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
In the previous Office Action issued October 29, 2025 (hereinafter “the previous Office Action”), claims 1-20 were pending.
This action is in response to the amendment and remarks filed March 25, 2026. In the amendment, claims 1-3, 5-8, 12-14, 16-18, and 20 were amended, claims 4 and 15 were canceled, and no claims were added. Thus, claims 1-3, 5-14, and 16-20 are pending.
The objections of claims 1-11, set forth in the previous Office Action, have been withdrawn in view of Applicant’s amendments and remarks.
The rejections of claims 4 and 15, set forth in the previous Office Action, have been withdrawn in view of the claims’ cancelation.
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 1-3, 5-14, and 16-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Claims 1-3 and 5-11 are directed to a method [process]. Claims 12-14 and 16-19 are directed to a device [machine]. Claim 20 is directed to a computer-readable storage medium [machine].
Regarding Claim 1:
Step 2A, Prong 1: The following limitations are directed to the abstract idea of a mental process [see MPEP 2106.04(a)(2) III. C.]. In particular, the claim recites mental processes that are concepts performed in the human mind or with pen and paper (including an observation, evaluation, judgement, or opinion).
(a) determining…based on the knowledge base entity, the query result value that provides a decision to the query
(b) constructing…a first proof tree that includes the first version of the knowledge base entity as a first node or a first edge
(c) constructing…a second proof tree that includes the second version of the knowledge base entity as a second node or a second edge
(d) determining…based on traversing the first proof tree and the second proof tree to determine that the first node or the first edge of the first proof tree is different than the second node or the second edge of the second proof tree, an explanation that explains a difference between the query result and a previous query result value provided with respect to the query
As drafted, under their broadest reasonable interpretation (BRI), in view of the specification, the above limitations cover concepts performed in the human mind (observation, evaluation, judgement, or opinion). Given a sufficiently small set of data, nothing in the claim prohibits this process from being performed mentally or with pen and paper.
Step 2A, Prong 2: There are no additional elements in this claim that integrate the judicial exception into a practical application.
The following additional elements are adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea [see MPEP 2106.05(f)] and therefore fails to integrate the judicial exception into a practical application.
(a) …by the automated reasoning engine, and…
(b) …by the automated reasoning engine…
(c) …by the automated reasoning engine…
(d) …by the automated reasoning engine, and…
outputting, by the automated reasoning engine, the explanation
The automated reasoning engine is the additional element performing the abstract ideas.
The following additional elements are directed to insignificant extra-solution activity to the judicial exception [see MPEP 2106.05(g)].
obtaining, by the automated reasoning engine, a query
obtaining, by the automated reasoning engine, from a knowledge base, and responsive to the query, a knowledge base entity that is representative of one or more explicit facts or one or more rules
obtaining, by the automated reasoning engine, provenance information that explains a history for the knowledge base entity
obtaining, by the automated reasoning engine, and based on the provenance information, a first version of the knowledge base entity and a second version of the knowledge base entity
Step 2B: There are no additional elements in this claim that amount to significantly more than the judicial exception.
The following additional elements are adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea [see MPEP 2106.05(f)] and therefore fails to amount to significantly more than the judicial exception.
(a) …by the automated reasoning engine, and…
(b) …by the automated reasoning engine…
(c) …by the automated reasoning engine…
(d) …by the automated reasoning engine, and…
outputting, by the automated reasoning engine, the explanation
The automated reasoning engine is the additional element performing the abstract ideas.
The following additional elements are directed to receiving or transmitting data over a network. The courts (as per Intellectual Ventures v. Symantec, 838 F.3d 1307, 1321; 120 USPQ2d 1353, 1362 (Fed. Cir. 2016)) have recognized receiving or transmitting data over a network as well-understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity to the judicial exception [see MPEP 2106.05(d) II.].
obtaining, by the automated reasoning engine, a query
obtaining, by the automated reasoning engine, from a knowledge base, and responsive to the query, a knowledge base entity that is representative of one or more explicit facts or one or more rules
obtaining, by the automated reasoning engine, provenance information that explains a history for the knowledge base entity
obtaining, by the automated reasoning engine, and based on the provenance information, a first version of the knowledge base entity and a second version of the knowledge base entity
Regarding Claim 2:
Step 2A, Prong 1: The claim recites the same abstract ideas as in claim 1.
The following limitations are directed to the abstract idea of a mental process [see MPEP 2106.04(a)(2) III. C.]. In particular, the claim recites mental processes that are concepts performed in the human mind (including an observation, evaluation, judgement, or opinion).
wherein determining the explanation comprises:
translating the difference into the explanation that explains the difference between the query result value and the previous query result value
Step 2A, Prong 2: There are no additional elements in this claim that integrate the judicial exception into a practical application.
Step 2B: There are no additional elements in this claim that amount to significantly more than the judicial exception.
Regarding Claim 3:
Step 2A, Prong 1: The claim recites the same abstract ideas as in claim 2.
The following limitations are directed to the abstract idea of a mental process [see MPEP 2106.04(a)(2) III. C.]. In particular, the claim recites mental processes that are concepts performed in the human mind (including an observation, evaluation, judgement, or opinion).
determining at least one of 1) the difference between the query result value and the previous query result value 2) a lack of existence of the knowledge base entity at a time of processing the previous query, and 3) addition of the knowledge base entity at a time after processing the previous query
Step 2A, Prong 2: There are no additional elements in this claim that integrate the judicial exception into a practical application.
Step 2B: There are no additional elements in this claim that amount to significantly more than the judicial exception.
Regarding Claim 5:
Step 2A, Prong 1: The claim recites the same abstract ideas as in claim 1.
The following limitations are directed to the abstract idea of a mental process [see MPEP 2106.04(a)(2) III. C.]. In particular, the claim recites mental processes that are concepts performed in the human mind (including an observation, evaluation, judgement, or opinion).
wherein determining the difference comprises determining that the first version of the knowledge base entity has changed relative to the second version of the knowledge base entity
Step 2A, Prong 2: There are no additional elements in this claim that integrate the judicial exception into a practical application.
Step 2B: There are no additional elements in this claim that amount to significantly more than the judicial exception.
Regarding Claim 6:
Step 2A, Prong 1: The claim recites the same abstract ideas as in claim 1.
