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 .
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.
Joint Inventors
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 05/10/2024 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). A certified copy of this document has been placed in the file wrapper. As such, the effective filing date of the instant application is considered 11/12/2021, coinciding with the filing date of the Japanese application to which foreign priority was requested.
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 1-6 are rejected under 35 U.S.C. 103 as being unpatentable over Hasunuma et al. (US20210197369, referred to as Hasunuma) in view of Tomotoshi et al. (JP2011107836, referred to as Tomotoshi).
Regarding claim 1: Hasunuma discloses: A robot control device comprising: a trained model constructed by being trained on work data when a human manipulates a robot so that the robot performs a series of operations, the work data including input data and output data, the input data being a state of the robot and its surroundings, the output data being corresponding human manipulation or the operation of the robot by the human manipulation; a control data acquirer acquiring control data of the robot to make the robot perform the operations, when the input data concerning the state of the robot and its surroundings are input to the trained model, by acquiring the output data concerning the human manipulation or the operation of the robot predicted accordingly from the trained model; [a progress degree acquirer acquiring a progress degree, when the input data are input to the trained model and the trained model outputs the corresponding output data, indicating to which degree of progress of the series of operations the output data correspond; and an information output part being capable of outputting the progress degree,] wherein the trained model can determine into which of a plurality of process operations, resulting from a division of the series of operations, the input data are classified, in the trained model, an output transition, being a temporal transition of the human manipulation so that the process operation is realized or the operation of the robot by the human manipulation, is defined in association with each classification, the trained model determines output corresponding to the input data from the output transition associated with the classification result of the input data and makes the output the output data, [a changing range of the progress degree is divided into a plurality of progress degree ranges to correspond to the plurality of the process operations, an order of the plurality of progress degree ranges corresponds to an order of the plurality of the process operations in the series of operations, and the progress degree acquired by the progress degree acquirer varies depending on an ordinal number of output corresponding to the input data in the output transition associated with the process operation, in the progress degree range corresponding to the process operation] that is a result of the classification of the input data performed by the trained model. ([0060] the operator operates the operation unit 20 ( operation device 21 ) . The operation unit 20 outputs the operator operation force to the switching device 30 , and the switching device 30 converts the operator operation force into an operation command and outputs the output to operate the robot 10. For example , when the operator inputs to the input unit 23 that the current work state is the work state 3 ( insertion ) , the operator operates the operation device 21 to operate the robot 10 to insert the workpiece 100 and tran sitions the work state into the work state 4 ( completed ) , that is , to complete the work . When the operator inputs to the input unit 23 that the current work state is the new work state 5 ( twist ) , the operator operates the robot 10 to operate the work by operating the operation device 21 and moves upward to be separated from the recess 110 , and the work state 5 ( twist ) is changed to the work state 1 ( in the air ) . [ 0061 ] At this time , the operation unit 20 outputs the operator operation force in which the operator operates the robot 10 so as to change the work state to the control unit 40 ( additional learning unit 43 ) , and the additional learning unit 43 acquire the operator operation force and state values ( S109 ) . For example , when the additional learning unit 43 detects that the operator operation force is input from the operation unit 20 , the additional learning unit 43 outputs a trigger signal to the timekeeping unit 46. Based on the trigger signal , the timekeeping unit 46 outputs a timer signal at a predetermined time interval ( 1 second in the present embodiment ) from the time when the trigger signal is input . Next , the additional learning unit 43 acquires the current state value ( for example , sensor information is acquired from the state detection sensors 11 to 13 ) , and acquires the operator operation force from the operation unit 20. The additional learning unit 43 stores an index having a numeri cal value of 0 , the state value , and the operation force ( that is , the operator operation force ) in association with each other . The additional learning unit 43 acquires the state value and the operator operation force every second based on the timer signal every second from the timekeeping unit 46. The additional learning unit 43 increments the index by 1 stores the index , the state value , and the operation force ( operator operation force ) until the completion of the operation of the robot 10 by the operation of the operator)
