DETAILED ACTION
Notice to Applicant
The following is a FINAL action upon examination of application number 18/369,724 filed on 09/18/2023. Claims 1-23 are pending in this application, and have been examined on the merits discussed below.
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Priority
Application 18/369,724 filed 09/18/2023 claims Priority from Provisional Application 63/408,008, filed 09/19/2022, claims Priority from Provisional Application 63/462,237, filed 04/26/2023, and claims Priority from Provisional Application 63/538,969, filed 09/18/2023.
Information Disclosure Statement
The information disclosure statement (IDS) filed on 02/15/2026 has been acknowledged. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Response to Amendment
In the response filed October 15, 2025, Applicant amended claims 1-2, 4, 8-9, 11, 15-16, and 18, and did not cancel any claims. No new claims were presented for examination.
Applicant's amendments to claim 15 are hereby acknowledged. The amendments are sufficient to overcome the previously issued claim rejection under 35 U.S.C. 112(b); accordingly, this rejection has been withdrawn.
Applicant's amendments to the claims are hereby acknowledged. The amendments are not sufficient to overcome the previously issued claim rejection under 35 U.S.C. 101; accordingly, this rejection has been maintained.
Response to Arguments
Applicant's arguments filed October 15, 2025, have been fully considered.
Applicant submits “It is respectfully submitted that Jacquemart teaches away from "automatically optimizing, utilizing the set of rewards and penalties, an allocation of the generated set of tasks with the identified set of actors and the identified set of equipment for travelling together through the distribution warehouse fulfilling the set of customer orders and the expected shipments of inventory within the execution schedule time period, within the set of constraints and capacities of the distribution warehouse locality" (Independent Claims 1, 8 and 15).” [Applicant’s Remarks, 10/15/2025, page 14]
The Examiner respectfully disagrees. In response to Applicant’s argument that “Jacquemart teaches away from "automatically optimizing, utilizing the set of rewards and penalties, an allocation of the generated set of tasks with the identified set of actors and the identified set of equipment for travelling together through the distribution warehouse fulfilling the set of customer orders and the expected shipments of inventory within the execution schedule time period, within the set of constraints and capacities of the distribution warehouse locality" (Independent Claims 1, 8 and 15),” it is noted that “the prior art’s mere disclosure of more than one alternative does not constitute a teaching away from any of these alternatives because such disclosure does not criticize, discredit, or otherwise discourage the solution claimed...” In re Fulton, 391 F.3d 1195, 1201, 73 USPQ2d 1141, 1146 (Fed. Cir. 2004). Accordingly, this argument is found unpersuasive.
Moreover, in response to Applicant’s argument, the Examiner notes that a prior art reference must either be in the field of applicant’s endeavor or, if not, then be reasonably pertinent to the particular problem with which the applicant was concerned, in order to be relied upon as a basis for rejection of the claimed invention. See In re Oetiker, 977 F.2d 1443, 24 USPQ2d 1443 (Fed. Cir. 1992). The Examiner points out that the rejection of claims 1-23 provided an articulated line of reasoning based on the teachings of the prior art, the knowledge of one skilled in the art, and the motivation to modify the prior art to arrive at the conclusion of obviousness of claimed invention, which is a permissible means to support the legal conclusion of the obviousness of the claimed subject matter. See KSR Int'l Co. v. Teleflex Inc., 550 U.S. at 418, 82 USPQ2d at 1396 (quoting In re Kahn, 441 F.3d 977, 988, 78 USPQ2d 1329, 1336 (Fed. Cir. 2006)). In this case, Jacquemart is directed towards system and method for managing a plurality of mobile robots for preparing orders for products stored in a warehouse. Haley is directed towards a method and system for optimizing order fulfillment. The combination of Jacquemart and Haley is directed to warehouse management systems. Haley is directed to subject matter that is analogous to the subject matter encompassed by Applicant’s disclosure. Similarly, Jacquemart is directed to subject matter that is analogous to the subject matter encompassed by Applicant’s disclosure, particularly a method for managing a plurality of autonomous mobile robots, referred to as picking robots, for order-picking of products stored in a warehouse having a plurality of product storage spaces, a plurality of areas for stock-picking by a plurality of operators, and a plurality of areas for the collection of products picked by the picking robots. It would have been obvious to one of ordinary skill in the art at the time of the invention to modify Jacquemart to include the teachings of Haley, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same functions as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. It would have been recognized that applying the techniques of Haley to the teachings of Jacquemart would have yielded predictable results because the level of ordinary skill in the art demonstrate by the references applied shows the ability to incorporate such inventory management features into similar systems. As described in the Final Office Action, dated April 15, 2025, “modifying Jacquemart to include Haley’s features…, in the manner claimed, would serve the motivation of providing good orchestration between the different tasks performed by workers/agents and areas of the warehouse/facility to reach maximum efficiency (Haley, paragraph 0026); and further obvious because the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.” As the claims have been given their "broadest reasonable interpretation consistent with the specification", the Examiner asserts that the scope and contents of the prior art have been determined, thereby satisfying the first factual inquiry set forth by Graham v. John Deere Co. The Examiner applied the teachings of Haley and Jacquemart, and determined the deficiencies, thereby ascertaining the differences between the prior art and the claims at issue. The Examiner has fulfilled the role of factfinder while resolving the Graham inquiries, as per MPEP 2141, and determined that the level of ordinary skill in the art is reflected by the prior art itself, thereby resolving the level of ordinary skill in the pertinent art. The Examiner asserts that the Graham factual inquiries have been properly resolved, resulting in a proper prima facie case of obviousness.
The Examiner further points out that a reference teaches away only when it discourages or criticizes the claimed approach. Jacquemart does not criticize or discredit optimizing task allocation among operators and equipment; rather, it consistently promotes real-time optimization of task distribution, routing, and coordination between human operators and robots (paragraphs 0019, 0024, 0121). For the reasons above, this argument is found unpersuasive.
Applicant submits “that neither Jacquemart, Haley or Thomas teach or suggest "automatically optimizing, utilizing the set of rewards and penalties, an allocation of the generated set of tasks with the identified set of actors and the identified set of equipment for travelling together through the distribution warehouse fulfilling the set of customer orders and the expected shipments of inventory within the execution schedule time period, within the set of constraints and capacities of the distribution warehouse locality" (Independent Claims 1, 8 and 15). That is, none of the cited art teaches or suggests that tasks for fulfilling the set of customer orders are optimized and allocated to actors and equipment as a set of sets, thereby optimizing order fulfillment and inventory shipments together. As a result, claims 1-23 are respectfully submitted to be in condition for allowance over the cited art as claims 1, 8 and 15 are the only independent claims and all other claims depend upon respectfully allowable claims 1, 8 and 15.” [Applicant’s Remarks, 10/15/2025, page 14]
In response to the Applicant’s argument “that neither Jacquemart, Haley or Thomas teach or suggest "automatically optimizing, utilizing the set of rewards and penalties, an allocation of the generated set of tasks with the identified set of actors and the identified set of equipment for travelling together through the distribution warehouse fulfilling the set of customer orders and the expected shipments of inventory within the execution schedule time period, within the set of constraints and capacities of the distribution warehouse locality" (Independent Claims 1, 8 and 15). That is, none of the cited art teaches or suggests that tasks for fulfilling the set of customer orders are optimized and allocated to actors and equipment as a set of sets, thereby optimizing order fulfillment and inventory shipments together,” it is noted that this argument is a mere allegation of patentability by the Applicant with no supporting rationale or explanation. Merely stating that the claims do not teach a feature does not offer any insight as to why the specific sections of the prior art relied upon by the Examiner fail to disclose the claimed features. Applicant's arguments amount to a general allegation that the claims define a patentable invention without specifically pointing out how the language of the claims patentably distinguishes them from the references. Moreover, the Examiner notes the limitations being argued by Applicant as being newly amended to the claims in the response filed 10/15/2025, which have been addressed in the updated rejection below. Applicant’s argument has been considered, but it pertains to amendments to independent claims 1/8/15 that are believed to be addressed via the updated ground of rejection under §103 set forth in the instant Office action, which incorporates a new reference and new citations to address the amended limitations in claim and supports a conclusion of obviousness of the amended claims. Moreover, in response to applicant's argument that the references fail to show certain features of the invention, it is noted that the features upon which applicant relies (i.e., tasks for fulfilling the set of customer orders are optimized and allocated to actors and equipment as a set of sets, thereby optimizing order fulfillment and inventory shipments together) are not recited in the rejected claim(s). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993).
Applicant submits “that neither Jacquemart or Haley teach or suggest "adjusting inventory levels in accordance with scheduling of the set of tasks for fulfilling customer orders and the expected shipments of inventory; wherein the automatic scheduling and optimizing takes into account scheduling of the adjusted inventory levels" (claims 2, 9 and 16).” [Applicant’s Remarks, 10/15/2025, page 14]
In response to the Applicant’s argument “that neither Jacquemart or Haley teach or suggest "adjusting inventory levels in accordance with scheduling of the set of tasks for fulfilling customer orders and the expected shipments of inventory; wherein the automatic scheduling and optimizing takes into account scheduling of the adjusted inventory levels,” the Examiner notes the limitations being argued by Applicant as being newly amended to the claims in the response filed 12/10/2025, which have been addressed in the updated rejection below. Applicant’s argument has been considered, but it pertains to amendments to dependent claims 2/9/16 that are believed to be addressed via the updated ground of rejection under §103 set forth in the instant Office action, which incorporates a new reference and new citations to address the amended limitations in claim and supports a conclusion of obviousness of the amended claims.
Applicant submits “that neither Jacquemart or Haley teach or suggest "wherein the operations of accessing a set of customer orders for fulfillment, accessing a set of rewards and penalties; accessing current inventory levels, accessing expected shipments of inventory, accessing a set of expected labor resources, automatically generating a set of tasks, automatically optimizing, utilizing the set of rewards and penalties, an allocation of the set of tasks, and automatically scheduling the allocated set of expected labor resources and equipment resources with the set of tasks for fulfilling the set of customer orders and the expected shipments of inventor, are updated dynamically and repeatedly within the planned schedule time period' (claims 4, 11 and 18).” [Applicant’s Remarks, 10/15/2025, page 15]
In response to the Applicant’s argument “that neither Jacquemart or Haley teach or suggest "wherein the operations of accessing a set of customer orders for fulfillment, accessing a set of rewards and penalties; accessing current inventory levels, accessing expected shipments of inventory, accessing a set of expected labor resources, automatically generating a set of tasks, automatically optimizing, utilizing the set of rewards and penalties, an allocation of the set of tasks, and automatically scheduling the allocated set of expected labor resources and equipment resources with the set of tasks for fulfilling the set of customer orders and the expected shipments of inventor, are updated dynamically and repeatedly within the planned schedule time period' (claims 4, 11 and 18),” the Examiner notes the limitations being argued by Applicant as being newly amended to the claims in the response filed 12/10/2025, which have been addressed in the updated rejection below. Applicant’s argument has been considered, but it pertains to amendments to dependent claims 4/11/18 that are believed to be addressed via the updated ground of rejection under §103 set forth in the instant Office action, which incorporates a new reference and new citations to address the amended limitations in claim and supports a conclusion of obviousness of the amended claims.
