DETAILED ACTION
This Action is responsive to claims filed 03/12/2026.
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 .
Status of the Claims
Claims 1, 5, 7, 9, 15, and 18 have been amended. Claims 1-20 are currently pending.
Response to Amendment
The amendments to the Specification have overcome the Objections to informalities.
Response to Arguments
The amendments to the independent claims have rendered the claim interpretation under 112(f) moot.
Applicant’s arguments, see Page 13, filed 03/12/2026, with respect to Claim 18 have been fully considered and are persuasive. The 35 U.S.C. 112(b) antecedent basis Rejection of Claim 18 has been withdrawn.
Applicant's arguments, see Page 13, filed 03/12/2026, with respect to the 35 U.S.C. 112(b) indefiniteness Rejection of Claims 1-20 have been fully considered but they are not persuasive.
The Applicant has replaced the word “fired” with “activated,” which, ultimately, was not the source of the ambiguity of the claimed language. The entire term/phrase “activated predicate” is structurally and/or functionally ambiguous. As previously cited, paragraphs [0060]-[0064] detail the term and its use, but offer little insight as to what the term actually represents within the user interface. Additional ambiguity is introduced with the generic use of the term “verb” in conjunction with the term “predicate” and reference to fired nodes in paragraph [0045]. The Applicant is encouraged to replace or augment the generic “predicate” term with specific language better defining the term’s role and/or implementation. See the updated 112(b) Rejection below.
Applicant's arguments, see Pages 9-12, filed 03/12/2026, with respect to the 35 U.S.C. 101 Rejection of Claims 1-20 have been fully considered but they are not persuasive.
The Examiner respectfully disagrees with the Applicant regarding the eligibility of the instant claims. On Page 10, the Applicant merely alleges the claim limitations are not practically performed within the human mind or with the aid of pen and paper, without sufficient structure or implementation reflecting this appearing in the claims. Although a human mind may, allegedly, be incapable of producing the claimed logical facts without specialized computing techniques (which the Examiner contends is not represented in the claims), there is no claimed structure or implementation precluding a human mind with the aid of pen and paper from generically showing said facts, as presently claimed. A human mind is more than capable of observing said logical facts, in whatever form they are produced computationally, taking them down on a piece of paper with a pen, and generically “showing” it. Likewise, the “selecting…”, “visualizing…”, and “showing trained rules…” do not, based on the BRI of the limitations, claim the actual computationally-generated elements within the limitation, but instead claim actions taken on, or observations of, said elements. These limitations, and the elements within them, are claimed so highly generally that a human mind performing the claimed steps is fully reasonable. A human mind is capable of generically selecting an action consisting of a generic verb and entity within a generic environment. The Examiner does this merely by typing (verb) this Action (entity) in their workspace (environment). Again, the limitation does not claim how the action, verb, entity, or environment is arrived at, merely their selection. The “visualizing…” limitation follows a similar thought pattern. Generic contrastive information, current, state, and goal state exist, are not claimed structurally or with implementation, and are merely visualized. Similarly, trained rules of a neuro-symbolic neural network exist (the BRI of the claimed language implies the NN is already trained, and the rules already exist) and are generically “show[ed]” without structure or implementation. The Examiner agrees with the applicant that the human mind is incapable of training the neural network, but what is claimed is not the training, but merely “showing” the rules used to train it.
On Page 11, the Applicant alleges a specific technological architecture, or specific arrangement of components is recited. The Examiner respectfully disagrees. The newly amended limitations amount to generic computing components performing manipulations or observations of data, without reciting particular structure or implementation precluding a human mind from performing the claimed steps.
The Applicant draws comparisons to Example 47 and the 2025 memorandum, which the Examiner respectfully disagrees with. The Applicant essentially draws this comparison without citing the similarities to these guidance(s) and essentially ignores the level of specificity recited in those example claims. Specificity which is lacking in the instant claims.
Given the highly generic nature of the additional elements, computing environment, and interpretable abstract idea mental process steps, the Examiner contends any specific improvement derived from the independent claims is essentially rooted in the interpretable abstract idea mental process steps of manipulating and observing pre-existing data, rather than rooted in specific structure or implementation of additional elements precluding their performance by the human mind. Per MPEP 2106.05(a), the specific improvement cannot come from the abstract idea mental process. See the updated 35 U.S.C. 101 Rejection below.
