, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Claims 2-3:
Further, Walsh discloses the following limitations:
wherein, based on the degree of coincidence determined by the degree of coincidence determination unit, information associating the robot work service provider and the degree of coincidence is presented to the robot work service client (see para [0059]-[0060], "The queue manager 102 may process the progression of an agent through the virtual queue and a consumable event 105 by assigning agents various states depending on their advancement through the process. System 100 stores these agent states into agent database 101b so that queue manager 102 can accurately update the virtual queue as stored in the queue database 101a. Before joining the virtual queue for a consumable event 105, an agent is in an “unqueued” state, which is stored in agent database 101b. When the queue manager 102 adds an agent to the virtual queue for the consumable event 105, the agent enters into a “in virtual queue” state and updates the current state of the agent from the previous “unqueued” state in agent database 101b. While waiting in the virtual queue for a summon to the consumable event 105, the agent updates to a “summon pending” state, replacing the previous “in virtual queue” state the agent was in within agent database 101b. An agent may update from the “in virtual queue” state to the “summon pending” state, for example, when the agent's Agent Score is first calculated. Once the agent receives a summon to the physical queue, the queue manager 102 updates the agent's status in agent database 101b to a “summoned” state. If the agent decides to postpone their attendance to the consumable event 105 after being summoned, the agent instead updates to a “postponed” state from the “summon pending” state in agent database 101b.").
wherein, based on the degree of coincidence determined by the degree of coincidence determination unit, information associating the robot work service client and the degree of coincidence is presented to the robot work service provider (see para [0059]-[0060], "The queue manager 102 may process the progression of an agent through the virtual queue and a consumable event 105 by assigning agents various states depending on their advancement through the process. System 100 stores these agent states into agent database 101b so that queue manager 102 can accurately update the virtual queue as stored in the queue database 101a. Before joining the virtual queue for a consumable event 105, an agent is in an “unqueued” state, which is stored in agent database 101b. When the queue manager 102 adds an agent to the virtual queue for the consumable event 105, the agent enters into a “in virtual queue” state and updates the current state of the agent from the previous “unqueued” state in agent database 101b. While waiting in the virtual queue for a summon to the consumable event 105, the agent updates to a “summon pending” state, replacing the previous “in virtual queue” state the agent was in within agent database 101b. An agent may update from the “in virtual queue” state to the “summon pending” state, for example, when the agent's Agent Score is first calculated. Once the agent receives a summon to the physical queue, the queue manager 102 updates the agent's status in agent database 101b to a “summoned” state. If the agent decides to postpone their attendance to the consumable event 105 after being summoned, the agent instead updates to a “postponed” state from the “summon pending” state in agent database 101b.").
Claim 4:
Further, Walsh discloses the following limitations:
wherein the first spatial information and the second spatial information include spatial information parameters related to a difficulty level of robot work, and wherein the degree of coincidence determination unit determines the degree of coincidence based on a value of the spatial information parameters (see para [0077], " System 100 may also monitor actual agent consumption times of the event to dynamically update its calculations regarding likely time by the agent consuming the event and enhance its ability to predict the amount of time other agents will spend consuming an event given their behaviour and preference data. The system 100 may store agent preference data and any data related to preference data such as agent location or movement data in agent database 101b such that queue manager 102 may access the data at any time and factor it into Agent Score calculations or machine learning operations." and see para [0114], "The queue manager 102 may also track what the cleaning robots were doing before being summoned, such as if a robot was actively completing another task, what sort of task the robot was doing, and how far along the task the robot was, and how long the cleaning robots would take to transit from its current location to the consumable event space. The queue manager 102 may consider the dynamic nature of these variables as robot transit time is impacted by other agent or traffic of people who are not queued for a consumable event and the time to complete a cleaning task may be impacted by the changes to the physical layout of consumable event 105 or event or activity that does not require queueing for space, e.g., the distribution of tables and chairs and the mess created as a result of a wedding with 100 guests is different from that of a wedding with 200 guests." where calculations regarding likely time and transit time can be considered a difficulty level given broadest reasonable interpretation).
