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 . In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
DETAILED ACTION
The following FINAL Office Action is in response to communication filed on 2/26/2026.
Priority
The Examiner has noted the Applicant claiming Priority from PCT Application PCT/KR2022/004482 filed 03/30/2022.
Status of Claims
Claims 1-4, 8-9, 11 are currently pending.
Claims 1-2, 4, 8-9 are currently amended.
Claims 5-7, 10, 12-15 are cancelled by Applicant.
Claims 1-4, 8-9, 11 are currently under examination and have been rejected as follows.
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The previously pending rejections under 35 USC 112 are withdrawn in view of the amendments.
The previously pending rejections under 35 USC 101 will be maintained. The 101 rejection is updated in view of the amendments.
The previously pending rejections under 35 USC 103 will be maintained. The 101 rejection is updated in view of the amendments.
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Response to Arguments
Regarding Applicant’s remarks pertaining to 35 USC 101:
Step 2A Prong 1:
Applicant argues on page 9 of remarks 2/26/2026:
“The Examiner asserts that the claims are directed to analyzing legislation and displaying results, allegedly constituting a business method or abstract idea.
“However, amended Claim 1 expressly recites a structured and interdependent computational pipeline that goes far beyond abstract evaluation of legislation.”
Examiner respectfully disagrees. Despite being performed by computers, the claims as amended still recite, describe or set forth monitoring legislation, receiving bill content information, extracting information from the bill content, and generating analysis of governmental regulation influence on business and industry based on the bill, which, Examiner submits, fall within fundamental economic principles and commercial or legal interactions each as Certain Methods of Organizing Human Activity. Examples from the claims as amended include “monitoring a bill and a legal regulation”, “storing template information”, “receiving basic information including main contents and proposal reasons of the bill”, “storing the basic information”, “forming extracted information from the basic information”, and “generating regulation influence analysis data based on the extracted information and the template information”, “calculating a second relevance between the specific company and the target bill”; “calculating a risk of the specific company by the target bill”, “generating regulation influence analysis data about the second relevance and regulation influence analysis data about the risk”, and “wherein when the basic information of the second proposed bill includes basic information about a member-proposed bill, it is determined that the preset item further includes a political party and legislative tendency of a member who proposes the second proposed bill”, etc.
Step 2A Prong 2:
Applicant argues on page 10 of remarks 2/26/2026:
“These steps form a unified computational sequence in which legislative data, company data, similarity equations, risk algorithms, visual parameter mapping, and model parameter updates are technically interdependent.
“This is not a mere automation of human evaluation. Rather, it is a specifically configured data-processing architecture implementing company-level regulatory impact quantification and iterative refinement.
“Accordingly, these steps form the unified computational sequence contributes to
integration into a practical technological application.”
Examiner respectfully disagrees. Independent claim 1 as amended include, from previously presented and now cancelled dependent claims, the additional computer-based elements “company information providing server”, “application programming interface (API)”, “learning model”, and “artificial intelligence module”. The functions of these additional computer-based elements are not descriptive beyond collecting, storing, mining, analyzing, calculating and presenting data, etc. For example, the claim limitation “performing supervised learning through a learning model based on training data that is stored in association with basic information of a first proposed bill, basic information of a first past bill, first past regulation influence analysis data, and first inference data” provides little detail of how the supervised learning is accomplished, the type of machine learning model applied, or how the computer technology itself is improved. The same can be said for “forming feedback information based on a difference between the second inference data and a pre-stored ground truth set”. Similarly, considering the amended claim limitation “regulation influence analysis data further includes: calculating a second relevance between the specific company and the target bill based on a predetermined similarity calculation equation for the second relevance; calculating a risk of the specific company by the target bill based on a predetermined first algorithm”, the limitation as claimed also lacks specifics about how the performed calculations or predetermined algorithm improve computer technology.
Applicant argues on page 11 of remarks 2/26/2026:
“The visual data is not generic presentation of text. It is a transformation of algorithmically calculated risk values into visual parameters (e.g., saturation levels) that are directly derived from computed quantitative outputs.
“Thus, the display is inseparably tied to the underlying computational processing. The visualization represents a technical mapping of numerical modeling results into visual
parameters, forming part of the overall processing architecture.
“Accordingly, the claimed output stage contributes to integration into a practical
technological application.”
Examiner respectfully disagrees. Even if, in arguendo, the visual data is not a generic presentation of text, which Examiner does not necessarily assert, the claims as amended are still akin to requiring the use of software to tailor information and provide it to the user on a generic computer (MPEP 2106.05(f)(2)(v)), invoking computers or other machinery merely as a tool to perform an existing process (MPEP 2106.05(f)(2)).
Applicant argues on page 11 of remarks 2/26/2026:
“The focus of the claim is therefore a technically implemented company-specific regulatory impact quantification system. The claimed invention solves a technical problem
how to automatically quantify, visualize, and iteratively refine company-level regulatory impact-using a structured combination of similarity equations, risk algorithms, associated
training data, and feedback-based model updating.
“This constitutes a specific practical application of computational techniques. By structuring legislative data, company data, similarity modeling, and feedback-driven learning within a unified computational architecture, the claimed invention improves the technical operation of computer-based regulatory impact assessment systems.”
Examiner respectfully disagrees. As submitted above, the functions of the additional computer-based elements are not descriptive beyond collecting, storing, mining, analyzing, calculating and presenting data, etc. The most detailed specifics regarding calculations performed appear to be at Applicant specification ¶ [100], [111], [152]: “… similarity calculation equation may include, for example, at least one of a mean square difference similarity, Cosine similarity, and a Pearson similarity equation.” The most detailed specifics regarding algorithms appear to be at Applicant specification ¶ [113]: “A predetermined first algorithm may be an algorithm that outputs the risk as ‘low’ when the second relevance is 0% to 30% in a case where the regulation is weakened and the regulation is strengthened, the risk as ‘medium’ when the second relevance is 30% to 60% in a case where the regulation is strengthened, and the risk as ‘high’ when the second relevance is 60% or greater in a case where the regulation is strengthened.” Learning model types are listed broadly at ¶ [130]. The claims and specification lack the specifics about how the performed calculations, predetermined algorithms, or learning models improve computer technology itself.
