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 office action is responsive to the amendment filed on 06/30/2025. As directed by the amendments claims 1, 6, 7 and 10 have been amended. No claims have been are canceled or added by this response. Thus, claims 1, 4 and 6-10 remain pending in the present application, of which claims 1, 6 and 7 are independent.
Regarding the 35 U.S.C § 101 Rejection:
Applicant's further arguments see pg. 8-13 filed 06/30/2025 have been fully considered but they are not persuasive.
APPLICANT ARGUMENT:
Applicant argues, “ These claims have been amended to more particularly point out and distinctly claim the subject matter, which does not recite any judicial exception (Step 2A - Prong 1: No), integrates the alleged abstract idea into a practical application (Step 2A - Prong 2: Yes), or amounts to significantly more than the judicial exception (Step 2B: Yes) [...] For example, claim 1 recites, inter alia [...] Other independent claims have been amended in a manner same as claim 1. Regarding Step 2A prong 1, the Office Action contends that the claimed process of estimating pathogenicity and calculating contribution degrees is a mental process performable with pen and paper. This characterization appears inconsistent the scope and nature of the claimed invention. The claimed invention is not simply estimating pathogenicity, but instead is providing an improved method for enhancing explainability of a machine learning model's inference result regarding pathogenicity s explained in the specification ([0003]), conventional machine learning models for this purpose are often "black boxes," making it difficult to understand the basis for their predictions [...] The claimed invention, however, takes a different approach, addressing the challenge of interpreting large graphs by employing a structured visualization. It generates a "first structure" representing classes of nodes, providing a high-level overview of the factors involved. A "second structure," coupled to the first, displays only those nodes within a selected class exceeding a contribution threshold [...] This focused presentation allows users to quickly identify the most influential factors within specific areas of interest. In addition, the highlighting in the claimed invention is not applied indiscriminately. By filtering out low-contribution nodes and focusing on specific classes within the first structure, the highlighting becomes much more effective in directing attention to the key drivers of the model's prediction. This targeted approach is significantly different from simply highlighting all elements in a large, complex graph, as done in the conventional system. These features, working together, provide a technical solution for enhanced explainability in complex machine learning models, enabling a deeper understanding of the model's reasoning. Therefore, we believe that claim 1 does not recite an abstract idea (a mere mental process), rather than a specific computer implementation to overcome the technological problem in the conventional system (Step 2A - Prong 1: NO). Second, even assuming arguendo that the claims recite a judicial exception, it is respectfully submitted that the claims recite the subject matter which integrates the alleged abstract idea into a practical application (Step 2A - Prong 2: Yes), or amounts to significantly more than the judicial exception (Step 2B: Yes). The claimed invention is integrated into the practical application of enhancing the explainability of machine learning models used for pathogenicity prediction. This addresses the real-world problem of interpreting the results of complex machine learning models, as described in [0003] and [0034]. The improved explainability provided by the claimed invention allows researchers and clinicians to better understand the basis for the model's predictions, leading to more informed decision-making regarding diagnosis and treatment. This is a concrete and tangible improvement over the "black box" nature of conventional models. The Office Action dismisses the additional elements as insignificant extra- solution activity. However, these elements are not mere data presentation but are specific techniques for manipulating and presenting information on the pathogenicity prediction in a way that enhances understanding. Further, the specific techniques for generating and coupling the first and second graph structures, along with the targeted highlighting achieved by filtering low-contribution nodes, are not conventional or routine. These techniques work together to address the specific technical problem of enhancing explainability in machine learning models, particularly in the context of pathogenicity prediction where large and complex datasets are common. The claimed invention is not simply organizing or displaying data; it's using a specific, unconventional combination of techniques to transform and present the information in a way that facilitates understanding and insights not readily available in the prior art. This targeted, structured approach distinguishes the claimed invention from generic computer functions or mere data presentation and demonstrates that it amounts to significantly more than the abstract idea. Hence, we believe that the claims recite the subject matter which integrates the alleged abstract idea into a practical application (Step 2A - Prong 2: Yes), or amounts to significantly more than the judicial exception (Step 2B: Yes). Accordingly, withdrawal of these rejections is respectfully requested. In a case where the Examiner believes that the current claim set should be rejected under 35 U.S.C. §101 as being directed to non-statutory subject matter, the Applicant would appreciate it if the Examiner could suggest claim amendments to overcome such rejection.
