DETAILED ACTION
This is a Final office action on the merits in application number 18/671,632. Claims 1-4 and 7-10 were amended and Claims 5 and 6 were cancelled. Claims 1-4 and 7-10 are pending and have been examined on the merits.
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 .
Response to Arguments
Regarding 35 USC 101:
Applicant asserts on pages 5-8 that they are solving the “technical” problem of the loss of human pickers’ specialized warehouse-picking knowledge due to labor turnover by obtaining, storing, recalling and providing optimized picking paths to new human pickers. Examiner holds that the loss of pickers’ specialized warehouse-picking knowledge is a business problem, not a technical problem, and documenting industrial methods and training new workers in the documented industrial methods is a common commercial practice. Applicant claims a general-purpose “path search algorithm” (presumably run on a general purpose computer) to obtain an optimized picking path for a warehouse, saving the resulting optimized path and then outputting it at some future time (to another picker) if a “similar” task arises. Examiner holds that using a “path search algorithm” to optimize a picking task in a warehouse is a common industrial method. Examiner holds that documenting an “optimal” picking path in a warehouse and using it to train new pickers is an abstract idea in the category of Certain Methods of Organizing Human Activity in the subcategory of Commercial or Legal interactions because it is a common commercial practice.
Applicant asserts on page 8 that the claimed "generating and displaying a graph indicating the obstacle area, the path area, and the picking movement path processed in the grid unit graph in response to a receipt of another picking task similar to the picking task, wherein the graph is composed of numbers of Os and 1s" is an “additional element” and integrates the abstract idea into a practical application and provides “significantly more” because it “improves upon the conventional picking movement path in a warehouse management system by first generating and storing an optimal picking movement path for a task and then later accessing the optimal picking movement path for another task or subsequent similar (task)”.
As discussed in the 35 USC 101 rejection, infra, MPEP 2106.05(a) defines the requirements for “Improvements to the functioning of a computer or to any other technology or technical field” and recites “In determining patent eligibility, examiners should consider whether the claim "purport(s) to improve the functioning of the computer itself" or "any other technology or technical field"…This consideration has also been referred to as the search for a technological solution to a technological problem… It is important to note, the judicial exception alone cannot provide the improvement. The improvement can be provided by one or more additional elements”. MPEP 2106.05(a)(I) also recites examples that are not sufficient to show an improvement in computer functionality: “(ii) Accelerating a process of analyzing audit log data when the increased speed comes solely from the capabilities of a general-purpose computer; (iii) Mere automation of manual processes, such as using a generic computer to process an application for financing a purchase… (viii) Arranging transactional information on a graphical user interface in a manner that assists traders in processing information more quickly”. MPEP 2106.05(a)(II) also recites examples that are not sufficient to show an improvement in any other technology: “(i) A commonplace business method being applied on a general purpose computer…(iii) Gathering and analyzing information using conventional techniques and displaying the result”). Applicant’s asserted “improvement” comes solely from the capabilities of (an unclaimed) general-purpose computer, merely automates a manual process, and uses conventional techniques to gather, analyze and display information (on unclaimed display hardware).
In this case generating a two-dimensional graph of 1s and 0s to illustrate a path to display to a (new) picker is part of the abstract idea and is not an additional element. Applicant has not presented any evidence that their particular display design choice of a graphical display of 1s and 0s solves the “problem” of passing down specialized warehouse-picking knowledge to future generations of workers any differently or more effectively than any other display design choice. While Examiner is not asserting Applicant claims a Mental Process, Examiner notes that, but for the use of (presumably) general-purpose computers and a general-purpose path optimization model, Applicant’s claims can be performed by a human picker using their memory and drawing a diagram on paper or on a white board for other human pickers to see.
Applicant’s arguments have been considered but are not persuasive, the rejection is maintained.
Regarding 35 USC 112:
The rejection under 35 USC 112 is withdrawn in view of Applicant’s amendments.
Regarding 35 USC 103:
Applicant asserts on pages 11-14 that the art of record does not teach the newly amended claims. As necessitated by amendment and discussed in the 35 USC 103 rejection, infra, Examiner now asserts the art of record further in view of Kumar.
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.
Examiner is using the “step” annotation from the flowchart of MPEP 2106 (III), and MPEP 2106.04 and MPEP 2106.05 for clarity.
Claims 1-4 and 7-10 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Appropriate correction is required.
