CTFR 18/172,448 CTFR 93529 DETAILED ACTION Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia 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 02/16/2026 has been entered. Claims 1-15 remain pending in the application. Claim Rejections - 35 USC § 101 07-04-01 AIA 07-04 35 U.S.C. 101 reads as follows: Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title. Claims 1-15 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Step 1 : Claims 1-5 are directed to a medium, claims 6-10 are directed to a method and claims 11-15 are directed to a device. Therefore, the claims are eligible under Step 1 for being directed to a manufacture, a process and a machine respectively. Independent claims 1, 6 and 11: Step 2A Prong 1: Claims recite: selecting a first edge from the plurality of edges - Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating data and selecting data based on judgement, which is observing, evaluating and judging that is practically capable of being performed in the human mind with the assistance of pen and paper; generating new graph data as neighborhood data from the obtained graph data, the generated new data having a second connection relationship between the plurality of nodes different from the first connection relationship, change connection of the first edge such that a third node connected to at least one of a first node and a second node located at both ends of the first edge via a number of edges, the number being equal to or less than a threshold, is located at one end of the first edge - Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of obtaining data and modifying data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper, or is a mathematical concept that is achievable through mathematical computation. Step 2A Prong 2 : This judicial exception is not integrated into a practical application because they recite the additional elements: A non-transitory computer-readable storage medium storing a data generation program for causing a computer to perform processing; a data generation device comprising: a memory; and processor circuitry coupled to the memory, the processor circuitry being configured to perform processing - These limitations amount to components of a general purpose computer that applies a judicial exception, by use of conventional computer functions (see MPEP § 2106.05(b)). obtaining graph data that includes a plurality of nodes and a plurality of edges connecting the plurality of nodes the step recited at a high level of generality, and amounts to mere data gathering, which is a form of insignificant extra-solution activity (see MPEP § 2106.05(g)). applying a Local Interpretable Model-agnostic Explanations (LIME) algorithm to the obtained graph data to change a first connection relationship between the plurality of nodes included in the obtained graph data, the applying of the LIME algorithm being limited to change connection of the first edge - the step recited at a high level of generality, and amounts to merely indicating a field of use or technological environment in which the judicial exception is performed (see MPEP § 2106.05(h)). Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claims are thus directed to the abstract idea. Step 2B : The claims do not include additional elements that amount to significantly more than the judicial exception. The additional elements: A non-transitory computer-readable storage medium storing a data generation program for causing a computer to perform processing; a data generation device comprising: a memory; and processor circuitry coupled to the memory, the processor circuitry being configured to perform processing - These limitations amount to components of a general purpose computer that applies a judicial exception, by use of conventional computer functions (see MPEP § 2106.05(b)). obtaining graph data that includes a plurality of nodes and a plurality of edges connecting the plurality of nodes - which is a well-understood, routine, conventional activity similar to receiving or transmitting data over a network described in MPEP 2106.05(d)(II) . applying a Local Interpretable Model-agnostic Explanations (LIME) algorithm to the obtained graph data to change a first connection relationship between the plurality of nodes included in the obtained graph data, the applying of the LIME algorithm being limited to change connection of the first edge - the step invokes computers or other machinery to apply the underlying judicial exception and generally linking the use of the judicial exception to indicate a field of use or technological environment. Thus, the additional elements, viewed individually or in combination, do not provide an inventive concept or otherwise amount to significantly more than the abstract idea itself. See MPEP 2106.05. Accordingly, these additional elements do not amount to significantly more than the judicial exception. As such, the claims are ineligible. Dependent claims 2, 7 and 12: Step 2A Prong 1: Claims recite: the generating includes processing of generating the new graph data that has a third connection relationship between the plurality of nodes different from the first connection relationship between the plurality of nodes of the graph data by changing connection of the first edge such that a fourth node connected to at least