Claim 5:
Further, Walsh discloses the following limitations:
wherein the spatial information parameter is defined in advance for each work type of robot work (see par a[0051], showing queue manager receiving physical layout and floorplan data which is then used in calculating an agent score), and a value of the spatial information parameter is determined for each work type of the robot work (see para [0114], "The queue manager 102 may also track what the cleaning robots were doing before being summoned, such as if a robot was actively completing another task, what sort of task the robot was doing, and how far along the task the robot was, and how long the cleaning robots would take to transit from its current location to the consumable event space. The queue manager 102 may consider the dynamic nature of these variables as robot transit time is impacted by other agent or traffic of people who are not queued for a consumable event and the time to complete a cleaning task may be impacted by the changes to the physical layout of consumable event 105 or event or activity that does not require queueing for space, e.g., the distribution of tables and chairs and the mess created as a result of a wedding with 100 guests is different from that of a wedding with 200 guests.", showing such data can be considered via variables)
Claims 6-8 are rejected under 35 U.S.C. 103 as being unpatentable over Walsh et al. (US 2020/0394584 A1) (hereinafter Walsh) in view of Michalowski et al. (US 11,209,798 B1) (hereinafter Michalowski) in view of Dewan et al. (US 2022/0164331 A1) (hereinafter Dewan).
Claim 6:
Walsh discloses the following limitations of claim 6:
An information processing apparatus comprising: at least one processor and a memory holding a program that makes the processor function (see para [0039], "FIG. 1 depicts an example of a system 100 for dynamically updating a virtual queue and summoning agents to a consumable event. System 100 comprises memory 101 and one or more queue managers 102 implemented by program code running on one or more processors. In this respect, system 100 is typically implemented on a server but may also be implemented in other ways, for example, using cloud-based service hosting.") as: a workability information acquisition unit configured to acquire, in association with a robot work service provider, workability information related to a space in which the robot work service provider can provide work (see para [0031], "A second type or class of agent is a human or a non-human entity that performs actions related to the consumable event or events as directed by the system. For example, these actions may be to support the participation of the first class of agent in the consumable event or to support the continued running of the consumable event. This second class of agent may be, for example, a cleaning personnel, a bus driver, or one or more automated vehicles or robots." and see para [0037], "The system may determine an Agent Score of an agent as a method to dynamically determine when to summon the agent to a consumable event. Agent Scores are calculated values determined by the system based on a variety of different factors to determine either or both the capacity of an agent to or how likely it is that an agent will arrive at the physical location of the consumable event within a target time window. An agent can have multiple Agent Scores at any time if an agent is in multiple virtual queues. In some examples, separate Agent Scores can also be maintained for an agent's progress through a consumable event relative to an estimated target time for Agents to consume the consumable event. Such Agent Scores can be used as factors in determining summon times for other agents in the virtual queue for the respective consumable event. Agent Scores are discussed in further detail below." and see para [0087], " In some embodiments of the present invention, the queue manager 102 receives physical space capacity limit data, the average time data indicating how long it has taken agents to consume the activity or event 105, a calculated estimated completion time for agents consuming the event 105, and actual dynamic time data of current agents attending the consumable event 105 from the consumable event 105, e.g., sensors, terminals, kiosks, or other local data sources of the consumable event 105, or venue 104. As agents currently attending or consuming a consumable event 105 make their way through the event and ultimately exit the event, the queue manager 102 tracks this agent flow rate data by calculating agent location and movement data received by the system 100 from the agent mobile devices 103 over the physical space data of the consumable event 105. Queue manager 102 may utilise this agent flow rate data and physical space capacity limit data, average agent consumption time data, estimated agent consumption completion time data, and dynamic agent consumption time data to calculate a further Agent Score for the agent which is used as an input for summoning further agents to the virtual queue for the consumable event 105. In one example, such an Agent Score indicates the likelihood that the agent will finish consuming the event within a target time (e.g., 5 minutes) which is then used in the determination of when to summon the next agent or agents in the virtual queue to the consumable event 105. For example, when agents are making their way through the consumable event 105 particularly quickly or slowly, the queue manager 102 will adjust the timing of when to summon the next agent or agents. As the system 100 may receive data defining the size of the physical queue from a computer or a staff device at the consumable event 105, the queue manager 102 may revise the queue by summoning additional agents if agents move through the physical queue (and consumable event) faster than anticipated or ramping down the summoning of agents when the physical queue becomes larger than ideal.”);