Step 1:
Independent Claim 1 and dependent Claims 2-4 and 7-10 recite a method (process) thereby falling into one of the four statutory categories of invention.
Step 2A, prong 1:
Applicant recites the following elements in Claim 1:
(Currently Amended) A method for optimizing a picking movement path in a warehouse management system (WMS), the method comprising: collecting reference information on a product and a worker who performs a picking task; by processing the reference information, forming, on a grid unit graph, an obstacle area, which is an area where the product and the worker cannot physically access, and a path area that is an area where the product and the worker can move; searching for a picking movement path of the product and the worker for the picking task based on the processed reference information, wherein the searching includes using a path search algorithm; selecting, based on an input of the worker, the picking movement path; storing the reference information and the picking movement path for the picking task in the WMS: and generating and displaying a graph indicating the obstacle area, the path area, and the picking movement path processed in the grid unit graph in response to a receipt of another picking task similar to the picking task, wherein the graph is composed of numbers of Os and 1s.
Examiner has bolded the elements that are part of the abstract idea.
Applicant claims running a general-purpose “path search algorithm” (presumably on a general-purpose computer) to obtain an optimized picking path for a warehouse, saving the resulting optimized path and then outputting it at some future time (to another picker) if a “similar” task arises. Examiner holds that using a general-purpose “path search algorithm” to optimize a picking task in a warehouse is a common industrial method. Examiner holds that documenting an “optimal” picking path in a warehouse and using it to train new pickers is an abstract idea in the category of Certain Methods of Organizing Human Activity in the subcategory of Commercial or Legal interactions because it is a common commercial practice.
Claim 1 thus recites an abstract idea. Dependent Claims 2-4 and 7-10 contain the same abstract idea by virtue of their dependency on Claim 1. Accordingly Claims 1-4 and 7-10 recite an abstract idea.
Step 2A, prong 2:
In addition to the abstract idea discussed above, Claim 1 also recites the following additional elements:
warehouse management system (WMS) – while Applicant does not recite a computer or processor in the claims or specification, Applicant recites in their specification ([page 7, lines 2-30] “a warehouse management system (WMS)”) which is a well-known type of software construct that runs on a general purpose computer. Applicant does not claim any specific WMS nor WMS technology. Applicant discloses general purpose WMS in name only with no detailed technical disclosure of any special features or benefits relating to Applicant’s inventive concept. Further, Applicant does not claim a computer but presumably this WMS runs on a general purpose computer.
MPEP 2106.05(f)(2) states “Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application”.
The additional element of WMS is broadly claimed and used in its ordinary capacity with no detailed technical disclosure of any special features or technologies and, thus, the additional element of WMS does not integrate the abstract idea into a practical application.
Applicant also recites the additional element of path search algorithm but does not claim any specific algorithm or technology. Applicant recites in their specification ([page 8, line 24 to page 9, line 3] “Dijkstra algorithm”, “traveling salesman problem algorithm” and “ACO algorithm”) which are software constructs running on a general purpose computer, in the broad category of shortest/optimal path in a graph algorithm that are well known in the software development art. Applicant does not claim a specific algorithm or technology and does not provide a detailed technical disclosure of any special features or benefits relating to Applicant’s inventive concept. For the same reasons as above, the software construct of path search algorithm is broadly claimed and used in its ordinary capacity with no detailed technical disclosure of any special features or technologies and, thus, the additional element of path search algorithm does not integrate the abstract idea into a practical application. Examiner notes that the claimed “information” and “graph” are data and are part of the abstract idea.
The claims as a whole do not integrate the abstract idea into a practical application because they do not impose any meaningful limitations on practicing the abstract idea. Claims 1-4 and 7-10 are therefore directed to an abstract idea.
Step 2B: As discussed above, Applicant claims the abstract idea of running a general-purpose “path search algorithm” (presumably on a general-purpose computer) to obtain an optimized picking path for a warehouse, saving the resulting optimized path and then outputting it at some future time (to another picker) if a “similar” task arises. As discussed above, Applicant also recites the additional elements of WMS and path search algorithm. As discussed above with respect to Step 2A, the claimed WMS and path search algorithm are software constructs recited at a high level of generality that amount to no more than instructions to apply the exception using general purpose computer systems. MPEP 2106.05(f) states that merely adding a general purpose computer or computer components to an abstract idea does not amount to significantly more, thus WMS and path search algorithm are not significantly more.
