DETAILED ACTION
Status of Claims
The following is a FIRST, NON-FINAL OFFICE ACTION for Application #19/190,171, filed on 04/25/2025, and a preliminary amendment filed on 06/30/2025. This application is a Continuation of Application #17/852,106, filed on 06/13/2024, and claims Priority to Provisional Application #63/216,413, filed on 06/29/2021.
Claims 21-40 are now pending and have been examined.
Claims 1-20 have been cancelled by the applicant.
Double Patenting
Claims 21, 28, and 35 are rejected on the ground of non-statutory double patenting as being unpatentable over claims 1 and 11 of Application #17/852,106 filed on 06/28/2022. (now U.S. Patent No. 12,314,877).
The non-statutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A non-statutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/process/file/efs/guidance/eTD-info-I.jsp.
CURRENT CLAIM 21 (as exemplary for claims 28 and 35 as well):
21. (New) A method comprising:
identifying, by one or more servers, a plurality of jobs to be performed using a plurality of robots and a plurality of material handling equipment, at least a portion of one or more jobs of the plurality of jobs being performed in combination with one or more people;
monitoring, by a monitor via one or more interfaces to the plurality of robots and the plurality of material handling equipment, a status and proximity of each of a plurality of robots and each of a plurality of material handling equipment with respect to a location of the plurality of jobs;
providing, by the one or more servers, as input to a model, the plurality of jobs and the status and proximity of each of the plurality of robots and plurality of materials handling equipment, identified from monitoring, to perform responsive portions of each of the plurality of jobs;
determining, by the one or more servers using outputs of the model, one or
more workflows for each of the plurality of jobs coordinating work assignments
of the one or more workflows among the plurality of robots and the plurality of material handling equipment to perform respective portions of each of the one or
more workflows;
distributing, by a work distributor configured on the one or more servers, the work assignments to the plurality of robots and the plurality of material handling equipment for performing the respective portions of each of the one or more workflows; and
displaying, by the monitor responsive to monitoring, via one or more user interfaces, a status of performance of respective work assignments by each of the plurality of robots and the plurality of material handling equipment for each of the workflows.
CLAIM 1 of U.S. Patent No. 12,314,877
(claim limitations that correspond to the above current claim 21 are lettered with the same corresponding letter as above)
1. (Currently Amended) A method for autonomously determining and distributing work
assignments across people, robots and material handling equipment, the method
comprising:
identifying, by one or more servers, a plurality of jobs to be performed
across a warehouse;
identifying, by the one or more servers, each of a plurality of people, a
plurality of robots and a plurality of material handling equipment available to
perform responsive portions of each of the plurality of jobs;
D. identifying, by the one or more servers, a model, the model trained using
as input identification of and a plurality of factors for each of the plurality of
people, the plurality of robots and the plurality of material handling equipment
across the warehouse; the model configured to provide outputs that identify one or
more workflows and coordination of work assignments for each person, robot and
material handling equipment available to perform the one or more workflows for
the one or more jobs, weights of the model being adjusted during training based
on an amount of and responsive to an error signal such that the model learns over
time;
establishing, by a monitor of the one or more servers, one or more interfaces to each of the plurality of robots and each of the plurality of material handling equipment using one or more application programming interfaces and protocols based at least on a type and configuration of each robot and each material handling equipment;
monitoring, by the monitor via the one or more interfaces, an availability, status of performance of one or more jobs and proximity of each of the plurality of robots and each of the plurality of material handling equipment;
autonomously determining, by the one or more servers from outputs of the model responsive to providing as input to the model the plurality of jobs and each of the plurality of people, plurality of robots and plurality of materials handling equipment identified as available to perform responsive portions of each of the plurality of jobs and respective status of performance of one or more jobs and proximity, workflows for each of the plurality of jobs coordinating work assignments for each of the plurality of people, the plurality of robots and the plurality of material handling equipment to perform respective portions of each of the workflows to perform the plurality of jobs;
autonomously distributing and coordinating, by a work distributor configured on the one or more servers responsive to and using the outputs from the model, the work assignments to each of the plurality of people, the plurality of robots and the plurality of material handling equipment for performing the respective portions of each of the workflows to perform the plurality of jobs; and
communicating, by the one or more servers, one or more work instructions via one or more APIs to computing devices of each of the plurality of people, and each of the plurality of robots and the plurality of material handling equipment to cause causing, by the one or more servers, each of the plurality of people, the plurality of robots and the plurality of material handling equipment to initiate performing their respective work assignments for each of the workflows to perform the plurality of jobs; and
monitoring, by the monitor, status of performance of respective work assignments for each of the plurality of people, and each of the plurality of robots and the plurality of material handling equipment for each of the workflows; and
displaying, by the monitor, via one or more user interfaces, status of performance of respective work assignments for each of the plurality of people, and each of the plurality of robots and the plurality of material handling equipment for each of the workflows.