EXAMINER RESPONSE: Examiner respectfully disagree, applicant argument is not persuasive. Amended independent claim 1 is rejected under 35 US.C. § 101 because the claims recites the limitation of:
generating a first structure including one or more classes, each of the one or more classes being a class to which one or more nodes included in the estimation target data belongs;
selecting, from among the plurality of nodes included in the estimation target data, one or more first nodes each of which is a node that belongs to a first class being any of the one or more classes and that has the associated contribution degree being equal to or larger than a threshold;
generating a second structure including the selected one or more first nodes;
wherein the process further includes calculating a total value of the contribution degree associated with the second node included in the plurality of nodes and the contribution degree associated with a third node coupled to the second node,
which are a mental process of evaluation and judgement. For example, a human can generate a primary structure that includes one or more classes, where each of the one or more classes is a class to which one or more nodes included in an estimation target data belong, can select one or more nodes from a plurality of nodes included in the estimation target data, can generate a secondary structure that includes the selected node and lastly can calculate a total value of the contribution degree by calculating the sum of two contribution degree such as 0.01 and 0.10. Therefore, the additional elements of:
A non-transitory computer-readable recording medium storing a display program for enhancing explainability of an inference result output from a machine learning model trained using knowledge graph technology to perform pathogenicity predictions, the display program comprising instructions which, when executed by a computer, cause the computer to execute a process, the process comprising:
acquiring, by using the machine learning model which outputs, in response to an input of estimation target data which is data in a graph structure indicating a relation between nodes generated using a triple of subject, predicate and object, that corresponds to a set of two nodes an edge acquired from a knowledge graph of a mutation, an estimation result which indicates that the mutation is the pathogenic or the benign and a contribution degree of each triple included in the estimation target data, the contribution degree associated with each of relations between a plurality of nodes included in the graph structure of the estimation target data with respect to the estimation result of the machine learning model;
... by using the machine learning model which outputs,...
wherein the graph does not include a second node, of which the associated contribution degree is less than the threshold among the plurality of nodes included in the estimation target data,
wherein the process further includes calculating a total value of the contribution degree associated with the second node included in the plurality of nodes and the contribution degree associated with a third node coupled to the second node,
wherein the displaying the graph includes displaying the graph including a third structure indicating the second node and the third node in a case that the total value is equal to or larger than a threshold,
wherein the displaying the graph includes displaying a relation between nodes contained in the graph in accordance with the contribution degree associated with the relation between the nodes in such a manner that the relation having a larger contribution degree is more highlighted
as disclosed above alone or in combination of the abstract idea are not sufficient to amount to significantly more than the judicial exception as they are mere insignificant extra solution activity in combination of generic computer functions being implemented with generic computer elements in a high level of generality to perform the disclosed abstract idea above. Although applicant argues, the features of the claim “ working together, provide a technical solution for enhanced explainability in complex machine learning models, enabling deeper understanding of the model’s reasoning”, when view as whole amended claim 1 recites an abstract idea of generating a “first structure” representing classes of nodes, selecting nodes form a plurality of nodes, generating a “second structure” and calculating a total value of a contribution degree.
Furthermore, examiner disagree that “ the claimed invention is integrated into the practical application of enhancing the explainability of machine learning models used for pathogenicity prediction” as mentioned in applicant argument, because the disclosure must provide sufficient details such that one of the ordinary skill in the art would recognize the claimed invention as providing an improvement. Although, applicant assert [0003] and [0034] outlies the technical problem being solved by the current claims, by evaluating the specification and the claims, the examiner concluded that the technical explanation of the asserted improvement is not present in the specification and that the claim does not reflect the asserted improvement ( see MPEP 2106.05(a)).
Therefore, amended independent claim 1 is not patent eligible. Independent claims 6 and 7 recites similar feature to those of claim 1, therefore the rejection of claim 1 applies. Claims 4, 8, and 9-10 are dependent on claims 1,6, and 7 therefore the rejection of claims 1, 6, and 7 applies. Accordingly, claims 1, 6-10 are not patent eligible under 35 USC § 101.
Regarding the 35 U.S.C § 103:
Applicant's further arguments see pg. 13-18 filed 06/30/2025 have been fully considered but they are not persuasive.
