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 .
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. § 119 (a)-(d). The certified copy has been filed in parent Application No. IN202321075843, filed on 11/06/2023.
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 1, 4, 6, 9, 11 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.
Claims 1, 6, 11 recite the limitation, “a manipulator robotic arm is manually actuated to perform… and joint angle data of the manipulator is recorded.” There is insufficient antecedent basis for this limitation in the claims.
Claims 4, 9, and 14 recite the limitation, “the temporal relationship” in claims 2, 7 and 12. However, there is no antecedent basis for this limitation in the claims.
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 (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 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 1–15 are rejected under 35 U.S.C. § 103 as being unpatentable over Gombolay et al. (U.S. 12,468, 279 B2) in view of Mandlekar et al. (U.S. 11,958,529 B2).
Regarding claim 1, Gombolay discloses a processor-implemented method comprising:
collecting, via an input/output interface, a plurality of videos of a long-horizon task demonstration (Per Fig. 1, Gombolay’s robotic arm recoder discloses video data where the location of the robotic arm actuatators 116 is recored to capture human-guided movements—i.e., a long-horizon task demonstration. Gombolay col. 6 lines 48–61. The robotic arm recorder 114 may track the location of the robotic arm actuators 116 and/or robotic arm 115 as coordinate system data or actuator settings in relation to, or synchronized with, acquired images or video data from the camera 120.) and a plurality of kinesthetic robot demonstration (a plurality of kinesthetic robot demonstration construed as dynamic movement primitives), wherein during the kinesthetic robot demonstration a manipulator robotic arm is manually actuated to perform a plurality of primitive actions (Per Fig. 1, Gombolay’s system 100 discloses a model where images are generated to describe dynamic movement primitives 109. Ibid. col. 5 lines 30–58. The model 107 of the source workpiece 106′ can be used in combination with dynamic movement primitives 109 (existing DMP or newly generated DMP) and new part instructions 111 to direct the robotic system 104′,) and joint angle data of the manipulator is recorded; (Per Fig. 9, Gombolay’s camera discloses multiple images when his robotic arm analyzes angles thereof. Ibid. col. 9 lines 27–52. [t]he camera captures five images at each waypoint: 1) a picture of the workpiece with the camera angled to the right; 2) a picture of the workpiece with the camera angled to the left; 3) a picture of the workpiece with the camera angled downwards; 4) a picture of the workpiece with the camera angled to the right; and 5) a picture of the workpiece with the camera centered on the workpiece.)
sampling, via one or more hardware processors, a plurality of frames at a plurality of time steps (a plurality of frames at a plurality of times steps construed as a real-time stream of images) from each of the plurality of videos using a uniform temporal sampling to identify a sequence of one or more sub-tasks in the plurality of videos of a task demonstration. (Per Fig. 1, Gombolay’s camera calibration system 123 samples intersection points to calculate robot base coordinates. Ibid. col. 8 lines 4–21. By sampling intersection points along a ground truth calibration surface or board, module 123 can establish sufficient reference points to compute the corresponding transform from the image coordinate to the camera location to the robot base coordinates.)
However, Gombolay fails to specifically disclose labeling, via the one or more hardware processors, the sampled plurality of frames with an associated sub-task of the one or more sub-tasks to generate a training dataset;
training, via the one or more hardware processors, a Task Sequencing Network (TSNet) using the generated training dataset comprising the long-horizon task demonstration to predict the sequence of one or more sub-tasks associated with the plurality of frames;
obtaining, via the one or more hardware processors, the one or more sub-tasks predicted with highest probability at each of the plurality of time steps, and removing one or more duplicate sub-tasks and extra blank tokens to predict the sequence of one or more sub-tasks using the trained TSNet;
building, via the one or more hardware processors, a manipulation graph from the predicted sequence of one or more sub-tasks to generate a parameterized trajectory based on the manipulation graph;
training, via the one or more hardware processors, one or more Dynamic Movement Primitive (DMP) models using the plurality of the kinesthetic robot task demonstration to build a Task Agnostic DMP Library (TADL) comprising DMPs associated with the one or more sub-tasks; and
selecting, via the one or more hardware processors, a relevant movement for the one or more sub-tasks from the TADL to execute an intended movement comprising of one or more sub-tasks on a manipulator robotic arm.