The current claim 21 is just a broader version of claim 1 of the patented claims. Therefore, this is considered an obvious variant of the patented claim 1 without the need for an additional prior art reference.
Therefore, as already stated above, claims 21, 28, and 35 are rejected, and by virtue of their dependency claims 22-27, 29-34, and 36-40 are also rejected and appropriate correction is required by one of the options stated above.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 24 and 31 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. Specifically, claims 24 and 31 recite communicating…to each of the plurality of robots…. But, the claims never state WHAT is being communicated. The claim goes on to say what is used to communicate and based on a type and configuration, but never what is actually being communicated. Therefore, the claims do not clearly and distinctly claim the applicant’s invention and appropriate correction is required.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 21-40 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The rationale for this finding is explained below.
Per Step 1 of the analysis, the claims are analyzed to determine if they are directed to statutory subject matter. Claim 21 claims a method, or process. A process is a statutory category for patentability. Claims 28 and 35 claim a system comprising one or more servers. Therefore, the systems are considered apparatuses. An apparatus is a statutory category for patentability.
Per Step 2A, Prong 1 of the analysis, the examiner must now determine if the claims are directed to an abstract idea or eligible subject matter. In the instant case, the independent claims 21, 28, and 35 are directed to an abstract idea. Specifically, the claims are directed to “identifying…a plurality of jobs to be performed using a plurality of robots and a plurality of material handling equipment, at least a portion of one or more jobs of the plurality of jobs being performed in combination with one or more people, monitoring the plurality of robots and the plurality of material handling equipment, a status and proximity of each of a plurality of robots and each of a plurality of material handling equipment with respect to a location of the plurality of jobs, determining one or more workflows for each of the plurality of jobs coordinating work assignments of the one or more workflows among the plurality of robots and the plurality of material handling equipment to perform respective portions of each of the one or more workflows, distributing the work assignments to the plurality of robots and the plurality of material handling equipment for performing the respective portions of each of the one or more workflows and monitoring a status of performance of respective work assignments by each of the plurality of robots and the plurality of material handling equipment for each of the workflows.” The claims are directed to an abstract idea, namely “certain methods of organizing human activity.” Specifically, the claims are directed to the activity of “commercial interactions, business relations.” A foreman or manager can identify the available entities to perform a task or complete a job, determine the assignments needed, assign the tasks to the plurality of available entities based on the most efficient or ideal use of the entities, and initiate the completion of the task or job as a whole as well as assign workflows. Such management work is common place on worksites ranging from construction to factories and warehouses, all which commonly include human workers and manually operated machinery, and often include such as robots an autonomous machinery. The claims simply automate the abstract idea using a computer. Therefore, the claims are directed to the abstract idea of “certain methods of organizing human activity,” specifically, “commercial interactions, business relations.” The claims are also secondarily directed to a mental process. A human operator such as a foreman or manager can identify the available entities to perform a task or complete a job, determine the assignments needed and the workflow of those assignments, assign the tasks to the plurality of available entities based on the most efficient or ideal use of the entities, and initiate the completion of the task or job as a whole. This can all be done as a mental task accompanied by verbal or written communication of some sort. The manager or foreman is making a judgment or opinion about work distribution based on an analysis of available entities. Such management work is common place on worksites ranging from construction to factories and warehouses, all which commonly include human workers and manually operated machinery, and often include such as robots an autonomous machinery. Therefore, the claims are secondarily directed to a mental process.
