DETAILED ACTION
This is in response to Request for Continued Examination (RCE) filed on 08/07/2025. Claims 1, 3-16, and 21 are pending in this Action. Claims 2 and 17-20 have been previously cancelled.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 08/07/2025 has been entered.
Remark
In the response filed 08/07/2025, claim 1 has been amended, no claim has been cancelled, and no new claim has been added.
The Applicant’s amendments and arguments regarding 35 USC 112 (a) and (b) rejections are accepted by the Examiner. Therefore, prior 35 USC 112(a) and (b) rejections are withdrawn.
Response to Arguments
Applicant's arguments filed 08/07/2025 have been fully considered but they are not persuasive.
With respect to 35 USC 101 rejections:
The applicant argues that:
Amended claim 1 describes specific computational steps that cannot practically or meaningfully be performed in a human mind. In particular, amended claim 1 states that calculating similarity scores and/or clustering citations is performed faster than applying filtering rules, that filtering rules are prioritized, and that high-priority rules are applied first, and low- priority rules are evaluated only if the preceding rules are inconclusive.
This structured, performance-driven sequence, which explicitly incorporates relative computational speed and rule priority, is adapted for a machine execution rather than a mental reasoning.
In fact, humans cannot deliberately reason in a way that mirrors this computational model, i.e. executing a faster mathematical process first and then selectively invoking a slower step, involving a rule-based logic based on conclusiveness and priority.
The human mind cannot deliberately structure its reasoning according to computational and timing efficiency, i.e. performing faster similarity scoring and clustering operations before slower sequential rule-based filtering based on assigned rule priorities. Human reasoning does not operate by benchmarking execution time at each step. Unlike a machine, a human cannot meaningfully enforce such layered control flow based on performance characteristics or conditional logic with deterministic consistency. Therefore, the claimed method is inherently non-mental and necessarily computer-implemented.
Therefore, amended claim 1 encompasses more than an abstract idea; it incorporates a non-conventional and non-generic arrangement of processing steps that address a specific technical issue in automated citation deduplication. Reliance on rule priority, conditional rule application and comparative performance constraints constitute an inventive concept that improves the system's functioning and cannot be divorced from its computer-implemented context.
The Examiner respectfully disagrees.
The Examiner contends that, given the claim its broadest and reasonable interpretation (BRI), the amended limitations of “wherein the step(s) of calculating a similarity score and/or clustering the citations is (are) faster than the step of applying filtering rules and Wherein the filtering rules have priorities and are sequentially applied” and “wherein high-priority filtering rules(s) is (are) applied first, and low-priority filtering rules(s) is (are) applied if previously applied rule(s) is no conclusive” are not required to be implemented by a computer and are not adapted for a machine execution. Under BRI, a human brain is capable of implementing a simple function faster than a complex function. For example, a human can process a mathematical calculation for determining a similarity score using a small equation much faster than process large and complex filter rules.
The limitation of “wherein the step(s) of calculating a similarity score and/or clustering the citations is (are) faster than the step of applying filtering rules” constitutes a mathematical calculation and observation, evaluation and/or judgment concepts which could be practically performed in the human mind. A person is able to calculate a small function faster than applying lengthy and complex filter rules. It is within the brain capability to implement a simpler function faster than complex and large operations.
Furthermore, the limitation of “wherein the filtering rules have priorities and are sequentially applied, and wherein high-priority filtering rules(s) is (are) applied first, and low-priority filtering rules(s) is (are) applied if previously applied rule(s) is no conclusive” involves observation, evaluation, and/or judgement concepts that could be practically performed in the human mind. A person can mentally determine priorities for the filter rules and apply the filtering rules accordingly.
Moreover, the current specification in paragraphs 5-6 hints that the process of deduplication of data objects could be “carried out manually”, but it might be time-consuming and susceptible to errors. As such, the claimed steps of deduplication of citations does not require a computer and could be carried out manually and mentally.
Additionally, the Examiner respectfully disagrees that the current claimed invention is similar to McRO v. Bandai Namco Games Am. Inc.
The basis for the McRO court's decision was that the claims were directed to an improvement in computer-related technology (allowing computers to produce "accurate and realistic lip synchronization and facial expressions in animated characters" that previously could only be produced by human animators), and thus did not recite a concept similar to previously identified abstract ideas. The claimed invention would improve computer-related technology by allowing computer performance of a function not previously performable by a computer. The criteria for determining whether a concept of invention is directed to a particular solution to a problem rather than a general idea of regarding an outcome of the problem itself is that the concept must improve computer-related technology by allowing a computer performance of a function not previously performed by a computer.”