one of the first node and the second node located at the both ends of the first edge via a number of edges, the number being equal to or less than the threshold, is located at the other end of the first edge - Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of obtaining data and modifying data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper, or is a mathematical concept that is achievable through mathematical computation. Step 2A Prong 2 & Step 2B : There are no additional elements recited so the claims do not provide a practical application and is not considered to be significantly more. As such, the claims are ineligible. Dependent claims 3, 8 and 13: Step 2A Prong 1: The claim recites the abstract ideas of claims 1, 6 and 11. Step 2A Prong 2 : This judicial exception is not integrated into a practical application because they recite the additional elements: wherein both the first connection relationship and the second connection relationship have connectivity - the step recited at a high level of generality, and amounts to insignificant application, which is a form of insignificant extra-solution activity (see MPEP § 2106.05(g)). Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claims are thus directed to the abstract idea. Step 2B : The claims do not include additional elements that amount to significantly more than the judicial exception. The additional elements: wherein both the first connection relationship and the second connection relationship have connectivity - the step recited at a high level of generality, and amounts to insignificant application, which is a form of insignificant extra-solution activity (see MPEP § 2106.05(g)). Accordingly, these additional elements do not amount to significantly more than the judicial exception. As such, the claims are ineligible. Dependent claims 4, 9 and 14: Step 2A Prong 1: Claims recite: wherein the selecting includes processing of selecting a new first edge from a plurality of edges included in the new graph data each time the new graph data is generated until the number of times the connection is changed in the processing of generating reaches a threshold - Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating data and selecting data based on judgement, which is observing, evaluating and judging that is practically capable of being performed in the human mind with the assistance of pen and paper. Step 2A Prong 2 & Step 2B : There are no additional elements recited so the claims do not provide a practical application and is not considered to be significantly more. As such, the claims are ineligible. Dependent claims 5, 10 and 15: Step 2A Prong 1: The claim recites the abstract ideas of claims 1, 6 and 11. Step 2A Prong 2 : This judicial exception is not integrated into a practical application because they recite the additional elements: wherein the new graph data is used to generate an approximate model that describes an inference result of a machine learning model that performs inference using the graph data as input - the step recited at a high level of generality, and amounts to more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)). Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claims are thus directed to the abstract idea. Step 2B : The claims do not include additional elements that amount to significantly more than the judicial exception. The additional elements: wherein the new graph data is used to generate an approximate model that describes an inference result of a machine learning model that performs inference using the graph data as input - the step recited at a high level of generality, and amounts to more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)). Accordingly, these additional elements do not amount to significantly more than the judicial exception. As such, the claims are ineligible. Claim Rejections - 35 USC § 103 07-20-aia AIA 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. 07-21-aia AIA Claim s 1-15 are rejected under 35 U.S.C. 103 as being unpatentable over Baker et al. (hereinafter Baker), US 20200334541 A1, in view of Xiong et al. (hereinafter Xiong), US 20170116315 A1, further in view of Ramamurthy et al. (hereinafter Ramamurthy), US 20210133610 A1 . Regarding independent claim 1 , Baker teaches a non-transitory computer-readable storage medium storing a data generation program for causing a computer to perform processing ([0398]) comprising: obtaining graph data that includes a plurality of nodes and a plurality of edges connecting the plurality of nodes ([0042] A network or directed graph is a set of elements, called “nodes,” with a binary relation on the set of ordered pairs of nodes. Conceptually, the network or graph is a set of nodes connected by directed arcs, where there is an arc from node A to node B in the graph if and only if the ordered pair (A, B) is in the binary relation; Fig. 2; [0100] In boxes 201 - 207 , the computer system implements feed-forward, back-propagation, and update computations for stochastic gradient descent training based on estimating the gradient of the objective with respect to each connection weight by performing for each training data item in a mini-batch, a feed-forward activation computation followed a back-propagation computation of the partial derivatives of the objective, averaged over