Applicant argues on page 12 of remarks 2/26/2026:
“This is a specific feedback-driven refinement framework in which legislative information, regulatory analysis outputs, and inference data are structurally associated and iteratively compared against predefined ground truth references. The ordered combination of structured association data and feedback-based parameter updating reflects a concrete technological mechanism for improving regulatory impact quantification accuracy.”
Examiner respectfully disagrees. The “learning model” and “artificial intelligence module” executed with training data, generating feedback, and updating parameters described as a technical mechanism in the claims and specification merely requires execution of an algorithm that can be performed by a generic computer component and provides insufficient detail regarding the operation of the algorithm. Such a generic recitation of “learning model” and “artificial intelligence module” alone are insufficient to show a practical application of the recited abstract idea.
Step 2B:
Applicant argues on page 13 of remarks 2/26/2026:
“These elements are not well-understood, routine, or conventional computer activities. Instead, they define a specifically configured regulatory impact quantification architecture tailored to company-level legislative risk modeling and iterative refinement. “Moreover, the claimed arrangement is not a mere aggregation of known elements. The similarity calculation, risk algorithm, visual parameter mapping, structured training data association, and feedback-based parameter updating operate together as an interdependent technical pipeline. Through this ordered combination, the claimed invention achieves concrete technological improvements, including automated company-specific regulatory impact quantification, structured visual representation of quantified risk, and iterative enhancement of predictive accuracy via ground-truth-based learning.” Examiner respectfully disagrees. Applicant specification lists well-known learning algorithms at ¶ [130], well-known hardware at ¶ [177], and well-known data extraction techniques at ¶ [146], [153]-[154]. Assuming, in arguendo, the claims and specification present a unique ordered combination of well-known technological elements in the field, the improvement to the actual technology itself is not sufficiently clear other than an entrepreneurial solution to monitoring bills and legal regulations and providing influence analysis data to specific enterprises. When evaluated per MPEP 2106.05(f)(2), the claims as amended represent mere invocation of computers to perform existing processes.
Accordingly, the previously pending rejections under 35 USC 101 will be maintained. The 101 rejection is updated in view of the amendments.
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Regarding Applicant’s remarks pertaining to 35 USC 103:
Applicant argues on page 16 of remarks 2/26/2026:
“Lee is directed to monitoring and providing policy or regulatory information. Its objective is to collect, organize, and deliver legislative or regulatory content….
“Lee does not recognize the problem of computing company-level regulatory risk using a predefined similarity equation and a dedicated risk algorithm. Nor does Lee provide any motivation to introduce company-specific quantitative modeling, percentage conversion of relevance, visual saturation mapping of risk, or ground-truth-based parameter updating.”
Examiner respectfully disagrees. Lee goes beyond delivering legislative and regulatory content, and implements a law enforcement guide algorithm applied to specific companies and specific regulation (see Lee ¶ [0023], [0050]). Within the broadest reasonable interpretation of the claims as amended, Lee discloses company-specific quantitative modeling at ¶ [0097], relevance calculation at ¶ [0075], and base data updating at ¶ [0018], [0019], [0050], [0119]. Visual saturation mapping of risk is disclosed by Eidelman at Figs. 25, 58C, 58D, 58E, 58H.
Applicant argues on page 16 of remarks 2/26/2026:
“Eidelman is primarily directed to policy-politician relationship analysis and prediction of political alignment within network structures. While Eidelman may involve text mining, NLP, and machine learning techniques, its technical objective concerns political network analysis and policy alignment prediction.
“The technical problem solved by the present invention-quantifying company-specific regulatory impact of a bill-is fundamentally different from analyzing political relationships.”
Examiner respectfully disagrees. Eidelman’s solution is also directed “to effectively understand and act on policy” (¶ [0003]). Analysis of the policy relationships leads to analysis of impact ratings of policies to specific organizations (see Eidleman ¶ [0002], [0377], [0421], [0423]). Lee and Eidelman both relate to the instant application as analogous art of industrial monitoring of governmental legal regulations. That said, Eidelman is not the primary teaching reference, but applied to cure the specific deficiencies of Lee, including calculating a relevance for a company through text mining or the use an API, generating influence analysis data about risk, converting data into visual data with saturation information on a user interface, and changing learning model parameters with feedback information.
Applicant argues on page 17 of remarks 2/26/2026:
“Accordingly, the claimed subject matter defines an interdependent computational
architecture in which:
(i) company-specific data integration and similarity-based quantification (Feature 1),
(ii) algorithmic risk modeling (Feature 2),
(iii) structured visual parameter mapping including saturation (Feature 3),
(iv) feedback-driven model refinement (Feature 4), and
(v) conditional expansion of preset analytical items based on bill type (Feature 5),
operate together within a single regulatory impact quantification framework.
“Neither Lee nor Eidelman, alone or in combination, discloses or suggests such a structurally adaptive and functionally cooperative configuration of Features 1-5.”
Examiner respectfully disagrees. Examiner points to disclosure of the features by Lee and Eidelman: Feature 1 at Lee ¶ [0075]; Feature 2 at Lee ¶ [0023]-[0024]; Feature 3 at Eidelman Figs. 25, 58C, 58D, 58E, 58H; Feature 4 at Lee ¶ [0050]; and Feature 5 at Eidelman Fig. 54, ¶ [0182], [0398]. Citations and further details are included in the 103 rejection section below.
Applicant argues on page 18 of remarks 2/26/2026:
“The intended user of the present invention is a company or a regulatory compliance professional within a company, whose primary concern is not political alignment analysis, but rather quantitative assessment and decision support regarding company-specific regulatory risk.