Applicant submits “The Office Action asserts, under Prong 1 of Step 2A, that the independent claims substantially recite concepts of create and compare maps with items, which the Examiner argues falls into the grouping of certain methods of organizing human activity such as fundamental economic practices or commercial or legal interactions (business relation) or principles and managing personal behavior or relationships or interactions between people following rules or instruction. Applicant respectfully submits that the claimed invention is not directed to a method of organizing human activity as the claims include "automatically providing a set of signals describing the optimized allocated set of tasks to the identified set of actors for performing of the allocated set of tasks with the equipment resources for fulfilling the set customer orders and unloading the expected shipments of inventory in response to the provided set of signals.” [Applicant’s Remarks, 10/15/2025, pages 15-16]
Applicant first argues under Step 2A Prong One of the eligibility inquiry that “the claimed invention is not directed to a method of organizing human activity.” However, Applicant’s argument is misplaced because Step 2A Prong One of the eligibility inquiry does not involve analysis or a determination as to whether or not the claims are directed to one of the groupings abstract idea groupings set forth in MPEP 2106. The “directed to” inquiry falls under Step 2A Prong Two, only after a finding under Step 2A Prong One that the claims recite an abstract idea. The analysis mandated under Step 2A Prong One requires Examiners to “Identify the specific limitation(s) in the claim under examination (individually or in combination) that the examiner believes recites and abstract idea.” Accordingly, the §101 rejection set forth in the previous and instant O0ffice action satisfied Step 2A Prong One of the §101 rejection by identifying the abstract idea and the specific limitations (via bold text) reciting the abstract idea, while further identifying the additional elements (via plain text) in order to clearly differentiate the recited abstract idea from the additional elements that are further evaluated under Step 2A Prong Two and Step 2B.
The Examiner maintains that claim 1 recites steps falling within the “Certain methods of organizing human activity” abstract idea grouping. The claim, when considered as whole, continues to recite an abstract idea, specifically of organizing human activity in the form of planning, allocating, and scheduling warehouse operations, including management of orders, labor resources, and inventory. The additional limitation cited by Applicant (i.e., automatically providing a set of signals describing the optimized allocated set of tasks to the identified set of actors for performing of the allocated set of tasks…”) is interpreted as communicating instructions or information to human actors. Such activity constitutes insignificant extra-solution activity. The claim does not recite any specific technological mechanism for generating or transmitting the signals. For the reasons abo, this argument is found unpersuasive.
Applicant submits “The MPEP specifies that "[o]ne way to demonstrate such integration is when the claimed invention improves the functioning of a computer or improves another technology or technical field." MPEP 2106.04(d)(I). Claims 1, 8 and 15 (as well as the dependent claims) improve the narrow technical field of scheduling the fulfillment of customer orders in a distribution warehouse. This is a highly technical approach that is a novel and unobvious improvement over the cited art that automatically enables automated and dynamic scheduling and allocation of tasks for fulfilling customer orders and unloading incoming shipments of inventory through the use of actors and equipment resources that is a significant advancement of the art. As a result, the amended claims are deemed to have overcome this 101 rejection.” [Applicant’s Remarks, 10/15/2025, page 16]
The Examiner respectfully disagrees. Under Step 2A, Prong Two of the eligibility inquiry, Applicant argues that “The MPEP specifies that "[o]ne way to demonstrate such integration is when the claimed invention improves the functioning of a computer or improves another technology or technical field." MPEP 2106.04(d)(I). Claims 1, 8 and 15 (as well as the dependent claims) improve the narrow technical field of scheduling the fulfillment of customer orders in a distribution warehouse.” The additional elements in exemplary claim 1 are: a processor, a memory storing program instructions, a set of signals, and the equipment resources, which merely serve to tie the abstract idea to a particular technological environment (computer-based operating environment) via generic computing hardware, software/instructions, which is not sufficient to amount to a practical application, as noted in MPEP 2106.05. Applicant has provided no facts/evidence, cited any portion of the Specification, nor provided a persuasive line of reasoning showing how the additional elements are integrated with the abstract idea to integrate the abstract idea into a practical application.
The Examiner further notes that the system is merely being used as a tool to implement the abstract idea which does not integrate the abstract idea into a practical application or amount to significantly more (See MPEP 2106.05). Lastly, the additional elements fail to integrate the abstract idea into a practical application because they fail to provide an improvement to the functioning of a computer or to any other technology or technical field, fail to apply the exception with a particular machine, fail to apply the judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, fail to effect a transformation of a particular article to a different state or thing, and fail to apply/use the abstract idea in a meaningful way beyond generally linking the use of the judicial exception to a particular technological environment.
Further, in response to Applicant’s argument that “claims 1, 8 and 15 (as well as the dependent claims) improve the narrow technical field of scheduling the fulfillment of customer orders in a distribution warehouse, the Examiner notes that improving an abstract idea, such as business or logistical process, does not constitute an improvement to a computer or other technology. The claims do not recite any specific improvement to computer functionality. Additionally, the claims do not provide any technological improvement to warehouse equipment or control systems. The recites “actors” and “equipment resources” are used as part of the abstract scheduling scheme, and the “signals” limitation merely communicates the results without specifying any technical mechanism or improvement in how such signals are generated, transmitted, or used. For the reasons above, this argument is found unpersuasive.
Applicant submits “Under Step 2B, it is respectfully submitted that the additional elements of claims 1, 8 and 15 provide significantly more than the recited abstract idea, because these claims, as amended, represents a non-routine improvement to the narrow technical field of scheduling the fulfillment of customer orders in a distribution warehouse. While it is respectfully maintained that the amended claims are patent eligible subject matter based on Step 2A, it is also submitted that the amended claims are patent eligible based on Step 2B.” [Applicant’s Remarks, 10/15/2025, page 16]
Applicant alludes to Step 2B of the eligibility inquiry by suggesting “that the additional elements of claims 1, 8 and 15 provide significantly more than the recited abstract idea, because these claims, as amended, represents a non-routine improvement to the narrow technical field of scheduling the fulfillment of customer orders in a distribution warehouse.” The Examiner respectfully disagrees and notes that the claims merely product a result in the form of “a set of signals” describing the optimized allocated set of tasks, which is not an improvement to the processor, memory, signals, equipment resources. These elements have been considered individually and in combination, or any other system or technology. The claims have not been shown to modify, reconfigure, manipulate, or transform the processor, memory, signals, equipment resources, or any technology in any discernible manner, much less yield an improvement thereto. There is no indication that any of the additional elements or the combination of elements amount to an improvement to the computer or to any technology. Their individual and collective functions merely provide generic computer implementation. Therefore, these additional claim elements do not amount to significantly more than the abstract idea itself. For the reasons above, thus argument is found unpersuasive.
For the reasons above, in addition to the reasons provided in the updated §101 rejection below, Applicant’s amendment and supporting arguments are not sufficient to overcome the §101 rejection.
16. Applicant’s remaining arguments either logically depend from the above-rejected arguments, in which case they too are unpersuasive for the reasons set forth above, or they are
directed to features which have been newly added via amendment. Therefore, this is now the Examiner's first opportunity to consider these limitations and as such any arguments regarding these limitations would be inappropriate since they have not yet been examined. A full rejection of these limitations will be presented later in this Office Action.
Claim Rejections - 35 USC § 101
17. 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.
18. Claims 1-23 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
19. Claims 1-23 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The eligibility analysis in support of these findings is provided below, in accordance with MPEP 2106.
With respect to Step 1 of the eligibility inquiry (as explained in MPEP 2106), it is first noted that the system (claims 1-7, 21), method (claims 8-14, 22) and computer program product (claims 15-20, 23) is directed to at least one potentially eligible category of subject matter (i.e., machine, process, and article of manufacture, respectively). As noted in paragraph 0121 of the Specification
“A computer readable storage medium, as used herein, is not to be construed as being transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission media (e.g., light pulses passing through a fiber-optic cable), or electrical signals transmitted through a wire.” Thus, Step 1 of the Subject Matter Eligibility test for claims 1-23 is satisfied.
With respect to Step 2A Prong One, it is next noted that the claims recite an abstract idea that falls into the “Certain Methods of Organizing Human Activity” abstract idea set forth in the MPEP 2106 because the claims recite steps for managing warehouse scheduling, which encompasses activity for managing personal behavior or relationships or interactions (e.g., following rules or instructions), and steps that can be performed in the human mind (including observation, evaluation, judgment, opinion), and therefore fall under the “Mental Processes” abstract idea grouping. With respect to independent claim 1, the limitations reciting the abstract idea are indicated in bold below: a processor; and a memory storing program instructions which when processed by the processor perform the operations of: identifying a set of constraints and capacities of the distribution warehouse locality; accessing a set of customer orders for fulfillment from the distribution warehouse locality within a planned schedule time period, each customer order including a priority for fulfillment; accessing a set of rewards and penalties for fulfillment of the set of customer orders; accessing current inventory levels for the distribution warehouse locality; accessing expected shipments of inventory to the distribution warehouse locality; accessing a set of expected labor resources at the distribution warehouse locality for fulfilling the set of customer orders and unloading the expected shipments of inventory within the planned schedule time period; automatically generating a set of tasks for completing each customer order and unloading the expected shipments of inventory; automatically optimizing, utilizing the set of rewards and penalties, an allocation of the set of tasks with the set of expected labor resources and equipment resources, within the set of constraints and capacities of the distribution warehouse locality, for each customer order and each expected shipment of inventory; automatically scheduling the allocated set of expected labor resources and equipment resources with the set of tasks for fulfilling the set of customer orders and the expected shipments of inventory; accessing an identified set of actors and an identified set of the equipment associated with the set of expected labor resources within an execution schedule time period; automatically optimizing, utilizing the set of rewards and penalties, an allocation of the generated set of tasks with the identified set of actors and the identified set of equipment for travelling together through the distribution warehouse fulfilling the set of customer orders and the expected shipments of inventory within the execution schedule time period, within the set of constraints and capacities of the distribution warehouse locality; and automatically providing a set of signals describing the optimized allocated set of tasks to the identified set of actors for performing of the allocated set of tasks with the equipment resources for fulfilling the set customer orders and unloading the expected shipments of inventory in response to the provided set of signals. These steps describe managing personal behavior or relationships or interactions (e.g., social activities, following rules or instructions) and are part of the abstract idea falling under “Certain Methods of Organizing Human Activity” and steps that can be performed in the human mind, and therefore fall under the “Mental Processes” abstract idea grouping.
Because the above-noted limitations recite steps falling within the “Certain methods of organizing human activity” abstract idea grouping and the “Mental Processes” abstract idea grouping, they have been determined to recite at least one abstract idea when evaluated under Step 2A Prong One of the eligibility inquiry.
With respect to Step 2A Prong Two, the judicial exception is not integrated into a practical application. With respect to independent claims 1, 8, and 15, the additional elements are: a processor, a memory storing program instructions, a set of signals, and the equipment resources (claim 1), code, a data processing system, a set of signals, and the equipment resources (claim 8), a computer readable storage medium having program instructions embodied therewith, a processing circuit of a computing device, a set of signals, and the equipment resources (claim 15). These additional elements have been evaluated, but fail to integrate the abstract idea into a practical application because they amount to using generic computing elements or computer-executable instructions (software) to perform the abstract idea, similar to adding the words “apply it” (or an equivalent), and merely serve to link the use of the judicial exception to a particular technological environment. See MPEP 2106.05(f) and 2106.05(h). In addition, these limitations fail to provide an improvement to the functioning of a computer or to any other technology or technical field, fail to apply the exception with a particular machine, fail to apply the judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, fail to effect a transformation of a particular article to a different state or thing, and fail to apply/use the abstract idea in a meaningful way beyond generally linking the use of the judicial exception to a particular technological environment.
Accordingly, because the Step 2A Prong One and Prong Two analysis resulted in the conclusion that the claims are directed to an abstract idea, additional analysis under Step 2B of the eligibility inquiry must be conducted in order to determine whether any claim element or combination of elements amount to significantly more than the judicial exception.
