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 .
Claim Status
Claims 1-26 are currently being examined.
Claim Interpretation
Claims 9 and 26 recite the limitation “wherein the pickers are human pickers and/or robotic pickers”. The shorthand of “and/or” introduces ambiguity as to which of the following combinations the limitation is intended to recite: human pickers AND robotic pickers (a combination); human pickers OR robotic pickers; human pickers OR robotic pickers OR a combination of both. Examiner has interpreted this limitation to encompass human pickers or robotic pickers or a combination of both.
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.
The limitations of claims 1-9, 13, 14, 16, and 25 have been mapped. Claims 10-12, 15, 17-24 and 26 repeat limitations in the mapped claims and are rejected on the same grounds as the mapped claims, summarized in Table 1 below.
Claim
Rejection
Mapping
Indp 10, 17
103: US 11053076 in view of US 10893107
See claim 1
11, 18
103: US 11053076 in view of US 10893107,
further in view of US 2019/0073611
See claim 2
12, 19
103: US 11053076 in view of US 10893107
See claim 3
15, 23
103: US 11053076 in view of US 10893107
See claim 7
20
103: US 11053076 in view of US 10893107,
further in view of US 2019/0073611
See claim 4
21
103: US 11053076 in view of US 10893107,
further in view of US 2019/0073611
See claim 5
22
103: US 11053076 in view of US 10893107
See claim 6
24
103: US 11053076 in view of US 10893107
See claim 8
26
103: US 11053076 in view of US 10893107
See claim 9
Claims 1, 3, 6-10, 12, 15-17, 19, 22-26 are rejected under 35 U.S.C. 103 as being unpatentable over Gopalakrishnan et al. (US 11053076) in view of Callari et al. (US 10893107).
Regarding independent claim 1, Gopalakrishnan et al. discloses:
An order fulfillment control system for a warehouse [See at least Col. 3, Lines 4-5], the order fulfillment control system comprising:
a controller configured to control fulfillment activities of the warehouse and to issue orders to pickers, wherein the controller is configured to adaptively control the issuance of the orders and to record operational data corresponding to the fulfillment activities in the warehouse; [See at least Fig. 3, Ref. Numeral 302; Col. 7, Lines 44-51; Col. 5, Lines 7-16; Col. 3, Lines 52-60; Col. 4, Lines 15-16 and 26-29]
a memory module configured to hold the operational data; [See at least Fig. 7, Ref. Numeral 710 (memory); Col. 4, Lines 15-16; Col. 12, Lines 12-14]
a current state data storage configured to hold live data corresponding to a current state of the warehouse defined by selected portions of the operational data; [See at least Fig. 1, Ref. Numeral 102; Col. 4, Lines 58-60; Col. 7, Lines 4-10]
an inference module comprising an order release control, wherein the inference module is operable to issue an order release recommendation to the controller when a set of live data is received from the current state data storage, and wherein the order release recommendation is defined by the order release control with respect to the set of live data; [See at least Col. 4, Lines 5-8 and 11-21; Figs. 5A-5C; Col. 5, Line 29-Col. 6, Line 15] and
While Gopalakrishnan et al. discloses a controller, memory module and data storage for operational and live data, an inference module for order release control and a plurality of priorities for optimal operation of the warehouse, Gopalakrishnan et al. does not explicitly disclose a retraining a model for order fulfillment using operational data and reinforcement learning. With respect to these limitations, Callari et al., directed to solving the same problem, managing resources and updating models in real-time for fulfillment, teaches:
a training module configured to retrain the order release control using reinforcement learning, wherein the training module is operable to perform the reinforcement learning [See at least Col. 6, Lines 54-58] using the operational data to retrain and update the order release control, wherein the training module is configured to retrain the order release control based upon a plurality of priorities for optimal operation of the warehouse. [See at least Col. 6, Lines 54-58; Col. 3, Lines 25-32 and 42-45; Col. 9, lines 20-27]
For examination purposes, the priorities for optimal operation of the warehouse including balancing orders between order channels, a proportionality of the orders released as compared to orders still awaiting release, and issuing order releases such that order channels are not starved nor congested has been construed to encompass the prioritization of orders of Gopalakrishnan et al. where the load forecaster projects hot and cold zones above or below a threshold, percent utilization and re-sequences zones based on real-time data to optimize performance. [See at least Figs. 3A-3B and 5A-5C; Col. 9, Lines 50-56; Col. 7, Lines 4-28; Col. 5, Lines 46-54]
It would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Gopalakrishnan et al. to incorporate the teachings of Callari et al. and combine the retraining for order fulfillment using operational data and reinforcement learning with the operational and live data-based order fulfillment system of Gopalakrishnan et al. The retraining for order fulfillment using operational data and reinforcement learning of Callari et al. allows the model to remain accurate and relevant as data distribution changes over time and adapt to new patterns or trends in the data, enabling increased efficiency and effectiveness.
