DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Objections
Claim 2 is objected to because of the following informalities:
Claim 2 recites the limitation “…wherein delay risk calculation unit calculates the delay risk…” in lines 2-3. The delay risk calculation unit is previously introduced in claim 1. Therefore, Examiner recommends correcting the limitation to instead read “…wherein the delay risk calculation unit calculates the delay risk…”.
Appropriate correction is required.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are:
“a conveyance information reception unit” introduced in claim 1;
“a delay risk calculation unit” introduced in claim 1;
“a task assignment unit” introduced in claim 1;
“a result output unit” introduced in claim 1;
“a path planning unit” introduced in claims 3 and 4;
“a conveyance simulation execution unit” introduced in claim 3;
“an actual measurement data evaluation unit” introduced in claim 4;
“an optimization unit” introduced in claims 7 and 8;
“a control command unit” introduced in claim 9; and
“a task execution status acquisition unit” introduced in claim 9.
For each of the above units which invoked corresponding 112(f) claim interpretations as having a generic placeholder (unit) coupled by functional language (configured to) without reciting sufficient structure, the specification reads “The CPU 601 executes a program (application software) read into the RAM 602 to control each processing unit of the arithmetic processing section 101 (FIG. 1)” [0070]. Thus, Examiner best interprets each of the above units to exemplify a function of a computer realized by software execution. As such, any such software or computer which performs the designated function of each unit will be considered pertinent when examining the prior art.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
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 1 and 13 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
On January 7, 2019, the USPTO released new examination guidelines setting forth a two-step inquiry for determining whether a claim is directed to non-statutory subject matter. According to the guidelines, a claim is directed to non-statutory subject matter if:
STEP 1: the claim does not fall within one of the four statutory categories of invention (process, machine, manufacture or composition of matter), or
STEP 2: the claim recites a judicial exception, e.g. an abstract idea, without reciting additional elements that amount to significantly more than the judicial exception, as determined using the following analysis:
STEP 2A (PRONG 1): Does the claim recite an abstract idea, law of nature, or natural phenomenon?
STEP 2A (PRONG 2): Does the claim recite additional elements that integrate the judicial exception into a practical application?
STEP 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception?
Using the two-step inquiry, it is clear that claims 1 and 13 are directed toward non-statutory subject matter, as shown below:
STEP 1: Do claims 1 and 13 fall within one of the statutory categories? Claim 1 is directed to a system and claim 13 is a method, and as such fall within one of the statutory categories.
STEP 2A (PRONG 1): Is the claim directed to a law of nature, a natural phenomenon or an abstract idea? Yes, claims 1 and 13 are directed to mental processes.
With regard to STEP 2A (PRONG 1), the guidelines provide three groupings of subject matter that are considered abstract ideas:
Mathematical concepts – mathematical relationships, mathematical formulas or equations, mathematical calculations;
Certain methods of organizing human activity – fundamental economic principles or practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations); managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions); and
Mental processes – concepts that are practicably performed in the human mind (including an observation, evaluation, judgment, opinion).
Regarding claim 1, the claim recites “…input task information related to a conveyance task and robot information related to a robot that executes the conveyance task…calculate a delay risk that is a probability that the conveyance task by the robot is delayed… execute task assignment processing that is processing of assigning the robot included in the robot information to the conveyance task included in the task information so that the delay risk is minimized…”. These limitations are exemplary of mental processes because a human, through the power of their own mind and/or with aid of a pen and paper, would be able to input, i.e., write down, information about a task and a robot, calculate a probability of delay in a conveyance task by judging the provided information, and then assign said tasks to said robots such that this predicted delay is minimized. These limitations are merely an example of evaluation and judgement with regard to a task scheduling process which can be mentally performed by a human and as such are abstract ideas.
Claim 13 shares similar limitations which recite “…calculating … a delay risk that is a probability that the conveyance task by the robot is delayed… executing … task assignment processing that is processing of assigning the robot included in the robot information to the conveyance task included in the task information so that the delay risk is minimized…” and as such are additionally abstract ideas which fall under the mental processes grouping for similar reasons as those stated above for claim 1.
STEP 2A (PRONG 2): Does the claim recite additional elements that integrate the judicial exception into a practical application? No, the claims do not recite additional elements that integrate the judicial exception into a practical application.
With regard to STEP 2A (prong 2), whether the claim recites additional elements that integrate the judicial exception into a practical application, the guidelines provide the following exemplary considerations that are indicative that an additional element (or combination of elements) may have integrated the judicial exception into a practical application:
an additional element reflects an improvement in the functioning of a computer, or an improvement to other technology or technical field;
an additional element that applies or uses a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition;
an additional element implements a judicial exception with, or uses a judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim;
an additional element effects a transformation or reduction of a particular article to a different state or thing; and
an additional element applies or uses the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception.
While the guidelines further state that the exemplary considerations are not an exhaustive list and that there may be other examples of integrating the exception into a practical application, the guidelines also list examples in which a judicial exception has not been integrated into a practical application:
an additional element merely recites the words “apply it” (or an equivalent) with the judicial exception, or merely includes instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea;
an additional element adds insignificant extra-solution activity to the judicial exception; and
an additional element does no more than generally link the use of a judicial exception to a particular technological environment or field of use.
Claims 1 and 13 do not recite any of the exemplary considerations that are indicative of an abstract idea having been integrated into a practical application. Also, as noted above, merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea is indicative that the judicial exception has not been integrated into a practical application. Therefore, given that each of the “units” are best understood to be software functions of a computer program executed by a processor and the arithmetic processing section is best understood to be the greater collection of those functions as a computer, each step which is performed by any such unit or arithmetic processing section is merely an “apply it” step/feature which utilizes a computer as a tool to implement the automated instruction of the abstract ideas.
