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 .
Status of the Claims
Claims 1-20 are pending for examination.
Claims 1, 8 and 15 are independent Claims.
Claims 1, 4, 6, 8, 11 and 13 are rejected under 35 U.S.C. §102.
Claims 2-3, 5, 6, 9-10, 12, 14-20 are rejected under 35 U.S.C. §103.
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, 6, 8, 11 and 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Anandan Kartha et al. (U.S. 2020/0126015 hereinafter Anandan) in view of Torresani et al. (U.S. 2017/0345245 hereinafter Torresani).
As Claim 1, Anandan teaches an artificial intelligence operations manager system comprising:
at least one computer processor operable with a memory storage medium (Anandan (¶00494 line 5-6), data storage devices and one or more hardware processors);
an inventory manager program that tracks selected inventory from at least one operational unit (Anandan (¶0044 line 1-5), current state of the supply chain (inventory) is tracked using IoT devices or other digital mechanism for capturing consumption) for comparison with operational performance thresholds (Anandan (¶0046, ¶0047), system compares between inventory and demand in order to come up with stock-out event), the inventory manager program further adapted to receive data about inventory availability and logistical accessibility from at least one or more of outside and inside at least one operational unit (Anandan (¶0027 last 6 lines, ¶0032 line 1-4), one or more state attributes include product levels and real time tracking information of plurality of delivery vehicles);
at least one or more of a manual inventory level and automated inventory level input interface to the inventory manager program for the at least one operational unit (Anandan (¶0032 last 4 lines, ¶0044 line 1-7), system provides real time information about store inventory), the automated inventory level input interface further having at least one sensor (Anandan (¶0044 line 1-5), current state of the supply chain (inventory) is tracked using IoT devices or other digital mechanism for capturing consumption) determining at least one or more of inventory level, the withdrawal of inventory, and the addition of inventory (Anandan (¶0046, ¶0047), system tracks inventory, demand and supply);
a data queue used by the inventory management program to arrange inventory management tasks according to selected operational performance thresholds (Anandan (¶0046, ¶0047, ¶0048), system compares between inventory and demand in order to come up with stock-out event) wherein the decision to change inventory levels within one or more operational cycles considers at least the cost and benefit of changing inventory levels to one or more selected levels of change compared to the cost and consequences of leaving inventory levels unchanged (Anandan (¶0041 line 8-16), system learns one or more constrains to maximize benefit for the retailer and usage of machine learning to minimize overall cost to the retailer. Comparisons are based on cost of out of stock, cost of obsoleteness/wastage and cost of the supply chain network), the data queue further adapted to be used to arrange inventory management tasks based either or both on changing individual inventory item levels and changing substantially together levels of two or more inventory items (Anandan (¶0029 line 2-9, ¶0040 last 5 lines), replenish plan is evaluated by environmental simulator. The simulator provides feedbacks on order decisions based on achievable overall performance. Demand predictor provides replenishment quantities within delivery slot(s)); and
a machine learning program supporting the inventory management program adapted to, on a determined cycle, at least one or more of assess data from the data queue and operational threshold (Anandan (¶0046, ¶0047), system compares between inventory (data queue) and demand (thresholds) in order to come up with stock-out event), recommend inventory management changes to either or both at least one human or at least one machine, (Anandan (¶0048), system recommends to use an extra delivery in order to meet the demand) monitor inventory management actions taken from recommendations by the at least one or more of the at least one human and the at least one machine (Anandan (¶0038), system monitors previous decisions in order to provide feedback to the machine learning), wherein the machine learning program adapts its performance to improve the cost, pace, and quality of inventory management performance (Anandan (¶0041 line 8-16), system learns one or more constrains to maximize benefit for the retailer and usage of machine learning to minimize overall cost to the retailer. Comparisons are based on cost of out of stock, cost of obsoleteness/wastage and cost of the supply chain network) by optimizing inventory level changes (Anandan (¶0041 line 3-4), optimize replenishment order) required to handle deviations from inventory use predictions, improving the accuracy of inventory use predictions (Anandan (¶0039, ¶0043, ¶0053 line 1-8), continual online training in order to react to change in system behavior and/or constraints. For example, real time sales, inventory consumption and/or demand predictor are used).