One of ordinary skill in the art would have had the capability to combine the retraining for order fulfillment using operational data and reinforcement learning of Callari et al. with the operational and live data-based order fulfillment system of Gopalakrishnan et al. and would have recognized that the combination would yield predictable results. Even in the combined context, the features of the operational and live data-based order fulfillment system of Gopalakrishnan et al. and the features of the retraining for order fulfillment using operational data and reinforcement learning of Callari et al. would be expected to function as intended, with each element in the combined context performing the same function as it did separately.
A person of ordinary skill in the art would be motivated to incorporate the teachings of Callari et al. because they are a known work in the same field of endeavor directed to solving the same problem, managing resources and updating models in real-time for fulfillment, which would prompt its use based on design improvements that are predictable and recognized by one of ordinary skill in the art.
Regarding claim 3, Gopalakrishnan et al. discloses:
The order fulfillment control system of claim 2, wherein the operational data is at least one of:
operational data recorded during performance of operational tasks within the warehouse; simulation data configured to simulate warehouse operations; and synthetic data configured to mimic the operational data. [See at least Col. 10, Lines 6-12]
Regarding claim 6, Gopalakrishnan et al. discloses:
The order fulfillment control system of claim 1, wherein the warehouse comprises at least one order channel, wherein each of the at least one order channel comprises a corresponding set of resources that are used to complete an order assigned to that order channel. [See at least Col. 3, Lines 60-64]
For examination purposes, the multiple pick lines of Gopalakrishnan et al. have been construed as equivalent to order channels.
Regarding claim 7, Gopalakrishnan et al. discloses:
The order fulfillment control system of claim 1, wherein the plurality of priorities for optimal operation of the warehouse comprises at least one of:
balancing orders between order channels of the at least one order channel, proportionality of the orders released as compared to the orders still awaiting release, and issuing order releases such that order channels of the at least one order channel are not starved nor congested. [See at least Col. 7, Lines 18-21; Col. 8, Line57-Col. 9, Line 25; Col. 9, Lines 50-56; Col. 7, Lines 4-28; Col. 5, Lines 46-54]
Regarding claim 8, Gopalakrishnan et al. discloses:
The order fulfillment control system of claim 1, wherein the controller is operable to direct the training module to retrain the order release control after a selected time interval or when a measured metric is determined to be outside of an operational window. [See at least Col. 6, Lines 15-20]
For examination purposes, the thresholds to determine cartons to induct into the system of Gopalakrishnan et al. and response when cartons does not these thresholds have been construed as being outside of an operational window.
Regarding claim 9, Gopalakrishnan et al. discloses:
The order fulfillment control system of claim 1, wherein the orders are picking orders, and wherein the pickers are human pickers and/or robotic pickers. [See at least Col. 5, Lines 7-15]
For examination purposes, the scope of the limitation has been construed to encompass human pickers or robotic pickers or a combination of both.
