DETAILED ACTION
This Non-Final Office Action is in response to the application filed on 01/07/2025.
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 .
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-30 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
An original claim may lack written description support when (1) the claim defines the invention in functional language specifying a desired result but the disclosure fails to sufficiently identify how the function is performed or the result is achieved or (2) a broad genus claim is presented but the disclosure only describes a narrow species with no evidence that the genus is contemplated. See Ariad Pharms., Inc. v. Eli Lilly & Co., 598 F.3d 1336, 1349-50 (Fed. Cir. 2010) (en banc).
While the Applicant specifies in claims 1 and 16 that “computing a first relevancy score for each of the categorized one or more first content documents based on expert-input from real-world, wherein the expert-input indicates a knowledge bank comprising impact of categorization on one or more entities”, there is no written content as to how or what specific algorithms are performed (i.e. formulas, algorithms, sequence of mathematical steps, process of determination, for example) in order to calculate the first relevancy score. As such, the disclosure does not objectively demonstrate that the applicant actually invented—was in possession of—the claimed subject matter.
While the Applicant specifies in claims 1 and 16 that “determining time-series data based on computing an impact factor associated with each of a set of attributes associated with the one or more second content documents”, there is no written content as to how or what specific algorithms are performed (i.e. formulas, algorithms, sequence of mathematical steps, process of determination, for example) in order to calculate the impact factor associated with each of a set of attributes associated with the second documents. As such, the disclosure does not objectively demonstrate that the applicant actually invented—was in possession of—the claimed subject matter.
While the Applicant specifies in claims 1 and 16 that “generating disruption indexes based on integrating the determined time- series data and one or more predefined knowledge bases”, there is no written content as to how or what specific algorithms are performed (i.e. formulas, algorithms, sequence of mathematical steps, process of determination, for example) in order to generate disruption indexes that a forecast is generated upon. As such, the disclosure does not objectively demonstrate that the applicant actually invented—was in possession of—the claimed subject matter.
While the Applicant specifies in claims 1 and 16 that “generating disruption indexes based on integrating the determined time- series data and one or more predefined knowledge bases”, there is no written content as to how or what specific algorithms are performed (i.e. formulas, algorithms, sequence of mathematical steps, process of determination, for example) in order to generate disruption indexes that a forecast is generated upon. As such, the disclosure does not objectively demonstrate that the applicant actually invented—was in possession of—the claimed subject matter.
While the Applicant specifies in claims 4 and 18 that “obtaining embeddings based on vectorization of textual data associated with the expert-input using an Artificial Intelligence (AI)model … compute the first relevancy score based on the output”, there is no written content as to how or what specific AI model are performed (i.e. formulas, algorithms, sequence of mathematical steps, process of determination, for example) in order to obtain embeddings based on vectorization of textual data associated with the expert-input leading to the calculation the first relevancy score. As such, the disclosure does not objectively demonstrate that the applicant actually invented—was in possession of—the claimed subject matter.
While the Applicant specifies in claims 6 and 21 that “compute a second relevancy score based on the historical input”, there is no written content as to how or what specific algorithms are performed (i.e. formulas, algorithms, sequence of mathematical steps, process of determination, for example) in order to compute the second relevancy score. As such, the disclosure does not objectively demonstrate that the applicant actually invented—was in possession of—the claimed subject matter.
The written description requirement can be satisfied if the particular steps, i.e., algorithm, necessary to perform the claimed function were “described in the specification.” In re Hayes Microcomputer Prods, Inc. Patent Litigation, 982 F.2d 1527, 1533-34, 25 USPQ2d 1241, (Fed. Cir. 1992).
As such, claims 1-30 are rejected as failing the written description requirement.