“Accordingly, a person of ordinary skill in the art would not have been motivated to
combine Lee's policy monitoring system with Eidelman's political network analysis framework in order to arrive at a company-specific regulatory impact quantification system as claimed.”
Examiner respectfully disagrees. Determination of political alignment of a politician proposing a bill by party and legislative tendency is disclosed in Applicant specification, among elsewhere, at ¶ [0170]-[0173].
Applicant argues on page 18 of remarks 2/26/2026:
“Lee does not recognize the problem of computing company-specific relevance and regulatory risk using a predetermined similarity calculation equation and a predefined risk algorithm, nor does it contemplate feedback-driven parameter updating.”
Examiner respectfully disagrees. As addressed above regarding Features 1, 2, and 4 described by Applicant on remarks page 17, see disclosures by Lee at ¶ [0075], [0023]-[0024], and ¶ [0050].
Applicant argues on page 19 of remarks 2/26/2026:
“…Eidelman does not teach or suggest applying its techniques to quantify bill-company regulatory impact using similarity equations, risk algorithms, visual saturation mapping, and ground-truth-based model updating.
“Accordingly, Eidelman does not provide a reasoned motivation to modify Lee's system to arrive at the claimed integrated regulatory impact quantification architecture.”
Examiner respectfully disagrees. Eidelman’s solution is also directed “to effectively understand and act on policy” (¶ [0003]). Analysis of the policy relationships leads to analysis of impact ratings of policies to specific organizations (see Eidleman ¶ [0002], [0377], [0421], [0423]). Lee and Eidelman both relate to the instant application as analogous art of industrial monitoring of governmental legal regulations. That said, Eidelman is not the primary teaching reference, but applied to cure the specific deficiencies of Lee, including calculating a relevance for a company through text mining or the use an API, generating influence analysis data about risk, converting data into visual data with saturation information on a user interface, and changing learning model parameters with feedback information.
Citations and additional details are included in the 103 rejection section below. Accordingly, the previously pending rejections under 35 USC 103 will be maintained. The 101 rejection is updated in view of the amendments.
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Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-4, 8-9, 11 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claims 1-4, 8-9, 11 are directed to a method or process which is a statutory category.
Step 2A Prong One: The claims recite, describe, or set forth a judicial exception of an abstract idea (see MPEP 2106.04(a)). Specifically, the claims recite, describe or set forth mitigating risk, agreements in the form of contracts, and legal obligations, including: “monitoring a bill and a legal regulation”, “storing template information”, “receiving basic information including main contents and proposal reasons of the target bill”, “storing the basic information”, “forming extracted information from the basic information”, “generating regulation influence analysis data based on the extracted information and the template information”, “calculating a second relevance between the specific company and the target bill”; “calculating a risk of the specific company by the target bill”, “generating regulation influence analysis data about the second relevance and regulation influence analysis data about the risk”, and “wherein when the basic information of the second proposed bill includes basic information about a member-proposed bill, it is determined that the preset item further includes a political party and legislative tendency of a member who proposes the second proposed bill”, etc. Monitoring legislation, receiving bill content information, extracting information from the bill content, and generating analysis of governmental regulation influence on business and industry based on the bill fall within mitigating risk as it pertains to fundamental economic principles, and agreements in the form of contracts and legal obligations as they pertain to commercial or legal interactions, each under the larger abstract grouping of Certain Methods of Organizing Human Activity (MPEP 2106.04(a)(2) II). Accordingly, the claims recite an abstract idea.
Step 2A Prong Two: Independent claim 1 recites the following additional computer-based elements: “computing system”, “memory”, “processor”, “user terminal”, “analysis server”, “company information providing server”, “application programming interface (API)”, “learning model”, and “artificial intelligence module”. The functions of these additional computer-based elements include examples such as monitoring a bill and legal regulation, storing template information, receiving basic bill proposal information, storing the basic information, extracting information through text mining, generating analysis data, outputting the analysis, providing specific company information, performing supervised learning about basic bill information, providing inference data, forming feedback based on differences between the inference data and ground truth data, etc. The additional elements are recited at a high level of generality (i.e. as a generic computer performing functions of collecting, storing, mining, analyzing, calculating and presenting data, etc.) such that they amount to no more than mere instructions to apply the exception using generic computer components. Therefore, these functions can be viewed as not meaningfully different than a business method or mathematical algorithm being applied on a general-purpose computer as tested per MPEP 2106.05(f)(2)(i), and requiring the use of software to tailor information and provide it to the user on a generic computer (MPEP 2106.05(f)(2)(v)), invoking computers or other machinery merely as a tool to perform an existing process (MPEP 2106.05(f)(2)).
The additional elements “learning model” and “artificial intelligence module” language merely requires execution of an algorithm that can be performed by a generic computer component and provides no detail regarding the operation of that algorithm. As such, the claim requirement amounts to mere instructions to implement the abstract idea on a computer, and, therefore, is not sufficient to make the claim patent eligible. See Alice, 573 U.S. at 226 (determining that the claim limitations “data processing system,” “communications controller,” and “data storage unit” were generic computer components that amounted to mere instructions to implement the abstract idea on a computer); October 2019 Guidance Update at 11–12 (recitation of generic computer limitations for implementing the abstract idea “would not be sufficient to demonstrate integration of a judicial exception into a practical application”). Such a generic recitation of “learning model” and “artificial intelligence module” alone is insufficient to show a practical application of the recited abstract idea.
The claims are directed to an abstract idea and the judicial exception does not integrate the abstract idea into a practical application.
Step 2B: According to MPEP 2106.05(f)(1), considering whether the claim recites only the idea of a solution or outcome i.e., the claims fail to recite the technological details of how the actual technological solution to the actual technological problem is accomplished. The recitation of claim limitations that attempt to cover an entrepreneurial and thus abstract solution to an entrepreneurial problem with no technological details on how the technological result is accomplished and no description of the mechanism for accomplishing the result do not provide significantly more than the judicial exception.
