DETAILED ACTION
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on January 8, 2026, has been entered.
Claims 1, 11, and 20 are amended.
Claims 1-7, 9-17, 19, and 20 are pending.
Response to Remarks/Amendments
35 USC §101 Rejections
The Applicant traverses the rejection of the claims as being directed to an ineligible abstract idea, contending that the present claims are subject matter eligible due to similarities with Example 46, claim 3. See Remarks p. 13. In response, the Examiner points out that Example 46 from claim 3 recites specific hardware to operate a gate to open. In contrast, the present claims do not recite a particular machine. The “autonomous machinery” in the claims has no particular structure, and the present claims merely include the “autonomous machinery” to recite a communication to perform an action. See exemplary independent claim 1. According to the independent claims, the autonomous machinery could be any of an autonomous vehicle or robot. None of those items has any particular structure. The present claims merely recite a communication to perform of a warehouse action that includes autonomously routing a good from a truck to a warehouse. This highly generalized recitation amounts to instructions to apply the judicial exception. Mere instructions to apply a judicial exception do not provide a practical application or significantly more than the recited judicial exception. See MPEP §2106.05(f). Moreover, automation of a manual task does not represent an improvement to a technology or technical field. See MPEP §2106.05(a)[I]{iii}.
The Applicant further contends that the hardware recited in the claims goes beyond generally linking the judicial exception to a technological environment. See Remarks pp. 15-16. As explained in the paragraphs, above, the Examiner disagrees. The claims merely recite implementation of the abstract idea of determining actions to meet operational requirements in a warehouse environment with autonomous machinery. Contrary to the Applicant’s assertions, the sorting gate of Example 46 has more structure than the “autonomous guided vehicle or an autonomous mobile robot” of the present claims. A sorting gate is an easily identifiable structure, while an “autonomous guided vehicle or an autonomous mobile robot” includes virtually any vessel with a motor and controller. The analysis provided indicates that claim 3 of example 46 is subject matter eligible because: “Thus, under any of the three embodiments, step (d) goes beyond merely automating the abstract ideas and instead actually uses the information obtained via the judicial exception to take corrective action by operating the gate and routing the animals in a particular way.” See October 2019 PEG Appendix 1. In contrast, the present claims merely recite a communication to be used to cause autonomous vehicles and robots to perform actions related to their intended purposes – “perform one or more of the plurality of warehouse actions.” See exemplary independent claim 1. Therefore, the recitation merely links the abstract idea of determining actions to meet operational requirements to a warehouse environment with automated machinery. Contrary to the Applicant’s assertions, the recitation of routing is highly generalized, because the recitation merely amounts to the performance of the machinery of its intended purpose in a typical environment. The Examiner additionally notes that the recited “physical path change” merely amounts to a routine updating of data. The recited “change” does not actually have a physical effect, because the “change” is in respect to a future path that has not occurred yet. In effect, the “physical path change” is routine operation of an autonomous vehicle or robot.
The rejection for lack of subject matter eligibility is updated and maintained.
35 USC §103 Rejections
Amendments to independent claims 1, 12, and 20 changed the scope of the claims, necessitating further search and consideration of the prior art. A new search returned the Smith reference, which is cited in the prior art rejections, below. The Applicant’s arguments with respect to the previously cited art are moot in light of the newly cited reference.
The rejection of the dependent claims stands or falls with the rejection of the independent claims.
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.
The Manual of Patent Examining Procedure (MPEP) provides detailed rules for determining subject matter eligibility for claims in §2106. Those rules provide a basis for the analysis and finding of ineligibility that follows.
Claims 1-7, 9-17, 19, and 20 are rejected under 35 U.S.C. 101. The claimed invention is directed to non-statutory subject matter because the claimed invention recites a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. Although claims(s) 1-7, 9-17, 19, and 20 are all directed to one of the four statutory categories of invention, the claims are directed to determining actions to meet operational requirements (as evidenced by exemplary independent claim 1; “performing . . . simulations of operations of the one or more warehouses . . . to determine a plurality of warehouse actions to meet one or more operational requirements”), an abstract idea. Certain methods of organizing human activity are ineligible abstract ideas, including managing personal behavior or relationships or interactions between people. See MPEP §2106.04(a). The limitations of exemplary claim 1 include: “receiving . . . a predictive analytics request;” “receiving . . . sensor data;” “inputting . . . data associated with [ ] one or more warehouses;” “performing . . . simulations of operations of the one or more warehouses . . . to determine a plurality of warehouse actions to meet one or more operational requirements;” and “communicating . . . the plurality of warehouse actions . . . “ The steps are all steps for managing personal behavior related to the abstract idea of determining actions to meet operational requirements that, when considered alone and in combination, are part of the abstract idea of determining actions to meet operational requirements. The dependent claims further recite steps for managing personal behavior that are part of the abstract idea of determining actions to meet operational requirements. These claim elements, when considered alone and in combination, are considered to be abstract ideas because they are directed to a method of organizing human activity which includes determining levels of inventory to meet anticipated demand.