However, in this case, as stated above, the claimed invention does not require a computer to perform the claimed steps and the entire claimed invention could be implemented mentally. It is important to note that in order for a method claim to improve computer functionality, the broadest reasonable interpretation of the claim must be limited to computer implementation. The Examiner contends that the claimed invention could be purely performed mentally.
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That is, a claim whose entire scope can be performed mentally, cannot be said to improve computer technology. Synopsys, Inc. v. Mentor Graphics Corp., 839 F.3d 1138, 120 USPQ2d 1473 (Fed. Cir. 2016). Similarly, a claimed process covering embodiments that can be performed on a computer, as well as embodiments that can be practiced verbally or with a telephone, cannot improve computer technology. See RecogniCorp, LLC v. Nintendo Co., 855 F.3d 1322, 1328, 122 USPQ2d 1377, 1381 (Fed. Cir. 2017) (process for encoding/decoding facial data using image codes assigned to particular facial features held ineligible because the process did not require a computer). See MPEP 2106.05(a)
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Furthermore, claim 1 does not explicitly recite “a computer” to implement the current invention. Even the current invention used a computer to implement the steps of duplication of citations, it would not be sufficient to improve the manner in which a computer functions, because it would evoke a computer merely as a tool to perform existing process. That is, the current claimed invention does not require a computer and its entire scope can be performed mentally, therefore, it cannot be said to improve computer technology.
Viewed as a whole, these additional claim element(s) do not provide meaningful limitation(s) to transform the abstract idea into a patent eligible application of the abstract idea such that the claim(s) amounts to significantly more than the abstract idea itself.
Therefore, rejections of claims 1, 3-16, and 21 under 35 USC 101 as being directed to non-statutory subject matter of abstract idea are maintained.
With respect to 35 USC 103 rejections:
Applicant argues that neither Huang nor Heller discloses the amended limitation of “wherein the step(s) of calculating a similarity score and/or clustering the citations is (are) faster than the step of applying filtering rules” to claim 1. The Examiner respectfully disagrees.
Based on Huang teachings in pages 2-5, 7, and 8, the step of calculating similarity score involves a mathematical calculation according to weights assigned to attributes of a document (e.g., titles or authors) and comparing them with a threshold value. Therefore, the step of calculating a similarity function (which is a mathematical function) could be inherently faster than step of applying a plurality of filtering rules comprising multiple operations. It is a common knowledge in the art that execution of a mathematical function could be faster than execution of a plurality of filtering rules with multiple operations, and it involves only routine skill in the art. A person of ordinary skill in the art can make the step of calculating a similarity score faster by selecting a short calculation than step of applying filter rules. As such, in Huang the step(s) of calculating a similarity score and/or clustering the citations could be faster than the step of applying filtering rules.
Furthermore, Applicant's arguments with respect to newly amended claim 1 that neither Huang nor Heller discloses the amended limitation of “Wherein the filtering rules have priorities and are sequentially applied, and wherein high-priority filtering rules(s) is (are) applied first, and low-priority filtering rules(s) is (are) applied if previously applied rule(s) is no conclusive” have been considered but are moot in view of the new ground(s) of rejection over the new reference Jefferies et al., US 6,807,576. The new combination of Huang, Heller, and Jefferies discloses the limitation of amended claim 1. See below for details.
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, 3-16, and 21 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter of abstract ideas.
Step 1:
Claims 1, 3-16, and 21 are directed to a method which is one of the statutory categories of invention.
Step 2A:
Prong 1:
Claim 1 is directed to an abstract idea without significantly more.
Claim 1 recites the steps of:
normalizing data comprised in metadata fields of a same metadata type; [constitutes concept which could be practically performed in the human mind. A person can manually normalize (e.g., converting data types) data]
calculating a similarity score between each pair of citations using the normalized data; [constitutes a mathematical concept]
clustering the citations based on the calculated similarity score; [constitutes concept which could be practically performed in the human mind. A person can manually and mentally group data objects based on similarity scores]
applying a filtering rule on the normalized data of citations in clusters for identifying equivalent data objects and flag identified duplicates; [constitutes an evaluation concept which could be practically performed in the human mind. A person can mentally apply filtering rule to identify similar data objects]
Wherein the step(s) of calculating a similarity score and/or clustering the citations is (are) faster than the step of applying filtering rules, [constitutes a mathematical calculation and observation, evaluation and/or judgment concepts which could be practically performed in the human mind. A person is able to calculate a small function faster than applying lengthy and complex filter rules. It is within the brain capability to implement a simpler function faster than complex and lengthy operations]
Wherein the filtering rules have priorities and are sequentially applied, and wherein high-priority filtering rules(s) is (are) applied first, and low-priority filtering rules(s) is (are) applied if previously applied rule(s) is no conclusive. [it involves observation, evaluation, and/or judgement concepts that could be practically performed in the human mind. A person can mentally determine priorities for the filter rules and apply the filtering rules accordingly]
The above-mentioned steps are processes that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. That is, nothing in the claim element precludes the step from practically being performed in a human mind or with pen and paper. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the “Mental Processes” grouping of abstract ideas (concepts performed in the human mind including an observation, evaluation, judgment, and opinion).