Regarding claim 16, Gopalakrishnan et al. discloses:
The method of claim 10 further comprising retraining the order release control after a selected time interval or when a measured metric is determined to be outside of an operational window, [See at least Col. 6, Lines 15-20] and wherein the measured metric is one or more of: production metrics and performance metrics. [See at least Col. 9, Lines 50-56; Col. 7, Lines 4-28 and 29-43; Col. 5, Lines 46-54]
For examination purposes, the minimum/maximum thresholds of containers in the entire system and specific module/level/zone and data indicating current load have been construed as equivalent to production metrics and zone utilization percentage has been construed as equivalent to performance metrics.
Regarding claim 25, Gopalakrishnan et al. discloses:
The non-transitory computer-readable medium of claim 24, wherein the measured metric is one or more of: production metrics and performance metrics. [See at least Col. 9, Lines 50-56; Col. 7, Lines 4-28 and 29-43; Col. 5, Lines 46-54]
For examination purposes, the minimum/maximum thresholds of containers in the entire system and specific module/level/zone and data indicating current load have been construed as equivalent to production metrics and zone utilization percentage has been construed as equivalent to performance metrics.
Independent claim 10 recites a method for controlling order fulfillment in a warehouse comprising the same steps performed by the system of claim 1. Independent claim 10 is rejected on the same grounds as independent claim 1.
Claims 12 and 15 depend from independent claim 10 and recite the same limitations as claims 3 and 7, respectively, and are rejected on the same grounds.
Independent claim 17 recites a non-transitory computer-readable medium with instructions stored thereon, that when executed on a processor, perform the same steps performed by the system of claim 1. Independent claim 17 is rejected on the same grounds as independent claim 1.
Claims 19 and 23 depend from independent claim 17 and recite the same limitations as claims 3 and 7, respectively, and are rejected on the same grounds.
Claims 22, 24, and 26 depend from independent claim 17 and recite the same limitations as claims 6, 8, and 9, respectively, and are rejected on the same grounds.
Claims 2, 4, 5, 11, 13, 18, 20, and 21 are rejected under 35 U.S.C. 103 as being unpatentable over Gopalakrishnan et al. (US 11053076) in view of Callari et al. (US 10893107) and further in view of Sahota et al. (US 2019/0073611).
Regarding claim 2, while Gopalakrishnan et al. discloses orders based on operation data stored in the memory module [See at least Col. 7, Lines 44-51; Col. 5, Lines 7-16; Col. 3, Lines 52-60; Col. 4, Lines 15-16 and 26-29; Col. 12, Lines 12-14] and Callari et al. teaches retraining the training module [See at least Col. 6, Lines 54-58; Col. 3, Lines 25-32 and 42-45; Col. 9, lines 20-27], the combination does not explicitly teach awarding numerical penalties and positive rewards. With respect to these limitations, Sahota et al., directed to solving the same problem, managing resources and updating models in real-time for fulfillment, teaches:
wherein the training module awards numerical penalties and positive rewards based upon evaluated results of the completion of orders as they are released by the order release control. [See at least Par. 0029 and 33]
It would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Gopalakrishnan et al. and Callari et al. to incorporate the teachings of Sahota et al. and combine the awards and penalties with the retraining with reinforcement learning of Gopalakrishnan et al. and Callari et al. The awards and penalties of Sahota et al. allows the model to remain accurate and relevant as data distribution changes over time and adapt to new patterns or trends in the data, enabling increased efficiency and effectiveness.
One of ordinary skill in the art would have had the capability to combine the awards and penalties of Sahota et al. with the retraining with reinforcement learning of Gopalakrishnan et al. and Callari et al. and would have recognized that the combination would yield predictable results. Even in the combined context, the features of the retraining with reinforcement learning of Gopalakrishnan et al. and Callari et al. and the features of the awards and penalties of Sahota et al. would be expected to function as intended, with each element in the combined context performing the same function as it did separately.
A person of ordinary skill in the art would be motivated to incorporate the teachings of Sahota et al. because they are a known work in the same field of endeavor directed to solving the same problem, managing resources and updating models in real-time for fulfillment, which would prompt its use based on design improvements that are predictable and recognized by one of ordinary skill in the art.