In addition to such “apply it” steps and features of the claim, claim 1 additionally recites “…output a task assignment result which is a result of the task assignment processing…” and claim 13 additionally recites “…outputting … a task assignment result that is a result of the task assignment processing…”. Such limitations are examples of mere data output, which is identified as insignificant extra-solution activity in MPEP 2106.05(g). Examiner ascertains that these limitations are necessary data output because all uses of the recited judicial exception require such data output.
STEP 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception? No, the claims do not recite additional elements that amount to significantly more than the judicial exception.
With regard to STEP 2B, whether the claims recite additional elements that provide significantly more than the recited judicial exception, the guidelines specify that the pre-guideline procedure is still in effect. Specifically, that examiners should continue to consider whether an additional element or combination of elements:
adds a specific limitation or combination of limitations that are not well-understood, routine, conventional activity in the field, which is indicative that an inventive concept may be present; or
simply appends well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, which is indicative that an inventive concept may not be present.
Claims 1 and 13 do not recite any specific limitation or combination of limitations that are not well-understood, routine, conventional (WURC) activity in the field. Limitations identified as “apply it” in step 2A qualify as apply it in step 2B as well.
CONCLUSION
Thus, since claims 1 and 13 are: (a) directed toward an abstract idea, (b) do not recite additional elements that integrate the judicial exception into a practical application, and (c) do not recite additional elements that amount to significantly more than the judicial exception, it is clear that claims 1 and 13 are directed towards non-statutory subject matter.
DEPENDENT CLAIMS
Dependent claims 2-12 and 14 do not recite any further limitations that cause the claim(s) to be patent eligible. Rather, the limitations of the dependent claims are directed toward additional aspects of the judicial exception and/or well-understood, routine and conventional additional elements that do not integrate the judicial exception into a practical application because the elements of the dependent limitations merely recite additional subject matter which can be practically performed by the human mind, or insignificant extra solution activity. Those limitations such as the control command unit of claim 9 and the path planning unit of claims 3 and 4 are not considered to be patent eligible because the robot is merely provided commands or the path is planned, but no such practical application as identified in the specification regarding a specific control of the robot is incorporated into the claim. Therefore, dependent claims 2-12 and 14 are not patent eligible under the same rationale as provided for in the rejection of claims 1 and 13.
Therefore, claims 1-14 are ineligible under 35 USC §101 and Applicant is advised to include additional features which are beyond reasonable human judgement and evaluation capabilities.
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 2-7 and 14 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.
Claim 2 recites “…distribution of the conveyance time of the robot..” in lines 4-5. There is insufficient antecedent basis for this limitation in the claim. No such conveyance time has been introduced and therefore it is unclear which conveyance time is being referred to. As such, Examiner will read the claim as “…distribution of a conveyance time of the robot…” such that any conveyance time will suffice when reviewing the prior art.
Claim 14 is rejected as having a similar limitation.
Claims 3-4 and 6 are rejected as being dependent on claim 2.
Claim 2 recites “…each combination of the conveyance task … and the robot…” in lines 6-8. Claim 5 recites “…for all the conveyance tasks…” in line 4. Claim 7 “…an optimal number of robots…” in lines 3-4. Claim 14 recites “…each combination of the conveyance task… and the robot…” in lines 7-9. Each of these limitations alludes to more than one conveyance task and/or more than one robot included in the task information or the robot information respectively. However, no such plurality of conveyance tasks or plurality of robots are claimed. Claim 1 and 13 only claim a single conveyance task and a single robot as input information for the system and method. As such the metes and bounds of claims 2, 5, 7, and 14 are unclear since these limitations rely upon a plurality of conveyance tasks and robots in the system. For the purpose of Examination, Examiner will consider any such prior art which has a plurality of tasks and robots performing the task assignment processing. Applicant should amend the claims to include such a plurality of conveyance tasks and robots within the system and method such that a combination of conveyance tasks and robots and multiple such conveyance tasks and robots may be considered within the scope of the claimed invention.
Claims 3-4 and 6 are rejected as being dependent on claim 2.
Examiner notes wherein the claims have been addressed below, in view of the prior art record, as best understood by the Examiner in light of the 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph rejections provided herein.
Claim Rejections - 35 USC § 102
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 (i.e., changing from AIA to pre-AIA ) 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.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1, 5, 9, 11, and 13 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Vishwakarma et al. (US 2024/0302837 A1; hereinafter “Vishwakarma”).