a first spatial information generation unit configured to generate, based on the workability information acquired by the workability information acquisition unit (see para [0087], "In some embodiments of the present invention, the queue manager 102 receives physical space capacity limit data, the average time data indicating how long it has taken agents to consume the activity or event 105, a calculated estimated completion time for agents consuming the event 105, and actual dynamic time data of current agents attending the consumable event 105 from the consumable event 105, e.g., sensors, terminals, kiosks, or other local data sources of the consumable event 105, or venue 104. As agents currently attending or consuming a consumable event 105 make their way through the event and ultimately exit the event, the queue manager 102 tracks this agent flow rate data by calculating agent location and movement data received by the system 100 from the agent mobile devices 103 over the physical space data of the consumable event 105. Queue manager 102 may utilise this agent flow rate data and physical space capacity limit data, average agent consumption time data, estimated agent consumption completion time data, and dynamic agent consumption time data to calculate a further Agent Score for the agent which is used as an input for summoning further agents to the virtual queue for the consumable event 105. In one example, such an Agent Score indicates the likelihood that the agent will finish consuming the event within a target time (e.g., 5 minutes) which is then used in the determination of when to summon the next agent or agents in the virtual queue to the consumable event 105. For example, when agents are making their way through the consumable event 105 particularly quickly or slowly, the queue manager 102 will adjust the timing of when to summon the next agent or agents. As the system 100 may receive data defining the size of the physical queue from a computer or a staff device at the consumable event 105, the queue manager 102 may revise the queue by summoning additional agents if agents move through the physical queue (and consumable event) faster than anticipated or ramping down the summoning of agents when the physical queue becomes larger than ideal." and see para [0073]-[0074], [0092], [0114]), first spatial information related to a space that is an object placement characteristic database representing positional relationships of a plurality of objects in a space in which the robot work service provider can provide work (see para [0046], "Queue manager 102 may learn agent preferences from how agents interact with their mobile device 103, how agents interact with consumable events 105 or other events or activities that do not require queueing for in the venue 104, and agent location and movement data. Queue manager 102 may track an agent's location and movement data through the agent's mobile device 103 in a number of ways including location beacons 108 within the venue 104, Wi-Fi triangulation, GPS, near-field communication, sequencing estimation, proprietary location tracking services as mobile device 103 applications, dead reckoning estimation, the agent interacting with entities or devices at known locations, computer vision, or other wireless communication methods. Agent preference, location, and movement data may also be sent by an agent's mobile device 103 to queue manager 102 and stored by agent records database 101b and processed by queue manager 102." and see para [0048], "As indicated above, system 100 may first receive data from agent mobile devices or other devices 103 when the associated agents join the virtual queues for a consumable event 105 and store this data in queue database 101a. System 100 may also receive data from agent mobile devices 103 and store the received data in agent records database 101b of memory 101. Agent records database 101b is used to compile information regarding agents currently in virtual queues for consumable events 105; queue manager 102 may then access agent records database 101b to determine an agent's Agent Score. Although agents are ordered in the virtual queue sequentially based on when they joined the virtual queue, as described further below, queue manager 102 may then use Agent Scores to determine the likelihood of each agent in the virtual queue arriving at the consumable event when summoned and then dynamically adjust when to summon an agent based on their Agent Scores. Queue manager 102 may then access queue database 101a and update the Agent Score fields of each agent in the virtual queue and use the current set of Agent Scores to determine when to summon an agent to the consumable event 105.");
a second spatial information generation unit configured to generate, based on the work request information acquired by the work request information acquisition unit (see para [0051], showing physical layout and floorplan data provided by the venue), second spatial information related to space comprising object type information and position information indicating a type of each of a plurality of objects present in a space in which the robot work service client desires work to be provided (see para [0046], "Queue manager 102 may learn agent preferences from how agents interact with their mobile device 103, how agents interact with consumable events 105 or other events or activities that do not require queueing for in the venue 104, and agent location and movement data. Queue manager 102 may track an agent's location and movement data through the agent's mobile device 103 in a number of ways including location beacons 108 within the venue 104, Wi-Fi triangulation, GPS, near-field communication, sequencing estimation, proprietary location tracking services as mobile device 103 applications, dead reckoning estimation, the agent interacting with entities or devices at known locations, computer vision, or other wireless communication methods. Agent preference, location, and movement data may also be sent by an agent's mobile device 103 to queue manager 102 and stored by agent records database 101b and processed by queue manager 102." and see para [0048], "As indicated above, system 100 may first receive data from agent mobile devices or other devices 103 when the associated agents join the virtual queues for a consumable event 105 and store this data in queue database 101a. System 100 may also receive data from agent mobile devices 103 and store the received data in agent records database 101b of memory 101. Agent records database 101b is used to compile information regarding agents currently in virtual queues for consumable events 105; queue manager 102 may then access agent records database 101b to determine an agent's Agent Score. Although agents are ordered in the virtual queue sequentially based on when they joined the virtual queue, as described further below, queue manager 102 may then use Agent Scores to determine the likelihood of each agent in the virtual queue arriving at the consumable event when summoned and then dynamically adjust when to summon an agent based on their Agent Scores. Queue manager 102 may then access queue database 101a and update the Agent Score fields of each agent in the virtual queue and use the current set of Agent Scores to determine when to summon an agent to the consumable event 105.");
a degree of coincidence determination unit configured to determine a degree of coincidence based on the object type information and the position information included in the third spatial information and the object type information and the position information included in the second spatial information (see para [0067], where the agent score can be considered to show a degree of coincidence given broadest reasonable interpretation).