In related art, Mandlekar discloses labeling, via the one or more hardware processors, the sampled plurality of frames with an associated sub-task of the one or more sub-tasks to generate a training dataset; (Per Fig. 40, Mandlekar labels ground truth data related to AI-assisted imaging data 4008 such that his machine learning model trains the annotated data to perform myriad inference tasks. Mandlekar col. 102 lines 15–45. [o]nce imaging data 4008 is received, AI-assisted annotation 4010 may be used to aid in generating annotations corresponding to imaging data 4008 to be used as ground truth data for a machine learning model.)
training, via the one or more hardware processors, a Task Sequencing Network (TSNet) using the generated training dataset comprising the long-horizon task demonstration to predict the sequence of one or more sub-tasks associated with the plurality of frames; (Per Fig. 14B, Mandlekar’s training logic 1115 discloses predicting operations according to calculating weight parameters in his neural network. Ibid. col. 23 lines 11–21. [i]nference and/or training logic 1115 may be used in system FIG. 14B for inferencing or predicting operations based, at least in part, on weight parameters calculated using neural network training operations,)
obtaining, via the one or more hardware processors, the one or more sub-tasks predicted (the one or more sub-tasks predicted construed as suboptimal data) with highest probability at each of the plurality of time steps, and removing one or more duplicate sub-tasks and extra blank tokens to predict the sequence of one or more sub-tasks using the trained TSNet; (Per Fig. 2, Mandlekar discloses suboptimal data where highest returns are rendered evaluating expected task returns. He determines whether state s2 is less than T steps–i.e., replacing extra blank tokens in a small number of timesteps. Ibid. col. 8 lines 48–58. Learning from suboptimal data: In an embodiment, the low-level goal-conditioned controller operates for a small number of timesteps, so the controller has no need to account for suboptimal actions.)
building, via the one or more hardware processors, a manipulation graph from the predicted sequence of one or more sub-tasks to generate a parameterized trajectory based on the manipulation graph; (Per Fig. 2, Mandlekar discloses a graph reach dataset demonstrating paths of the robot. Ibid. col. 6 lines 41–56. [t]he Graph Reach dataset 202 consists of demonstrated paths from the start location at the top to the goal location at the bottom.) training, via the one or more hardware processors, one or more Dynamic Movement Primitive (DMP) models using the plurality of the kinesthetic robot task demonstration to build a Task Agnostic DMP Library (TADL) comprising DMPs associated with the one or more sub-tasks; and (Per Fig. 41, Mandlekar discloses image reconstruction for inference tasks with data augmentation library. Ibid. col. 109 line 45 – col. 110 line 17. [a] data augmentation library (e.g., as one of services 4020) may be used to accelerate these operations.)
selecting, via the one or more hardware processors, a relevant movement for the one or more sub-tasks from the TADL to execute an intended movement comprising of one or more sub-tasks on a manipulator robotic arm. (Per Fig. 36, Mandlekar’s system discloses whether its pending task pool is selected to execute scheduled tasks from his scheduler unit 3612. Ibid. col. 92 line 64 – col. 93 line 17. [t]hat task is evicted from that active task pool for GPC 3618 and another task from a pending task pool is selected and scheduled for execution on GPC 3618.)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to incorporate the teachings of Mandlekar into the teachings of Gombolay to conduct diverse longer-horizon tasks improving abilities of complex tasks in machine learning. Ibid. col. 1 lines 8–17.
