DETAILED ACTION
Claims 1-23 are presented for examination
This office action is in response to submission of application on 29-DECEMBER-2022.
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 .
Response to Amendment
The amendment filed on 14-APRIL-2026 in response to the non-final office action mailed 14-JANURARY-2026 has been entered. Claims 1-23 remain pending in the application.
With regards to the 101 rejection, the rejection to claim 1 has been overcome by the applicant’s amendments.
With regards to the 103 rejections, the applicant’s amendments to the claims have not overcome the rejections to claims 1-23 as the former prior art sufficiently teaches the newly added limitations of the amended claims.
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) 1-5, 8-9, 15-17, 18, and 23 are rejected under 35 U.S.C. 103 as being unpatentable over TSAGKARIS et al. (E.P. Pub. No. EP 3295611 B1) in view of RIGOTTI et al. (U.S. Pub. No. US 20200394531 A1).
Regarding claim 1, TSAGKARIS teaches the invention as substantially claimed, including:
A method performed by a node in a telecommunications network for selecting a preferred action to take from a plurality of proposed actions in order to achieve a target network configuration in the telecommunications network, the method comprising: obtaining the plurality of proposed actions, wherein the step of obtaining the plurality of proposed actions comprises using a machine learning (ML) model for predicting actions to perform in order to move a network configuration from a current configuration towards the target network configuration; ([0042] In one embodiment, Q-based Reinforcement Learning is used to obtain the Reinforcement Learning data and recommend actions (i.e., changes in network configuration parameters). In reality, the huge number of possible Situations in a network makes it impossible to maintain the Q-based Reinforcement Leaning results in a conventional table. Instead, the Analyzer effectively maintains this information in the above-described deep neural network (e.g., a Deep Q-Neural Network). [0044] The deep neural network predicts how far each Situation is from each other Situation in terms of the least number of steps to move from one Situation (i.e., state) to another. In one embodiment, the deep neural networks are initially trained with historical data from a network operators databases. [0045] The tables in Figures 4a and 4b illustrate how the internal information processed by the deep neural networks would look like in human-friendly form. The table in Figure 4a depicts the expected total number of sequential steps (i.e., actions) required to reach one Situation (column) from another Situation (row) in some random time instance. A "step" represents an action that transition the communications network from one state to another. If there are multiple steps between two Situations, then the transition involves going through intermediate Situations. As the system collects more data on the communications network over time, it is usually able to reduce the number of steps (i.e., actions) identified to move from one Situation to another, such that the path between many Situations may be reduced to a single step.
) and selecting the preferred action, based on the results of the evaluating; and performing the preferred action. ( [0040] The deep neural network provides the Analyzer with an understanding of the distance between Situation states, and this understanding enables the Analyzer to predict how changes in one or more communications network configuration parameters will enable the communications network to move from one state to another. From all the potential actions, the Analyzer selects an optimal action as described below. ([0047] As stated above, an action is a change to one or more communications network configuration parameters. An example of an action is adjusting the antenna tilt of a communications network element. The tilt angle is a network configuration parameter. For instance, the value of the parameter may have three discrete values 0, +15, -15. As part of the Reinforcement Learning process, the system observes and explores the effect on communications network performance when the configuration parameter takes on different values under specific time, location, weather, etc. conditions. In this example, the action involves one network configuration parameter, but other actions could involve changes to multiple network configuration parameters. The Analyzer may use a simulator to simulate possible actions and fine tune the deep neural network and the transition probability matrices, thereby further improving its predictions and recommendations.))
While TSAGKARIS does teach selection a preferred action from a plurality of proposed actions it does not explicitly teach:
evaluating each of the plurality of proposed actions predicted by the ML model compared to the target network configuration using a computational argumentation process comprising a quantitative bipolar argumentation method;
However, in analogous art that similar utilizes neural networks to make a decision on an action to be taken, RIGOTTI teaches:
evaluating each of the plurality of proposed actions compared to the target network configuration using a computational argumentation process comprising a quantitative bipolar argumentation method; ([0032] After the agents identification, the trusted protagonist generates arguments to justify the acceptance of its claim in the interest of its manager and sends them, initiating a dialogue, to the connected trusted antagonists.
[0033] Each connected trusted antagonist verifies the compliance of the trusted protagonist's arguments with its claim, within its given constraints and limits, in order to decide on its engagement in a negotiation with the trusted protagonist and so, if positive, becoming a trusted engaged antagonist.