Anandan may not explicitly disclose:
including strategies to influence customer purchasing behavior by prioritizing specific inventory items through sales promotion tactics, to align actual inventory usage with predicted usage patterns, thereby being configured to improve prediction accuracy and optimizing inventory availability.
Torresani teaches:
including strategies to influence customer purchasing behavior by prioritizing specific inventory items through sales promotion tactics (Torresani (¶0045 line 9-18), “may cause display module 420 to provide an offer (e.g., a discount on a type of item of product) based on the quantity of items of product. In some implementations, management module 410 may dynamically alter prices of products offered by vending device 240 based on data obtained by management module 410 ( e.g., demand data, supply data, or restocking data, such as a predicted amount of time before a technician will arrive at vending device 240 to perform restocking, a predicted set of items of product that are able to be restocked, or the like).”), to align actual inventory usage with predicted usage patterns, thereby being configured to improve prediction accuracy and optimizing inventory availability (Torresani (¶0084 line 1-8, “cloud server 220 may determine an alteration to an offer of an item for sale. For example, based on data indicating that a technician is to restock a particular type of item of product within a threshold amount of time, cloud server 220 may determine that a discount to a price of the particular type of item of product is to be offered to cause the particular item of product to be sold out (improve predication accuracy) when the technician arrives (optimizing inventory availability)”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify prediction module of Anandan instead be a advertising module taught by Torresani, with a reasonable expectation of success. The motivation would be so that “analytics module may predict a reduction in cost or increase in sales associated with altering a supply chain or inventory level, and may cause service orchestrator module 520 to cause the alteration to the supply chain or inventory level” (Torresani (¶0061 last 5 lines)).
As Claim 4, besides Claim 1, Anandan in view of Torresani teaches wherein the artificial intelligence operations manager further renders controllable at least one or more equipment members (Anandan (¶0044 last 6 lines), output is converted by post-processing (IT systems) into a set of implementable decisions), the equipment members having at least one computer processor operable with a memory storage medium, the artificial intelligence operations manager adapted to either or both control and advise on equipment member performance to further optimize the use of resources (Anandan (¶0053 line 11-15), replenish orders are tailored for need of destination node with least cost to the overall supply chain network in terms of out of stock, wastage and supply chain operation).
As Claim 6, besides Claim 1, Anandan teaches wherein a machine learning program supports the inventory management program and is designed to, on a substantially continual cycle (Anandan (¶0039 line 4-7), continual online training in order to react to change in system behavior and/or constraints), at least one or more of assesses data from the data queue and operational performance thresholds (Anandan (¶0046, ¶0047, ¶0048), system compares between inventory and demand in order to come up with stock-out event), recommends inventory management changes to either or both at least one human or at least one machine, and monitors inventory management actions taken from recommendations by the at least one or more of the at least one human and the at least one machine (Anandan (¶0048), system recommends to use an extra delivery in order to meet the demand).
As Claim 8, the Claim is rejected for the same reasons as Claim 1.
As Claim 11, the Claim is rejected for the same reasons as Claim 4.
As Claim 13, the Claim is rejected for the same reasons as Claim 6.
Claim(s) 2-3, 5, 7, 9-10, 12 and 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Anandan in view of Torresani in further view of Borke et al. (U.S. 2022/0215339 hereinafter Borke).
As Claim 2, besides Claim 1, Anandan in view of Torresani teaches:
through which to orient the inventory management program and the associated machine learning program wherein the selection of operational goals may be set either or both manually or machine assisted, and where the goals include, but are not limited to, controlling costs, and optimizing inventory availability (Anandan (¶0053 line 11-15), replenish orders are tailored for need of destination node with least cost to the overall supply chain network in terms of out of stock, wastage and supply chain operation).