APPLICANT ARGUMENT:
Applicant argues, claim 1 recites inter alia, the features of claim 1 “distinguishes over each of Fuji, Kato, and Neville, and thus over their combination [..] Fuji fails to disclose "displaying a graph in which, within the graph structure, a first structure indicating a first class to which one node or a plurality of nodes belongs and a second structure indicating a first node that belongs to the first class and has the associated contribution degree being equal to or larger than a threshold, are coupled to each other" as recited in previous claim 1 [...] While Fuji uses a graph to visualize the basis of an inference result (Figure 5), it lacks the claimed structured presentation. Fuji merely presents a graph indicating relationships between various elements, without the claimed organization and filtering based on contribution degrees. Thus, Fuji does not disclose the above noted features, namely, "generating a first structure including one or more classes, each of the one or more classes being a class to which one or more nodes included in the estimation target data belongs", "selecting, from among the plurality of nodes included in the estimation target data, one or more first nodes each of which is a node that belongs to a first class being any of the one or more classes and that has the associated contribution degree being equal to or larger than a threshold", "generating a second structure including the selected one or more first nodes", and "displaying a graph including the first structure and the second structure such that the second structure is coupled to the first class in the first structure" [... Furthermore] While Kato does identify such nodes, it does not teach or suggest using these nodes to generate a "second structure" that is then coupled to a "first structure" representing classes of nodes. Kato merely identifies high contribution nodes within the context of a single, hierarchical tree diagram representing a discussion flow, without any notion of separate structures or classes. Furthermore, Kato's selection of nodes above a threshold is not limited to nodes belonging to a specific class, as required by the claimed invention [...] Kato lacks this targeted approach and therefore does not teach or suggest the claimed invention's structured presentation for enhanced explainability. Thus, Kato does not appear to cure the above deficiencies in Fuji. Further, nothing has been cited or found in Neville to cure the above deficiencies of Fuji. Therefore, it is submitted that claim 1 patentably distinguishes over any combination of Fuji, Kato, and Neville, for at least the above reasons [...] Among other things, a prima facie case of obviousness must establish that the asserted combination of references teaches or suggests each and every element of the claimed invention. In view of the distinction of claim 1 noted above, at least one claimed element is not present in the asserted combination of references. Hence, the Office Action fails to establish a prima facie case of obviousness vis-h-vis claim 1. Because claim 4 depends from independent claim 1, claim 4 incorporates all the elements of independent claim 1 and plus additional patentable features. The additional reference McAteer, however, fails to cure the deficiencies pointed out above with respect to independent claim 1. Thus, the dependent claim should likewise be in a condition for allowance for at least the reasons stated above with respect to independent claim 1. Independent claims 6 and 7, although differing in scope and/or statutory class, patentably define over the cited references at least for reasons analogous to the reasons stated above for the patentability of claim 1. Accordingly, it is respectfully submitted that claims 6 and 7, and the various claims depending therefrom, patentably define over the cited references. Withdrawal of the rejections is respectfully requested”.
EXAMINER RESPONSE: Examiner respectfully disagree, Fuji does disclose the newly added limitations of independent claims 1, 6 and 7. To be specific Fuji, sec: 4.2 Evaluation of Explainable AI., p. 63, left col. para 1 teaches a graph being displayed to the user, for example, Figure 5 teaches the graph that can be displayed to the user which contains a plurality of nodes, specifically, factors having a large effect (contribution) on inference result as circle nodes and classes such as Disease and Mutation. In addition, Fuji does teaches “generating a first structure including one or more classes, each of the one or more classes being a class to which one or more nodes included in the estimation target data belongs” specifically Fig. 2 teaches generating a “basis” for reaching the inference result from the input (estimation target data) by using a knowledge graph and Fig. 5 teaches the basis generated examples includes a first structure ( class – Disease)). Moreover, the applicant specification, lacks of support for how the one or more first nodes each of which is a node that belongs to a first class being any of the one or more classes and that has the associated contribution degree being equal to or larger than a threshold are being selected from among the plurality of nodes included in the estimation target data, nevertheless, Fuji Fig. 1 teaches input graph data (estimation target data) which includes a plurality of nodes and Fuji p. 59, left col. para. 2, teaches extracting (selecting) graph data features (nodes) utilizing Deep Tensor which is then used to generate the output, see also Fig. 2. In addition, Fig. 5 teaches a plurality of nodes, specifically, factors having a large effect on inference result as circle nodes and classes such as Disease and Mutation. In addition, Fuji also teaches generating the second structure including the selected one or more first nodes that can be seen in Fig. 5 where the basis generated examples includes a second structure ( class – Mutation) that includes a selected node (circle nodes).