The additional elements alone or in combination do not improve the functioning of a computer or any other technology or technological field. The additional elements alone or in combination do not apply the judicial exception to a particular (non-general purpose) machine. The additional elements alone or in combination do not effect a transformation or reduction of a particular article to a different state or thing. Applicant does not claim or teach in their specification any special purpose hardware or improvements thereof. Therefore, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Dependent Claims 2, 3 and 4 contain the same abstract idea by virtue of their dependency on Claim 1 and further limit the content of reference information. Dependent Claims 7-9 contain the same abstract idea by virtue of their dependency on Claim 1 and further limit processing the reference information. Dependent Claim 10 contains the same abstract idea by virtue of its dependency on Claim 1 and further limit storing the reference information.
Claims 1-4 and 7-10 are not patent eligible.
Claim Rejections - 35 USC § 112
The rejections under 35 USC 112 are withdrawn in view of Applicant’s amendments.
Claim Rejections - 35 USC § 103
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 (i.e., changing from AIA to pre-AIA ) 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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-4 and 7-10 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Publication 2024/0149443 (Fox) in view of U.S. Patent Publication 2018/0218311 (Kumar) in view of U.S. Patent Publication 2018/0330258 (Harris).
Regarding Claim 1:
Fox teaches a system of using models to optimize picking paths in a warehouse. Fox teaches: (Currently Amended) A method for optimizing a picking movement path… the method comprising: collecting reference information on a product ([0021] “the model is trained based on the properties of the transported items”).
in a warehouse management system (WMS), ([0015] "warehouse control system")
(collecting reference information on) a worker who performs a picking task; ([0082] “availability of mobile robots 103 and their current locations” and [0091] “the current location of mobile robots 103 may be tracked by IoT sensors 104, where such information is provided to tracking engine 304. In one embodiment, tracking engine 304 determines the distance between the current location of mobile robot(s) 103 organized to form the identified optimal movement path and the target location using a map of warehouse 101, such as map 206 stored in mobile robot 103, which may also be stored in a storage device (e.g., memory, disk unit) of warehouse control system 102”).
by processing the reference information; ([0021] “the model is trained to predict the optimal path based on historical information pertaining to the movement of items using various movement paths (e.g., individual movement paths…)… After training the model to determine an optimal movement path for transporting items using mobile robots and receiving certain information pertaining to the transportation of the items, such as the source and target locations, the number of items to be transported and the properties of the items to be transported, simulations of mobile robots transporting the items from the source location to the target location using various movement paths are performed”).
… searching for a picking movement path of the product and the worker for the picking task based on the processed reference information ([0021] “An optimal movement path for transporting such items from the source location to the target location is identified using the trained model based on the simulated movement paths, the historical times for the movement of the items using various movement paths, the number of items to be transported and the properties of the items”).
storing the reference information and the picking movement path for the picking task in the WMS: ([0031] “stored in a database 105 connected to warehouse control system 102”).
While Fox teaches the robots using a 2D floorplan map to find products (see at least [0044]) and Fox teaches a picking path that avoids obstacles (see at least [0047]), Fox does not specifically teach forming, on a grid unit graph, an obstacle area, which is an area where the product and the worker cannot physically access, and a path area that is an area where the product and the worker can move; …and generating and displaying a graph indicating the obstacle area, the path area, and the picking movement path processed in the grid unit graph in response to a receipt of another picking task similar to the picking task, wherein the graph is composed of numbers of Os and 1s. Kumar, in the same field of art, teaches a system that generates optimized pick paths and displays them on a grid with 1s and 0s, Kumar teaches: ([0061] “The method 160 may include the steps of identifying the items with aisle locations that match map data or XY coordinates… The method 160 includes the steps of transmitting the store map and pick path in block 174. For example, transmitting, by the server computing device 104 or system 100, a representation of the store map and the optimized pick path to the user computing device 107. The method 160 further includes displaying the store map overlaid with the optimized pick path on a graphical display of the user computing device 107 in block 176” and ([0066] “The optimization algorithm starts in block 180 with Item-Aisle mapping by finding the four (4) corner points of the retail store 106 and dividing the two dimensional array of the retail store 106 (array with 1s and 0s) into small grids with some configurable width and height. The optimization algorithm, for each aisle (cluster of modules), based on their bay types and aisle types, marks them as obstacles (1) in the 2-dimensional array and the rest is free space (0) as Aisle-Block XY data in block 182 and Blockers in block 184”). It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to use a grid with 1s and 0s, as taught by Kumar, in the system taught by Fox, due to simple substitution of one known element for another to yield predictable results. The design choice of using 1s and 0s on a grid, as taught by Kumar, is a more mathematical format and communication of the optimal path to an autonomous robot to avoid obstacles in this format could potentially be more easily understood by robots, as taught by Fox (see Fox in at least [0045-0049]).
