DETAILED ACTION
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 1-16 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Claim 1 is directed to statutory ineligible subject matter of determining permission of reception of a vehicle within a platoon based on mental steps.
Claim 1, An electronic device comprising:
a processor; and
a memory configured to store instructions which, when executed individually or collectively by the processor, cause the electronic device to:
form a configuration space comprising a starting point, a target point, and map information for generating a moving path of a robot;
predict, based on the configuration space, a current position of the robot, and a progress corresponding to the current position with respect to the moving path;
obtain a sample corresponding to an arbitrary point in a portion the configuration space, based on the progress; and
generate the moving path by expanding a tree until the tree reaches the target point from the current position, by adding the sample to the tree, wherein the tree represents a hierarchical data structure for generating the moving path.
Step 1: Statutory Category - Yes – the claim recited an apparatus including at least one step / functional limitation.
Step 2A: Prong One Evaluation: Judicial Exception – Yes – Mental processes
Claim(s) is to be analyzed to determine whether it recites subject matter that falls within one of the following groups of abstract ideas: a) mathematical concepts, b) mental processes, and/or c) certain methods of organizing human activity.
The Office submits that the foregoing bolded limitation(s) constitutes judicial exceptions in terms of “mental processes” because under its broadest reasonable interpretation, the claim covers performance using mental processes.
The claim 1 recites the limitations (i) form a configuration space comprising a starting point, a target point, and map information for generating a moving path of a robot; (ii) predict, based on the configuration space, a current position of the robot, and a progress corresponding to the current position with respect to the moving path; and (iii) generate the moving path by expanding a tree until the tree reaches the target point from the current position, by adding the sample to the tree... .
Under the broadest reasonable interpretation, these limitations, as drafted, are simple processes that cover performance of these limitations in the mind but for the recitation of generic computer components. That is, other than reciting “electronic device,” “processor” and “memory” and nothing in the claim element precludes the step from practically being performed in the mind.
The form and generating limitations cover performance of the limitation in the mind. For instance, a remote user, using pen and paper, drawing / forming a map space / environment and generating a robot path from a start to destination points within a space / environment. Thus, this step limitation recites a mental process, which is an abstract idea.
The predict limitation covers performance of the limitation in the mind. For instance, the remote user, using pen and paper, predicting a robot current position within the space / environment as it moving within a path. Thus, this step limitation recites a mental process, which is an abstract idea.
The generate limitation covers performance of the limitation in the mind. For instance, the remote user, using pen and paper, generating the path by expanding a tree of multiple point options to the destination point using hierarchical data structure. Thus, this step limitation recites a mental process, which is an abstract idea.
Hence, the claim recites mental processes and is not eligible.
Step 2A: Prong Two Evaluation: Practical Application - No
Claim(s) is evaluated whether as a whole it integrates the recited judicial exception into a practical application. As noted in the 2019 PEG, it must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception. The courts have indicated that additional elements merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application.”
In the present case, the additional limitations beyond the above-noted abstract idea are as follows (where the underlined portions are the “additional limitations” while the bolded portions continue to represent the “abstract idea”).
The judicial exception is not integrated into a practical application. The claim recites the flowing additional element(s): “ obtain a sample corresponding to an arbitrary point in a portion the configuration space, based on the progress.”
The obtain limitation(s) / step(s) is recited at a high-level of generality (e.g., as a general means for gathering information related to space / environment points along the robot path) and amount to mere data gathering, which is a form of insignificant extra-solution activity – per MPEP 2106.05(g).
The “electronic device” and “processor” that facility the form, generating, predict and generate limitation(s) / step(s) are general recited processor(s) that “apply” the otherwise mental perform steps using a generic or general-purpose computer(s) and are recited at a high level of generality to merely automate the mental steps as indicated above.
The “memory” is recited as a general computer element for performing insignificant extra solution activity to store instruction(s) to be executed by a generic computer component (e.g., generic processor).
The combination of these additional element(s) is also no more than mere instruction to gather data and apply an exception using a generic computer component. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limitation on the practicing of the abstract idea.
Step 2B Evaluation: Invention Concept - No
The claim(s) is evaluated whether the claim as a whole amount to significantly more than the recited exception, i.e., whether any additional element, or combination of additional elements, adds an inventive concept to the claim.