The following limitations are directed to the abstract idea of a mental process [see MPEP 2106.04(a)(2) III. C.]. In particular, the claim recites mental processes that are concepts performed in the human mind (including an observation, evaluation, judgement, or opinion).
wherein the previous query result value is the same as the query result value
wherein determining the difference comprises traversing the first proof tree and the second proof tree to identify that at least one node or edge of the first proof tree is different than a corresponding node or edge of the second proof tree
Step 2A, Prong 2: There are no additional elements in this claim that integrate the judicial exception into a practical application.
Step 2B: There are no additional elements in this claim that amount to significantly more than the judicial exception.
Regarding Claim 7:
Step 2A, Prong 1: The claim recites the same abstract ideas as in claim 1.
The following limitations are directed to the abstract idea of a mental process [see MPEP 2106.04(a)(2) III. C.]. In particular, the claim recites mental processes that are concepts performed in the human mind (including an observation, evaluation, judgement, or opinion).
wherein constructing the first proof tree includes: applying the first version of the knowledge base entity to a first version of different knowledge base entity determined using second provenance information to obtain a new knowledge base entity representative of an implicit fact
adding a new node to the first proof tree that is representative of the new knowledge base entity
Step 2A, Prong 2: There are no additional elements in this claim that integrate the judicial exception into a practical application.
The following additional element is adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea [see MPEP 2106.05(f)] and therefore fails to integrate the judicial exception into a practical application.
the first version of the different knowledge base entity occurring at a concurrent time as the first version of the knowledge base entity
The following additional element does not meaningfully limit the judicial exception [see MPEP 2106.05(e)]. The claim simply recites additional information regarding the characteristics of the provenance information. Therefore, the additional element does not integrate the abstract ideas into a practical application.
wherein the provenance information comprises first provenance information
Step 2B: There are no additional elements in this claim that amount to significantly more than the judicial exception.
The following additional element is adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea [see MPEP 2106.05(f)] and therefore fails to amount to significantly more than the judicial exception.
the first version of the different knowledge base entity occurring at a concurrent time as the first version of the knowledge base entity
The following additional element does not meaningfully limit the judicial exception [see MPEP 2106.05(e)]. The claim simply recites additional information regarding the characteristics of the provenance information. Therefore, the additional element does not amount to significantly more than the judicial exception.
wherein the provenance information comprises first provenance information
Regarding Claim 8:
Step 2A, Prong 1: The claim recites the same abstract ideas as in claim 1.
The following limitations are directed to the abstract idea of a mental process [see MPEP 2106.04(a)(2) III. C.]. In particular, the claim recites mental processes that are concepts performed in the human mind (including an observation, evaluation, judgement, or opinion).
wherein constructing the first proof tree includes:
deriving, from the first version of the knowledge base entity, a first version of an implicit fact
adding the first version of the implicit fact to the first proof tree as a first additional node
wherein constructing the second proof tree includes:
deriving, from the second version of the knowledge base entity, a second version of the implicit fact
adding the second version of the implicit fact to the second proof tree as a second additional node
Step 2A, Prong 2: There are no additional elements in this claim that integrate the judicial exception into a practical application.
Step 2B: There are no additional elements in this claim that amount to significantly more than the judicial exception.
Regarding Claim 9:
Step 2A, Prong 1: The claim recites the same abstract ideas as in claim 1.
The following limitations are directed to the abstract idea of a mental process [see MPEP 2106.04(a)(2) III. C.]. In particular, the claim recites mental processes that are concepts performed in the human mind (including an observation, evaluation, judgement, or opinion).
wherein the provenance information includes data identifying one or more of a creator of the knowledge base entity, a time at which the knowledge base entity was created or changed, a date at which the knowledge base entity was created or changed, or a reason indicating why the knowledge base entity was created or changed
Step 2A, Prong 2: There are no additional elements in this claim that integrate the judicial exception into a practical application.
Step 2B: There are no additional elements in this claim that amount to significantly more than the judicial exception.
Regarding Claim 10:
Step 2A, Prong 1: The claim recites the same abstract ideas as in claim 1.
The following limitations are directed to the abstract idea of a mental process [see MPEP 2106.04(a)(2) III. C.]. In particular, the claim recites mental processes that are concepts performed in the human mind (including an observation, evaluation, judgement, or opinion).
wherein the explanation further explains a history of change that resulted in the difference between the query result value and the previous query result value
Step 2A, Prong 2: There are no additional elements in this claim that integrate the judicial exception into a practical application.
Step 2B: There are no additional elements in this claim that amount to significantly more than the judicial exception.
Regarding Claim 11:
Step 2A, Prong 1: The claim recites the same abstract ideas as in claim 1.
The following limitations are directed to the abstract idea of a mental process [see MPEP 2106.04(a)(2) III. C.]. In particular, the claim recites mental processes that are concepts performed in the human mind (including an observation, evaluation, judgement, or opinion).
wherein determining the query result value includes:
deriving, based on application of the first knowledge base entity to the second knowledge base entity, an implicit fact
determining, based on the implicit fact, the query result value
Step 2A, Prong 2: There are no additional elements in this claim that integrate the judicial exception into a practical application.
The following additional element is directed to insignificant extra-solution activity to the judicial exception [see MPEP 2106.05(g)].
wherein obtaining a knowledge base entity comprises obtaining a first knowledge base entities that is representative of a rule and a second knowledge base entity that is representative of an explicit fact
Step 2B: There are no additional elements in this claim that amount to significantly more than the judicial exception.
The following additional element is directed to receiving or transmitting data over a network. The courts (as per Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362) have recognized receiving or transmitting data over a network as well-understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity to the judicial exception [see MPEP 2106.05(d) II.].
wherein obtaining a knowledge base entity comprises obtaining a first knowledge base entities that is representative of a rule and a second knowledge base entity that is representative of an explicit fact
Regarding Claim 12:
Claim 12 is a device claim corresponding to method claim 1.
Step 2A, Prong 1: The claim recites the same abstract ideas as in claim 1.
Step 2A, Prong 2: There are no additional elements in this claim that integrate the judicial exception into a practical application. The analysis of claim 12 at this step mirror that of claim 1, with the exception the following limitations.
