DETAILED ACTION
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
2. The Amendment filed on December 5, 2025, has been entered. The examiner acknowledges the amendments to claims 1, and 16.
Rejections under 35 U.S.C. § 103: Applicant’s argues for claim 1 that an initial operational state of the retail facility is not disclosed or suggested by prior art or that certain types of data are not included in prior art or are unique or novel to the invention. Examiner disagrees noting that Saigh and Evans both extensively describe data-driven operations employing automation in planning and execution of operations. The initial operational state of the retail facility is discussed in Saigh, [0136], [0103], customer-related data in [0148], associate-related data [0153], [0107], and retail facility data [0107], [0131]. Dynamic reallocation of resources, optimization and configuration and described by Evans [0012]-[0014] along with recalibrating based new data points and adapting to pattern changes, [0015]. The resulting capabilities described in the prior art detail a connected and integrated system for organizing, managing and delivering retail logistics delivery operations.
Arguments that reallocation of tasks assigned to mobile robot resources is not addressed by Keating are now overcome with updated art.
Arguments surrounding prior art (Evans) disclosure of a supply chain optimizer claiming it is not an optimizer for a retail facility appear short-sighted. It seems that a retail facility would rely on a supply chain to support operations, hence an optimized supply chain would enable supporting retail optimization, so any specific novelty or innovation beyond this is not apparent. Additionally, claims that Evans does not discuss the operational state of a retail facility ignores the supply chain optimizer that is connected to Enterprise Data Systems and stakeholder communication devices, where these stakeholders are defined as operations managers, supply chain managers, planners, distributors and retailers, [0036].
The common element in these arguments is an apparent lack of novelty being revealed as part of the invention. Precise data terminology, the use of data, the optimization factors, are each expressed at a level of detail that enables prior art to be reasonably interpreted as providing similar capabilities. It is understood that with subject matter eligibility issues overcome by an acknowledged practical application, a rapid allowance with respect to prior art is desirable. It is the Examiner’s opinion that the claim language and technical detail especially in the area of data definition and optimization provided have yet to describe innovation and novelty to a level that supports allowance of the current claims. In view of this, the request for withdrawal of rejections under 35 U.S.C. § 103 is denied.
Claim Rejections 35 U.S.C. §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.
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 1-8 and 12-19 are rejected under 35 U.S.C. § 103 as being taught by Saigh (US
20070187183 A1) hereafter Saigh, “System, Method And Process For Computer Controlled Delivery Of Classified Goods And Services Through An Amalgamated Drive-thru Complex,” in view of Keating, (US 20160347541 A1), hereafter Keating, “Automated Storage and Retrieval System,” in further view of Evans (US 20210158259 A1), hereafter Evans, “Orchestrated Intelligent Supply Chain Optimizer,” in further view of Hance, (US 10787315 B2), hereafter Hance, “Dynamic Truck Route Planning Between Automated Facilities.”
Regarding Claim 1, A retail facility control system for a retail facility having mobile
robot resources and retail facility associate resources, Saigh teaches, (operation, control and management of a retail facility or complex,[0002], transport system could include a guidance system controlling any mechanical technology, electromagnetic technology, robotic technology, cart system, conveyor system or electronic delivery system, [0019], facility complex 100 processes orders received from order placement stations 102 when customers order in person through a facility processing means (not shown, but hereafter referred to as the Comprehensive Order Response Enterprise system, or the CORE system) which may be electronic, computer, software or hardware based, or any combination thereof, [0087], and the present invention's CORE system is adapted to communicate with a cellular phone in a private and or public network to accept orders of any SIC/NAIC good or service within each drive-thru classification. [ ] This type of communication stream may also benefit the retail tenant, who, through the CORE system, can promote a good or use the communication stream for order confirmation, purchase confirmation, time of pickup confirmation, cost confirmation, payment conformation and delivery processing, Saigh, [0075]), the retail facility control system comprising:
at least one retail facility operational subsystem comprising:
an automated storage and retrieval system (ASRS) configured to automatically store and
retrieve respective products of a plurality of products to and from corresponding designated
storage locations in the ASRS and facilitate fulfillment of a customer order that includes at
least one product from the plurality of products, wherein the ASRS includes the mobile
robot resources; Saigh does not teach, Keating teaches, (automated storage and retrieval system may operate in a retail distribution center or warehouse to, for example, fulfill orders received from retail stores, [0018], and the automated storage and retrieval system 100 may include in-feed and out-feed transfer stations 170, 160, input and output vertical lift modules 150A, 150B (generally referred to as lift modules 150), a storage structure 130, and a number of autonomous rovers, [0019]),
Saigh and Keating are considered to be analogous to the claimed invention because they are both in the field of retail supply chain operations. It would have been obvious to one of ordinary skill in the art before the effective filing date to combine the operational control and management of logistics techniques of Saigh with the automated storage and retrieval capabilities of Keating to incorporate fatigue considerations with respect to loading from the automated storage and retrieval system, Keating, [0007].