Furthermore applicant argues, “Kato’s selection of nodes above a threshold is not limited to nodes belonging to a specific class, as required by the claimed invention” however, as shown above Fuji teaching overcome this deficiency. Therefore, Fuji, Kato and Neville in combination teach or suggest the limitations presented in amended independent claims 1, 6 and 7. Claims 4, 8, 9-10 depends of claims 1,6 and 7, thus the rejection of claims 1, 6, and 7 are incorporated. Accordingly, claims 1, 6-10 are not patent eligible under 35 USC § 103.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1, 4 and 6-10 rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Independent claims 1, 6, and 7 recites the newly added limitation “...enhancing explainability of an inference result output from a machine learning model trained using knowledge graph technology to perform pathogenicity predictions...”. However, the specification does not contain support in how explainability of an inference result output from a machine learning model is being enhanced. In addition, amended claims 1, 6 and 7 recites the limitation “selecting, from among the plurality of nodes included in the estimation target data, one or more first nodes each of which is a node that belongs to a first class being any of the one or more classes and that has the associated contribution degree being equal to or larger than a threshold”, yet the specification does not provide support for selecting from among the plurality of nodes included in the estimation target data, one or more first nodes each of which is a node that belongs to a first class being any of the one or more classes and that has the associated contribution degree being equal to or larger than a threshold. Rather, [0082] teaches “the structure generation unit 53 sequentially selects each node included in the estimation result stored in the estimation result DB 19” , [0123] teaches “the analysis section 50 selects one unprocessed node” and [0124] teaches “the analysis section 50 selects the class of the selected node”. Therefore, specification does not provides details in how “enhancing explainability of an inference result output from a machine learning model trained using knowledge graph technology to perform pathogenicity predictions” and “selecting, from among the plurality of nodes included in the estimation target data, one or more first nodes each of which is a node that belongs to a first class being any of the one or more classes and that has the associated contribution degree being equal to or larger than a threshold” as disclosed in the amended claims 1,6, and 7.
Claims 4, 8-10 are dependent on claims 1,6, and 7. Therefore, the rejection of claims 1,6 and 7 applies.
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 1, 4 and 6-10 are 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.
Claim 1 recites the limitation "the pathogenic" and “the benign” in line 12. There is insufficient antecedent basis for this limitation in the claim. For purpose of examination, examiner is interpreting these limitations as "a pathogenic" and “a benign”.
Claims 6 and 7 are independent and recites similar features to those of claim 1, thus are rejected for reasons set forth in the rejection of claim 1.
Claims 4, 8-10 are dependent on claims 1,6, and 7. Therefore, the rejection of claims 1,6 and 7 applies.
Claim Rejections - 35 USC § 101
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, 4, and 6-10 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, 4 and 8 are non-transitory computer-readable recording medium type claim. Claims 6 and 9 are an information processing apparatus type claim. Claim 7 and 10 are a display method type claim. Therefore, claims 1, 4, and 6-10 are directed to either a process, machine, manufacture or composition of matter.
Regarding claim 1: 2A Prong 1:
generating a first structure including one or more classes, each of the one or more classes being a class to which one or more nodes included in the estimation target data belongs; (mental process – of generating first structure including one or more classes, each of the one or more classes being a class to which one or more nodes included in the estimation target data belongs can be performed by the human mind with the help of pen and paper (e.g., judgment)).
selecting, from among the plurality of nodes included in the estimation target data, one or more first nodes each of which is a node that belongs to a first class being any of the one or more classes and that has the associated contribution degree being equal to or larger than a threshold; (mental process – of selecting one or more first nodes each of which is a node that belongs to a first class being any of the one or more classes and that has the associated contribution degree being equal to or larger than a threshold from among the plurality of nodes included in the estimation target data can be performed by the human mind with the help of pen and paper (e.g., judgement & evaluation)).
generating a second structure including the selected one or more first nodes; (mental process – of generating second structure including the selected one or more first nodes can be performed by the human mind with the help of pen and paper (e.g., judgment )).
wherein the process further includes calculating a total value of the contribution degree associated with the second node included in the plurality of nodes and the contribution degree associated with a third node coupled to the second node, ( mental process – of calculating a total value of the contribution degree can be performed by the human mind with the aid of pen and paper. For example, as shown in applicant specification [0118] a total values can be obtained by calculating the sum of two contribution degree 0.01 and 0.10 which can be done utilizing a pen and paper (e.g., evaluation)).