The following additional elements are adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea [see MPEP 2106.05(f)] and therefore fails to integrate the judicial exception into a practical application.
A device configured to explain a query result value determined by an automated reasoning engine, the device comprising: one or more memories configured to store the automated reasoning engine; and a computation engine executing one or more processors, the computation engine configured to execute the automated reasoning engine to:
Step 2B: There are no additional elements in this claim that amount to significantly more than the judicial exception. The analysis of claim 12 at this step mirror that of claim 1, with the exception the following limitations.
The following additional elements are adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea [see MPEP 2106.05(f)] and therefore fails to amount to significantly more than the judicial exception.
A device configured to explain a query result value determined by an automated reasoning engine, the device comprising: one or more memories configured to store the automated reasoning engine; and a computation engine executing one or more processors, the computation engine configured to execute the automated reasoning engine to:
Regarding Claims 13-14 and 16-19:
Step 1: Claims 13-14 and 16-19 are directed to a device, corresponding to method claims 2-3, 5-7, and 11. In particular, 13:2, 14:3, 16:5, 17:6, 18:7, 19:11.
Step 2A, Prong 1: The claim recites the same abstract ideas as in claims 2-7 and 11.
Step 2A, Prong 2: There are no additional elements in this claim that integrate the judicial exception into a practical application. The analysis of claims 13-14 and 16-19 at this step mirror that of claims 2-3, 5-7, and 11.
Step 2B: There are no additional elements in this claim that amount to significantly more than the judicial exception. The analysis of claims 13-14 and 16-19 at this step mirror that of claims 2-3, 5-7, and 11.
Regarding Claim 20:
Claim 20 is a computer-readable storage medium claim corresponding to method claim 1.
Step 2A, Prong 1: The claim recites the same abstract ideas as in claim 1.
Step 2A, Prong 2: There are no additional elements in this claim that integrate the judicial exception into a practical application. The analysis of claim 20 at this step mirror that of claim 1, with the exception the following limitations.
The following additional elements are adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea [see MPEP 2106.05(f)] and therefore fails to integrate the judicial exception into a practical application.
A non-transitory computer-readable storage medium having stored thereon instructions that, when executed, cause one or more processors to execute a computation engine configured to:
Step 2B: There are no additional elements in this claim that amount to significantly more than the judicial exception. The analysis of claim 20 at this step mirror that of claim 1, with the exception the following limitations.
The following additional elements are adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea [see MPEP 2106.05(f)] and therefore fails to amount to significantly more than the judicial exception.
A non-transitory computer-readable storage medium having stored thereon instructions that, when executed, cause one or more processors to execute a computation engine configured to:
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.
Claims 1-3, 5-14, and 16-20 are rejected under 35 U.S.C. 103 as being unpatentable over Bagchi et al. (US 20120078837), hereinafter Bagchi, in view of Kishimoto et al. (US 20200250557), hereinafter Kishimoto..
Regarding Claim 1:
Bagchi discloses:
A method of explaining a query result value determined by an automated reasoning engine, the method comprising:
Bagchi, [0060], “The method receives answers from the question-answering system in item 210. For each query submitted, the question-answering system 110 returns a list of answers, their confidences, evidence dimensions, and evidence sources.”
obtaining, by the automated reasoning engine, from a knowledge base, and responsive to the query, a knowledge base entity that is representative of one or more explicit facts or one or more rules
Bagchi, [0051], “In item 202, the method receives input about the current problem. The method can receive a user inquiry through the input/output module in the form of a free-form query, a free-form statement, and/or keyword search, etc. The input from the problem can be multi-modal, such as text, audio, images, and video. The text can be unstructured, such as paragraphs of problem description in natural language, or structured, such as the content derived from a database.”
determining, by the automated reasoning engine, and based on the knowledge base entity, the query result value that provides a decision to the query
Bagchi, [0049], “The computer processor 104/110 also automatically generates a plurality of diagnosis answers for each diagnosis query, and calculates confidence values for each of the answers based on numerical values for several dimensions of evidence that are relevant to the problem-solving domain.”
[0068], “The decision-maker 108 can also drill down deeper into each answer and dimension of evidence and examine the supporting pieces of evidence that justify the answer along that dimension.”
In para. 49, Bagchi discloses automatically generating diagnosis answers [determining, by the automated reasoning engine and based on the knowledge base entity, the query result value]. Para. 68 further specifies each answer has supporting pieces of evidence that justify the answer along a dimension [the query result value that provides a decision to the query].
obtaining, by the automated reasoning engine, provenance information that explains a history for the knowledge base entity
Bagchi, [0063], “Thus, the method outputs the queries, the answers, the corresponding confidence values, the links to the evidence sources, and the numerical value of each evidence dimension using the input/output module upon user inquiry. Additionally, the decision maker can explore each evidence dimension further by viewing each piece of evidence and explore its associated provenance. For example, a piece of evidence may be a supporting passage, reasoning chain, or database fact. Similarly, examples of associated provenances include journal articles, textbooks, and databases. Further, when outputting the numerical value of each evidence dimension , this embodiment can illustrate the amount each evidence dimension contributes to a corresponding confidence value (on a scale or percentage basis, for example) and illustrate how changes in each of the numerical value of each evidence dimension produce changes in the corresponding confidence value.”
obtaining, by the automated reasoning engine, and based on the provenance information, a first version of the knowledge base entity and a second version of the knowledge base entity
Bagchi, [0039], “In general, the embodiments herein are intended to allow interaction to occur over a period of time and to support an iterative refinement approach. Therefore, one aspect of embodiments herein is a repository of all relevant analysis and decisions made to date. This repository 106 contains a representation of the reasoning and decision process not only as an efficiency mechanism, but allows the system to re-evaluate assumptions and decisions in light of new evidence relevant to the case. This allows users to interact with this representation, accepting, rejecting, or modifying it to as they think necessary to explore alternative solutions based on the users' insights into the validity or importance of the evidence or reasoning chain. This repository 106 is not only useful in the current evolving decision making interaction, but can be used to track the provenance of decisions that were made in the past and allow notification of actions to take based on newly arriving information that comes possibly years after decisions were made. For example, if a new study reports a contraindication for a drug in a given situation, the system could use this repository 106 of prior analysis to reevaluate its conclusions and provide relevant notification of alternative therapies to a patient that has been on this drug for years.”