With respect to Step 2B of the eligibility inquiry, it has been determined that the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. With respect to independent claims 1, 8, and 15, the additional elements are: a processor, a memory storing program instructions, a set of signals, and the equipment resources (claim 1), code, a data processing system, a set of signals, and the equipment resources (claim 8), a computer readable storage medium having program instructions embodied therewith, a processing circuit of a computing device, a set of signals, and the equipment resources (claim 15). These elements have been considered individually and in combination, but fail to add significantly more to the claims because they amount to using generic computing elements or instructions (software) to perform the abstract idea, similar to adding the words “apply it” (or an equivalent), and merely serve to link the use of the judicial exception to a particular technological environment and does not amount to significantly more than the abstract idea itself. Notably, Applicant’s Specification describes that generic computer devices that may be used to implement the invention, which cover virtually any computing device under the sun (Specification at paragraph [0105]). Accordingly, the generic computer involvement in performing the claim steps merely serves to generally link the use of the judicial exception to a particular technological environment, which does not add significantly more to the claim. See, e.g., Alice Corp., 134 S. Ct. 2347, 110 USPQ2d 1976.).
In addition, when taken as an ordered combination, the ordered combination adds nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements integrates the abstract idea into a practical application. Their collective functions merely provide generic computer implementation. Therefore, when viewed as a whole, these additional claim elements do not provide meaningful limitations to transform the abstract idea into a practical application of the abstract idea or that, as an ordered combination, amount to significantly more than the abstract idea itself.
Dependent claims 2-7, 9-14, and 16-23 recite the same abstract ideas as recited in the independent claims by reciting steps/details for managing personal behavior or relationships or interactions (e.g., following rules or instructions) and steps that can be performed in the human mind (including observation, evaluation, judgment, opinion). For example, dependent claims 2-7 and 21 recite “further comprising adjusting inventory levels in accordance with scheduling of the set of tasks for fulfilling customer orders and the expected shipments of inventory; wherein the automatic scheduling and optimizing takes into account scheduling of the adjusted inventory levels,” “further comprising accessing an identified set of automated machines associated with the set of expected labor resources within the execution schedule time period; wherein the allocation of the set of tasks with the identified set of actors for fulfilling the set of customer orders within the execution schedule time period includes automatically optimizing the allocation of the set of tasks with the identified set of automated machines for fulfilling the set of customer orders,”
“wherein the operations of accessing a set of customer orders for fulfillment, accessing a set of rewards and penalties; accessing current inventory levels, accessing expected shipments of inventory, accessing a set of expected labor resources, automatically generating a set of tasks, automatically optimizing, utilizing the set of rewards and penalties, an allocation of the set of tasks, and automatically scheduling the allocated set of expected labor resources and equipment resources with the set of tasks for fulfilling the set of customer orders and the expected shipments of inventor, are updated dynamically and repeatedly within the planned schedule time period,” “wherein the allocation of the set of tasks with the set of expected labor resources for each customer order is automatically updated; and wherein the allocation of the set of tasks with the identified set of actors for fulfilling the set of customer orders within the execution schedule time period is dynamically updated in response,” “wherein the allocation of the set of tasks with the set of labor resources for the set of customer orders is dynamically optimized, utilizing the set of rewards and penalties within the set of constraints and capacities of the distribution warehouse locality, when incoming shipments are delayed,” “wherein the allocation of the set of tasks with the set of labor resources for the set of customer orders is dynamically optimized, utilizing the set of rewards and penalties within the set of constraints and capacities of the distribution warehouse locality, when a portion of the identified set of actors are missing,” “further comprising accessing a set of equipment resources at the distribution warehouse locality for fulfilling the set of customer orders within the planned schedule time period; wherein automatically optimizing includes automatically optimizing, utilizing the set of rewards and penalties, an allocation of the set of tasks with the set of expected labor resources and the set of equipment resources, within the set of constraints and capacities of the distribution warehouse locality, for each customer order; wherein automatically scheduling includes automatically scheduling the allocated set of expected labor resources, the set of equipment resources, and the set of tasks for fulfilling the set of customer orders; wherein accessing an identified set of actors includes accessing an identified set of actors associated with the set of expected labor resources and an identified set of equipment associated with the set of equipment resources, within an execution schedule time period; and wherein automatically optimizing includes automatically optimizing, utilizing the set of rewards and penalties, an allocation of the set of tasks with the identified set of actors and the identified set of equipment for fulfilling the set of customer orders within the execution schedule time period, within the set of constraints and capacities of the distribution warehouse locality,” which are details for managing resources and that can be performed in the human mind (including observation, evaluation, judgment, opinion). The other dependent claims have been evaluated as well, but similar to dependent claims 2-7 and 21, recite details/steps that merely refine the same abstract idea recite in the independent claims. Accordingly, these steps are part of the same abstract idea(s) set forth in the independent claims. The additional elements recited in the dependent claims include a set of automated machines (claims 3, 10, 17) and identified ser of automated machines (claims 4, 11, 18), and a set of equipment resources (claims 21-23) are directed to generic computing elements and instructions/software that serve to tie the abstract to a particular technological environment, similar to simply adding the words “apply it” to the abstract idea, which is not sufficient to integrate the abstract idea into a practical application or add significantly more.
The ordered combination of elements in the dependent claims (including the limitations inherited from the parent claim(s)) add nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide generic computer implementation. Accordingly, the subject matter encompassed by the dependent claims fails to amount to a practical application or significantly more than the abstract idea itself.
For more information, see MPEP 2106.
Claim Rejections - 35 USC § 103
20. In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
21. 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 of this title, 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.
22. The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
23. This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
24. Claims 1, 3-6, 8, 10-13, 15, and 17-23 are rejected under 35 U.S.C. 103 as being unpatentable over Jacquemart et al., Pub. No.: US 2023/0259878 A1, [hereinafter Jacquemart], in view of Haley et al., Pub. No.: US 2024/0046204 A1, [hereinafter Haley], in further view of Prasad et al., Pub. No.: US 2023/0410029 A1, [hereinafter Prasad].
As per claim 1, Jacquemart teaches a data processing system for automatically and dynamically planning and executing distribution warehouse scheduling and operations, the data processing system (paragraph 0001: “The field of the invention is that of logistics, in particular for preparing orders within a warehouse for storing products to be shipped.”; paragraphs 0028, 0029) comprising:
a processor (paragraph 0144: “a processor”; paragraph 0145); and
a memory storing program instructions which when processed by the processor perform the operations of (paragraph 0144):
identifying a set of constraints and capacities of the distribution warehouse locality (paragraph 0010: “There is therefore a need to provide a new approach which can adapt in real time to these different constraints while optimising the overall performance of the system, that is to say the performance of the operators and the robots.”; paragraph 0062: “This optimisation takes into account all the constraints internal to the warehouse in which the technique is implemented.”; paragraph 0023, discussing that not only the characteristics specific to the physical configuration of the warehouse are taken into account, but also characteristics related to the stored products, the lists of orders to be prepared; paragraphs 0075, 0229);
accessing a set of customer orders for fulfillment from the distribution warehouse locality within a planned schedule time period, each customer order including a priority for fulfillment (paragraph 0013, discussing receiving, by an order manager, data for managing the warehouse comprising at least one plurality of order data; paragraph 0022, discussing that the step of generating and transmitting data for managing the warehouse further comprises a sub-step of receiving and processing data relating to the picking operators, to an inventory of the products stored in the warehouse and to a list of orders to be prepared; paragraph 0023, discussing that not only the characteristics specific to the physical configuration of the warehouse are taken into account, but also characteristics related to the stored products, the lists of orders to be prepared; paragraph 0103, discussing taking into account the number of pickings to be performed in a given period of time; paragraph 0107, discussing a task generator that generates, for a given period of time, tasks each comprising sub-tasks which may contain products from different orders; paragraph 0108);
accessing current inventory levels for the distribution warehouse locality (paragraph 0022, discussing that the step of generating and transmitting data for managing the warehouse further comprises a sub-step of receiving and processing data relating to the picking operators, to an inventory of the products stored in the warehouse and to a list of orders to be prepared; paragraph 0087, discussing that the data relating to the inventory of the products present in the warehouse concern the positioning of the products in the warehouse, the stock of the products, the type of packaging of the products, etc.; paragraph 0096, discussing a warehouse modelling sub-module that is capable of receiving and processing data from the navigation graph generator and the warehouse data analyser. This sub-module is therefore capable of storing all the data relating to the warehouse such as the state of the warehouse in real time and the navigation routes of the warehouse, the data on the inventory and zones in order to associate routes within the warehouse in real time depending on the current state of the warehouse);
accessing a set of expected labor resources at the distribution warehouse locality for fulfilling the set of customer orders within the planned schedule time period (paragraph 0025, discussing that the step of processing the data for managing the warehouse and of delivering the order scheduling data comprises a sub-step of storing the data relating to the picking operators, this data comprising at least one indicator from the number of picking operators present in the warehouse, their performance, their experience and their statistics; paragraph 0085, discussing that the data relating to the operators concern, for example, the number of operators present in the warehouse according to the time of the day, their working hours (planning), the maximum weight that can be picked per hour and/or per day of work for each operator, their experience (efficiency and picking statistics), etc.; paragraph 0198, discussing storing data regarding the number of picking operators in the warehouse, their performance, their experience and their statistics; paragraph 0108, 0231);
automatically generating a set of tasks for completing each customer order (paragraph 0092, discussing that the order manager is a module of the management system which, taking into account the data transmitted thereto by the picking manager, is capable of processing the list of orders depending on the current state of the warehouse and the picking operators so as to determine an optimal picking and collection strategy and thus generate robotic tasks to collect the products picked by the picking operators; paragraph 0106, discussing that the function of the task generator is to divide/split/cut the list of orders to be prepared into a plurality of tasks which each comprise sub-tasks, the sub-tasks each corresponding to a journey/movement of a product collection robot in the warehouse; paragraph 0107, discussing that the task generator generates, for a given period of time, tasks each comprising sub-tasks which may contain products from different orders. In this manner, the picking and the collection of the products can be distributed over several picking operators and over several collection robots in order to optimise the overall effort to execute all the orders; paragraph 0108, discussing that the task generator generates, for a given period of time, picking tasks for the picking operators; paragraph 0160);
automatically optimizing an allocation of the set of tasks with the set of expected labor resources and equipment resources, within the set of constraints and capacities of the distribution warehouse locality, for each customer order (paragraph 0019, discussing that the proposed solution is therefore based on the determination of an optimal scheduling proposal for orders to be prepared and the actual implementation of this scheduling, taking into account real-time data likely to modify the determined optimal scheduling. In this manner, the preparation of orders is optimised in real time for all its aspects (optimisation of picking, collection, use of robots…); paragraph 0024, discussing that the step of processing the data for managing the warehouse and of delivering the order scheduling data comprises a sub-step of generating an optimal routing of the movements of the picking operators and the collection robots within the warehouse. In this manner, the method optimises, in real time, the movements of the operators and the robots, so as to schedule the preparation of orders according to an optimal scheme; paragraph 0062, discussing that this optimisation takes into account all the constraints internal to the warehouse in which the technique is implemented; paragraph 0103, discussing that the function of the zone generator is to determine in real time the number of picking zones to be implemented in the warehouse in order to optimise the workload of each picking operator. To do this, the zones are determined in view of the occupancy, in real time, of the aisles; paragraph 0108, discussing that the task generator generates, for a given period of time, picking tasks for the picking operators. These picking tasks are generated in particular by taking into account the number of picking zones and the number of picking operators. Thus, each picking operator can pick a maximum of products while minimising his movements since he only picks products in the picking zone allocated to him. The performance of each picking operator is therefore optimized; paragraph 0104);
automatically scheduling the allocated set of expected labor resources and the set of tasks for fulfilling the set of customer orders (paragraph 0019, discussing that the proposed solution is therefore based on the determination of an optimal scheduling proposal for orders to be prepared and the actual implementation of this scheduling; paragraph 0024, discussing that the step of processing the data for managing the warehouse and of delivering the order scheduling data comprises a sub-step of generating an optimal routing of the movements of the picking operators and the collection robots within the warehouse. In this manner, the method optimizes the movements of the operators and the robots, so as to schedule the preparation of orders according to an optimal scheme; paragraph 0158, discussing delivering order scheduling data and generating an optimal routing of the movements of the picking operators and the collection robots within the warehouse);