[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.
[0061] The present invention applies in integrated way distributed ledger technology, computational argumentation and inference, vocal argumentation and root of trust based security.
[0062] In the following description the term manager 2 is referred to an entity comprising means to define and activate some claims with their constraints and limits. A claim or issue is a statement essentially arguable but used as a primary point to support or prove an argument. Argumentation is, in fact, a discourse in which a set of propositions is put forward to justify the acceptance of a questioned proposition—the standpoint or claim. Claims are the main components of an argument; identifying and using them correctly are essential to framing an argument in a debate. Whereas formal arguments are static, such as one might find in a textbook or research article, argumentative dialogue is dynamic. It serves as a published record of justification for an assertion.
[0063] In the following description the term agent, acting as protagonist 1 or antagonist 3, is referred to an entity comprising means to accept in input claims from a manager 2 with their constraints and limits and to manage their finalization by interacting with other agents to find an agreement on the handling of distributed ledger objects.
[0064] For exemplificative purpose and without limiting the scope of the present invention a manager 2 or an agent, protagonist 1 or antagonist 3, can be, indifferently and interchangeably, a silicon-based technology entity like an electronic device such as a wearable device or a smartphone or an IoT device or, in general, a computerized device or robots or it can be a carbon-based entity like a human or an animal. So it is possible that one antagonist agent 3 is human and the protagonist agent 1 is not a human agent or vice versa. The agents can interact also with other trusted or non-trusted entities such as external databases and websites and exchange data with them. (as seen here, the method has an agent, manager, protagonist, and antagonist. Each role allows the method to either support or attack the argument, making the method quantitative and bipolar))
It would have been obvious to a person skilled in the art before the effective filing date of the invention to have combined with RIGOTTI’s teaching of a computational argumentation process and, with TSAGKARIS’ teaching of a method to choose a proposed action, to realize, with a reasonable expectation of success, a method that chooses a proposed action, as in TSAGKARIS, using a computational computation process, as in RIGOTTI. A person of ordinary skill would have been motivated to make this combination to be prepared to increase efficiency (RIGOTTI [0043]).
Regarding claim 2, RIGOTTI further teaches:
The method of claim1, wherein the step of evaluating each of the plurality of proposed actions compared to the target network configuration using a computational argumentation process comprises, for each proposed action: determining, according to the computational argumentation process, one or more arguments for the proposed action, wherein the one or more arguments comprise arguments in favour of the proposed action being likely to achieve the target network configuration, and/or arguments against the proposed action being likely to achieve the target network configuration. ([0032] After the agents identification, the trusted protagonist generates arguments to justify the acceptance of its claim in the interest of its manager and sends them, initiating a dialogue, to the connected trusted antagonists.
[0033] Each connected trusted antagonist verifies the compliance of the trusted protagonist's arguments with its claim, within its given constraints and limits, in order to decide on its engagement in a negotiation with the trusted protagonist and so, if positive, becoming a trusted engaged antagonist.
[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.)
Regarding claim 3, TSAGKARIS further teaches:
The method of claim 2, wherein the step of obtaining the plurality of proposed actions further comprises obtaining, for each proposed action of the plurality of proposed actions: a prediction of a change in a value of a key performance indicator, KPI, that is predicted to result from the proposed action, and a confidence value reflecting a confidence in the respective prediction; ([0043] An "action" is a change in one or more communications network configuration parameters. Each row in the table indicates how an action taken in a given Situation is expected to affect KPI values. When an action is actually taken in a Situation, the Analyzer is able to assess how well it predicted the corresponding KPI values (immediately and longer-term) and adjust the deep neural network model accordingly. Over time, this increases the degree of confidence the Analyzer has in the effect an action will have on a Situation (immediately and longer term).)
RIGOTTI further teaches:
and wherein the step of determining one or more arguments comprises determining a first argument based on the obtained prediction and the corresponding confidence values. ([0029] Each agent executes an application to obtain the claim defined by the manager with its given constraints and limits. This application could be selected from a dedicated application repository.)
