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 .
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 10/29/2025 has been entered.
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.
Claims 16-23 and 47-53 are rejected under 35 U.S.C. 103 as being unpatentable over Wei US 2021/0217418 A1 in view of Gao et al. (Gao) US 2019/0324795 A1 and Mohalik et al. (Mohalik) US 2023/00096832
In regard to claim 16, Wei disclose A system for planning actions to accomplish tasks, the system comprising: (abstract,[0007]-[0008] facilitating accomplishing tasks)
a communications interface of a digital computational learning system; (Fig. 1, Fig. 2, [[0007]-[0009] [0042]-[0047][0054]-[0063] [0103]-[0106] a voice based interface and a network interface of system 200 which is a ML system)
a network of the digital computational learning system configured to learn, automatically, configured to learn, automatically, a plurality of interconnected actor perceiver predictor (APP) nodes of a knowledge graph stored on the digital computational learning system, ([0007]-[0008] [0042]-[0063] [0075]-[0092][0108]-[0118] [0146] Fig. 16, Fig. 22, a network with learning unit to generate a direct graph with a set of nodes interconnected (the nodes represent a state which have four types: Context, Action, Intent and Expect) the states of the nodes are stored which represent the knowledge graph, knowledge graph information is stored in the state management unit of the network ) and determine a sequence of actions for accomplishing a task by selecting and chaining, dynamically, at least a portion of the plurality of APP nodes of the plurality of APP nodes learned; ([0007]-[0008] [0042]-[0063][0075]-[0092][0102]-[0118] [0146] Fig. 16-22, and learns sequential prediction of the next context based on the existing contexts and identify proper actions, determine routes from model learned for facilitating accomplishing tasks and refine and connect the nodes based on the learning, with a directed graph generated based on the expected actions and link multiple actions together with action chain to finish the action plan) and
a supervisory system of the digital computational learning system configured to effect a change to the sequence of actions determined by interpreting natural language input and causing the planning neural network to update the selecting and chaining of the at least a portion of the plurality of APP nodes of the knowledge graph based on the interpreting, the natural language input received via the communications interface. ([0049]-[0063] [0065]-[0071] [0075]-[0092] [0103]-[0106] the system to update the sequence of tasks by the context of the utterance and the ML network to refine the directed graph with nodes connected representing the sequence of tasks in the knowledge graph based on the conversation received by the voice based interface)
But Wei fail to explicitly disclose “a planning neural network of the digital computational learning system, a deep queue-learning (DQL) neural network of the digital computational learning system, the planning neural network configured to employ the DQL neural network to accelerate or advance determination of the sequence of actions for accomplishing the task;”
Gao disclose a planning neural network of the digital computational learning system, a deep queue-learning (DQL) neural network of the digital computational learning system, the planning neural network configured to employ the DQL neural network to accelerate or advance determination of the sequence of actions for accomplishing the task; ([0017]-[0029][0032]-[0050] a neural network of the system, (first NN) and deep Q learning network (second NN) of the system, using the deep q learning network to enable selecting a multi-step action such as a sequence of actions for completing a task with a regard in response to completing the task)
It would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made to incorporate Gao’s method of composite task execution into Wei’s invention as they are related to the same field endeavor of facilitating accomplishing tasks based on a natural language conversation. The motivation to combine these arts, as proposed above, at least because Gao’s composite task execution by using deep Q network would help to provide more network learning method into Guo’s system. Therefore it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made that providing network learning method using deep Q network would facilitate task completion and therefore improve user experience using the system.
But Wei ang Gao fail to explicitly disclose “each of the plurality of interconnected APP nodes comprising an action-controller component including an instance of planner including a plurality of allied planners; employ, via at least one of the plurality of allied planners, the DQL neural network.”
Mohalik disclose each of the plurality of interconnected APP nodes comprising an action-controller component including an instance of planner including a plurality of allied planners; (Fig. 1, [0044]-[0058] interconnected nodes with AI planners, each AI planner corresponding to a task to support execution of the task)
employ, via at least one of the plurality of allied planners, the DQL neural network. (Fig. 1, [0014] [0044]-[0058][0276]-[0278] to trigger to select the model to act via the AI planner based on the plan here it disclose the trigger action)
It would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made to incorporate Mohalik’s communication action network into Gao and Wei’s invention as they are related to the same field endeavor of facilitating accomplishing tasks. The motivation to combine these arts, as proposed above, at least because Mohalik’s action network with planners would help to provide more decision making method into Guo and Wei’s system. Therefore it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made that providing decision making method based on generated sequence of action with planners would facilitate task completion and therefore improve user experience using the system.