Per Step 2A, Prong 2 of the analysis, the examiner must now determine if the claims integrate the abstract idea into a practical application. The additional elements of the claims include the recitation of “one or more servers” “by a monitor via one or more interfaces,” and “a work distributor configured on one or more servers.” However, these components are considered generic recitations of technical elements which are recited at a high level of generality. These components are being used as “tools to automate the abstract idea” (see MPEP 2106.05 (f)), and do not integrate the abstract idea into a practical application. They are not recitations of a special purpose computer or transformation (see MPEP 2106.05 (b) and (c)). The additional elements of the claims also include “providing, as input to a model, the plurality of jobs and the status of proximity of each of the plurality of robots and the plurality of materials handling equipment, identified from monitoring, to perform responsive portions of each of the plurality of jobs” as well as “determining…using outputs of the model.” However, these additional elements are recited at a high level of generality and are considered the equivalent of “apply it” or “using a computer as a tool to automate the abstract idea” (see MPEP 2106.05 (f)). The claims merely describe at a very broad level the input into a model and using an output to determine workflows. The “use” of the model is only described as outputs in response to the inputs, the model outputting the results. There is no detail whatsoever as to how the model is “used,” other than describing what the outputs are, much less any technical step taken as a response to or improved by the use of the model. Therefore the additional elements do not integrate the abstract idea into a practical application. The examiner refers the applicant to the recent 2024 Guidance Update on Patent Subject Matter Eligibility Including on Artificial Intelligence published in the Federal Register on July 17th, 2024. The update and the examples describe the level of technical detail that is considered sufficient or 101 eligible when the claims include such as the training and/or use of a machine learning model or other such similar model. The additional elements in the claims also include “monitoring by a monitor via one or more interfaces” and “displaying, by the monitor responsive to monitoring, via one or more user interfaces….” Absent further detail, these limitations are considered insignificant extra-solution activity and further are considered “receiving and/or transmission of data over a network” is listed in the MPEP 2106.05 (d) (II) (i) as an example of conventional computer functioning (see “receiving or transmitting data over a network,” citing Symantec, “sending messages over a network,” citing buySAFE v Google, and “presenting content” citing OIP Techs v Amazon.com). Therefore these additional elements do not integrate the abstract idea into a practical application.
Per Step 2B of the analysis, the examiner must now determine if the claims include limitations that are “significantly more” than the abstract idea by demonstrating an improvement to another technology or technical field, an improvement to the functioning of the computer itself, or meaningful limitations beyond generally linking the use of an abstract idea to a particular technological environment. The additional elements of the claims include the recitation of “one or more servers” “by a monitor via one or more interfaces,” and “a work distributor configured on one or more servers.” However, these components are considered generic recitations of technical elements which are recited at a high level of generality. These components are being used as “tools to automate the abstract idea” (see MPEP 2106.05 (f)), and are not considered significantly more than the abstract idea itself. They are not recitations of a special purpose computer or transformation (see MPEP 2106.05 (b) and (c)). The additional elements of the claims also include “providing, as input to a model, the plurality of jobs and the status of proximity of each of the plurality of robots and the plurality of materials handling equipment, identified from monitoring, to perform responsive portions of each of the plurality of jobs” as well as “determining…using outputs of the model.” However, these additional elements are recited at a high level of generality and are considered the equivalent of “apply it” or “using a computer as a tool to automate the abstract idea” (see MPEP 2106.05 (f)). The claims merely describe at a very broad level the input into a model and using an output to determine workflows. The “use” of the model is only described as outputs in response to the inputs, the model outputting the results. There is no detail whatsoever as to how the model is “used,” other than describing what the outputs are, much less any technical step taken as a response to or improved by the use of the model. Therefore the additional elements are not considered significantly more. The examiner refers the applicant to the recent 2024 Guidance Update on Patent Subject Matter Eligibility Including on Artificial Intelligence published in the Federal Register on July 17th, 2024. The update and the examples describe the level of technical detail that is considered sufficient or 101 eligible when the claims include such as the training and/or use of a machine learning model or other such similar model. The additional elements in the claims also include “monitoring by a monitor via one or more interfaces” and “displaying, by the monitor responsive to monitoring, via one or more user interfaces….” Absent further detail, these limitations are considered insignificant extra-solution activity and further are considered “receiving and/or transmission of data over a network” is listed in the MPEP 2106.05 (d) (II) (i) as an example of conventional computer functioning (see “receiving or transmitting data over a network,” citing Symantec, “sending messages over a network,” citing buySAFE v Google, and “presenting content” citing OIP Techs v Amazon.com). Therefore these additional elements are not considered significantly more than the abstract idea itself.
When considered as an ordered combination, the claims still are considered to be directed to an abstract idea. The claims the logical set of steps for identifying the entities available for a task, determining work assignments, distributing work assignments, and causing the initiation of performing and monitoring the work assignments. Therefore, the ordered combination does not lead to a determination of significantly more.