Regarding claim 6, Gombolay discloses a system comprising:
a memory storing instruction; (Fig. 1, 118 a robotic arm controller)
one or more Input/Output (I/O) interfaces; and (Fig. 1, 124 a workspace estimation module 124)
one or more hardware processors coupled to the memory via the one or more I/O interfaces, wherein the one or more hardware processors are configured by the instructions to:
collect a plurality of videos of a long-horizon task demonstration (Per Fig. 1, Gombolay’s robotic arm recoder discloses video data where the location of the robotic arm actuatators 116 is recored to capture human-guided movements—i.e., a long-horizon task demonstration. Gombolay col. 6 lines 48–61. The robotic arm recorder 114 may track the location of the robotic arm actuators 116 and/or robotic arm 115 as coordinate system data or actuator settings in relation to, or synchronized with, acquired images or video data from the camera 120.) and a plurality of kinesthetic robot demonstration (a plurality of kinesthetic robot demonstration construed as dynamic movement primitives), wherein during the kinesthetic robot demonstration a manipulator robotic arm is manually actuated to perform a plurality of primitive actions (Per Fig. 1, Gombolay’s system 100 discloses a model where images are generated to describe dynamic movement primitives 109. Ibid. col. 5 lines 30–58. The model 107 of the source workpiece 106′ can be used in combination with dynamic movement primitives 109 (existing DMP or newly generated DMP) and new part instructions 111 to direct the robotic system 104′,) and joint angle data of the manipulator is recorded; (Per Fig. 9, Gombolay’s camera discloses multiple images when his robotic arm analyzes angles thereof. Ibid. col. 9 lines 27–52. [t]he camera captures five images at each waypoint: 1) a picture of the workpiece with the camera angled to the right; 2) a picture of the workpiece with the camera angled to the left; 3) a picture of the workpiece with the camera angled downwards; 4) a picture of the workpiece with the camera angled to the right; and 5) a picture of the workpiece with the camera centered on the workpiece.)
sample a plurality of frames at a plurality of time steps from each of the plurality of videos using a uniform temporal sampling to identify a sequence of one or more sub-tasks in the plurality of videos of a task demonstration. (Per Fig. 1, Gombolay’s camera calibration system 123 samples intersection points to calculate robot base coordinates. Ibid. col. 8 lines 4–21. By sampling intersection points along a ground truth calibration surface or board, module 123 can establish sufficient reference points to compute the corresponding transform from the image coordinate to the camera location to the robot base coordinates.)
However, Gombolay fails to specifically disclose label the sampled plurality of frames with an associated sub-task of the one or more sub-tasks to generate a training dataset;
train a Task Sequencing Network (TSNet) using the generated training dataset comprising the long-horizon task demonstration to predict the sequence of one or more sub-tasks associated with the plurality of frames;
obtain the one or more sub-tasks predicted with highest probability at each of the plurality of time steps, and removing one or more duplicate sub-tasks and extra blank tokens to predict the sequence of one or more sub-tasks using the trained TSNet;
build a manipulation graph from the predicted sequence of one or more sub-tasks to generate a parameterized trajectory based on the manipulation graph;
train one or more Dynamic Movement Primitive (DMP) models using the plurality of the kinesthetic robot task demonstration to build a Task Agnostic DMP Library (TADL) comprising DMPs associated with the one or more sub-tasks; and
select a relevant movement for the one or more sub-tasks from the TADL to execute an intended movement comprising of one or more sub-tasks on a manipulator robotic arm.
In related art, Mandlekar discloses label the sampled plurality of frames with an associated sub-task of the one or more sub-tasks to generate a training dataset; (Per Fig. 40, Mandlekar labels ground truth data related to AI-assisted imaging data 4008 such that his machine learning model trains the annotated data to perform myriad inference tasks. Mandlekar col. 102 lines 15–45. [o]nce imaging data 4008 is received, AI-assisted annotation 4010 may be used to aid in generating annotations corresponding to imaging data 4008 to be used as ground truth data for a machine learning model.)