In regard to claim 17, Wei and Gao, Mohalik disclose The system of Claim 16, the rejection is incorporated herein.
But Wei and Mohalik fail to explicitly disclose “wherein the planning neural network is further configured to provide at least one partial reward to the DQL neural network.”
Gao disclose wherein the planning neural network is further configured to provide at least one partial reward to the DQL neural network. ([0026]-[0030] providing reward in response to completing the task)
It would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made to incorporate Gao’s method of composite task execution into Mohalik and Wei’s invention as they are related to the same field endeavor of facilitating accomplishing tasks based on a natural language conversation. The motivation to combine these arts, as proposed above, at least because Gao’s composite task execution by using deep Q network with reward would help to provide more network learning method into Mohalik and Wei’s system. Therefore it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made that providing network learning method using deep Q network with reward would facilitate task completion and therefore improve user experience using the system.
In regard to claim 18, Wei and Gao, Mohalik disclose The system of Claim 17, the rejection is incorporated herein.
But Wei and Mohalik fail to explicitly disclose “wherein the task includes at least one subtask and the at least one partial reward represents completion of the at least one subtask.”
Gao disclose wherein the task includes at least one subtask and the at least one partial reward represents completion of the at least one subtask. ([0026]-[0030] [0033]-[0042] tasks includes subtasks and providing reward in response to completing a subtask)
It would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made to incorporate Gao’s method of composite task execution into Mohalik and Wei’s invention as they are related to the same field endeavor of facilitating accomplishing tasks based on a natural language conversation. The motivation to combine these arts, as proposed above, at least because Gao’s composite task execution by using deep Q network with reward would help to provide more network learning method into Mohalik and Wei’s system. Therefore it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made that providing network learning method using deep Q network with reward would facilitate task completion and therefore improve user experience using the system.
In regard to claim 19, Wei and Gao, Mohalik disclose The system of Claim 18, the rejection is incorporated herein.
But Wei and Mohalik fail to explicitly disclose “wherein completion of the at least one subtask is based on successful execution of at least one action of the sequence of actions determined.”
Gao disclose wherein completion of the at least one subtask is based on successful execution of at least one action of the sequence of actions determined. [0003]-[0005] [0044]-[0047] [0051] completion of the subtask is based on the completion of the at least of one action in the multi-step actions)
It would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made to incorporate Gao’s method of composite task execution into Mohalik and Wei’s invention as they are related to the same field endeavor of facilitating accomplishing tasks based on a natural language conversation. The motivation to combine these arts, as proposed above, at least because Gao’s composite task execution by using deep Q network with reward would help to provide more network learning method into Mohalik and Wei’s system. Therefore it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made that providing network learning method using deep Q network with reward would facilitate task completion and therefore improve user experience using the system.
In regard to claim 20, Wei and Gao, Mohalik disclose The system of Claim 16, the rejection is incorporated herein.
But Wei and Mohalik fail to explicitly disclose “wherein the DQL neural network is configured to suggest actions to accelerate or advance the planning neural network’s determination of the sequence of actions for accomplishing the task.”
Gao disclose wherein the DQL neural network is configured to suggest actions to accelerate or advance the planning neural network’s determination of the sequence of actions for accomplishing the task. ([0017]-[0029] [0032]-[0042] deep Q learning network, using the deep q learning network to enable selecting the next action in a sequence of actions for completing a task with a regard in response to completing the task to help advance)
It would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made to incorporate Gao’s method of composite task execution into Mohalik and Wei’s invention as they are related to the same field endeavor of facilitating accomplishing tasks based on a natural language conversation. The motivation to combine these arts, as proposed above, at least because Gao’s composite task execution by using deep Q network with reward would help to provide more network learning method into Mohalik and Wei’s system. Therefore it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made that providing network learning method using deep Q network with reward would facilitate task completion and therefore improve user experience using the system.