Regarding claim 4, TSAGKARIS further teaches:
The method of claim 3, wherein the first argument comprises: an argument in favor of the proposed action if the obtained prediction of the change in the value of the KPI suggests that the outcome of the proposed action will change the KPI in a direction consistent with the target network configuration; ([0046] The table in Figure 4b is a stochastic matrix with the multi-step transition probabilities among Situations (i.e. the sum probabilities for reaching one Situation (column) from another Situation (row), by taking the steps (actions) shown in the corresponding cells in the table in Figure 4a). The higher the value of the cell, the higher the probability of a successful transition. Cells with values smaller than 0.1 indicate a highly improbable transition. It should be noted that a large number of steps does not necessarily mean small probability and vice versa. For Situations associated with KPIs below a specified threshold, the Analyzer eventually identifies an optimal action for the Situation. An optimal action is one that, with a threshold degree of confidence, will move the communications network from such a Situation to an improved Situation (i.e., a state with better KPI values) by changing only one or a "few" communications network configuration parameters, wherein a "few" may be defined by an administrator of the Analyzer or configurable by a communications network operator. In one embodiment, recommended actions are only those in which a Situation can be improved in one step with a threshold degree of confidence.) or an argument against the proposed action if the obtained prediction of the change in value of the KPI suggests that the outcome of the proposed action will change the KPI in a direction inconsistent with the target network configuration. ([0046] The table in Figure 4b is a stochastic matrix with the multi-step transition probabilities among Situations (i.e. the sum probabilities for reaching one Situation (column) from another Situation (row), by taking the steps (actions) shown in the corresponding cells in the table in Figure 4a). The higher the value of the cell, the higher the probability of a successful transition. Cells with values smaller than 0.1 indicate a highly improbable transition. It should be noted that a large number of steps does not necessarily mean small probability and vice versa. For Situations associated with KPIs below a specified threshold, the Analyzer eventually identifies an optimal action for the Situation. An optimal action is one that, with a threshold degree of confidence, will move the communications network from such a Situation to an improved Situation (i.e., a state with better KPI values) by changing only one or a "few" communications network configuration parameters, wherein a "few" may be defined by an administrator of the Analyzer or configurable by a communications network operator. In one embodiment, recommended actions are only those in which a Situation can be improved in one step with a threshold degree of confidence.)
Regarding claim 5, TSAGKARIS further teaches:
The method of claim 3, wherein the first argument comprises an argument against the proposed action if the KPI can be shown to be unaffected by the proposed action. ([0046] The table in Figure 4b is a stochastic matrix with the multi-step transition probabilities among Situations (i.e. the sum probabilities for reaching one Situation (column) from another Situation (row), by taking the steps (actions) shown in the corresponding cells in the table in Figure 4a). The higher the value of the cell, the higher the probability of a successful transition. Cells with values smaller than 0.1 indicate a highly improbable transition. It should be noted that a large number of steps does not necessarily mean small probability and vice versa. For Situations associated with KPIs below a specified threshold, the Analyzer eventually identifies an optimal action for the Situation. An optimal action is one that, with a threshold degree of confidence, will move the communications network from such a Situation to an improved Situation (i.e., a state with better KPI values) by changing only one or a "few" communications network configuration parameters, wherein a "few" may be defined by an administrator of the Analyzer or configurable by a communications network operator. In one embodiment, recommended actions are only those in which a Situation can be improved in one step with a threshold degree of confidence.)
Regarding claim 8, TSAGKARIS further teaches:
The method of claim1, wherein the step of obtaining the plurality of proposed actions comprises requesting a model to predict an action that will achieve the target network configuration. ([0039] The Analyzer uses the statistical model created from unsupervised clustering to predict future Situations that will result in non-normal communications network conditions (step 175). In other words, the Analyzer uses the statistical model to determine whether any Situations associated with non-normal network conditions are likely to arise at some future point. Because of the time component in each row and the segmentation of situations clusters based on the communications network state (e.g., "critical," "poor," "normal," etc.), the system can provide incident detection by time series and regression analysis with constraints/thresholds coming from the human operator. For example, it can predict the value of any KPI in some future time point (e.g., in 1 hour, Monday evening) given some particular communications network parameter configuration and environment condition context and produce a warning after comparing that predicted value against an operator's threshold (e.g., drop call rate < 10%). Thereafter, the Analyzer validates this result using standard hypothesis testing techniques for confidence bands.