Prong 2:
This judicial exception in claim 1 is not integrated into a practical application. Claim 1 recites the additional steps of “inputting an input dataset comprising the set of citations;”, “outputting a deduplicated dataset comprising only citations that were not identified as duplicates”, and “storing the deduplicated dataset” which could be considered as insignificant extra solution activities of inputting, outputting, and
storing data. See MPEP 2106.04(d) and 2106.05(g).
Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea. See MPEP 2106.04(d) and 2106.05(g).
Step 2B:
Claim 1 does not include additional elements that are sufficient to amount to significantly more than the judicial exception. Claim 1 recites the additional steps of “inputting an input dataset comprising the set of citations;”, “outputting a deduplicated dataset comprising only citations that were not identified as duplicates”, and “storing the deduplicated dataset” which could be considered as well-understood, conventional, and routine activities of inputting, outputting, and storing data. See MPEP 2106.04(d) and 2106.05(g). Therefore, the claims are not patent eligible.
Regarding dependent claims 3-11, 14,15, and 21,
The dependent claims further include data description and the additional steps for normalizing, filtering, calculating scores (mathematical concept), updating, clustering, and manual verification that could be performed mentally failing to integrate the judicial exception into a practical application or to amount significantly to more than abstract idea.
Regarding dependent claims 12,
the dependent claims additional generic computer functions for notifying a user by displaying data that are considered to be insignificant extra solution and/or well-understood routine computer routine failing to integrate the judicial exception into a practical application or to amount significantly to more than abstract idea.
Regarding dependent claim 13,
The dependent claims additional steps for generic computer function of inputting data that is considered to be insignificant extra solution and/or well-understood routine computer routine failing to integrate the judicial exception into a practical application or to amount significantly to more than abstract idea.
The dependent claims further recite the additional step for merging, normalizing,
calculating similarity score, clustering, filtering, deleting, and updating data that could be performed mentally failing to integrate the judicial exception into a practical application or to amount significantly to more than abstract idea.
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.
Claims 1, 3-5, 7-9, 11, and 13-20 are rejected under 35 U.S.C. 103 as being unpatentable over Huang et al., WO 2017/096777 (Huang, hereafter) in view of Heller et
al., US 12,067,366 (Heller, hereafter) and further in view of Jeffries et al., US 6,807,576 (Jeffries, hereafter).
Regarding claim 1,
Huang discloses a computer-implemented method for deduplication of bibliographic citations in a set of citations originating from different databases, wherein each data object is provided with metadata fields; the method includes the steps:
inputting an input dataset comprising the set of [data] (See Huang: at least the highlighted sections in pages 2 and 7, acquiring documents from at least one website source);
normalizing data comprised in metadata fields of a same metadata type (See Huang: at least the highlighted sections in pages 2 and 7, normalizing or standardizing attributes (i.e., metadata) of document including title, author, abstract, publication source, publication time, and the like);
calculating a similarity score between each pair of [data] (See Huang: at least the highlighted sections in pages 2-4 and 8, calculating a similarity score between documents using normalized attributes or metadata (e.g., title, author, publication data));
clustering the [data] (See Huang: at least the highlighted sections in pages 2, 4, 8, and 11, clustering similar documents to obtain second sets);
applying a filtering rule on the normalized data of [data] (See Huang: at least the highlighted sections in pages 2, 4-5, and 7, screening and filtering the qualified document collection (e.g., the Hamming distance compared to a preset threshold) to identify similar documents and identifying duplicate documents);
outputting a deduplicated dataset comprising only [data] (See Huang: at least the highlighted sections in pages 4, 6, and 7, identifying and displaying the same or duplicate documents);
wherein the step(s) of calculating a similarity score and/or clustering the citations is (are) faster than the step of applying filtering rules (Based on Huang above-mentioned teachings, the step of calculating similarity involves a mathematical calculation according to weights assigned to attributes of a document (e.g., titles or authors) and comparing them with a threshold value. Therefore, the step of calculating a similarity function (which is a mathematical function) could be inherently faster than step of applying a plurality of filtering rules comprising multiple operations. It is a common knowledge in the art that execution of a mathematical function could be faster than execution of a plurality of filtering rules with multiple operations, and it involves only routine skill in the art. A person of ordinary skill in the art can make the step of calculating a similarity score faster by selecting a short calculation than step of applying filter rules).