Hasunuma does not explicitly disclose: a progress degree acquirer acquiring a progress degree, when the input data are input to the trained model and the trained model outputs the corresponding output data, indicating to which degree of progress of the series of operations the output data correspond; and an information output part being capable of outputting the progress degree … a changing range of the progress degree is divided into a plurality of progress degree ranges to correspond to the plurality of the process operations, an order of the plurality of progress degree ranges corresponds to an order of the plurality of the process operations in the series of operations, and the progress degree acquired by the progress degree acquirer varies depending on an ordinal number of output corresponding to the input data in the output transition associated with the process operation, in the progress degree range corresponding to the process operation
Hasunuma does not disclose the following limitations, however Tomotoshi, from an analogous field of endeavor, further teaches: a progress degree acquirer acquiring a progress degree, when the input data are input to the trained model and the trained model outputs the corresponding output data, indicating to which degree of progress of the series of operations the output data correspond; and an information output part being capable of outputting the progress degree … a changing range of the progress degree is divided into a plurality of progress degree ranges to correspond to the plurality of the process operations, an order of the plurality of progress degree ranges corresponds to an order of the plurality of the process operations in the series of operations, and the progress degree acquired by the progress degree acquirer varies depending on an ordinal number of output corresponding to the input data in the output transition associated with the process operation, in the progress degree range corresponding to the process operation ([0093] ] Finally, from the data recorded in the column of “work ID” (l), the earliest time is extracted as “start time” and the latest time is “completion time” for each ID, as shown in FIGS. Work specific data (D5-1, D5-2) can be generated. [0094] Referring to the work specifying data (D5) shown in FIGS. 12 and 13, which work item (w) is started or completed in the entire work (W) (such as the work item currently being executed). The progress status ((c) first progress in FIG. 20) can be grasped. However, with such data alone, the progress status (p. 2 (d) of FIG. 20, second progress, progress of work item (w) (p) )) Can not be judged. [0095] On the other hand, as one method (prior art), the scheduled work time (h) of the work data (D3) as shown in FIG. 10 is compared with the time elapsed after the start of the work, and the work is performed at that rate. It is conceivable to estimate (calculate) the degree of progress. However, in this method, in the middle of work (actual work) by an operator, for example, incidental work such as material loading, tool maintenance, meeting, adjustment, etc. is performed irregularly, or actual work (net work). When an undefined operation other than the above occurs, the progress degree cannot be estimated with high accuracy. [0096] As a solution to the above, in the present embodiment, the progress estimation unit 142 estimates the degree of progress with higher accuracy and includes data (sensor data (D2)) of each storage unit (131 to 135). ) To estimate the progress (p) of each work item (w). [0097] S202 (S40 to S70) corresponds to the above-described second progress grasping / estimating process ((d) in FIG. 20). It is the following about the processing method which estimates the progress (p) of work item (w) unit by the progress estimation part 142 in S40-S70. For example, for each work, the time ratio of the net work and the accompanying work, the energy consumption during the work, the walking amount during the work, and the like are known from the past experience. Such information can be used for estimation (determination).)
Hasunuma and Tomotoshi are analogous art to the claimed invention since they are from the similar field of robotic processing methods for continuous improvement and user informatics. It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention, with a reasonable expectation for success, to modify the operator teaching processing disclosed in Hasunuma to enable the process degree tracking and presentation taught in Tomotoshi.
The motivation for modification would have been to provide the processing method disclosed in Hasunuma with the method applied to tracking progress as taught in Tomotoshi.
