DETAILED ACTION
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 .
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d), with respect to Korean parent Application No. KR10-2022-0027280, filed on 3/3/2022.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 8/30/2024 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Notice to Applicant
Claims 1-3, 5-8 and 10 are presently amended.
Claim 4 is cancelled.
Claims 1-3 and 5-10 are pending.
Response to Amendment
Applicant’s amendments are acknowledged.
Response to Arguments
Applicant's arguments filed 6/30/2026 have been fully considered in view of further consideration of statutory law, Office policy, precedential common law, and the cited prior art as necessitated by the amendments to the claims, and are persuasive in-part for the reasons set forth below.
35 USC § 101 Rejections
First, Applicant argues that “…claims 1 and 10, as amended, integrate the alleged abstract idea
into a practical application. As explained in the specification, existing techniques for analyzing
unstructured data to predict stock price volatility "do not provide grounds for prediction results,
and thus brokerage firm employees or individual stock investors, who use prediction results, do
not trust the prediction results or have limitations to using the prediction results." Published
Specification ("Spec.") at para. [0005]. There was accordingly "a demand for a method for
providing a market explanation event, which predicts the direction of a stock price by extracting
and analyzing an event based on unstructured data, and explains grounds for prediction results or
the cause of actual stock price rise or fall." Spec. at para. [0006].
Claims 1 and 10 address this problem through a specific technical mechanism: "the event
extraction module 121 may input the acquired unstructured data to an event extraction model
which is an unsupervised learning model, and may extract an event whose correlation with a
stock price index is greater than or equal to a pre-set first threshold value." Spec. at para. [0039].
Claim 1, for example, recites language which corresponds to this benefit by reciting "a step of
extracting, by the market explanation event extraction system, an event that influences a stock
market based on the acquired unstructured data, wherein the step of extracting the event
comprises inputting the acquired unstructured data to an unsupervised learning model, and
identifying the event based on the acquired unstructured data."
In addition, Applicant respectfully submits that "inputting the acquired unstructured data
to an unsupervised learning model, and identifying the event based on the acquired unstructured
data" as recited my claims 1 and 10 is not itself part of the mental process or certain-methods-of-
organizing-human-activity concepts identified by the Office, since it cannot be performed in the
human mind” [Arguments, pages 2-3].
In response, Applicant’s arguments are considered and are persuasive in-part. First, and with respect the to the argument that “"inputting the acquired unstructured data to an unsupervised learning model, and identifying the event based on the acquired unstructured data" as recited my claims 1 and 10 is not itself part of the mental process or certain-methods-of- organizing-human-activity concepts identified by the Office, since it cannot be performed in the human mind”, Examiner disagrees and observes that the abstract idea groupings of “mental processes” and “certain methods of organizing human activity”, are separate and distinct. Whether or not the recited steps can be performed in the human mind is a consideration for the abstract idea grouping of “mental processes”, not “certain methods of organizing human activity”.
Thus, Examiner observes that the recitation of an unsupervised learning model in the independent claims obviates the “mental processes” rejection.
However, Examiner respectfully maintains that the present invention recites concepts identified as “certain methods of organizing human activity”. In particular, the present limitations describe steps for fundamental economic principles or practices, which includes hedging, insurance, and mitigating risk. Specifically, analyzing stock market data to explain events and predict price movement is considered to describe steps for hedging and mitigating risk of adverse pricing events. As such, claims 1 and 10 recite concepts identified as abstract ideas.
Further, with regard to the assertion that the present claims recite a technological solution to a technological problem, Examiner respectfully disagrees. In particular, Examiner maintains that the additional elements are not recited or integrated into the claims at a level of particularity which could be considered to demonstrate significantly more than the judicial exception. Specifically, Examiner observes that the computer implemented and unsupervised learning model elements are considered to amount to no more than mere instructions to apply the exception (i.e. “apply it”) using a generic computer component (see MPEP 2106.05(f)), like the following MPEP example:
i. A commonplace business method or mathematical algorithm being applied on a general purpose computer, Alice Corp. Pty. Ltd. V. CLS Bank Int’l, 573 U.S. 208, 223, 110 USPQ2d 1976, 1983 (2014); Gottschalk v. Benson, 409 U.S. 63, 64, 175 USPQ 673, 674 (1972); Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015);
Accordingly, these additional elements do not integrate the abstract idea into a practical application. The remaining dependent claims do not recite any new additional elements, and thus do not integrate the abstract idea into a practical application. Thus, Examiner respectfully maintains that the present invention recites a judicial exception without significantly more. As such, Examiner remains unpersuaded.