Walsh, however, does not specifically disclose a work request information acquisition unit configured to acquire, in association with a robot work service client, work request information related to a space in which the robot work service client desires work to be provided. In analogous art, Michalowski discloses the following limitations:
a work request information acquisition unit configured to acquire, in association with a robot work service client, work request information related to a space in which the robot work service client desires work to be provided (col 9, line 4 to col 10, line 54, "The planner subsystem 302 processes the layout parameters 304 to determine an optimized workspace layout and to generate a workspace layout specification 314 that that describes the optimized workspace layout. To do this, the planner subsystem 302 can include a layout constraint generator 322, a layout evaluation engine 324, a layout performance simulator 326, and a layout selection engine 328. Further, the planner subsystem 302 interacts with a workspace layout engine 306, an optimization manager 308, and, optionally, a user terminal 312 in the course of determining the optimized workspace layout and generating the workspace layout specification 314. In general, the planner subsystem 302 iteratively evaluates candidate workspace layouts generated by the workspace layout engine 306 with respect to an objective function 320 provided by the optimization manager 308. The planner subsystem 302 can provide feedback to the workspace layout engine 306 to guide its generation or selection of candidate workspace layouts, and the top candidate workspace layout(s) can be provided as an output of the planner subsystem 302 as the top “optimized” workspace layouts. The layout constraint generator 322 processes the layout parameters 304 and, optionally, inputs from user terminal 312 and simulation and evaluation results from previously simulated and evaluated candidate workspace layouts, to generate a set of layout constraints 316. The layout constraints 316 describe constraints for candidate workspace layout generation by the workspace layout engine 306. In some implementations, the layout constraints 316 include parameters that have been directly imported from the layout parameters 304, such as product design specification parameters 304a, fabrication process parameters 304b, environmental specification parameters 304c, and/or resource catalog parameters 304d. In some implementations, the layout constraint generator 322 derives at least some of the layout constraints 316 from the layout parameters 304, where such constraints are not directly specified in the layout parameters 304 themselves. For example, the layout constraint generator 322 can process a blueprint or map of the workspace, as indicated by the environmental specification parameters 304c, to determine relevant features of the workspace (e.g., dimensions or a size of the workspace) that can be used by the workspace layout engine 306 to generate a candidate workspace layout. The layout constraint generator 322 can structure, format, and/or filter information from the layout parameters 304 into data that can be processed by the workspace layout engine 306. Additionally, the layout constraint generator 322 can define additional constraints that are not specified in the layout parameters 304 but that further guide the generation or selection of candidate workspace layouts, such as instructions to maintain a portion of the layout without change from a previously evaluated workspace layout or instructions to change a portion of the layout from a previously evaluated workspace layout. The workspace layout engine 306 provides to the planner subsystem 302 candidate layout data 318 that describes a particular candidate workspace layout. The layout evaluation engine 324 then evaluates the candidate workspace layout to assess how well it optimizes one or more specified workspace layout criteria. Each workspace layout criterion can represent a measure of quality of the candidate workspace layout, such as the layout's compliance with resource, process, and/or design constraints, a speed at which tasks can be executed in view of the layout, and a cost of implementing the layout (e.g., a cost of the robots and/or other equipment or resources necessary to implement the layout). The workspace layout criteria to be applied by the system 300 can be set by default or may be specified by user input through the user terminal 312. The optimization manager 308 is configured to maintain the applicable set of workspace layout criteria and to configure the planner subsystem 302 to utilize the applicable set of workspace layout criteria to evaluate candidate workspace layouts. In some implementations, the optimization manager 308 generates an objective function 320 based on multiple workspace layout criteria. For example, the objective function 320 may be a weighted sum of workspace criteria that, when evaluated, provides an overall quality score for a candidate workspace layout. The optimization manager 308 provides the objective function 320 to the layout evaluation engine 324, and the layout evaluation engine 324 uses the objective function 320 to assess the quality of each candidate workspace layout.");