[0040] The Analyzer then combines the re-trained deep neural network model with the reinforcement learning results to identify one or more changes to network configuration parameters that has at least a threshold probability (e.g., 0.8) of changing the state of the predicted Situation to a normal communications network state while having negligible side effects on the longer-term (i.e., beyond the time frame of the Situation) condition of the communications network (step 180). The deep neural network provides the Analyzer with an understanding of the distance between Situation states, and this understanding enables the Analyzer to predict how changes in one or more communications network configuration parameters will enable the communications network to move from one state to another. From all the potential actions, the Analyzer selects an optimal action as described below.)
Regarding claim 9, RIGOTTI further teaches:
The method of claim 2, wherein the step of obtaining the plurality of proposed actions comprises requesting a model to predict an action that will achieve the target network configuration, obtaining the plurality of proposed actions further comprises obtaining an indication of an accuracy of the model that predicted the proposed action, and; ([0038] The controller may comprise a long short-term memory (LSTM) network. The controller may comprise a number of controller parameters. The controller may operate a policy (e.g., it may perform a selected operation based on functionality set out in the policy). The controller parameters may influence the policy, such that modifying the parameters may modify the outcome of actions taken by the policy. The controller may be trained by updating the controller parameters. For example, a reward may be established which the controller seeks to maximize, e.g., this may be to reduce a variance associated with a baseline average for the controller. [0045] When a loss function is being minimized, stopping criteria may be introduced. For example, stopping criteria may inhibit a model from being over-trained, e.g., trained too specifically to its training data set that the model struggles to generalize to unseen data. The stopping criteria may include use of a validation data set to identify performance of the model on a separate data set being above a threshold level. For example, stopping criteria may be based on an indication of accuracy for the model, e.g., once above a threshold level, the model may be deemed sufficiently trained.) the step of determining one or more arguments comprises determining a third argument based on the indication of accuracy of the model. ([0029] Each agent executes an application to obtain the claim defined by the manager with its given constraints and limits. This application could be selected from a dedicated application repository.)
Regarding claim 16, TSAGKARIS further teaches:
The method of claim 1, wherein the target network configuration is expressed in terms of a set of key performance indicators, KPIs, to be met in the telecommunications network. ([0015] The Analyzer receives as input large amounts of data in a data warehouse from disparate sources, wherein the imported data includes: (1) data related to communications network components (e.g., cell ID, cell location, configuration parameters and associated time stamps; (2) KPI indicators for the communications network and associated time stamps; and)
Regarding claim 20, RIGOTTI further teaches:
A computer program product comprising a computer readable medium, the computer readable medium having computer readable code embodied therein, the computer readable code being configured such that, on execution by a suitable computer or processor, the computer or processor is caused to perform the method of claim1 . ([0098] At least one application selection module 22 to handle an application selection phase to select from at least one applications repository 23 the at least one application to be used by the at least one application processing module 5 to obtain the at least one claim defined with its given constraints and limits. According to the present invention and with reference to FIG. 6, it is possible that the agent's at least one application selection module 22 initiates the application selection phase considering applications available in the at least one applications repository 23 that could be stored by the agent or that could be discovered in or retrieved from external directories or repositories. The application repository 23 could be an application container, providing access to a plurality of software applications, that may include a set of executable applications and a set of associated system files required to execute them like runtime components, such as files, environment variables and libraries, necessary to run the desired software. It is worth to point out that the applications used by the agents could be continuously updated by the agents or by other actors managing the agents repositories or directories.)
Regarding claim 18 and 23, they comprise of limitations similar to those of claims 1-2 and are therefore rejected for similar rationale.
Claim(s) 6-7 and 10 are rejected under 35 U.S.C. 103 as being unpatentable over TSAGKARIS et al. (E.P. Pub. No. EP 3295611 B1), RIGOTTI et al. (U.S. Pub. No. US 20200394531 A1) in view of BRUNO et al. (U.S. Pub. No. US 20220060966 A1).
Regarding claim 6, while TSAGKARIS, as modified by RIGOTTI, does teach claim 2 which claim 6 is dependent upon, it does not explicitly teach:
The method of claim 2, wherein the step of obtaining the plurality of proposed actions further comprises obtaining, for each proposed action of the plurality of proposed actions: a feasibility parameter related to a feasibility of the proposed action;
However, in analogous art that similarly teaches using a neural network to make a decision, BRUNO teaches:
The method of claim 2, wherein the step of obtaining the plurality of proposed actions further comprises obtaining, for each proposed action of the plurality of proposed actions: a feasibility parameter related to a feasibility of the proposed action; ([0024] According to examples of the present disclosure, the method may further comprise using the parameter to evaluate the feasibility of a potential new path between microwave nodes in the microwave network in the event of a change in the link topology of the microwave network.)