Under step 2A of the subject matter eligibility analysis, a claim that recites a judicial exception must be evaluated to determine whether the claim provides a practical application of the judicial exception. Additional elements of the independent claims amount to generic computer hardware that does not provide a practical application (a processor, devices, and sensors in independent claim 1; a system with a memory, processors, devices, and sensors in independent claim 11; and a computer readable medium, devices, and sensors in independent claim 20). See MPEP §2106.04(d)[I]. Autonomous machinery is also recited in the claims, but the autonomous machinery does not have any particular structure. Therefore, the autonomous machinery merely amounts to generic computing hardware and/or a technological environment for implementing the abstract idea. See MPEP §2105.05(b) and (h). The claims do not recite an improvement to another technology or technical field, nor do they recite an improvement to the functioning of the computer itself. See MPEP §2106.05(a). The claims do recite the use of machine learning, but the abstract idea of determining actions to meet operational requirements is generally linked to a machine learning environment for implementation. Therefore, the machine learning merely amounts to a technological environment that does not provide a practical application or significantly more than the abstract idea. See MPEP §2106.05(h). The claims require no more than a generic computer (a processor, devices, and sensors in independent claim 1; a system with a memory, processors, devices, and sensors in independent claim 11; and a computer readable medium, devices, and sensors in independent claim 20) to implement the abstract idea, which does not amount to significantly more than an abstract idea. See MPEP §2106.05(f). Because the claims only recite use of a generic computer, they do not apply the judicial exception with a particular machine. See MPEP §2106.05(b). For these reasons, the claims do not provide a practical application of the abstract idea, nor do they amount to significantly more than an abstract idea under step 2B of the subject matter eligibility analysis. Using a generic computer to implement an abstract idea does not provide an inventive concept. Therefore, the claims recite ineligible subject matter under 35 USC §101.
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.
Claim(s) 1-4, 9-12, 19, and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20030093307 A1 to Renz et al. (hereinafter ‘RENZ’) in view of US 20190062055 A1 to Hance et al. (hereinafter ‘HANCE’), US 20020169658 A1 to Adler et al. (hereinafter ‘ADLER’), and US 20210049532 A1 to Smith et al. (hereinafter ‘SMITH’).
Claim 1 (Currently Amended)
RENZ discloses a method comprising: receiving, by one or more processors of a warehouse management system (see ¶[0018]; at least one processor coupled to a storage unit), a predictive analytics request associated with one or more warehouses (see ¶[0053]-[0055]; monitor and predict future inventory levels by modeling variability in demand and supply related supply chain activities. See also ¶[0036] and [0049]-[0050]; integrate the company warehouse management system).
RENZ does not specifically disclose, but HANCE discloses, receiving, by the one or more processors and from one or more sensors in the one or more warehouses, sensor data (see ¶[0005] and [0027]-[0028]; use sensor data from robots to reconcile inventory changes. A computer readable medium with instructions executable my one or more processors).
RENZ further discloses, in response to receiving the predictive analytics request, inputting, by the one or more processors, data associated with the one or more warehouses (see ¶[0010] and [0015]-[0017]; extract relevant supply chain data form multiple systems, including inventory sum, demand sum, and an orders sum during a time interval) into a warehouse management model to determine one or more predictive analytics associated with the one or more warehouses (see again ¶[0053]-[0055]; monitor and predict future inventory levels by modeling variability in demand and supply related supply chain activities), wherein the warehouse management model is trained via machine learning to determine the one or more predictive analytics (see again ¶[0053]-[0055]; machine-learning techniques may be used to recognize patterns of behavior from historical data).
RENZ does not specifically disclose, but ADLER discloses, wherein the warehouse management model is trained (see ¶[0022], [0025], [0033]-[0034], and [0046]; logistics, including shipment verification, and order management. Specify "what-if" scenarios that extrapolate current conditions and trends in the economy and markets and permit the injection of singular events such as wars, recessions, bankruptcies, etc. Improve inventory management. Order management and delivery logistics Simulated behaviors reflect both causal relationships between business entities (e.g., principles of economic theory relating price to supply and demand. See also ¶[0076]; scenario planning is carries forward iteratively over time using feedback loops), using training data that include historical data of the one or more warehouses categorized into a plurality of different scenarios, to operate differently in the plurality of different scenarios associated with a plurality of different operational requirements associated with the one or more warehouses (see again ¶[0022], [0025], [0033]-[0034], and [0046]; specify "what-if" scenarios that extrapolate current conditions and trends in the economy and markets and permit the injection of singular events such as wars, recessions, bankruptcies, etc. Improve inventory management. Order management and delivery logistics Simulated behaviors reflect both causal relationships between business entities (e.g., principles of economic theory relating price to supply and demand).
RENZ does not specifically disclose, but ADLER discloses, wherein the warehouse management model is trained (see ¶[0076]; scenario planning is carries forward iteratively over time using feedback loops).
RENZ further discloses using the historical data that include pairs of past due-in item information regarding items that were due to be sent to the one or more warehouses and past receipt information regarding items that were actually received by one or more warehouses for each of a plurality of items (see ¶[0041], [0047], and [0054]-[0064]; predictive data includes demand, orders, and inventory. The available and planned inventory is checked against the date requested by the customer and appropriate quantities. Monitor the quantity of an SKU on the shelf. Predict future orders based on the inventory at the beginning of a period measured after the day’s shipment arrives, the quantity ordered, and items on order).
RENZ does not explicitly disclose, but SMITH discloses, to forecast a receipt discrepancy between due-in items and received items in a future shipment (see ¶[0011], [0020]-[0021], [0026], and claims 1-3; a part shortage may be shortages to customer orders as well as shortages when component suppliers do not support the part forecast. The database includes data of supplier commits. Historical issues include availability and shortages. Receive a query from a user regarding a potential shortage of a manufacturing component. Anticipate when a shortage may occur. Training data includes a shortage. Compare predicted consumption to forecast to advise users of upcoming shortages due to actual demand greater than the forecast),
wherein the one or more predictive analytics include the forecasted receipt discrepancy in the future shipment (see again ¶[0011], [0020]-[0021], [0026], and claims 1-3; a part shortage may be shortages to customer orders as well as shortages when component suppliers do not support the part forecast. The database includes data of supplier commits. Historical issues include availability and shortages. Receive a query from a user regarding a potential shortage of a manufacturing component. Anticipate when a shortage may occur. Training data includes a shortage. Compare predicted consumption to forecast to advise users of upcoming shortages due to actual demand greater than the forecast).