and a degree of coincidence determination unit configured to determine a degree of coincidence based on the object type information and the position information included in the third spatial information and the object type information and the position information included in the second spatial information (col 9, line 4 to col 10, line 54, where it would be obvious to one of ordinary sill in the art that "the optimization manager 308 generates an objective function 320 based on multiple workspace layout criteria. For example, the objective function 320 may be a weighted sum of workspace criteria that, when evaluated, provides an overall quality score for a candidate workspace layout. The optimization manager 308 provides the objective function 320 to the layout evaluation engine 324, and the layout evaluation engine 324 uses the objective function 320 to assess the quality of each candidate workspace layout." would also consider the work robot provider and position information when optimizing the workspace).
It would have been obvious to one or ordinary skill in the art at the time of the invention to combine the teachings of Michalowski with Walsh because including acquiring work information related to a space in which the work robot work is to be provided enables workspaces that are more suitable to automated processes (see Michalowski, col 1, line 27-65).
Moreover, it would have been obvious to one of ordinary skill in the art at the time of the invention to include the system for robotic workspace layout planning as taught by Michalowski in the queue management system of Walsh, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Walsh and Michalowski, however, do not specifically disclose a third spatial information generation unit configured to search the object placement characteristic database using the object type information and the position information included in the second spatial information as a key, and generate third spatial information related to a space comprising the object type information and the position information. In analogous art, Dewan discloses the following limitations:
a third spatial information generation unit configured to search the object placement characteristic database using the object type information and the position information included in the second spatial information as a key, and generate third spatial information related to a space comprising the object type information and the position information (see para [0019], "Embodiments of the present invention provide EAM systems that may be configured to communicate with third-party APIs to provide solutions for clients managing assets having spatial data attributes. For example, EAM systems may include management functionality to visualize the spatial relationships among assets and other mapped features. EAM systems may extend capabilities of third-party APIs by adding maps for assets, locations, work order tracking, and service requests. The maps include spatial attributes for each asset to be viewed for various management purposes. Each asset may be stored in a database in an index table for later access or for processing functions. Indexing data enables more efficient searching operations to be performed on the multitudes of assets stored in the database index table" and see para [0022]-[0023], "Embodiments of the present invention describe computer-implemented methods, computer program products, and systems for efficient spatial indexing. In an embodiment, the computer-implemented method may include one or more processors configured for obtaining, from a database, index data representing one or more assets, wherein each of the one or more assets comprise an asset identifier and a spatial attribute. The index data may be representative of a library of assets stored in a database and organized in an index table based on certain attributes. Each asset may include an asset identifier to identify one particular asset and distinguish it from another asset. Each asset may also include a spatial attribute corresponding to its relative position within a geographical location. The computer-implemented method may include one or more processors configured for generating a location hash for each of the one or more assets using the respective asset identifier and the respective spatial attribute. For example, generating a location hash may include transforming a string of characters into a usually shorter fixed-length value or key that represents the original string. A location hash may be used to anchor a part of an asset address in a database. A location hash may be used to index and retrieve assets in a database because it is faster to find the assets using the shorter hashed key than to find it using the original value. Thus, in this embodiment, a location hash may be generated for each asset using at least the asset identifier and the spatial attribute. The asset identifier may be some combination of numeric, alphabetic, or alphanumeric characters. The location hash may be a geohash including a point value, a radius value, and one of the one or more precision values. In an embodiment, the spatial attribute may include a longitudinal value and a latitudinal value corresponding to a geographical location.")