a retail execution system, communicatively coupled to the ASRS and configured to receive ASRS data associated with operation of the ASRS, Saigh teaches, (A computer system may be employed within the facility 100 to monitor or otherwise process facility operations, ordering, packaging, shipping, or traffic, [0103], the retail execution system is further communicatively coupled to a plurality of retail applications and configured to obtain retail facility customer-related data, retail facility associate-related data, and retail facility related data from the plurality of retail applications, where the retail facility associate resources interact with the retail execution system; (The computer system is configured to be communicatively compatible with most of the current retail and wholesale operating programs and computer operating systems, [0103]),
at least one data repository, communicatively coupled to the retail execution system,
configured to store the ASRS data, (The computer system may also be configured with a database for storage and retention of transactional data to allow maximum data storage and ease of access to stored data, [0103]), the retail facility customer-related data, (many exemplary information services may be offered to a customer according to the present invention, whether while the customer is at the facility or through other communication means [ ]. For example, personal inventory data may be available which maintains data related to all purchase information made by the customer (e.g., what was purchased, how often, how much and at what price, [0148], and the CORE system 27 is adapted to process or otherwise
authenticate identification of the customer who is purchasing the ordered good or service, verify the customer's ability to purchase the ordered good or service, and if authentication and identification is confirmed, deliver the ordered good or service to the customer. [ ] By using a biometric identification system, for example, the unique of properties of each customer can be initially stored be retained on file for subsequent confirmation to whom is picking up the prescription. Such biometric verification methodologies include, but are not limited to, fingerprints, eye patterns, visual (face) identification, license scanning, voice, vehicle identification, non-invasive cell scans and others. Such verification can also be used to determine whether the customer has previously ordered goods, or if the customer
is a member of a qualified customer membership plan, [0101]), the retail facility associate-related data, (The disclosed computer system, in still another embodiment, may be configured to manage such clerical operations as [ ]staffing levels, [0107]), and the retail facility-related data, (the CORE system 27 may be adapted to communicate with similar remote systems to check the inventory at other facilities and have selected items held for later customer retrieval, shipped to the next closest facility, or order the goods for retrieval by the customer at a different location. The CORE system 27 may also be adapted to allow remote customer communication (such as, via an optional audio/video device) from one facility to another facility. Thus, the present invention contemplates consumer communication with other facility data or personnel. The CORE system 27 may also be adapted to allow the customer to access another computer network such as the Internet, and may allow for transmission of electronic mail communications. These features provides great flexibility for a customer, and allows a customer to order for another office, co-worker or emergency need at another location and be in control of the purchases, whether at a local facility or a remote facility, [0156]), where the retail facility customer related data, retail facility associate-related data and retail facility-related data define an initial operational state of the retail facility;
a control circuit; Saigh teaches, (the present invention's computer systems may employ various computing systems, including memory elements, digital signal processing elements, look-up tables, databases, and the like, which may carry out a variety of functions under the control of one or more microprocessors or other control devices, [0028]), and
a solver module communicatively coupled with the at least one data repository so as to
register the initial operational state based on the retail facility customer-related data, retail
facility associate-related data, and retail facility-related data defining the initial operational
state, (A computer system (not shown in FIGS. 1-4) may be employed within the facility 100 to monitor or otherwise process facility operations, ordering, packaging, shipping, or traffic, [ ] the computer system will also allow communication amongst the tenants within the facility, or amongst tenants and customers. [ ] the computer system is configured to be communicatively compatible with most of the current retail and wholesale operating programs and computer operating systems in commercial use [ ] the computer system may also be configured with a database for storage and retention of transactional data to allow maximum data storage and ease of access to stored data. [0103], and the disclosed computer system, in still another embodiment, may be configured to manage such clerical operations as order confirmation, printing of receipts, order pick lists, lane control, "day" reports, inventory reports, and tenant reports. Further business and office management tasks such as new customer rate, current customer flow, activity rate, data entry rate, cash flow, goal tracking, service error rate, productivity levels, staffing levels, inventory control, service selection, customer, awareness, customer trends, tenant changes, tenant value(s), product value(s), legal issues, and external links may be managed or otherwise controlled by the computer system, [0107]), and the CORE system 27 includes enterprise resource planning software for the management of each SIC or NAICS goods and service classification requirements. DRWMS 28 is adapted to dynamically control most, if not all, of the physical functions of the facility 100, [0131].