RENZ does not specifically disclose, but ADLER discloses, wherein the data include the sensor data and a particular scenario out of the plurality of scenarios (see ¶[0022], [0025], [0033]-[0034], and [0046]; specify "what-if" scenarios that extrapolate current conditions and trends in the economy and markets and permit the injection of singular events such as wars, recessions, bankruptcies, etc. Improve inventory management. Order management and delivery logistics Simulated behaviors reflect both causal relationships between business entities (e.g., principles of economic theory relating price to supply and demand).
RENZ further discloses performing, by the one or more processors, simulations of operations of the one or more warehouses based on the one or more predictive analytics to determine a plurality of warehouse actions to meet one or more operational requirements associated with the particular scenario (see ¶[0067]-[0073]; the agent model simulates separate processes that govern the ordering policy: ordering as a result of experienced consumer demand and ordering to stock-up in anticipation of future promotions. Keep inventory low while not running out of stock).
RENZ does not specifically disclose, but HANCE discloses, communicating, by the one or more processors, the plurality of warehouse actions to a plurality of devices associated with the one or more warehouses to operate the plurality of devices according to the plurality of warehouse actions to meet the one or more operational requirements, including sending machine-readable instructions, including a dynamically updated set of routing parameters, to one or more autonomous machinery of the plurality of devices to operate the one or more autonomous machinery to autonomously physically perform one or more of the plurality of warehouse actions, including executing a physical path change, based on the dynamically updated set of routing parameters, to autonomously route an unloaded good unloaded from a specific truck to a specific warehouse of the one or more warehouses, wherein the one or more autonomous machinery include one or more of an autonomous guided vehicle or autonomous mobile robot (see abstract; a plurality of robots are deployed at each warehouse. Cause at least one robot to prepare for pickup the item that satisfies the order at the selected warehouse. See also ¶[0052]-[0053] and [0058]; receive navigation instructions from a single control system. Change the path of the vehicle to a third location in a supply chain based on the occurrence of an event, including low inventory).
RENZ discloses adaptive networks for supply and demand planning that forecast supply and demand to govern an ordering policy in a warehouse management system. HANCE discloses robot inventory updates for order routing that includes causing a robot in a warehouse management system to prepare an order for pickup to satisfy an order. It would have been obvious to include causing the robot to prepare for pickup as taught by HANCE in the system executing the method of RENZ with the motivation to govern an ordering policy and satisfy orders.
RENZ discloses adaptive networks for supply and demand planning that forecast supply and demand to govern an ordering policy in a warehouse management system. ADLER discloses a system for modeling and analyzing strategic business management decisions that includes scenario modeling of war scenarios for better order and inventory management. It would have been obvious to include the scenario modeling as taught by ADLER in the system executing the method of RENZ with the motivation to govern an ordering policy in a warehouse management system.
RENZ discloses adaptive networks for supply and demand planning that forecast supply and demand to govern an ordering policy in a warehouse management system. SMITH discloses a supply chain disruption advisor that includes anticipated when shortages may occur through training data that includes historical shortages where suppliers do not support the part forecast or shortages to customer orders. It would have been obvious to include the shortage data as taught by SMITH in the system executing the method of RENZ with the motivation to anticipate shortages and determine solutions to shortages (see SMITH abstract).
Claim 2 (Original)
The combination of RENZ, HANCE, ADLER, and SMITH discloses the method as set forth in claim 1.
RENZ further discloses wherein the one or more predictive analytics associated with the one or more warehouses include at least one of: a predicted demand forecast for the one or more warehouses or a predicted arriving items forecast for the one or more warehouses (see again ¶[0053]-[0055]; monitor and predict future inventory levels by modeling variability in demand and supply related supply chain activities. Predict orders).
Claim 3 (Original)
The combination of RENZ, HANCE, ADLER, and SMITH discloses the method as set forth in claim 2.
RENZ further discloses wherein the predicted arriving items forecast for the one or more warehouses include indications of items forecasted to be delivered to the one or more warehouses for a specified time period and indications of one or more details of each of the items forecasted to be delivered to the one or more warehouses (in Claim 2, above, the element of “predicted arriving items” is claimed in the alternative. Therefore, the prior art meets the limitations of claim 3 by teaching the elements of Claim 2).
Claim 4 (Original)
The combination of RENZ, HANCE, ADLER, and SMITH discloses the method as set forth in claim 3.
RENZ further discloses wherein the one or more details of each of the items forecasted to be delivered to the one or more warehouses include an item condition (in Claims 2 and 3, above, the element of “predicted arriving items” is claimed in the alternative. Therefore, the prior art meets the limitations of claim 3 by teaching the elements of Claim 2 and 3).
Claim 9 (Previously Presented)
The combination of RENZ, HANCE, ADLER, and SMITH discloses the method as set forth in claim 1.
RENZ does not specifically disclose, but ADLER discloses, wherein the plurality of different scenarios include a peace time scenario and a wartime scenario (see ¶[0022], [0025], [0033]-[0034], and [0046]; specify "what-if" scenarios that extrapolate current conditions and trends in the economy and markets and permit the injection of singular events such as wars, recessions, bankruptcies, etc. Improve inventory management. Order management and delivery logistics Simulated behaviors reflect both causal relationships between business entities (e.g., principles of economic theory relating price to supply and demand. Examiner Note: a scenario that does not consider war is a peace time scenario) .
RENZ discloses adaptive networks for supply and demand planning that forecast supply and demand to govern an ordering policy in a warehouse management system. ADLER discloses a system for modeling and analyzing strategic business management decisions that includes scenario modeling of war scenarios for better order and inventory management. It would have been obvious to include the scenario modeling as taught by ADLER in the system executing the method of RENZ with the motivation to govern an ordering policy in a warehouse management system.
Claim 10 (Previously Presented)
The combination of RENZ, HANCE, ADLER, and SMITH discloses the method as set forth in claim 2.
