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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 .
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.
Claim(s) 1-11, 14, 15 and 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Jacobs (U.S. 2021/0081750 hereinafter Jacobs) in view of Halim et al. (U.S. 2018/0218267 hereinafter Halim).
As Claim 1, Jacobs teaches a computer-implemented method for consolidating explanations associated with one or more actions proposed based on a current state of a system and an intent, the method comprising:
acquiring a first explanation and a second explanation (Jacobs (¶0040 line 1-8, fig. 2 item 220), “the coordinator agent receives, from each of the plurality of additional agents, a response to the constraint proposal either rejecting the constraint proposal CA or returning one or more further constraints to be CA satisfied, i.e. by providing a set of counter-proposal constraints (i.e. constraint counter-proposal CA') that are additional constraints or constraints that are alternatives to one of more of the constraints in the set of proposed constraints”), wherein the first and second explanations are associated with a proposed action or are associated with different actions, and wherein each of the first and second explanations includes one or more constraints (Jacobs (¶0040 last 7 lines, fig. 2 item 220), “each respective one of the plurality of additional agents will return a constraint counter-proposal CA' if a solution compatible with constraint proposal CA will be accepted if the solution additionally satisfies the additional or alternative constraints specified by the constraint counter-proposal CA' (for example, the agent is willing to execute at most 3 of the proposed tasks).”), each constraint representing a requirement to satisfy a problem corresponding to the intent (Jacobs (¶0039 line 5-7, fig. 2 item 230), “a constraint proposal CA to each of a plurality of additional agents ( e.g. the plurality of additional agents 120) involved in the optimization”);
combining constraints from the first and second explanations to form a set of constraints D (Jacobs (¶0041 line 6-8, fig. 2 item 230), “the protocol returns to 210 where the coordinator agent proposes a new constraint proposal CA to each of the plurality of additional agents.” Jacobs (¶0038 last 5 lines, fig. 2 item 240), “the protocol proceeds to 240 where the coordinator agent determines an optimal solution satisfying all constraints of both CA and CA'.”);
,wherein the solution represents a consolidated explanation based on the first and second explanations (Jacobs (¶0038 last 5 lines, fig. 2 item 240), “the protocol proceeds to 240 where the coordinator agent determines an optimal solution satisfying all constraints of both CA and CA'.”).
Jacobs may not explicitly disclose:
generating a planning problem P =<K, A, I, G, Cost>, wherein K consists of a set of predicates F in a domain of the system and the set of constraints D, wherein A represents a set of possible actions associated with the first explanation and/or the second explanation, I represents an initial state of the system, G represents a goal state of the system corresponding to the intent, and Cost represents cost values associated with each constraint in the set of constraints; and
determining a solution for the planning problem P
Halim teaches:
generating a planning problem P =<K, A, I, G, Cost>, wherein K consists of a set of predicates F in a domain of the system and the set of constraints D (Halim (¶0044 line 8-11), “a domain model can describe characteristics of the domain, actions that can be performed in the domain which describes the transitions between states, a set of fluents,”), wherein A represents a set of possible actions associated with the first explanation and/or the second explanation, I represents an initial state of the system, G represents a goal state of the system corresponding to the intent, and Cost represents cost values associated with each constraint in the set of constraints (Halim (¶0050), “Definition 1 A planning problem with action costs is a tuple P=(F, A, I, G), where F is a finite set of fluent symbols, A is a set of actions with preconditions, Pre( a), add effects, Add(a), delete effects, Del(a), and non-negative action costs, Cost(a), I defines the initial state, and G defines the goal state”); and
determining a solution for the planning problem P (Halim (¶0051 last 5 lines), “it is said to be optimal if it has minimal cost, or there exists no other plan that has a better cost than this plan. A planning problem P can have more than one optimal plan”)
Jacobs discloses a system/method for generate consolidated constraint based on agent’s explanations. Halim disclose a system/method to use artificial intelligent for solving planning problem with a specific constraint. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify constraints of Jacobs instead be planning problem constraints taught by Halim, with a reasonable expectation of success. The motivation would be to allow “plan projector component 104 can also include artificial intelligence planning component 208 can employ an artificial intelligence planner to determine a solution to the goal recognition problem by transforming the goal recognition problem (e.g., a plan recognition problem associated with a set of possible goals) into an artificial intelligence
planning problem” (Halim (¶0059 line 1-7)).