Regarding claim 2: The combination of Hasunuma and Tomotoshi teaches: The robot control device according to claim 1,
Hasunuma further discloses: comprising an operation label storage part stores an operation label including information expressing a content of the process operation associated with the process operation, wherein the information output part can output the operation label. ([0033] The display device 22 is a dot matrix type display such as a liquid crystal or an organic EL . The display device 22 is arranged in the vicinity of the operation device 21 and displays information on the work performed by the robot system 1 based on a video signal , for example , a notification signal described later . When the operation device 21 is arranged at a position away from the robot 10 , the display device 22 may display an image in the vicinity of the robot 10. The input unit 23 is a key or the like that receives the input of the work state by the operator at the time of additional learning described later , and outputs the input work )
Regarding claim 3: The combination of Hasunuma and Tomotoshi teaches: The robot control device according to claim 1,
Hasunuma further discloses: wherein in the work data, the output data include, associating with the progress degree, the human manipulation or the operation of the robot by the human manipulation. ([0060] the operator operates the operation unit 20 ( operation device 21 ) . The operation unit 20 outputs the operator operation force to the switching device 30 , and the switching device 30 converts the operator operation force into an operation command and outputs the output to operate the robot 10. For example , when the operator inputs to the input unit 23 that the current work state is the work state 3 ( insertion ) , the operator operates the operation device 21 to operate the robot 10 to insert the workpiece 100 and tran sitions the work state into the work state 4 ( completed ) , that is , to complete the work . When the operator inputs to the input unit 23 that the current work state is the new work state 5 ( twist ) , the operator operates the robot 10 to operate the work by operating the operation device 21 and moves upward to be separated from the recess 110 , and the work state 5 ( twist ) is changed to the work state 1 ( in the air ) . [ 0061 ] At this time , the operation unit 20 outputs the operator operation force in which the operator operates the robot 10 so as to change the work state to the control unit 40 ( additional learning unit 43 ) , and the additional learning unit 43 acquire the operator operation force and state values ( S109 ) . For example , when the additional learning unit 43 detects that the operator operation force is input from the operation unit 20 , the additional learning unit 43 outputs a trigger signal to the timekeeping unit 46. Based on the trigger signal , the timekeeping unit 46 outputs a timer signal at a predetermined time interval ( 1 second in the present embodiment ) from the time when the trigger signal is input . Next , the additional learning unit 43 acquires the current state value ( for example , sensor information is acquired from the state detection sensors 11 to 13 ) , and acquires the operator operation force from the operation unit 20. The additional learning unit 43 stores an index having a numeri cal value of 0 , the state value , and the operation force ( that is , the operator operation force ) in association with each other . The additional learning unit 43 acquires the state value and the operator operation force every second based on the timer signal every second from the timekeeping unit 46. The additional learning unit 43 increments the index by 1 stores the index , the state value , and the operation force ( operator operation force ) until the completion of the operation of the robot 10 by the operation of the operator)
Regarding claim 4: The combination of Hasunuma and Tomotoshi teaches: The robot control device according to claim 1,
Hasunuma further discloses: wherein the work data are divided into process operation data, each corresponding to one process operation, and the trained model is constructed by clustering for the process operation data. ([0079] The calculation of the progress degree will be described with reference to FIG . 12. In the present embodi ment , as shown in FIG . 12 , the progress degree is calculated in consideration of the cluster obtained by clustering the states of the robot 10 that can be acquired in chronological order ( time series ) and the operation history of the robot 10 . [ 0080 ] The state of the robot 10 described above can be expressed as a multidimensional vector ( feature vector ) including the sensor information from the state detection sensors 11 to 13 and the calculation operation force of the model . The feature vector changes variously in the process of the robot 10 performing a series of operations . The feature vector may include not only the value of the sensor infor mation and the calculation operation force at the present time , but also the past history of the sensor information and the calculation operation force.)
Regarding claim 5: The combination of Hasunuma and Tomotoshi teaches: A robot system comprising: the robot control device according to claim 1;
Hasunuma further discloses: and the robot. ([0010] the robot system includes a robot , a state detection sensor , a time keeping unit , a learning control unit, an operation device, and a switching device)
Regarding claim 6: Rejected using the same rationale as claim 1.
Conclusion
The prior art made of record, and not relied upon, considered pertinent to applicant' s disclosure or directed to the state of art is listed on the enclosed PTO-892.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ATTICUS A CAMERON whose telephone number is 703-756-4535. The examiner can normally be reached M-F 8:30 am - 4:30 pm.
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/ATTICUS A CAMERON/ /JASON HOLLOWAY/ Primary Examiner, Art Unit 3658 Examiner, Art Unit 3658A
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