Dependent claim 11 recites the additional elements “computer program stored in a medium”. The functions of this additional computer-based element includes “executing the method of claim 1”. This additional element is also recited at a high level of generality (i.e. as a generic computer performing functions of executing computer code, etc.) such that it amounts to no more than mere instructions to apply the exception using generic computer components.
Further, dependent claims 2-4, 8-9 merely incorporate the additional elements recited in claim 1 along with further narrowing of the abstract idea of claim 1 and their execution of the abstract idea. Specifically, the dependent claims narrow the “computing system”, “memory”, “processor”, “user terminal”, “analysis server”, “company information providing server”, “application programming interface (API)”, “learning model”, and “artificial intelligence module” to capabilities such as generating, matching, including, calculating, mining, determining, forming, providing, outputting, changing, extracting, and transmitting various forms of data such as regulation influence analysis, present items, provisions, laws, promotions, stakeholders, objectives, industries, companies, relevance, percentages, learning data, feedback, text, bill contents, parameters, etc. which, when evaluated per MPEP 2106.05(f)(2) represent mere invocation of computers to perform existing processes. Therefore, the additional elements recited in the claimed invention individually and in combination fail to integrate a judicial exception into a practical application (Step 2A prong two) and for the same reasons they also fail to provide significantly more (Step 2B). Thus, claims 1-4, 8-9, 11 are reasoned to be patent ineligible.
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REJECTIONS BASED ON PRIOR ART
Examiner Note: Some rejections will contain bracketed comments preceded by an “EN” that will denote an examiner note. This will be placed to further explain a rejection.
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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 of this title, 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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-4, 8-9, 11 are rejected under 35 U.S.C. 103 as being unpatentable over:
Lee at al. KR 102057169 B1, hereinafter Lee in view of
Eidelman et al. US 20230214753 A1, hereinafter Eidelman. As per,
Regarding claims 1, 11: Lee teaches:
A method (claim 1) / A computer program stored in a medium for executing the method of claim 1 (claim 11) for monitoring a bill and a legal regulation, which is performed by a computing system including at least one memory and at least one processor, the method comprising:
storing template information including a preset item (Lee ¶ [0065]: The corporate service unit 240 uses the law enforcement guide information stored in the database 250 to generate a law enforcement guide table including a task name and a schedule to be implemented in the company and interwork with the corporate terminal 100…. Mid-¶ [0020]: The company's customized chemicals related laws implementation guide system, characterized in that it is configured to automatically convert to the relevant legal information in the database, and to apply the searched legal information to the format in the form of a predetermined matrix to the corresponding corporate terminal Is provided);
receiving, from a user terminal, basic information including main contents and proposal reasons of the bill (Lee ¶ [0061]: That is, when the legal information is authorized from the corporate terminal 100, the corporate service unit 240 determines whether or not a corporate implementation target is customized based on a list of the provisions presented for each law by the law enforcement determination algorithm. ¶ [0065]: …Based on the information recorded in the law enforcement guide table from the corporate terminal 100, the relevant law information, the law enforcement method and the penalty information issued when the law deviates from the standard condition so as not to deviate from the standard condition including the law enforcement period and the chemical standard value provide law enforcement performance alarm information, including the corporate terminal 100);
storing the basic information in an analysis server (Lee ¶ [0023]: Statutory information is stored, and the law enforcement determination algorithm and the law enforcement guide algorithm is configured to determine the law to be implemented on the basis of subordinate laws and regulations of chemicals related laws, and is configured to generate law enforcement guide information);
forming extracted information from the basic information [..], based on the preset item (Lee ¶ [0051]: The law enforcement determination algorithm generation unit 220 generates a law enforcement determination algorithm for extracting related subordinate law information based on company information including general company information and facility information, facility information, authentication information, and handling material information. [0052]: That is, the law enforcement determination algorithm may extract the law information related to the chemicals handled by the company by comparing the company information and the information corresponding to the "who" separator of each sub-statute information registered in the database 250);
generating regulation influence analysis data based on the extracted information and the template information (Lee mid-¶ [0018]: In addition to judging compliance with the law on the basis of the satisfaction of the standard conditions including the value of the standard, and the legal information corresponding to the corporate information variable, the method of implementing the relevant law, and the failure of the relevant law[,] provide law enforcement performance alarm information including penalty information to the corresponding company terminal, but give correlation [EN: analysis data] to second company information variable that affects [EN: influences] each company information variable by statute or clause and do[es] not affect [each company information variable].); and
outputting a regulation influence analysis interface for the bill or legal regulation to the user terminal, based on the generated regulation influence analysis data (Lee mid-¶ [0018]: After the information is generated, it is stored in a database, and using the corporate law enforcement guide information, including the corporate service department that provides the law enforcement guide service through the corresponding corporate terminal, the corporate service department is to include the law enforcement guide information. Mid-¶ [0061]: Provided to the corresponding corporate terminal 100 in a format divided into a matrix form, the corporate law provisions information for "what", the corporate information for the implementation of the law for "who", the representative provisions for the "how" and its implementation It provides information on how to implement the law, "when", information on when to implement the law, "where", information on filing the law, and "penalty" on the administrative disposition, penalties, and penalties).