In regard to claim 21, Wei and Gao, Mohalik disclose The system of Claim 16, the rejection is incorporated herein.
But Wei and Mohalik fail to explicitly disclose “wherein the graph-based planner is further configured to filter, based on a knowledge database of successful actions, a set of possible actions available to the DQL neural network to train on.”
Gao disclose wherein the graph-based planner is further configured to filter, based on a knowledge database of successful actions, a set of possible actions available to the DQL neural network to train on. ([0016][0017] [0046]-[0049][0055]- [0065] identify a minimal number of actions for a plurality of subtasks corresponding to a composite task based on the reward with the goal and with a maximum likelihood estimation for training)
It would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made to incorporate Gao’s method of composite task execution into Mohalik and Wei’s invention as they are related to the same field endeavor of facilitating accomplishing tasks based on a natural language conversation. The motivation to combine these arts, as proposed above, at least because Gao’s composite task execution by using deep Q network with reward would help to provide more network learning method into Mohalik and Wei’s system. Therefore it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made that providing network learning method using deep Q network with reward would facilitate task completion and therefore improve user experience using the system.
In regard to claim 22, Wei and Gao, Mohalik disclose The system of Claim 16, the rejection is incorporated herein.
But Wei and Mohalik fail to explicitly disclose “wherein the natural language input is associated with an object of an environment within which the system is deployed.”
Gao disclose wherein the natural language input is associated with an object of an environment within which the system is deployed. (Fig. 1, [0019][0082] user simulator can detect the user dialog, in virtual reality system, for example)
It would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made to incorporate Gao’s method of composite task execution into Mohalik and Wei’s invention as they are related to the same field endeavor of facilitating accomplishing tasks based on a natural language conversation. The motivation to combine these arts, as proposed above, at least because Gao’s composite task execution by using user simulator would help to receive user input into Mohalik and Wei’s system. Therefore it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made that using user simulator to detect user input would facilitate task completion from a user and therefore improve user experience using the system.
In regard to claim 23, Wei and Gao, Mohalik disclose The system of Claim 22, the rejection is incorporated herein.
But Wei and Mohalik fail to explicitly disclose “wherein the environment is a simulated or real- world environment.”
Gao disclose wherein the environment is a simulated or real- world environment. (Fig. 1, [0019][0082] user simulator can detect the user dialog, in AR or VR system, for example)
It would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made to incorporate Gao’s method of composite task execution into Mohalik and Wei’s invention as they are related to the same field endeavor of facilitating accomplishing tasks based on a natural language conversation. The motivation to combine these arts, as proposed above, at least because Gao’s composite task execution by using user simulator would help to receive user input into Mohalik and Wei’s system. Therefore it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made that using user simulator to detect user input would facilitate task completion from a user and therefore improve user experience using the system.
In regard to claims 47-53, claims 47-53 are method claims corresponding to the system claims 16-21, 22+23 above and, therefore, are rejected for the same reasons set forth in the rejections of claims 16-21, 22+23.
Response to Arguments
Applicant’s arguments with respect to claims 16-23 and 47-53 filed on 11/29/2025 have been considered but are moot because the arguments do not apply to the current rejection.
Conclusion
The prior art made of record and not relied upon is considered pertinent to Applicant's disclosure.
U.S. Patent Documents PATENT DATE INVENTOR(S) TITLE
US 11003856 B2 2021-05-11 Kiros et al.
Processing Text Using Neural Networks
Kiros et al. disclose Methods, systems, and apparatus including computer programs encoded on a computer storage medium, for generating a data set that associates each text segment in a vocabulary of text segments with a respective numeric embedding. In one aspect, a method includes providing, to an image search engine, a search query that includes the text segment; obtaining image search results that have been classified as being responsive to the search query by the image search engine, wherein each image search result identifies a respective image; for each image search result, processing the image identified by the image search result using a convolutional neural network, wherein the convolutional neural network has been trained to process the image to generate an image numeric embedding for the image; and generating a numeric embedding for the text segment from the image numeric embeddings for the images identified by the image search results… see abstract.
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XUYANG XIA
Primary Examiner
Art Unit 2143
/XUYANG XIA/Primary Examiner, Art Unit 2143