DETAILED ACTION
This Final Office Action is in response to the application filed on 01/07/2025 and the Amendment & Remark filed on 07/06/2026.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
Claims 3, 4, 6-9, 12-14, 18, 19, 21-24 and 27-29 are canceled.
Claims 1, 10-11, 16 and 25-26 are amended.
Claims 1, 2, 5, 10-12, 15-17, 20, 25, 26 and 30 are pending.
Claim Rejections - 35 USC § 112
The previous rejection under 35 USC 112(a) is withdrawn in view of the Amendment filed on 07/06/2026.
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, 2, 5, 10-12, 15-17, 20, 25, 26 and 30 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
As an initial matter, the claims as a whole are to a process and an apparatus, which falls within one or more statutory categories. (Step 1: YES) The recitation of the claimed invention is then further analyzed as follow, in which the abstract elements are boldfaced.
Claim 1 recites:
A method for financial forecasting, the method comprising:
categorizing one or more first content documents into a plurality of categories of interest, wherein the one or more first content documents are obtained from a plurality of content sources;
determining one or more second content documents among the categorized one or more first content documents based on correlating a first relevancy score associated with each of the categorized one or more first content documents with a first predefined threshold score, wherein the one or more second content documents correspond to one or more entities;
determining time-series data based on a set of attributes associated with the one or more second content documents, wherein the set of attributes includes at least one of a first sentiment, a hot index, a second sentiment, uniqueness, a category, an industry and duration of the one or more second content document;
obtaining one or more predefined knowledge bases from a plurality of knowledge base platforms, wherein the one or more predefined knowledge bases comprise at least one of a Consumer Price Index (CPI), a Purchasing Managers Index (PMI), industrial production, Gross Domestic Product (GDP), Exchange-Traded Fund (ETF) baseline, forex, sector-specific ETF, commodities, and stocks data;
generating disruption indexes based on integrating the determined time- series data and one or more predefined knowledge bases, wherein the disruption indexes indicate variables for training a time-series model, wherein generating the disruption indexes comprises one-hot encoding one or more nominal variables and aggregating the one or more nominal variables by summing values of the one or more nominal variables, calculating a mean score for one or more interval variables, and addressing missing values using unconditional mean imputation; and
generating a forecast of the one or more entities based on the generated disruption indexes using the time-series model.
Claim 16 recites:
A system for financial forecasting, the system comprising:
a memory; and
at least one processor in communication with the memory, wherein the at least one processor is configured to:
categorize one or more first content documents into a plurality of categories of interest, wherein the one or more first content documents are obtained from a plurality of content sources;
determine one or more second content documents among the categorized one or more first content documents based on correlating a first relevancy score associated with each of the categorized one or more first content documents with a first predefined threshold score, wherein the one or more second content documents correspond to one or more entities;
determine time-series data based on a set of attributes associated with the one or more second content documents, wherein the set of attributes includes at least one of a first sentiment, a hot index, a second sentiment, uniqueness, a category, an industry and duration of the one or more second content document;
obtain one or more predefined knowledge bases from a plurality of knowledge base platforms, wherein the one or more predefined knowledge bases comprise at least one of a Consumer Price Index (CPI), a Purchasing Managers Index (PMI), industrial production, Gross Domestic Product (GDP), Exchange-Traded Fund (ETF) baseline, forex, sector-specific ETF, commodities, and stocks data;
generate disruption indexes based on integrating the determined time- series data and one or more predefined knowledge bases, wherein the disruption indexes indicate variables for training a time-series model, wherein generating the disruption indexes comprises one-hot encoding one or more nominal variables and aggregating the one or more nominal variables by summing values of the one or more nominal variables, calculating a mean score for one or more interval variables, and addressing missing values using unconditional mean imputation; and
generate a forecast of the one or more entities based on the generated disruption indexes using the time-series model.
Claim 2 and 17 similarly recite:
wherein categorizing the one or more first content documents comprises:
identifying content in the one or more first content documents, wherein the content comprises at least one of, realty-based content, sports-based content, finance-based content, stocks-based content, lifestyle-based content, pandemic- based content, natural hazards-based content, and travel-based content; and
filtering the one or more first content documents based on checking accuracy of the one or more first content documents, thereby categorizing the one or more first content documents into the plurality of categories of interest.