The first version of the knowledge base is being interpreted as repository before the re-evaluation of current up to date facts on how to treat a disease with a drug. Bagchi gives an example of a decision that was made previous to new knowledge on how to treat a disease with a drug. Bagchi then states a new study, that provides information that’s contradictory to how the drug is treated that is then added to the repository. Bagchi then states how analysis of this study could change treatment. The repository after the addition if the study is interpreted as the second version of the knowledge base.
outputting, by the automated reasoning engine, the explanation
Bagchi, [0063], “In item 214, the method displays information to support decision-making. The list of answers is displayed along with answer confidences for the decision-maker 108 to evaluate (see Figure 4 for an example). Thus, the method outputs the queries, the answers, the corresponding confidence values, the links to the evidence sources, and the numerical value of each evidence dimension using the input/output module upon user inquiry. Additionally, the decision maker can explore each evidence dimension further by viewing each piece of evidence and explore its associated provenance. For example, a piece of evidence may be a supporting passage, reasoning chain, or database fact. Similarly, examples of associated provenances include journal articles, textbooks, and databases. Further, when outputting the numerical value of each evidence dimension , this embodiment can illustrate the amount each evidence dimension contributes to a corresponding confidence value (on a scale or percentage basis, for example) and illustrate how changes in each of the numerical value of each evidence dimension produce changes in the corresponding confidence value.”
The ability for the user to explore their evidence dimension and explore its associated provenance is interpreted as the explanation.
Bagchi does not explicitly disclose:
constructing, by the automated reasoning engine, a first proof tree that includes the first version of the knowledge base entity as a first node or a first edge
constructing, by the automated reasoning engine, a second proof tree that includes the second version of the knowledge base entity as a second node or a second edge
determining, by the automated reasoning engine, and based on traversing the first proof tree and the second proof tree to determine that the first node or the first edge of the first proof tree is different than the second node or the second edge of the second proof tree, an explanation that explains a difference between the query result value and a previous query result value provided with respect to the query
However, in the same field, analogous art Kishimoto teaches:
constructing, by the automated reasoning engine, a first proof tree that includes the first version of the knowledge base entity as a first node or a first edge
Kishimoto, [0016], “In one aspect, an explanation component may determine (e.g., compute) a set of formal explanations (e.g., proof trees) as to why a policy claim was deemed invalid and/or valid). The explanation proof tree(s) may contain all relevant rules that were violated or satisfied by the policy claim. An active learning component may identify one or more explanations to the user. One or more explanations may be scored and ranked based on a scoring function that may be customized by the user. Those of the explanations that are most relevant explanations to the policy claim may be automatically selected (or selected by a user). Scores of the rules in a knowledge domain may be adjusted based on those rules included in the explanations selected (e.g., automatically and/or by the user). The score of each rule indicates and/or measures the probability (e.g., likelihood) that the rule will be included in a subsequent explanation.”
constructing, by the automated reasoning engine, a second proof tree that includes the second version of the knowledge base entity as a second node or a second edge
Kishimoto, [0017], “In one aspect, one or more rules may be extracted from one or more segments of text data of a policy data source according to an active learning operation. The active learning may be applied to a previously extracted set of rules and revise, correct, update, and/or modify the scores of the rules in a knowledge base "KB" or knowledge domain. Active learning may be used to learn how to score the rules and assign and/or modify assigned scores/weights of the rules in the KB. Thus, the active learning may be used to build, maintain, update, and/or construct the KB. In this way, learning policy explanations may be employed using active learning.”
[0057], “For example, each rule has a score stored in the knowledge base and is adjusted based on the user feedback. The score of the explanation is calculated by using scores of the rules. More specifically, a theorem prover (e.g., automated theorem component 480 of Fig. 4) constructs a proof tree and the score of the proof tree is defined to be a sum of the scores of the rules appearing in the proof tree. The theorem prover attempts to find a proof tree that has the smallest score as compared to other scores. Then, an explanation is generated by a chain of rules in the proof trees (i.e., a portion of the proof tree), and the score of an explanation is the sum of the costs of the rules appearing in the chain. The theorem prover extracts several explanations (i.e., several chains of the rules) to present them to the user. The present invention regards the rules appearing in the explanation selected by the user as critical rules (e.g., which may have a defined level of priority), and the scores of these rules are reduced. It should be noted that one or more costs and/or the scores of rules in the explanations that are not selected may be increased.”
The knowledge base being able to be changed in para. 17 and another proof tree being created in para. 57 is being interpreted as the second knowledge base and second proof tree being created.
determining, by the automated reasoning engine, and based on traversing the first proof tree and the second proof tree to determine that the first node or the first edge of the first proof tree is different than the second node or the second edge of the second proof tree, an explanation that explains a difference between the query result value and a previous query result value provided with respect to the query
Kishimoto, [0057], “More specifically, a theorem prover (e.g., automated theorem component 480 of Fig. 4) constructs a proof tree and the score of the proof tree is defined to be a sum of the scores of the rules appearing in the proof tree. The theorem prover attempts to find a proof tree that has the smallest score as compared to other scores. Then, an explanation is generated by a chain of rules in the proof trees (i.e., a portion of the proof tree), and the score of an explanation is the sum of the costs of the rules appearing in the chain. The theorem prover extracts several explanations (i.e., several chains of the rules) to present them to the user. The present invention regards the rules appearing in the explanation selected by the user as critical rules (e.g., which may have a defined level of priority), and the scores of these rules are reduced.”
Kishimoto clarifies what scores are in ¶ [0058], “Each rule is associated with a score representing a probability (e.g., likelihood) of including that rule in an explanation. The scoring function associated with an explanation (proof tree) that can be defined by the user. The scoring function may optimize one or more criteria (e.g., number of rules included, maximize sum of scores of included rules, etc.). A new set of rules with updated scores may be generated. Each rule may have a relevant natural text snippet (e.g., a few words, phrase, or defined number of words) associated with the rule (e.g., the text fragment from which the rule was originally extracted). A user and/or artificial intelligence ("AI") operation may be used to revise the rule score and/or explanations.”)