While Fox also teaches using an algorithm to determine the optimal picking path (see at least [0069] The algorithm iteratively makes predictions on the training data as to the optimal movement path to be utilized by mobile robots 103 in transporting the items from a source location to a target location until the predictions achieve the desired accuracy as determined by an expert. Examples of such learning algorithms include nearest neighbor, Naïve Bayes, decision trees, linear regression, support vector machines and neural networks”), and Fox also teaches creating a map/graph of the physical locations of items/pickers in a warehouse (see at least [0044] “a map 206 defining a floor plan 207, such as the floor plan of warehouse 101”), Fox does not teach a specific “path search algorithm”. Harris teaches a graph-based machine learning system that finds the shortest path through graphs. Harris teaches the claimed: wherein the searching includes using a path search algorithm ([0064] “optimization technique that finds optimal paths through graphs. The optimization technique may be configured to determine paths to reach an end goal that are shortest or the least expensive as it scans through the domains or regions of one or more graphs of the information space. An example of an optimization technique that may be used at step 410 may include an ant colony optimization algorithm”).
While Fox teaches that the robots are autonomous (see at least [0013] “a mobile robot (also referred to as a “moveable robot”) is an automatic machine that is capable of locomotion. Mobile robots have the capability to move around in their environment and are not fixed to one physical location. Mobile robots can be “autonomous” in that the mobile robots are capable of navigating an uncontrolled environment without the need for physical or electro-mechanical guidance devices. That is, autonomous mobile robots are devices capable of performing tasks and moving around an environment, such as a warehouse, without the need for human intervention” and [0015] “autonomous mobile robots identify and sidestep any obstacles they come across as they move items within the warehouse”), and teaches that the robots can receive optimal picking paths from the system, but Fox does not specifically teach the claimed selecting, based on an input of the worker, the picking movement path. Harris teaches a path search optimization model with an output in the form of recommendations that an autonomous entity can select: ([0109] “generate a recommendation that may be accepted or acting upon by a user” and [0152] “The remembered values may then be used to output a requested prediction. For example, the remembered values may be sent to a user device as a list of recommendations” and see [0156]-166]).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to use the “ant colony optimization algorithm”, as taught by Harris, to find the optimal movement path through a physical space because Harris suggests that the ACO algorithm is very well suited to dynamically changing environments (such as a warehouse, as taught by Fox): ([0105] “At step 650a, each of the agents may update local path information to indicate the calculated cost of a proposed solution within its searched region of the graph. The agents may be initialized again at a next iteration to search for a less costly solution. Steps 620 to steps 650a may be continuously cycled until the cost requirement is finally met. As the agents search for a next solution state, the agents may be affected by the trail levels of each path as determined by local pheromones recorded in previous iterations. A particular agent may be influenced by the costs of paths within the specific portion of the domain being searched, as calculated during a previous iteration, which may lead the agent to choose a different path or different point A at the current iteration. For example, the probability of a path being chosen by an agent during an iteration may be a parameter that depends on the cost calculated for the path during the previous iteration. Therefore, there may be a higher probability that the path chosen by an agent during each consecutive iteration may be one that has a reduced cost in comparison to paths chosen at previous iterations, thus allowing the colony of agents to better meet the cost requirement after each cycle” and [0195-0196] “Embodiments of the invention allow for autonomous learning in a dynamic environment through the detection of new features. Prior methods for configuring an artificial intelligence model for a dynamically changing environment required manually re-building the AI model on the fly. In other words, prior methods required those skilled in the art to constantly redefine new rules for the AI model when features used for predictions no longer became predictive. This could prove to be very time-consuming as any given information space may continuously be changing… (prior methods) may still fail to make accurate predictions in cases of subtle or incomplete concept drifts, or in cases in which a drift in an explanatory variable has not been transformed in the AI such as in the case of strong concept drift. Embodiments of the invention can provide a more efficient and accurate method of operating in a dynamic environment by executing an algorithm that may autonomously detect and compile new features that are highly predictive at the environment's current state”). Examiner notes that Applicant teaches the same algorithm in ([page 8, line 25] “an ant colony optimization (ACO) algorithm may be used”).