Regarding claim 4, Gopalakrishnan et al. discloses:
The order fulfillment control system of claim 2, wherein the plurality of picking orders corresponds to an historical day’s quantity of picking orders completed on that day. [See at least Col. 8, Lines 62-67]
Regarding claim 5, Gopalakrishnan et al. discloses:
The order fulfillment control system of claim 2, wherein the plurality of picking orders corresponds to a hypothetical day’s quantity of picking orders to be completed that day. [See at least Col. 8, Lines 22-26]
Regarding claim 13, Gopalakrishnan et al. discloses:
The method of claim 11, wherein the plurality of picking orders comprises at least one of one or more historical day’s quantity of picking orders completed on respective days, and at least one hypothetical day’s quantity of picking orders to be completed on a hypothetical day. [See at least Col. 8, Lines 22-26 and 62-67]
Claim 11 depends from independent claim 10 and recite the same limitations as claim 2 and is rejected on the same grounds.
Claims 18, 20, and 21 depend from independent claim 17 and recite the same limitations as claims 2, 4, and 5, respectively, and are rejected on the same grounds.
Claim 14 is rejected under 35 U.S.C. 103 as being unpatentable over Gopalakrishnan et al. (US 11053076) in view of Callari et al. (US 10893107) and further in view of Hance et al. (US 2019/0062055).
Regarding claim 14, while Gopalakrishnan et al. discloses multiple order channels and coordinating between order channels [See at least Col. 3, Lines 60-64], it does not explicitly disclose upstream vs. downstream resources. With respect to these limitations, Hance et al., directed to solving the same problem, managing resources and updating models in real-time for fulfillment, teaches:
wherein each of the plurality of order channels comprises a corresponding set of downstream resources and associated requirements, and wherein two or more order channels of the plurality of order channels share upstream resources. [See at least Par. 0070, 0071]
It would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Gopalakrishnan et al. and Callari et al. to incorporate the teachings of Hance et al. and detail upstream and downstream resources with the order channels of Gopalakrishnan et al. and Callari et al. The detail upstream and downstream resources of Hance et al. allows the model to remain accurate and relevant as data distribution changes over time and adapt to new patterns or trends in the data, enabling increased efficiency and effectiveness.
One of ordinary skill in the art would have had the capability to combine the upstream and downstream resources of Hance et al. with the order channels of Gopalakrishnan et al. and Callari et al. and would have recognized that the combination would yield predictable results. Even in the combined context, the features of the order channels of Gopalakrishnan et al. and Callari et al. and the features of the upstream and downstream resources of Hance et al. would be expected to function as intended, with each element in the combined context performing the same function as it did separately.
A person of ordinary skill in the art would be motivated to incorporate the teachings of Hance et al. because they are a known work in the same field of endeavor directed to solving the same problem, managing resources and updating models in real-time for fulfillment, which would prompt its use based on design improvements that are predictable and recognized by one of ordinary skill in the art.
Examiner's Note
Prior Art: Examiner has cited particular paragraphs and figures in the references as applied to the claims set forth hereinabove for the convenience of the Applicant. While the specified citations are representative of the teachings in the art and are applied to specific limitations within the individual claims, other passages and figures in the cited references may be applicable, as well. It is respectfully requested that the Applicant, in preparing any response to the Office Action, fully consider the references in their entirety as potentially teaching all or part of the claimed invention, in addition to the context of the passage(s) as taught by the prior art or as disclosed by the Examiner. Applicant is reminded that the Examiner is required to give the broadest reasonable interpretation to the language of the claims. Furthermore, the Examiner is not limited to Applicant’s definitions that are not specifically set forth in the claims.
English Translations: If a prior art reference has been relied upon to map the claim limitations that is in a language other than English, Examiner has provided both the original reference and an English translation of the reference as attachments to the Office Action. Applicant is encouraged to refer to the provided English translation for cited pages and/or paragraphs in the mapping of prior art to claims.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure [See PTO-892 Notice of References Cited] because the prior art references contain subject matter that relates to one or more of Applicant’s claim limitations.
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/EM/Examiner, Art Unit 3655
/JACOB S. SCOTT/Supervisory Patent Examiner, Art Unit 3655