As Claim 2, besides Claim 1, Jacobs in view of Halim teaches wherein determining a solution for the planning problem comprises determining whether the planning problem P can be solved without relaxing one or more constraints in the set of constraints D (Jacobs (¶0041 line 4-8), “If one or more of the plurality of additional agents has rejected the constraint proposal CA, the protocol returns to 210 where the coordinator agent proposes a new constraint proposal CA to each of the plurality of additional agents”), and
if it is determined that the planning problem can be solved without relaxing one or more constraints in the set of constraints D, determining that the first and second explanations are complementary explanations as the solution of the planning problem (Jacobs (¶0041 last 7 lines), “if none of the plurality of additional agents has rejected the constraint proposal, the protocol proceeds to 240 where the coordinator agent determines an optimal solution satisfying all constraints of both CA and CA'.”), or
if it is determined that the planning problem P cannot be solved without relaxing one or more constraints in the set of constraints D, determining a plan as the solution for the planning problem.
As Claim 3, besides Claim 2, Jacobs in view of Halim teaches wherein determining a plan as the solution for the planning problem comprises:
assigning a respective plan label to each of a plurality of subsets of constraints from the set of constraints D, wherein each plan label is indicative of a respective candidate plan for the planning problem P (Jacobs (¶0045 line 1-7, fig. 4 item 430), “coordinator agent determines that one or more additional or alternative constraints C* specified in one or more respective constraint counter-proposals are CA' problematic constraints, i.e. constraints that will have a detrimental impact on the desired optimization, e.g. unnecessarily lead to an increased value of a quantity to be minimized (such as total cost or total time to completion”);
determining a combination cost value for each of the plurality of candidate plans, wherein the combination cost value is a combination of a plan cost value and a constraint cost value, the plan cost value representing a cost of executing the respective candidate plan (Jacobs (¶0048 line 5-9), “When agent 1 executes task i, nonnegative costs c_il have to be paid. The costs of agent 2 executing the task are defined as c_i2 accordingly. In an embodiment, the objective function is to minimize the total cost:”), and
the constraint cost value representing a total violation cost caused by the respective subset of constraints of the respective candidate plan (Jacobs (¶0048 line 5-9), “When agent 1 executes task i, nonnegative costs c_il have to be paid. The costs of agent 2 executing the task are defined as c_i2 accordingly. In an embodiment, the objective function is to minimize the total cost:”); and
selecting a candidate plan with the lowest combination cost value as the solution for the planning problem (Jacobs (¶0048 line 5-9), “When agent 1 executes task i, nonnegative costs c_il have to be paid. The costs of agent 2 executing the task are defined as c_i2 accordingly. In an embodiment, the objective function is to minimize the total cost:”).
As Claim 4, besides Claim 3, Jacobs in view of Halim teaches wherein determining a combination cost value for a respective candidate plan comprises:
acquiring, from a knowledge base of the system, at least one of:
an individual cost value for each constraint in the subset of constraints of the respective candidate plan, and an aggregate cost value for one or more subgroups of constraints in the subset of constraints of the respective candidate plan (Jacobs (¶0048 line 5-9), “When agent 1 executes task i, nonnegative costs c_il have to be paid. The costs of agent 2 executing the task are defined as c_i2 accordingly. In an embodiment, the objective function is to minimize the total cost:”);
determining the constraint cost value for the respective candidate plan by summing the at least one of:
individual cost values and aggregate cost values (Jacobs (¶0048 line 5-9), “When agent 1 executes task i, nonnegative costs c_il have to be paid. The costs of agent 2 executing the task are defined as c_i2 accordingly. In an embodiment, the objective function is to minimize the total cost:”); and
determining the combination cost value by combining the constraint cost value with the plan cost value associated with the respective candidate plan (Jacobs (¶0048 line 5-9), “When agent 1 executes task i, nonnegative costs c_il have to be paid. The costs of agent 2 executing the task are defined as c_i2 accordingly. In an embodiment, the objective function is to minimize the total cost:”).