Applicant's arguments, see Pages 13-17, filed 03/12/2026, with respect to the 35 U.S.C. 103 Rejection(s) of Claims 1-20 have been fully considered but they are not persuasive.
In response to applicant's arguments against the references individually, one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986).
Cited reference Lyu is not relied upon to a graphical user interface or the manipulation of it. Cited reference Vojír is not relied upon to teach action candidates taken in an environment. The Examiner maintains the motivation to combine the previously cited references as detailed on Page 16 of the Office Action filed 12/12/2025.
The Examiner maintains, given the high level of generality recited in the current claims, that the action language recited in Lyu (Page 3), specifically “An action description D in the language BC [57] includes two kinds of symbols on signature σ, fluent constants that represent the properties of the world, and action constants , representing actions that influences the world.”, reads on a generic action taken in a generic environment, an action reads broadly on a generic “verb”, and fluent constant effected by action broadly reads on generic (and ambiguous) “predicates.” Given that Vojír is relied upon to teach a graphical user interface allowing a user to make fine-grained manipulations, and the overall ambiguity surrounding the verbs, predicates, their activation, and their role within the user interface, the Examiner contends a combination of Lyu and Vojír sufficiently reads on the generic “highlighting” of said fluent constant/action pair/visualization, as a combination of Lyu and Vojír would allow the manipulation of said elements, which, reasonably, would highlight the elements, as is found in most user interfaces when an element is interacted with.
In regards to the Applicant’s argument to Claim 5: Applicant’s arguments have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 3 and 4 rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claims 3 and 4 recite the limitation "the trained rules analyzer" in Claim 3. There is insufficient antecedent basis for this limitation in the claim. The recitation of a trained rules analyzer has been amended out of Claim 1.
Claims 1-20 rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
The entire term/phrase “activated predicate” is structurally and/or functionally ambiguous. Paragraphs [0060]-[0064] address the term in the context of user selection(s), but it is ambiguous what the term actually represents within the graphical user interface. The Examiner currently interprets this element as an operation executed as a result of an interface element being selected (an insert operation executing as a result of an insert button of a GUI being pressed, or a list of locations to perform an insertion (candidate predicates?) being displayed for selection, as examples). Additional ambiguity is introduced with the generic use of the term “verb” in conjunction with the term “predicate” and reference to fired nodes in paragraph [0045]. The Applicant is encouraged to replace or augment the generic “predicate” term with specific language better defining the term’s role and/or implementation.
Claim Rejections - 35 USC § 101
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claims 1-20 rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more; and because the claims as a whole, considering all claim elements both individually and in combination, do not amount to significantly more than the abstract idea, see Alice Corporation Pty. Ltd. v. CLS Bank International, et al, 573 U.S. (2014). In determining whether the claims are subject matter eligible, the Examiner applies the 2019 USPTO Patent Eligibility Guidelines. (2019 Revised Patent Subject Matter Eligibility Guidance, 84 Fed. Reg. 50, Jan. 7, 2019.)
Step 1:
Claims 1-8 recite a computer system, which falls under the statutory category of a machine. Claims 9-14 recite a computer-implemented method, which falls under the statutory category of a process. Claims 15-20 recite a computer program product, which falls under the statutory category of a manufacture.
Step 2A – Prong 1:
Claim 1 recites an abstract idea, law of nature, or natural phenomenon. The limitations of “selecting an action from possible candidates taken in an environment, wherein the action comprises a pair of a verb and an entity;”, “showing one or more logical facts that are extracted from natural observation sentences of the environment;”, “visualizing contrastive information for a current state and a goal state which is from external knowledge;”, and “showing trained rules in a neuro-symbolic neural network for neuro-symbolic artificial intelligence,”
A human mind is capable of generically selecting an action consisting of a generic verb and entity within a generic environment. Again, the limitation does not claim how the action, verb, entity, or environment is arrived at, merely their selection. The “visualizing…” limitation follows a similar thought pattern. Generic contrastive information, current, state, and goal state exist, are not claimed structurally or with implementation, and are merely visualized. Similarly, trained rules of a neuro-symbolic neural network exist (the BRI of the claimed language implies the NN is already trained, and the rules already exist) and are generically “show[ed]” without structure or implementation. The Examiner agrees with the applicant that the human mind is incapable of training the neural network, but what is claimed is not the training, but merely “showing” the rules used to train it.