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
A non-transitory computer-readable recording medium storing a display program for enhancing explainability of an inference result output from a machine learning model trained using knowledge graph technology to perform pathogenicity predictions, the display program comprising instructions which, when executed by a computer, cause the computer to execute a process, the process comprising: (This is directed to using computers or other machinery merely as a tool to perform an existing process. See MPEP 2106.05(f)).
acquiring, (This is understood to be insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g)).
... by using the machine learning model which outputs,... (This is directed to using computers or other machinery merely as a tool to perform an existing process. See MPEP 2106.05(f)).
displaying a graph including the first structure and the second structure such that the second structure is coupled to the first class in the first structure, (This is understood to be insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g)).
wherein the graph does not include a second node, of which the associated contribution degree is less than the threshold among the plurality of nodes included in the estimation target data, (The specification of data to be stored is understood to be a field of use limitation. See MEPE 2106.05(h)).
wherein the process further includes calculating a total value of the contribution degree associated with the second node included in the plurality of nodes and the contribution degree associated with a third node coupled to the second node, (This is understood to be insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g)).
wherein the displaying the graph includes displaying the graph including a third structure indicating the second node and the third node in a case that the total value is equal to or larger than a threshold, (This is understood to be insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g)).
wherein the displaying the graph includes displaying a relation between nodes contained in the graph in accordance with the contribution degree associated with the relation between the nodes in such a manner that the relation having a larger contribution degree is more highlighted (This is understood to be insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g)).
The additional elements as disclosed above alone or in combination do not integrate the judicial exception into practical application as they are mere insignificant extra solution activity in combination of generic computer functions being implemented with generic computer elements in a high level of generality to perform the disclosed abstract idea above.
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
A non-transitory computer-readable recording medium storing a display program for enhancing explainability of an inference result output from a machine learning model trained using knowledge graph technology to perform pathogenicity predictions, the display program comprising instructions which, when executed by a computer, cause the computer to execute a process, the process comprising: (This is directed to using computers or other machinery merely as a tool to perform an existing process. See MPEP 2106.05(f)).
acquiring, ( This is directed to well understood, routine of presenting offers and gathering statistics. See MPEP 2106.05 (d)(II)).
... by using the machine learning model which outputs,... (This is directed to using computers or other machinery merely as a tool to perform an existing process. See MPEP 2106.05(f)).
displaying a graph including the first structure and the second structure such that the second structure is coupled to the first class in the first structure, ( This is directed to well understood, routine of presenting offers and gathering statistics. See MPEP 2106.05 (d)(II)).
wherein the graph does not include a second node, of which the associated contribution degree is less than the threshold among the plurality of nodes included in the estimation target data, (The specification of data to be stored is understood to be a field of use limitation. See MEPE 2106.05(h)).
wherein the process further includes calculating a total value of the contribution degree associated with the second node included in the plurality of nodes and the contribution degree associated with a third node coupled to the second node, ( This is directed to well understood, routine of presenting offers and gathering statistics. See MPEP 2106.05 (d)(II)).
wherein the displaying the graph includes displaying the graph including a third structure indicating the second node and the third node in a case that the total value is equal to or larger than a threshold, ( This is directed to well understood, routine of presenting offers and gathering statistics. See MPEP 2106.05 (d)(II)).
wherein the displaying the graph includes displaying a relation between nodes contained in the graph in accordance with the contribution degree associated with the relation between the nodes in such a manner that the relation having a larger contribution degree is more highlighted ( This is directed to well understood, routine of presenting offers and gathering statistics. See MPEP 2106.05 (d)(II)).
The additional elements as disclosed above in combination of the abstract idea are not sufficient to amount to significantly more than the judicial exception as they are mere insignificant extra solution activity in combination of generic computer functions being implemented with generic computer elements in a high level of generality to perform the disclosed abstract idea above.