As cited above in para. 16, Kishimoto discloses one or more explanation proof trees to be scored and ranked, the scoring of the trees is interpreted as the traversing of the trees, and the ranking is interpreting as the difference/comparison of the trees [based on traversing the first proof tree and the second proof tree to determine that the first node or the first edge of the first proof tree is difference than the second node or the second edge of the second proof tree]. Further in paras. 57-58, scores are based on rules and how likely they are to show up in certain explanations. Thus, these explanations being outputted is being interpreted as different proof tree query results being compared [explains a difference between the query result value and previous query result value provided with respect to the query].
Bagchi, Kishimoto, and the instant application are analogous art because they are all directed to automated systems.
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Bagchi with Kishimoto to use proof trees in order to generate more meaningful explanations of the reasoning process. “As such, the present invention provides for learning policy explanations in a computing environment by a processor are provided. In one embodiment, the present invention provides one or more explanations justifying validity or invalidity of a claim based on one or more rules extracted from one or more segments of text data of a policy data source using a machine learning operation. Thus, the present invention may generate meaningful explanations even if the policy claim violates two or more rules. The explanations may be shorter explanations and contain only the most relevant information to a user” (Kishimoto, [0015]).
Regarding Claim 2:
As discussed above, Bagchi in view of Kishimoto teach [the] method of claim 1, and Bagchi further discloses:
translating the difference into the explanation that explains the difference between the query result value and the previous query result value
Bagchi, [0064], “Further, the embodiments herein automatically and continuously update the diagnosis answers, the corresponding confidence values, and the numerical value of each evidence dimension based on revisions to the problem case information to produce revised queries, answers, corresponding confidence values, etc. (using the question-answering module). This method can also automatically output the revised queries, answers, and/or corresponding confidence values when a difference threshold is exceeded. This "difference threshold" can comprise a time period (e.g., hours, weeks, months, etc.), the amount one or more answers change (e.g., percentage change, polarity (yes/no) change, number of answers changing, etc.) and/or an amount of confidence value changes (percent confidence change, confidence polarity change, etc.).”
The difference threshold is being interpreted as the difference between the query result value and the previous query result value.
Regarding Claim 3:
As discussed above, Bagchi in view of Kishimoto teach [the] method of claim 2, and Bagchi further discloses:
determining at least one of 1) the difference between the query result value and the previous query result value 2) a lack of existence of the knowledge base entity at a time of processing the previous query, and 3) addition of the knowledge base entity at a time after processing the previous query
Bagchi, ¶ [0064], “Further, the embodiments herein automatically and continuously update the diagnosis answers, the corresponding confidence values, and the numerical value of each evidence dimension based on revisions to the problem case information to produce revised queries, answers, corresponding confidence values, etc. (using the question-answering module). This method can also automatically output the revised queries, answers, and/or corresponding confidence values when a difference threshold is exceeded. This ‘difference threshold’ can comprise a time period (e.g., hours, weeks, months, etc.), the amount one or more answers change (e.g., percentage change, polarity (yes/no) change, number of answers changing, etc.) and/or an amount of confidence value changes (percent confidence change, confidence polarity change, etc.). The difference threshold is being interpreted as the difference between the query result value and the previous query result value.”
Regarding Claim 5:
As discussed above, Bagchi in view of Kishimoto teach [the] method of claim 1, and Kishimoto further discloses:
determining the difference comprises determining that the first version of the knowledge base entity has changed relative to the second version of the knowledge base entity
Kishimoto, [0017], “In one aspect, one or more rules may be extracted from one or more segments of text data of a policy data source according to an active learning operation. The active learning may be applied to a previously extracted set of rules and revise, correct, update, and/or modify the scores of the rules in a knowledge base "KB" or knowledge domain. Active learning may be used to learn how to score the rules and assign and/or modify assigned scores/weights of the rules in the KB. Thus, the active learning may be used to build, maintain, update, and/or construct the KB. In this way, learning policy explanations may be employed using active learning.”
[0057], “For example, each rule has a score stored in the knowledge base and is adjusted based on the user feedback. The score of the explanation is calculated by using scores of the rules. More specifically, a theorem prover (e.g., automated theorem component 480 of Fig. 4) constructs a proof tree and the score of the proof tree is defined to be a sum of the scores of the rules appearing in the proof tree. The theorem prover attempts to find a proof tree that has the smallest score as compared to other scores. Then, an explanation is generated by a chain of rules in the proof trees (i.e., a portion of the proof tree), and the score of an explanation is the sum of the costs of the rules appearing in the chain. The theorem prover extracts several explanations (i.e., several chains of the rules) to present them to the user. The present invention regards the rules appearing in the explanation selected by the user as critical rules (e.g., which may have a defined level of priority), and the scores of these rules are reduced. It should be noted that one or more costs and/or the scores of rules in the explanations that are not selected may be increased.”
The first knowledge base is being interpreted as the knowledge before alterations stated in paragraph 17. The second knowledge base is being interpreted as the second knowledge base and second proof tree being created in paragraph 57. The scores and rank are being interpreted as determining the difference between knowledge bases.
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Bagchi with Kishimoto to use proof trees to find differences between knowledgebase versions in order to generate more meaningful explanations of the reasoning process. “As such, the present invention provides for learning policy explanations in a computing environment by a processor are provided. In one embodiment, the present invention provides one or more explanations justifying validity or invalidity of a claim based on one or more rules extracted from one or more segments of text data of a policy data source using a machine learning operation. Thus, the present invention may generate meaningful explanations even if the policy claim violates two or more rules. The explanations may be shorter explanations and contain only the most relevant information to a user” (Kishimoto, [0015]).
Regarding Claim 6:
As discussed above, Bagchi in view of Kishimoto teach [the] method of claim 1, and Kishimoto further discloses:
wherein determining the difference comprises traversing the first proof tree and the second proof tree to identify that at least one node or edge of the first proof tree is different than a corresponding node or edge of the second proof tree
Kishimoto, [0058], “Each rule is associated with a score representing a probability (e.g., likelihood) of including that rule in an explanation. The scoring function associated with an explanation (proof tree) that can be defined by the user. The scoring function may optimize one or more criteria (e.g., number of rules included, maximize sum of scores of included rules, etc.). A new set of rules with updated scores may be generated. Each rule may have a relevant natural text snippet (e.g., a few words, phrase, or defined number of words) associated with the rule (e.g., the text fragment from which the rule was originally extracted). A user and/or artificial intelligence ("AI") operation may be used to revise the rule score and/or explanations.”