and the solver module is configured to be executed by the control circuit to:
access business priorities and operational goals defined for a retail facility; Saigh does not teach, Evans teaches, (The Cognitive Self-Modelling Supply Chain system described herein automatically identifies the RIGHT configuration to meet the strategic goals of the organization with the optimal service cost settings across the network. OI models and projects the future supply chain based on the variables and constraints in the data, considers the millions of options that are available in real time and identifies a costed and optimized solution which is the best fit for the organization, [0013]),
Saigh and Evans are both considered to be analogous to the claimed invention because they are both in the field of retail supply chain operations. It would have been obvious to one of ordinary skill in the art before the effective filing date to combine the operational control and management of logistics techniques of Saigh with the automatic identification of the right configuration to meet strategic goals of Evans [0013], to ensure the configuration remains correct as new challenges and risks surface, Evans, [0014],
and reallocate task assignments of one or more of the mobile robot resources and retail facility associate resources to optimize retail facility workflow, Saigh does not teach, Hance teaches, (One or more robots and/or other actors, such as human actors, human-driven vehicles, and autonomous vehicles, can operate throughout a supply chain. The supply chain can include various regions, such as buildings, roadways, flight routes, and seaways. Some of these regions are associated with one or more spaces, such as the interior of part or all of a building and/or its surrounding outdoor regions, where robots and/or the other actors perform tasks and/or otherwise utilize the space(s) together, [1: 14-22], the warehouse and supply-chain coordinator can coordinate groups of robots efficiently to optimize the loading and unloading of vehicles at a facility, such as a loading dock of a warehouse, [11:29-32], when the cargo delivery request is for cargo to be delivered from a first automated warehouse to a second automated warehouse, the request may involve reallocating one or more robots from the first automated warehouse to the second automated warehouse in addition to or instead of other freight. Mobile and/or fixed robots may be designed to allow for easy reallocation to upscale or downscale a particular warehouse's fleet based on projected future workload. By forecasting the number of robots needed at each warehouse for a future time period, robots can be allocated to satisfy projected demand. Additionally, a control system may consider both truck utilization and robot utilization across a network of automated warehouses, [6:16-30]. Example warehouse control services include, but are not limited to: yard management services for vehicle arrivals to the warehouse, such as routing trucks and/or other vehicles to appropriate loading docks, staging locations, and/or parking locations; services related to coordinating robots and/or other agents with vehicle arrivals at (a loading dock of) the warehouse based upon vehicle traceability and real-time ETA for maximizing warehouse throughput; services related to controlling robots within the warehouse to move inventory; [6:64 - 7:6], and instructions and/or messages communicated between the warehouse and supply-chain coordinator and robots are communicated between the warehouse and supply-chain coordinator and other agents than robots, such as, but not limited to, human agents, material handling systems operated and/or managed by humans, and human-assisted robotic devices. Examples of material handling systems include, but are not limited to, electrical systems, mechanical systems, and electro-mechanical systems, such as automated pallet wrapping machines, powered doors, etc., [11:55-64].