Claims 5 and 20 similarly recite:
wherein determining the one or more second content documents comprise determining the one or more second content documents when the first relevancy score exceeds the first predefined threshold score.
Claims 10 and 25 similarly recite:
wherein the set of attributes includes the first sentiment, and obtaining the first sentiment comprises obtaining expert views from the real-world in response to the one or more second content documents.
Claims 11 and 26 similarly recite:
wherein the set of attributes includes the hot index, and the hot index indicates topics associated with the one or more second content documents trending beyond a predefined range of numbers.
Claims 15 and 30 similarly recite:
wherein generating the forecast comprises generating the forecast in a time-series pattern using the time-series model.
Based on the limitations above, the claims describe a process that covers conducting financial forecast. Conducting financial forecast is considered to be a fundamental economic practice / commercial interaction, which falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. As such, the claim(s) recite(s) a Judicial Exception. (Step 2A prong one: Yes)
This analysis then evaluates whether the claims as a whole integrates the recited Judicial Exception into a practical application of the exception. In particular, the claims recite the additional element(s) of “processer” as a mere tool to perform the steps of the Judicial Exception, which encompasses no more than Mere Instruction to Apply.
For example, the limitation “categorizing one or more first content documents into a plurality of categories of interest, wherein the one or more first content documents are obtained from a plurality of content sources” encompasses no more than generically invoking a processor to apply the Judicial Exception step of categorizing one or more first content document into categories of interest;
the limitation “determining one or more second content documents among the categorized one or more first content documents based on correlating a first relevancy score associated with each of the categorized one or more first content documents with a first predefined threshold score, wherein the one or more second content documents correspond to one or more entities” encompasses no more than generically invoking a processor to apply the Judicial Exception step of determining one or more second content documents based on correlating the first relevancy score with a predetermined threshold score;
the limitation “determining time-series data based on a set of attributes associated with the one or more second content documents, wherein the set of attributes includes at least one of a first sentiment, a hot index, a second sentiment, uniqueness, a category, an industry and duration of the one or more second content document” encompasses no more than generically invoking a processor to apply the Judicial Exception step of determining time-series data based on a set of attributes associated with the one or more second content documents;
the limitation “obtaining one or more predefined knowledge bases from a plurality of knowledge base platforms, wherein the one or more predefined knowledge bases comprise at least one of a Consumer Price Index (CPI), a Purchasing Managers Index (PMI), industrial production, Gross Domestic Product (GDP), Exchange-Traded Fund (ETF) baseline, forex, sector-specific ETF, commodities, and stocks data” encompasses no more than generically invoking a processor to apply the Judicial Exception step of obtaining the one or more predefined knowledge bases;
the limitation “generating disruption indexes based on integrating the determined time- series data and one or more predefined knowledge bases, wherein the disruption indexes indicate variables for training a time-series model” encompasses no more than generically invoking a processor to apply the Judicial Exception step of generating the disruption indexes based on integrating the determined time-series data and one or more predefined knowledge bases;
the limitation “wherein generating the disruption indexes comprises one-hot encoding one or more nominal variables and aggregating the one or more nominal variables by summing values of the one or more nominal variables, calculating a mean score for one or more interval variables, and addressing missing values using unconditional mean imputation” encompasses no more than generically invoking a processor to apply the Judicial Exception step of generating the disruption indexes one-hot encoding one or more nominal variables, aggregating the nominal variables by summing values of the nominal variables, calculating a mean score for one or more interval and addressing missing values;
the limitation “generating a forecast of the one or more entities based on the generated disruption indexes” encompasses no more than generically invoking a processor to apply the Judicial Exception step of generating a forecast of the one or more entities based on the disruption indexes;
the limitation “identifying content in the one or more first content documents, wherein the content comprises at least one of, realty-based content, sports-based content, finance-based content, stocks-based content, lifestyle-based content, pandemic- based content, natural hazards-based content, and travel-based content” encompasses no more than generically invoking a processor to apply the Judicial Exception step of identifying content in the one or more first content documents;
the limitation “filtering the one or more first content documents based on checking accuracy of the one or more first content documents, thereby categorizing the one or more first content documents into the plurality of categories of interest” encompasses no more than generically invoking a processor to apply the Judicial Exception step of filtering the one or more first content documents based checking accuracy of the documents;