Regarding claim 1, Vishwakarma discloses a conveyance task management system (“Workload control circuitry” of Fig. 4.) comprising:
a conveyance information reception unit configured to input task information related to a conveyance task and robot information related to a robot that executes the conveyance task (“The machine readable instructions and/or the operations 600 of FIG. 6 begin at block 602, at which the local variable analysis circuitry identifies pending task(s) to be performed and properties associated with the task(s) (e.g., the pending task data 414). For example, the local variable analysis circuitry 402 of the example workload control circuitry 122 of FIG. 4 identifies pending or uncompleted task(s) in connection with orders received via the management engine 220. The task properties can include, for instance, good(s) to be retrieved for the order(s), fulfillment deadlines, etc.” [0108]. “At block 604, the local variable analysis circuitry 402 identifies autonomous robots 116, 118, 120, 200 that are available to perform tasks or expected to be available within a threshold period of time based on, for instance, data provided by the vehicle control circuitry 210 of the respective robots 116, 118, 120, 200 (e.g., the robot status data 412). The local variable analysis circuitry 402 identifies properties associated with robots 116, 118, 120, 200 such as robot type” [0109]. Thus, the local variable analysis circuitry, i.e., conveyance information reception unit, is configured to input pending task data, i.e., task information related to a conveyance task, and robot information related to available robots that execute the conveyance tasks.);
a delay risk calculation unit configured to calculate a delay risk that is a probability that the conveyance task by the robot is delayed (“At block 608, the global variable analysis circuitry 404 of the example workload control circuitry 122 executes the global variable analysis model(s) 316 to determine or predict a likelihood of the presence of conditions in the environment (e.g., the warehouse 102) that can affect (e.g., negatively impact) performance efficiency of the robot(s) 116, 118, 120, 200 in performing the pending tasks. For example, based on the environment status data 418 (e.g., image data of the environment generated by the sensors 121, 123, 125 associated with the robots, other equipment, and/or user devices) and the global variable analysis model(s) 316, the global variable analysis circuitry 404 can predict instances of congestion at particular area(s) of the warehouse 102 at particular times” [0111]. Thus, the global variable analysis circuitry, i.e., delay risk calculation unit, is configured to calculate the likelihood that conditions which negatively impact performance efficiency, i.e., a delay risk that is a probability that the conveyance task by the robot is delayed.);
a task assignment unit configured to execute task assignment processing that is processing of assigning the robot included in the robot information to the conveyance task included in the task information so that the delay risk is minimized (“At block 610, the scheduling circuitry 406 of the example workload control circuitry 122 executes the workload assignment model(s) 318 to generate the adjusted task assignment data 422. In particular, the scheduling circuitry 406 executes the workload assignment model(s) in view of the initial task assignment data 416 and the global condition data 420 to select a pending task to be performed by a particular autonomous robot 116, 118, 120, 200 to minimize disruptions (e.g., navigational disruptions) to the robot 116, 118, 120, 200 during performance of the task in view of current or expected conditions at the warehouse 102” [0112]. Thus, the scheduling circuitry, i.e., task assignment unit, is configured to execute workload assignment model(s), i.e., execute task assignment processing, in view of the initial assignment data, i.e., the robot included in the robot information and conveyance task included in the task information, to select a particular robot to perform the pending task so that disruptions are minimized, i.e., delay risk is minimized.); and
a result output unit configured to output a task assignment result which is a result of the task assignment processing (“At block 612, the robot interface circuitry 400 of the example workload control circuitry 122 of FIG. 4 transmits instructions to the robot(s) 116, 118, 120, 200 based on the adjusted task assignment data 422 to cause the robot(s) 116, 118, 120, 200 to perform the task(s)” [0113]. Thus, the robot interface circuitry, i.e., result output unit, outputs a task assignment result as a robot instruction to perform the tasks which the robots were assigned to, i.e., a result of the task assignment processing.).
Regarding claim 5, Vishwakarma discloses the conveyance task management system according to Claim 1,
wherein the delay risk calculation unit obtains the delay risk for all the conveyance tasks included in the task information (In step 610, the system reevaluates via the scheduling circuitry information regarding each task which is to be assigned based on the global condition data acquired from the global variable analysis circuitry, i.e., the delay risk calculation unit, and therefore obtains the delay risk for all conveyance tasks included in the task information.), and
the task assignment unit assigns the robot to the conveyance task so that a sum or a maximum value of the delay risks is minimized (“Examples disclosed herein selectively assign pending or uncompleted task(s) to an autonomous robot to increase efficiency of the robot in performing the task(s) in view of properties of the task(s) (e.g., characteristics of the goods associated with the task, completion deadline) as well as conditions in the environment (e.g., a warehouse) in which the robot is located that can affect performance of the task(s) by the robot. Some examples disclosed herein schedule performance of task(s) by an autonomous robot to minimize congestion in the warehouse due to the presence of other autonomous robots, individual workers, and/or other types of equipment (e.g., manually operated equipment) in the warehouse” [0024]. Thus, the system which assigns tasks evaluates for an increased efficiency in task performance, i.e., a minimization of the sum of all factors regarding the delay risk, or determines the minimum congestion for any such robot which is assigned a task, i.e., a minimization of the maximum value of the congestion delay risk. “Instead, in this example, the workload control circuitry 122 instructs the first autonomous vehicle 116 to perform that second task to retrieve the second product 124 from the second inventory storage location 106. As illustrated in FIG. 1, the second inventory storage location 106 is located in a less congested area than the first inventory storage location 104. Thus, the first autonomous vehicle 116 can navigate to and perform the second task more efficiently than the first task. In some examples, the first autonomous vehicle 116 navigates to and performs the second task more efficiently than the first task even if the first autonomous vehicle 116 travels a further distance to the second inventory storage location 106 than the first inventory storage location 104 due to the lack of congestion at the second inventory storage location 106” [0037]. Thus, in this example, the robot is assigned to the task which requires a further distance but results in a less congested travel route, i.e., assignment based on the minimization of a maximum congestion value. “Thus, the workload control circuitry 122 of FIG. 1 considers (a) local variables with respect to properties of tasks to be performed and current or expected availability of one or more types of autonomous robots to perform the tasks and (b) global variables with respect to conditions in the warehouse 102 that can affect travel of the autonomous robots and performance of the tasks. As disclosed herein, the workload control circuitry 122 executes machine learning models to assign tasks to the autonomous vehicles 116, 118, 120 to facilitate (e.g., optimize, maximize) navigational efficiency of the autonomous vehicles 116, 118, 120 and, thus, efficiency in execution of the tasks” [0042]. Thus, the robots are assigned based on a maximum efficiency based on a combination data regarding the properties of the task, the expected availability, and global conditions of the warehouse, i.e., a minimization of a sum of all delay risk factors resulting in the maximum efficiency.).