Saigh and Hance are both considered to be analogous to the claimed invention because they are both in the field of retail supply chain operations. It would have been obvious to one of ordinary skill in the art before the effective filing date to combine the operational control and management of logistics techniques of Saigh with the larger planning allocation and control of robotic and human logistic resources of Hance to enable the warehouse and supply-chain coordinator to coordinate and optimize various elements in one or more supply chains, [6:33-35], by defining a recommended operational plan for the retail facility based on the retail facility customer-related data, the retail facility associate-related data, the retail facility related data, and the ASRS data, wherein the recommended operational plan effects dynamic reallocation of the task assignments to the one or more of the mobile robot resources and retail facility associate resources effecting coordination thereof to optimize operation of the retail facility registered by the solver module consistent with one or more of the business priorities and the operational goals so that the optimal operation registered by the solver module characterizes an optimized operational state compared to the initial state. Saigh does not teach, Evans teaches, (the invention finds an optimal model that ensures the balance between cost and service is optimized and profitability maximized, the system parameters are configured to respond to both current and future risks, variability is built into plans enabling maximized efficiency, and human error and bias are eliminated from the planning process such that pro-active rather than reactive behavior becomes the norm, [0015]. The best solution is one that balances your segmented service level targets against current and future opportunities. It is dynamic; continually recalibrating to take account of new data points and changes to underlying variability patterns and trends in the data. It will adapt in the face of the most extreme challenges facing the enterprise, [0012]).
Saigh and Evans are both considered to be analogous to the claimed invention because they are both in the field of retail supply chain operations. It would have been obvious to one of ordinary skill in the art before the effective filing date to combine the operational control and management of logistics techniques of Saigh with the dynamic update capabilities of Evans to enable transformational quantifiable improvement in service levels and network efficiencies simultaneously, Evans, [0016].
Regarding claim 2, The retail facility control system of claim 1, wherein the retail execution system is configured to control the ASRS in accordance with the recommended operational plan, Saigh teaches, (operation, control and management of a retail facility or complex,[0002], transport system could include a guidance system controlling any mechanical technology, electromagnetic technology, robotic technology, cart system, conveyor system or electronic delivery system, [0019], facility complex 100 processes orders received from order placement stations, [0087]).
Regarding claim 3, The retail facility control system of claim 1, wherein the solver
module, in defining the recommended operational plan is configured to process at least the retail facility customer-related data, the retail facility associate-related data, and the facility-related data using one or more optimization algorithms. Saigh does not teach, Evans teaches, (the Data Management Module 220 configures connections to data from customer enterprise data systems (EDS) and receives these data in both asynchronous processes(batch data import) and synchronous processes (ongoing/live data feeds). These data are then combined with any system data previously stored in System Data Archive 260 to create are presentation of the supply chain network. This data is of four distinct types (examples are not exhaustive): Item Master Data (fixed data related to the item): code, description, price, UOM etc.; Item Parameter Data which determines how the network is currently planned. Examples are Current Safety Stock Levels, Delivery Frequency, Order Multiples; Supply Chain Variability data—Examples are product-level monthly demand history, delivery performance history or characteristics; Strategic Objective Parameters/Constraints such as Service Targets, Site sensitivity to complexity, cost of holding stock, Transport Costs etc., Evans, [0037].
Saigh and Evans are both considered to be analogous to the claimed invention because they are both in the field of retail supply chain operations. It would have been obvious to one of ordinary skill in the art before the effective filing date to combine the operational control and logistics management techniques of Saigh with the data processing of Evans to enable transformational quantifiable improvement in service levels and network efficiencies simultaneously, Evans, [0016].
Regarding claim 4, The retail facility control system of claim 1, further comprising:
a simulator module configured to be executed by the control circuit to apply one or more simulator machine learning models to the recommended operational plan and simulate events at the retail facility to predict future states, and evaluate the predicted future states relative to the recommended operational plan, Saigh does not teach, Evans teaches, (results prioritization module applies analytical techniques (machine learning and AI in some embodiments) to determine which recommendations are most important to act on, [0081] and ensure compliance with both physical limitations of systems and corporate governance and strategy, [Claim 6]).
Saigh and Evans are both considered to be analogous to the claimed invention because they are both in the field of retail supply chain operations. It would have been obvious to one of ordinary skill in the art before the effective filing date to combine the operational control and logistics management techniques of Saigh with the machine learning and AI of Evans to enable transformational quantifiable improvement in service levels and network efficiencies simultaneously, Evans, [0016].