accessing an identified set of actors and an identified set of the equipment associated with the set of expected labor resources within an execution schedule time period (paragraph 0011, discussing a method for managing a plurality of autonomous mobile robots, called collection robots, for preparing orders for products stored in a warehouse comprising a plurality of spaces for storing products, a plurality of zones for picking the products by a plurality of operators and a plurality of zones for collecting the products picked by the collection robots; paragraph 0026, discussing the step of processing the data for managing the warehouse and of delivering the order scheduling data comprises a sub-step of generating a picking strategy comprising a plurality of picking tasks for the picking operators and collecting tasks for the mobile robots; paragraph 0093, discussing that the order manager manages the orders in real time to establish an optimal strategy for picking and collecting the products in the warehouse in order to carry out the preparation of the orders. From all the data transmitted thereto, the order manager is therefore capable of delivering command scheduling data being in the form of a navigation graph (also called navigation route, or code navigation of the robots) of the robots in the warehouse depending, in particular, on the list of orders and the real-time constraints within the warehouse, such as for example the congestion of an aisle or a picking operator in behind on their tasks; paragraph 0109, discussing that the task generator also generates, for a given period of time, the collection tasks for the robots based on the different collection/grouping points of products associated with each zone and on the availability and capacity of the robots of the fleet of robots; paragraph 0110);
automatically optimizing an allocation of the generated set of tasks with the identified set of actors and the identified set of equipment for travelling together through the distribution warehouse fulfilling the set of customer orders within the execution schedule time period, within the set of constraints and capacities of the distribution warehouse locality (paragraph 0019, discussing that the preparation of orders is optimised for all its aspects (optimisation of picking, collection, use of robots…); paragraph 0021, discussing that these sub-steps allow in particular taking into consideration all physical specificities of the warehouse, such as for example directions of circulation, prohibited or highly regulated zones, so as to optimise the navigation of the collection robots. Moreover, the method takes into account these characteristics of the warehouse in real time and can therefore reassess the navigation of the robots as soon as a change occurs, such as for example an accidental congestion (collision between two robots, falling of products…) of an aisle or a zone of the warehouse; paragraph 0121, discussing that depending on the data relating to the state of the robots, that is to say depending on the traffic and the movements of the robots and more generally the state of the fleet of robots, the fleet manager is then capable of modifying/adapting the scheduling of the tasks/sub-tasks generated by the order manager by redistributing/delegating these tasks/sub-tasks. In other words, the fleet manager is capable of modifying the optimal strategy provided by the order manager in order to adapt it to the real-time state of the warehouse. For example, the fleet manager can decide to reroute a task to a robot or to sequence the tasks differently relative to the optimal strategy provided by the order manager; paragraph 0021); and
automatically providing a set of signals describing the optimized allocated set of tasks to the identified set of actors (paragraph 0093, discussing that the order manager manages the orders in real time to establish an optimal strategy for picking and collecting the products in the warehouse in order to carry out the preparation of the orders. From all the data transmitted thereto, the order manager is therefore capable of delivering command scheduling data being in the form of a navigation graph (also called navigation route, or code navigation of the robots) of the robots in the warehouse depending, in particular, on the list of orders and the real-time constraints within the warehouse, such as for example the congestion of an aisle or a picking operator in behind on their tasks; paragraph 0154, discussing generating and transmitting, by the fleet manager, a plurality of movement commands to the collection robots, taking into account at least the determined collection and picking zones and the order scheduling data; paragraph 0161).
Jacquemart does not explicitly teach accessing a set of rewards and penalties for fulfillment of the set of customer orders; accessing expected shipments of inventory to the distribution warehouse locality; accessing a set of expected labor resources at the distribution warehouse locality for fulfilling the set of customer orders and unloading the expected shipments of inventory within the planned schedule time period; automatically generating a set of tasks for completing each customer order and unloading the expected shipments of inventory; automatically optimizing, utilizing the set of rewards and penalties, an allocation of the set of tasks with the set of expected labor resources and equipment resources, within the set of constraints and capacities of the distribution warehouse locality, for each customer order and each expected shipment of inventory; automatically scheduling the allocated set of expected labor resources and equipment resources with the set of tasks for fulfilling the expected shipments of inventory; automatically optimizing, utilizing the set of rewards and penalties, an allocation of the generated set of tasks with the identified set of actors and the identified set of equipment for travelling together through the distribution warehouse fulfilling the expected shipments of inventory within the execution schedule time period; and automatically providing a set of signals describing the optimized allocated set of tasks to the identified set of actors for performing of the allocated set of tasks with the equipment resources for fulfilling the set customer orders and unloading the expected shipments of inventory in response to the provided set of signals. Haley in the analogous art of order fulfillment systems teaches:
accessing a set of rewards and penalties for fulfillment of the set of customer orders (paragraph 0004, discussing methods and a system for a highly flexible solution to dynamically respond to changing warehouse operations and order conditions for both individual agents or workers and for changing facility objectives; paragraph 0052, discussing that at each point in time, a learning agent is provided a description of the current state of the environment. The agent takes an action within this environment, and after this interaction, observes a new state of the environment. The agent receives a positive reward to promote desired behaviors, or a negative reward to deter undesired behaviors. This selection of an action, and an evaluation of the result is repeated for a plurality of possible decisions for a particular decision point; paragraph 0053, discussing that the learning paradigm of RL (reinforcement learning) has been found to be very effective in interactive control tasks. The agent is defined as the decision-making system, which maps the environment state to a set of actions for each agent (robotic and humanoid pickers and autonomous vehicles (AMRs)). The agent would be informed about the location of various items, other agents and possibly orders of the other agents. Based on such information, the agent selects an action and subsequently receives the newly reached state of the environment as well as a positive or negative numerical reward feedback. Agents are given a positive reward for good actions (such as completing an order or picking a single item) and a negative reward for bad actions (e.g., waiting too long). Such agents receive rewards according to the cumulative effect of their actions over time, as opposed to the reward for a single good or bad action);
accessing expected shipments of inventory to the distribution warehouse locality (paragraphs 0036-0038, discussing that even relatively simple workflows (algorithms) within a facility require good orchestration between the different tasks and areas to reach maximum efficiency. One of the biggest problems is that operators and supervisors in the areas have only “local” visibility to the tasks that are performed (in their area) and do not have a view of the entire facility in real-time. In other words, they are unaware of the state of operations in other areas of the facility. This can create problems for the optimum operation of the facility. This disconnect between individual local operations within the facility and the operation of the entire facility, can include: Shipping area has limited space to store final orders before giving them to the carriers. If the picking area starts to process too many orders, it will overwhelm the shipping area, to the point of creating gridlocks where there is no more space for the incoming picking orders. The opposite is also a problem, where the shipping area is underutilized by waiting for orders to be picked, or missing delivery of orders because they came too late to be loaded into the carrier truck. Picking has a dependency from replenishment. If the product is not in the right place and at the right time for picking, the pickers will have to wait for the product to arrive to complete the orders (picker productivity lost, shipping productivity lost); paragraph 0039, discussing that to help in coordinating operations in the facility, conventional control systems, unable to constantly monitor the facility make use of “waves” to coordinate each of the areas and ensure availability of storage space and resources in the downstream areas. An individual wave is a small plan of tasks and resources. Each area of the facility will finish a wave before starting the next wave. This serves as a checkpoint in a broader plan…To help with wave transitions, staging buffers (conveyor or floor space) between the different areas are used to store inventory or order boxes coming from one area to another);
automatically optimizing, utilizing the set of rewards and penalties, an allocation of the set of tasks with the set of expected labor resources and the equipment resources, within the set of constraints and capacities of the distribution warehouse locality, for each customer order and each expected shipment of inventory (paragraph 0055, discussing that an objective of the exemplary training system is to train a reinforcement learning algorithm to determine the strategy for allocating AMR (autonomous mobile robots) and picker movements to optimize the order throughput for a specific time frame. Allocating AMR and picker movements: the algorithm is configured to decide where each AMR and picker should go next, at every point a decision can be made for their next location. Optimize order throughput is defined as minimizing time to compete all orders; paragraph 0058, discussing that the multi-agent perspective of the exemplary order fulfillment facility includes a number of agents representing decision points and attendant decision spaces specific to each software subsystem, collection of robots, fixed automation systems, resource/decision management systems, etc. Deep reinforcement learning via multi-agent training allows for a collaborative learning approach among such agents to take advantage of cooperative strategies that improve warehousing/facility key performance indicators (KPIs) (throughput, cycle time, labor utilization, etc.). These agents learn by interacting with the dynamic environment—in this case a fulfillment center/facility—whose present state is determined by previously taken actions and exogenous factors. At each time step, the agent perceives the state of the environment and takes an action, causing the environment to transit into a new state with some obtained reward. This reward signal evaluates the quality of each transition and is used by the agent to maximize the cumulative reward throughout the course of interaction; paragraph 0059, discussing that within restricted domains of warehouse/facility operation, micro-agents may cooperate to jointly maximize rewards for a specific subsystem in an extension of MDP called a Markov game. While micro-agents focus on decentralized actions to optimize specific subsystems, at a higher level of hierarchical control, a macro-agent, or “orchestrator”, can centrally direct cooperative orchestration across functional areas to drive optimal operating points of the global system. The system state of the fulfillment center/facility that can be used to generate observations for reinforcement learning (RL) training at this level of control includes inventory, workers, known and forecasted order demand, and shipping/receiving related due dates. For example, circumstances such as an urgent shipping deadline approaching, inbound staging nearing capacity, or the depletion of forward inventory might prompt the orchestrator to proactively address such competing exigencies in an intelligent way in order to maximize the sum of KPI-derived rewards defined according to the relevant business considerations);
automatically scheduling the allocated set of expected labor resources and equipment resources with the set of tasks for fulfilling the expected shipments of inventory (paragraph 0055, discussing that an objective of the exemplary training system is to train a reinforcement learning algorithm to determine the strategy for allocating AMR (autonomous mobile robots) and picker movements to optimize the order throughput for a specific time frame. Allocating AMR and picker movements: the algorithm is configured to decide where each AMR and picker should go next, at every point a decision can be made for their next location. Optimize order throughput is defined as minimizing time to compete all orders); and
automatically optimizing, utilizing the set of rewards and penalties, an allocation of the set of generated tasks with the identified set of actors and the identified set of equipment for fulfilling the set of customer orders and the expected shipments of inventory within the execution schedule time period, within the set of constraints and capacities of the distribution warehouse locality (paragraph 0058, discussing that the multi-agent perspective of the exemplary order fulfillment facility includes a number of agents representing decision points and attendant decision spaces specific to each software subsystem, collection of robots, fixed automation systems, resource/decision management systems, etc. Deep reinforcement learning via multi-agent training allows for a collaborative learning approach among such agents to take advantage of cooperative strategies that improve warehousing/facility key performance indicators (KPIs) (throughput, cycle time, labor utilization, etc.). These agents learn by interacting with the dynamic environment—in this case a fulfillment center/facility—whose present state is determined by previously taken actions and exogenous factors. At each time step, the agent perceives the state of the environment and takes an action, causing the environment to transit into a new state with some obtained reward. This reward signal evaluates the quality of each transition and is used by the agent to maximize the cumulative reward throughout the course of interaction; paragraph 0059, discussing that within restricted domains of warehouse/facility operation, micro-agents may cooperate to jointly maximize rewards for a specific subsystem in an extension of MDP called a Markov game. While micro-agents focus on decentralized actions to optimize specific subsystems, at a higher level of hierarchical control, a macro-agent, or “orchestrator”, can centrally direct cooperative orchestration across functional areas to drive optimal operating points of the global system. The system state of the fulfillment center/facility that can be used to generate observations for reinforcement learning (RL) training at this level of control includes inventory, workers, known and forecasted order demand, and shipping/receiving related due dates. For example, circumstances such as an urgent shipping deadline approaching, inbound staging nearing capacity, or the depletion of forward inventory might prompt the orchestrator to proactively address such competing exigencies in an intelligent way in order to maximize the sum of KPI-derived rewards defined according to the relevant business considerations).