35 USC § 102/103 Rejections
First, Applicant argues that “The Office contends that Groll discloses "wherein the step of extracting the event comprises inputting the acquired unstructured data to an unsupervised learning model," citing to para. [0050]. See Office Action at page 13. Applicant respectfully disagrees.
Groll discloses "categorization of events 303 and extraction of event parameters 304 may
be carried out by a human expert or alternatively/additionally by computer-aided clustering mechanisms such as self-organizing maps (SOM, single-layer rectangular-shaped neural networks with unsupervised learning)." Groll at para. [0050].
Accordingly, Groll discloses extracting event parameters using neural networks. Groll however, does not use the neural networks to perform event identification, let alone, "wherein the step of extracting the event comprises inputting the acquired unstructured data to an unsupervised learning model, and extracting the event comprises identifying the event based on the acquired unstructured data," as recited by claim 1” [Arguments, pages 3-5].
In response, Applicant’s arguments are considered but are not persuasive. Examiner respectfully disagrees and maintains that von Groll anticipates the above-argued limitation. In particular, Examiner first observes that the neural network of von Groll explicitly relies upon unsupervised learning based on acquired unstructured data. Further, Examiner maintains that von Groll’s neural network is used to perform event identification through a categorization process. Specifically, Examiner directs the Applicant to (von Groll, ¶ 50, It is straight forward that significant events can strongly impact the price of a related financial instrument. The example provided herein shows how a particular product withdrawal event can be defined for the purposes of this methodology with the parameters specified in Table 2. This categorization of events 303 and extraction of event parameters 304 may be carried out by a human expert or alternatively/additionally by computer-aided clustering mechanisms such as self-organizing maps (SOM, single-layer rectangular-shaped neural networks with unsupervised learning). (discloses unsupervised learning) Where the human expert is judging out of experience and business insight which parameters are relevant, the computer-aided mechanisms look for commonalities of media coverage features in each event class to extract parameters as promising candidates. The definition of the event classes and the event parameters can be refined in a feedback loop via sub-process 105).
Here von Groll discloses that a process for inputting the acquired unstructured data to an unsupervised learning model, and extracting the event by identifying the event (i.e. through categorization) based on the acquired unstructured data. Thus, Examiner respectfully maintains that von Groll anticipates the above-argued limitation. As such, Examiner remains unpersuaded.
Second, Applicant argues that “In rejecting claim 4, the Office relies on paragraphs [0050], [0057], and [0062]. Applicant respectfully submits that Groll fails to teach or suggest, inter alia, "determining an event whose prediction accuracy is greater than or equal to a second threshold value among the selected events as the market explanation event." Paragraphs [0057] and [0062] of Groll merely disclose comparing a prediction with the actual price movement for post-mortem analysis and model refinement. Paragraph [0050], cited by the Examiner for the threshold limitation, only describes event categorization and extraction of event parameters using clustering techniques. It does not disclose evaluating the prediction accuracy of selected events against a threshold value or determining a market explanation event based on such threshold. Accordingly, the cited passages of Groll, either individually or collectively, do not teach "determining an event whose prediction accuracy is greater than or equal to a second threshold value among the selected events as the market explanation event," as recited in amended claim 1” [Arguments, page 5].