When considering the dependent claims, claims 22 and 23 are recited at a high level of generality and are considered the equivalent of “apply it” or “using a computer as a tool to automate the abstract idea” (see MPEP 2106.05 (f)). The claims merely describe at a very broad level the “training” of the model using the three data points without any detail whatsoever as to how the model is trained. The “using” of the model is only described as outputs in response to the inputs, the model outputting the results. There is no detail whatsoever as to how the model is “used,” other than describing what the outputs are, much less any technical step taken as a response to or improved by the use of the model. For claim 23, the additional element recites that the weights are adjusted over time based on an error signal and that the “model learns over time.” This again is recited at a high level of generality and the “model learning over time” is considered insignificant extra-solution activity and is merely descriptive. Therefore, the additional elements do not integrate the abstract idea into a practical application. The examiner refers the applicant to the recent 2024 Guidance Update on Patent Subject Matter Eligibility Including on Artificial Intelligence published in the Federal Register on July 17th, 2024. The update and the examples describe the level of technical detail that is considered sufficient or 101 eligible when the claims include such as the training and/or use of a machine learning model or other such similar model. Claim 24, absent further detail, is considered “receiving and/or transmission of data over a network” is listed in the MPEP 2106.05 (d) (II) (i) as an example of conventional computer functioning (see “receiving or transmitting data over a network,” citing Symantec, “sending messages over a network,” citing buySAFE v Google). Therefore, this additional element is not considered significantly more. The use of API’s, as recited, absent further detail, is considered a generic recitation of a technical element which is recited at a high level of generality. The API’s are being used as “tools to automate the abstract idea” (see MPEP 2106.05 (f)), and are not considered significantly more than the abstract idea itself. They are not recitations of a special purpose computer or transformation (see MPEP 2106.05 (b) and (c)). The protocols/instructions are simply being transmitted to the API of the robot or component. Further, the examiner takes Official Notice that it is old and well known in the computer arts at the time of filing of the application to transmit instructions to an API of a device or machine. Claim 25 is considered part of the abstract idea, as communicating workflows could be done manually and by programming as part of a business operation by the manager or foreman, and if a computer is used this would simply be automating the abstract idea after the workflows are determined. Claims 26, 27, and 38 are considered part of the abstract idea, as the type of factors considered in determining workflows does not change the analysis and determination that could be done by a manager as part of a business process or mental evaluation. Claim 36, absent further detail, is considered insignificant extra-solution activity and further are considered “receiving and/or transmission of data over a network” is listed in the MPEP 2106.05 (d) (II) (i) as an example of conventional computer functioning (see “receiving or transmitting data over a network,” citing Symantec, “sending messages over a network,” citing buySAFE v Google, and “presenting content” citing OIP Techs v Amazon.com. Therefore, this additional element does not integrate the abstract idea into a practical application and is not considered significantly more. Claim 37 is recited at a high level of generality and are considered the equivalent of “apply it” or “using a computer as a tool to automate the abstract idea” (see MPEP 2106.05 (f)). Therefore, this additional limitation is not considered significantly more. Claims 39 and 40 are considered part of the abstract idea, as modifying workflows or availability can be done as part of a business decision or mental process.
Therefore, claims 21-40 are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter. See Alice Corporation Pty. Ltd. Vs. CLS Bank International et al., 2014 (please reference link to updated publicly available Alice memo at http://www.uspto.gov/patents/announce/alice_pec_25jun2014.pdf as well as the USPTO January 2019 Updated Patent Eligibility Guidance.)
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103(a) are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 21-22, 24-29, 31-35, and 36-38 are rejected under 35 U.S.C. 103 as being unpatentable over Kelly, et al., Patent No. 10,062,042 B1 in view of Kisiler, et al., Pre-Grant Publication No. 2021/0188430 A1 and in further view of John, et al., Pre-Grant Publication No. 2021/0132947 A1.