the mini-batch and used for a stochastic gradient descent update of the learned parameters, the connection weights and node biases. The feed-forward computation, the back-propagation computation, and the iterative update for each mini-batch are well-known to those skilled in the art of training neural networks); selecting a first edge from the plurality of edges (Fig. 2, 208; [0114] In box 208 , the computer system also decides for a node pair <A, B> with an existing direct connection from A to B, whether to delete that connection) ; and generating new graph data as neighborhood data from the obtained graph data by applying a Local Interpretable Model-agnostic Explanations (LIME) algorithm to the obtained graph data to change a first connection relationship between the plurality of nodes included in the obtained graph data , the generated new data having a second connection relationship between the plurality of nodes different from the first connection relationship , the applying of the LIME algorithm being limited to change connection of the first edge ([0106] In boxes 208 - 210 , the computer system performs operations specific to the self-organizing learning process for a set with a strict partial order, based on the concepts discussed in the introduction; [0107] In box 208 , the computer system decides whether to add a connection that is in the transitive closure of the current network or delete a connection whose node pair is not a cover pair; [0108] In one embodiment, to decide whether to add a connection from node A to node B to the current network, the computer system makes an estimate of the expected improvement in the objective that may be achieved by a modified network that includes the additional connection; [0114] In box 208 , the computer system also decides for a node pair <A, B> with an existing direct connection from A to B, whether to delete that connection. For this decision, the computer system estimates CDC(A, B), i.e., the “Cost of Deleting the Connection” from A to B. The function CDC(A, B) is only defined for ordered pairs <A, B> for which there is a direct connection) such that a third node connected to at least one of a first node and a second node located at both ends of the first edge via a number of edges, the number being equal to or less than a threshold , is located at one end of the first edge ([0126] For example, there may be a bound on the rate at which new arcs can be added dependent on the number of arcs that have been deleted. As another example, in addition to only adding arcs with the largest magnitude objective function partial derivatives, there can be a threshold value not allowing any new arc to be added unless the magnitude of its objective function exceeds the threshold value. The threshold value can be adjusted by fixed rules or by the learning coach 220 to help match the rates of arc creation and deletion to the strategy for the current situation; Figs. 22A-22C; [0071]-[0083] Note: Examiner maps AC arc as first edge, A and C as first node and second node, D as third node. Graph in Fig. 22C (i.e new data) is different from graph in Fig. 22A by changing connection of arc AC where third node D connected to A via 1 edge, is located at one end of the first edge ). Baker doesn’t explicitly disclose a third node connected to a node via a number of edges, the number being equal to or less than a threshold, applying a Local Interpretable Model-agnostic Explanations (LIME) algorithm to the obtained graph data to change a first connection relationship between the plurality of nodes included in the obtained graph data, the applying of the LIME algorithm being limited to change connection of the first edge. However, in the same field of endeavor, Xiong teaches a third node connected to a node via a number of edges, the number being equal to or less than a threshold ([0023] The clustering engine 202 obtains data in the form of tables, such as the table 212 , from the relational database 210 . The clustering engine 202 then clusters the nodes in the data according to their connectivity, i.e., such that each cluster contains nodes that are close to each other according to some metric (e.g., within a threshold distance); [0024] For instance, in one embodiment, where a graph is unweighted, the nodes that are closest to each other (e.g., separated by no more than a threshold number of edges) may be grouped in the same cluster. In an alternative embodiment, where a graph is weighted, the nodes that are closest to each other (e.g., separated by no more than a threshold weighted distance/sum of edge weights) may be grouped in the same cluster. In this case, two nodes placed in the same cluster could have a relatively large number of edges between them; however, the sum of the weights of these edges may be less than the threshold). It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of clustering the nodes in a graph such that each cluster contains nodes that are close to each other according to some metric as suggested in Xiong into Baker’s system because both of these systems are addressing controlling a nodal network. This modification would have been motivated by the desire to improve graph traversal efficiency and facilitate graph-based traversal (Xiong, [0015]). The combination of Baker and Xiong does not explicitly