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 1-30 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;
computing a first relevancy score for each of the categorized one or more first content documents based on expert-input from real-world, wherein the expert-input indicates a knowledge bank comprising impact of categorization on one or more entities;
determining one or more second content documents among the categorized one or more first content documents based on correlating the first relevancy score with a first predefined threshold score, wherein the one or more second content documents correspond to the one or more entities;
determining time-series data based on computing an impact factor associated with each of a set of attributes associated with the one or more second content documents, wherein the impact factor indicates a significant quantification of a subsequent impact corresponding to the one or more entities in response to the one or more second content documents;
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; and
generating a forecast of the one or more entities based on the generated disruption indexes.
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;
compute a first relevancy score for each of the categorized one or more first content documents based on expert-input from real-world, wherein the expert-input indicates a knowledge bank comprising impact of categorization on one or more entities;
determine one or more second content documents among the categorized one or more first content documents based on correlating the first relevancy score with a predefined threshold score, wherein the one or more second content documents correspond to the one or more entities;
determine time-series data based on computing an impact factor associated with each of a set of attributes associated with the one or more second content documents, wherein the impact factor indicates a significant quantification of a subsequent impact corresponding to the one or more entities in response to the one or more second content documents;
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; and
generate a forecast of the one or more entities based on the generated disruption indexes.
Claims 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 3 and 18 similarly recite:
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.
Claims 4 and 19 similarly recite:
wherein computing the first relevancy score comprises:
obtaining embeddings based on vectorization of textual data associated with the expert-input using an Artificial Intelligence (Al) model;
extracting output from the embeddings based on implementing Retrieval-Augmented Generation (RAG) technique, wherein the output indicates at least one of, semantic similarity scores, comments, and meta information associated with the expert-input; and
computing the first relevancy score based on the output.
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 6 and 21 similarly recite:
wherein prior to determining the one or more second content documents, the method comprises:
obtaining historical input based on correlating one or more past content documents and the one or more first content documents, wherein the historical
input indicates events associated with the one or more first content documents that occurred in the past; and
computing a second relevancy score based on the historical input.
Claims 7 and 22 similarly recite:
wherein determining the one or more second content documents comprise determining the one or more second content documents when the second relevancy score exceeds a second predefined threshold score.
Claims 8 and 23 similarly recite:
wherein prior to determining the time-series data, the method comprises: obtaining the set of attributes based on analyzing the one or more second content documents.
Claims 9 and 24 similarly recite:
wherein obtaining the set of attributes comprises obtaining at least one of, the first relevancy score, a first sentiment, a hot index, a second sentiment, uniqueness, a category, an industry, and duration of the one or more second content documents.
Claims 10 and 25 similarly recite:
wherein 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 hot index indicates topics associated with the one or more second content documents trending beyond a predefined range of numbers.
Claims 12 and 27 similarly recite:
wherein obtaining the second sentiment indicates obtaining at least one of, a positive impact, negative impact, and a neutral impact on the one or more entities using a sentiment model based on the one or more second content documents.
Claims 13 and 28 similarly recite:
wherein prior to generating the disruption indexes, the method comprises:
obtaining the 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) and a Purchasing Managers Index (PMI), industrial production, Gross Domestic Product (GDP), Exchange-Traded Fund (ETF) baseline, forex, sector-specific ETF, commodities, and stocks data.