Regarding claim 5, The retail facility control system of claim 4, wherein the simulator module is configured to simulate one or more of associate task performance and ASRS product retrieval performance. Singh does not teach, Evans teaches, (Given an input feature vector for the supply chain, the predicted future behavior may be determined by a variety of algorithmic methods. For example, one might train an AI system to predict future performance based on the input feature vector and supply chain network attributes, [0072]),
Saigh and Evans are both considered to be analogous to the claimed invention because they are both in the field of retail supply chain operations. It would have been obvious to one of ordinary skill in the art before the effective filing date to combine the operational control and logistics management techniques of Saigh with the performance prediction algorithms of Evans to enable transformational quantifiable improvement in service levels and network efficiencies simultaneously, Evans, [0016].
Regarding claim 6, The retail facility control system of claim 1, wherein the solver module is configured to iteratively define multiple recommended operational plans that could be implemented at the retail facility and predicted to enhance operation of the retail facility consistent with one or more of the business priorities and the operational goals, Saigh does not teach, Evans teaches, (this process may be carried out in an iterative fashion in conjunction with Future Performance Predictor 450, [0076], and Optimizer 150 analyses the complex flow of products through the supply chain and large number of variables impacting its behavior as a means of determining the best flow of product and the right location to buffer against risk with stock. It uses AI to decipher patterns of flow and variability across the network as well as the performance and parameter sensitivities of different parts of the supply chain to identify potential improvements. For example, in consumer retail goods, the distribution of different products can be highly complex, with different packaging and storage facilities delivering products with different transportation constraints, [0114] and other strategic constraints might express business imperatives, such as reducing overall inventory in parts of the supply chain, or ensuring that the risk of a stockout of a critical product or group of products be below a specific level, [0062]).
Saigh and Evans are both considered to be analogous to the claimed invention because they are both in the field of retail supply chain operations. It would have been obvious to one of ordinary skill in the art before the effective filing date to combine the operational control and logistics management techniques of Saigh with the AI predicted performance of Evans to enable transformational quantifiable improvement in service levels and network efficiencies simultaneously, Evans, [0016].
Regarding claim 7, The retail facility control system of claim 6, further comprising:
a simulator module configured to be executed by the control circuit to apply one or more simulator machine learning models to each of the multiple recommended operational plans and simulate events at the retail facility to predict future states, Saigh does not teach, Evans teaches (one might use a simulation approach to generate an ensemble of different future behaviors and then compute estimates and confidence levels of future outcomes based upon these ensembles, [0072], and
evaluating the predicted future states relative to one or more of the multiple recommended operational plans and identifying a first recommended operational plan, (a formal sensitivity analysis can be performed so that an estimate of the future outcome can be broken down into its constituent uncertainties and ranked so that the customer can be warned of the items that are the most pressing, [0072], of the multiple recommended operational plans predicted to satisfy an optimization criteria within a criteria threshold, (the AI modelling algorithm seeks to identify based on the input parameters both imported and computational the optimal configuration response to meet targeted parameter constraints with the current and predicted levels of variability as identified in the input data, [0074]).
Saigh and Evans are both considered to be analogous to the claimed invention because they are both in the field of retail supply chain operations. It would have been obvious to one of ordinary skill in the art before the effective filing date to combine the operational control and logistics management techniques of Saigh with the future outcome analysis of Evans to enable transformational quantifiable improvement in service levels and network efficiencies simultaneously, Evans, [0016].
Regarding claim 8, The retail facility control system of claim 7, wherein the retail execution system is configured to adjust the operation of the ASRS and to control an associate resource to adjust deployment of one or more associates at the retail facility in executing the first recommended operational plan. Saigh does not teach, Evans teaches, (the systems and methods provide the capability to configure supply chain systems so as to: balance between cost and service is optimized and profitability maximized; [0009], it is dynamic; continually recalibrating to take account of new data points and changes to underlying variability patterns and trends in the data, [0012], the user may interact with these results in a number of ways, including but not limited to accepting the recommended parameters, modifying the recommended parameters, rejecting the recommended parameters, commenting on results, initiating requests and actions based on the results (for example, a request to modify a network or system constraint, or change a Service Level value for one or more products or segments, [0047], and the Orchestrated Intelligent Optimization Module 230 is designed to exploit these tradeoffs to provide maximal flexibility, accuracy and performance (including user experience) during the identification of optimal supply chain operational parameters, [0076].