Regarding Claims 2, 3 and 4:
Fox in view of Kumar and Harris teaches all of the elements of Claim 1. Fox also teaches: (currently amended) The method according to claim 1, wherein the reference information includes at least one of product warehousing information, product ordering information, location information of the product and the worker and spatial information about a work site. (currently amended) The method according to claim 2, wherein the product warehousing information and the product ordering information are information about warehousing and ordering number, product number and warehousing time, and the location information of the product and the worker is information about current location and location to move. (currently amended) The method according to claim 2, wherein the spatial information is formed by processing the work site into the grid unit graph. ([0003] “source location for a plurality of items to be picked. The method additionally comprises obtaining a target location to receive the plurality of items transported from the source location” and [0044] “a map 206 defining a floor plan 207, such as the floor plan of warehouse 101”).
Regarding Claim 7:
Fox in view of Kumar and Harris teaches all of the elements of claim 1. Fox does not specifically teach: (currently amended) The method according to claim 1, wherein the path search algorithm includes searching a path where a sum of weights when moving from one point to another point on the grid unit graph becomes a minimum value. Harris teaches this: ([0078] “The total cost of this path may be the sum of edge weights along the path” and [0049] “an edge weight may represent the length of a route, the capacity of a communication line, or the energy required to move from one state to the next. The connections between nodes, as defined by corresponding edges and weights, may be evaluated in order to determine optimal routes or movements, such as to reach an end goal in the most efficient manner. For example, the edges in a graph for a communication network can be evaluated to find an optimal route for delivering a message to all nodes using the least amount of resources or in the least amount of time (i.e. traveling salesman problem). Various optimization techniques may be used to find optimal paths in a graph”).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to use the “ant colony optimization algorithm”, as taught by Harris, to find the optimal movement path through a physical space because Harris suggests that the ACO algorithm is very well suited to dynamically changing environments (such as a warehouse, as taught by Fox): ([0105] “At step 650a, each of the agents may update local path information to indicate the calculated cost of a proposed solution within its searched region of the graph. The agents may be initialized again at a next iteration to search for a less costly solution. Steps 620 to steps 650a may be continuously cycled until the cost requirement is finally met. As the agents search for a next solution state, the agents may be affected by the trail levels of each path as determined by local pheromones recorded in previous iterations. A particular agent may be influenced by the costs of paths within the specific portion of the domain being searched, as calculated during a previous iteration, which may lead the agent to choose a different path or different point A at the current iteration. For example, the probability of a path being chosen by an agent during an iteration may be a parameter that depends on the cost calculated for the path during the previous iteration. Therefore, there may be a higher probability that the path chosen by an agent during each consecutive iteration may be one that has a reduced cost in comparison to paths chosen at previous iterations, thus allowing the colony of agents to better meet the cost requirement after each cycle” and [0195-0196] “Embodiments of the invention allow for autonomous learning in a dynamic environment through the detection of new features. Prior methods for configuring an artificial intelligence model for a dynamically changing environment required manually re-building the AI model on the fly. In other words, prior methods required those skilled in the art to constantly redefine new rules for the AI model when features used for predictions no longer became predictive. This could prove to be very time-consuming as any given information space may continuously be changing… (prior methods) may still fail to make accurate predictions in cases of subtle or incomplete concept drifts, or in cases in which a drift in an explanatory variable has not been transformed in the AI such as in the case of strong concept drift. Embodiments of the invention can provide a more efficient and accurate method of operating in a dynamic environment by executing an algorithm that may autonomously detect and compile new features that are highly predictive at the environment's current state”). Examiner notes that Applicant teaches the same algorithm in ([page 8, line 25] “an ant colony optimization (ACO) algorithm may be used”).