Under the 2019 PEG, a conclusion that an additional element is insignificant extra-solution activity in Step 2A should be reevaluated in Step 2B.
Here, the obtain step(s) / limitation(s) was considered to be extra-solution activity in Step 2A, and thus it is re-evaluated in Step 2B to determine if the claim recites additional element that amount to significant more than the judicial exception. Per MPEP 2106.05(g), mere gathering and applying data are deemed to be directed to insignificant extra solution activity. This step(s) / limitation(s) does not contain any improvement for the path search technology.
The “electronic device,” “processors,” and “memory” (e.g., generic computer components) to perform insignificant extra-solution activities in Step 2A, and thus they are reevaluated in Step 2B to determine if they are more than what is well-understood, routine, conventional activity in the field. The background of the specification does not provide any indication that said “electronic device,” “processors,” and “memory” are anything other than possible generic, off the-shelf computer component, and the Symantec, TLI, and OIP Techs. court decisions cited in MPEP 2106.05(d)(II) indicate that mere collection / receipt of data (e.g., processing stored / available data to apply the otherwise mental determination) over a processor to obtain a result is a well-understood, routine, and conventional function when it is claimed in a merely generic manner (as it is here).
For these reasons, there is no inventive concept in the claim, and thus the claim is not patent eligible.
Regarding claims 2-3, the claim do not contain additional element that would integrate the identified mental exception, as cited on claim(s) above, into a practical application in a manner that impose a meaningful limit on the judicial exception. There is no inventive step in 2B per the same reasoning as explained above. The claim is not patent eligible.
Regarding claim 4, the additional element “ … using a neural network based on the configuration space and the current position” is evaluated in Prong 2 of 2A. Per the specification, the neural network is recited at high level of generality for providing computerized network functions as tool to perform an existing mental process. The neural network limitation does not amount to more than a recitation of the words "apply it" (or an equivalent), such as mere instructions to implement an abstract idea on a computer network. This does not integrate the identified judicial exception (mental) into practical application, MPEP 2106.05(f).
The claim does not recite any additional element that amount to significant more that the judicial exception since the specification discloses a generic computer component – (Step 2B). Therefore, the claim does not amount to more than the abstract idea itself.
Regarding claim 5, the additional element “ … the tree comprises a rapidly-exploring random tree (RRT) …. determine the progress by comparing the current position with a point included in the moving path ” are evaluated in Prong 1 of 2A and it is directed to mental process. For instance, the remote user, using pen and paper, determining a progress of the robot movement to a target destination by comparing its current position with a point in the moving path by using expanding tree of multiple point options. Thus, this step limitation recites a mental process, which is an abstract idea.
Hence, the claim recites mental processes and is not eligible.
Regarding claim 6, the additional elements (i) “determine, …, an area including a predicted next position is in the configuration space;” and (ii) “generate the sample by uniformly sampling a point included in the area” are evaluated in Prong 1 of 2A and it is directed to mental process. For instance, the remote user, using pen and paper, determining an area within the space / environment where the robot needs to travel and generate the path within the determined area by expanding a tree of multiple point options to the destination point. Thus, this step limitation recites a mental process, which is an abstract idea.
Hence, the claim recites mental processes and is not eligible.
Regarding claim 7, the additional element “generate the sample by sampling a point that satisfies the constraint” is evaluated in Prong 1 of 2A and it is directed to mental process. For instance, the remote user, using pen and paper, generating the path by expanding a tree of multiple point options to the destination point that satisfies traveling constraint – e.g., avoiding collision. Thus, this step limitation recites a mental process, which is an abstract idea.
Hence, the claim recites mental processes and is not eligible.
Regarding claim 8, the claim do not contain additional element that would integrate the identified mental exception, as cited on claim(s) above, into a practical application in a manner that impose a meaningful limit on the judicial exception. There is no inventive step in 2B per the same reasoning as explained above. The claim is not patent eligible.
Claim 9, An operating method of an electronic device, the operating method comprising:
forming a configuration space comprising a starting point, a target point, and map information for generating a moving path of a robot;
predicting, based on the configuration space, a current position of the robot, and a progress corresponding to the current position with respect to the moving path;
obtaining a sample corresponding to an arbitrary point in a portion of area in the configuration space, based on the progress; and
generating the moving path by expanding a tree until the tree reaches the target point from the current position, by adding the sample to the tree, wherein the tree represents a hierarchical data structure for generating the moving path.