Saigh and Evans are both considered to be analogous to the claimed invention because
they are both in the field of retail supply chain operations. It would have been obvious to one of
ordinary skill in the art before the effective filing date to combine the operational control and
logistics management techniques of Saigh with supply chain system configuration adjustments
of Evans to enable transformational quantifiable improvement in service levels and network
efficiencies simultaneously, Evans, [0016].
Regarding claim 12, The retail facility control system of claim 1, wherein the solver module is configured to apply a machine learning model to one or more of retail facility customer-related data, retail facility associate-related data, and retail facility-related data from multiple different retail facilities in determining the recommended operational plan. Saigh does not teach, Evans teaches, (Optimization Module 230, including segmentation of products based on most recent available data and determination of current supply chain network (which in some embodiments includes use of machine learning and AI models to recommend improvements upon an existing network and facilitate implementation of these improvements, a.k.a. AI-augmented network design optimization), predicting future performance of the supply chain network for different supply chain parameter settings based on a variety of supply chain network configuration assumptions, and analysis of optimal supply chain parameter settings given both strategic objectives (e.g. desired Service Levels (SL) for individual products or groups of products) and system-level constraints (e.g. upper limits of the number of orders a supply site can service per time period),and optimization of the presentation of results to planners and other users (in some embodiments, machine learning and/or AI models are used to recommend contents and display parameters of supply chain optimization results, [0044]).
Saigh and Evans are both considered to be analogous to the claimed invention because they are both in the field of retail supply chain operations. It would have been obvious to one of ordinary skill in the art before the effective filing date to combine the operational control and logistics management techniques of Saigh with the machine learning model approach of Evans to enable transformational quantifiable improvement in service levels and network efficiencies simultaneously, Evans, [0016].
Regarding claim 13, The retail facility control system of claim 1, further comprises a second retail execution system associated with a second retail facility, wherein the solver module, Saigh does not teach, Evans teaches, (Future Performance Predictor in which future performance of the supply chain for each fixed set of operational parameters is predicted for the supply chain defined in Supply Chain Attributes Definition Module, [0069]), is configured to apply a first machine learning model to the retail facility customer-related data, the retail facility associate-related data, and the retail facility-related data in determining the recommended operational plan; (the AI modelling algorithm seeks to identify based on the input parameters both imported and computational the optimal configuration response to meet targeted parameter constraints with the current and predicted levels of variability as identified in the input data, [0074]), and wherein the second retail execution system is configured to apply the first machine learning model to additional data corresponding to the second retail facility to determine a second recommended operational plan corresponding to the second retail facility; (one might use a simulation approach to generate an ensemble of different future behaviors and then compute estimates and confidence levels of future outcomes based upon these ensembles, Evans, [0072]).
Saigh and Evans are both considered to be analogous to the claimed invention because
they are both in the field of retail supply chain operations. It would have been obvious to one of ordinary skill in the art before the effective filing date to combine the operational control and logistics management techniques of Saigh with current and predicted levels of variability in developing an ensemble of behaviors and outcomes as taught by Evans to enable transformational quantifiable improvement in service levels and network efficiencies simultaneously, Evans, [0016].
Regarding Claim 14, The retail facility control system of claim 1, wherein the solver module is configured to access business priorities and operational goals defined for multiple retail facilities, and define multiple recommended operational plans each configured to be implemented at a respective one of the multiple retail facilities predicted to respectively enhance operation of each of the retail facilities consistent with one or more of the business priorities and the operational goals, Saigh does not teach, Evans teaches, (this process may be carried out in an iterative fashion in conjunction with Future Performance Predictor 450, [0076], and Optimizer 150 analyses the complex flow of products through the supply chain and large number of variables impacting its behavior as a means of determining the best flow of product and the right location to buffer against risk with stock. It uses AI to decipher patterns of flow and variability across the network as well as the performance and parameter sensitivities of different parts of the supply chain to identify potential improvements. For example, in consumer retail goods, the distribution of different products can be highly complex, with different packaging and storage facilities delivering products with different transportation constraints, [0114] and other strategic constraints might express business imperatives, such as reducing overall inventory in parts of the supply chain, or ensuring that the risk of a stockout of a critical product or group of products be below a specific level, [0062]).