Step 2A – Prong 2:
The additional elements of claim 1 do not integrate the abstract idea into a judicial exception. The claim recites the additional elements “a computer system”, “a processor”, “storage media”, and “program instructions” which are recognized as generic computer components recited at a high level of generality. Although they have and execute instructions to perform the abstract idea itself, this also does not serve to integrate the abstract idea into a practical application as it merely amounts to instructions to "apply it." (See MPEP 2106.04(d)(2) indicating mere instructions to apply an abstract idea does not amount to integrating the abstract idea into a practical application).
The additional elements of “a pair of a verb and an entity”, “logical facts that are extracted from natural observation sentences of the environment”, “a neuro-symbolic neural network”, and “an activated predicate” are recognized as non-generic computer components, but are recited at a high level of generality and are found to generally link the abstract idea to a particular technological environment or field of use (See MPEP 2106.05(h)).
The additional elements recited in the limitation “wherein, in response to a first user selection of the action, highlighting each pair of the verb and the entity and a n activated predicate corresponding to the first user selection.” is found to be mere instructions to apply the abstract idea mental process step of selecting an action preceding it (See MPEP 2106.05(f)).
Step 2B:
The additional elements of claim 1 do not amount to more than the judicial exception. The only limitation on the performance of the described system is a limitation reciting “a computer system”, “a processor”, “storage media”, and “program instructions”. These elements are insufficient to transform a judicial exception to a patentable invention because the recited elements are considered insignificant extra-solution activity (generic computer system, processing resources, links the judicial exception to a particular, respective, technological environment). The claim thus recites computing components only at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using generic computer components; mere instructions to apply an exception using a generic computer component cannot provide an inventive concept (see MPEP 2106.05(f)).
The additional elements of “a pair of a verb and an entity”, “logical facts that are extracted from natural observation sentences of the environment”, “a neuro-symbolic neural network”, and “a fired predicate” are recognized as non-generic computer components, but are recited at a high level of generality and are found to generally link the abstract idea to a particular technological environment or field of use (See MPEP 2106.05(h)).
The additional elements recited in the limitation “wherein, in response to a first user selection of the action, highlighting each pair of the verb and the entity and a n activated predicate corresponding to the first user selection.” is found to be mere instructions to apply the abstract idea mental process step of selecting an action preceding it (See MPEP 2106.05(f)).
Taken alone or in ordered combination, these additional elements do not amount to significantly more than the above-identified abstract idea. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation.
For the reasons above, claim 1 is rejected as being directed to non-patentable subject matter under §101. This rejection applies equally to independent claims 9 and 15.
Claim 9 recites similar limitations to Claim 1, with the exception of “A computer-implemented method comprising:” (generic computer components). The limitations of “displaying one or more logical facts that are extracted from natural observation sentences of the environment;” and “displaying trained rules in a neuro-symbolic neural network for neuro-symbolic artificial intelligence,” have been analyzed under Step 2A – Prong 2 and reanalyzed under Step 2B and found to be mere post-solution activity or well-understood, routine, and conventional activity (See MPEP 2106.05(g)(iii)(first list) and MPEP 2106.05(d)(II)(iv)(third list)); therefore, both Claims are similarly rejected.
Claim 15 recites similar limitations to Claims 1 and 9, with the exception of “A computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a processor to cause the processor to perform operations comprising:” (generic computer components); therefore, both Claims are similarly rejected.
Dependent Claims:
Claim 2 (claims 10 and 16) recites additional elements found to generally link the abstract idea to a particular technology or field of use (See MPEP 2106.05(h)).
Claim 3 (claims 11 and 17) recites an abstract idea mental process step “presents the trained rules” and instructions to apply it “by using the plurality of nodes and edges.”
Claim 4 (claims 12 and 18) recites further instructions to apply the abstract idea mental process step of making a selection “responsive to a second user selection of a node or edge and an edit option, the neuro-symbolic neural network changes the neuro-symbolic neural network to change the trained rules”
Claim 5 recites an abstract idea mental process step “add a node to the neuro-symbolic neural network.”
Claim 6 (claims 13 and 19) recites an abstract idea mental process step “adding the node” and pre- or post-extra-solution data transmittal steps “receiving a selection of a predicate from multiple predicate candidates; receiving an initiation of adding a node based on the selected verb and selected predicate;”
Claim 7 recites an abstract idea mental process step “delete a node from the neuro-symbolic neural network.”