Regarding claim 9, Vishwakarma discloses the conveyance task management system according to Claim 1, including:
a control command unit configured to give a control command to the robot, based on the task assignment result (“The robot interface circuitry 400 transmits instructions to the vehicle control circuitry 210 of the respective autonomous vehicle(s) 116, 118, 120, 200 that have been assigned tasks based on the adjusted task assignment data 422. The instructions cause the autonomous vehicle(s) 116, 118, 120, 200 to travel and perform the tasks” [0086]. Thus, the robot interface circuitry is a functional equivalent to the control command unit as it is configured to provide instructions, i.e., give a control command, to the robot based on the task assignment result.); and
a task execution status acquisition unit configured to acquire, from the robot, task execution status data including an execution status of the conveyance task of the robot (“At block 614, the monitoring circuitry 408 of the example workload control circuitry 122 of FIG. 4 monitors performance and/or completion of the task(s) assigned to the autonomous robot(s) 116, 118, 120, 200 by the scheduling circuitry 406. For example, based on outputs of the vehicle control circuitry 210; the other equipment 126, 128; the user device(s) 132; and/or the sensor(s) 121, 123, 125, 127, the monitoring circuitry 408 can track performance of the task(s) by the robot(s) 116, 118, 120, 200, identify changes or unexpected in conditions in the warehouse 102 that can affect performance of the tasks, etc. For instance, the monitoring circuitry 408 can detect that unexpected congestion in the warehouse 102 is causing a duration for completion of a task by a particular robot 116, 118, 120, 200 to increase” [0114]. Thus, the monitoring circuitry functions as a task execution status acquisition unit to acquire, from the robot, the task performance and completion information, i.e., task execution status data, which tracks the performance, i.e., execution status, of the conveyance task of the robot.).
Regarding claim 11, Vishwakarma discloses the conveyance task management system according to Claim 9,
wherein the task execution status acquisition unit acquires the task execution status data from the robot (As identified in the rejection of claim 9, the task execution status data is received from the vehicle control circuitry and sensors and thus acquires the task execution status data from the robot.), and
the task assignment unit acquires the task execution status data from the task execution status acquisition unit and executes the task assignment processing according to the acquired task execution status data (“At block 616, the monitoring circuitry 408 determines if the task assignment(s) should be revised in view of the monitoring. At block 618, the local variable analysis circuitry 402, the global variable analysis circuitry 404, and/or the scheduling circuitry 406 can modify the evaluation of the tasks, the evaluation of the warehouse conditions, and/or the task assignments in response to the monitoring to affect (e.g., adjust) the performance of the task(s) by the robot(s) 116, 118, 120, 200 (e.g., via instructions output by the robot control circuitry 400)” [0115]. Thus, the monitoring circuitry, i.e., task execution status acquisition unit, determines whether or not the task assignments should be modified. This determination, i.e., the task execution status data, is transmitted to the scheduling circuitry, i.e., task assignment unit, executes the task assignment processing to adjust task assignments based on the task execution status data as described above.).
Regarding claim 13, Vishwakarma discloses a conveyance task management method that is executed by a conveyance task management system having an arithmetic processing section (“Disclosed herein are example systems, apparatus, and methods that provide for scheduling of tasks to be performed by autonomous robots (e.g., autonomous vehicles) by considering performance efficiency of the autonomous robots in completing the tasks” [0023]. Thus, there is a conveyance task management method executed by the system which has the arithmetic processing section (workload control circuitry and other arithmetic processing features) of Fig. 4.), the conveyance task management method comprising:
a conveyance data input step of inputting by the arithmetic processing section, task information related to a conveyance task and robot information related to a robot that executes the conveyance task (“The machine readable instructions and/or the operations 600 of FIG. 6 begin at block 602, at which the local variable analysis circuitry identifies pending task(s) to be performed and properties associated with the task(s) (e.g., the pending task data 414). For example, the local variable analysis circuitry 402 of the example workload control circuitry 122 of FIG. 4 identifies pending or uncompleted task(s) in connection with orders received via the management engine 220. The task properties can include, for instance, good(s) to be retrieved for the order(s), fulfillment deadlines, etc.” [0108]. “At block 604, the local variable analysis circuitry 402 identifies autonomous robots 116, 118, 120, 200 that are available to perform tasks or expected to be available within a threshold period of time based on, for instance, data provided by the vehicle control circuitry 210 of the respective robots 116, 118, 120, 200 (e.g., the robot status data 412). The local variable analysis circuitry 402 identifies properties associated with robots 116, 118, 120, 200 such as robot type” [0109]. Thus, the local variable analysis circuitry performs an input step to input pending task data, i.e., task information related to a conveyance task, and robot information related to available robots that execute the conveyance tasks.);
a delay risk calculation step of calculating by the arithmetic processing section, a delay risk that is a probability that the conveyance task by the robot is delayed (“At block 608, the global variable analysis circuitry 404 of the example workload control circuitry 122 executes the global variable analysis model(s) 316 to determine or predict a likelihood of the presence of conditions in the environment (e.g., the warehouse 102) that can affect (e.g., negatively impact) performance efficiency of the robot(s) 116, 118, 120, 200 in performing the pending tasks. For example, based on the environment status data 418 (e.g., image data of the environment generated by the sensors 121, 123, 125 associated with the robots, other equipment, and/or user devices) and the global variable analysis model(s) 316, the global variable analysis circuitry 404 can predict instances of congestion at particular area(s) of the warehouse 102 at particular times” [0111]. Thus, the global variable analysis circuitry performs a delay risk calculation step to calculate the likelihood that conditions which negatively impact performance efficiency, i.e., a delay risk that is a probability that the conveyance task by the robot is delayed.);