Although, Huang discloses identifying duplicate documents, Huang does not explicitly teach citations, deduplication of bibliographic citations, outputting a deduplicated dataset comprising only citations that are not identified as duplicates, and storing the deduplicated dataset.
On the other hand, Heller discloses deduplication of citation, outputting reduplicated citations (See Heller: at least Fig. 14, and 35:34 through 36:15). Furthermore, it is obvious to a person of ordinary skill in the art that a deduplicated citation could be stored in a database or a storage. Therefore, it would have been obvious to one of ordinary skill in the art before the time the invention was effectively filed to modify the teachings of Huang with Heller’s teaching in order to implement above function with reasonable expectation of success. The motivation for doing so would have been to help in determining significance of a citation and improve data storage by eliminating citation duplicate.
The combination of Huang and Heller discloses the limitations as stated above including applying filters to documents. However, it does not explicitly teach Wherein the filtering rules have priorities and are sequentially applied, and wherein high-priority filtering rules(s) is (are) applied first, and low-priority filtering rules(s) is (are) applied if previously applied rule(s) is no conclusive.
On the other hand, Jefferies discloses applying filter rules in order and applying a filter rule with higher priority and then applying a lower priority rule (See Jefferies: at least 2:9-12, 9:27-33, and 4:57 to 5:7). Therefore, it would have been obvious to one of ordinary skill in the art before the time the invention was effectively filed to modify the teachings of the combination of Huang and Heller with Jefferies’s teaching in order to implement above function with reasonable expectation of success. The motivation for doing so would have been to improve efficiency of the method by removing irrelevant results based on filter rule hierarchical priorities.
Regarding claim 3,
the combination of Huang, Heller, Jefferies discloses wherein the metadata fields include data of different data types (See Huang: at least the highlighted sections in pages 2 and 7).
Regarding claim 4,
the combination of Huang, Heller, Jefferies discloses wherein the step of normalizing metadata fields comprises the step of converting the data of different data types into a common data type (See Huang: at least the highlighted sections in pages 2, 7-8, 11 and 17 and Fig. 3, converting data form various format (e.g., publication or author data)).
Regarding claim 5,
the combination of Huang, Heller, Jefferies discloses wherein the step of normalizing metadata fields comprises the step of harmonizing strings comprised in metadata fields of the same metadata type by removing special characters (See Huang: at least the highlighted sections in pages 2, 7-8, 11 and 17 and Fig. 3, e.g., removing special characters such as punctuations).
Regarding claim 7,
the combination of Huang, Heller, Jefferies discloses wherein the step of normalizing data included in the metadata fields comprises the step of translating strings representing a word comprised in metadata fields of the same metadata type into a common language using natural language processing (See Huang: at
least the highlighted sections in pages 2, 7-8, 11 and 17 and Fig. 3).
Regarding claim 8,
the combination of Huang, Heller, Jefferies discloses wherein the step of calculating the similarity score includes the step of calculating the similarity score based on
data included in the metadata fields of the same metadata type (See Huang: at
least the highlighted sections in pages 2, 4, 8, and 11).
Regarding claim 9,
the combination of Huang, Heller, Jefferies discloses wherein the similarity score being calculated using a string metric algorithm (See Huang: at least the highlighted sections in pages 2, 4, 8, and 11, e.g., Hamming distance).
Regarding claim 11,
the combination of Huang, Heller, Jefferies discloses wherein the filtering rule verifies whether data in normalized data field of the same type of two citations in one cluster are equal or concurring and is applied separately to more than one metadata type (See Huang: at least the highlighted sections in pages 2, 4-5, and 7 and Heller: at least Fig. 14, and 35:34 through 36:15).
Regarding claim 13,
the combination of Huang, Heller, Jefferies discloses - inputting a second input dataset comprising a different second set of citations; - inputting the deduplicated dataset comprising the deduplicated set of citations; - merging the second input dataset and the deduplicated dataset for providing a merged input dataset;- repeating the steps of normalizing data, calculating the similarity score, clustering the citations, applying the filtering rule, and deleting identified equivalent citations in the merged input dataset; - updating the deduplicated dataset using the deduplicated merged input dataset (See Huang: at least the highlighted sections in pages 2, 4-5, and 7 and Heller: at least Fig. 14, and 35:34 through 36:15).