wherein the method further comprises receiving, when logging-in to the computing
system using an account of a specific company, information about the specific company
provided from a company information providing server [..] (Lee mid-¶ [0018]: After the information is generated, it is stored in a database, and using the corporate law enforcement guide information, including the corporate service department that provides the law enforcement guide service through the corresponding corporate terminal, the corporate service department is to include the law enforcement guide information),
wherein the generating of the regulation influence analysis data further includes:
calculating a second relevance between the specific company and the target bill based on a predetermined similarity calculation equation for the second relevance (Lee ¶ [0075]: In addition, the corporate service unit 240 divides the penalty information (penalty) previously registered according to the statutes and the provisions into the database 100 into three penalty types of administrative disposition, imprisonment, and fine, and according to the penalty type according to the preset algorithm. The score is calculated, and the importance level is set from each corporate terminal 100 for each penalty type [EN: thus yielding multiple relevance calculations], and the importance score [EN: relevance] for each law per company is calculated based on the importance set by the corporate terminal 100);
calculating a risk of the specific company by the target bill based on a predetermined first algorithm (Lee end-¶ [0023]: Statutory [EN: bill] information is stored, and the law enforcement determination algorithm and the law enforcement guide [EN: predetermined] algorithm is configured to determine the law to be implemented on the basis of subordinate laws and regulations of chemicals related laws, and is configured to generate law enforcement guide information [EN: including risk]. ¶ [0024]: In addition, the corporate service department may be required to comply with legislative standards for each company information variable, including the content of chemicals, daily handling, storage and storage, annual handling, handling area, handling equipment capacity, and power consumption of handling facilities. Providing different statutory-based alarm information for baseline proximity conditions corresponding to the ratio of 80-90% and over-baseline conditions [EN: calculated risk] corresponding to the ratio of 110-120% of the statutory implementation standard, provided that statutory information and proximity alarm information including penalty information generated in the event of non-compliance with the law [EN: risk event], and excess alarm information including the statutory information corresponding to the reference condition and the benefit information generated when the reference condition is fulfilled when the condition exceeds the reference value);
generating regulation influence analysis data about the second relevance [..] (Lee end-¶ [0018]: …calculate the importance score for each statute for each company. The higher the statute or clause, the higher the importance score, and the shorter the law enforcement alarm period, the shorter the observance of the statute. ¶ [0019]: In addition, the information management unit updates the database on the basis of the revised legal information [EN: regulation influence analysis data], when the revision information on the chemical laws related to the collection, the algorithm generation unit applies the revised legal information to the pre-registered law enforcement determination algorithm and the law enforcement guide algorithm. And each algorithm is updated, and the corporate service department is configured to update the law enforcement guide information by applying the corporate information variable necessary to determine compliance with the relevant laws and regulations to the law enforcement guide algorithm);
converting the regulation influence analysis data about the second relevance into a percentage (Lee mid-¶ [0075]: At this time, the importance level is set to a value of "0 or more and less than 1" as the score weight [EN: values from 0 to 1 are easily convertible to percentages], and the sum of the importance levels for the three penalty types is set to "1". This is to set different importance for administrative disposition, imprisonment, and fine in consideration of each company's situation, and to provide a law enforcement alarm service tailored to the company's situation);
[..]; and
outputting the percentage [..] to the user terminal based on the information about the specific company (Lee mid-¶ [0075]: The score is calculated, and the importance level is set from each corporate terminal 100 for each penalty type, and the importance score for each law per company is calculated based on the importance set by the corporate
terminal 100),
wherein the second relevance and [..] are included in the preset item (Lee ¶ [0061]: …Provided to the corresponding corporate terminal 100 in a format divided into a matrix form, the corporate law provisions information for "what", the corporate information for the implementation of the law for "who", the representative provisions for the "how" and its implementation. It provides information on how to implement the law, "when", information on when to implement the law, "where", information on filing the law, and "penalty" on the administrative disposition… and penalties),
[..]
wherein the method further comprises:
performing [computation on] data that is stored in association with basic information of a first proposed bill, basic information of a first past bill, first past regulation influence analysis data, and first inference data (Lee ¶ [0119]: At this time, the law enforcement guide apparatus 200 compares the revised law [EN: proposed bill] and the previous law [EN: past bill] and analyzes the difference information, searches for the target company to apply the update based on the analyzed amendment difference information, and the law enforcement guide information for the searched company. ¶ [0019]: In addition, the information management unit updates the database on the basis of the revised legal information [EN: regulation influence analysis data], when the revision information on the chemical laws related to the collection, the algorithm generation unit applies the revised legal information to the pre-registered law enforcement determination algorithm and the law enforcement guide algorithm. And each algorithm is updated, and the corporate service department is configured to update the law enforcement guide information [EN: with inference data] by applying the corporate information variable necessary to determine compliance with the relevant laws and regulations to the law enforcement guide algorithm);
[..]
outputting second inference data based on the learning data received from the learning model, when basic information of a second proposed bill, basic information of a
second past bill, and second past regulation influence analysis data are input (Lee ¶ [0050]: In addition, the information management unit 210 compares the collected revised laws [EN: multiple proposed bills, i.e. first and second] and subordinate statute data with previously registered pre-revised data [EN: basic information from past bills], extracts the changes, calls the statute information to which the extracted changes are applied, and the previous applicable law provisions. The legal provision information of the database 250 is updated based on the changed legal provision information in comparison with the information, and the changed legal provision information is converted into the law enforcement determination algorithm generator 220 and the law enforcement guide algorithm generator 230 [EN: as regulation influence analysis data] and provided to the corporate service unit 240);
forming feedback information based on a difference between the second inference
data and a pre-stored [data] (Lee mid-¶ [0050]: The legal provision information of the database 250 is updated based on the changed legal provision information in comparison with the information); and
[..].
Although Lee teaches monitoring legislation; generating influence analysis and calculating relevance for an enterprise; performing computations based on multiple proposed bills, past bills, past regulation influence, and inference data; forming feedback based on the difference between the proposed and past bills; and generating influence analysis data based on basic information of a legislative bill; Lee does not specifically teach calculating a relevance for a company through text mining or the use an API; generating influence analysis data about risk; converting data into visual data with saturation information on a user interface; including risk as a preset item; using a supervised learning model, artificial intelligence model, or ground truth data to perform computations; nor changing learning model parameters with feedback information.