RENZ further discloses wherein the data associated with the one or more warehouses include one or more of: logistical data, historical data (see ¶[0033]; historic data over longer time horizons), time series data (see again ¶[0033]; historic data over longer time horizons), demand pattern data (see ¶[0053]; recognize patterns of behavior from historical data around consumption), warehouse execution data (see ¶[0047]; the planning module 90 sends a message to an execution module 96 along a message path 98. The planning module 90 and the execution module 96 may be components within a supply chain management application 100 or may be separate stand-alone components. After receiving the message from the planning module 90, the execution module 96 sends a first message to a manufacturing facility 102 along a message path 104 and a second message to a distribution center 106 along a message path 108. Based on this series of messages, the store is able to replenish its shelf with the item 82), warehouse communications data (see again ¶[0047]; the planning module 90 sends a message to an execution module 96 along a message path 98. The planning module 90 and the execution module 96 may be components within a supply chain management application 100 or may be separate stand-alone components. After receiving the message from the planning module 90, the execution module 96 sends a first message to a manufacturing facility 102 along a message path 104 and a second message to a distribution center 106 along a message path 108. Based on this series of messages, the store is able to replenish its shelf with the item 82), autonomous vehicle data, weather data, road conditions data, human resources data, ordering data (see abstract; and order quantity and an order level), purchasing data (see ¶[0047]; the number of items being purchased at that time), shipping data (see ¶[0036]; track shipments in transit), or air traffic data.
Claim 11 (Currently Amended)
RENZ discloses a computing system comprising: memory; and one or more processors (see ¶[0018]; at least one processor coupled to a storage unit) configured to: receive a predictive analytics request associated with one or more warehouses (see ¶[0053]-[0055]; monitor and predict future inventory levels by modeling variability in demand and supply related supply chain activities. See also ¶[0036] and [0049]-[0050]; integrate the company warehouse management system).
RENZ does not specifically disclose, but HANCE discloses, receive, from one or more sensors in the one or more warehouses, sensor data (see ¶[0005] and [0027]-[0028]; use sensor data from robots to reconcile inventory changes. A computer readable medium with instructions executable my one or more processors).
RENZ further discloses, in response to receiving the predictive analytics request, input data associated with the one or more warehouses (see ¶[0010] and [0015]-[0017]; extract relevant supply chain data form multiple systems, including inventory sum, demand sum, and an orders sum during a time interval) into a warehouse management model to determine one or more predictive analytics associated with the one or more warehouses (see again ¶[0053]-[0055]; monitor and predict future inventory levels by modeling variability in demand and supply related supply chain activities), wherein the warehouse management model is trained via machine learning to determine the one or more predictive analytics (see again ¶[0053]-[0055]; machine-learning techniques may be used to recognize patterns of behavior from historical data).
wherein the warehouse management model is trained via machine learning to determine the one or more predictive analytics (see again ¶[0053]-[0055]; machine-learning techniques may be used to recognize patterns of behavior from historical data).
RENZ does not specifically disclose, but ADLER discloses, wherein the warehouse management model is trained (see ¶[0022], [0025], [0033]-[0034], and [0046]; logistics, including shipment verification, and order management. Specify "what-if" scenarios that extrapolate current conditions and trends in the economy and markets and permit the injection of singular events such as wars, recessions, bankruptcies, etc. Improve inventory management. Order management and delivery logistics Simulated behaviors reflect both causal relationships between business entities (e.g., principles of economic theory relating price to supply and demand. See also ¶[0076]; scenario planning is carries forward iteratively over time using feedback loops), using training data that include historical data of the one or more warehouses categorized into a plurality of different scenarios, to operate differently in the plurality of different scenarios associated with a plurality of different operational requirements associated with the one or more warehouses (see again ¶[0022], [0025], [0033]-[0034], and [0046]; specify "what-if" scenarios that extrapolate current conditions and trends in the economy and markets and permit the injection of singular events such as wars, recessions, bankruptcies, etc. Improve inventory management. Order management and delivery logistics Simulated behaviors reflect both causal relationships between business entities (e.g., principles of economic theory relating price to supply and demand).
RENZ further discloses wherein the warehouse management model is trained using the historical data that include pairs of past due-in item information regarding items that were due to be sent to the one or more warehouses and past receipt information regarding items that were actually received for each of a plurality of items (see ¶[0041], [0047], and [0054]-[0064]; predictive data includes demand, orders, and inventory. The available and planned inventory is checked against the date requested by the customer and appropriate quantities. Monitor the quantity of an SKU on the shelf. Predict future orders based on the inventory at the beginning of a period measured after the day’s shipment arrives, the quantity ordered, and items on order).
RENZ does not explicitly disclose, but SMITH discloses, to forecast a receipt discrepancy between due-in items and received items in a future shipment (see ¶[0011], [0020]-[0021], [0026], and claims 1-3; a part shortage may be shortages to customer orders as well as shortages when component suppliers do not support the part forecast. The database includes data of supplier commits. Historical issues include availability and shortages. Receive a query from a user regarding a potential shortage of a manufacturing component. Anticipate when a shortage may occur. Training data includes a shortage. Compare predicted consumption to forecast to advise users of upcoming shortages due to actual demand greater than the forecast),
wherein the one or more predictive analytics include the forecasted receipt discrepancy in the future shipment (see again ¶[0011], [0020]-[0021], [0026], and claims 1-3; a part shortage may be shortages to customer orders as well as shortages when component suppliers do not support the part forecast. The database includes data of supplier commits. Historical issues include availability and shortages. Receive a query from a user regarding a potential shortage of a manufacturing component. Anticipate when a shortage may occur. Training data includes a shortage. Compare predicted consumption to forecast to advise users of upcoming shortages due to actual demand greater than the forecast),
RENZ does not specifically disclose, but ADLER discloses, and wherein the data include the sensor data and a particular scenario out of the plurality of scenarios (see ¶[0022], [0025], [0033]-[0034], and [0046]; specify "what-if" scenarios that extrapolate current conditions and trends in the economy and markets and permit the injection of singular events such as wars, recessions, bankruptcies, etc. Improve inventory management. Order management and delivery logistics Simulated behaviors reflect both causal relationships between business entities (e.g., principles of economic theory relating price to supply and demand).