train a Task Sequencing Network (TSNet) using the generated training dataset comprising the long-horizon task demonstration to predict the sequence of one or more sub-tasks associated with the plurality of frames; (Per Fig. 14B, Mandlekar’s training logic 1115 discloses predicting operations according to calculating weight parameters in his neural network. Ibid. col. 23 lines 11–21. [i]nference and/or training logic 1115 may be used in system FIG. 14B for inferencing or predicting operations based, at least in part, on weight parameters calculated using neural network training operations,)
obtain the one or more sub-tasks predicted with highest probability at each of the plurality of time steps, and removing one or more duplicate sub-tasks and extra blank tokens to predict the sequence of one or more sub-tasks using the trained TSNet; (Per Fig. 2, Mandlekar discloses suboptimal data where highest returns are rendered evaluating expected task returns. He determines whether state s2 is less than T steps–i.e., replacing extra blank tokens in a small number of timesteps. Ibid. col. 8 lines 48–58. Learning from suboptimal data: In an embodiment, the low-level goal-conditioned controller operates for a small number of timesteps, so the controller has no need to account for suboptimal actions.)
build a manipulation graph from the predicted sequence of one or more sub-tasks to generate a parameterized trajectory based on the manipulation graph; (Per Fig. 2, Mandlekar discloses a graph reach dataset demonstrating paths of the robot. Ibid. col. 6 lines 41–56. [t]he Graph Reach dataset 202 consists of demonstrated paths from the start location at the top to the goal location at the bottom.) train one or more Dynamic Movement Primitive (DMP) models using the plurality of the kinesthetic robot task demonstration to build a Task Agnostic DMP Library (TADL) comprising DMPs associated with the one or more sub-tasks; and (Per Fig. 41, Mandlekar discloses image reconstruction for inference tasks with data augmentation library. Ibid. col. 109 line 45 – col. 110 line 17. [a] data augmentation library (e.g., as one of services 4020) may be used to accelerate these operations.)
select a relevant movement for the one or more sub-tasks from the TADL to execute an intended movement comprising of one or more sub-tasks on a manipulator robotic arm. (Per Fig. 36, Mandlekar’s system discloses whether its pending task pool is selected to execute scheduled tasks from his scheduler unit 3612. Ibid. col. 92 line 64 – col. 93 line 17. [t]hat task is evicted from that active task pool for GPC 3618 and another task from a pending task pool is selected and scheduled for execution on GPC 3618.)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to incorporate the teachings of Mandlekar into the teachings of Gombolay to conduct diverse longer-horizon tasks improving abilities of complex tasks in machine learning. Ibid. col. 1 lines 8–17.
Regarding claim 11, Gombolay discloses one or more non-transitory machine-readable information storage mediums comprising one or more instructions which when executed by one or more hardware processors cause:
collecting, via an input/output interface, a plurality of videos of a long-horizon task demonstration (Per Fig. 1, Gombolay’s robotic arm recoder discloses video data where the location of the robotic arm actuatators 116 is recored to capture human-guided movements—i.e., a long-horizon task demonstration. Gombolay col. 6 lines 48–61. The robotic arm recorder 114 may track the location of the robotic arm actuators 116 and/or robotic arm 115 as coordinate system data or actuator settings in relation to, or synchronized with, acquired images or video data from the camera 120.) and a plurality of kinesthetic robot demonstration (a plurality of kinesthetic robot demonstration construed as dynamic movement primitives), wherein during the kinesthetic robot demonstration a manipulator robotic arm is manually actuated to perform a plurality of primitive actions (Per Fig. 1, Gombolay’s system 100 discloses a model where images are generated to describe dynamic movement primitives 109. Ibid. col. 5 lines 30–58. The model 107 of the source workpiece 106′ can be used in combination with dynamic movement primitives 109 (existing DMP or newly generated DMP) and new part instructions 111 to direct the robotic system 104′,) and joint angle data of the manipulator is recorded; (Per Fig. 9, Gombolay’s camera discloses multiple images when his robotic arm analyzes angles thereof. Ibid. col. 9 lines 27–52. [t]he camera captures five images at each waypoint: 1) a picture of the workpiece with the camera angled to the right; 2) a picture of the workpiece with the camera angled to the left; 3) a picture of the workpiece with the camera angled downwards; 4) a picture of the workpiece with the camera angled to the right; and 5) a picture of the workpiece with the camera centered on the workpiece.)