It would have been obvious to a person skilled in the art before the effective filing date of the invention to have combined with BRUNO’s teaching of a feasibility parameter and, with TSAGKARIS’, as modified by RIGOTTI, teaching of a method to choose a proposed action, to realize, with a reasonable expectation of success, a method that chooses a proposed action, as in TSAGKARIS as modified by RIGOTTI, using a feasibility parameter, as in BRUNO. A person of ordinary skill would have been motivated to make this combination to be reduce costs (BRUNO [0003]).
RIGOTTI further teaches:
and wherein the step of determining one or more arguments comprises determining a second argument based on the feasibility parameter. ([0029] Each agent executes an application to obtain the claim defined by the manager with its given constraints and limits. This application could be selected from a dedicated application repository.)
Regarding claim 7, BRUNO further teaches:
The method of claim6, wherein the second argument comprises an argument in favor of the proposed action if the feasibility parameter indicates that the proposal is feasible; ([0025] According to examples of the present disclosure, using the parameter quantifying, for a microwave node in the microwave network, the extent to which the microwave path associated with the microwave node is reconfigurable, to evaluate the feasibility of a potential new path between microwave nodes in the microwave network may comprises identifying a change in the link topology of the microwave network. Using the parameter may further comprise identifying a path between first and second microwave nodes in the microwave network, which path is impacted by the change in topology, the path comprising at least one microwave link, identifying a potential new path between the first and second microwave nodes, which path maintains connectivity between the first and second nodes in the changed link topology, and evaluating the feasibility of the potential new path on the basis of parameters quantifying the extent to which the microwave paths associated with the first and second microwave nodes are reconfigurable.
[0026] According to examples of the present disclosure, the method may further comprise, if the potential new path between the first and second microwave nodes is evaluated as feasible, instructing the first and second microwave nodes to reconfigure their associated microwave paths to form the potential new path.) or an argument against the proposed action if the feasibility parameter indicates that the proposal is unfeasible. ([0025] According to examples of the present disclosure, using the parameter quantifying, for a microwave node in the microwave network, the extent to which the microwave path associated with the microwave node is reconfigurable, to evaluate the feasibility of a potential new path between microwave nodes in the microwave network may comprises identifying a change in the link topology of the microwave network. Using the parameter may further comprise identifying a path between first and second microwave nodes in the microwave network, which path is impacted by the change in topology, the path comprising at least one microwave link, identifying a potential new path between the first and second microwave nodes, which path maintains connectivity between the first and second nodes in the changed link topology, and evaluating the feasibility of the potential new path on the basis of parameters quantifying the extent to which the microwave paths associated with the first and second microwave nodes are reconfigurable.
[0026] According to examples of the present disclosure, the method may further comprise, if the potential new path between the first and second microwave nodes is evaluated as feasible, instructing the first and second microwave nodes to reconfigure their associated microwave paths to form the potential new path.)
Regarding claim 10, BRUNO further teaches:
The method of claim 9, wherein the third argument comprises an argument in favor of the proposed action if the indication of accuracy suggests that the model that predicted the proposed action historically has an accuracy greater than a threshold accuracy level; ([0025] According to examples of the present disclosure, using the parameter quantifying, for a microwave node in the microwave network, the extent to which the microwave path associated with the microwave node is reconfigurable, to evaluate the feasibility of a potential new path between microwave nodes in the microwave network may comprises identifying a change in the link topology of the microwave network. Using the parameter may further comprise identifying a path between first and second microwave nodes in the microwave network, which path is impacted by the change in topology, the path comprising at least one microwave link, identifying a potential new path between the first and second microwave nodes, which path maintains connectivity between the first and second nodes in the changed link topology, and evaluating the feasibility of the potential new path on the basis of parameters quantifying the extent to which the microwave paths associated with the first and second microwave nodes are reconfigurable.