RENZ further discloses, perform simulations of operations of the one or more warehouses based on the one or more predictive analytics to determine a plurality of warehouse actions to meet one or more operational requirements associated with the particular scenario (see ¶[0067]-[0073]; the agent model simulates separate processes that govern the ordering policy: ordering as a result of experienced consumer demand and ordering to stock-up in anticipation of future promotions. Keep inventory low while not running out of stock).
RENZ does not specifically disclose, but HANCE discloses, communicate the plurality of warehouse actions to one or more devices associated with the one or more warehouses to operate the plurality of devices according to the plurality of warehouse actions to meet the one or more operational requirements, including sending machine-readable instructions, including a dynamically updated set of routing parameters, to one or more autonomous machinery of the plurality of devices to enable the one or more autonomous machinery to autonomously physically perform one or more of the plurality of warehouse action, including executing a physical path change, based on the dynamically updated set of routing parameters, to autonomously route an unloaded good unloaded from a specific truck to a specific warehouse of the one or more warehouses, wherein the one or more autonomous machinery include one or more of an autonomous guided vehicle or autonomous mobile robot (see abstract; a plurality of robots are deployed at each warehouse. Cause at least one robot to prepare for pickup the item that satisfies the order at the selected warehouse. See also ¶[0052]-[0053] and [0058]; receive navigation instructions from a single control system. Change the path of the vehicle to a third location in a supply chain based on the occurrence of an event, including low inventory).
RENZ discloses adaptive networks for supply and demand planning that forecast supply and demand to govern an ordering policy in a warehouse management system. HANCE discloses robot inventory updates for order routing that includes causing a robot in a warehouse management system to prepare an order for pickup to satisfy an order. It would have been obvious to include causing the robot to prepare for pickup as taught by HANCE in the system executing the method of RENZ with the motivation to govern an ordering policy and satisfy orders.
RENZ discloses adaptive networks for supply and demand planning that forecast supply and demand to govern an ordering policy in a warehouse management system. ADLER discloses a system for modeling and analyzing strategic business management decisions that includes scenario modeling of war scenarios for better order and inventory management. It would have been obvious to include the scenario modeling as taught by ADLER in the system executing the method of RENZ with the motivation to govern an ordering policy in a warehouse management system.
RENZ discloses adaptive networks for supply and demand planning that forecast supply and demand to govern an ordering policy in a warehouse management system. SMITH discloses a supply chain disruption advisor that includes anticipated when shortages may occur through training data that includes historical shortages where suppliers do not support the part forecast or shortages to customer orders. It would have been obvious to include the shortage data as taught by SMITH in the system executing the method of RENZ with the motivation to anticipate shortages and determine solutions to shortages (see SMITH abstract).
Claim 12 (Original)
The combination of RENZ, HANCE, ADLER, and SMITH discloses the computing system as set forth in claim 11.
RENZ further discloses wherein the one or more predictive analytics associated with the one or more warehouses include at least one of: a predicted demand forecast for the one or more warehouses or a predicted arriving items forecast for the one or more warehouses (see again ¶[0053]-[0055]; monitor and predict future inventory levels by modeling variability in demand and supply related supply chain activities. Predict orders).
Claim 19 (Previously Presented)
The combination of RENZ, HANCE, ADLER, and SMITH discloses the computing system as set forth in claim 11.
RENZ does not specifically disclose, but ADLER discloses, wherein the plurality of different scenarios include a peace time scenario and a wartime scenario (see ¶[0022], [0025], [0033]-[0034], and [0046]; specify "what-if" scenarios that extrapolate current conditions and trends in the economy and markets and permit the injection of singular events such as wars, recessions, bankruptcies, etc. Improve inventory management. Order management and delivery logistics Simulated behaviors reflect both causal relationships between business entities (e.g., principles of economic theory relating price to supply and demand. Examiner Note: a scenario that does not consider war is a peace time scenario) .
RENZ discloses adaptive networks for supply and demand planning that forecast supply and demand to govern an ordering policy in a warehouse management system. ADLER discloses a system for modeling and analyzing strategic business management decisions that includes scenario modeling of war scenarios for better order and inventory management. It would have been obvious to include the scenario modeling as taught by ADLER in the system executing the method of RENZ with the motivation to govern an ordering policy in a warehouse management system.
Claim 20 (Currently Amended)
RENZ discloses a non-transitory computer-readable medium storing instructions (see ¶[0018]; at least one processor coupled to a storage unit) that, when executed by one or more processors of a computing system, cause the one or more processors to: receive a predictive analytics request associated with one or more warehouses (see ¶[0053]-[0055]; monitor and predict future inventory levels by modeling variability in demand and supply related supply chain activities. See also ¶[0036] and [0049]-[0050]; integrate the company warehouse management system).
RENZ does not specifically disclose, but HANCE discloses, receive, from one or more sensors in the one or more warehouses, sensor data (see ¶[0005] and [0027]-[0028]; use sensor data from robots to reconcile inventory changes. A computer readable medium with instructions executable my one or more processors).
RENZ further discloses in response to receiving the predictive analytics request, input data associated with the one or more warehouses (see ¶[0010] and [0015]-[0017]; extract relevant supply chain data form multiple systems, including inventory sum, demand sum, and an orders sum during a time interval) into a warehouse management model to determine one or more predictive analytics associated with the one or more warehouses (see again ¶[0053]-[0055]; monitor and predict future inventory levels by modeling variability in demand and supply related supply chain activities), wherein the warehouse management model is trained via machine learning to determine the one or more predictive analytics (see again ¶[0053]-[0055]; machine-learning techniques may be used to recognize patterns of behavior from historical data).