Step 1: Statutory Category - Yes – the claim recited a method including at least one step / functional limitation.
Step 2A: Prong One Evaluation: Judicial Exception – Yes – Mental processes
Claim(s) is to be analyzed to determine whether it recites subject matter that falls within one of the following groups of abstract ideas: a) mathematical concepts, b) mental processes, and/or c) certain methods of organizing human activity.
The Office submits that the foregoing bolded limitation(s) constitutes judicial exceptions in terms of “mental processes” because under its broadest reasonable interpretation, the claim covers performance using mental processes.
The claim 9 recites the limitations (i) forming a configuration space comprising a starting point, a target point, and map information for generating a moving path of a robot; (ii) predicting, based on the configuration space, a current position of the robot, and a progress corresponding to the current position with respect to the moving path; and (iii) generating the moving path by expanding a tree until the tree reaches the target point from the current position, by adding the sample to the tree... .
Under the broadest reasonable interpretation, these limitations, as drafted, are simple processes that cover performance of these limitations in the mind and nothing in the claim element precludes the step from practically being performed in the mind.
The forming and generating limitations cover performance of the limitation in the mind. For instance, a remote user, using pen and paper, drawing / forming a map space / environment and generating a robot path from a start to destination points within a space / environment. Thus, this step limitation recites a mental process, which is an abstract idea.
The predicting limitation covers performance of the limitation in the mind. For instance, the remote user, using pen and paper, predicting a robot current position within the space / environment as it moving within a path. Thus, this step limitation recites a mental process, which is an abstract idea.
The generating limitation covers performance of the limitation in the mind. For instance, the remote user, using pen and paper, generating the path by expanding a tree of multiple point options to the destination point using hierarchical data structure. Thus, this step limitation recites a mental process, which is an abstract idea.
Hence, the claim recites mental processes and is not eligible.
Step 2A: Prong Two Evaluation: Practical Application - No
Claim(s) is evaluated whether as a whole it integrates the recited judicial exception into a practical application. As noted in the 2019 PEG, it must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception. The courts have indicated that additional elements merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application.”
In the present case, the additional limitations beyond the above-noted abstract idea are as follows (where the underlined portions are the “additional limitations” while the bolded portions continue to represent the “abstract idea”).
The judicial exception is not integrated into a practical application. The claim recites the flowing additional element(s): “ obtaining a sample corresponding to an arbitrary point in a portion of area in the configuration space, based on the progress.”
The obtaining limitation(s) / step(s) is recited at a high-level of generality (e.g., as a general means for gathering information related to space / environment points along the robot path) and amount to mere data gathering, which is a form of insignificant extra-solution activity – per MPEP 2106.05(g).
The combination of these additional element(s) is also no more than mere instruction to gather data and apply an exception using a generic computer component. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limitation on the practicing of the abstract idea.
Step 2B Evaluation: Invention Concept - No
The claim(s) is evaluated whether the claim as a whole amount to significantly more than the recited exception, i.e., whether any additional element, or combination of additional elements, adds an inventive concept to the claim.
Under the 2019 PEG, a conclusion that an additional element is insignificant extra-solution activity in Step 2A should be reevaluated in Step 2B.
Here, the obtaining step(s) / limitation(s) was considered to be extra-solution activity in Step 2A, and thus it is re-evaluated in Step 2B to determine if the claim recites additional element that amount to significant more than the judicial exception. Per MPEP 2106.05(g), mere gathering and applying data are deemed to be directed to insignificant extra solution activity. This step(s) / limitation(s) does not contain any improvement for the path search technology.
For these reasons, there is no inventive concept in the claim, and thus the claim is not patent eligible.
Regarding claims 10-11, the claim do not contain additional element that would integrate the identified mental exception, as cited on claim(s) above, into a practical application in a manner that impose a meaningful limit on the judicial exception. There is no inventive step in 2B per the same reasoning as explained above. The claim is not patent eligible.
Regarding claim 12, the additional element “ … using a neural network based on the configuration space and the current position” is evaluated in Prong 2 of 2A. Per the specification, the neural network is recited at high level of generality for providing computerized network functions as tool to perform an existing mental process. The neural network limitation does not amount to more than a recitation of the words "apply it" (or an equivalent), such as mere instructions to implement an abstract idea on a computer network. This does not integrate the identified judicial exception (mental) into practical application, MPEP 2106.05(f).
