DETAILED ACTION
Response to Amendment
This action is in response to the remark entered on September 18th, 2025.
Claims 1 - 7 and 9 - 11 are pending in current application.
Claims 1 – 7 and 9 – 11 are amended.
Information Disclosure Statement
The information disclosure statement (IDS) submitted is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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, 2 and 5 - 11 are rejected under 35 U.S.C. 103 as being unpatentable over Feniello et al, US Pat Pub No.2015/0331416 in view of Burger et al, US Pat Pub No. 2019/0070725.
Regarding claims 1, 10 and 11, Feniello et al shows a proposition setting device (See at least Para 0025 for robot task program) comprising: a memory storing instructions (See at least Para 0079 for system memory 16); hardware processors configured to execute the instructions (See at least Para 0079 for central processing unit and graphical processing unit) to:
an abstract state setting means configured to set an abstract state being a state abstracting each objects in a workspace based on a measurement result in the workspace where each robot works (See at least Para 0002 and 00004 for objects separated by features and characterized by orientation, location, perceptual characteristics at starting configuration as starting state for in Para 0001 also on Para 0024 and 0027 for object state in pose information captured using camera 100; also on Para 0005 for each program for the robot to reposition as each robot to operate; also on Para 0032, 0033 and 0035 for DSL for object repositioning with each word as state/tack);
set a propositional area represents a proposition concerning each of objects by an area based on the abstract state ( See at Para 0024 and 0025 for workspace 104 with virtual workspace generated where the object proposition based upon as the propositional area for repositioning tasks for objects based upon object measured state characteristic discussed on Para 0035 - 0037; also on Para 0066 for the objects identified in the workspace with object reposition primitive with scene- reposition pair, Para 0004);
and relative area information concerning relative areas of each of the objects (See at least Para 0028 and 00044 for the perceptual object characteristic includes the size or shape of the objects as the information concerning relative area of object during scene- reposition pair on Para 0004);
generate an operation sequence of robot based on the abstract state and propositional area (See at least para 0021 for robot operation as sorting, knitting and packaging; see at least Para 0025 for operation sequence/task on objects in workspace; See at least Para 0035 – 0037 for repositioning tasks for objects based upon object measured state characteristic discussed on Para 0035 – 0037; also on Para 0004 and 0025 for scene- reposition pair for each task/operation sequence for each scene generated); however, Feniello does not shows the workspace where each of a plurality of robot works.
Burger et al shows the workspace where each of a plurality of robot works (See at least Para 0066 for pluarlity of robots work together in a workspace using team model with set of action and switch transition for actions between each robot agent).
It would have been obvious for one of ordinar y skill in the art, at the time of filing, to provide plurality of work robot as exhibited by Burger, for the robot task program discussed by Feniello, since Feniello discussed the intention for teaching robots carry out tasks purpose on Para 0001 and 0003 for the robot task operation.
Regarding claim 2, Feniello et al shows the hardware processors further configured to execute the instructions (See at least Para 0024 for computing device 108) to:
set the propositional area based on the abstract state and the relative area information (See at least Para 0025 for operation sequence/task on objects in workspace; also on least Para 0035 – 0037 for repositioning tasks for objects based upon object measured state characteristic discussed on Para 0035 – 0037; also on Para 0004 and 0025 for scene- reposition pair for each task/operation sequence); propositional area (See at least Para 0033 for state consist object properties; Para 0004 and 0025 for scene- reposition pair for each task/operation sequence with each individual scene generated);
Burger et al further shows determine whether or not a plurality of proposition is to be integrated including the proposition (See at least Para 0029 and 0030 for nondeterministic finite automation each including proposition and state; also on Para 0024 and 0025 for a sequence not accepted);
set the proposition being integrated (See at least Para 0026 for a robot agent mission integrates a set of task including the proposition), based on the plurality of the proposition determined to be integrated (See at least Para 0026 for a robot agent mission integrates a set of task including the proposition).
It would have been obvious for one of ordinary skill in the art, at the time of filing, to provide the propositional integration of Burger, for the robot operating task propositional area, in order to provide multiple robot teaching integration, as desired and discussed by Feniello et al.
Regarding claim 5, Feniello et al shows hardware processors further configured to execute the instructions (See at least Para 0024 for computing device 108) to:
divide each operable area of the robot (See at least Para 0025 for each tasks equipped to have each own individual scene-reposition pair)
specified based on each of a plurality of propositional areas including the proposition area ( See at least Para 0025 for each tasks equipped to have its own individual scene-reposition pair divided where each scene as operable area based on proposition tasks; also at Para 0024 and 0025 for workspace 104 with virtual workspace generated where the object proposition based upon as the propositional area for repositioning tasks for objects based upon object–– measured state characteristic);
set the propositional area corresponding to each divided operable area (See at least Para 0025 for each tasks equipped to have its own individual scene-reposition pair divided where each scene as operable area based on proposition tasks where each task as repositioning task captured with scene- reposition pair individual for workspace virtual rendering on Para 0024).