Regarding Claims 21, 28, and 35, Kelly teaches:
A method (system) comprising:
identifying, by one or more servers, a plurality of jobs to be performed
at least a portion of one or more jobs of the plurality of jobs being performed in
combination with one or more people (see Abstract, Column 2, lines 14-46, Column 3, lines 9-23, Column 4, lines 22-Column 5, line 34, Column 7, lines 1-34)
monitoring, by a monitor via one or more interfaces a status and proximity of each of the people with respect to a location of the plurality of jobs (see at least Column 2, lines 13-16, Column 3, lines 13-23, and Column 9, lines 51-59)
determining, by the one or more servers, one or more workflows for each of the plurality of jobs coordinating work assignments of the one or more workflows among the plurality of people to perform respective portions of each of the one or more workflows (see Column 3, lines 8-23, Column 14, lines 14-34, Column 7, lines 1-34, Column 8, lines 3-24)
distributing, by a work distributor configured on the one or more servers, the work assignments to the plurality of people for performing the respective portions of each of the one or more workflows (see Abstract, Column 2, lines 2-46, Column 3, lines 8-26, Column 4, line 54-Column 5, line 34, and Column 8, lines 3-56)
Kelly, however, does not appear to specify:
identifying, by one or more servers, a plurality of jobs to be performed
using a plurality of robots and a plurality of material handling equipment
monitoring, by a monitor via one or more interfaces to the plurality of
robots and the plurality of material handling equipment, a status and proximity of
each of a plurality of robots and each of a plurality of material handling
equipment with respect to a location of the plurality of jobs;
determining, by the one or more servers, one or more workflows for each of the plurality of jobs coordinating work assignments of the one or more workflows among the plurality of robots and the plurality of material handling equipment to perform respective portions of each of the one or more workflows
distributing, by a work distributor configured on the one or more servers,
the work assignments to the plurality of robots and the plurality of material
handling equipment for performing the respective portions of each of the one or
more workflows
Kisiler teaches:
identifying, by one or more servers, a plurality of jobs to be performed
using a plurality of robots and a plurality of material handling equipment (see [0054] in which “task tools” are various equipment for doing a task and “task robots” are described in [0055] as various types of robots that can be assigned various tasks; see also Abstract, [0112], [0122]-[0124] in which task tools and task robots are identified that can complete various ones of the plurality of needed tasks)
monitoring, by a monitor via one or more interfaces to the plurality of
robots and the plurality of material handling equipment, a status and proximity of
each of a plurality of robots and each of a plurality of material handling equipment with respect to a location of the plurality of jobs (see [0087]-[0088], [0094]-[0095], and [0103] in which the workflow of task robots and task tools are monitored in real time with sensors; see also [0101], [0109]-[0114], and [0122]-[0124] in which the workflow is determined based on location, time needed, and other factors)
determining, by the one or more servers, one or more workflows for each of the plurality of jobs coordinating work assignments of the one or more workflows among the plurality of robots and the plurality of material handling equipment to perform respective portions of each of the one or more workflows (see [0054]-[0057], [0076], [0109]-[0119] which details the whole process of task initiation and assigning the tasks to task robots and/or task tools, and [0121]-[0124] which details the process of task execution; see also [0101], [0109]-[0114], and [0122]-[0124] in which the task assignment includes identification of workflows for the task assignments)
distributing, by a work distributor configured on the one or more servers,
the work assignments to the plurality of robots and the plurality of material
handling equipment for performing the respective portions of each of the one or
more workflows (see [0054]-[0057], [0076], [0109]-[0119] which details the whole process of task initiation and assigning the tasks to task robots and/or task tools, and [0121]-[0126] which further details the process of task assignment and execution; see also [0101], [0109]-[0114], and [0122]-[0124] in which the task assignment includes identification and coordination of workflows for the task assignments)
It would be obvious to one of ordinary skill in the art to combine Kisiler with Kelly because Kelly already teaches similar identification of a plurality of tasks and distributing tasks to the available workers based on a variety of factors, and does so autonomously, using technology, but does not also distribute the work to robot and equipment entities, and doing so would allow for better use of non-human entities and would allow for more efficient overall workflow and time management even when involving robots and equipment, which are commonly found in such as warehouse and factory settings such as that described in Kelly, and identifying and coordinating workflows would also allow for better time scheduling and best use of resources.
It would also be obvious to one of ordinary skill in the art to combine Kisiler with Kelly because Kelly already teaches similar status monitoring of workers, and monitoring the equipment and robots would allow for better use of non-human entities and would allow for more efficient overall workflow and time management even when involving robots and equipment, which are commonly found in such as warehouse and factory settings such as that described in Kelly.