Regarding claim 6: is rejected under the same rational of claim 1. Claim 6 only recites the additional elements of An information processing apparatus ...comprising: a memory; and a processor coupled to the memory and configured to... which is directed to using computers or other machinery merely as a tool to perform an existing process. See MPEP 2106.05(f).
Regarding claim 7: is rejected under the same rational of claim 1. Claim 7 only recites the additional elements of A display method implemented by a computer... the display method comprising... which is directed to using computers or other machinery merely as a tool to perform an existing process. See MPEP 2106.05(f).
Regarding claim 4: 2A Prong 1: wherein the calculating a sum total calculates the sum total in a case that the second node is a node that is coupled to the first node and belongs to the first class, and the associated contribution degree is equal to or greater than a threshold ( mental process – of calculating a sum total can be performed by the human mind with the aid of pen and paper (e.g., evaluation)).
2A Prong 2 & 2B: None.
Regarding claim 8: 2A Prong 1:
wherein the process further includes extracting knowledge designated by an administrator or from the knowledge graph and inserting the knowledge into the estimation result (mental process – of extracting knowledge from a knowledge graph and insert the knowledge into an estimation result can be performed by the human mind with the aid of pen and paper ( e.g. evaluation)).
2A Prong 2 and 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
The non-transitory computer-readable recording medium storing the display program for causing the computer to execute the process according to claim 1... (This is directed to using computers or other machinery merely as a tool to perform an existing process. See MPEP 2106.05(f)).
Regarding claim 9: 2A Prong 1:
...extracts knowledge designated by an administrator or from the knowledge graph and inserts the knowledge into the estimation result (mental process – of extracting knowledge from a knowledge graph and insert the knowledge into an estimation result can be performed by the human mind with the aid of pen and paper ( e.g. evaluation)).
2A Prong 2 and 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
The information processing apparatus... wherein the processor... (This is directed to using computers or other machinery merely as a tool to perform an existing process. See MPEP 2106.05(f)).
Regarding claim 10: See rejection of claim 8, same rational applies. Claim 10, only recites the additional element of The display method according to claim 7... which is directed to using computers or other machinery merely as a tool to perform an existing process. See MPEP 2106.05(f)).
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.
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.
Claims 1, and 6-10 are rejected under 35 U.S.C. 103 as being unpatentable over Fuji et al. “Explainable AI Through Combination of Deep Tensor and Knowledge Graph” in further view of Kato et al. US 2017/0091307 A1 (hereinafter Kato) in further view of Neville et al. US 2022/0189581 A1 (hereinafter Neville).
Regarding claim 1:
Fuji teaches ...enhancing explainability of an inference result output from a machine learning model trained using knowledge graph technology to perform pathogenicity predictions,... (Fuji Abstract, Fig. 2 & 4 provides an artificial intelligence technology that utilizes Deep Tensor to learn relationships between genetic mutations and pathogenicity from public databases in order to logically explain the reasons and bases of the inference results).
acquiring, by using the machine learning model which outputs, in response to an input of estimation target data which is data in a graph structure indicating a relation between nodes generated using a triple of subject, predicate and object, that corresponds to a set of two nodes an edge acquired from a knowledge graph of a mutation, an estimation result which indicates that the mutation is the pathogenic ( Fuji p. 60, sec. 3.1 Identification of inference factors by Deep Tensor, para. 1, teaches inputting the graph data into a neural network. Further, p. 60, sec. 3.2 Basis forming by using Knowledge graph Technology, para. 1, Figure. 2, teaches using graph data (input of estimation target data) for learning and making inferences, and teaches the graph data can be provided to Deep Tensor by extracting partial graphs from the knowledge graph to produce an output which can be views as the “estimation result” of the neural network. Moreover, Fuji p. 60, sec. 3. Developed technology, para. 1 and Figure 1 & 3 teaches the output, outputs both the inference result and reason such as inference factors (contribution degree) that contributed greatly to the inference result. In addition, Fuji p. 61-63, sec. 4.2 Evaluation of Explainable AI, para. 1-3, teaches performing an experiment using a knowledge graph, in the field of bioinformatic for inference and teaches using “Deep Tensor to learn relationships between genetic mutations and pathogenicity from public databases”. Figure. 4 “Application of developed technology” teaches outputting “Inferred relationship with disease”, thus disclosing the use of a machine learning model to estimate if a genetic mutation leads to a disease such as a pathogenic mutation and Figure 5 and p. 60, sec. 3. Developed technology, para. 3, teaches the graph data of the knowledge graph, used for inference in the context of bioinformatics, includes “triple” such as the genetic mutation (subject), genes (predicate) and drugs (object) as well factors that significantly contribution to the inference result as circle nodes).