RENZ does not specifically disclose, but ADLER discloses, wherein the warehouse management model is trained (see ¶[0022], [0025], [0033]-[0034], and [0046]; logistics, including shipment verification, and order management. Specify "what-if" scenarios that extrapolate current conditions and trends in the economy and markets and permit the injection of singular events such as wars, recessions, bankruptcies, etc. Improve inventory management. Order management and delivery logistics Simulated behaviors reflect both causal relationships between business entities (e.g., principles of economic theory relating price to supply and demand. See also ¶[0076]; scenario planning is carries forward iteratively over time using feedback loops), using training data that include historical data of the one or more warehouses categorized into a plurality of different scenarios, to operate differently in a plurality of different scenarios associated with a plurality of different operational requirements associated with the one or more warehouses (see again ¶[0022], [0025], [0033]-[0034], and [0046]; specify "what-if" scenarios that extrapolate current conditions and trends in the economy and markets and permit the injection of singular events such as wars, recessions, bankruptcies, etc. Improve inventory management. Order management and delivery logistics Simulated behaviors reflect both causal relationships between business entities (e.g., principles of economic theory relating price to supply and demand).
RENZ further discloses, wherein the warehouse management model is trained using the historical data that include pairs of past due-in item information regarding items that were due to be sent to the one or more warehouses and receipt information regarding items that were actually received by one or more warehouses for each of a plurality of items (see ¶[0041], [0047], and [0054]-[0064]; predictive data includes demand, orders, and inventory. The available and planned inventory is checked against the date requested by the customer and appropriate quantities. Monitor the quantity of an SKU on the shelf. Predict future orders based on the inventory at the beginning of a period measured after the day’s shipment arrives, the quantity ordered, and items on order).
RENZ does not explicitly disclose, but SMITH discloses, to forecast a receipt discrepancy between due-in items and received items in a future shipment (see ¶[0011], [0020]-[0021], [0026], and claims 1-3; a part shortage may be shortages to customer orders as well as shortages when component suppliers do not support the part forecast. The database includes data of supplier commits. Historical issues include availability and shortages. Receive a query from a user regarding a potential shortage of a manufacturing component. Anticipate when a shortage may occur. Training data includes a shortage. Compare predicted consumption to forecast to advise users of upcoming shortages due to actual demand greater than the forecast),
wherein the one or more predictive analytics include the forecasted receipt discrepancy in the future shipment (see again ¶[0011], [0020]-[0021], [0026], and claims 1-3; a part shortage may be shortages to customer orders as well as shortages when component suppliers do not support the part forecast. The database includes data of supplier commits. Historical issues include availability and shortages. Receive a query from a user regarding a potential shortage of a manufacturing component. Anticipate when a shortage may occur. Training data includes a shortage. Compare predicted consumption to forecast to advise users of upcoming shortages due to actual demand greater than the forecast).
RENZ does not specifically disclose, but ADLER discloses, wherein the data include the sensor data and a particular scenario out of the plurality of scenarios (see ¶[0022], [0025], [0033]-[0034], and [0046]; specify "what-if" scenarios that extrapolate current conditions and trends in the economy and markets and permit the injection of singular events such as wars, recessions, bankruptcies, etc. Improve inventory management. Order management and delivery logistics Simulated behaviors reflect both causal relationships between business entities (e.g., principles of economic theory relating price to supply and demand).
RENZ further discloses, perform simulations of operations of the one or more warehouses based on the one or more predictive analytics to determine a plurality of warehouse actions to meet one or more operational requirements associated with the particular scenario (see ¶[0067]-[0073]; the agent model simulates separate processes that govern the ordering policy: ordering as a result of experienced consumer demand and ordering to stock-up in anticipation of future promotions. Keep inventory low while not running out of stock).
RENZ does not specifically disclose, but HANCE discloses, communicate the plurality of warehouse actions to one or more devices associated with the one or more warehouses to operate the plurality of devices according to the plurality of warehouse actions to meet the one or more operational requirements, including sending machine-readable instructions, including a dynamically updated set of routing parameters, to one or more autonomous machinery of the plurality of devices to operate the one or more autonomous machinery to autonomously physically perform one or more of the plurality of warehouse action, including executing a physical path change, based on the dynamically updated set of routing parameters, to autonomously route an unloaded good unloaded from a specific truck to a specific warehouse of the one or more warehouses, wherein the one or more autonomous machinery include one or more of an autonomous guided vehicle or an autonomous mobile robot (see abstract; a plurality of robots are deployed at each warehouse. Cause at least one robot to prepare for pickup the item that satisfies the order at the selected warehouse. See also ¶[0052]-[0053] and [0058]; receive navigation instructions from a single control system. Change the path of the vehicle to a third location in a supply chain based on the occurrence of an event, including low inventory).
RENZ discloses adaptive networks for supply and demand planning that forecast supply and demand to govern an ordering policy in a warehouse management system. HANCE discloses robot inventory updates for order routing that includes causing a robot in a warehouse management system to prepare an order for pickup to satisfy an order. It would have been obvious to include causing the robot to prepare for pickup as taught by HANCE in the system executing the method of RENZ with the motivation to govern an ordering policy and satisfy orders.
RENZ discloses adaptive networks for supply and demand planning that forecast supply and demand to govern an ordering policy in a warehouse management system. ADLER discloses a system for modeling and analyzing strategic business management decisions that includes scenario modeling of war scenarios for better order and inventory management. It would have been obvious to include the scenario modeling as taught by ADLER in the system executing the method of RENZ with the motivation to govern an ordering policy in a warehouse management system.
RENZ discloses adaptive networks for supply and demand planning that forecast supply and demand to govern an ordering policy in a warehouse management system. SMITH discloses a supply chain disruption advisor that includes anticipated when shortages may occur through training data that includes historical shortages where suppliers do not support the part forecast or shortages to customer orders. It would have been obvious to include the shortage data as taught by SMITH in the system executing the method of RENZ with the motivation to anticipate shortages and determine solutions to shortages (see SMITH abstract).