Regarding claim 6, Feniello et al shows set an area in which a relative area of the relative areas as the propositional area (See at least Para 0025 for each tasks equipped to have its own individual scene-reposition pair divided where each scene as operable area based on proposition tasks where each task as repositioning task captured with scene- reposition pair individual for workspace virtual rendering on Para 0024),
represented by the relative area information is defined in the workspace (See at least Para 0025 for each tasks equipped to have its own individual scene-reposition pair divided where each scene as operable area based on proposition tasks where each task as repositioning task captured with scene- reposition pair individual for workspace virtual rendering on Para 0024) based on a position and a posture of each object set as the abstract state (See at least Para 0027 and 0028 for initial pose and destination pose of object set forth for the scene- reposition pairs for reposition task; also on Para 0033 for state consist value for object repositioning task and rec store object properties also on figure 3).
Regarding claim 7, Feniello et al shows
extract the relative area information corresponding to each of the objects specified based on the measurement result (See at least Para 0025 for each tasks equipped to have its own individual scene-reposition pair divided; each scene and each task as repositioning task captured with scene- reposition pair individual for workspace virtual rendering on Para 0024 based on camera image and further rendering virtual workspace),
from a database in which the relative area information representing a relative area of the relative areas (See at least Para 0024 for repositioning tasks synthesized form database),
corresponding to a type of each of the objects is associated with the type of each of the objects (See at least Para 0027 and 0028 for each object characterized with perceptual object characteristics; also on at least Para 0024 and 0025 for workspace 104 with virtual workspace generated where the object proposition based upon as the propositional area for repositioning tasks for objects based upon object measured state characteristic discussed on Para 0035 - 0037),
set the propositional area based on the relative area information being extracted (See at least Para 0033 for repositioning task with state and rec set forth for object property; also on at least Para 0024 and 0025 for workspace 104 with virtual workspace generated where the object proposition based upon as the propositional area for repositioning tasks for objects based upon object measured state characteristic discussed on Para 0035 - 0037).
Regarding claim 9, Feniello shows convert a task to be executed by the robot into a first logical formula based on a logic (See at least Para 0075 for robotic task program executed with logic operators; also on Para 0034 for primitive build upon the if then logic statement on figure 4);
propositional area(See at Para 0024 and 0025 for workspace 104 with virtual workspace generated where the object proposition based upon as the propositional area for repositioning tasks for objects based upon object measured state characteristic discussed on Para 0035 - 0037; also on Para 0066 for the objects identified in the workspace with object reposition primitive with scene- reposition pair, Para 0004);
Burger further shows a task to be executed by the robot into a first temporal logic formula (See at least Para 0007 and 0009 for temporal logic used to express temporal logic formula on Para 0010 - 0014 );
generating a time step logical formula as a second logical formula representing a state for each time step to execute the task from the first logical formula (See at least Para 0016 for non-deterministic finite automation has set of states; also on Para 0007 for sequence of linear time temporal defined over sequence of propositions as function of time index t ; also Para 0024 for sequence of proposition applied to non-deterministic finite automation);
generate an abstract model abstracting dynamic in the workspace based the abstract state and the proposition (See at least Para 0026 for given mission M based upon set of tasks; also on Para 0089 for agent models and generating product model including agent model and finite automation);
generate a time series of control inputs for robot by an optimization using the abstract model and the time step logical formula as constraint conditions (See at least Para 0118 for model discrete constraint; also on Para 0109 for minimize robot team cost for agent/robot models with cost function).
It would have been obvious for one of ordinary sill in the art, at the time of filing, to provide optimization control of exhibited by Burger, for the multiple robot control teaching desired by Feniello, in order to further enhance the robot agent collaboration, as desired by both Feniello and Burger.
Claim Objections
Claims 3 and 4 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Gonzalez et al, US Pat Pub No. 2020/0125472, autonomous automotive/robot test task automated generation using spatial temporal logic.
Buerger et al, US Pat Pub No. 2019/0070725, robot operation control using temporal logic for robot behavior task planning with state and proposition, agent model as abstraction of places in the environment map.
Leon et al, US Pat Pub No. 2019/0317455, industrial robot interacts with environment with sensor for multidimensional value for dimension and value range as measurement of object, selected state and proposition for temporal spatial logic, safety restrictions in an operation domain.
Nishi, US Pat Pub No. 2015/030948, overlapping propositional area for autonomous system operation, logic operation.
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Ian JEN whose telephone number is (571)270-3274. The examiner can normally be reached 11AM - 7PM.
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/Ian Jen/Primary Examiner, Art Unit 3657
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