Jacquemart is directed towards system and method for managing a plurality of mobile robots for preparing orders for products stored in a warehouse. Haley is directed towards a method and system for optimizing order fulfillment. Therefore they are deemed to be analogous as they both are directed towards warehouse management systems. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Jacquemart with Haley because the references are analogous art because they are both directed to solutions for order fulfillment and warehouse management, which falls within applicant’s field of endeavor (managing distribution warehouse operations), and because modifying Jacquemart to include Haley’s features for including accessing a set of rewards and penalties for fulfillment of the set of customer orders, accessing expected shipments of inventory to the distribution warehouse locality, automatically optimizing, utilizing the set of rewards and penalties, an allocation of the set of tasks with the set of expected labor resources and the equipment resources, within the set of constraints and capacities of the distribution warehouse locality, for each customer order and each expected shipment of inventory, automatically scheduling the allocated set of expected labor resources and equipment resources with the set of tasks for fulfilling the expected shipments of inventory, and automatically optimizing, utilizing the set of rewards and penalties, an allocation of the set of generated tasks with the identified set of actors and the identified set of equipment for fulfilling the set of customer orders and the expected shipments of inventory within the execution schedule time period, within the set of constraints and capacities of the distribution warehouse locality, in the manner claimed, would serve the motivation of providing good orchestration between the different tasks performed by workers/agents and areas of the warehouse/facility to reach maximum efficiency (Haley, paragraph 0026); and further obvious because the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
The Jacquemart-Haley combination does not explicitly teach accessing a set of expected labor resources at the distribution warehouse locality for unloading the expected shipments of inventory within the planned schedule time period; automatically generating a set of tasks for unloading the expected shipments of inventory; and automatically providing a set of signals describing the optimized allocated set of tasks to the identified set of actors for performing of the allocated set of tasks with the equipment resources for unloading the expected shipments of inventory in response to the provided set of signals. However, Prasad in the analogous art of warehouse management system teaches these concepts. Prasad teaches:
accessing a set of expected labor resources at the distribution warehouse locality for unloading the expected shipments of inventory within the planned schedule time period (paragraph 0009, discussing systems and devices for automating the tracking, monitoring, and scheduling of facility events, including the loading and unloading of assets at a facility, such as a storage facility, warehouse, distribution center,…, and the like; paragraph 0033, discussing that the system also receives storage area data from the storage area sensor system (e.g., sensors mounted on the facility, storage racks, equipment, personnel, and the like) as well loading/unloading data from loading/unloading sensor systems (e.g., sensors mounted on the facility, bay doors, equipment, personnel, and the like). In some cases, the system may receive the identification data for the event personnel or other input data within the storage data via an input system at the loading/unloading area; paragraph 0036, discussing that the scheduling system may also be configured to estimate delivery times, unloading operation times, loading operation times, and the like via the data as well as historical data (e.g., historical facility performance times), current staffing, current equipment states, facility storage capacity and utilization (e.g., more inventory may cause increased load and unloading times), number of shipments/delivery's, vehicle types, container types, jurisdictional rules (e.g., country, state, city based shipping rules, environmental rules, vehicle operation rules, and the like), and the like; paragraph 0072, discussing that the time estimation instructions may be configured to estimate arrival time and/or operation times associated with operations of the facility based at least in part on known data about the assets, vehicles, facility personnel, equipment, and the like. In some cases, the time estimation instructions may utilize one or more machine learned models to assist with estimating or predicting the arrival time, operation time, or scheduling of events. In some cases, the one or more machine learned models may be trained using historical data associate with operation executing time, type, with varying personnel and equipment assigned);
automatically generating a set of tasks for unloading the expected shipments of inventory (paragraph 0036, discussing that the scheduling system may also be configured to estimate delivery times, unloading operation times, loading operation times, and the like via the data as well as historical data (e.g., historical facility performance times), current staffing, current equipment states, facility storage capacity and utilization (e.g., more inventory may cause increased load and unloading times), number of shipments/delivery's, vehicle types, container types, jurisdictional rules (e.g., country, state, city based shipping rules, environmental rules, vehicle operation rules, and the like), and the like.; paragraph 0037, discussing that it should be understood that in some examples, the system may update schedules, cancel operations, add operations or the like in substantially real-time as additional data s received by the system . For example, the system may substitute unloading or loading events between vehicles based on the additional data indicating that a vehicle is ahead of schedule or behind schedule and the like; paragraph 0063, discussing that the scheduling system may schedule an unloading operation associated with the asset. For instance, if the asset is already in a loading area the process may advance, otherwise the system may schedule a retrieval operation if the asset is within, for instance, a warehouse storage area. Otherwise, if the asset is still on a trailer, the system may schedule an unloading event for the trailer. In some cases, the system may notify other facility personnel that an unloading event or operation has been scheduled for the collected or retrieved container, trailer, vehicle, or the like. Again, the system may send a notification to a mobile device associated with the desired facility personnel); and
automatically providing a set of signals describing the optimized allocated set of tasks to the identified set of actors for performing of the allocated set of tasks with the equipment resources for unloading the expected shipments of inventory in response to the provided set of signals (paragraph 0035, discussing that the scheduling system may track and provide notifications as to the location of the assets to facility personnel and/or operators to improve the loading and unloading operations. For example, the notifications may be provided to devices assigned to facility operators or personnel based on an assignment to an event or operation (e.g., loading/unloading, packaging, picking, or the like); paragraph 0052, discussing that the system may send an alert to a facility operator or personnel associated with the location of the asset. For example, the system may alert the operator or personnel that the asset is located in an unloaded trailer at a specified location within the yard. In some cases, the alert may include identifiers for the unloaded trailer, container, or vehicle. The alert may also include descriptions, photographs, or image data of the unloaded trailer, container, or vehicle, and the like to assist the facility operator or personnel in locating the asset. The alert may also include any equipment recommended for retrieving or moving the unloaded trailer, container, or vehicle to the unloading zone or area. In some cases, the alert may also include a region associated with the facility that the assets, unloaded trailer, container, or vehicle may be located within; paragraph 0061, discussing that the system may send an alert to a facility operator or personnel associated with the location or region of the asset. For example, the system may alert the operator or personnel that the asset is located in an unloaded trailer at a specified region within the yard. In some cases, the alert may include identifiers for the unloaded trailer, container, or vehicle. The alert may also include descriptions (e.g., color, labels, size, type, and the like), photographs, or image data of the unloaded trailer, container, or vehicle, and the like to assist the facility operator or personnel in locating the asset. The alert may also include any equipment recommended for retrieving or moving the unloaded trailer, container, or vehicle to the unloading zone or area. In some cases, the alert may also include a region associated with the facility that the assets, unloaded trailer, container, or vehicle may be located within; paragraph 0063, discussing that the scheduling system may schedule an unloading operation associated with the asset. For instance, if the asset is already in a loading area the process may advance, otherwise the system may schedule a retrieval operation if the asset is within, for instance, a warehouse storage area. Otherwise, if the asset is still on a trailer, the system may schedule an unloading event for the trailer. In some cases, the system may notify other facility personnel that an unloading event or operation has been scheduled for the collected or retrieved container, trailer, vehicle, or the like. Again, the system may send a notification to a mobile device associated with the desired facility personnel).
The Jacquemart-Haley combination describes features related to task management and warehouse planning. Prasad is directed towards a warehouse system for asset tracking and load scheduling. Therefore they are deemed to be analogous as they both are directed towards warehouse management systems. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the Jacquemart-Haley combination with Prasad because the references are analogous art because they are both directed to solutions for warehouse management, which falls within applicant’s field of endeavor (managing distribution warehouse operations), and because modifying the Jacquemart-Haley combination to include Prasad’s features for including accessing a set of expected labor resources at the distribution warehouse locality for unloading the expected shipments of inventory within the planned schedule time period; automatically generating a set of tasks for unloading the expected shipments of inventory; and automatically providing a set of signals describing the optimized allocated set of tasks to the identified set of actors for performing of the allocated set of tasks with the equipment resources for unloading the expected shipments of inventory in response to the provided set of signals, in the manner claimed, would serve the motivation of scheduling use of the loading and unloading areas based on an optimization to improve the overall flow of assets in and out of the facility (Prasad, paragraph 0021); and further obvious because the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
As per claim 3, the Jacquemart-Haley combination teaches the data processing system of claim 1. Jacquemart further teaches further comprising accessing an identified set of automated machines associated with the set of expected labor resources within the execution schedule time period (paragraph 0049, discussing that for a picker to load an item onto an AMR, both workers have to be located at the location of that particular item…The picker may be either a robotic picker or a humanoid picker; paragraph 0127, discussing that when all the tasks of a robot are performed, the fleet manager transmits new tasks/subtasks to the task manager of the robot. In other words, the task managers of the robots respond/react to the fleet manager in real time in order to ensure/guarantee an accurate control of the robots; paragraph 0154, discussing generating and transmitting, by the fleet manager, a plurality of movement commands to the collection robots, taking into account at least the determined collection and picking zones and the order scheduling data; paragraph 0109),
wherein the allocation of the set of tasks with the identified set of actors for fulfilling the set of customer orders within the execution schedule time period includes automatically optimizing the allocation of the set of tasks with the identified set of automated machines for fulfilling the set of customer orders (paragraph 0019, discussing that the proposed solution is therefore based on the determination of an optimal scheduling proposal for orders to be prepared and the actual implementation of this scheduling; paragraph 0024, discussing that the step of processing the data for managing the warehouse and of delivering the order scheduling data comprises a sub-step of generating an optimal routing of the movements of the picking operators and the collection robots within the warehouse. In this manner, the method optimizes the movements of the operators and the robots, so as to schedule the preparation of orders according to an optimal scheme; paragraph 0109, discussing that the task generator also generates, for a given period of time, the collection tasks for the robots based on the different collection/grouping points of products associated with each zone and on the availability and capacity of the robots of the fleet of robots; paragraph 0158, discussing delivering order scheduling data and generating an optimal routing of the movements of the picking operators and the collection robots within the warehouse).