Anandan in view of Torresani does not explicitly disclose:
wherein a user may set operational goals via a dashboard assembly
Borke teaches:
wherein a user may set operational goals via a dashboard assembly (Borke (¶0216 line 1-4), screen 940 includes option for user to select the threshold amount for re-order functionality)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify stock out threshold of Anandan in view of Torresani instead be a user input threshold taught by Borke, with a reasonable expectation of success. The motivation would be to conveniently and advantageously “provide automatic tracking of inventory ( e.g., consumable products) and re-ordering (such as automatically re-ordering or by notifying the user in some capacity) in an environment, such as a household. In this regard, various devices (e.g., manual dispensers, automated dispensers, various consumable product holders, spindles, storage devices, etc.) may be provided with one or more sensors configured to detect one or more characteristics regarding the consumable product” (Bork (¶0004 line 1-9)).
As Claim 3, besides Claim 2, Anandan in view of in view of Torresani in further view of Borke teaches wherein goals for controlling costs and optimizing inventory availability are supported by further goals including, but not limited to, targeting customers (Borke (¶0216 line 1-4), screen 940 includes option for user to select the threshold amount for re-order functionality), developing menus (Borke (¶0222 line 7-15, fig. 44), system provides the screen with items ready for reordering), optimizing logistics, and forecasting the behavior of individuals (Anandan (¶0053 line 11-15), replenish orders are tailored for need of destination node with least cost to the overall supply chain network in terms of out of stock, wastage and supply chain operation) wherein further data sources (Borke (¶0182 last 5 lines), amount of consumable product remaining), rules (Borke (¶0182 last 5 lines), positioning another sensors), weights (Borke (¶0182 last 5 lines), weight based sensor), and variables (Borke (¶0182 last 5 lines), further threshold level) pertain to operational performance measures inclusive of time (Borke (¶0182 line 1-7), time-of-flight sensor), space (Borke (¶0182 last 5 lines), further threshold level), material (Borke (¶0182 last 5 lines), amount of consumable product remaining), and risk (Borke (¶0236), error with tracking device) wherein further data sources (Borke (¶0182 last 5 lines), amount of consumable product remaining) as each of these performance measures are defined by the given user as data input (Borke (¶0216 line 1-4), screen 940 includes option for user to select the threshold amount for re-order functionality) and assessed data output (Borke (¶0222 line 7-15, fig. 44), system provides the screen with items ready for reordering) (Teaching Suggestion Motivation).
As Claim 5, besides Claim 1, Anandan in view of Torresani does not explicitly disclose:
wherein the automated inventory level input further has the at least one sensor determining at least one or more of inventory level, the withdrawal of inventory, and the addition of inventory, wherein the sensor detects at least one or more of weight cues, optical cues, tag cues such as RFID and optical codes, image cues such as an image of the inventory itself, gate cues such as inventory passing through a door or light beam, inventory scanners, point-of-sale scanners, automated purchase scanners, and scanners built into smartphones or tablets.
Borke teaches:
wherein the automated inventory level input further has the at least one sensor determining at least one or more of inventory level, the withdrawal of inventory, and the addition of inventory, wherein the sensor detects at least one or more of weight cues, optical cues, tag cues such as RFID and optical codes, image cues such as an image of the inventory itself, gate cues such as inventory passing through a door or light beam, inventory scanners, point-of-sale scanners, automated purchase scanners, and scanners built into smartphones or tablets (Borke (¶0182 line 1-7), time-of-flight sensor provides inventory level).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify stock out threshold of Anandan in view of Torresani instead be a user input threshold taught by Borke, with a reasonable expectation of success. The motivation would be to conveniently and advantageously “provide automatic tracking of inventory ( e.g., consumable products) and re-ordering (such as automatically re-ordering or by notifying the user in some capacity) in an environment, such as a household. In this regard, various devices (e.g., manual dispensers, automated dispensers, various consumable product holders, spindles, storage devices, etc.) may be provided with one or more sensors configured to detect one or more characteristics regarding the consumable product” (Bork (¶0004 line 1-9)) (Teaching Suggestion Motivation).
As Claim 7, besides Claim 1, Anandan in view of Torresani does not explicitly disclose:
wherein the artificial intelligence operations manager is adapted to input data for inventory to reflect that inventory moving to a different stage to include at least one or more of thawing, mixing, cooking, and opening a seal wherein the conditions of the time, space, material, and risk associated with the associated inventory may change.