a task assignment step of executing by the arithmetic processing section, task assignment processing that is processing of assigning the robot included in the robot information to the conveyance task included in the task information so that the delay risk is minimized (“At block 610, the scheduling circuitry 406 of the example workload control circuitry 122 executes the workload assignment model(s) 318 to generate the adjusted task assignment data 422. In particular, the scheduling circuitry 406 executes the workload assignment model(s) in view of the initial task assignment data 416 and the global condition data 420 to select a pending task to be performed by a particular autonomous robot 116, 118, 120, 200 to minimize disruptions (e.g., navigational disruptions) to the robot 116, 118, 120, 200 during performance of the task in view of current or expected conditions at the warehouse 102” [0112]. Thus, the scheduling circuitry performs a task assignment step to execute workload assignment model(s), i.e., execute task assignment processing, in view of the initial assignment data, i.e., the robot included in the robot information and conveyance task included in the task information, to select a particular robot to perform the pending task so that disruptions are minimized, i.e., delay risk is minimized.); and
a result output step of outputting by the arithmetic processing section, a task assignment result that is a result of the task assignment processing (“At block 612, the robot interface circuitry 400 of the example workload control circuitry 122 of FIG. 4 transmits instructions to the robot(s) 116, 118, 120, 200 based on the adjusted task assignment data 422 to cause the robot(s) 116, 118, 120, 200 to perform the task(s)” [0113]. Thus, the robot interface circuitry performs a result output step and outputs a task assignment result as a robot instruction to perform the tasks which the robots were assigned to, i.e., a result of the task assignment processing.).
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 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.
Claims 2-4, 6, 8, and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Vishwakarma.
Regarding claim 2, Vishwakarma teaches the conveyance task management system according to Claim 1.
However, Vishwakarma does not explicitly teach but rather implies …wherein delay risk calculation unit calculates the delay risk based on a probability density distribution of the conveyance time of the robot (“The scheduling circuitry 406 can assign the second uncompleted pending task to the first autonomous vehicle 116 if, as a result of execution of the workload assignment model(s) 318, the scheduling circuitry 406 determines that the first autonomous vehicle 116 will be able to complete the second pending task with less navigation disruptions than the first pending task. For example, congestion in the warehouse associated with the location of the first uncompleted task (as indicated by the global condition data 420) can weigh against assigning the first uncompleted task to the first autonomous vehicle 116 because of the likelihood that the first autonomous vehicle 116 will encounter disruptions (e.g., reduced speeds, unplanned stops, increased duration to complete the task) when traveling to or at the location of the first task. Put another way, as result of execution of the workload assignment model(s) 318, the scheduling circuitry 406 selects the second pending task for the first autonomous vehicle 116 because the first autonomous vehicle 116 will achieve better performance condition(s) (e.g., task completion efficiency) for the second pending task than if the first autonomous vehicle 116 was assigned the first pending task" [0081]. Thus, although Vishwakarma is not explicit in determining a probability density distribution of the conveyance time of the robot, the likelihood of disruptions over the time travelling to the location, i.e., conveyance time, is an implied teaching of a probability density distribution for completing a specified task at the determined task location. See also [0037-0039] which details how the likelihood of congestion is treated as a continuous variable in determining task assignment. See also [0078-0079] regarding the global variable analysis model used to predict congestion within a threshold period of time or particular times at particular areas of the warehouse.), and
the probability density distribution is set for each combination of the conveyance task included in the task information and the robot included in the robot information (Paragraphs [0080-0084] detail the above probability density distribution as one which evaluates all possible tasks available to be completed and the robots available to complete them and determines the predicted distribution for completion time based on the environment (i.e., congestion), the type of task to be assigned, and the possible interference between agents which would result from a specified combination of robot and conveyance task assignments.).
Regarding claim 3, Vishwakarma teaches the conveyance task management system according to Claim 2, including:
a path planning unit configured to plan a movement path of the robot for the conveyance task (“The autonomous vehicles 116, 118, 120 travel in the warehouse 102 to particular inventory storage locations 104, 106, 108, 110, 112 in response to instructions received from workload control circuitry 122” [0031]. Thus, the workload control circuitry, i.e., path planning unit, plans a movement path of the robot for the conveyance task.); and
a conveyance simulation execution unit which performs simulation and obtains the probability density distribution for the conveyance task by using the movement path planned by the path planning unit (“In some examples, the scheduling circuitry 406 can assign a third pending task to a third autonomous vehicle 120, where the third pending task is associated with a same location in the warehouse 102 as the second task assigned to the first autonomous vehicle 116. For instance, the scheduling circuitry 406 can assign the third task to the third autonomous vehicle 120 if performance of the third task will not interfere with performance of the second task by the first autonomous vehicle 116 at the location. For example, the scheduling circuitry 406 can cause the third autonomous vehicle 120 to travel to the first location such that the arrival times of the first and third autonomous vehicles 116, 120 at the location is staggered (e.g., to prevent congestion)” [0083]. Thus, when the scheduling circuitry is in the process of assigning tasks, as with the example above, the scheduling circuitry evaluates the predicted congestion and time to travel to a task location, i.e., probability distribution, based on the projected travel path of the autonomous robot. Such an evaluation is effectively a simulation because the evaluation of these scenarios occur prior to assigning the task.),
wherein the delay risk calculation unit calculates the delay risk based on the probability density distribution obtained by the conveyance simulation execution unit (As identified in the rejection of claim 2, the evaluation of task assignment is based on a delay risk calculation by the scheduling circuitry, i.e., delay risk calculation unit, based on the prediction of congestion and comparison of planned robot tasks which contribute to congestion over time at specified locations during the conveyance task, i.e., probability density distribution obtained by the conveyance simulation execution unit.).