However, Eidelman in analogous art of industrial monitoring of governmental legal regulations teaches or suggests:
forming extracted information from the basic information through text mining (Eidelman ¶ [0122]: As used herein, "scraping" or "scraping the Internet" may include any manner of data aggregation, by machine or manual effort, including but not limited to crawling across websites, identifying links and changes to websites, data transfer through API's, FTP's, GUI, direct database connections through, e.g. using SQL, parsing and extraction of website pages [EN: text mining], or any other suitable form of data acquisition. ¶ [0316]: In step 2203, the server may analyze the text data…. In yet further embodiments, the text analysis module 1809 may also determine the levels of influence of each comment (e.g., based on the author of the comment), and weigh each comment based on an influence level. Mid-¶ [0471]: In some embodiments, document content may be analyzed using known natural language processing algorithms…. For example, entity agreement module may parse the text of a user uploaded document, apply named entity recognition model to identify policymaker and policy name mentions, apply topic identification model to identify the issue(s) contained therein, with associated confidence score for issue identification, apply a sentiment/stance detection model…);
receiving [..] information about the specific company [..] through an application programming interface (API) provided from the company information providing server (Eidelman ¶ [0122]: As used herein, "scraping" or "scraping the Internet" may include any manner of data aggregation, by machine or manual effort, including but not limited to crawling across websites, identifying links and changes to websites, data transfer through API's…. End-¶ [0165]: In some embodiments, the server may receive proprietary [EN: company] information by scraping a source provided by the user. For example, a user may provide access to proprietary source, e.g. an internal network, database, and/or an API);
generating [..] regulation influence analysis data about the risk (See Eidelman Fig. 13A with influence analysis data about risks of pharmaceutical regulations, including issues (regulations), issue types, weights, impacts, and desired outcomes; and related text);
converting the regulation influence analysis data about the risk into visual data based on the risk corresponding to the regulation influence analysis data about the risk (See Eidelman examples of color-saturated visual charts depicting regulation-based risks and related text: Fig. 25 shows a pie chart depicting sentiments about a proposed bill; Fig. 58C forecasts industrial activity based on a policy; Fig. 58D depicts relationships between regulation topics and organizational relevance; Fig. 58E depicts geographic distribution of company assets with relevant regulations; and 58H illustrates positive vs. negative media coverage of a company in a regulatory context);
outputting [..] the visual data to the user terminal based on the information about the specific company (Eidelman ¶ [0519]: In some embodiments, displaying the issue graph may include displaying information extracted from the issue graph via one or more user interfaces. FIGS. 58A, 58B, 58C, 58D, 58E, 58F, 58G, 58H, and 58I illustrate example user interface elements for presenting an issue graph),
wherein [..] and the risk are included in the preset item (See Eidelman Fig. 13A: risks such as policy “Impact” included as fields in the template),
wherein the visual data includes saturation information corresponding to the risk (See Eidelman examples of color-saturated visual charts depicting regulation-based risks: Figs. 25, 58C, 58D, 58E, 58H),
performing supervised learning through a learning model based on training data [..] (Eidelman ¶ [0376]: In some embodiments, a machine-trained model may be trained to extract relationships from unstructured data. For example, a training algorithm, such as an artificial neural network may receive training data in the form of unstructured data. The training data may be labeled such that relationships described herein are identified. As a result, a model may be trained to identify relationships within the unstructured data. Consistent with the present disclosure, various other machine learning algorithms may be used, including a logistic regression, a linear regression… a random forest, a K-Nearest Neighbor (KNN) model (for example as described above)… a decision tree, a cox proportional hazards regression model, a Naive Bayes model, a Support Vector Machines (SVM) model, a gradient boosting algorithm [EN: the models listed are all examples of supervised learning models], or any other form of machine learning model or algorithm);
forming learning data through machine learning of the learning model; providing an artificial intelligence module with the learning data; (Eidelman ¶ [0376]: …For example, a training algorithm, such as an artificial neural network [EN: artificial intelligence model] may receive training data in the form of unstructured data);
forming [..] information based on [..] a pre-stored ground truth set (Eidelman end-¶ [0492]: For example, the machine-trained model may be trained to analyze structured [EN: prestored ground truth set] or unstructured data and identify links between policymakers and organizations, as described above); and
changing a parameter of the learning model based on the feedback information (Eidelman ¶ [0213]: In other embodiments, users may provide feedback on one or more parameters used by the model, such as the features used and weighting of those features. Users may also provide feedback on an individual feature or weight, or a grouping of features or weights together, where the grouping may be determined by the user or the system. ¶ [0217]: In other embodiments, feedback on the model, such as the predicted output, or sub-parts of the model, such as weighting or parameters, may result in the model being recomputed. Any feedback, including modified features, weights, or new features may be computed by the system on all data, only for a subset of data or models, including, for example, proprietary data, as indicated by the default profile settings or user profile settings), and
wherein when the basic information of the second proposed bill includes basic information about a member-proposed bill, it is determined that the preset item further includes a political party and legislative tendency of a member who proposes the second proposed bill (See Eidelman Fig. 54: Democrat and Republican labels for congressional representatives. Mid-¶ [0182]: …The system may use a uni-dimensional or multidimensional space to map the ideological leanings of the policymaker. For example, with a uni-dimensional space, a policymaker may be scored as more "conservative" or more "liberal"; with a multi-dimensional space, a policymaker may be scored as more "conservative" or more "liberal" on one issue (e.g., healthcare) and scored separately as more "conservative" or more "liberal" on other issues (e.g., immigration). Mid-¶ [0398]: The issue graph may also indicate that these documents [EN: proposed bill] are authored by various persons, including, e.g., Legislator 2 [EN: member], who is considered to be similar, based on voting record, party affiliation, cosponsoring activity, professional or educational background, etc. to other persons, including, e.g., Other Legislators).
Eidelman and Lee are found as analogous art of industrial monitoring of governmental legal regulations. It would have been obvious to one skilled in the art, before the effective filing date of the invention, to have modified Lee’s enterprise legal guidance system and method to have included Eidelman’s teachings around calculating a relevance for a company through text mining or the use an API; generating influence analysis data about risk; converting data into visual data with saturation information on a user interface; including risk as a preset item; using a supervised learning model, artificial intelligence model, or ground truth data to perform computations; nor changing learning model parameters with feedback information. The benefit of these additional features would have assisted organizations in effectively understanding and acting on policy making, within the broader political context to other policy, people, organizations, and events. (Eidelman ¶ [0003]). The predictability of such modifications and/or variations, would have been corroborated by the broad level of skill of one of ordinary skills in the art as articulated by Lee in view of Eidelman (see MPEP 2143 G).