**The examiner notes that “robots” and “material handling equipment” are broadly interpreted in light of the applicant’s filed specification at pages 22, line 7-23, line 10 in which the equipment is described broadly as possibly including a variety of options including equipment that is “controllable by a human operator” as well as even AI programmed. Therefore, the examiner broadly interprets the material handling equipment to be any kind of equipment that is assigned to a task even if it is controlled by a human or robot operator. If the applicant wishes to have a narrower interpretation, the examiner suggests further amendment to more narrowly define what the MHE is in the current claims.**
Kelly and Kisiler, however, does not appear to specify:
providing, by the one or more servers, as input to a model, the plurality of
jobs and the status and proximity of each of the plurality of robots and plurality of
materials handling equipment, identified from monitoring, to perform responsive
portions of each of the plurality of jobs
determining, by the one or more servers using outputs of the model, one or
more workflows
displaying, by the monitor responsive to monitoring, via one or more user
interfaces, a status of performance of respective work assignments by each of the
plurality of robots and the plurality of material handling equipment for each of the
workflows
John teaches:
providing, by the one or more servers, as input to a model, the plurality of
jobs and the status and proximity of each of the plurality of robots and plurality of
materials handling equipment, identified from monitoring, to perform responsive
portions of each of the plurality of jobs and determining, by the one or more servers using outputs of the model, one or more workflows (see at least Figures 4 and 6 and [0086]-[0090] and [0093]-[00984] in which a machine learning model is trained on past personnel, task assignment, and workflow execution data in order to assign tasks and identify workflow for current resources for completing a current job)
displaying, by the monitor responsive to monitoring, via one or more user
interfaces, a status of performance of respective work assignments by each of the
plurality of robots and the plurality of material handling equipment for each of the
workflows (see Figures 12, 16, and 19 and [0097], [0101], and [0104])
It would be obvious to one of ordinary skill in the art to combine John with Kelly and Kisiler because Kelly already teaches identification of a plurality of tasks and distributing tasks to the available workers based on a variety of factors, and does so autonomously, using technology, and Kisiler teaches distributing the work to robot and equipment entities to achieve more efficient overall workflow and time management even when involving robots and equipment, which are commonly found in such as warehouse and factory settings such as that described in Kelly, and identifies and coordinates workflows. Further, Kelly specifically teaches the use of machine learning such as a neural network, which is a form of model, to complete one or more of the overall scheduling and assigning tasks in at least Column 8, lines 52-56, and using the trained model of John would allow the decisions on personnel and task assignment and workflows to be more efficiently decided by using intelligent learning which would allow for large historical data pools to be best used for applying to the current situation by the trained model.
Regarding Claims 22 and 29, the combination of Kelly, Kisiler, and John teaches:
the method of claim 21…
John further teaches:
establishing, by the one or more servers, the model, the model trained using as a plurality of factors for each of the plurality of robots and the plurality of material handling equipment and the model configured to provide outputs that identify one or more workflows and coordination of work assignments for each robot and material handling equipment available to perform the one or more workflows for the one or more jobs (see at least Figures 4 and 6 and [0086]-[0090] and [0093]-[00984] in which a machine learning model is trained on past personnel, task assignment, and workflow execution data in order to assign tasks and identify workflow for current resources for completing a current job)
It would be obvious to one of ordinary skill in the art to combine John with Kelly and Kisiler because Kelly already teaches identification of a plurality of tasks and distributing tasks to the available workers based on a variety of factors, and does so autonomously, using technology, and Kisiler teaches distributing the work to robot and equipment entities to achieve more efficient overall workflow and time management even when involving robots and equipment, which are commonly found in such as warehouse and factory settings such as that described in Kelly, and identifies and coordinates workflows. Further, Kelly specifically teaches the use of machine learning such as a neural network, which is a form of model, to complete one or more of the overall scheduling and assigning tasks in at least Column 8, lines 52-56, and using the trained model of John would allow the decisions on personnel and task assignment and workflows to be more efficiently decided by using intelligent learning which would allow for large historical data pools to be best used for applying to the current situation by the trained model.
**The examiner notes that while John does not teach the training data or outputs to be specifically for “robots, persons, and material handling equipment,” Kelly and Kisiler have already been shown to teach those specific entities, and so John is being used to show the trained model being trained and used for a similar identification of task assignment and workflows for a similar set of multiple entities for completing a job.**
Regarding Claims 24 and 31, the combination of Kelly, Kisiler, and John teaches:
the method of claim 21…
Kisiler further teaches:
communicating, by the monitor via one or more interfaces to each of the plurality of robots and the plurality of material handling equipment using one or more application programming interfaces and protocols based at least on a type and configuration of each robot and each material handling equipment (see [0103] and [0123]-[0128])
It would be obvious to one of ordinary skill in the art to combine Kisiler with Kelly because Kelly already teaches autonomous distribution of workflows to workers in a factory or similar setting, and distribution to equipment and robots would allow for better use of non-human entities and would allow for more efficient overall workflow and time management even when involving robots and equipment, which are commonly found in such as warehouse and factory settings such as that described in Kelly.
Regarding Claims 25 and 32, the combination of Kelly, Kisiler, and John teaches:
the method of claim 21…
Kisiler further teaches:
communicating, by the one or more servers, one or more work instructions to each of the plurality of robots and the plurality of material handling equipment, wherein the one or more work instructions instruct the plurality of robots and the plurality of material handling equipment to perform their respective work assignments for each of the workflows to perform the plurality of jobs (see [0103] and [0123]-[0128])
It would be obvious to one of ordinary skill in the art to combine Kisiler with Kelly because Kelly already teaches autonomous distribution of workflows to workers in a factory or similar setting, and distribution to equipment and robots would allow for better use of non-human entities and would allow for more efficient overall workflow and time management even when involving robots and equipment, which are commonly found in such as warehouse and factory settings such as that described in Kelly.