Regarding claim 4, Vishwakarma teaches the conveyance task management system according to Claim 2, including:
a path planning unit configured to plan a movement path of the robot for the conveyance task (“The autonomous vehicles 116, 118, 120 travel in the warehouse 102 to particular inventory storage locations 104, 106, 108, 110, 112 in response to instructions received from workload control circuitry 122” [0031]. Thus, the workload control circuitry, i.e., path planning unit, plans a movement path of the robot for the conveyance task.); and
an actual measurement data evaluation unit which stores actual measurement data when the robot actually executes the conveyance task (“At block 620, the feedback circuitry 410 of the example workload control circuitry 122 provides feedback for the machine learning model training circuitry 300. The feedback can be indicative of instances when the autonomous robot(s) 116, 118, 120, 200 experienced navigational disruptions due to congestion in the warehouse 102 (e.g., examples of unsuccessful scheduling), instances in which the robot(s) 116, 118, 120, 200 were not able to efficiency complete a task due to robot type, instances when the robot(s) 116, 118, 120, 200 did not experience navigational disruptions when performing a task (e.g., examples of successful scheduling)” [0116]. Thus, the feedback circuitry, i.e., actual measurement data evaluation unit, stores the actual measurement data when the robot actually executes the conveyance task as feedback data for the machine learning models.), and obtains the probability density distribution from the conveyance time included in the actual measurement data for the movement path planned by the path planning unit for the conveyance task (See [0059-0067] for details regarding training data for the associated models which perform task assignment. Specifically, Paragraph [0064] describes training data including job performance metrics by robots during levels of congestion, thus obtaining a probability density distribution as previously described from the performance metric, i.e., conveyance time, included in the actual measurement data for the movement path planned by the workload control circuitry, i.e., path planning unit, for the conveyance task.),
wherein the delay risk calculation unit calculates the delay risk based on the probability density distribution obtained by the actual measurement data evaluation unit (“The data from the feedback circuitry 410 can be used by the machine learning model training circuitry 300 of FIG. 3 to adjust or refine the model(s) 312, 316, 318” [0090]. Thus, the data from the feedback circuitry, i.e., actual measurement data evaluation unit, provides data to the machine learning model training circuitry to determine the probability density distribution and calculate the delay risk based on such trained models.).
Regarding claim 6, Vishwakarma teaches the conveyance task management system according to Claim 2, wherein the delay risk calculation unit obtains the conveyance time by using a representative value of the probability density distribution (“In particular, as a result of execution of the workload assignment model(s) 318, the scheduling circuitry 406 assigns a task to an autonomous vehicle 116, 118, 120, 200 such that the task will be completed by the autonomous vehicle 116, 118, 120, 200 to optimize (e.g., increase, maximize, minimize negative effects on) the performance efficiency metrics associated with completion of the task. The performance efficiency metrics can include, for instance, a duration of time for completion of the task, the amount of congestion in the warehouse 102 experienced by the autonomous vehicle 116, 118, 120, 200 when performing the task, the number of disruptions (e.g., unplanned stops) during travel of the autonomous vehicle 116, 118, 120, 200 while completing the task, a success rate in meeting shipping deadlines for an order associated with the task, and/or other conditions that can affect efficiency of the autonomous vehicle 116, 118, 120, 200 in performing the task” [0080]. Thus, based on the global conditions acquired by the global condition analysis circuitry, i.e., delay risk calculation unit, the scheduling circuitry which further evaluates the delay risk regarding each task, i.e., acts as a functional equivalent to further the features of the delay risk calculation unit, determines performance efficiency metrics based on the above described conditions, i.e., representative values, which result in the probability density distribution determined in the rejection of Claim 2.).