Further, the claimed invention could have also been viewed as a mere combination of old elements in a similar field of industrial monitoring of governmental legal regulations. In such combination each element would have merely performed the same function as it did separately. Thus, one of ordinary skill in the art would have recognized that, given existing technical ability to combine the elements, as evidenced by Lee in view of Eidelman above, the to- be combined elements would have fit together like pieces of a puzzle in a logical, complementary, technologically feasible and/or economically desirable manner. Thus, it would have been reasoned that the results of the combination would have been predictable (see MPEP 2143 A).
Regarding claim 2: Lee / Eidelman teaches all the limitations of claim 1 above.
Lee further teaches:
wherein the generating of the regulation influence analysis data based on the extracted information and the template information includes generating the regulation influence analysis data by matching the extracted information to each preset item in the template information (Lee ¶ [0060]: When the corporate information including chemical substance information is input through the corporate terminal 100, the corporate service unit 240 provides the corporate terminal 100 with legal performance information in a matrix form corresponding to the relevant legal provisions. ¶ [0061]: …Provided to the corresponding corporate terminal 100 in a format divided into a matrix form, the corporate law provisions information for "what", the corporate information for the implementation of the law for "who", the representative provisions for the "how" and its implementation. It provides information on how to implement the law, "when", information on when to implement the law, "where", information on filing the law, and "penalty" on the administrative disposition… and penalties).
Regarding claim 3: Lee / Eidelman teaches all the limitations of claim 1 above.
Lee further teaches:
wherein the preset item includes a regulation affairs name, a regulation provision, a delegated law, a type, pre-announcement of legislation, a background of promotion, necessity of government intervention, a regulation objective, a regulation content, a regulated group, a stakeholder, cost benefit analysis, influence evaluation, sunset setting, priority permission, post-regulation application, a cost management system, a regulated industry, a close-up industry, and a related company (Lee ¶ [0061]: …Provided to the corresponding corporate terminal 100 in a format divided into a matrix form, the corporate law provisions information for "what" [EN: affairs name, provision, law, type, etc.], the corporate information for the implementation of the law for "who" [EN: necessity, group, stakeholder, etc.], the representative provisions for the "how" [EN: post regulation application, etc.] and its implementation. It provides information on how to implement the law, "when", information on when to implement the law, "where", information on filing the law, and "penalty" on the administrative disposition… and penalties).
Examiner notes that the information of different data in the preset item in claim 3 is construed as nonfunctional descriptive material and is given no patentable weight because these items have no functional relationship with a computer in the claim. This is the same as the example in MPEP 2111.05 of a table containing batting averages, where the information only has significance to a user reading the information. Nonetheless, for compact prosecution, art is applied to the “names” of the data.
Regarding claim 4: Lee / Eidelman teaches all the limitations of claim 3 above.
Lee further teaches:
wherein the generating of the regulation influence analysis data based on the extracted information and the template information further includes:
calculating a first relevance between the target bill and the extracted information corresponding to each of the regulated group and the stakeholder, the regulated industry, the close-up industry, and the related company, based on a predetermined similarity calculation equation for the first relevance (Lee ¶ [0075]: In addition, the corporate service unit 240 divides the penalty information (penalty) previously registered according to the statutes and the provisions into the database 100 into three penalty types of administrative disposition, imprisonment, and fine, and according to the penalty type according to the preset algorithm. The score is calculated, and the importance level is set from each corporate terminal 100 for each penalty type, and the importance score [EN: relevance] for each law per company is calculated based on the importance set by the corporate terminal 100. ¶ [0047]: In addition, the company information stored in the database 250 includes basic information, authentication information and substance information. Basic information is company basic information (general administrative information including company name, address, contact information), contact information, detailed information and additional information. In particular, detailed information includes business address, headquarter / branch classification, location classification, total area of business, standard including industrial classification name, standard industrial classification number…); and
generating regulation influence analysis data about the first relevance (Lee end-¶ [0018]: …calculate the importance score for each statute for each company. The higher the statute or clause, the higher the importance score, and the shorter the law enforcement alarm period, the shorter the observance of the statute. ¶ [0019]: In addition, the information management unit updates the database on the basis of the revised legal information [EN: regulation influence analysis data], when the revision information on the chemical laws related to the collection, the algorithm generation unit applies the revised legal information to the pre-registered law enforcement determination algorithm and the law enforcement guide algorithm. And each algorithm is updated, and the corporate service department is configured to update the law enforcement guide information by applying the corporate information variable necessary to determine compliance with the relevant laws and regulations to the law enforcement guide algorithm).
Regarding claim 8: Lee / Eidelman teaches all the limitations of claim 1 above.
Lee further teaches:
the generating of the regulation influence analysis data based on the extracted information and the template information further includes generating the regulation influence analysis data based on [..] the basic information including the main contents and the proposal reasons of the target bill, and the second inference data (Lee mid-¶ [0018]: Algorithm generation unit that generates the statute implementation algorithm to determine the statute to be implemented based on information and chemicals and the statute guide algorithm to generate the guide information [EN: influence analysis] for the implementation of the relevant statute by each statute, handled by the company from the corporate terminal. Company information containing chemical substances present. In addition, by applying the company information including chemical substance information to the law enforcement determination algorithm, the relevant law is extracted from the relevant company, and the extracted law to be implemented is applied to the law enforcement guide algorithm. End ¶ [0018]: Calculate and add the penalty score for each type of penalty to calculate the importance score for each statute for each company [EN: inference data]. The higher the statute or clause, the higher the importance score, and the shorter the law enforcement alarm period, the shorter the observance of the statute).
Although Lee teaches generating influence analysis data based on basic information and inference data; Lee does not specifically teach doing so through text mining.