[0056], “A claim may be marked as valid and/or invalid with respect to the policy rules. A set of explanations may be generated in the form of proof trees using an automated theorem prover. Each of the explanations may be ranked according to their relevance (as defined by the user or to optimize a set of criteria - e.g., size of the explanations in terms of rules included). A user may select a subset of these explanations. The scores of the rules in knowledge base may be adjusted based on the rules included in the explanations selected and not selected by the user.”
The scores are based on the rules likely hood of existing in the proof tree and explanation. The ranking is based on the scores and is interpreted as pointing out at least one different node (rule) in the tree.
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Bagchi with Kishimoto to use proof trees and identify at least 1 node or edge difference in order to generate more meaningful explanations of the reasoning process. “As such, the present invention provides for learning policy explanations in a computing environment by a processor are provided. In one embodiment, the present invention provides one or more explanations justifying validity or invalidity of a claim based on one or more rules extracted from one or more segments of text data of a policy data source using a machine learning operation. Thus, the present invention may generate meaningful explanations even if the policy claim violates two or more rules. The explanations may be shorter explanations and contain only the most relevant information to a user” (Kishimoto, [0015]).
Regarding Claim 7:
As discussed above, Bagchi in view of Kishimoto teach [the] method of claim 1, and Bagchi further discloses:
wherein the provenance comprises first provenance information
Bagchi, [0039], “Therefore, one aspect of embodiments herein is a repository of all relevant analysis and decisions made to date. This repository 106 contains a representation of the reasoning and decision process not only as an efficiency mechanism, but allows the system to re-evaluate assumptions and decisions in light of new evidence relevant to the case. This allows users to interact with this representation, accepting, rejecting, or modifying it to as they think necessary to explore alternative solutions based on the users' insights into the validity or importance of the evidence or reasoning chain. This repository 106 is not only useful in the current evolving decision making interaction, but can be used to track the provenance of decisions that were made in the past and allow notification of actions to take based on newly arriving information that comes possibly years after decisions were made. For example, if a new study reports a contraindication for a drug in a given situation, the system could use this repository 106 of prior analysis to reevaluate its conclusions and provide relevant notification of alternative therapies to a patient that has been on this drug for years.”
The first provenance information is being interpreted as repository before the re-evaluation of current up to date facts on how to treat a disease with a drug. Bagchi gives an example of a decision that was made previous to new knowledge on how to treat a disease with a drug and the relevant provenance of decisions made that were made previously.
Kishimoto further discloses:
wherein constructing the first proof tree includes; applying the first version of the knowledge base entity to a first version of different knowledge base entity determined using second provenance information to obtain a new knowledge base entity representative of an implicit fact, the first version of the different knowledge base entity occurring at a concurrent time as the first version of the knowledge base entity
Kishimoto, [0085], “In one aspect, one or more explanations may be generated by leveraging one or more parts of the proof tree. For example, one or more candidate explanations may be generated as follows. Explanation 1: "Bob's claim is invalid, because it is an over limit claim, and he is an adult, and does not request participating insurance ("PAR"). Explanation 2-"Bob's claim is invalid, because the claim is an over limit claim and he is not a child." The user may select the most useful explanation of all generated. The costs of the rules used for an explanation selected by the user may be reduced, and the costs (e.g., scores) of the rules used for an explanation that are not selected by the user may be increased.”
In this example, Kishimoto shows an example proof tree and shows how the implicit fact of Bob being an adult can be inferred by him being labeled as not a child which is equivalent to a rule/fact stating that Bob is an adult. This example can be used for the second tree as well as Kishimoto in previous paras. 16 and 57 states multiple proof trees can be generated from the rules to generate explanations.
adding a new node to the first proof tree that is representative of the new knowledge base entity
Kishimoto, [0074], “A database ("DB" or knowledge domain) of rules may be enriched, enhanced, updated, replaced, and/or added to using the extracted rules, as in block 508. That is, the extraction of one or more rules, concepts and topics may include, but is not limited to, performing knowledge extraction from natural language text documents including reading input text; transforming the input text into a machine understandable knowledge representation so as to provide knowledge libraries (e.g., within the database/knowledge domain) from said documents; and using semantic based means for extracting concepts and their interrelations from said input text. Knowledge structures of the database/knowledge domain may be used consisting of rules, or other concepts and topics, such as rule-like obligations and violations, and the interrelations of the rule-like obligations and violations.“
Knowledge domain is being interpreted as being synonymous with the knowledge domain that was stated in para. 16. Being that it is interpreted this way the addition of facts/rules to the knowledge domain stated above would then relate to the knowledge domain which is used to create the proof tree which would be adding a node to the proof tree. Can be applied to both proof trees.
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Bagchi with Kishimoto to construct proof trees by applying said knowledge base entities with said provenance information to create new knowledge base entities and adding them to the tree as a new node in order to generate more meaningful explanations of the reasoning process. “As such, the present invention provides for learning policy explanations in a computing environment by a processor are provided. In one embodiment, the present invention provides one or more explanations justifying validity or invalidity of a claim based on one or more rules extracted from one or more segments of text data of a policy data source using a machine learning operation. Thus, the present invention may generate meaningful explanations even if the policy claim violates two or more rules. The explanations may be shorter explanations and contain only the most relevant information to a user” (Kishimoto, [0015]).
Regarding Claim 8:
As discussed above, Bagchi in view of Kishimoto teach [the] method of claim 1, and Kishimoto further discloses:
deriving, from the first version of the knowledge base entity, a first version of an implicit fact
Kishimoto, [0085], “In one aspect, one or more explanations may be generated by leveraging one or more parts of the proof tree. For example, one or more candidate explanations may be generated as follows. Explanation 1: "Bob's claim is invalid, because it is an over limit claim, and he is an adult, and does not request participating insurance ("PAR"). Explanation 2- "Bob's claim is invalid, because the claim is an over limit claim and he is not a child." The user may select the most useful explanation of all generated. The costs of the rules used for an explanation selected by the user may be reduced, and the costs (e.g., scores) of the rules used for an explanation that are not selected by the user may be increased.”