generating a first structure including one or more classes, each of the one or more classes being a class to which one or more nodes included in the estimation target data belongs; ( According to applicant specification, [0127] a corresponding class is a first structure. For which Fuji Fig. 2 teaches generating a “basis” for reaching the inference result from the input (estimation target data) by using a knowledge graph and Fig. 5 teaches the basis generated examples includes a first structure ( class – Disease)).
selecting, from among the plurality of nodes included in the estimation target data, one or more first nodes each of which is a node that belongs to a first class being any of the one or more classes and that has the associated contribution degree ( Fuji Fig. 1 teaches input graph data (estimation target data) which includes a plurality of nodes and Fuji p. 59, left col. para. 2, teaches extracting (selecting) graph data features (nodes) utilizing Deep Tensor which is then used to generate the output, see also Fig. 2. In addition, Fig. 5 teaches a plurality of nodes, specifically, factors having a large effect on inference result as circle nodes and classes such as Disease and Mutation).
generating a second structure including the selected one or more first nodes; ( Fuji Fig. 5 teaches the basis generated examples includes a second structure ( class – Mutation) that includes a selected node (circle nodes)).
displaying a graph including the first structure and the second structure such that the second structure is coupled to the first class in the first structure, ( To clarify Fuji sec: 4.2 Evaluation of Explainable AI., p. 63, left col. para 1 teaches a graph being displayed to the user. Furthermore, Fuji Figure 5 teaches a plurality of nodes, specifically, factors having a large effect on inference result as circle nodes and classes such as Disease and Mutation . Further, Fuji pg. 62, right column, 1st and 2nd paragraph, teaches that in Figure 5, “the solid lines (edges) represent the relationship among those nodes, while the broken lines represent specific connection between a genetic mutation, its gene, a drug, and the related disease. This enables the user to visualize complex relationship and to “construct a graph of associated knowledge that extends to candidate diseases as a basis”).
Fuji does not teach A non-transitory computer-readable recording medium storing a display program for... the display program comprising instructions which, when executed by a computer, cause the computer to execute a process, the process comprising: acquiring, by using the machine learning model which outputs,.. an estimation result which indicates that the mutation is the pathogenic or the benign; ...contribution degree being equal to or larger than a threshold; wherein the graph does not include a second node, of which the associated contribution degree is less than the threshold among the plurality of nodes included in the estimation target data, wherein the process further includes calculating a total value of the contribution degree associated with the second node included in plurality of nodes and the contribution degree associated with a third node coupled to the second node, wherein the displaying the graph includes displaying the graph including a third structure indicating the second node and the third node in a case that the total value is equal to or larger than a threshold, wherein the displaying the graph includes displaying a relation between nodes contained in the graph in accordance with the contribution degree associated with the relation between the nodes in such a manner that the relation having a larger contribution degree is more highlighted.
Nevertheless, Kato teaches the following:
A non-transitory computer-readable recording medium storing a display program for... the display program comprising instructions which, when executed by a computer, cause the computer to execute a process, the process comprising: (Kato Fig. 3 element 30 teaches a display controlling unit, and [0006] teaches a non-transitory computer readable recording medium that causes a computer to execute a process).