[0026] According to examples of the present disclosure, the method may further comprise, if the potential new path between the first and second microwave nodes is evaluated as feasible, instructing the first and second microwave nodes to reconfigure their associated microwave paths to form the potential new path.)
or an argument against the proposed action if the indication of accuracy suggests that the model that predicted the proposed action historically has an accuracy less than a threshold accuracy level. ([0025] According to examples of the present disclosure, using the parameter quantifying, for a microwave node in the microwave network, the extent to which the microwave path associated with the microwave node is reconfigurable, to evaluate the feasibility of a potential new path between microwave nodes in the microwave network may comprises identifying a change in the link topology of the microwave network. Using the parameter may further comprise identifying a path between first and second microwave nodes in the microwave network, which path is impacted by the change in topology, the path comprising at least one microwave link, identifying a potential new path between the first and second microwave nodes, which path maintains connectivity between the first and second nodes in the changed link topology, and evaluating the feasibility of the potential new path on the basis of parameters quantifying the extent to which the microwave paths associated with the first and second microwave nodes are reconfigurable.
[0026] According to examples of the present disclosure, the method may further comprise, if the potential new path between the first and second microwave nodes is evaluated as feasible, instructing the first and second microwave nodes to reconfigure their associated microwave paths to form the potential new path.)
Claim(s) 11-12 are rejected under 35 U.S.C. 103 as being unpatentable over TSAGKARIS et al. (E.P. Pub. No. EP 3295611 B1), RIGOTTI et al. (U.S. Pub. No. US 20200394531 A1) in view of FILIPPOVA et al. (U.S. Pub. No. US 20190287649 A1).
Regarding claim 11, while TSAGKARIS, as modified by RIGOTTI, does teach claim 2 which claim 11 is dependent upon, it does not explicitly teach:
The method of claim 2 wherein the one or more arguments are hierarchically linked such that the one or more arguments comprise: a first subset of the one or more arguments that directly support the proposed action, and a second subset of the one or more arguments that support the first subset of the one or more arguments; and/or a third subset of the one or more arguments that directly oppose the proposed action, and a fourth subset of the one or more arguments that support the opposition of the proposed action by the third subset of the one or more arguments.
However, in analogous art that similarly teaches using a neural network to make a decision, FILIPPOVA teaches:
The method of claim 2 wherein the one or more arguments are hierarchically linked such that the one or more arguments comprise: a first subset of the one or more arguments that directly support the proposed action, and a second subset of the one or more arguments that support the first subset of the one or more arguments; ([0215] As disclosed herein, a cross validation procedure partitions the filtered training data into different pairs of a training subset and a validation subset at a predetermined percentage split. For example, the first training subset and first validation subset depicted at step 410 represent an 80:20 split during one fold of a k-fold cross-validation experiment.) and/or a third subset of the one or more arguments that directly oppose the proposed action, and a fourth subset of the one or more arguments that support the opposition of the proposed action by the third subset of the one or more arguments.
It would have been obvious to a person skilled in the art before the effective filing date of the invention to have combined with FILIPOVA’s teaching of subsets of data supporting data used to confirm an argument and, with TSAGKARIS’, as modified by RIGOTTI, teaching of a method to choose a proposed action and computational argumentation processes, to realize, with a reasonable expectation of success, a method that chooses a proposed action, as in TSAGKARIS as modified by RIGOTTI, and verifies that choice with subsets of data, as in FILIPOVA. A person of ordinary skill would have been motivated to make this combination to improve accuracy (FILIPPOVA [0004]).
Regarding claim 12, FILIPPOVA further teaches:
The method of claim 11, further comprising: generating a visual representation comprising the one or more arguments, wherein the one or more arguments are arranged in the visual representation so as to indicate the first subset of the one or more arguments, the second subset of the one or more arguments, and a manner in which the first subset of the one or more arguments and the second subset of the one or more arguments are hierarchically linked. ([0125] In some embodiments, a user may use I/O module 120 to manipulate data that is available either on a local device or can be obtained via a network connection from a remote service device or another user device. For example, I/O module 120 can allow a user to a keyboard or a touchpad to perform data analysis via a graphical user interface (GUI). In some embodiments, a user can manipulate data via voice control. In some embodiments, user authentication is required before a user is granted access to the data being requested.)
Claim(s) 13-14 are rejected under 35 U.S.C. 103 as being unpatentable over TSAGKARIS et al. (E.P. Pub. No. EP 3295611 B1), RIGOTTI et al. (U.S. Pub. No. US 20200394531 A1) in further view of BYRON et al. (U.S. Pub. No. US 10726338 B2).