As Claim 5, besides Claim 4, Jacobs in view of Halim teaches wherein acquiring at least one of individual cost values and aggregate cost values comprises:
acquiring the at least one of individual cost values and aggregate cost values from a previous solution plan of a planning problem stored in the knowledge base, wherein the previous solution plan and constraints associated with the previous solution plan match those of the respective candidate plan (Jacobs (¶0048 line 5-9), “When agent 1 executes task i, nonnegative costs c_il have to be paid. The costs of agent 2 executing the task are defined as c_i2 accordingly. In an embodiment, the objective function is to minimize the total cost:”).
As Claim 6, besides Claim 4, Jacobs in view of Halim teaches wherein each individual cost value and/or each aggregate cost value is based on at least one of: the intent, one or more associated service level agreements, and a cost associated with the intent (Jacobs (¶0048 line 5-9), “When agent 1 executes task i, nonnegative costs c_il have to be paid. The costs of agent 2 executing the task are defined as c_i2 accordingly. In an embodiment, the objective function is to minimize the total cost:”).
As Claim 7, besides Claim 3, Jacobs in view of Halim teaches wherein determining a combination cost value is performed using a constraint solving technique (Halim (¶0081), “where P is the planning problem with action costs as defined in Definition 1, and k is the number of plans to find”) and a trained machine learning model (Halim (¶0080), “Artificial intelligence planning component 208 can include a plan component 304 that can determine a solution to the artificial intelligence planning problem using any suitable artificial intelligence planner to determine a set of plans and a set of goals.”).
As Claim 8, besides Claim 1, Jacobs in view of Halim teaches wherein the one or more actions are proposed by a recommender module in the system, and wherein the one or more actions are proposed based on one of:
a rule-based process for inferring actions given a state of the system (Jacobs (¶0040 last 7 lines, fig. 2 item 220), “each respective one of the plurality of additional agents will return a constraint counter-proposal CA' if a solution compatible with constraint proposal CA will be accepted if the solution additionally satisfies the additional or alternative constraints specified by the constraint counter-proposal CA' (for example, the agent is willing to execute at most 3 of the proposed tasks).”), a logic-based process for inferring actions given a state of the system, a machine learning based recommending process trained using datasets encompassing states of the system and corresponding actions taken, and a reinforcement learning based process.
As Claim 9, besides Claim 1, Jacobs in view of Halim teaches wherein each of the first explanation and the second explanation corresponds to one of:
a proof, a derivation, or a trace of rules applied to infer the respective proposed action given a state of the system (Jacobs (¶0040 last 7 lines, fig. 2 item 220), “each respective one of the plurality of additional agents will return a constraint counter-proposal CA' if a solution compatible with constraint proposal CA will be accepted if the solution additionally satisfies the additional or alternative constraints specified by the constraint counter-proposal CA' (for example, the agent is willing to execute at most 3 of the proposed tasks).”), a set of mutually satisfiable constraints indicating corresponding variables and the value intervals within which the constraints remain satisfiable, one or more features or predicates and their corresponding values that have the greatest impact on the proposed action, and one or more properties or predicates and their corresponding values that are achieved executing the proposed action.
As Claim 10, besides Claim 1, Jacobs in view of Halim teaches wherein the system is at least part of a communication network, and the intent represents an aggregate operational goal to be reached by the communication network (Halim (¶0045 line 4-11), “The domain knowledge in a particular domain can be represented by one or more graphical maps (e.g., Mind Maps). The graphical map can be created in a knowledge engineering tool which produces an XML representation of the graphical map which can serve as an input to domain component 202. Domain component 202 can then translate the graphical map into an AI planning problem automatically.”).
As Claim 11, besides Claim 1, Jacobs in view of Halim teaches wherein the method is performed at a combined planner (Halim (¶0050), “Definition 1 A planning problem with action costs is a tuple P=(F, A, I, G), where F is a finite set of fluent symbols, A is a set of actions with preconditions, Pre( a), add effects, Add(a), delete effects, Del(a), and non-negative action costs, Cost(a), I defines the initial state, and G defines the goal state”) and explainer module in the system (Halim (¶0077 line 1-6), “includes the final state s together with its "explanation", it. Each action sequence it, comprises of actions that explain or discard the observations ( e.g., [ a, a0... , an]) and T many future actions reachable in T steps after the last observation, om is either explained or discarded, according to the domain description”).