Borke teaches:
wherein the artificial intelligence operations manager is adapted to input data for inventory to reflect that inventory moving to a different stage to include at least one or more of thawing, mixing, cooking, and opening a seal (Borke (¶0192 line 1-5), bulk storage device includes cooking material such as flour, cereal, can coffee …) wherein the conditions of the time (Borke (¶0182 line 1-7), time-of-flight sensor), space (Borke (¶0182 last 5 lines), further threshold level), material (Borke (¶0182 last 5 lines), amount of consumable product remaining), and risk (Borke (¶0236), error with tracking device) associated with the associated inventory may change (Borke (¶0193 line 8-12), system provides indicator for the bulk storage product).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify stock out threshold of Anandan in view of Torresani instead be a user input threshold taught by Borke, with a reasonable expectation of success. The motivation would be to conveniently and advantageously “provide automatic tracking of inventory ( e.g., consumable products) and re-ordering (such as automatically re-ordering or by notifying the user in some capacity) in an environment, such as a household. In this regard, various devices (e.g., manual dispensers, automated dispensers, various consumable product holders, spindles, storage devices, etc.) may be provided with one or more sensors configured to detect one or more characteristics regarding the consumable product” (Bork (¶0004 line 1-9)) (Teaching Suggestion Motivation).
As Claim 9-10, the Claims are rejected for the same reasons as Claim 2-3, respectively.
As Claim 12, the Claim is rejected for the same reasons as Claim 5.
As Claim 14, the Claim is rejected for the same reasons as Claim 7.
Claim(s) 15-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Anandan and Torresani in view of Borke in further view of Gomez-Rosado et al. (U.S. 2015/0213547 hereinafter Gomez).
As Claim 15, besides Claim 1, Anandan teaches an artificial intelligence operations manager system comprising:
at least one computer processor operable with a memory storage medium (Anandan (¶00494 line 5-6), data storage devices and one or more hardware processors);
an inventory manager program that tracks selected inventory from at least one operational unit (Anandan (¶0044 line 1-5), current state of the supply chain (inventory) is tracked using IoT devices or other digital mechanism for capturing consumption) for comparison with operational performance thresholds (Anandan (¶0046, ¶0047), system compares between inventory and demand in order to come up with stock-out event), the inventory manager program further adapted to receive data about inventory availability and logistical accessibility from at least one or more of outside and inside at least one operational unit (Anandan (¶0027 last 6 lines, ¶0032 line 1-4), one or more state attributes include product levels and real time tracking information of plurality of delivery vehicles);
at least one or more of a manual inventory level and automated inventory level input interface to the inventory manager program for the at least one operational unit (Anandan (¶0032 last 4 lines, ¶0044 line 1-7), system provides real time information about store inventory), the automated inventory level input interface further having at least one sensor determining at least one or more of inventory level, the withdrawal of inventory, and the addition of inventory (Anandan (¶0046, ¶0047), system tracks inventory, demand and supply);
a data queue used by the inventory management program to arrange inventory management tasks according to selected operational performance thresholds (Anandan (¶0046, ¶0047, ¶0048), system compares between inventory and demand in order to come up with stock-out event) wherein the decision to change inventory levels within one or more operational cycles considers at least the cost and benefit of changing inventory levels to one or more selected levels of change compared to the cost and consequences of leaving inventory levels unchanged (Anandan (¶0041 line 8-16), system learns one or more constrains to maximize benefit for the retailer and usage of machine learning to minimize overall cost to the retailer. Comparisons are based on cost of out of stock, cost of obsoleteness/wastage and cost of the supply chain network), the data queue further adapted to be used to arrange inventory management tasks based either or both on changing individual inventory item levels and changing substantially together levels of two or more inventory items (Anandan (¶0029 line 2-9, ¶0040 last 5 lines), replenish plan is evaluated by environmental simulator. The simulator provides feedbacks on order decisions based on achievable overall performance. Demand predictor provides replenishment quantities within delivery slot(s)); and