As per claim 4, the Jacquemart-Haley-Prasad combination teaches the data processing system of claim 1. Jacquemart further teaches wherein the operations of accessing a set of customer orders for fulfillment, accessing current inventory levels, of inventory, accessing a set of expected labor resources, automatically generating a set of tasks, and automatically scheduling the allocated set of expected labor resources and equipment resources with the set of tasks for fulfilling the set of customer orders and the expected shipments of inventor, are updated dynamically and repeatedly within the planned schedule time period (paragraph 0096, discussing that the warehouse modelling sub-module is capable of receiving and processing data from the navigation graph generator and the warehouse data analyser. This sub-module is therefore capable of storing all the data relating to the warehouse such as the state of the warehouse a in real time and the navigation routes 1of the warehouse, the data on the inventory and zones in order to associate routes within the warehouse in real time depending on the current state of the warehouse. In other words, this sub-module aims at updating the optimal routing within the warehouse, depending on the congestion of the aisles for example; paragraph 0114, discussing that the task generator proposes a picking strategy, at a precise moment and for a given period of time, which is optimal both for the picking operators and for the picking robots; paragraph 0115, discussing that these constraints relating to the real-time situation within the warehouse are taken into account by the fleet manager; paragraph 0121, discussing that depending on the data relating to the state of the robots, that is to say depending on the traffic and the movements of the robots and more generally the state of the fleet of robots, the fleet manager is then capable of modifying/adapting the scheduling of the tasks/sub-tasks generated by the order manager by redistributing/delegating these tasks/sub-tasks. In other words, the fleet manager is capable of modifying the optimal strategy provided by the order manager in order to adapt it to the real-time state of the warehouse. For example, the fleet manager can decide to reroute a task to a robot or to sequence the tasks differently relative to the optimal strategy provided by the order manager; paragraph 0122, discussing that certain constraints related to the occupation of the warehouse are taken into account by the fleet manager. These real-time constraints are also transmitted to the order manager so that the latter can consequently modify the picking strategy for the next time period. Thus, the picking strategy generated by the order manager is regularly updated depending on the state of the warehouse observed by the robot fleet manager; paragraph 0229, discussing that the proposed technique is easily scalable and has a very high real-time reactivity depending on the evolving constraints which are encountered in the warehouse. The picking zones are not fixed and can change many times, within the same day if necessary, in order to respond in real time to the orders to be prepared and in order to optimise the workload depending on the profile of each picking operator).
Jacquemart does not explicitly teach wherein the operations of accessing a set of rewards and penalties, accessing expected shipments, and automatically optimizing, utilizing the set of rewards and penalties, an allocation of the set of tasks, are updated dynamically and repeatedly within the planned schedule time period. However, Haley in the analogous art of order fulfillment systems teaches this concept. Haley teaches:
wherein the operations of accessing a set of rewards and penalties, accessing expected shipments, and automatically optimizing, utilizing the set of rewards and penalties, an allocation of the set of tasks, are updated dynamically and repeatedly within the planned schedule time period (paragraph 0005, discussing that the training module retrains the algorithm using reinforcement learning techniques. The training module performs the reinforcement learning on the operational data to retrain and update the algorithms. Operational data may be used for offline reinforcement training, but online reinforcement training may also take place using facility simulation. The training module also retrains a macro algorithm according to a first set of priorities for optimal operation of the warehouse, and to train a plurality of micro algorithms according to corresponding second sets of priorities for optimal operation of a particular location and/or activity within the warehouse. The controller adaptively controls the fulfillment activities using the updated algorithms…; paragraph 0023, discussing an AI-based procedure for the control of macro-agent (orchestrator) and micro-agent in a warehouse environment based on algorithm tuning and training such that the macro-agent is trained to find optimal operational strategies; paragraph 0025, discussing that controller of the warehouse is configured to provide artificial intelligence (AI) control and optimization of agent tasks in the warehouse. An exemplary AI controller, using algorithms that are tuned via deep reinforcement learning, is configured to control different types of workers (agents) in the warehouse and to optimize various objectives (global and local) of the warehouse. Those objectives can include, for example, time for order completion/order lead-time, traffic and congestion, quantity of workers (e.g., pickers, vehicles, and robots), energy usage, travel distance, labor cost, and pallet stability and pick pattern; paragraph 0040, discussing that if the system controller can monitor the different areas and resource in near real-time, the system controller can release work incrementally as resources downstream free up, the use of waves could be eliminated. This is the main principle of “waveless” systems. However, such control systems require more intelligent algorithms which have been tuned to the operational conditions of the warehouse/facility (order profile, storage capacity, labor and machine performance, etc.). These algorithms will work well if the operational conditions do not change much. However, once those operational conditions begin to change, the algorithms will need to be adjusted/tuned to the new conditions. Each area of the facility needs to run efficiently, but also needs to be aware of the downstream areas to not overflow or starve them. The amount of data needed to monitor, and the parameters needed to tune could be too much for humanoid interaction to tune accurately in a complex environment; paragraph 0041, discussing that modern order fulfillment can rapidly change the operational conditions from one day to the next, or even from one hour to another, as well as those seasonal changes, and there needs to be a way to rapidly tune the control system to adapt to those changes. Examples of the changes include different order profiles (small or large, single- or multi-unit orders), labor skills/performance, delivery time constraints, etc.). This is where artificial intelligence (AI) and machine learning techniques can be used to react and adapt the controlling algorithms quickly to changes and to keep the facility running at a peak or optimal performance; paragraphs 0043, 0044, 0057).
Jacquemart is directed towards system and method for managing a plurality of mobile robots for preparing orders for products stored in a warehouse. Haley is directed towards a method and system for optimizing order fulfillment. Therefore they are deemed to be analogous as they both are directed towards warehouse management systems. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Jacquemart with Haley because the references are analogous art because they are both directed to solutions for order fulfillment and warehouse management, which falls within applicant’s field of endeavor (managing distribution warehouse operations), and because modifying Jacquemart to include Haley’s feature for including wherein the operations of accessing a set of rewards and penalties, accessing expected shipments, and automatically optimizing, utilizing the set of rewards and penalties, an allocation of the set of tasks, are updated dynamically and repeatedly within the planned schedule time period, in the manner claimed, would serve the motivation of providing good orchestration between the different tasks performed by workers/agents and areas of the warehouse/facility to reach maximum efficiency (Haley, paragraph 0026); and further obvious because the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
As per claim 5, the Jacquemart-Haley-Prasad combination teaches the data processing system of claim 1. Jacquemart further teaches wherein the allocation of the set of tasks with the set of expected labor resources for each customer order is automatically updated (paragraph 0019, discussing that the proposed solution is therefore based on the determination of an optimal scheduling proposal for orders to be prepared and the actual implementation of this scheduling, taking into account real-time data likely to modify the determined optimal scheduling. In this manner, the preparation of orders is optimised in real time for all its aspects (optimisation of picking, collection, use of robots…); paragraph 0121, discussing that depending on the data relating to the state of the robots, that is to say depending on the traffic and the movements of the robots and more generally the state of the fleet of robots, the fleet manager is then capable of modifying/adapting the scheduling of the tasks/sub-tasks generated by the order manager by redistributing/delegating these tasks/sub-tasks. In other words, the fleet manager is capable of modifying the optimal strategy provided by the order manager in order to adapt it to the real-time state of the warehouse. For example, the fleet manager can decide to reroute a task to a robot or to sequence the tasks differently relative to the optimal strategy provided by the order manager); and
wherein the allocation of the set of tasks with the identified set of actors for fulfilling the set of customer orders within the execution schedule time period is dynamically updated in response (paragraph 0019, discussing that the proposed solution is therefore based on the determination of an optimal scheduling proposal for orders to be prepared and the actual implementation of this scheduling, taking into account real-time data likely to modify the determined optimal scheduling. In this manner, the preparation of orders is optimised in real time for all its aspects (optimisation of picking, collection, use of robots…; paragraph 0096, discussing that the warehouse modelling sub-module is capable of storing all the data relating to the warehouse such as the state of the warehouse in real time and the navigation routes of the warehouse, the data on the inventory and zones in order to associate routes within the warehouse in real time depending on the current state of the warehouse. In other words, this sub-module aims at determining/updating the optimal routing within the warehouse, depending on the congestion of the aisles for example; paragraphs 0104, 0114, 0122).
As per claim 6, the Jacquemart-Haley-Prasad combination teaches the data processing system of claim 5. Jacquemart further teaches wherein the allocation of the set of tasks with the set of labor resources for the set of customer orders is dynamically optimized (paragraph 0019, discussing that the proposed solution is therefore based on the determination of an optimal scheduling proposal for orders to be prepared and the actual implementation of this scheduling, taking into account real-time data likely to modify the determined optimal scheduling. In this manner, the preparation of orders is optimised in real time for all its aspects (optimisation of picking, collection, use of robots…; paragraph 0062, discussing that the optimisation takes into account all the constraints internal to the warehouse in which the technique is implemented. These internal constraints are mostly imposed and inflexible, but often evolving, such as for example priorities between the received orders and the departure times of the delivery trucks or even the conflicts of trajectories between the robots, the directions of circulation in the warehouse, etc.; paragraph 0096, discussing that the warehouse modelling sub-module is capable of storing all the data relating to the warehouse such as the state of the warehouse in real time and the navigation routes of the warehouse, the data on the inventory and zones in order to associate routes within the warehouse in real time depending on the current state of the warehouse. In other words, this sub-module aims at determining/updating the optimal routing within the warehouse, depending on the congestion of the aisles for example; paragraph 0121, discussing that the fleet manager is capable of modifying the optimal strategy provided by the order manager in order to adapt it to the real-time state of the warehouse; paragraph 0158).
Jacquemart does not explicitly teach utilizing the set of rewards and penalties within the set of constraints and capacities of the distribution warehouse locality, when incoming shipments are delayed. However, Haley in the analogous art of order fulfillment systems teaches this concept. Haley teaches:
wherein the allocation of the set of tasks with the set of labor resources for the set of customer orders is dynamically optimized, utilizing the set of rewards and penalties within the set of constraints and capacities of the distribution warehouse locality, when incoming shipments are delayed (paragraph 0053, discussing that the learning paradigm of RL (reinforcement learning) has been found to be very effective in interactive control tasks. The agent is defined as the decision-making system, which maps the environment state to a set of actions for each agent (robotic and humanoid pickers and autonomous vehicles (AMRs). The agent would be informed about the location of various items, other agents and possibly orders of the other agents. Based on such information, the agent selects an action and subsequently receives the newly reached state of the environment as well as a positive or negative numerical reward feedback. Agents are given a positive reward for good actions (such as completing an order or picking a single item) and a negative reward for bad actions (e.g., waiting too long). Such agents receive rewards according to the cumulative effect of their actions over time, as opposed to the reward for a single good or bad action; paragraphs 0036-0038, discussing that even relatively simple workflows (algorithms) within a facility require good orchestration between the different tasks and areas to reach maximum efficiency. One of the biggest problems is that operators and supervisors in the areas have only “local” visibility to the tasks that are performed (in their area) and do not have a view of the entire facility in real-time. In other words, they are unaware of the state of operations in other areas of the facility. This can create problems for the optimum operation of the facility. This disconnect between individual local operations within the facility and the operation of the entire facility, can include: Shipping area has limited space to store final orders before giving them to the carriers. If the picking area starts to process too many orders, it will overwhelm the shipping area, to the point of creating gridlocks where there is no more space for the incoming picking orders. The opposite is also a problem, where the shipping area is underutilized by waiting for orders to be picked, or missing delivery of orders because they came too late to be loaded into the carrier truck. Picking has a dependency from replenishment. If the product is not in the right place and at the right time for picking, the pickers will have to wait for the product to arrive to complete the orders (picker productivity lost, shipping productivity lost; paragraph 0055, discussing that an objective of the exemplary training system is to train a reinforcement learning algorithm to determine the strategy for allocating AMR (autonomous mobile robots) and picker movements to optimize the order throughput for a specific time frame. Allocating AMR and picker movements: the algorithm is configured to decide where each AMR and picker should go next, at every point a decision can be made for their next location. Optimize order throughput is defined as minimizing time to compete all orders; paragraph 0058, discussing that the multi-agent perspective of the exemplary order fulfillment facility includes a number of agents representing decision points and attendant decision spaces specific to each software subsystem, collection of robots, fixed automation systems, resource/decision management systems, etc. Deep reinforcement learning via multi-agent training allows for a collaborative learning approach among such agents to take advantage of cooperative strategies that improve warehousing/facility key performance indicators (KPIs) (throughput, cycle time, labor utilization, etc.). These agents learn by interacting with the dynamic environment—in this case a fulfillment center/facility—whose present state is determined by previously taken actions and exogenous factors. At each time step, the agent perceives the state of the environment and takes an action, causing the environment to transit into a new state with some obtained reward. This reward signal evaluates the quality of each transition and is used by the agent to maximize the cumulative reward throughout the course of interaction; paragraph 0059, discussing that within restricted domains of warehouse/facility operation, micro-agents may cooperate to jointly maximize rewards for a specific subsystem in an extension of MDP called a Markov game. While micro-agents focus on decentralized actions to optimize specific subsystems, at a higher level of hierarchical control, a macro-agent, or “orchestrator”, can centrally direct cooperative orchestration across functional areas to drive optimal operating points of the global system. The system state of the fulfillment center/facility that can be used to generate observations for reinforcement learning (RL) training at this level of control includes inventory, workers, known and forecasted order demand, and shipping/receiving related due dates. For example, circumstances such as an urgent shipping deadline approaching, inbound staging nearing capacity, or the depletion of forward inventory might prompt the orchestrator to proactively address such competing exigencies in an intelligent way in order to maximize the sum of KPI-derived rewards defined according to the relevant business considerations; paragraphs 0065-0071, discussing that the multi-agent reinforcement learning/hierarchical reinforcement learning (MARL/HRL) approach described above promises new heights of flexibility in fulfillment compared to current/conventional methods. Such an approach will: *Dynamically adjust workflows to current conditions. *Optimize the facility holistically as opposed to compartmentally. *Adapt to variable conditions in order volume and composition. *Improve proactivity of decision making…).