In response, Applicant’s arguments are considered but are not persuasive. Examiner respectfully disagrees and maintains that von Groll anticipates the above-argued limitation. In particular, Examiner directs the Applicant to (von Groll, ¶ 26, The building (104) and execution (604) of these models--numerical in nature--require the properties of the financial instruments (and/or product sales) and media coverage to be captured in quantitative parameters. Examples of communication parameters are volume of articles published per day, the weight of the publications in which the articles are published, the tonality of the articles. Tonality is a parameter to capture the sentiment expressed in the media, for example in determining if an article on a particular issue as positive or negative from the perspective of the subject company. In order to apply tonality in the disclosed methodology, it needs to be cast into quantitative terms. Further parameters may be the editorial attention time span for a specific issue (e.g. number of days until messages are subsiding to 10% of maximum coverage per day), (discloses threshold attention span parameters for modeling) any reference to stock price impact in early media coverage or any reference to other problems in the same company within the initial coverage. As used herein, the phrase "media coverage" may include any publicly available information from various types of sources, including newspapers, radio, online reporting, magazines, news bulletins or feeds, press releases, television, court filings (e.g., including a court filing initiating a lawsuit, an appeal, or a judicial decision), blog postings, tweets, etc.).
Here von Groll discloses that a process for determining an event whose prediction accuracy is greater than or equal to a second threshold value among the selected events as the market explanation event by utilizing a threshold attention span (e.g. a number of days until messages are subsiding to 10% of maximum coverage per day) to determine a degree to which the event is affecting the selected stock’s price. Thus, Examiner respectfully maintains that von Groll anticipates the above-argued limitation. As such, Examiner remains unpersuaded.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-3 and 5-10 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.
Step 1: Claims 1-3 and 5-10 are directed to statutory categories, namely a process (claims 1-3 and 5-9), and a machine (claim 10).
Step 2A, Prong 1: Claims 1 and 10, in part, recite the following abstract idea:
…A market explanation event extraction method comprising: a step of acquiring, by…, stock price index data and unstructured data, wherein the unstructured data is of a text type for predicting a stock price; a step of extracting, by…, an event that influences a stock market based on the acquired unstructured data, wherein the step of extracting the event comprises inputting the acquired unstructured data to … and identifying the event based on the acquired unstructured data; a step of predicting, by… , a future direction of the stock price by analyzing the extracted event; and a step of selecting, by …, an event that matches a result of predicting the future direction by comparing the result of predicting the future direction and an actual stock price index, and extracting a market explanation event that explains a cause of a rise or fall of the stock price based on the selected event, wherein the step of extracting the market explanation event comprises: a step of selecting events that match the result of predicting the future direction by comparing the result of predicting the future direction and the actual stock price index; and a step of determining an event whose prediction accuracy is greater than or equal to a second threshold value among the selected events as the market explanation event. [Claim 1],
…configured to acquire a stock price index data and unstructured data, wherein the unstructured data is of a text type for predicting a stock price; …configured to: extract an event that influences a stock market based on the acquired unstructured data, by inputting the acquired unstructured data to … and identifying the event based on the acquired unstructured data, predict a future direction of a stock price by analyzing the extracted event, select an event that matches a result of predicting the future direction by comparing the result of predicting the future direction and an actual stock price index, and extract a market explanation event that explains a cause of a rise or fall of the stock price based on the selected event, wherein… is further configured to: select events that match the result of predicting the future direction by comparing the result of predicting the future direction and the actual stock price index; and determine an event whose prediction accuracy is greater than or equal to a second threshold value among the selected events as the market explanation event [Claim 10].
These concepts are not meaningfully different than the following concepts identified by the MPEP:
Concepts relating to certain methods of organizing human activity. The aforementioned limitations describe steps for fundamental economic principles or practices, which includes hedging, insurance, and mitigating risk. Specifically, analyzing stock market data to explain events and predict price movement is considered to describe steps for hedging and mitigating risk of adverse pricing events. As such, claims 1 and 10 recite concepts identified as abstract ideas.