Regarding claim 13, while TSAGKARIS, as modified by RIGOTTI, does teach claim 2 which claim 13 is dependent upon, it does not explicitly teach:
The method of claim 2 further comprising determining a weighting representing a strength of each of the one or more arguments for each proposed action.
However, in analogous art that similarly teaches using a neural network to make a decision, BYRON teaches:
The method of claim 2 further comprising determining a weighting representing a strength of each of the one or more arguments for each proposed action. ((paragraph 35, lines 14-28) In the synthesis stage 360, the large number of scores generated by the various reasoning algorithms are synthesized into confidence scores or confidence measures for the various hypotheses. This process involves applying weights to the various scores, where the weights have been determined through training of the statistical model employed by the QA pipeline 300 and/or dynamically updated. For example, the weights for scores generated by algorithms that identify exactly matching terms and synonym may be set relatively higher than other algorithms that are evaluating publication dates for evidence passages. The weights themselves may be specified by subject matter experts or learned through machine learning processes that evaluate the significance of characteristics evidence passages and their relative importance to overall candidate answer generation.)
It would have been obvious to a person skilled in the art before the effective filing date of the invention to have combined with BYRON’s teaching of weighting data and, with TSAGKARIS’, as modified by RIGOTTI, teaching of a method to choose a proposed action and computational argumentation processes, to realize, with a reasonable expectation of success, a method that chooses a proposed action using argumentation computational processes, as in TSAGKARIS as modified by RIGOTTI, and weights the arguments generated as data, as in BYRON. A person of ordinary skill would have been motivated to make this combination to improve accuracy (BYRON, Paragraph 23, lines 4-25).
Regarding claim 14, BYRON further teaches:
The method of claim 13, wherein the step of evaluating each of the plurality of proposed actions compared to the target network configuration using a computational argumentation process further comprises: combining the strengths of each of the one or more arguments for each proposed action, to determine an overall strength for the respective proposed action; ((paragraph 35, lines 29-38) The weighted scores are processed in accordance with a statistical model generated through training of the QA pipeline 300 that identifies a manner by which these scores may be combined to generate a confidence score or measure for the individual hypotheses or candidate answers. This confidence score or measure summarizes the level of confidence that the QA pipeline 300 has about the evidence that the candidate answer is inferred by the input question, i.e. that the candidate answer is the correct answer for the input question.) and selecting the preferred action, based on the overall strengths of the proposed actions. ((paragraph 35, lines 39-54) The resulting confidence scores or measures are processed by a final confidence merging and ranking stage 370 which compares the confidence scores and measures to each other, compares them against predetermined thresholds, or performs any other analysis on the confidence scores to determine which hypotheses/candidate answers are the most likely to be the correct answer to the input question. The hypotheses/candidate answers are ranked according to these comparisons to generate a ranked listing of hypotheses/candidate answers (hereafter simply referred to as “candidate answers”). From the ranked listing of candidate answers, at stage 380, a final answer and confidence score, or final set of candidate answers and confidence scores, are generated and output to the submitter of the original input question via a graphical user interface or other mechanism for outputting information.)
Response to Arguments
Applicant’s arguments filed 14-APRIL-2026 have been fully considered, but they are found to be non-persuasive
With regards to the applicant’s remarks regarding the 103 rejection in the non-final action, the applicant argues that the prior art does not teach the newly amended claims 1 and 18. The examiner acknowledges this argument and has adjusted the prior art of TSAGKARIS and RIGOTTI to disclose the newly added limitations. Namely, new paragraphs from TSAGKARIS have been added to show the newly added limitation of claim 1 while RIGOTTI’s mapping to the canceled claim 17 now present in claim 1 has been altered to better clarify the intention of the mapping. Further, the examiner has adjusted all dependent claims accordingly.
Namely, Applicant argues that RIGOTTI fails to disclose a quantitative bipolar argumentation method, as required by newly amended claim 1. The mapping of RIGOTTI paragraph 64 has been added to show the claimed method present within RIGOTTI. Namely, RIGOTTI has a method has an agent, manager, protagonist, and antagonist. Each role allows the method to either support or attack the argument, making the method quantitative and bipolar, as is required by amended claim 1.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SKIELER A KOWALIK whose telephone number is (571)272-1850. The examiner can normally be reached 8-5.
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/SKIELER ALEXANDER KOWALIK/Examiner, Art Unit 2142 /Mariela Reyes/Supervisory Patent Examiner, Art Unit 2142
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