Jacquemart is directed towards system and method for managing a plurality of mobile robots for preparing orders for products stored in a warehouse. Haley is directed towards a method and system for optimizing order fulfillment. Therefore they are deemed to be analogous as they both are directed towards warehouse management systems. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Jacquemart with Haley because the references are analogous art because they are both directed to solutions for order fulfillment and warehouse management, which falls within applicant’s field of endeavor (managing distribution warehouse operations), and because modifying Jacquemart to include Haley’s feature for including wherein the allocation of the set of tasks with the set of labor resources for the set of customer orders is dynamically optimized, utilizing the set of rewards and penalties within the set of constraints and capacities of the distribution warehouse locality, when incoming shipments are delayed, in the manner claimed, would serve the motivation of providing good orchestration between the different tasks performed by workers/agents and areas of the warehouse/facility to reach maximum efficiency (Haley, paragraph 0026); and further obvious because the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
As per claim 21, the Jacquemart-Haley-Prasad combination teaches the data processing system of claim 1. Jacquemart further teaches further comprising accessing a set of equipment resources at the distribution warehouse locality for fulfilling the set of customer orders within the planned schedule time period (paragraph 0011, discussing a method for managing a plurality of autonomous mobile robots, called collection robots, for preparing orders for products stored in a warehouse comprising a plurality of spaces for storing products, a plurality of zones for picking the products by a plurality of operators and a plurality of zones for collecting the products picked by the collection robots; paragraph 0109, discussing that the task generator also generates, for a given period of time, the collection tasks for the robots based on the different collection/grouping points of products associated with each zone and on the availability and capacity of the robots of the fleet of robots; paragraph 0110);
wherein automatically optimizing includes automatically optimizing an allocation of the set of tasks with the set of expected labor resources and the set of equipment resources, within the set of constraints and capacities of the distribution warehouse locality, for each customer order (paragraph 0019, discussing that the proposed solution is therefore based on the determination of an optimal scheduling proposal for orders to be prepared and the actual implementation of this scheduling, taking into account real-time data likely to modify the determined optimal scheduling. In this manner, the preparation of orders is optimised in real time for all its aspects (optimisation of picking, collection, use of robots…; paragraph 0024, discussing that the step of processing the data for managing the warehouse and of delivering the order scheduling data comprises a sub-step of generating an optimal routing of the movements of the picking operators and the collection robots within the warehouse. In this manner, the method optimises, in real time, the movements of the operators and the robots, so as to schedule the preparation of orders according to an optimal scheme; paragraph 0062, discussing that this optimisation takes into account all the constraints internal to the warehouse in which the technique is implemented; paragraph 0103, discussing that the function of the zone generator is to determine in real time the number of picking zones to be implemented in the warehouse in order to optimise the workload of each picking operator. To do this, the zones are determined in view of the occupancy, in real time, of the aisles; paragraph 0108, discussing that the task generator 124 generates, for a given period of time, picking tasks for the picking operators. These picking tasks are generated in particular by taking into account the number of picking zones and the number of picking operators. Thus, each picking operator can pick a maximum of products while minimising his movements since he only picks products in the picking zone allocated to him. The performance of each picking operator is therefore optimized; paragraph 0104);
wherein automatically scheduling includes automatically scheduling the allocated set of expected labor resources, the set of equipment resources, and the set of tasks for fulfilling the set of customer orders (paragraph 0019, discussing that the proposed solution is therefore based on the determination of an optimal scheduling proposal for orders to be prepared and the actual implementation of this scheduling; paragraph 0024, discussing that the step of processing the data for managing the warehouse and of delivering the order scheduling data comprises a sub-step of generating an optimal routing of the movements of the picking operators and the collection robots within the warehouse. In this manner, the method optimizes the movements of the operators and the robots, so as to schedule the preparation of orders according to an optimal scheme; paragraph 0114, discussing that the task generator proposes a picking strategy for a given period of time, which is optimal both for the picking operators and for the picking robots; paragraph 0158, discussing delivering order scheduling data and generating an optimal routing of the movements of the picking operators and the collection robots within the warehouse);
wherein accessing an identified set of actors includes accessing an identified set of actors associated with the set of expected labor resources and an identified set of equipment associated with the set of equipment resources, within an execution schedule time period (paragraph 0025, discussing that the step of processing the data for managing the warehouse and of delivering the order scheduling data comprises a sub-step of storing the data relating to the picking operators, this data comprising at least one indicator from the number of picking operators present in the warehouse, their performance, their experience and their statistics; paragraph 0026, discussing the step of processing the data for managing the warehouse and of delivering the order scheduling data comprises a sub-step of generating a picking strategy comprising a plurality of picking tasks for the picking operators and collecting tasks for the mobile robots; paragraph 0093, discussing that the order manager manages the orders in real time to establish an optimal strategy for picking and collecting the products in the warehouse in order to carry out the preparation of the orders. From all the data transmitted thereto, the order manager is therefore capable of delivering command scheduling data being in the form of a navigation graph (also called navigation route, or code navigation of the robots) of the robots in the warehouse depending, in particular, on the list of orders and the real-time constraints within the warehouse, such as for example the congestion of an aisle or a picking operator in behind on their tasks; paragraph 0109, discussing that the task generator also generates, for a given period of time, the collection tasks for the robots based on the different collection/grouping points of products associated with each zone and on the availability and capacity of the robots of the fleet of robots; par); and
wherein automatically optimizing includes automatically optimizing, an allocation of the set of tasks with the identified set of actors and the identified set of equipment for fulfilling the set of customer orders within the execution schedule time period, within the set of constraints and capacities of the distribution warehouse locality (paragraph 0019, discussing that the preparation of orders is optimised for all its aspects (optimisation of picking, collection, use of robots…); paragraph 0021, discussing that these sub-steps allow in particular taking into consideration all physical specificities of the warehouse, such as for example directions of circulation, prohibited or highly regulated zones, so as to optimise the navigation of the collection robots. Moreover, the method takes into account these characteristics of the warehouse in real time and can therefore reassess the navigation of the robots as soon as a change occurs, such as for example an accidental congestion (collision between two robots, falling of products…) of an aisle or a zone of the warehouse; paragraph 0121, discussing that depending on the data relating to the state of the robots, that is to say depending on the traffic and the movements of the robots and more generally the state of the fleet of robots, the fleet manager is then capable of modifying/adapting the scheduling of the tasks/sub-tasks generated by the order manager by redistributing/delegating these tasks/sub-tasks. In other words, the fleet manager is capable of modifying the optimal strategy provided by the order manager in order to adapt it to the real-time state of the warehouse. For example, the fleet manager can decide to reroute a task to a robot or to sequence the tasks differently relative to the optimal strategy provided by the order manager; paragraph 0021).
Does not explicitly teach utilizing the set of rewards and penalties, an allocation of the set of tasks with the set of expected labor resources and the set of equipment resources, within the set of constraints and capacities of the distribution warehouse locality, for each customer order; and utilizing the set of rewards and penalties, an allocation of the set of tasks with the identified set of actors and the identified set of equipment for fulfilling the set of customer orders within the execution schedule time period, within the set of constraints and capacities of the distribution warehouse locality. However, Haley in the analogous art of order fulfillment systems teaches these concepts. Haley teaches:
wherein automatically optimizing includes automatically optimizing, utilizing the set of rewards and penalties, an allocation of the set of tasks with the set of expected labor resources and the set of equipment resources, within the set of constraints and capacities of the distribution warehouse locality, for each customer order (paragraph 0055, discussing that an objective of the exemplary training system is to train a reinforcement learning algorithm to determine the strategy for allocating AMR (autonomous mobile robots) and picker movements to optimize the order throughput for a specific time frame. Allocating AMR and picker movements: the algorithm is configured to decide where each AMR and picker should go next, at every point a decision can be made for their next location. Optimize order throughput is defined as minimizing time to compete all orders); and
wherein automatically optimizing includes automatically optimizing, utilizing the set of rewards and penalties, an allocation of the set of tasks with the identified set of actors and the identified set of equipment for fulfilling the set of customer orders within the execution schedule time period, within the set of constraints and capacities of the distribution warehouse locality (paragraph 0058, discussing that the multi-agent perspective of the exemplary order fulfillment facility includes a number of agents representing decision points and attendant decision spaces specific to each software subsystem, collection of robots, fixed automation systems, resource/decision management systems, etc. Deep reinforcement learning via multi-agent training allows for a collaborative learning approach among such agents to take advantage of cooperative strategies that improve warehousing/facility key performance indicators (KPIs) (throughput, cycle time, labor utilization, etc.). These agents learn by interacting with the dynamic environment—in this case a fulfillment center/facility—whose present state is determined by previously taken actions and exogenous factors. At each time step, the agent perceives the state of the environment and takes an action, causing the environment to transit into a new state with some obtained reward. This reward signal evaluates the quality of each transition and is used by the agent to maximize the cumulative reward throughout the course of interaction; paragraph 0059, discussing that within restricted domains of warehouse/facility operation, micro-agents may cooperate to jointly maximize rewards for a specific subsystem in an extension of MDP called a Markov game. While micro-agents focus on decentralized actions to optimize specific subsystems, at a higher level of hierarchical control, a macro-agent, or “orchestrator”, can centrally direct cooperative orchestration across functional areas to drive optimal operating points of the global system. The system state of the fulfillment center/facility that can be used to generate observations for reinforcement learning (RL) training at this level of control includes inventory, workers, known and forecasted order demand, and shipping/receiving related due dates. For example, circumstances such as an urgent shipping deadline approaching, inbound staging nearing capacity, or the depletion of forward inventory might prompt the orchestrator to proactively address such competing exigencies in an intelligent way in order to maximize the sum of KPI-derived rewards defined according to the relevant business considerations).