Regarding claim 8, Vishwakarma teaches the conveyance task management system according to Claim 1,
including an optimization unit configured to optimize a layout of a conveyance area in which the robot executes the conveyance task (Paragraphs [0030-0041], the disclosure describes various arrangements of task scheduling to move automated vehicles, machines, and other users throughout the warehouse in a distributed/dynamic layout. “Thus, the workload control circuitry 122 of FIG. 1 considers (a) local variables with respect to properties of tasks to be performed and current or expected availability of one or more types of autonomous robots to perform the tasks and (b) global variables with respect to conditions in the warehouse 102 that can affect travel of the autonomous robots and performance of the tasks. As disclosed herein, the workload control circuitry 122 executes machine learning models to assign tasks to the autonomous vehicles 116, 118, 120 to facilitate (e.g., optimize, maximize) navigational efficiency of the autonomous vehicles 116, 118, 120 and, thus, efficiency in execution of the tasks” [0042]. “In some examples, to minimize disruption to the autonomous vehicles 116, 118, 120, 200 in performing pending tasks, the scheduling circuitry 406 can assign tasks and/or generate schedule(s) of task(s) to be performed by the other equipment 126, 128 (e.g., manually operated equipment) and/or individual(s) 130 to avoid interference with performance of the task(s) assigned to the autonomous vehicle(s) 116, 118, 120, 200. For example, a manually operated forklift may be assigned a first task to retrieve an object for an order but is likely to block or substantially block an aisle in the warehouse 102 while performing the first task. The second autonomous vehicle 118 of FIG. 2 may be assigned a second task that also involves retrieving an item from the same aisle. In this example, the scheduling circuitry 406 can schedule the forklift to perform the first task after the second autonomous vehicle 118 has traveled through the aisle in connection with performance of the second task” [0085]. Thus, the scheduling circuitry 406 operates as the optimization unit which optimizes the layout of the conveyance area, i.e., paths and timing for different machines/vehicles/users regarding the facility, in which the robot executes the task.),
wherein the task assignment unit performs the task assignment processing plural times while changing the layout (The adjusted task assignment data is generated by the scheduling circuitry, i.e., task assignment unit, after the initial task assignment data to consider environmental features such as congestion and robot path distribution (see [0112]). Thus, the task assignment unit performs the task assignment processing plural times while changing the distribution of the scheduled paths and assignments, i.e., layout.), and
the optimization unit derives an evaluation index related to the delay risk for each of the layouts, and obtains the optimal layout using the evaluation index (The scheduling circuitry, i.e., optimization unit, optimizes performance efficiency metrics, i.e., evaluation indices related to the delay risk, for each layout to optimize the task assignment and therefore the task layout ([0112]).).
Regarding claim 14, Vishwakarma teaches the conveyance task management method according to Claim 13.
However, Vishwakarma does not explicitly teach but rather implies …wherein in the delay risk calculation step, the arithmetic processing section calculates the delay risk based on a probability density distribution of the conveyance time of the robot (“The scheduling circuitry 406 can assign the second uncompleted pending task to the first autonomous vehicle 116 if, as a result of execution of the workload assignment model(s) 318, the scheduling circuitry 406 determines that the first autonomous vehicle 116 will be able to complete the second pending task with less navigation disruptions than the first pending task. For example, congestion in the warehouse associated with the location of the first uncompleted task (as indicated by the global condition data 420) can weigh against assigning the first uncompleted task to the first autonomous vehicle 116 because of the likelihood that the first autonomous vehicle 116 will encounter disruptions (e.g., reduced speeds, unplanned stops, increased duration to complete the task) when traveling to or at the location of the first task. Put another way, as result of execution of the workload assignment model(s) 318, the scheduling circuitry 406 selects the second pending task for the first autonomous vehicle 116 because the first autonomous vehicle 116 will achieve better performance condition(s) (e.g., task completion efficiency) for the second pending task than if the first autonomous vehicle 116 was assigned the first pending task" [0081]. Thus, although Vishwakarma is not explicit in determining a probability density distribution of the conveyance time of the robot, the likelihood of disruptions over the time travelling to the location, i.e., conveyance time, is an implied teaching of a probability density distribution for completing a specified task at the determined task location. See also [0037-0039] which details how the likelihood of congestion is treated as a continuous variable in determining task assignment. See also [0078-0079] regarding the global variable analysis model used to predict congestion within a threshold period of time or particular times at particular areas of the warehouse.), and
the probability density distribution is set for each combination of the conveyance task included in the task information and the robot included in the robot information (Paragraphs [0080-0084] detail the above probability density distribution as one which evaluates all possible tasks available to be completed and the robots available to complete them and determines the predicted distribution for completion time based on the environment (i.e., congestion), the type of task to be assigned, and the possible interference between agents which would result from a specified combination of robot and conveyance task assignments.).
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Vishwakarma in view of Lyu et al. (“Approach to Integrated Scheduling Problems Considering Optimal Number of Automated Guided Vehicles and Conflict-Free Routing in Flexible Manufacturing Systems”, 2019; hereinafter “Lyu”).
Regarding claim 7, Vishwakarma teaches the conveyance task management system according to Claim 1.
However, Vishwakarma does not explicitly teach …including an optimization unit which obtains an optimal number of the robots,
wherein the task assignment unit performs the task assignment processing plural times while changing the number of the robots, and
the optimization unit derives an evaluation index related to the delay risk for each of the number of the robots and obtains an optimal number of the robots using the evaluation index.
Lyu, pertinent to the problem at hand, teaches …including an optimization unit which obtains an optimal number of the robots (As detailed in Section C (Pages 74919-74920), the goal of the method is to obtain an optimal number of robots for the scheduling problem.),
wherein the task assignment unit performs the task assignment processing plural times while changing the number of the robots (As detailed on Page 74919, the experimental process is described to test 3-5 experiments using different numbers of vehicles wherein each test is executed multiple times to achieve an average and minimum makespan, i.e., task completion time.), and
the optimization unit derives an evaluation index related to the delay risk for each of the number of the robots and obtains an optimal number of the robots using the evaluation index (As detailed also on Page 74919, the goal of the optimization problem is to minimize makespan, i.e., task completion time for all tasks. The secondary goal is then to reduce the “invalid waiting time”. The configuration of the number of robots which generates a minimum makespan while also minimizing invalid waiting time, i.e., both factors which are a total evaluation index related to the delay risk, is the optimal number of robots (see Fig. 9).).
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the task scheduling system of Vishwakarma to include the optimization methods which obtain an optimal number of robots as taught by Lyu with a reasonable expectation of success. One of ordinary skill in the art would have been motivated to make such a modification because using the optimal number of AGVs rather than a fixed number of AGVs will optimize transportation resources and decrease costs, while further resulting in the optimal transportation resources to obtain minimum makespans and satisfy delivery time (Lyu, Page 74921).