Regarding Claim 8:
Fox in view of Kumar and Harris teaches all of the elements of claims 1 and 7. Fox does not specifically teach: (currently amended) The method according to claim 7, wherein the sum of weights is at least one weight suitable for purpose of the picking movement path and comprises minimum cost, minimum distance, heuristic order, and negative weight. Harris teaches this: ([0064] “the autonomous learning algorithm may begin by detecting new features through an optimization technique that finds optimal paths through graphs. The optimization technique may be configured to determine paths to reach an end goal that are shortest or the least expensive as it scans through the domains or regions of one or more graphs of the information space. An example of an optimization technique that may be used at step 410 may include an ant colony optimization algorithm”).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to use the “ant colony optimization algorithm”, as taught by Harris, to find the optimal movement path through a physical space because Harris suggests that the ACO algorithm is very well suited to dynamically changing environments (such as a warehouse, as taught by Fox): ([0105] “At step 650a, each of the agents may update local path information to indicate the calculated cost of a proposed solution within its searched region of the graph. The agents may be initialized again at a next iteration to search for a less costly solution. Steps 620 to steps 650a may be continuously cycled until the cost requirement is finally met. As the agents search for a next solution state, the agents may be affected by the trail levels of each path as determined by local pheromones recorded in previous iterations. A particular agent may be influenced by the costs of paths within the specific portion of the domain being searched, as calculated during a previous iteration, which may lead the agent to choose a different path or different point A at the current iteration. For example, the probability of a path being chosen by an agent during an iteration may be a parameter that depends on the cost calculated for the path during the previous iteration. Therefore, there may be a higher probability that the path chosen by an agent during each consecutive iteration may be one that has a reduced cost in comparison to paths chosen at previous iterations, thus allowing the colony of agents to better meet the cost requirement after each cycle” and [0195-0196] “Embodiments of the invention allow for autonomous learning in a dynamic environment through the detection of new features. Prior methods for configuring an artificial intelligence model for a dynamically changing environment required manually re-building the AI model on the fly. In other words, prior methods required those skilled in the art to constantly redefine new rules for the AI model when features used for predictions no longer became predictive. This could prove to be very time-consuming as any given information space may continuously be changing… (prior methods) may still fail to make accurate predictions in cases of subtle or incomplete concept drifts, or in cases in which a drift in an explanatory variable has not been transformed in the AI such as in the case of strong concept drift. Embodiments of the invention can provide a more efficient and accurate method of operating in a dynamic environment by executing an algorithm that may autonomously detect and compile new features that are highly predictive at the environment's current state”). Examiner notes that Applicant teaches the same algorithm in ([page 8, line 25] “an ant colony optimization (ACO) algorithm may be used”).
Regarding Claim 9:
Fox in view of Kumar and Harris teaches all of the elements of claims 1 and 2. Fox does not specifically teach: (currently amended) The method according to claim 2, wherein the path search algorithm is an algorithm suitable for a situation by considering at least one of a distance between vertices, constraints for picking equipment and situations occurring at the work site. Harris teaches this: ([0175] “The graph may be sharded into multiple overlapping subsets based on their relative distance to one another… Each of these subsets may be treated as individual graphs by a given processing unit in a distributed parallel processing framework, such as in the case of distributed GPUs, wherein each processing unit or processing core may act as an agent performing optimization (e.g. performing ant colony optimization to detect new features). According to embodiments of the invention, a graph may be sharded based on an individual problem being solved and may be done such that the system is biased towards a given solution that best fits the individual problem” and [0180] “The cost may be calculated according to agent rules that may have been defined at initialization such as based on total distance between nodes”).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to use the “ant colony optimization algorithm”, as taught by Harris, to find the optimal movement path through a physical space because Harris suggests that the ACO algorithm is very well suited to dynamically changing environments (such as a warehouse, as taught by Fox): ([0105] “At step 650a, each of the agents may update local path information to indicate the calculated cost of a proposed solution within its searched region of the graph. The agents may be initialized again at a next iteration to search for a less costly solution. Steps 620 to steps 650a may be continuously cycled until the cost requirement is finally met. As the agents search for a next solution state, the agents may be affected by the trail levels of each path as determined by local pheromones recorded in previous iterations. A particular agent may be influenced by the costs of paths within the specific portion of the domain being searched, as calculated during a previous iteration, which may lead the agent to choose a different path or different point A at the current iteration. For example, the probability of a path being chosen by an agent during an iteration may be a parameter that depends on the cost calculated for the path during the previous iteration. Therefore, there may be a higher probability that the path chosen by an agent during each consecutive iteration may be one that has a reduced cost in comparison to paths chosen at previous iterations, thus allowing the colony of agents to better meet the cost requirement after each cycle” and [0195-0196] “Embodiments of the invention allow for autonomous learning in a dynamic environment through the detection of new features. Prior methods for configuring an artificial intelligence model for a dynamically changing environment required manually re-building the AI model on the fly. In other words, prior methods required those skilled in the art to constantly redefine new rules for the AI model when features used for predictions no longer became predictive. This could prove to be very time-consuming as any given information space may continuously be changing… (prior methods) may still fail to make accurate predictions in cases of subtle or incomplete concept drifts, or in cases in which a drift in an explanatory variable has not been transformed in the AI such as in the case of strong concept drift. Embodiments of the invention can provide a more efficient and accurate method of operating in a dynamic environment by executing an algorithm that may autonomously detect and compile new features that are highly predictive at the environment's current state”). Examiner notes that Applicant teaches the same algorithm in ([page 8, line 25] “an ant colony optimization (ACO) algorithm may be used”).