Regarding Claims 26 and 33, the combination of Kelly, Kisiler, and John teaches:
the method of claim 21…
Kisiler further teaches:
wherein the plurality of factors of each of the plurality of robots include one or more of the following: robot capabilities, availability, proximity, qualifications, status, routing, traffic management, charging, maintenance, utilization and prioritization of the plurality of jobs (see [0119] in which factors for task assignment including availability, capabilities, and location; see also [0122]-[0125])
It would be obvious to one of ordinary skill in the art to combine Kisiler with Kelly because Kelly already teaches factors for worker selection for tasks, and use of specific robots based on factors would allow for better use of non-human entities and would allow for more efficient overall workflow and time management even when involving robots, which are commonly found in such as warehouse and factory settings such as that described in Kelly.
Regarding Claims 27 and 34, the combination of Kelly, Kisiler, and John teaches:
the method of claim 21…
Kisiler further teaches:
wherein the plurality of factors of each of the plurality of material handling equipment include one or more of the following: material handling capabilities, availability, proximity, qualifications, status, routing, traffic management, maintenance, utilization and prioritization of the plurality of jobs (see [0122]-[0125] in which capabilities, availability, and location are factors in identification of tools for a task)
It would be obvious to one of ordinary skill in the art to combine Kisiler with Kelly because Kelly already teaches factors for worker selection for tasks, and use of equipment based on factors would allow for better use of non-human entities and would allow for more efficient overall workflow and time management even when involving equipment, which is commonly found in such as warehouse and factory settings such as that described in Kelly.
Regarding Claim 36, the combination of Kelly, Kisiler, and John teaches:
the system of claim 35
John further teaches:
display, responsive monitoring, on one or more user interfaces of a display, a status of performance of respective work assignments by each of the plurality of robots (see Figures 12, 16, and 19 and [0097], [0101], and [0104])
It would be obvious to one of ordinary skill in the art to combine John with Kelly and Kisiler because Kelly already teaches identification of a plurality of tasks and distributing tasks to the available workers based on a variety of factors, and does so autonomously, using technology, and Kisiler teaches distributing the work to robot and equipment entities to achieve more efficient overall workflow and time management even when involving robots and equipment, which are commonly found in such as warehouse and factory settings such as that described in Kelly, and identifies and coordinates workflows. Further, Kelly specifically teaches the use of machine learning such as a neural network, which is a form of model, to complete one or more of the overall scheduling and assigning tasks in at least Column 8, lines 52-56, and using the trained model of John would allow the decisions on personnel and task assignment and workflows to be more efficiently decided by using intelligent learning which would allow for large historical data pools to be best used for applying to the current situation by the trained model.
**The examiner notes that the limitation “display…a status of performance of respective work assignments by each of the plurality of robots” is very broadly claimed, as any display of the status of a workflow or task, especially if there is only one robot tasked with one task, would read on this claim as written, and it is unclear what is being displayed.**
Regarding Claim 37, the combination of Kelly, Kisiler, and John teaches:
the system of claim 35
Kelly further teaches:
wherein the one or more data structures of the one or more workflows are further configured to identify one of a logical condition or a decision point for progressing through the one or more workflows (see Abstract, Column 9, lines 21-65)
Kisiler further teaches:
wherein the one or more data structures of the one or more workflows are further configured to identify one of a logical condition or a decision point for progressing through the one or more workflows (see Abstract, [0115]-[0119] and [0141]-[0147])
Regarding Claim 38, the combination of Kelly, Kisiler, and John teaches:
the system of claim 35
Kelly further teaches:
wherein the one or more data structures of the one or more workflows are further configured to identify one or more factors for performing the one or more work assignments (see Column 2, lines 40-46 and Column 4, lines 63-67 which teach locations, certifications, experience, Column 3, lines 8-23 which teach workload, efficiency, skill level, location, and priority of new tasks; see also Column 7, lines 1-34)
Claims 23 and 30 are rejected under 35 U.S.C. 103 as being unpatentable over Kelly, et al., Patent No. 10,062,042 B1 in view of Kisiler, et al., Pre-Grant Publication No. 2021/0188430 A1 and in further view of John, et al., Pre-Grant Publication No. 2021/0132947 A1 and in further view of Pei, et al., Pre-Grant Publication No. 2022/0318898 A1.