Claim 8 (claims 14 and 20) recites abstract idea mental process steps “highlighting the selected node;” and “deleting the selected node and any associated edges connected to the selected node” and pre- or post-extra-solution data transmittal steps “receiving a selection of a node to be deleted;” and “receiving an initiation of deletion of the selected node;”
Claim Rejections - 35 USC § 103
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 1-4, 7, 9-12, and 15-18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lyu et al. (TDM: Trustworthy Decision-Making via Interpretability Enhancement, 2021), hereinafter Lyu, and Vojír et al. (Editable machine learning models? A rule-based framework for user studies of explainability, 2020), hereinafter Vojír.
In regards to claim 1: The present invention claims: “A computer system comprising: a processor set; one or more computer-readable storage media; and program instructions stored on the one or more computer-readable storage media to cause the processor set to perform operations comprising…an action from possible candidates taken in an environment, wherein the action comprises a pair of a verb and an entity; …one or more logical facts that are extracted from natural observation sentences of the environment; …contrastive information for a current state and a goal state which is from external knowledge; …trained rules in a neuro-symbolic neural network for neuro-symbolic artificial intelligence,” Lyu describes an action language defining actions and states based on logical or environmental facts (Pages 3-4, Action Language BC: “An action description D in the language BC [57] includes two kinds of symbols on signature σ, fluent constants that represent the properties of the world [Emphasis added, mapping to “natural observation sentences of the environment”], and action constants , representing actions that influences the world.”), mapping a set of symbolic tasks to subtasks to achieve a goal state to optimize a reward and discount (Pages 4-5, B. Interpretability Enhancement via Symbolic Representation), and comparisons between a current state and goal state (Page 6, Algorithm 1). The Examiner maintains, given the high level of generality recited in the current claims, that the action language recited in Lyu (Page 3), specifically “An action description D in the language BC [57] includes two kinds of symbols on signature σ, fluent constants that represent the properties of the world, and action constants , representing actions that influences the world.”, reads on a generic action taken in a generic environment, an action reads broadly on a generic “verb”, and fluent constant effected by action(s) broadly reads on generic (and ambiguous) “predicates.”
Lyu reads on the general concept of the symbolic/reinforcement learning environment of the claims. The Examiner submits, based on the Applicant’s Specification paragraphs [0042]-[0045] and [0052], the claims broadly recite elements of a graphical user interface displaying various relevant elements of the neuro-symbolic neural network or environmental states relevant to the model. Lyu fails to explicitly teach the graphical user interface or procedural attributes claimed in:
“selecting…”, “showing…”, “visualizing…”, and “showing…” However, Vojír, in a similar field of endeavor of interpretable rule-based models teaches “The presented web-based rule editing software allows the user to perform common editing actions such as modify rule (add or remove attribute), delete rule, create new rule, or reorder rules. To observe the effect of a particular edit on predictive performance, the user can validate the rule list against a selected dataset using a scoring procedure.” (Abstract). Vojír’s system allows users to delete rules and edit rules (Figure 1).
“wherein, in response to a first user selection of the action, highlighting each pair of the verb and the entity and a fired predicate corresponding to the first user selection.” Given the ambiguous nature of the use of “predicate” in Applicant’s Specification paragraphs [0060]-[0064], the Examiner interprets this feature broadly to represent a function indicated by a visualization being activated on selection of the visualization (an insert operation being performed on pressing an “Insert” button of a GUI, for example). Vojír’s Figure 1 describes the rule editor “with one loaded rule (zoo dataset). a Shows a list of conditions of the currently edited rule decomposed to individual blocks corresponding to attribute names (hair, legs, aquatic), values (such as False, True and 4) and syntactic elements (round brackets, is, and). The user can delete selected block (block legs is selected), insert new syntactic blocks by dragging them from the palette displayed below (b), insert new attributes (c) and their values (d), or edit the values of Confidence and Support (e)” (Page 791).
Given that Vojír is relied upon to teach a graphical user interface allowing a user to make fine-grained manipulations, and the overall ambiguity surrounding the verbs, predicates, their activation, and their role within the user interface, the Examiner contends a combination of Lyu and Vojír sufficiently reads on the generic “highlighting” of said fluent constant/action pair/visualization, as a combination of Lyu and Vojír would allow the manipulation of said elements, which, reasonably, would highlight the elements, as is found in most user interfaces when an element is interacted with.