The claim does not recite any additional element that amount to significant more that the judicial exception since the specification discloses a generic computer component – (Step 2B). Therefore, the claim does not amount to more than the abstract idea itself.
Regarding claim 13, the additional element “ … the tree comprises a rapidly-exploring random tree (RRT) …. determining the progress by comparing the current position with a point included in the moving path ” are evaluated in Prong 1 of 2A and it is directed to mental process. For instance, the remote user, using pen and paper, determining a progress of the robot movement to a target destination by comparing its current position with a point in the moving path by using expanding tree of multiple point options to the destination point. Thus, this step limitation recites a mental process, which is an abstract idea.
Hence, the claim recites mental processes and is not eligible.
Regarding claim 14, the additional elements (i) “determining, …, an area including a predicted next position is in the configuration space” and (ii) “generating the sample by uniformly sampling a point included in the area” are evaluated in Prong 1 of 2A and it is directed to mental process. For instance, the remote user, using pen and paper, determining an area within the space / environment where the robot needs to travel and generate the path within the determined area by expanding a tree of multiple point options to the destination point. Thus, this step limitation recites a mental process, which is an abstract idea.
Hence, the claim recites mental processes and is not eligible.
Regarding claim 15, the additional element “generating the sample by sampling a point that satisfies the constraint” is evaluated in Prong 1 of 2A and it is directed to mental process. For instance, the remote user, using pen and paper, generating the path by expanding a tree of multiple point options to the destination point that satisfies traveling constraint – e.g., avoiding collision. Thus, this step limitation recites a mental process, which is an abstract idea.
Hence, the claim recites mental processes and is not eligible.
Regarding claim 16, the claim do not contain additional element that would integrate the identified mental exception, as cited on claim(s) above, into a practical application in a manner that impose a meaningful limit on the judicial exception. There is no inventive step in 2B per the same reasoning as explained above. The claim is not patent eligible.
Claim Rejections - 35 USC § 102
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 1-3, 5-11 and 13-16 are rejected under 35 U.S.C. 102(a)(1) / 102(a)(2) as being anticipated by Kim et al. (Pub. No.: US 2011/0106306 A1).
Regarding claim 1, Kim et al. disclose a path planning apparatus for a robot having a controller (par. 57 and Figure 5, 17-19) comprising:
a processor (e.g., processor – par. 11); and
a memory (e.g., a memory (par. 123)) configured to store instructions which, when executed individually or collectively by the processor (e.g., the memory configured to store program instructions and be executed by the processor (par. 123)), cause the electronic device to:
form a configuration space comprising a starting point, a target point, and map information for generating a moving path of a robot (e.g., forming “a configuration space having start point and goal point” to generate a motion path of robot / manipulator (par. 17 and 20));
predict, based on the configuration space, a current position of the robot, and a progress corresponding to the current position with respect to the moving path (e.g., determining a new sample gnew in a tree that satisfies a constraint and continuously extending the tree T until the sample gnew reaches the goal point G (par. 69, 73, 89 and 71), which covers the predicting limitation. Figures 10-12 show a robot / manipulator’s motion path comprising multiple samples g added to the tree as the robot moves along the path (par. 71, 72, 89 and 90). Figures 17-18 show the robot / manipulator moving along the motion path to perform a task at the gold point);
obtain a sample corresponding to an arbitrary point in a portion the configuration space, based on the progress (e.g., “selecting a closest node from nodes belonging to the tree T in a certain point g randomly sampled in the C-Space” (par. 69 and 74), which covers obtaining a sample corresponding to an arbitrary point ); and
generate the moving path by expanding a tree until the tree reaches the target point from the current position, by adding the sample to the tree (e.g., generate a motion path of a manipulator of the robot by implementing a Rapidly-exploring Random Tree (RRT) to connect the start point and the goal point via a path (par. 11, 17 and 68)), wherein the tree represents a hierarchical data structure for generating the moving path (e.g., adding new sample to a tree T1 if the constraint and the goal score condition are satisfied (par. 71 and Figures 8-12) to form a motion path (par. 72) in a hierarchical connection segment to a goal point).
Note: the term “hierarchical data structure” is defined as tree (spec. Pub. par. 16, 53).