teach applying a Local Interpretable Model-agnostic Explanations (LIME) algorithm to the obtained graph data to change a first connection relationship between the plurality of nodes included in the obtained graph data, the applying of the LIME algorithm being limited to change connection of the first edge. However, in the same field of endeavor, Ramamurthy teaches applying a Local Interpretable Model-agnostic Explanations (LIME) algorithm to the obtained graph data to change a first connection relationship between the plurality of nodes included in the obtained graph data, the applying of the LIME algorithm being limited to change connection of the first edge ([0038] The system and method shown accomplish precisely this, where given a linear or non-linear local explainability technique such as LIME we propose a (meta-) method that can build multilevel explanations with monotonically increasing and (explicitly) controllable cohesion from the leaves to the root of the constructed tree; [0042] Given the omnipresence of multilevel explanations across various real world settings, the present system and method proposes a novel model agnostic multilevel explanation (MAME) method that can take a local explainability technique such as LIME (Local Interpretable Model Explanations) along with a dataset and can generate multiple explanations for each of the examples corresponding to different degrees of cohesion (i.e. parameter tying) between (explanations of) the examples, where each such degree determines a level in our multilevel explanation tree and is explicitly controllable. At the extremes, the leaves would correspond to independent local explanations as would be the case using standard local explainability techniques (viz. LIME), while the root of the tree would correspond to practically a single explanation given the high degree of cohesion between all the explanations at this level; [0051] The present system and method propose a meta-method that can build multilevel explanations using base explanations from a local explanation model with monotonically increasing and explicitly controllable cohesion from the leaves to the root of the constructed tree; [0052] The present Model-agnostic Multilevel explanations method (MAME) can take a local explainability technique such as LIME along with a dataset and can generate multiple explanations for each of the examples corresponding to different degrees of cohesion between (explanations of) the examples, where each such degree determines a level in our multilevel explanation tree and is explicitly controllable; [0077] Referring to FIG. 4, the system 100 shows the method of using a computing device to explain one or more predictions of a machine learning model. The method includes receiving by a computing device a pre-trained artificial intelligence model with one or more predictions 40 , receiving by the computing device a dataset for the pre-trained artificial intelligence model containing a plurality of training datapoints 42 , receiving by the computing device a coordinate wise map of the plurality of training datapoints 44 , sampling by the computing device a neighborhood of datapoints around each of the training datapoints 46 , generating by the computing device a multilevel explanation tree 48 , linking the neighborhood of datapoints around each of the training datapoints to the one or more predictions, leaves of the multilevel explanation tree representing the neighborhood of datapoints around each of the training datapoints and distances between leaves of the multilevel explanation tree indicating differences between values of the neighborhood of datapoints 50 , and utilizing by the computing device the leaves of the multilevel explanation tree representing the neighborhood of datapoints to explain one or more predictions of the machine learning model 52 , to provide an output 54 ). It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of generating a multilevel explanation tree, linking neighborhood of datapoints around each of a plurality of training datapoints to the one or more predictions using LIME as suggested in Ramamurthy into Baker and Xiong’s system because both of these systems are addressing updating graph connectivity to reduce the optimality of the assignment of nodes to clusters. This modification would have been motivated by the desire for obtaining consistent, multilevel explanations for a group of examples (viz. the training set) (Ramamurthy, [0006]). Regarding dependent claim 2 , the combination of Baker, Xiong and Ramamurthy teaches all the limitations as set forth in the rejection of claim 1 that is incorporated. Baker and Xiong teach wherein the generating includes processing of generating the new graph data that has a third connection relationship between the plurality of nodes different from the first connection relationship between the plurality of nodes of the graph data by changing connection of the first edge (Baker [0106] In boxes 208 - 210 , the computer system performs operations specific to the self-organizing learning process for a set with a strict partial order, based on the concepts discussed in the introduction; [0107] In box 208 , the computer system decides whether to add a connection that is in the transitive closure of the current network or delete a connection