Claim(s) 5 and 13-15 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20030093307 A1 to RENZ et al. in view of US 20190062055 A1 to HANCE et al., US 20020169658 A1 to ADLER et al., and US 20210049532 A1 to SMITH et al. as applied to claims 1 and 2 above, and further in view of US 20210312488 A1 to Wick (hereinafter ‘WICK’).
Claim 5 (Original)
The combination of RENZ, HANCE, ADLER, and SMITH discloses the method as set forth in claim 2.
The combination of RENZ, HANCE, ADLER, and SMITH does not specifically disclose, but WICK discloses, wherein the predicted demand forecast for the one or more warehouses include indications of items forecasted to be demanded from the one or more warehouses for a specified time period and indications of one or more details of each of the items forecasted to be demanded from the one or more warehouses (see ¶[0065]-[0068] and [0073]; generate predictions at daily intervals. Product data includes physical parameters. Demand forecasts may cover a time interval).
RENZ discloses adaptive networks for supply and demand planning that forecast supply and demand to govern an ordering policy in a warehouse management system. WICK discloses demand forecasting with machine learning with applications in warehouse management (see ¶[0021]) that includes forecasting demand intervals and providing product data that includes the physical characteristics of products. It would have been obvious to include the physical characteristics of products with forecasted demand as taught by WICK in the system executing the method of RENZ with the motivation to provide an ordering policy in a warehouse management system.
Claim 13 (Original)
The combination of RENZ, HANCE, ADLER, and SMITH discloses the computing system as set forth in claim 12.
The combination of RENZ, HANCE, ADLER, and SMITH does not specifically disclose, but WICK discloses, wherein the predicted arriving items forecast for the one or more warehouses include indications of items forecasted to be delivered to the one or more warehouses for a specified time period and indications of one or more details of each of the items forecasted to be delivered to the one or more warehouses (see ¶[0065]-[0068] and [0073]; generate predictions at daily intervals. Product data includes physical parameters. Demand forecasts may cover a time interval).
RENZ discloses adaptive networks for supply and demand planning that forecast supply and demand to govern an ordering policy in a warehouse management system. WICK discloses demand forecasting with machine learning with applications in warehouse management (see ¶[0021]) that includes forecasting demand intervals and providing product data that includes the physical characteristics of products. It would have been obvious to include the physical characteristics of products with forecasted demand as taught by WICK in the system executing the method of RENZ with the motivation to provide an ordering policy in a warehouse management system.
Claim 14 (Original)
The combination of RENZ, HANCE, ADLER, SMITH, and WICK discloses the computing system as set forth in claim 13.
RENZ does not specifically disclose, but WICK discloses, wherein the one or more details of each of the items forecasted to be delivered to the one or more warehouses include an item condition (see ¶[0068]; any categorical characteristic or quality of a product. Physical parameters include color).
RENZ discloses adaptive networks for supply and demand planning that forecast supply and demand to govern an ordering policy in a warehouse management system. WICK discloses demand forecasting with machine learning with applications in warehouse management (see ¶[0021]) that includes forecasting demand intervals and providing product data that includes the physical characteristics of products. It would have been obvious to include the physical characteristics of products with forecasted demand as taught by WICK in the system executing the method of RENZ with the motivation to provide an ordering policy in a warehouse management system.
Claim 15 (Original)
The combination of RENZ, HANCE, ADLER, and SMITH discloses the computing system as set forth in claim 12.
The combination of RENZ, HANCE, ADLER, and SMITH does not specifically disclose, but WICK discloses, wherein the predicted demand forecast for the one or more warehouses include indications of items forecasted to be demanded from the one or more warehouses for a specified time period and indications of one or more details of each of the items forecasted to be demanded from the one or more warehouses (see ¶[0065]-[0068] and [0073]; generate predictions at daily intervals. Product data includes physical parameters. Demand forecasts may cover a time interval).
RENZ discloses adaptive networks for supply and demand planning that forecast supply and demand to govern an ordering policy in a warehouse management system. WICK discloses demand forecasting with machine learning with applications in warehouse management (see ¶[0021]) that includes forecasting demand intervals and providing product data that includes the physical characteristics of products. It would have been obvious to include the physical characteristics of products with forecasted demand as taught by WICK in the system executing the method of RENZ with the motivation to provide an ordering policy in a warehouse management system.
Claim(s) 6 and 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20030093307 A1 to RENZE et al. in view of US 20190062055 A1 to HANCE et al., US 20020169658 A1 to ADLER et al., and US 20210049532 A1 to SMITH et al. as applied to claim 1 above, and further in view of US 20210173387 A1 to Raza et al. (hereinafter ‘RAZA’).
Claim 6 (Previously Presented)
The combination of RENZ, HANCE, ADLER, and SMITH discloses the method as set forth in claim 1.
The combination of RENZ, HANCE, ADLER, and SMITH does not specifically disclose, but RAZA discloses, wherein performing the simulations of operations of the one or more warehouses based on the one or more predictive analytics to determine the plurality of warehouse actions to meet the one or more operational requirements further comprises: performing, by the one or more processors, the simulations of operations of the one or more warehouses based on the one or more predictive analytics to determine one or more root causes of the one or more warehouses not meeting the one or more operational requirements (see ¶[0066] and [0074-[0076]; highlight root causes and types of bottlenecks and prints/outputs to the dashboard likely causes to short-term, mid-term, and long-term bottlenecks. See also ¶[0042] and [0053]; services such as artificial intelligence, process analytics, digitization, predictive analytics, blockchain, and cryptographic ledgers, will be supplementary components to further enhance this invention's ability to improve throughput and reduce all visible and invisible bottlenecks. Predict future bottlenecks); and
determining, by the one or more processors, the one plurality of warehouse actions to be performed to meet the one or more operational requirements (see ¶[0002], [0053], and ¶[0067]; share resolutions to prevent reoccurrences of bottlenecks).