Regarding claim 14,
the combination of Huang, Heller, Jefferies discloses wherein updating includes the step of replacing citations comprised in the deduplicated dataset with citations comprised in the deduplicated merged input dataset (See Huang: at least the highlighted sections in pages 2, 4-5, and 7 and Heller: at least Fig. 14, and 35:34 through 36:15).
Regarding claim 15,
the combination of Huang, Heller, Jefferies discloses wherein updating includes the step of adding citations not yet comprised in the deduplicated dataset from the deduplicated merged input dataset into the deduplicated dataset (See Huang: at least the highlighted sections in pages 2, 4-5, and 7 and Heller: at least Fig. 14, and 35:34 through 36:15).
Regarding claim 16,
the combination of Huang, Heller, Jefferies discloses comprising a step of outputting a second duplicate dataset containing only identified equivalent citations from the merged input dataset (See Huang: at least the highlighted sections in pages 2, 4-5, and 7 and Heller: at least Fig. 14, and 35:34 through 36:15).
Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Huang et al., WO 2017/096777 (Huang, hereafter) in view of Heller et al., US 12,067,366 further in view of Jeffries et al., US 6,807,576 and further in view of Benke et al., US 2023/0090601 (Benke, hereafter).
The combination of Huang, Heller, Jefferies discloses normalizing attributes or metadata of the documents by removing characters, however, it does not explicitly teach wherein the removal of special characters comprises the step of removing an URL prefix.
On the other hand, Benke discloses normalizing a document by removing a URL (See Benke: at least para 87). Therefore, it would have been obvious to one of ordinary skill in the art before the time the invention was effectively filed to modify the teachings of the combination of Huang, Heller, Jefferies with Benke’s teaching in order to implement above function with reasonable expectation of success. The motivation for doing so would have been to improve identifying the duplicate documents by reducing the variations.
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Huang et al., WO 2017/096777 (Huang, hereafter) in view of Heller et al., US 12,067,366 further in view of Jeffries et al., US 6,807,576 and further in view of Ferreira et al., US 2024/0176795 (Ferreira, hereafter).
The combination of Huang, Heller, Jefferies discloses wherein the string metric algorithm is provided as an edit distance algorithm; however, it does not explicitly teach Levenshtein distance algorithm.
On the other hand, Ferreira discloses using Levenshtein distance algorithm to calculate similarity (See Ferreira: at least para 49). Therefore, it would have been obvious to one of ordinary skill in the art before the time the invention was effectively filed to modify the teachings of the combination of Huang, Heller, Jefferies with Ferreira’s teaching in order to implement above function with reasonable expectation of success. The motivation for doing so would have been to improve similarity calculation
taking advantage of Levenshtein distance algorithm benefits.
Claim 12 is rejected under 35 U.S.C. 103 as being unpatentable over Huang et al., WO 2017/096777 (Huang, hereafter) in view of Heller et al., US 12,067,366 and further in view of Jeffries et al., US 6,807,576 further in view of Cialdea, JR. et al., US 2015/0032730 (Cialdea, hereafter).
The combination of Huang, Heller, Jefferies discloses identifying equivalent data objects based on the entered filtering rule or on a pre-defined filtering rule, however, it does not explicitly teach comprising the step of notifying a user by displaying a dialog for entering the filtering rule.
On the other hand, Cialdea discloses asking a user to specify filtering rules to be applied to data on a GUI (See Cialdea: at least para 46 and Fig. 5). Therefore, it would have been obvious to one of ordinary skill in the art before the time the invention was effectively filed to modify the teachings of the combination of Huang, Heller, Jefferies with Cialdea’s teaching in order to implement above function with reasonable expectation of success. The motivation for doing so would have been to improve identifying duplicate documents by allowing a user to specify filtering conditions.
Claim 21 is rejected under 35 U.S.C. 103 as being unpatentable over Huang et al., WO 2017/096777 (Huang, hereafter) in view of Heller et al., US 12,067,366 further in view of Jeffries et al., US 6,807,576 and further in view of Sim, US 2022/0247706.
The combination of Huang, Heller, Jefferies discloses applying a filtering rule, however, it does not explicitly teach the application of the filtering rule includes a conditional step of manual verification.
On the other hand, Sim discloses a user verifies setting of filter conditions (See Sim: at least para 89 and Fig. 7). Therefore, it would have been obvious to one of ordinary skill in the art before the time the invention was effectively filed to modify the teachings of the combination of Huang, Heller, Jefferies with Sim’s teaching in order to implement above function with reasonable expectation of success. The motivation for doing so would have been to improve functionality of the method by allowing a user to verify the filtering rules.
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/Hares Jami/ Primary Examiner, Art Unit 2164
11/05/2025