including adjustable threshold priorities of at least one or more of cost control versus availability, availability importance versus non-importance, frequency by which to permit non-availability, staffing, and risk tolerance by which cost control and availability priorities may change, the adjustable threshold priorities adjusted by at least one or more of people and computers, the decisions to be made by which of the at least one or more of people and computers also adjustable (Anandan (¶0041 line 8-16), system learns one or more constrains to maximize benefit for the retailer and usage of machine learning to minimize overall cost to the retailer. Comparisons are based on cost of out of stock, cost of obsoleteness/wastage and cost of the supply chain network);
a machine learning program supporting the inventory management program adapted to, on a determined cycle, at least one or more of assess data from the data queue and operational threshold (Anandan (¶0046, ¶0047), system compares between inventory (data queue) and demand (thresholds) in order to come up with stock-out event), recommend inventory management changes to either or both at least one human or at least one machine, (Anandan (¶0048), system recommends to use an extra delivery in order to meet the demand) monitor inventory management actions taken from recommendations by the at least one or more of the at least one human and the at least one machine (Anandan (¶0038), system monitors previous decisions in order to provide feedback to the machine learning), wherein the machine learning program adapts its performance to improve the cost, pace, and quality of inventory management performance (Anandan (¶0041 line 8-16), system learns one or more constrains to maximize benefit for the retailer and usage of machine learning to minimize overall cost to the retailer. Comparisons are based on cost of out of stock, cost of obsoleteness/wastage and cost of the supply chain network) by optimizing inventory level changes (Anandan (¶0041 line 3-4), optimize replenishment order) required to handle deviations from inventory use predictions, improving the accuracy of inventory use predictions, and recommending inventory use promotions whereby changes in the pace of inventory use improve prediction accuracy (Anandan (¶0039, ¶0043, ¶0053 line 1-8), continual online training in order to react to change in system behavior and/or constraints. For example, real time sales, inventory consumption and/or demand predictor are used).
Anandan may not explicitly disclose:
including strategies to influence customer purchasing behavior by prioritizing specific inventory items through sales promotion tactics, to align actual inventory usage with predicted usage patterns, thereby being configured to improve prediction accuracy and optimizing inventory availability.
Torresani teaches:
including strategies to influence customer purchasing behavior by prioritizing specific inventory items through sales promotion tactics (Torresani (¶0045 line 9-18), “may cause display module 420 to provide an offer (e.g., a discount on a type of item of product) based on the quantity of items of product. In some implementations, management module 410 may dynamically alter prices of products offered by vending device 240 based on data obtained by management module 410 ( e.g., demand data, supply data, or restocking data, such as a predicted amount of time before a technician will arrive at vending device 240 to perform restocking, a predicted set of items of product that are able to be restocked, or the like).”), to align actual inventory usage with predicted usage patterns, thereby being configured to improve prediction accuracy and optimizing inventory availability (Torresani (¶0084 line 1-8, “cloud server 220 may determine an alteration to an offer of an item for sale. For example, based on data indicating that a technician is to restock a particular type of item of product within a threshold amount of time, cloud server 220 may determine that a discount to a price of the particular type of item of product is to be offered to cause the particular item of product to be sold out (improve predication accuracy) when the technician arrives (optimizing inventory availability)”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify prediction module of Anandan instead be a advertising module taught by Torresani, with a reasonable expectation of success. The motivation would be so that “analytics module may predict a reduction in cost or increase in sales associated with altering a supply chain or inventory level, and may cause service orchestrator module 520 to cause the alteration to the supply chain or inventory level” (Torresani (¶0061 last 5 lines)).