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Vishwakarma in view of Orita (US 2006/0265103 A1).
Regarding claim 10, Vishwakarma teaches the conveyance task management system according to Claim 9,
…the task assignment unit acquires the task execution status data from the task execution status acquisition unit and executes the task assignment processing based on the acquired task execution status data (“At block 616, the monitoring circuitry 408 determines if the task assignment(s) should be revised in view of the monitoring. At block 618, the local variable analysis circuitry 402, the global variable analysis circuitry 404, and/or the scheduling circuitry 406 can modify the evaluation of the tasks, the evaluation of the warehouse conditions, and/or the task assignments in response to the monitoring to affect (e.g., adjust) the performance of the task(s) by the robot(s) 116, 118, 120, 200 (e.g., via instructions output by the robot control circuitry 400)” [0115]. Thus, the monitoring circuitry, i.e., task execution status acquisition unit, determines whether or not the task assignments should be modified. This determination, i.e., the task execution status data, is transmitted to the scheduling circuitry, i.e., task assignment unit, executes the task assignment processing to adjust task assignments based on the task execution status data as described above.).
However, Vishwakarma does not explicitly teach …wherein the task execution status acquisition unit acquires the task execution status data from the robot at predetermined time intervals…
Orita, pertinent to the problem at hand, teaches …wherein the task execution status acquisition unit acquires the task execution status data from the robot at predetermined time intervals (“The main control unit 40 further generates data indicative of status of the robot R (status information) at predetermined time intervals, and transmits the generated status information through the wireless communication unit 60 to the robot control apparatus 3. Hereupon, the status information is information used by the robot control apparatus 3 that will be described later to determine whether any two robots R will possibly come upon each other. In the present embodiment, the status information includes: (i) coordinate data indicative of the current position of the robot R (current position information); (ii) battery level information indicative of a remaining amount of charge in the battery 70 installed in the robot R; and (iii) data composed of a task ID indicative of contents of a task that the robot R is currently executing and a progress indicative of the stages of development of the task corresponding to the task ID (task information)” [0060]. Thus, there is a main control unit which generates, i.e., acquires, task execution status data from the robot at predetermined time intervals.)…
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the task execution status acquisition as taught by Vishwakarma to include the task execution status acquisition updates at predetermined time intervals as taught by Orita with a reasonable expectation of success. One of ordinary skill in the art would have been motivated to receive task execution status updates at predetermined time intervals because by regularly checking for task execution status at predetermined time intervals, the system may identify unpredicted task delay factors early and thus increase task completion efficiency.
Claim 12 is rejected under 35 U.S.C. 103 as being unpatentable over Vishwakarma in view of Aisu (US 2020/0293063 A1).
Regarding claim 12, Vishwakarma teaches the conveyance task management system according to Claim 1,
including an input/output terminal having a screen (“In the example of FIG. 2, a user workload application 224 is executed by the processor circuitry 215 of the user device 202. In some examples, the user workload application 224 can receive instructions from the workload control circuitry 122 with respect to, for instance, task(s) or workload(s) assigned to the particular user (e.g., the user 130) with which the user device 202 is associated. The task(s) and/or workload(s) can be displayed via a display screen 226 of the user device 202. A user can provide inputs via the user workload application 224, such as whether a task has been completed, whether a task is unable to be completed, etc. The user task(s) or workload(s) can be associated with, for instance, a particular order” [0049]. Thus, there is an input/output terminal having a screen on the user device.)…
However, since the output of the display is directed to tasks which are assigned to a user, Vishwakarma does not explicitly teach …wherein the input/output terminal displays the task assignment result and the delay risk on the screen.
Aisu, pertinent to the problem at hand, teaches …wherein the input/output terminal displays the task assignment result and the delay risk on the screen (“The display device 603 displays data or information output from the travel planning device 100. While the display device 603 is, for example, an LCD (Liquid Crystal Display), a CRT (Cathode-Ray Tube), and a PDP (Plasma Display Panel), the display device 603 is not limited to this. The data or the information output from the computer device 600 can be displayed by this display device 603” [0174]. As described in [0157] and [0160-0161], part of the data which is output by the travel timing schedule. As such, there is an input/output terminal that displays the task assignment result and delay risk for the resulting schedule on the screen.).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the data output of Vishwakarma to include a display of the task assignment result and delay risk on a screen as taught by Aisu with a reasonable expectation of success. One of ordinary skill in the art would have been motivated to make such a modification because by displaying this information, the user monitoring the manufacturing system can verify the results of the operation and observe the real time task completion expectancies. This verification leads to increased efficiency of the system because the user can directly observe when abnormalities or significant disruptions occur in the scheduling problem. Such a modification is a mere combination of prior art elements according to known methods to yield predictable results (see MPEP 2143.I(A)).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 2025/0244776 A1, US 2020/0074848 A1, US 2020/0406460, US 2023/0013246, US 2021/0286373, and US 2022/0075381 A1 are task allocation systems which aim to minimize task completion time and disruptions in a robotic task completion system and describe probability distributions for task completion efficiencies and time.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SIDNEY L MOLNAR whose telephone number is (571)272-2276. The examiner can normally be reached 9 A.M. to 4 P.M. EST Monday-Friday.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jonathan (Wade) Miles can be reached at (571) 270-7777. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/S.L.M./Examiner, Art Unit 3656
/WADE MILES/Supervisory Patent Examiner, Art Unit 3656
Prosecution Insights