Both Lyu and Vojír highlight the need for interpretability and transparency in machine learning models. “it is not reasonable to trust systems that are beyond our comprehension, and typical machine learning and data-driven decision-making are black-box paradigms that impede interpretability. Therefore, it is critical to establish computational trustworthy decision-making mechanisms enhanced by interpretability-aware strategies.” (Lyu, Abstract) and “we argue that interpretability research also needs to consider the task of humans editing the model, not least due to the existing or forthcoming legal requirements on the right of human intervention. In this article, we focus on rule models as these are directly interpretable as well as editable” (Vojír, Abstract). It would have been obvious to one of ordinary skill in the art at the time of the Applicant’s filing to implement a system such as Lyu’s into a graphical user interface such as Vojír’s in order to manage the training rules of the reinforcement learning system and/or edit or remove training rules or subtasks from the model to improve the interpretability and realize the improvements of human-editing as highlighted by Vojír.
In regards to claim 2: The present invention claims: “wherein the neuro-symbolic neural network comprises a plurality of nodes and edges.” See Lyu, Figure 2 for the symbolic visualization being nodes and edges, it would stand to reason a combination of Lyu and Vojír would depict such a graph.
In regards to claim 3: The present invention claims: “wherein, the trained rules analyzer presents the trained rules by using the plurality of nodes and edges.” A combination of Lyu and Vojír reasonably reads on the general recitation of the nodes and edges of graph representing the rules or state of a reinforcement learning model (if the symbolic representation showing the nodes and edges between states and subtasks/rules in Lyu Figure 2 were being edited by the rules editing interface of Vojír, for example).
In regards to claim 4: The present invention claims: “wherein, responsive to a second user selection of a node or edge and an edit option, the neuro-symbolic neural network changes the neuro-symbolic neural network to change the trained rules.” See above where Vojír is expressly designed to edit or delete the training rules (Figure 1). In a combination of Lyu and Vojír, editing the rules as a user is capable of in Vojír’s interface would necessarily change the network or optimality of subtasks (nodes, in the visual representation) of the model of Lyu.
In regards to claim 7: The present invention claims: “wherein the operations further enable a user to delete a node from the neuro-symbolic neural network.” See above where Vojír is expressly designed to edit or delete the training rules (Figure 1). In a combination of Lyu and Vojír, deleting the rules as a user is capable of in Vojír’s interface would necessarily change the network or optimality of subtasks (nodes, in the visual representation) of the model of Lyu.
In regards to claim 9-12: Claims 9-12 recite similar limitations to Claims 2-4, with the exception of “A computer-implemented method comprising:”, “displaying one or more logical facts that are extracted from natural observation sentences of the environment;”, and “and displaying trained rules in a neuro-symbolic neural network for neuro-symbolic artificial intelligence,” of claim 9. The combination of Lyu and Vojír continues to read on the generic recitation of “displaying” given Vojír is designed as a graphical user interface. Therefore, both sets of claims are similarly rejected.
In regards to claim 15-18: Claims 15-18 recite similar limitations to Claims 2-4, with the exception of “A computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a processor to cause the processor to perform operations comprising:”, “displaying one or more logical facts that are extracted from natural observation sentences of the environment;”, and “and displaying trained rules in a neuro-symbolic neural network for neuro-symbolic artificial intelligence,” of claim 15. The combination of Lyu and Vojír continues to read on the generic recitation of “displaying” given Vojír is designed as a graphical user interface. Therefore, both sets of claims are similarly rejected.
Claim(s) 5-6, 8, 13-14, and 19-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lyu and Vojír as applied to claims 1, 9, and 15 above, and further in view of Beinecke et al. (Interactive explainable AI platform for graph neural networks, 2022), hereinafter Beinecke.