In this example Kishimoto shows an example proof tree and shows how the implicit fact of Bob being an adult can be inferred by him being labeled as not a child which is equivalent to a rule/fact stating that Bob is an adult. This example can be used for the second tree as well as Kishimoto in previous paras. 16 and 57 states multiple proof trees can be generated from the rules to generate explanations.
adding the first version of the implicit fact to the first proof tree as a first additional node
Kishimoto, [0074], “A database ("DB" or knowledge domain) of rules may be enriched, enhanced, updated, replaced, and/or added to using the extracted rules, as in block 508. That is, the extraction of one or more rules, concepts and topics may include, but is not limited to, performing knowledge extraction from natural language text documents including reading input text; transforming the input text into a machine understandable knowledge representation so as to provide knowledge libraries (e.g., within the database/knowledge domain) from said documents; and using semantic based means for extracting concepts and their interrelations from said input text. Knowledge structures of the database/knowledge domain may be used consisting of rules, or other concepts and topics, such as rule-like obligations and violations, and the interrelations of the rule-like obligations and violations.”
Knowledge domain is being interpreted as being synonymous with the knowledge domain that was stated in para. 16. Being that it is interpreted this way the addition of facts/rules to the knowledge domain stated above would then relate to the knowledge domain which is used to create the proof tree which would be adding a node to the proof tree. Can be applied to both proof trees.
deriving, from the second version of the knowledge base entity, a second version of the implicit fact
Kishimoto, [0085], “In one aspect, one or more explanations may be generated by leveraging one or more parts of the proof tree. For example, one or more candidate explanations may be generated as follows. Explanation 1: "Bob's claim is invalid, because it is an over limit claim, and he is an adult, and does not request participating insurance ("PAR"). Explanation 2- "Bob's claim is invalid, because the claim is an over limit claim and he is not a child." The user may select the most useful explanation of all generated. The costs of the rules used for an explanation selected by the user may be reduced, and the costs (e.g., scores) of the rules used for an explanation that are not selected by the user may be increased.”
In this example Kishimoto shows an example proof tree and shows how the implicit fact of Bob being an adult can be inferred by him being labeled as not a child which is equivalent to a rule/fact stating that Bob is an adult. This example can be used for the second tree as well as Kishimoto in previous paras. 16 and 57 states multiple proof trees can be generated from the rules to generate explanations.
adding the second version of the implicit fact to the second proof tree as a second additional node
Kishimoto [0074], “A database ("DB" or knowledge domain) of rules may be enriched, enhanced, updated, replaced, and/or added to using the extracted rules, as in block 508. That is, the extraction of one or more rules, concepts and topics may include, but is not limited to, performing knowledge extraction from natural language text documents including reading input text; transforming the input text into a machine understandable knowledge representation so as to provide knowledge libraries (e.g., within the database/knowledge domain) from said documents; and using semantic based means for extracting concepts and their interrelations from said input text. Knowledge structures of the database/knowledge domain may be used consisting of rules, or other concepts and topics, such as rule-like obligations and violations, and the interrelations of the rule-like obligations and violations.”
Knowledge domain is being interpreted as being synonymous with the knowledge domain that was stated in para. 16. Being that it is interpreted this way the addition of facts/rules to the knowledge domain stated above would then relate to the knowledge domain which is used to create the proof tree which would be adding a node to the proof tree. Can be applied to both proof trees.
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Bagchi with Kishimoto to construct proof trees to differentiate in order to generate more meaningful explanations of the reasoning process. “As such, the present invention provides for learning policy explanations in a computing environment by a processor are provided. In one embodiment, the present invention provides one or more explanations justifying validity or invalidity of a claim based on one or more rules extracted from one or more segments of text data of a policy data source using a machine learning operation. Thus, the present invention may generate meaningful explanations even if the policy claim violates two or more rules. The explanations may be shorter explanations and contain only the most relevant information to a user” (Kishimoto, [0015]).
Regarding Claim 9:
As discussed above, Bagchi in view of Kishimoto teach [the] method of claim 1, and Bagchi further discloses:
wherein the provenance information includes data identifying one or more of a creator of the knowledge base entity, a time at which the knowledge base entity was created or changed, a date at which the knowledge base entity was created or changed, or a reason indicating why the knowledge base entity was created or changed
Bagchi, [0052], “The input information can come in over time. The input may be triggered by a change in the problem condition, the result of additional tests or procedures performed, or a response to a query for more information generated by the decision-maker 108. In addition, the information within the domain knowledge content 102 can change according to evolving demographic changes, evolving medical discoveries, evolving medication conflicts, evolving side effect information, etc. This time-stamped information is recorded in the repository 106 in the system.”
Regarding Claim 10:
As discussed above, Bagchi in view of Kishimoto teach [the] method of claim 1, and Bagchi further discloses:
wherein the explanation further explains a history of change that resulted in the difference between the query result value and the previous query result value
Bagchi, [0039], “In general, the embodiments herein are intended to allow interaction to occur over a period of time and to support an iterative refinement approach. Therefore, one aspect of embodiments herein is a repository of all relevant analysis and decisions made to date. This repository 106 contains a representation of the reasoning and decision process not only as an efficiency mechanism, but allows the system to re-evaluate assumptions and decisions in light of new evidence relevant to the case. This allows users to interact with this representation, accepting, rejecting, or modifying it to as they think necessary to explore alternative solutions based on the users' insights into the validity or importance of the evidence or reasoning chain. This repository 106 is not only useful in the current evolving decision making interaction, but can be used to track the provenance of decisions that were made in the past and allow notification of actions to take based on newly arriving information that comes possibly years after decisions were made. For example, if a new study reports a contraindication for a drug in a given situation, the system could use this repository 106 of prior analysis to reevaluate its conclusions and provide relevant notification of alternative therapies to a patient that has been on this drug for years.”