the limitation “wherein obtaining the expert-input comprises obtaining the expert-input from the real-world based on correlating the categorized one or more first content documents and an impact made on the one or more entities in response to events associated with each of the categorized one or more first content documents” encompasses no more than generically invoking a processor to apply the Judicial Exception step of obtaining expert-input based on correlating the categorized first content document and an impact made on the one or more entities in response to events associated with each of the categorized first content documents;
the limitation “wherein determining the one or more second content documents comprise determining the one or more second content documents when the first relevancy score exceeds the first predefined threshold score” encompasses no more than generically invoking a processor to apply the Judicial Exception step of determining the one or more second content documents when the first relevancy score exceeds the first predefined threshold score;
the limitation “wherein the set of attributes includes the hot index, and the hot index indicates topics associated with the one or more second content documents trending beyond a predefined range of numbers” encompasses no more than generically invoking a processor to apply the Judicial Exception step of determining the time- series data based on hot index indicating topics associated with one or more second content documents trending beyond a predefined range of numbers;
the limitation “wherein generating the forecast comprises generating the forecast in a time-series pattern using the time-series model” encompasses no more than generically invoking a processor to apply the Judicial Exception step of generating the forecast in a time-series pattern;
Other than being generally linked to the steps of the Judicial Exception, the additional elements in the above step(s) is/are recited at a high-level of generality, without technological detail of how the particular steps are performed technologically.
The additional element(s) of “memory” and/or “non-transitory storage medium” are generically recited to store data and/or instructions of the Judicial Exception.
The additional element(s) of “using an Artificial Intelligence (Al) model” and “based on implementing Retrieval-Augmented Generation (RAG) technique” are generically recited to perform the obtaining and extracting steps described only by a result-oriented solution with insufficient detail for how the steps are accomplished.
Indeed, the instant claims (1) attempted to cover a solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result; (2) used of a computer or other machinery in its ordinary capacity for economic or other tasks or simply added a general purpose computer or computer components after the fact to the Judicial Exception and (3) generally applied the Judicial Exception to a generic computing environment without limitation indicative of practical application (See MPEP 2106.04(d)I). Thus, the claims are no more than Mere Instruction to Apply the Judicial Exception (See MPEP 2106.05(f)) or adding insignificant extra-solution activity to the judicial exception (See MPEP 2106.05(g)), which do not integrate the cited Judicial Exception into practical application (Step 2A prong two: No) The claims are directed to a Judicial Exception.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a processor to conduct financial forecast amounts to no more than mere instructions to apply the exception using generic computer components. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. No additional element currently recited in the claims amount the claims to be significantly more than the cited abstract idea. (Step 2B: No)
Therefore, claims 1, 2, 5, 10-12, 15-17, 20, 25, 26 and 30 are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Response to Arguments
Applicant's arguments filed on 07/06/2026 have been fully considered but they are not persuasive.
Regarding the applicant’s argument that the claims integrate the Judicial Exception into practical application, the examiner respectfully disagrees. The applicant contended that the claims “impose a specific structure on heterogeneous data before the data is used by the time-series model” and “do not preempt all techniques for financial forecasting …are limited to a specific forecasting pipeline in which selected content documents are converted into time-series data, integrated with predefined knowledge bases, processed through the recited encoding, aggregation, mean calculation, and imputation steps, and then used as disruption indexes for a time-series model”. However, the examiner noted that because a judicial exception is not eligible subject matter, Bilski, 561 U.S. at 601, 95 USPQ2d at 1005-06 (quoting Chakrabarty, 447 U.S. at 309, 206 USPQ at 197 (1980)), if there are no additional claim elements besides the judicial exception, or if the additional claim elements merely recite another judicial exception, that is insufficient to integrate the judicial exception into a practical application. See, e.g., RecogniCorp, LLC v. Nintendo Co. Particularly, time series model is a mathematical concept that is not eligible for patent and adding the ineligible mathematical concept to the financial forecasting would be insufficient to integrate the Judicial Exception into practical application. As such, the argument is not persuasive.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CHO KWONG whose telephone number is (571)270-7955. The examiner can normally be reached 9am - 5pm EST M-F.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, MICHAEL W ANDERSON can be reached at 571-270-0508. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/CHO YIU KWONG/Primary Examiner, Art Unit 3693
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