In regards to claim 5: While Vojír Figure 1 shows that the user may add a rule. In a combination of Lyu and Vojír, adding a rule as a user is capable of in Vojír’s interface would necessarily change or add a node representation of the rule or subtask to the model of Lyu. The combination of Lyu and Vojír fails to explicitly teach: “wherein the operations further enable a user to add a node to the neuro-symbolic neural network.” However, Beinecke, in a similar field of endeavor of visualizing and editing graph neural networks for interpretability, describes in Figure 1 “Interact window with its seven components. 1: Drop down selection of a patient graph, with information on the patient graphs, 2: Performance scores of GNN on test data and predict, retrain, and download button, 3: Specifications for graph visualization (node/edge colouring, node sorting, and how many nodes to display), 4: Graph visualisation, 5: Information on nodes and edges, 6: Graph modifications (node/edge deletion/addition), 7: Log print of actions performed by the user.” (Page 2). This interface shows a selected node (Section 1), and the option to add the selected node (Section 6).
Beinecke echoes the trustworthiness and interpretability of AI systems concerns of Lyu and Vojír (Abstract). It would have been obvious to one of ordinary skill in the art at the time of the Applicant’s filing when designing an interface in which one was capable of deleting nodes representing subtasks or rules such as in a combination of Lyu and Vojír to make it clear to the user which node or edge is to be removed by highlighting the selected node or edge before deletion.
In regards to claim 6: The present invention claims: “wherein adding a node to the neuro-symbolic neural network comprises: receiving a selection of a predicate from multiple predicate candidates; receiving an initiation of adding a node based on the selected verb and selected predicate; and adding the node.” Given the ambiguous use of the term “predicate” in Applicant’s Specification paragraphs [0060]-[0064], the Examiner interprets this feature broadly to represent a function indicated by a visualization being fired on selection of the visualization (an insert operation being performed on pressing an “Insert” button of a GUI or a list of locations to perform an insertion (candidate predicates?) being displayed for selection, as examples). Vojír Figure 1 offers multiple options to edit a rule in Figure 1 (“insert new syntactic blocks by dragging them from the palette displayed below (b)”, and Lyu Figure 2 indicates multiple subtasks stemming from a given node, representing different actions or rules from a given state. It would follow when inserting a rule/subtask that a user would be presented with multiple options to drag into and edit the rule when creating a rule in Vojír’s interface before insertion into Lyu’s visual representation. See above how a combination of Lyu, Vojír, and Beinecke would read on inserting a node via an interface.
In regards to claim 8: The present invention claims: “wherein deleting a node from the neuro-symbolic neural network comprises: receiving a selection of a node to be deleted; highlighting the selected node; receiving an initiation of deletion of the selected node; and deleting the selected node and any associated edges connected to the selected node.” Beinecke describes in Figure 1 “Interact window with its seven components. 1: Drop down selection of a patient graph, with information on the patient graphs, 2: Performance scores of GNN on test data and predict, retrain, and download button, 3: Specifications for graph visualization (node/edge colouring, node sorting, and how many nodes to display), 4: Graph visualisation, 5: Information on nodes and edges, 6: Graph modifications (node/edge deletion/addition), 7: Log print of actions performed by the user.” (Page 2). This interface shows a selected node (Section 1), and the option to delete the selected node (Section 6).
In regards to claims 13-14: Claims 13-14 recites similar limitations to Claims 6 and 8, with the exception of “A computer-implemented method comprising:”, “displaying one or more logical facts that are extracted from natural observation sentences of the environment;”, and “and displaying trained rules in a neuro-symbolic neural network for neuro-symbolic artificial intelligence,” of claim 9. The combination of Lyu, Vojír, and Beinecke continues to read on the generic recitation of “displaying” given Vojír and Beinecke are designed as graphical user interfaces. Therefore, both sets of claims are similarly rejected.
In regards to claims 19-20: Claims 19-20 recite similar limitations to Claims 6 and 8, with the exception of “A computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a processor to cause the processor to perform operations comprising:”, “displaying one or more logical facts that are extracted from natural observation sentences of the environment;”, and “and displaying trained rules in a neuro-symbolic neural network for neuro-symbolic artificial intelligence,” of claim 15. The combination of Lyu, Vojír, and Beinecke continues to read on the generic recitation of “displaying” given Vojír and Beinecke are designed as graphical user interfaces. Therefore, both sets of claims are similarly rejected.
Conclusion
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 GRIFFIN T BEAN whose telephone number is (703)756-1473. The examiner can normally be reached M - F 7:30 - 4:30.
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, Li Zhen can be reached at (571) 272-3768. 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.
/GRIFFIN TANNER BEAN/ Examiner, Art Unit 2121
/Li B. Zhen/ Supervisory Patent Examiner, Art Unit 2121
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