Regarding Claim 10:
Fox in view of Kumar and Harris teaches all of the elements of claim 1. While Fox teaches that times are used in the simulation model: ([0077] “a simulation is performed by implementing a simulation model of warehouse 101. In one embodiment, the simulation model is loaded with data pertaining to the different processing times and speeds for mobile robots 103 to transport various items, based on the number of such items as well as the dimensions, weight, etc. of such items”), Fox does not specifically teach: (currently amended) The method according to claim 1, wherein the storing the picking movement path includes converting the picking movement path into a database and storing it in the WMS in a time series manner. Harris teaches this: ([0159] “the graph database may be recorded to and called by Data API/Graph Query 850. A user's position in a graph may be determined by its characteristics and connection to other users such as through transactions conducted between two users (e.g. between consumer and merchant) or by any other information relating to a user profile. Connections between users may be expressed as factual edges representing actual characteristics such as location, time”).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to use the “ant colony optimization algorithm”, as taught by Harris, to find the optimal movement path through a physical space because Harris suggests that the ACO algorithm is very well suited to dynamically changing environments (such as a warehouse, as taught by Fox): ([0105] “At step 650a, each of the agents may update local path information to indicate the calculated cost of a proposed solution within its searched region of the graph. The agents may be initialized again at a next iteration to search for a less costly solution. Steps 620 to steps 650a may be continuously cycled until the cost requirement is finally met. As the agents search for a next solution state, the agents may be affected by the trail levels of each path as determined by local pheromones recorded in previous iterations. A particular agent may be influenced by the costs of paths within the specific portion of the domain being searched, as calculated during a previous iteration, which may lead the agent to choose a different path or different point A at the current iteration. For example, the probability of a path being chosen by an agent during an iteration may be a parameter that depends on the cost calculated for the path during the previous iteration. Therefore, there may be a higher probability that the path chosen by an agent during each consecutive iteration may be one that has a reduced cost in comparison to paths chosen at previous iterations, thus allowing the colony of agents to better meet the cost requirement after each cycle” and [0195-0196] “Embodiments of the invention allow for autonomous learning in a dynamic environment through the detection of new features. Prior methods for configuring an artificial intelligence model for a dynamically changing environment required manually re-building the AI model on the fly. In other words, prior methods required those skilled in the art to constantly redefine new rules for the AI model when features used for predictions no longer became predictive. This could prove to be very time-consuming as any given information space may continuously be changing… (prior methods) may still fail to make accurate predictions in cases of subtle or incomplete concept drifts, or in cases in which a drift in an explanatory variable has not been transformed in the AI such as in the case of strong concept drift. Embodiments of the invention can provide a more efficient and accurate method of operating in a dynamic environment by executing an algorithm that may autonomously detect and compile new features that are highly predictive at the environment's current state”). Examiner notes that Applicant teaches the same algorithm in ([page 8, line 25] “an ant colony optimization (ACO) algorithm may be used”).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KIMBERLY S BURSUM whose telephone number is (571)272-8213. The examiner can normally be reached M-F 9:30 AM - 6:30 PM.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Florian (Ryan) m Zeender can be reached at 571-272-6790. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/KIMBERLY S. BURSUM/Examiner, Art Unit 3627
/FLORIAN M ZEENDER/Supervisory Patent Examiner, Art Unit 3627