Regarding Claim 11:
As discussed above, Bagchi in view of Kishimoto teach [the] method of claim 1, and Bagchi further discloses:
wherein obtaining a knowledge base entity comprises obtaining a first knowledge base entities that is representative of a rule and a second knowledge base entity that is representative of an explicit fact, and wherein determining the query result value includes: deriving, based on application of the first knowledge base entity to the second knowledge base entity, an implicit fact; and determining, based on the implicit fact, the query result value
Bagchi, [0039], “In general, the embodiments herein are intended to allow interaction to occur over a period of time and to support an iterative refinement approach. Therefore, one aspect of embodiments herein is a repository of all relevant analysis and decisions made to date. This repository 106 contains a representation of the reasoning and decision process not only as an efficiency mechanism, but allows the system to re-evaluate assumptions and decisions in light of new evidence relevant to the case. This allows users to interact with this representation, accepting, rejecting, or modifying it to as they think necessary to explore alternative solutions based on the users' insights into the validity or importance of the evidence or reasoning chain. This repository 106 is not only useful in the current evolving decision making interaction, but can be used to track the provenance of decisions that were made in the past and allow notification of actions to take based on newly arriving information that comes possibly years after decisions were made. For example, if a new study reports a contraindication for a drug in a given situation, the system could use this repository 106 of prior analysis to reevaluate its conclusions and provide relevant notification of alternative therapies to a patient that has been on this drug for years.”
[0040], “Lastly, in general, all embodiments herein are meant to inform the decision making process and allow the decision-maker 108 to view alternatives and associated confidences in proposed answers, explore the evidence and reasoning process the system used to come to its conclusions, and to get feedback on what additional information, if provided, would result in changing the answers.”
The first version of the knowledge base is being interpreted as repository before the re-evaluation of current up to date facts on how to treat a disease with a drug. Bagchi gives an example of a decision that was made previous to new knowledge on how to treat a disease with a drug. Bagchi then states a new study, that provides information that’s contradictory to how the drug is treated that is then added to the repository. Bagchi then states how analysis of this study could change treatment. The repository after the addition if the study is interpreted as the second version of the knowledge base. The implicit fact that derived from both knowledge bases is being interpreted as the contradictory information that cause the answer which in this case affects how the patient is treated which is the result.
Regarding Claim 12:
Claim 12 is a device claim corresponding to method claim 1 and is rejected for at least the same reasons as given in the rejection of claim 1, with the exception of the following limitations:
Bagchi discloses:
one or more memories configured to store the automated reasoning engine
Bagchi, [0079], “Any combination of one or more computer readable medium(s) may be utilized. The computer readable medium may be a computer readable signal medium or a computer readable storage medium. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing.”
a computation engine executing one or more processors, the computation engine configured to execute the automated reasoning engine to
Bagchi, [0049], “This ‘computer processor’ 104/110 automatically analyzes the problem case information in order to identify semantic concepts, relations, and data and automatically generates at least one diagnosis query from the semantic concepts, relations and data. The computer processor 104/110 also automatically generates a plurality of diagnosis answers for each diagnosis query, and calculates confidence values for each of the answers based on numerical values for several dimensions of evidence that are relevant to the problem-solving domain.”
Regarding Claims 13-14 and 16-19:
Claims 13-14 and 16-19 are device claims corresponding to method claims 2-3, 5-7, and 11 and are rejected for the same reasons as given in the rejection of claim 2-3, 5-7, and 11. In particular, 13:2, 14:3, 16:5, 17:6, 18:7, 19:11.
Regarding Claim 20:
Claim 20 is a non-transitory computer-readable storage medium claim corresponding to method claim 1 and is rejected for at least the same reasons as given in the rejection of claim 1, with the exception of the following limitations:
Bagchi discloses:
A non-transitory computer-readable storage medium having stored thereon instructions that, when executed, cause one or more processors to execute a computation engine configured to:
Bagchi, [0079], “Any combination of one or more computer readable medium(s) may be utilized. The computer readable medium may be a computer readable signal medium or a computer readable storage medium.”
Response to Arguments
Applicant's arguments filed March 25, 2026 (“Remarks”) have been fully considered, but they are not persuasive.
35 U.S.C. § 101:
Remarks, p. 10. Applicant argues the claims appear to be considered piecemeal. Examiner respectfully disagrees. Although the claim limitations are broken up for further rejection analysis, the independent claims are directed to an abstract idea even when considered as a whole. In particular, the claim reflects explaining a query result value determined by an automated reasoning engine which directed to a judicial exception. Additionally, “by the automated reasoning engine” throughout the entirety of the claim is recited at a generic, high level, and merely uses the computer as a tool to perform the recited abstract ideas.
Remarks, pp. 10-14. Applicant argues the independent claims recite an improvement to the existing automated reasoning technological process. Applicant cites paras. [0059]-[0060] of the specification on p. 11 as well as paras. [0022]-[0025] on pp. 13-14 to explain the improvement to the technological process. Examiner respectfully disagrees. Although the specification describes an improvement, these improvements are not reflected within the claim. For example, the claim does not recite nor reflect the use of natural language to aid users unaccustomed to computer programming languages to more naturally understand the explanation. As another example, the claim does not recite nor reflect users validating explanations. As such, the claims do not provide a clear improvement to the existing technological process.
35 U.S.C. § 103:
Remarks, pp. 14-17. Applicant argues with respect to amended claim 1, and corresponding claims 12 and 20, that Kishimoto does not disclose or suggest the subject matter of amended claim 1. In particular, Applicant argues there is no comparison of proof trees constructed at different points in time to provide an explanation for the providence of the query result. Examiner respectfully disagrees. As discussed above under section 103, claim 1 is rejected under the combination of Bagchi in view of Kishimoto. In particular, Bagchi teaches provenance and an iterative refinement approach that occurs over a period of time in para. 39, and Kishimoto teaches scoring and ranking explanation proof trees in para. 16. In other words, it is the combination of Bagchi in view of Kishimoto that teaches the amended claim 1 by ranking/comparing explanation proof trees as taught by Kishimoto, which occur over a period of time as taught by Bagchi.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Ma et al. (US 20050256865) relates to unstructured or structured queries for databases.
Greystoke et al. (US 20150012467) relates to decision systems.
Kundu et al. (US 10515170) relates to proof trees.
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.
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/S.H.P./Examiner, Art Unit 2125
/KAMRAN AFSHAR/Supervisory Patent Examiner, Art Unit 2125
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