Claims 14 and 29 similarly recite:
wherein prior to determining the time- series data, the method comprises: ranking the one or more second content documents based on the impact factor
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 “computing a first relevancy score for each of the categorized one or more first content documents based on expert-input from real-world, wherein the expert-input indicates a knowledge bank comprising impact of categorization on one or more entities” encompasses no more than generically invoking a processor to apply the Judicial Exception step of computing a first relevancy score for each of the categorized first content documents;
the limitation “determine one or more second content documents among the categorized one or more first content documents based on correlating the first relevancy score with a predefined threshold score, wherein the one or more second content documents correspond to the 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 computing an impact factor associated with each of a set of attributes associated with the one or more second content documents, wherein the impact factor indicates a significant quantification of a subsequent impact corresponding to the one or more entities in response to the one or more second content documents” encompasses no more than generically invoking a processor to apply the Judicial Exception step of determining time-series data based on computing the impact factor associated with each of a set of attributes associated with second content documents;
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 “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 “obtaining embeddings based on vectorization of textual data associated with the expert-input using an Artificial Intelligence (Al) model; extracting output from the embeddings based on implementing Retrieval-Augmented Generation (RAG) technique, wherein the output indicates at least one of, semantic similarity scores, comments, and meta information associated with the expert-input; and computing the first relevancy score based on the output” encompasses no more than generically invoking a processor to apply the Judicial Exception step of obtaining embeddings using an AI model, extracting output using RAG technique and computing the first relevancy score;
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 “obtaining historical input based on correlating one or more past content documents and the one or more first content documents, wherein the historical input indicates events associated with the one or more first content documents that occurred in the past; and
computing a second relevancy score based on the historical input” encompasses no more than generically invoking a processor to apply the Judicial Exception step of obtaining historical input based on correlating one or more past content documents and the one or more first content documents and computing the second relevancy score;
the limitation “wherein determining the one or more second content documents comprise determining the one or more second content documents when the second relevancy score exceeds a second 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 second relevancy score exceeds a second predefined threshold score;
the limitation “wherein prior to determining the time-series data, the method comprises: obtaining the set of attributes based on analyzing the one or more second content documents” encompasses no more than generically invoking a processor to apply the Judicial Exception step of obtaining the set of attributes based on analyzing the one or more second content documents;
the limitation “wherein obtaining the set of attributes comprises obtaining at least one of, the first relevancy score, a first sentiment, a hot index, a second sentiment, uniqueness, a category, an industry, and duration of the one or more second content documents” encompasses no more than generically invoking a processor to apply the Judicial Exception step of obtaining the aforementioned attributes;
the limitation “wherein obtaining the first sentiment comprises obtaining expert views from the real-world in response to the one or more second content documents” encompasses no more than generically invoking a processor to apply the Judicial Exception step of obtaining expert views from the real-world in response to the one or more second content documents;
the limitation “wherein 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 obtaining the hot index;
the limitation “wherein obtaining the second sentiment indicates obtaining at least one of, a positive impact, negative impact, and a neutral impact on the one or more entities using a sentiment model based on the one or more second content documents” encompasses no more than generically invoking a processor to apply the Judicial Exception step of obtaining at least one of, a positive impact, negative impact, and a neutral impact on the one or more entities using a sentiment model;
the limitation “obtaining the 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) and 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 “wherein prior to determining the time- series data, the method comprises: ranking the one or more second content documents based on the impact factor” encompasses no more than generically invoking a processor to apply the Judicial Exception step of ranking the one or more second content documents based on the impact factor prior to determining the time- series data;
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 1-30 are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Examiner Note
Based on prior searches, the prior art deemed closest to the instant claims is Mangipudi et al. (US 2016/0171540) Mangipudi discloses forecasting based on time-series analysis of categorized contents. However, Mangipudi fails to disclose the ordered combination of “computing a first relevancy score for each of the categorized one or more first content documents based on expert-input from real-world, wherein the expert-input indicates a knowledge bank comprising impact of categorization on one or more entities; determining one or more second content documents among the categorized one or more first content documents based on correlating the first relevancy score with a first predefined threshold score, wherein the one or more second content documents correspond to the one or more entities; determining time-series data based on computing an impact factor associated with each of a set of attributes associated with the one or more second content documents, wherein the impact factor indicates a significant quantification of a subsequent impact corresponding to the one or more entities in response to the one or more second content documents; 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; and generating a forecast of the one or more entities based on the generated disruption indexes”. No combination of prior art was found to render the claims obvious without applying improper hindsight. Thus, claims 1-30 are found to be novel and non-obvious.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Mangipudi et al. (US 2016/0171540)
Siig et al. (US 20140058775)
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/CHO YIU KWONG/Primary Examiner, Art Unit 3693
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