...contribution degree being equal to or larger than a threshold ( Kato Abstract teaches a creativity aiding server identifies “one or more of the nodes of which the degree of contribution is equal to or higher than a threshold value”).
wherein the process further includes calculating a total value of the contribution degree associated with the second node included in the plurality of nodes and the contribution degree associated with a third node coupled to the second node, (Kato [0068-0069] teaches the total degree of contribution is being calculated by adding the score from the specific nodes, as illustrated in Fig. 13. Such that, user A total contribution is determined by adding the score of the nodes No. 1, 7 and 16, which results in the degree of contribution being calculated as "4+1+1=6 points"”).
wherein the displaying the graph includes displaying the graph including a third structure indicating the second node and the third node in a case that the total value is equal to or larger than a threshold, ( Kato teaches [0077] “the display controlling unit 30 is a processing unit that changes the display method of any of the tree diagrams stored in the tree diagram DB 14” this enable the display controlling unit to adjust the display format based on user interaction, which could imply showing graph representing the degree of contribution for specific nodes is equal to or higher than a threshold value).
wherein the displaying the graph includes displaying a relation between nodes contained in the graph in accordance with the contribution degree associated with the relation between the nodes in such a manner that the relation having a larger contribution degree is more highlighted ( Kato [0097] teaches “it is also possible to display the nodes in various colors, to make it possible to distinguish a node on which information, opinions, and the like are concentrated and a node having a high evaluation score”).
Kato is also in the same field of endeavor as Fuji (data analysis). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include the functionality of a display controlling unit and a method for calculating the contribution degree, as being disclosed and taught by Kato, in the system taught by Fuji to yield the predictable results of improve the display of data to a user such that “it is possible to provide sufficient creativity support” ( Kato [0159]).
Despite that, Fuji teaches using a machine learning model to estimate if a mutation is pathogenic, obtaining a contribution degree from the input data, and Kato teaches calculating the contribution degree. Both Fuji and Kato do not suggest acquiring, by using the machine learning model which outputs,.. an estimation result which indicates that the mutation is the pathogenic or the benign and wherein the graph does not include a second node, of which the associated contribution degree is less than the threshold among the plurality of nodes included in the estimation target data.
However, Neville teaches the following:
acquiring, by using the machine learning model which outputs,.. an estimation result which indicates that the mutation is the pathogenic or the benign (Neville [0073] teaches a classification algorithm (machine learning model) that classifies genetic variances as either “pathogenic or benign”. Further, [0074] teaches “variant classification is carried out by a machine learning algorithm which, given the input data (estimation target data) , calculates the posterior probability that the variant is pathogenic versus the probability that it is benign. The probability is a numerical value ranging from 0 to 100% incorporating graded measures of the evidence available for the pathogenicity of a given genetic variant”. Therefore, the classification the algorithm can be view as the estimation whether the mutation (variance) is either pathogenic or the benign and the posterior probability can be viewed as the estimation results which provides a numerical value or measure).
wherein the graph does not include a second node, of which the associated contribution degree is less than the threshold among the plurality of nodes included in the estimation target data,( Neville Figs 8A, 8B and 8C teach graphs. Further, Neville [0144] teaches “translating the genetic, biological and/or clinical features directly causative for the pathogenicity of a genetic variant into variables, represented as nodes in the hierarchical Bayesian network... the relationship between these features is translated into connections between the nodes, representing the causal influence between the features” and [0147] teaches calculating mutual information (MI) score between the nodes in the graph to remove unnecessary dependencies, specifically connections with MI scores less that a threshold such as <0.0001 are assumed to be null and removed).
Neville is also in the same field of endeavor as Fuji and Kato (data analysis). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include the functionality of a machine learning model being able to estimates whether a mutation is pathogenic or benign and remove nodes that have a contribution degree is less than the threshold as being disclosed and taught by Neville, in the system taught by Fuji and Kato to yield the predictable results of improve the predictive performance of the model ( Neville [0089]).
Regarding claim 6: is rejected under the same rational of claim 1. Claim 6 only recites the additional elements of An information processing apparatus ...comprising: a memory; and a processor coupled to the memory and configured to... for which Kato Fig. 18 and [0102] teaches an information processing apparatus, a memory (element 10c) and a processor (element 10d) coupled to the memory.
Regarding claim 7: is rejected under the same rational of claim 1. Claim 7 only recites the additional elements of A display method implemented by a computer... the display method comprising... which Fuji sec: 4.2 Evaluation of Explainable AI., p. 63, left col. para 1 teaches a display method that can be presented to the user.
Regarding claim 8:
Fuji, Kato and Neville teach The non-transitory computer-readable recording medium storing the display program for causing the computer to execute the process according to claim 1. Specifically, Fuji teaches wherein the process further includes extracting knowledge designated by an administrator or from the knowledge graph and inserting the knowledge into the estimation result ( Fuji p. 60, sec. 3.2 Basis forming by usi