Regarding claim 2, Kim et al. disclose a path planning apparatus for a robot wherein the starting point comprises a shape at a starting position in the configuration space before the robot performs a motion (e.g., Figures 17 shows a position shape of the robot at the start point before the robot performs a motion (par. 101 and Figure 17). Figure 18 shows the motion path of the robot).
Regarding claim 3, Kim et al. disclose a path planning apparatus for a robot wherein the target point comprises a shape at a target position in the configuration space at which the robot performs a task (e.g., Figures 18 shows a position shape of the robot at the goal point along the motion path (par. 102 and Figure 18)).
Regarding claim 5, Kim et al. disclose a path planning apparatus for a robot wherein the tree comprises a rapidly-exploring random tree (RRT) (e.g., implement Rapidly-exploring Random Tree (RRT) algorithm to generate the motion path to the goal point G (par. 68-69 and 23)), and wherein the instructions, when executed individually or collectively by the processor, further cause the electronic device to: determine the progress by comparing the current position with a point included in the moving path (e.g., Figures 17-18 show the robot / manipular moving along the motion path to perform a task and Figure 19 shows the robot / manipulator grasping and moving an object A in a z-direction using the RRT path (par. 101-102 and Figures 17-18), which requires the controller to compare robot’ position and the generated motion / RRT path as the robot moves along a path).
Regarding claim 6, Kim et al. disclose a path planning apparatus for a robot wherein the instructions, when executed individually or collectively by the processor, further cause the electronic device to:
determine, based on the progress, an area including a predicted next position is in the configuration space (e.g., generate new sample gnew within working area to be added to the tree using a C-Space, which requires to determine the location of the new sample gnew within the working area (par. 71-72 and Figures 7-12) ); and
generate the sample by uniformly sampling a point included in the area (e.g., Figure7-10 show generated new sample gnew added to the motion path (par. 71-72 and Figures 7-10), which covers uniformly sampling a point included in the area).
Regarding claim 7, Kim et al. disclose a path planning apparatus for a robot wherein the instructions, when executed individually or collectively by the processor, further cause the electronic device to: based on a constraint being added to the configuration space, generate the sample by sampling a point that satisfies the constraint (e.g., adding new sample to a tree T1 if the constraint and the goal score condition are satisfied (par. 71 and Figures 8-12) to form a motion path for the manipulator (par. 72)).
Regarding claim 8, Kim et al. disclose a path planning apparatus for a robot wherein the instructions, when executed individually or collectively by the processor, further cause the electronic device to: add noise to the sample to obtain a noisy sample; and add the noisy sample to the tree (e.g., Figure 13 show added nodes / samples n1-n4 to the motion path that do not satisfy the constraint (par. 74 and Figure 14)).
Note: per specification disclosure, the term “add noise” is interpreted as sample point added to the tree (pub.: par. 123 and 124)
Regarding claim 9, Kim et al. disclose a path planning method for a robot having a controller (par. 57 and Figure 5, 17-19) comprising:
forming a configuration space comprising a starting point, a target point, and map information for generating a moving path of a robot (e.g., forming “a configuration space having s start point and goal point” to generate a motion path of robot / manipulator (par. 17 and 20));
predicting, based on the configuration space a current position of the robot, a progress corresponding to the current position with respect to the moving path (e.g., determining a new sample gnew in a tree that satisfies a constraint and continuously extending the tree T until the sample gnew reaches the goal point G (par. 69, 73, 89 and 71), which covers the predicting limitation. Figures 10-12 show a robot / manipulator’s motion path comprising multiple samples g added to the tree as the robot moves along the path (par. 71, 72, 89 and 90). Figures 17-18 show the robot / manipulator moving along the motion path to perform a task at the gold point);
obtaining a sample corresponding to an arbitrary point in a portion of area in the configuration space, based on the progress (e.g., “selecting a closest node from nodes belonging to the tree T in a certain point g randomly sampled in the C-Space” (par. 69 and 74), which covers obtaining a sample corresponding to an arbitrary point ); and
generating the moving path by expanding a tree until the tree reaches the target point from the current position, by adding the sample to the tree (e.g., generate a motion path of a manipulator of the robot by implementing a Rapidly-exploring Random Tree (RRT) to connect the start point and the goal point via a path (par. 11, 17 and 68)), wherein the tree represents a hierarchical data structure for generating the moving path (e.g., adding new sample to a tree T1 if the constraint and the goal score condition are satisfied (par. 71 and Figures 8-12) to form a motion path (par. 72) in a hierarchical connection segment to a goal point).