RENZ discloses adaptive networks for supply and demand planning that forecast supply and demand to govern an ordering policy in a warehouse management system. RAZA discloses determining root causes of bottlenecks and sharing resolutions to bottlenecks. It would have been obvious to determine root causes of bottlenecks and share resolutions as taught by RAZA in the system executing the method of RENZ with the motivation to prevent and remedy bottlenecks.
Claim 16 (Currently Amended)
The combination of RENZ, HANCE, ADLER, and SMITH discloses the computing system as set forth in claim 11.
The combination of RENZ, HANCE, ADLER, and SMITH does not specifically disclose, but RAZA discloses, wherein to perform the simulations of operations of the one or more warehouses based on the one or more predictive analytics to determine the plurality of warehouse actions to meet the one or more operational requirements, the one or more processors are further configured to: perform the simulations of operations of the one or more warehouses based on the one or more predictive analytics to determine one or more root causes of the one or more warehouses not meeting the one or more operational requirements (see ¶[0066] and [0074-[0076]; highlight root causes and types of bottlenecks and prints/outputs to the dashboard likely causes to short-term, mid-term, and long-term bottlenecks. See also ¶[0042] and [0053]; services such as artificial intelligence, process analytics, digitization, predictive analytics, blockchain, and cryptographic ledgers, will be supplementary components to further enhance this invention's ability to improve throughput and reduce all visible and invisible bottlenecks. Predict future bottlenecks); and
determining the plurality of warehouse actions to be performed to meet the one or more operational requirements (see ¶[0002], [0053], and ¶[0067]; share resolutions to prevent reoccurrences of bottlenecks).
RENZ discloses adaptive networks for supply and demand planning that forecast supply and demand to govern an ordering policy in a warehouse management system. RAZA discloses determining root causes of bottlenecks and sharing resolutions to bottlenecks. It would have been obvious to determine root causes of bottlenecks and share resolutions as taught by RAZA in the system executing the method of RENZ with the motivation to prevent and remedy bottlenecks.
Claim(s) 7 and 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20030093307 A1 to RENZE et al., US 20190062055 A1 to HANCE et al., US 20020169658 A1 to ADLER et al., US 20210049532 A1 to SMITH et al., and US 20210173387 A1 to RAZA et al. as applied to claims 1 and 6, above, and further in view of US 20180300435 A1 to Eckman et al. (hereinafter ‘ECKMAN’).
Claim 7 (Previously Presented)
The combination of RENZ, HANCE, ADLER, SMITH, and RAZA discloses the method as set forth in claim 6.
The combination of RENZ, HANCE, ADLER, SMITH, and RAZA does not specifically disclose, but ECKMAN discloses, wherein determining the plurality of warehouse actions to be performed to meet the one or more operational requirements further comprises:
determining, by the one or more processors, current operating parameters of the one or more warehouses (see abstract; determine an optimal warehouse automation design given a variety of parameters that are specific to the warehouse);
determining, by the one or more processors, operating parameters of a simulation of operations of the one or more warehouses that meets the one or more operational requirements out of the simulations of operations of the one or more warehouses (see again abstract; simulate warehouse automation designs, given a variety of parameters that are specific to the warehouse, such as the expected customer inventory demands over time); and
determining, by the one or more processors, the plurality of warehouse actions to be performed to meet the one or more operational requirements based at least in part on one or more differences between the current operating parameters of the one or more warehouses and the operating parameters of the simulation of operations of the one or more warehouses that meets the one or more operational requirements (see abstract and ¶[0009]; determine whether the current automation design for a warehouse failed to meet one or more performance metrics under simulation. Determine an optimal automation design for the warehouse based on performance metrics. Generate a revised design and determine whether the design is an improvement.).
RENZ discloses adaptive networks for supply and demand planning that forecast supply and demand to govern an ordering policy in a warehouse management system. ECKMAN discloses automated warehouse design and simulations that includes optimizing design based on expected inventory demands and simulated performance metrics. It would have been obvious to include the expected demand and simulated performance metrics as taught by ECKMAN in the system executing the method of RENZ with the motivation to govern an ordering policy and optimize the performance of a warehouse management system.
Claim 17 (Previously Presented)
The combination of RENZ, HANCE, ADLER, SMITH, and RAZA discloses the computing system as set forth in claim 16.
The combination of RENZ, HANCE, ADLER, SMITH, and RAZA does not specifically disclose, but ECKMAN discloses, wherein to determine the plurality of warehouse actions to be performed to meet the one or more operational requirements, the one or more processors are further configured to: determine current operating parameters of the one or more warehouses (see abstract; determine an optimal warehouse automation design given a variety of parameters that are specific to the warehouse);
determine operating parameters of a simulation of operations of the one or more warehouses that meets the one or more operational requirements out of the simulations of operations of the one or more warehouses (see again abstract; simulate warehouse automation designs, given a variety of parameters that are specific to the warehouse, such as the expected customer inventory demands over time); and
determine the plurality of warehouse actions to be performed to meet the one or more operational requirements based at least in part on one or more differences between the current operating parameters of the one or more warehouses and the operating parameters of the simulation of operations of the one or more warehouses that meets the one or more operational requirements (see abstract and ¶[0009]; determine whether the current automation design for a warehouse failed to meet one or more performance metrics under simulation. Determine an optimal automation design for the warehouse based on performance metrics. Generate a revised design and determine whether the design is an improvement.).
RENZ discloses adaptive networks for supply and demand planning that forecast supply and demand to govern an ordering policy in a warehouse management system. ECKMAN discloses automated warehouse design and simulations that includes optimizing design based on expected inventory demands and simulated performance metrics. It would have been obvious to include the expected demand and simulated performance metrics as taught by ECKMAN in the system executing the method of RENZ with the motivation to govern an ordering policy and optimize the performance of a warehouse management system.
Conclusion
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/RICHARD N SCHEUNEMANN/Primary Examiner, Art Unit 3624