Jacquemart is directed towards system and method for managing a plurality of mobile robots for preparing orders for products stored in a warehouse. Haley is directed towards a method and system for optimizing order fulfillment. Therefore they are deemed to be analogous as they both are directed towards warehouse management systems. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Jacquemart with Haley because the references are analogous art because they are both directed to solutions for order fulfillment and warehouse management, which falls within applicant’s field of endeavor (managing distribution warehouse operations), and because modifying Jacquemart to include Haley’s features for including automatically optimizing, utilizing the set of rewards and penalties, an allocation of the set of tasks with the set of expected labor resources and the set of equipment resources, within the set of constraints and capacities of the distribution warehouse locality, for each customer order; and automatically optimizing, utilizing the set of rewards and penalties, an allocation of the set of tasks with the identified set of actors and the identified set of equipment for fulfilling the set of customer orders within the execution schedule time period, within the set of constraints and capacities of the distribution warehouse locality, in the manner claimed, would serve the motivation of providing good orchestration between the different tasks performed by workers/agents and areas of the warehouse/facility to reach maximum efficiency (Haley, paragraph 0026); and further obvious because the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Claims 8 and 15 recites substantially similar limitations that stand rejected via the art citations and rationale applied to claim 1, as discussed above. Further, as per claim 8, the Jacquemart-Haley-Prasad combination teaches a method of automatically and dynamically planning and executing schedules and operations of a distribution warehouse locality (paragraphs 0011, 0024, 0034). As per claim 15, the Jacquemart-Haley-Prasad combination teaches a computer program product for automatically and dynamically planning and executing distribution warehouse scheduling and operations, the computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions processed by a processing circuit of a computing device to cause the computing device to perform a method (paragraph 0033, 0144, 0146).
Claims 10 and 17 recite substantially similar limitations that stand rejected via the art citations and rationale applied to claim 3, as discussed above.
Claims 11 and 18 recite substantially similar limitations that stand rejected via the art citations and rationale applied to claim 4, as discussed above.
Claims 12 and 19 recite substantially similar limitations that stand rejected via the art citations and rationale applied to claim 5, as discussed above.
Claims 13 and 20 recite substantially similar limitations that stand rejected via the art citations and rationale applied to claim 6, as discussed above.
Claims 22 and 23 recite substantially similar limitations that stand rejected via the art citations and rationale applied to claim 21, as discussed above.
25. Claims 2, 9, and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Jacquemart in view of Haley, in view of Prasad, in further view of Wing et al., Pub. No.: US 2023/0325769 A1, [hereinafter Wing].
As per claim 2, the Jacquemart-Haley-Prasad combination teaches the data processing system of claim 1. Although not explicitly taught by the Jacquemart-Haley-Prasad combination, Wing in the analogous art of inventory control system teaches further comprising adjusting inventory levels in accordance with scheduling of the set of tasks for fulfilling customer orders and the expected shipments of inventory; wherein the automatic scheduling and optimizing takes into account scheduling of the adjusted inventory levels (paragraph 0002, discussing that a replenishment process may involve the flow of inventory item units from the distribution center to the store responsive to transfer orders, but may be based on, e.g., inventory levels, re-order points, stocking levels, picking and packing management, order lead times, and/or transit times, among other similar factors. Still further, in supply chain systems of significant size and complexity decisions regarding how best to allocate inventory across that supply chain are highly complex, due to the changing demand, inventory levels, etc. at each location; paragraph 0008, discussing that the systems and methods include receiving constraint definitions related to an inventory action (e.g., an inventory transfer or a purchase order) and automatically adjusting at least one inventory action based on an inventory issue caused by the inventory action; paragraph 0011, discussing causing the user device to connect with an inventory management system, receive inventory data, scheduled inventory actions, and inputs selecting at least one constraint definition, send the at least one constraint definition to the inventory management system, and receive at least one updated scheduled inventory action, wherein the inventory management system automatically generated the at least one updated scheduled inventory action in response to detecting an inventory issue caused by the at least one constraint definition; paragraph 0088, discussing adjusting scheduled inventory actions. In some examples, only the received adjustments are made. In some embodiments, other inventory actions are automatically adjusted based on the received adjustments. For example, if one order is rescheduled then the future orders are similarly rescheduled. In some examples, the simulation system is used to simulate various changes and determine an optimal set of adjustments to make in addition to the received adjustments; paragraph 0116, discussing adjusting inventory data based on received inputs. The inventory data and projections are updated based on the inputs moving inventory within the supply chain; paragraphs 0089, 0090)
The Jacquemart-Haley-Prasad combination describes features related to task management and warehouse planning. Wing is directed towards methods and systems for inventory planning and control. Therefore they are deemed to be analogous as they both are directed towards task management systems. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the Jacquemart-Haley-Prasad combination with Wing because the references are analogous art because they are both directed to solutions for task and inventory management, which falls within applicant’s field of endeavor (managing distribution warehouse operations), and because modifying the Jacquemart-Haley-Prasad combination to include Wing’s feature for including further comprising adjusting inventory levels in accordance with scheduling of the set of tasks for fulfilling customer orders and the expected shipments of inventory; wherein the automatic scheduling and optimizing takes into account scheduling of the adjusted inventory levels, in the manner claimed, would serve the motivation of facilitating transfer a set of items between distribution centers, or among other nodes (Wing, paragraph 0115); and further obvious because the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Claims 9 and 16 recite substantially similar limitations that stand rejected via the art citations and rationale applied to claim 2, as discussed above.
26. Claims 7 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Jacquemart in view of Haley, in view of Prasad, in further view of Thomas et al., Pub. No.: US 2020/0411168 A1, [hereinafter Thomas].
As per claim 7, the Jacquemart-Haley-Prasad combination teaches the data processing system of claim 5. Jacquemart further teaches wherein the allocation of the set of tasks with the set of labor resources for the set of customer orders is dynamically optimized (paragraph 0019, discussing that the proposed solution is therefore based on the determination of an optimal scheduling proposal for orders to be prepared and the actual implementation of this scheduling, taking into account real-time data likely to modify the determined optimal scheduling. In this manner, the preparation of orders is optimised in real time for all its aspects (optimisation of picking, collection, use of robots…; paragraph 0062, discussing that the optimisation takes into account all the constraints internal to the warehouse in which the technique is implemented. These internal constraints are mostly imposed and inflexible, but often evolving, such as for example priorities between the received orders and the departure times of the delivery trucks or even the conflicts of trajectories between the robots, the directions of circulation in the warehouse, etc.; paragraph 0096, discussing that the warehouse modelling sub-module is capable of storing all the data relating to the warehouse such as the state of the warehouse in real time and the navigation routes of the warehouse, the data on the inventory and zones in order to associate routes within the warehouse in real time depending on the current state of the warehouse. In other words, this sub-module aims at determining/updating the optimal routing within the warehouse, depending on the congestion of the aisles for example; paragraph 0121, discussing that the fleet manager is capable of modifying the optimal strategy provided by the order manager in order to adapt it to the real-time state of the warehouse; paragraph 0158).
Jacquemart does not explicitly teach utilizing the set of rewards and penalties within the set of constraints and capacities of the distribution warehouse locality, when a portion of the identified set of actors are missing. However, Haley in the analogous art of order fulfillment systems teaches this concept. Haley teaches:
wherein the allocation of the set of tasks with the set of labor resources for the set of customer orders is dynamically optimized, utilizing the set of rewards and penalties within the set of constraints and capacities of the distribution warehouse locality, when a portion of the identified set of actors are missing (paragraph 0001, discussing computer-implemented techniques for managing tasks of a dynamic system with limited resources using a machine-learning and combinatorial optimization framework; paragraph 0053, discussing that the task management module can utilize the predicted demand information, the predicted TAT information, and/or the predicted staff availability information to manage supply and demand, facilitate compliance with defined SLAs for the tasks, facilitate determining how to prioritize performance of the tasks, and optimizing allocation of available resources of the dynamic system for the tasks in real-time (e.g., in association with the current operating state of the dynamic system). For example, in some embodiments, the task management module can evaluate the predicted demand for tasks over an upcoming timeframe in view of the resources of the dynamic system that are available for fulfilling the tasks to determine whether the available resources (e.g., workers, supplies, equipment, etc.) are sufficient. The task management modules can also determine whether the available resources are sufficient in view of any defined resource requirements/restrictions for the tasks. The task management module can further generate a notification in real-time in scenarios in which the available resources are determined to insufficient to satisfy the forecasted demand and/or resources requirements/restrictions for forecasted tasks. In another embodiment, the task management module can evaluate the predicted TATs (turnaround times) for the currently pending tasks in view of defined performance requirements for one or more of the TATs to identify potential violations to the performance requirements; paragraph 0083, discussing that the resource monitoring component can facilitate determining when the available system resources are insufficient in view of the current and expected demand (e.g., the currently pending and predicted tasks). In this regard, the resource monitoring component can determine the amount and/or type of resources that are needed to satisfy the current and forecasted demand provided in the predictive output data; paragraph 0088, discussing that the resource allocation component can determine how to optimize resource allocation based on the current constraints of the system, defined rules of the system, and an optimization objective for the system, such as increasing throughput, maximizing the number of tasks performed, equalizing the distribution of the workload amongst available staff, and the like; paragraph 0106, discussing that the resource allocation component can employ an optimization problem that aims to determine how to allocate resources with an objective of maximizing the rewards minus the penalties expressed mathematically by Equation 1; paragraph 0116).
The Jacquemart-Haley-Prasad combination describes features related to task management and warehouse planning. Thomas is directed towards a machine-learning and combinatorial optimization framework for managing tasks of a dynamic system. Therefore they are deemed to be analogous as they both are directed towards task management systems. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the Jacquemart-Haley-Prasad combination with Thomas because the references are analogous art because they are both directed to solutions for task management, which falls within applicant’s field of endeavor (managing distribution warehouse operations), and because modifying the Jacquemart-Haley-Prasad combination to include Thomas’ feature for including wherein the allocation of the set of tasks with the set of labor resources for the set of customer orders is dynamically optimized, utilizing the set of rewards and penalties within the set of constraints and capacities of the distribution warehouse locality, when a portion of the identified set of actors are missing, in the manner claimed, would serve the motivation of facilitating managing and prioritizing tasks in which a limited amount of resources are available for performing the tasks (Thomas, paragraph 0030); and further obvious because the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Claim 14 recites substantially similar limitations that stand rejected via the art citations and rationale applied to claim 7, as discussed above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Mains et al., Pub. No.: US 2023/0056103 A1 – describes an apparatus and method for freight delivery and pick-up. Further describes that upon assignment of a loading dock, the administrative personnel may cause a message to be sent to the warehouse operators at the loading/unloading facility as to which loading dock was selected and indicating that the driver is about to arrive for a pick up or delivery. Such a message may also indicate whether any specific equipment will be required to accomplish the loading/unloading operation. Loading/unloading personnel then proceed to the assigned loading dock.
Lee et al., Pub. No.: US 2020/0167726 A1 – describes a system method for providing integrated unloading and loading plans using cloud service.
Mains, Jr. et al., Pub. No.: US 2019/0066033 A1 – describes sending a message to a facility worker providing instructions to proceed to an assigned dock and to begin loading or unloading a shipment.
Zhen, Lu, et al. "Scheduling multiple types of equipment in an automated warehouse." Annals of Operations Research 322.2 (2023): 1119-1141 – describes a mixed-integer programming model to optimize the assignment of pallets to the related equipment and the storage locations during inbound process, as well as the sequencing handling activities of these equipment.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. 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 extension fee 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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/Darlene Garcia-Guerra/
Primary Examiner, Art Unit 3625
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