It would have been obvious to one or ordinary skill in the art at the time of the invention to combine the teachings of Dewan with Walsh and Michalowski because including such searching functionality and using a spatial information as a key enables more effective indexing and retrieval of such data (see Dewan, para [0002]-[0004]).
Moreover, it would have been obvious to one of ordinary skill in the art at the time of the invention to include the method for efficient spatial indexing as taught by Dewan in the Walsh and Michalowski combination, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Claims 7-8:
Further, Walsh discloses the following limitations:
wherein the first spatial information and the second spatial information include spatial information parameters related to a difficulty level of robot work, and wherein the degree of coincidence determination unit determines the degree of coincidence based on a value of the spatial information parameters (see para [0077], " System 100 may also monitor actual agent consumption times of the event to dynamically update its calculations regarding likely time by the agent consuming the event and enhance its ability to predict the amount of time other agents will spend consuming an event given their behaviour and preference data. The system 100 may store agent preference data and any data related to preference data such as agent location or movement data in agent database 101b such that queue manager 102 may access the data at any time and factor it into Agent Score calculations or machine learning operations." and see para [0114], "The queue manager 102 may also track what the cleaning robots were doing before being summoned, such as if a robot was actively completing another task, what sort of task the robot was doing, and how far along the task the robot was, and how long the cleaning robots would take to transit from its current location to the consumable event space. The queue manager 102 may consider the dynamic nature of these variables as robot transit time is impacted by other agent or traffic of people who are not queued for a consumable event and the time to complete a cleaning task may be impacted by the changes to the physical layout of consumable event 105 or event or activity that does not require queueing for space, e.g., the distribution of tables and chairs and the mess created as a result of a wedding with 100 guests is different from that of a wedding with 200 guests." where calculations regarding likely time and transit time can be considered a difficulty level given broadest reasonable interpretation).
wherein the spatial information parameter is defined in advance for each work type of robot work (see par a[0051], showing queue manager receiving physical layout and floorplan data which is then used in calculating an agent score), and a value of the spatial information parameter is determined for each work type of the robot work (see para [0114], "The queue manager 102 may also track what the cleaning robots were doing before being summoned, such as if a robot was actively completing another task, what sort of task the robot was doing, and how far along the task the robot was, and how long the cleaning robots would take to transit from its current location to the consumable event space. The queue manager 102 may consider the dynamic nature of these variables as robot transit time is impacted by other agent or traffic of people who are not queued for a consumable event and the time to complete a cleaning task may be impacted by the changes to the physical layout of consumable event 105 or event or activity that does not require queueing for space, e.g., the distribution of tables and chairs and the mess created as a result of a wedding with 100 guests is different from that of a wedding with 200 guests.", showing such data can be considered via variables)
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Kwak et al. (US 2025/0196346 A1), a cooperation control method of a multi-robot comprises detecting information on a user or information on surroundings of the user using sensors, recognizing an intention or a situational context of the user based on the detected information, selecting a task that a plurality of robots are able to cooperatively perform in response to the recognized intention or situational context of the user, and requesting a service provision to each of the plurality of robots that are able to cooperatively perform the selected task
Saeki (JP 2024174118 A), a system to a work orderer to easily select an operator having a skill level appropriate to the work having been ordered by the work orderer that includes work request information containing a work content and work time period for the work content having been input by a work orderer; a requested skill level determination unit which determines a requested skill level indicating a skill of remote operation requested to an operator by the work orderer on the basis of the work request information received by the communication unit; an operator database which stores a skill level of each of a plurality of operators; and a list information generation unit 13 which extracts operators having skill levels of the requested skill level or higher from the operator database, and generates the operator list information for presenting the extracted operators to the work orderer
Zhou et al. "Multirobot collaborative task dynamic scheduling based on multiagent reinforcement learning with heuristic graph convolution considering robot service performance", a paper on the problem of multirobot collaborative task scheduling considering the degradation of industrial robot performance and the recovery of robot performance through intervention of compensation measures, a robot collaborative task scheduling method based on multiagent reinforcement learning with heuristic graph convolution
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/SUJAY KONERU/
Primary Examiner, Art Unit 3624
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