sampling a plurality of frames at a plurality of time steps from each of the plurality of videos using a uniform temporal sampling to identify a sequence of one or more sub-tasks in the plurality of videos of a task demonstration. (Per Fig. 1, Gombolay’s camera calibration system 123 samples intersection points to calculate robot base coordinates. Ibid. col. 8 lines 4–21. By sampling intersection points along a ground truth calibration surface or board, module 123 can establish sufficient reference points to compute the corresponding transform from the image coordinate to the camera location to the robot base coordinates.)
However, Gombolay fails to specifically disclose labeling the sampled plurality of frames with an associated sub-task of the one or more sub-tasks to generate a training dataset;
training a Task Sequencing Network (TSNet) using the generated training dataset comprising the long-horizon task demonstration to predict the sequence of one or more sub-tasks associated with the plurality of frames;
obtaining the one or more sub-tasks predicted with highest probability at each of the plurality of time steps, and removing one or more duplicate sub-tasks and extra blank tokens to predict the sequence of one or more sub-tasks using the trained TSNet;
building a manipulation graph from the predicted sequence of one or more sub-tasks to generate a parameterized trajectory based on the manipulation graph;
training one or more Dynamic Movement Primitive (DMP) models using the plurality of the kinesthetic robot task demonstration to build a Task Agnostic DMP Library (TADL) comprising DMPs associated with the one or more sub-tasks; and
selecting a relevant movement for the one or more sub-tasks from the TADL to execute an intended movement comprising of one or more sub-tasks on a manipulator robotic arm.
In related art, Mandlekar discloses labeling the sampled plurality of frames with an associated sub-task of the one or more sub-tasks to generate a training dataset; (Per Fig. 40, Mandlekar labels ground truth data related to AI-assisted imaging data 4008 such that his machine learning model trains the annotated data to perform myriad inference tasks. Mandlekar col. 102 lines 15–45. [o]nce imaging data 4008 is received, AI-assisted annotation 4010 may be used to aid in generating annotations corresponding to imaging data 4008 to be used as ground truth data for a machine learning model.)
training a Task Sequencing Network (TSNet) using the generated training dataset comprising the long-horizon task demonstration to predict the sequence of one or more sub-tasks associated with the plurality of frames; (Per Fig. 14B, Mandlekar’s training logic 1115 discloses predicting operations according to calculating weight parameters in his neural network. Ibid. col. 23 lines 11–21. [i]nference and/or training logic 1115 may be used in system FIG. 14B for inferencing or predicting operations based, at least in part, on weight parameters calculated using neural network training operations,)
obtaining the one or more sub-tasks predicted with highest probability at each of the plurality of time steps, and removing one or more duplicate sub-tasks and extra blank tokens to predict the sequence of one or more sub-tasks using the trained TSNet; (Per Fig. 2, Mandlekar discloses suboptimal data where highest returns are rendered evaluating expected task returns. He determines whether state s2 is less than T steps–i.e., replacing extra blank tokens in a small number of timesteps. Ibid. col. 8 lines 48–58. Learning from suboptimal data: In an embodiment, the low-level goal-conditioned controller operates for a small number of timesteps, so the controller has no need to account for suboptimal actions.)
building a manipulation graph from the predicted sequence of one or more sub-tasks to generate a parameterized trajectory based on the manipulation graph; (Per Fig. 2, Mandlekar discloses a graph reach dataset demonstrating paths of the robot. Ibid. col. 6 lines 41–56. [t]he Graph Reach dataset 202 consists of demonstrated paths from the start location at the top to the goal location at the bottom.) training one or more Dynamic Movement Primitive (DMP) models using the plurality of the kinesthetic robot task demonstration to build a Task Agnostic DMP Library (TADL) comprising DMPs associated with the one or more sub-tasks; and (Per Fig. 41, Mandlekar discloses image reconstruction for inference tasks with data augmentation library. Ibid. col. 109 line 45 – col. 110 line 17. [a] data augmentation library (e.g., as one of services 4020) may be used to accelerate these operations.)