As Claim 14, besides Claim 1, Jacobs in view of Halim teaches wherein determining a solution for the planning problem P is performed using an automatic planning progress (Halim (¶0080), “Artificial intelligence planning component 208 can include a plan component 304 that can determine a solution to the artificial intelligence planning problem using any suitable artificial intelligence planner to determine a set of plans and a set of goals.”) and a constraint relaxation process (Jacobs (¶0041 line 4-8), “If one or more of the plurality of additional agents has rejected the constraint proposal CA, the protocol returns to 210 where the coordinator agent proposes a new constraint proposal CA to each of the plurality of additional agents”).
As Claim 15 and 17, the Claims are rejected for the same reasons as Claim 1.
Claim(s) 12 and 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Jacobs in view of Halim in further view of Rigotti et al. (U.S. 2020/0394531 hereinafter Rigotti).
As Claim 12, besides Claim 1, Jacobs in view of Halim may not explicitly disclose: wherein the one or more actions are proposed using a machine learning model trained on datasets encompassing states of the systems and actions taken corresponding to respective states, or wherein the one or more actions are proposed using a reinforcement learning agent trained on simulators of the system.
Rigotti teaches:
wherein the one or more actions are proposed using a machine learning model trained on datasets encompassing states of the systems (Rigotti (¶0014 middle portion), “major obstacle in developing automatic argumentation mining techniques was the scarcity of relevant high-quality annotated data. IBM developed the first dataset to address this need. It includes 2,683 argument elements, collected in the context of 33 controversial topics, organized under a simple claim-evidence structure. To train a Deep Neural Network (DNN) to predict thematic similarity between sentences … The IBM model, trained over these data, outperformed state-of-the-art methods (Annotated argument elements and Identifying similar sentences ACL, 2018)”) and actions taken corresponding to respective states (Rigotti (¶0084 middle portion), “With the argument engagement module agents are aimed in the evaluation of possible conclusions/claims by considering reasons (arguments and counter-arguments) for and against them, providing a support for and against the conclusions/claims, through a mixture of dialectical and logical reasoning”), or wherein the one or more actions are proposed using a reinforcement learning agent trained on simulators of the system.
Jacobs discloses a system/method for negotiate constraint. Rigotti discloses a system and method for using argumenta and counter-argument to negotiate constraints. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify negotiation method of Jacobs instead be a argument and counter-argument exchanges taught by Rigotti, with a reasonable expectation of success. The motivation would be to allow “users to participate in online debates and to analyse these discussions as well as their inherent viewpoints revealed by argumentation semantics” (Rigotti (¶0016 bottom)).
As Claim 13, besides Claim 1, Jacobs in view of Halim may not explicitly disclose: wherein the first explanation is acquired from a first subsystem of the system or a first external entity, and the second explanation is from a second subsystem of the system or a second external entity.
Rigotti teaches:
wherein the first explanation is acquired from a first subsystem of the system or a first external entity, and the second explanation is from a second subsystem of the system or a second external entity (Rigotti (¶0034), “The trusted protagonist and the trusted engaged antagonists, through computational argumentation and inference, possibly using and enriching knowledge models available in a dedicated repository, exchange arguments and counterarguments in order to negotiate the handling of distributed ledger objects, required to obtain their claims”).
Jacobs discloses a system/method for negotiate constraint. Rigotti discloses a system and method for using argumenta and counter-argument to negotiate constraints. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify negotiation method of Jacobs instead be a argument and counter-argument exchanges taught by Rigotti, with a reasonable expectation of success. The motivation would be to allow “users to participate in online debates and to analyse these discussions as well as their inherent viewpoints revealed by argumentation semantics” (Rigotti (¶0016 bottom)).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Lawrence et al. (U.S. 2022/0147819) discloses a debater system for better prediction.
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/NHAT HUY T NGUYEN/Primary Examiner, Art Unit 2147
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