However, Eidelman in analogous art of industrial monitoring of governmental legal regulations teaches or suggests:
mining data, which is obtained by performing text mining (Eidelman ¶ [0122]: As used herein, "scraping" or "scraping the Internet" may include any manner of data aggregation, by machine or manual effort, including but not limited to crawling across websites, identifying links and changes to websites, data transfer through API's, FTP's, GUI, direct database connections through, e.g. using SQL, parsing and extraction of website pages [EN: text mining], or any other suitable form of data acquisition. ¶ [0316]: In step 2203, the server may analyze the text data…. In yet further embodiments, the text analysis module 1809 may also determine the levels of influence of each comment (e.g., based on the author of the comment), and weigh each comment based on an influence level. Mid-¶ [0471]: In some embodiments, document content may be analyzed using known natural language processing algorithms…. For example, entity agreement module may parse the text of a user uploaded document, apply named entity recognition model to identify policymaker and policy name mentions, apply topic identification model to identify the issue(s) contained therein, with associated confidence score for issue identification, apply a sentiment/stance detection model…).
Rationales to have modified / combined Lee / Eidelman are above and reincorporated.
Regarding claim 9: Lee / Eidelman teaches all the limitations of claim 1 above.
Although Lee teaches generating influence analysis data based on basic information of a legislative bill, Lee does not specifically teach determining relevant overseas cases and similar legislative cases, extracting their contents, and delivering to the user.
However, Eidelman in analogous art of industrial monitoring of governmental legal regulations teaches or suggests:
determining a relevant overseas case and a similar legislative case corresponding to the basic information including the main contents and the proposal reasons of the target bill using a predetermined second algorithm; extracting main contents of the relevant overseas case and the similar legislative case (Eidelman mid-¶ [0494]: the server may determine, based on client independent system computed relationships [EN: via predetermined second algorithm] (e.g., similarities found in the US bill compared to a bill in a foreign country, or similarities found in a US legislator compared to a legislator in a foreign country), the server may generate an issue graph of policy/policymakers user has not interacted with before and compute the issue graph measures described above. Memory 1200 may further instruct database access module 1210 to search database 1212 for issue graph data stored therein. In some aspects, if issue graph data is not available, action execution module 1206 may scrape the Internet to obtain information [EN: extract main contents] for graph generator module to generate one or more issue graphs);
transmitting the main contents of the relevant overseas case and the similar legislative case to the user terminal; and outputting the main contents of the relevant overseas case and the similar legislative case to the user terminal (Eidelman mid-¶ [0250]: FIG. 13A is diagrammatic illustration of a GUI 1300 presenting a list of user-selectable agenda issues for performing internet-based agenda data analysis. In particular, FIG. 13A illustrates an exemplary GUI presenting a list of user-selectable agenda issues for performing an issue-based analysis of legislature alignment with organizational posture. GUI 1300 may be displayed as part of a screen executed by a software application on, for example, a personal device 300, 400. The screen may take the form of a web browser or web page consistent with the present disclosure. Mid-¶ [0494]: For example, if the server determines that an additional agenda issue is relevant to an issue selected as being of interest to the organization, the server may include the additional agenda issue and the policymakers relevant to the additional agenda issue in the issue graph model [EN: output on user terminal]).
Eidelman and Lee are found as analogous art of industrial monitoring of governmental legal regulations. It would have been obvious to one skilled in the art, before the effective filing date of the invention, to have modified Lee’s enterprise legal guidance system and method to have included Eidelman’s teachings around determining relevant overseas cases and similar legislative cases, extracting their contents, and delivering to the user. The benefit of these additional features would have assisted organizations in effectively understanding and acting on policy making, within the broader political context to other policy, people, organizations, and events. (Eidelman ¶ [0003]). The predictability of such modifications and/or variations, would have been corroborated by the broad level of skill of one of ordinary skills in the art as articulated by Lee in view of Eidelman (see MPEP 2143 G).
Further, the claimed invention could have also been viewed as a mere combination of old elements in a similar field of industrial monitoring of governmental legal regulations. In such combination each element would have merely performed the same function as it did separately. Thus, one of ordinary skill in the art would have recognized that, given existing technical ability to combine the elements, as evidenced by Lee in view of Eidelman above, the to- be combined elements would have fit together like pieces of a puzzle in a logical, complementary, technologically feasible and/or economically desirable manner. Thus, it would have been reasoned that the results of the combination would have been predictable (see MPEP 2143 A).
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Conclusion
The following art is made of record and considered pertinent to Applicant’s disclosure:
Brooks; Bradford et al. US 20180285892 A1, Sentiment and analytics for predicting future legislation.
DeAngelis; Stephen F. et al. US 20120131542 A1, Systems and methods for creating reusable software components based on regulatory and policy documents to ensure compliance with the documents for integration with automated systems.
Pesci-Anderson; Jennifer et al. US 20130096955 A1, System and method for compliance and operations management.
Brown, Michael Wayne et al. US 20020107698 A1, Apparatus, methods and computer programs for determining estimated impact of proposed legislation.
HIDENORI OCHIAI et al. BR 112019026386 A2, Legal information processing system, method and program.
Bommarito II, Michael J., Daniel Martin Katz, and Eric M. Detterman. "LexNLP: Natural language processing and information extraction for legal and regulatory texts." Research handbook on big data law. Edward Elgar Publishing, 2021. 216-227. https://arxiv.org/pdf/1806.03688
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THIS ACTION IS MADE FINAL. 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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Any inquiry concerning this communication or earlier communications from the examiner should be directed to REED M. BOND whose telephone number is (571) 270-0585. The examiner can normally be reached Monday - Friday 8:00 am - 5:00 pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Patricia Munson can be reached at (571) 270-5396. The fax phone number for the organization where this application or proceeding is assigned is (571) 273-8300.
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/REED M. BOND/Examiner, Art Unit 3624 April 2, 2026
/HAMZEH OBAID/Primary Examiner, Art Unit 3624