selecting a relevant movement for the one or more sub-tasks from the TADL to execute an intended movement comprising of one or more sub-tasks on a manipulator robotic arm. (Per Fig. 36, Mandlekar’s system discloses whether its pending task pool is selected to execute scheduled tasks from his scheduler unit 3612. Ibid. col. 92 line 64 – col. 93 line 17. [t]hat task is evicted from that active task pool for GPC 3618 and another task from a pending task pool is selected and scheduled for execution on GPC 3618.)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to incorporate the teachings of Mandlekar into the teachings of Gombolay to conduct diverse longer-horizon tasks improving abilities of complex tasks in machine learning. Ibid. col. 1 lines 8–17.
Regarding claim 2, Gombolay as modified by Mandlekar, discloses the processor-implemented method, wherein the TSNet comprises a Convolutional Neural Network and a Recurrent Neural Network. (Per Fig. 14C, Mandlekar discloses CNN and RNN to optimize his hardware accelerators. Mandlekar col. 26 lines 5–25. [a]ccelerator(s) 1414 could be used for targeted workloads (e.g., perception, convolutional neural networks (“CNNs”), recurrent neural networks (“RNNs”), etc.) that are stable enough to be amenable to acceleration.)
Regarding claim 3, Gombolay as modified by Mandlekar, discloses the processor-implemented method, wherein the CNN is trained to classify the plurality of frames. (Per Fig. 14C, Mandlekar discloses a CNN for classifying visual data. Mandlekar col. 32 lines 45–64. [S]oC(s) 1404 use a CNN for classifying environmental and urban sounds, as well as classifying visual data.)
Regarding claim 4, Gombolay as modified by Mandlekar, discloses the processor-implemented method, wherein the RNN is trained to learn the temporal relationship between the plurality of frames. (Mandlekar discloses a RNN to analyze image data. Mandlekar col. 10 lines 7–20. [a] Recurrent Neural Network (“RNN”) variant of Behavioral Cloning called BC-RNN, and a Batch-Constrained Q-Learning (“BCQ”) baseline, which is a state-of-the-art Batch Reinforcement Learning algorithm for continuous control.)
Regarding claim 5, Gombolay as modified by Mandlekar, discloses the processor-implemented, wherein the CNN and the RNN are trained using a Connectionist Temporal Classification (CTC) loss function. (Per Fig. 12, Mandlekar discloses a loss function in his training framework 1204. Mandlekar col. 16 lines 5–32. [t]raining framework 1204 trains untrained neural network 1206 repeatedly while adjust weights to refine an output of untrained neural network 1206 using a loss function and adjustment algorithm,)
Regarding claim 7, it has been rejected in the same manner as claim 2.
Regarding claim 8, it has been rejected in the same manner as claim 3.
Regarding claim 9, it has been rejected in the same manner as claim 4.
Regarding claim 10, it has been rejected in the same manner as claim 5.
Regarding claim 12, it has been rejected in the same manner as claim 2.
Regarding claim 13, it has been rejected in the same manner as claim 3.
Regarding claim 14, it has been rejected in the same manner as claim 4.
Regarding claim 15, it has been rejected in the same manner as claim 5.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Chao et al. (U.S. 11,893,468 B2) discloses techniques to identify a goal of a demonstration.
Contact
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BENEDICT LEE whose telephone number is (571)270-0390. The examiner can normally be reached 10:00-17:00 (EST).
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, Stephen R. Koziol can be reached at (408) 918-7630. 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.
/BENEDICT E LEE/Examiner, Art Unit 2665
/Stephen R Koziol/Supervisory Patent Examiner, Art Unit 2665
Prosecution Insights