whose node pair is not a cover pair; [0108] In one embodiment, to decide whether to add a connection from node A to node B to the current network, the computer system makes an estimate of the expected improvement in the objective that may be achieved by a modified network that includes the additional connection; [0114] In box 208 , the computer system also decides for a node pair <A, B> with an existing direct connection from A to B, whether to delete that connection. For this decision, the computer system estimates CDC(A, B), i.e., the “Cost of Deleting the Connection” from A to B. The function CDC(A, B) is only defined for ordered pairs <A, B> for which there is a direct connection) such that a fourth node connected to at least one of the first node and the second node located at the both ends of the first edge via a number of edges, the number being equal to or less than the threshold, is located at the other end of the first edge (Baker [0126] For example, there may be a bound on the rate at which new arcs can be added dependent on the number of arcs that have been deleted. As another example, in addition to only adding arcs with the largest magnitude objective function partial derivatives, there can be a threshold value not allowing any new arc to be added unless the magnitude of its objective function exceeds the threshold value. The threshold value can be adjusted by fixed rules or by the learning coach 220 to help match the rates of arc creation and deletion to the strategy for the current situation; Baker Figs. 22A-22C; [0071]-[0083] Note: Examiner maps AC arc as first edge, A and C as first node and second node, D as third node. Graph in Fig. 22C (i.e new data) is different from graph in Fig. 22A by changing connection of arc AC where third node D connected to A via 1 edge, is located at one end of the first edge ; Xiong [0023] The clustering engine 202 obtains data in the form of tables, such as the table 212 , from the relational database 210 . The clustering engine 202 then clusters the nodes in the data according to their connectivity, i.e., such that each cluster contains nodes that are close to each other according to some metric (e.g., within a threshold distance); Xiong [0024] For instance, in one embodiment, where a graph is unweighted, the nodes that are closest to each other (e.g., separated by no more than a threshold number of edges) may be grouped in the same cluster. In an alternative embodiment, where a graph is weighted, the nodes that are closest to each other (e.g., separated by no more than a threshold weighted distance/sum of edge weights) may be grouped in the same cluster. In this case, two nodes placed in the same cluster could have a relatively large number of edges between them; however, the sum of the weights of these edges may be less than the threshold. Examiner notes that FIG. 2 is a flow chart for the iterative training process. Thus, the other end of the edge is processed in the iterative training process ). Regarding dependent claim 3 , the combination of Baker, Xiong and Ramamurthy teaches all the limitations as set forth in the rejection of claim 2 that is incorporated. Baker teaches wherein both the first connection relationship and the second connection relationship have connectivity ([0071] FIG. 22A depicts a feed-forward network consisting of nodes A-D with four cover pairs: (A, C), (A, D), (B, C), and (B, D); [0071] If the arc between node B and node D is deleted and replaced with a new arc oriented in the opposite direction, as depicted in dashed lines in FIG. 22B, then the network architecture has been altered such that there are now only three cover pairs: (A, D), (B, C), and (D, B). In other words, in addition to the (B, D) cover pair being reversed, (A, C) is no longer a cover pair because it is now no longer true that there is no element X such that A<X<C. This is because A is now also connected to node C through nodes B and D (i.e., X includes B and/or D). This change in the network architecture can be represented visually by FIG. 22C, for example). Regarding dependent claim 4 , the combination of Baker, Xiong and Ramamurthy teaches all the limitations as set forth in the rejection of claim 2 that is incorporated. Baker teaches wherein the selecting includes processing of selecting a new first edge from a plurality of edges included in the new graph data each time the new graph data is generated until the number of times the connection is changed in the processing of generating reaches a threshold ([0099] FIG. 2 is a flow chart for the iterative training process; [0151] In box 212 , the computer system checks to see if a stopping criterion is met. If so, it terminates. If not, it starts the processing of the next epoch. Stopping criteria includes reaching a specified limit in the number of epochs, achieving a specified target error rate, or converging to a stationary point). Regarding dependent claim 5 , the combination of Baker, Xiong and Ramamurthy teaches all the limitations as set forth in the rejection of claim 2 that is incorporated. Baker teaches wherein the new graph data is used to generate an approximate model that describes an inference result of a machine learning model that performs inference using the graph data as input ([0119] it may be a good strategy to have policies and