Anandan in view of Torresani does not explicitly disclose:
a notification protocol to address deviations from performance thresholds by changing at least one or more of inventory levels, preparation of inventory, state of inventory, and staffing to one or more selected levels of change, the state of inventory to include at least one or more of thawing, mixing, cooking, and opening a seal wherein the conditions of the time, space, material, and risk associated with the associated inventory may change ;
a notification protocol to assess if changing inventor levels to one or more selected levels of change reduced deviations from performance thresholds, positive and negative results updated to the artificial intelligence operations manager; and
Borke teaches:
a notification protocol to address deviations from performance thresholds by changing at least one or more of inventory levels, preparation of inventory, state of inventory, and staffing to one or more selected levels of change, the state of inventory to include at least one or more of thawing, mixing, cooking, and opening a seal (Borke (¶0192 line 1-5), bulk storage device includes cooking material such as flour, cereal, can coffee …) wherein the conditions of the time (Borke (¶0182 line 1-7), time-of-flight sensor), space (Borke (¶0182 last 5 lines), further threshold level), material (Borke (¶0182 last 5 lines), amount of consumable product remaining), and risk (Borke (¶0236), error with tracking device) associated with the associated inventory may change (Borke (¶0193 line 8-12), system provides indicator for the bulk storage product. Borke (¶0222 line 7-15, fig. 44), system provides the screen with items ready for reordering);
a notification protocol to assess if changing inventor levels to one or more selected levels of change reduced deviations from performance thresholds (Borke (¶0222 line 7-15, fig. 44), system provides the screen with items ready for reordering), positive and negative results (Borke (¶0236), error with tracking device) updated to the artificial intelligence operations manager (Borke (¶0193 line 8-12), system provides indicator for the bulk storage product. Borke (¶0222 line 7-15, fig. 44), system provides the screen with items ready for reordering); and
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify stock out threshold of Anandan in view of Torresani instead be a user input threshold taught by Borke, with a reasonable expectation of success. The motivation would be to conveniently and advantageously “provide automatic tracking of inventory ( e.g., consumable products) and re-ordering (such as automatically re-ordering or by notifying the user in some capacity) in an environment, such as a household. In this regard, various devices (e.g., manual dispensers, automated dispensers, various consumable product holders, spindles, storage devices, etc.) may be provided with one or more sensors configured to detect one or more characteristics regarding the consumable product” (Bork (¶0004 line 1-9)).
Anandan in view of Torresani in further view of Borke does not explicitly disclose:
inter-day and end-of-day operational performance thresholds
Gomez teaches:
inter-day and end-of-day operational performance thresholds (Gomez (¶0256 line 1-5), goal or metric is reset at end of day or quarter (inter day))
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify stock out threshold of Anandan in view of Torresani in further view of Borke instead be a inter-day and end-of-day thresholds taught by Gomez, with a reasonable expectation of success. The motivation would be so that “business owner or store manager may be interested in which items sell best at specific times of day, in order to determine what is worth preparing at various times in the day” and “the interface may be used by a sales clerk ( e.g., a provider employee) who may be more interested in total sales for similar times of day in the last week to determine which inventory to have on hand during that period or perhaps which items to suggest to consumers during that period” (Gomez (¶0199 line 6-8, ¶0200 line 1-6)).
As Claim 16, the Claim is rejected for the same reasons as Claim 3.
As Claim 17, the Claim is rejected for the same reasons as Claim 4.
As Claim 18, the Claim is rejected for the same reasons as Claim 5.
As Claim 19, the Claim is rejected for the same reasons as Claim 6.
As Claim 20, Anandan in view of Borke in further view of Gomez teaches wherein at least one or more Internet of Things (IoT) networked sensors may communicate inventory use information to the artificial intelligence operations manager system (Anandan (¶0044 line 1-5), current state of the supply chain (inventory) is tracked using IoT devices or other digital mechanism for capturing consumption).
Response to Amendment
Rejection of the Claims Under 35 U.S.C.§102:
Claims 2-3, 5, 7, 9-10, 12 and 14 over Anandan in view of Borke:
As Claim 1, 8 and 15, Applicant argues that Anandan in view of Borke does not disclose “an active intervention where the machine learning system is configured to recommend promotions to modulate consumption rates” (second to last paragraph of page 11 in the remarks).
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Applicant’s arguments are not persuasive because new reference Torresani teaches the limitation. See the current rejection(s) for details.
Claims 15-20 over anandan in view of Borke in futher view of Gomez:
As Claim 1, 8 and 15, Applicant argues that Anandan in view of Borke in futher view of Gomez does not disclose the proactive machine learning mechanism (last paragraph of page 13 in the remarks).
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Applicant’s arguments are not persuasive because new reference Torresani teaches the limitation. See the current rejection(s) for details.
Other Claims are rejected for the same reasons of the independent Claims above.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/NHAT HUY T NGUYEN/ Primary Examiner, Art Unit 2147