Saigh and Evans are both considered to be analogous to the claimed invention because
they are both in the field of retail supply chain operations. It would have been obvious to one of ordinary skill in the art before the effective filing date to combine the operational control and logistics management techniques of Saigh with the multiple facility planning analysis approach of Evans to enable transformational quantifiable improvement in service levels and network efficiencies simultaneously, Evans, [0016].
Regarding Claim 15, The retail facility control system of claim 1, wherein the solver module is configured to access business priorities and operational goals defined for a retail entity controlling multiple different retail facilities located at different geographic locations and comprising the retail facility, and define the recommended operational plan to be implemented relative to two or more of the multiple different retail facilities predicted to respectively enhance operation of each of the two or more of multiple different retail facilities consistent with one or more of the business priorities and the operational goals, Saigh does not teach, Evans teaches, (this process may be carried out in an iterative fashion in conjunction with Future Performance Predictor 450, [0076], and Optimizer 150 analyses the complex flow of products through the supply chain and large number of variables impacting its behavior as a means of determining the best flow of product and the right location to buffer against risk with stock. It uses AI to decipher patterns of flow and variability across the network as well as the performance and parameter sensitivities of different parts of the supply chain to identify potential improvements. For example, in consumer retail goods, the distribution of different products can be highly complex, with different packaging and storage facilities delivering products with different transportation constraints, [0114] and other strategic constraints might express business imperatives, such as reducing overall inventory in parts of the supply chain, or ensuring that the risk of a stockout of a critical product or group of products be below a specific level, [0062]).
Saigh and Evans are both considered to be analogous to the claimed invention because
they are both in the field of retail supply chain operations. It would have been obvious to one of ordinary skill in the art before the effective filing date to combine the operational control and logistics management techniques of Saigh with the multiple facility planning analysis approach of Evans to enable transformational quantifiable improvement in service levels and network efficiencies simultaneously, Evans, [0016].
Claims 16-19 are rejected for reasons corresponding to those provided for Claims 1-15. In these claims, the absence of hardware and software elements in the method does not change the rational for the rejections under 35 U.S.C § 103 or the referenced prior art. Saigh teaches a system, method, and process for delivery of classified goods and serviced items through a vehicle drive-thru facility, for goods or services orderable via an electronic or computer medium and a computer controlled transport system for the variable or high speed retrieval and delivery of amalgamated classified goods or services at any location within the facility or adjacent to the facility, Saigh, [Abstract].
Claims 9-11, 20 are rejected under 35 U.S.C. § 103 as being taught by Saigh (US
20070187183 A1) hereafter Saigh, “System, Method And Process For Computer Controlled Delivery Of Classified Goods And Services Through An Amalgamated Drive-thru Complex,” in view of Keating, (US 20160347541 A1), hereafter Keating, “Automated Storage and Retrieval System,” in further view of Evans (US 20210158259), hereafter Evans, “Orchestrated Intelligent Supply Chain Optimizer,” in further view of Hance, (US 10787315 B2), hereafter Hance, “Dynamic Truck Route Planning Between Automated Facilities,” in further view of Kumar, (WO2020070758 A2), hereafter Kumar, “Systems and Methods for Simulation of Humans by Human Twin.”
Regarding claim 9, The retail facility control system of claim 4, wherein the simulator
module is configured to apply digital twins implementing digital models to simulate one or more processes within respective one or more virtual environments. Saigh does not teach, Kumar teaches (Digital twin(s) are virtual replicas of physical devices that data scientists and other professionals can use to run simulations before actual devices are built and deployed. They are also changing how technologies such as [ ] analytics are optimized.
Saigh and Kumar are both considered to be analogous to the claimed invention because
they are both in the field of logistics management and optimization. It would have been obvious to one of ordinary skill in the art before the effective filing date to combine the operational control and logistics management techniques of Saigh with the digital twins of Kumar to not only focus on automation, but also focus on holistic or personalized criteria, that humans can achieve in order to optimize the environment, Kumar, [Abstract].