design controls that make it easy to create new arcs and to make them active. One embodiment of this strategy is to introduce a specified number of new arcs per updated cycle. These new arcs could be chosen, for example, primarily based on the magnitudes of the partial derivatives of the objective. However, other considerations and trade-offs would also need to be taken into account. This strategy could be implemented by a number of design rules controlled by hyperparameters. In one embodiment, these hyperparameters could be flexibly controlled by a learning coach 220 ; [0120] This learning coach 220 is a second machine learning system that learns to model the effect of the hyperparameters on the effectiveness of applying the associated learning strategy to the learning process of the first machine learning system. In addition, the learning coach 220 can take additional measurement of the state of the first machine learning system and the rate of progress of its learning and learn to optimize the hyperparameters to achieve the best final performance and learn the optimum network architecture and weight parameters as quickly as possible, with some specified trade-off between these dual objectives). Regarding independent claim 6 , it is a method claim that corresponding to the medium of claim 1. Therefore, it is rejected for the same reason as claim 1 above. Regarding dependent claim 7 , it is a method claim that corresponding to the medium of claim 2. Therefore, it is rejected for the same reason as claim 2 above. Regarding dependent claim 8 , it is a method claim that corresponding to the medium of claim 3. Therefore, it is rejected for the same reason as claim 3 above. Regarding dependent claim 9 , it is a method claim that corresponding to the medium of claim 4. Therefore, it is rejected for the same reason as claim 4 above. Regarding dependent claim 10 , it is a method claim that corresponding to the medium of claim 5. Therefore, it is rejected for the same reason as claim 5 above. Regarding dependent claim 7 , it is a method claim that corresponding to the medium of claim 2. Therefore, it is rejected for the same reason as claim 2 above. Regarding independent claim 11 , it is a device claim that corresponding to the medium of claim 1. Therefore, it is rejected for the same reason as claim 1 above. Baker further teaches a data generation device comprising: a memory; and processor circuitry coupled to the memory, the processor circuitry being configured to perform processing (Fig. 24; [0395]). Regarding dependent claim 12 , it is a device claim that corresponding to the medium of claim 2. Therefore, it is rejected for the same reason as claim 2 above. Regarding dependent claim 13 , it is a device claim that corresponding to the medium of claim 3. Therefore, it is rejected for the same reason as claim 3 above. Regarding dependent claim 14 , it is a device claim that corresponding to the medium of claim 4. Therefore, it is rejected for the same reason as claim 4 above. Regarding dependent claim 15 , it is a device claim that corresponding to the medium of claim 5. Therefore, it is rejected for the same reason as claim 5 above. Response to Arguments Applicant's arguments filed 02/16/2026 have been fully considered. Each of applicant’s remarks is set forth, followed by examiner’s response. (1) Regarding claim rejection under 35 U.S.C. §101, Applicant alleges the claimed invention, particularly in amended claim 1, goes beyond a mere mental process or abstract mathematical calculation. The amended claim clearly specifies "generating new graph data as neighborhood data from the obtained graph data by applying a Local Interpretable Model-agnostic Explanations (LIME) algorithm to the obtained graph data". The human mind is not equipped to perform such complex operations on large-scale graph data, as required by the LIME algorithm, nor to systematically apply specific rules for edge re-connection while maintaining graph topological properties. The volume and complexity of graph data, and the iterative application of the LIME algorithm in the described manner, render this process impractical for human mental execution, even with primitive tools. Thus, the claimed invention does not merely recite a mental process. Furthermore, the claimed generation of "neighborhood data" using a LIME algorithm, which involves modifying a graph data structure based on specific parameters (like a threshold number of edges) and ensuring the resulting graph retains certain properties (like connectivity), represents a transformation of data that is inherently technical. This is not a generalized mathematical concept but a specific, algorithmically defined process applied to a particular data structure (graph data) for a technical purpose (model interpretability). Thus, the amended claim 1 does not recite a judicial exception (Step 2A - Prong 1: No). It rather involves a concrete transformation of data. Applicant further argues the amended claim 1 clearly defines a specific computer-implemented process that provides a technical solution to a technical problem inherent in the field of machine learning, thereby reflecting an improvement in technology. The claimed invention provides a specific technical solution to this technical problem by "generating new graph data as neighborhood