Regarding claim 10, Kim et al. disclose a path planning method for a robot having a controller, wherein the starting point comprises a shape at a starting position in the configuration space before the robot performs a motion (e.g., Figures 17 shows a position shape of the robot at the start point before the robot performs a motion (par. 101 and Figure 17). Figure 18 shows the motion path of the robot).
Regarding claim 11, Kim et al. disclose a path planning method for a robot having a controller, wherein the target point comprises a shape at a target position in the configuration space at which the robot performs a task (e.g., Figures 18 shows a position shape of the robot at the goal point along the motion path (par. 102 and Figure 18)).
Regarding claim 13, Kim et al. disclose a path planning method for a robot having a controller, wherein the tree comprises a rapidly-exploring random tree (RRT) (e.g., implement Rapidly-exploring Random Tree (RRT) algorithm to generate the motion path to the goal point G (par. 68-69 and 23)), and wherein the predicting of the progress comprises: determining the progress by comparing the current position with a point included in the moving path (e.g., Figures 17-18 show the robot / manipular moving along the motion path to perform a task and Figure 19 shows the robot / manipulator grasping and moving an object A in a z-direction using the RRT path (par. 101-102 and Figures 17-18), which requires the controller to compare robot’ position and the generated motion / RRT path as the robot moves along a path).
Regarding claim 14, Kim et al. disclose a path planning method for a robot having a controller, wherein the obtaining of the sample comprises:
determining, based on the progress, an area including a predicted next position in the configuration space (e.g., generate new sample gnew within working area to be added to the tree using a C-Space, which requires to determine the location of the new sample gnew within a working area (par. 71-72 and Figures 7-12) ); and
generating the sample by uniformly sampling a point included in the area (e.g., Figure7-10 show generated new sample gnew added to the motion path (par. 71-72 and Figures 7-10), which covers uniformly sampling a point included in the area).
Regarding claim 15, Kim et al. disclose a path planning method for a robot having a controller, wherein the obtaining of the sample comprises: based on a constraint being added the configuration space, generating the sample by sampling a point that satisfies the constraint (e.g., adding new sample to a tree T1 if the constraint and the goal score condition are satisfied (par. 71 and Figures 8-12) to form a motion path for the manipulator (par. 72)).
Regarding claim 16, Kim et al. disclose a path planning method for a robot having a controller, wherein the generating of the moving path comprises: adding noise to the sample to obtain a noisy sample; and adding the noisy sample to the tree (e.g., Figure 13 show added nodes / samples n1-n4 to the motion path that do not satisfy the constraint (par. 74 and Figure 14)).
Note: per specification disclosure, the term “add noise” is interpreted as sample point added to the tree (pub.: par. 123 and 124).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102 of this title, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim 4 and 12 are rejected under 35 U.S.C. 103 as being unpatentable over Kim et al. (Pub. No.: US 2011/0106306 A1) in view of Minoya et al. (Pub. No.: US 2021/0237270 A1).
Regarding claims 4 and 12, Kim et al. failed to specifically disclose predicting the progress using a neural network based on the configuration space and the current position.
However, Minoya teaches a trajectory generation apparatus and method configured to estimate a robot’s hand 201 at a next time using machine learning and Rapidly Exploring Random Tree algorithm (par. 34, 37 and 20 and Figure 1).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention (AIA ) to modify the robot’s controller taught by Kim et al., such that the controller estimates a robot’s hand at a next time using machine learning and Rapidly Exploring Random Tree algorithm, in view of Minoya, with reasonable expectation of success, since doing so would have achieved the benefit of reaching a goal joint state without interfering with an obstacle while the robot performs a task (par. 37 and 24).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Li et al. (US 2022/0396289 A1) is directed to a neural network path planning configured to calculate a plurality of paths for a vehicle / robot using Rapidly Exploring Random Tree (RRT) algorithm.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Jorge O. Peche whose telephone number is (571)270-1339. The examiner can normally be reached Monday-Friday 8:30 AM - 5:30 PM.
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/Jorge O Peche/Examiner, Art Unit 3656
Prosecution Insights