Regarding Claims 23 and 30, the combination of Kelly, Kisiler, and John teaches:
the method of claim 21…
Kelly, Kisiler, and John, however, does not appear to specify:
wherein one or more weights of the model are adjusted based on an amount of and responsive to an error signal such that the model learns over time
Pei teaches:
wherein one or more weights of the model are adjusted based on an amount of and responsive to an error signal such that the model learns over time (see Figure 12D and [0102] in which the model weights are adjusted based on the error signals)
It would be obvious to one of ordinary skill in the art to combine Pei with Kelly, Kisiler, and John because Kelly already teaches use of neural networks and John already teaches a trained AI model, and adjusting weights of the model based on the error messages would allow for a better feedback loop and overall continued training and learning of the model.
Claims 39 and 40 are rejected under 35 U.S.C. 103 as being unpatentable over Kelly, et al., Patent No. 10,062,042 B1 in view of Kisiler, et al., Pre-Grant Publication No. 2021/0188430 A1 and in further view of John, et al., Pre-Grant Publication No. 2021/0132947 A1 and in further view of Pandya, et al., Pre-Grant Publication No. 2018/0158016 A1.
Regarding Claim 39, the combination of Kelly, Kisiler, and John teaches:
the system of claim 35
Kelly, Kisiler, and John, however, does not appear to specify:
wherein the one or more servers are further configured with executable instructions to modify, responsive to monitoring, a workflow of the one or more workflows
Pandya teaches:
wherein the one or more servers are further configured with executable instructions to modify, responsive to monitoring, a workflow of the one or more workflows (see Abstract, [0027]-[0028], [0030]-[0031], [0137], and claim 9 in which workflows for robots are optimized and modified dynamically as availability of robots, dynamics in the workflow and tasks, and other factors change during execution of tasks and workflow)
It would be obvious to one of ordinary skill in the art to combine Pandya with Kelly, Kisiler, and John because Kelly and Kisiler already teach execution and optimization of workflows in such as a warehouse or local area environment, but do not discuss availability leading to modification of a workflow, but Kelly does discuss keeping in mind personnel shifts and hours and Kisiler discusses such as maintenance issues that could arise, and modifying workflow according to availability would ensure task completion and workflow success even when availability becomes a challenge for a particular entity.
Regarding Claim 40, the combination of Kelly, Kisiler, and John teaches:
the system of claim 35
Kelly, Kisiler, and John, however, does not appear to specify:
wherein the one or more servers are further configured with executable instructions to modify, responsive to monitoring a status or availability of the plurality of robots, a work assignment for a robot of the plurality of robots
Pandya teaches:
wherein the one or more servers are further configured with executable instructions to modify, responsive to monitoring a status or availability of the plurality of robots, a work assignment for a robot of the plurality of robots (see Abstract, [0027]-[0028], [0030]-[0031], [0137], and claim 9 in which workflows for robots are optimized and modified dynamically as availability of robots, dynamics in the workflow and tasks, and other factors change during execution of tasks and workflow)
It would be obvious to one of ordinary skill in the art to combine Pandya with Kelly, Kisiler, and John because Kelly and Kisiler already teach execution and optimization of workflows in such as a warehouse or local area environment, but do not discuss availability leading to modification of a workflow, but Kelly does discuss keeping in mind personnel shifts and hours and Kisiler discusses such as maintenance issues that could arise, and modifying workflow according to availability would ensure task completion and workflow success even when availability becomes a challenge for a particular entity.
Conclusion
The following prior art references was not relied upon in this office action but is considered pertinent to the claimed invention:
Gupta, Pre-Grant Publication No. 2019/0066013 A1- teaches a system for intelligent assignment of tasks for optimum workflow and efficiency in a work setting, the tasks assigned to both human workers and robots.
Lem, Pre-Grant Publication No. 2019/0303819 A1- teaches warehouse workflow optimization through task assignments to various entities in the warehouse.
Hildmann, et al., Pre-Grant Publication No. 2015/0294251 A1- teaches task assignments to various agents in various workplace settings, the agents including people, drones, and equipment such as delivery trucks.
Huang, et al., Pre-Grant Publication No. 2019/0205792 A1- teaches the use of machine learning models to schedule and manage task assignments and workflows for a software task or other similar jobs
Any inquiry of a general nature or relating to the status of this application or concerning this communication or earlier communications from the Examiner should be directed to Luis A. Brown whose telephone number is 571.270.1394. The Examiner can normally be reached on M-F 8:30am-4:30pm EST. If attempts to reach the examiner by telephone are unsuccessful, the Examiner’s supervisor, JESSICA LEMIEUX can be reached at 571.270.3445.
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/LUIS A BROWN/Primary Examiner, Art Unit 3626