data from the obtained graph data by applying a Local Interpretable Model-agnostic Explanations (LIME) algorithm to the obtained graph data to change a first connection relationship between the plurality of nodes included in the obtained graph data, the generated new data having a second connection relationship between the plurality of nodes different from the first connection relationship, the applying of the LIME algorithm being limited to change connection of the first edge such that a third node connected to at least one of a first node and a second node located at both ends of the first edge via a number of edges, the number being equal to or less than a threshold, is located at one end of the first edge." The claimed invention incorporates a unique and non-conventional combination of elements that provides a significant contribution beyond any abstract idea, thereby embodying an inventive concept. As to point (1), Examiner respectfully disagrees. The recitation of “applying a Local Interpretable Model-agnostic Explanations (LIME) algorithm” in limitation "generating new graph data as neighborhood data from the obtained graph data by applying a Local Interpretable Model-agnostic Explanations (LIME) algorithm to the obtained graph data" merely indicates a field of use or technological environment in which the judicial exception is performed. Although the additional element “applying a Local Interpretable Model-agnostic Explanations (LIME) algorithm” limits the identified judicial exceptions “generating new graph data as neighborhood data from the obtained graph data by applying a Local Interpretable Model-agnostic Explanations (LIME) algorithm to the obtained graph data” this type of limitation merely confines the use of the abstract idea to a particular technological environment (neural networks) and thus fails to add an inventive concept to the claims. See MPEP 2106.05(h). Even when viewed in combination, these additional elements do not integrate the recited judicial exception into a practical application (Step 2A, Prong Two: NO), and the claim is directed to the judicial exception. (Step 2A: YES). As explained with respect to Step 2A, Prong Two, the additional element of “applying a Local Interpretable Model-agnostic Explanations (LIME) algorithm” are at best mere instructions to “apply” the abstract ideas, which cannot provide an inventive concept. See MPEP 2106.05(f). As discussed in Step 2A, Prong Two above, the recitation of a non-transitory computer-readable storage medium storing a data generation program for causing a computer to perform processing; a data generation device comprising: a memory; and processor circuitry coupled to the memory, the processor circuitry being configured to perform processing amounts to no more than mere instructions to apply the exception using a generic computer component. Even when considered in combination, these additional elements represent mere instructions to implement an abstract idea or other exception on a computer and insignificant extra-solution activity, which do not provide an inventive concept. (Step 2B: NO). (2) Applicant’s prior art arguments with respect to the pending claims have been considered but they are moot in view of the new ground(s) of rejections presented above. Conclusion 07-40 AIA Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL . See MPEP § 706.07(a). 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 AMY P HOANG whose telephone number is (469)295-9134. The examiner can normally be reached M-TH 8:30-5:00PM. 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, JENNIFER WELCH can be reached at 571-272-7212. 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. /AMY P HOANG/ Examiner, Art Unit 2143 /JENNIFER N WELCH/ Supervisory Patent Examiner, Art Unit 2143 Application/Control Number: 18/172,448 Page 2 Art Unit: 2143 Application/Control Number: 18/172,448 Page 3 Art Unit: 2143 Application/Control Number: 18/172,448 Page 4 Art Unit: 2143 Application/Control Number: 18/172,448 Page 5 Art Unit: 2143 Application/Control Number: 18/172,448 Page 6 Art Unit: 2143 Application/Control Number: 18/172,448 Page 7 Art Unit: 2143 Application/Control Number: 18/172,448 Page 8 Art Unit: 2143 Application/Control Number: 18/172,448 Page 9 Art Unit: 2143 Application/Control Number: 18/172,448 Page 10 Art Unit: 2143 Application/Control Number: 18/172,448 Page 11 Art Unit: 2143 Application/Control Number: 18/172,448 Page 12 Art Unit: 2143 Application/Control Number: 18/172,448 Page 13 Art Unit: 2143 Application/Control Number: 18/172,448 Page 14 Art Unit: 2143 Application/Control Number: 18/172,448 Page 15 Art Unit: 2143 Application/Control Number: 18/172,448 Page 16 Art Unit: 2143 Application/Control Number: 18/172,448 Page 17 Art Unit: 2143 Application/Control Number: 18/172,448 Page 18 Art Unit: 2143 Application/Control Number: 18/172,448 Page 19 Art Unit: 2143 Application/Control Number: 18/172,448 Page 20 Art Unit: 2143 Application/Control Number: 18/172,448 Page 21 Art Unit: 2143 Application/Control Number: 18/172,448 Page 22 Art Unit: 2143 Application/Control Number: 18/172,448 Page 23 Art Unit: 2143 Application/Control Number: 18/172,448 Page 24 Art Unit: 2143 Application/Control Number: 18/172,448 Page 25 Art Unit: 2143
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