Regarding claim 10, The retail facility control system of claim 1, further comprises:
a simulator module configured to be executed by the control circuit to run a simulation of one or more site operational events, Saigh does not teach, Evans teaches, (Evans teaches (one might use a simulation approach to generate an ensemble of different future behaviors and then compute estimates and confidence levels of future outcomes based upon these ensembles, [0072]),
Saigh and Evans are both considered to be analogous to the claimed invention because they are both in the field of retail supply chain operations. It would have been obvious to one of ordinary skill in
the art before the effective filing date to combine the operational control and logistics management techniques of Saigh with simulation ensemble approach to outcome analysis of Evans to enable transformational quantifiable improvement in service levels and network efficiencies simultaneously, Evans, [0016], based on a digital representation of at least one system executed operation and at least one human executed operation predicting states of operation corresponding to the site operational events and evaluate whether the predicted states meet the recommended operational plan.
Saigh does not teach, Kumar teaches (Digital twin(s) are virtual replicas of physical devices that data scientists and other professionals can use to run simulations before actual devices are built and deployed. They are also changing how technologies such as [ ] analytics are optimized. In recent times, the technology behind digital twins has expanded to include larger items such as buildings, factories and even cities, and some have said people and processes can have the digital twin(s), expanding the concept even further, [003]. The step of simulating comprises simulating behavior and activities of humans via the human twin for optimizing the real-time operating environment. The embodiment of the present disclosure further facilitates generating the set of recommendations 205 with the set of simulated information 204 for identifying a set of optimal values on behavior and activities changes of humans. The embodiments of the proposed disclosure facilitate optimizing the real-time operating environment by generating one or more new models or by generating information via a tabular representation to a user, or by any other means thereof, based upon the set of simulated information 204, the set of optimum values and the generated set of recommendations 205, [052]. The embodiment, thus provides for the optimization of the real-time operating environment by implementing the human twin, wherein the human twin comprises simulating humans with physical or real time environment using the digital twin, for optimizing the real-time operating environment. Moreover, the embodiments herein further provides simulating behavior and activities of humans via the human twin for optimizing the real-time operating environment, [060], and FIG. 3.
Saigh and Kumar are both considered to be analogous to the claimed invention because they are both in the field of logistics management and optimization. It would have been obvious to one of ordinary skill in the art before the effective filing date to combine operational control and logistics management techniques of Saigh with the digital twins of Kumar to not only focus on automation, but also focus on holistic or personalized criteria, that humans can achieve in order to optimize the environment, [Kumar, Abstract].
Regarding claim 11, The retail facility control system of claim 10, further comprising:
a site optimization module configured to be executed by the control circuit to direct control of retail facility operational subsystems to implement the recommended operational plan at the retail facility. Saigh teaches, (controlling, operating, and directing a vehicle drive-thru or drive-up facility complex which inventories a wide variety of convenience non-durable goods as well as durable goods that lend themselves in size and sales volume to the structural facility and surrounding demographics. The facility complex may provide thousands of convenience items such as a grocery store, drug store, and items found in major discount retailers, factories, and general merchandise stores, all from pre-selected tenants. In addition, the facility structure is linked to e-commerce products ordered via the Internet, [0022], and to monitor or otherwise process facility operations, [0103], and allows an overall integration to provide total business review and management capabilities, [0106]).
Claims 20 is rejected for reasons corresponding to those provided for Claims 9-10. In these claims, the absence of hardware and software elements in the method does not change the rational for the rejections under 35 U.S.C § 103 or the referenced prior art. Saigh teaches a system, method, and process for delivery of classified goods and serviced items through a vehicle drive-thru facility, for goods or services orderable via an electronic or computer medium and a computer controlled transport system for the variable or high speed retrieval and delivery of amalgamated classified goods or services at any location within the facility or adjacent to the facility, Saigh, [Abstract].
Conclusion
The prior art made of record and not relied upon is considered pertinent to
applicant's disclosure or directed to the state of the art is listed on the enclosed PTO-892.
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/MB/
Patent Examiner, Art Unit 3624
/MEHMET YESILDAG/Primary Examiner, Art Unit 3624