Dependent claims 2-3 and 5-9 recite limitations relative to the independent claims, including, for example:
…wherein the step of extracting the event comprises extracting an event whose correlation with a stock index is greater than or equal to a pre-set first threshold value [Claim 2],
…wherein the step of predicting the future direction comprises: a step of analyzing the extracted event and classifying an event type into a market event that influences an entirety or a part of the stock market, a corporation event that influences a specific corporation, or an industry event that influences a specific industry; and a step of predicting the future direction of the stock price according to the classified event type; and a step of calculating a prediction accuracy of the predicted direction [Claim 3],
…further comprising a step of analyzing, by the market explanation event extraction system, a plurality of market explanation events, and grouping market explanation events of the plurality of market explanation events whose correlation with one another is greater than or equal to a pre-set third threshold value [Claim 5],
…wherein the step of grouping comprises grouping market explanation events of the plurality of market explanation events whose correlation with one another is greater than or equal to the pre-set third threshold value with reference to a prediction accuracy, a range of a target influenced, and a range of stock price fluctuation caused by an event [Claim 6],
The limitations of these dependent claims are merely narrowing the abstract idea identified in the independent claims, and thus, the dependent claims also recite abstract ideas.
Step 2A, Prong 2: This judicial exception is not integrated into a practical application. In particular, claims 1 and 10 only recite the following additional elements –
…a market explanation event extraction system; … the market explanation event extraction system… an unsupervised learning model…; …the market explanation event extraction system…; …the market explanation event extraction system… [Claim 1],
A market explanation event extraction system comprising: a communication system…; at least one processor… an unsupervised learning model… [Claim 10].
The apparatus, learning model and executable instructions are recited at a high-level of generality (see MPEP § 2106.05(a)), like the following MPEP examples:
iii. Mere automation of manual processes, such as using a generic computer to process an application for financing a purchase, Credit Acceptance Corp. v. Westlake Services, 859 F.3d 1044, 1055, 123 USPQ2d 1100, 1108-09 (Fed. Cir. 2017) or speeding up a loan-application process by enabling borrowers to avoid physically going to or calling each lender and filling out a loan application, LendingTree, LLC v. Zillow, Inc., 656 Fed. App'x 991, 996-97 (Fed. Cir. 2016) (non-precedential);
viii. Arranging transactional information on a graphical user interface in a manner that assists traders in processing information more quickly, Trading Technologies v. IBG LLC, 921 F.3d 1084, 1093-94, 2019 USPQ2d 138290 (Fed. Cir. 2019);
iii. Gathering and analyzing information using conventional techniques and displaying the result, TLI Communications, 823 F.3d at 612-13, 118 USPQ2d at 1747-48.
Furthermore, the computer implemented element is considered to amount to no more than mere instructions to apply the exception using a generic computer component (see MPEP 2106.05(f)), like the following MPEP example:
i. A commonplace business method or mathematical algorithm being applied on a general purpose computer, Alice Corp. Pty. Ltd. V. CLS Bank Int’l, 573 U.S. 208, 223, 110 USPQ2d 1976, 1983 (2014); Gottschalk v. Benson, 409 U.S. 63, 64, 175 USPQ 673, 674 (1972); Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015);
Accordingly, these additional elements do not integrate the abstract idea into a practical application.
The remaining dependent claims do not recite any new additional elements, and thus do not integrate the abstract idea into a practical application.
Step 2B: Claims 1 and 10 and their underlying limitations, steps, features and terms, considered both individually and as a whole, do not include additional elements that are sufficient to amount to significantly more than the judicial exception for the following reasons:
Independent Claims 1 and 10 only recite the following additional elements -
…a market explanation event extraction system; … the market explanation event extraction system… an unsupervised learning model…; …the market explanation event extraction system…; …the market explanation event extraction system… [Claim 1],
A market explanation event extraction system comprising: a communication system…; at least one processor… an unsupervised learning model… [Claim 10].
These elements do not amount to significantly more than the abstract idea for the reasons discussed in 2A prong 2 with regard to MPEP 2106.05(a) and MPEP 2106.05(f). By the failure of the elements to integrate the abstract idea into a practical application there, the additional elements likewise fail to amount to an inventive concept that is significantly more than an abstract idea here, in Step 2B.
As such, both individually or in combination, these limitations do not add significantly more to the judicial exception.
The remaining dependent claims do 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 dependent claims do not recite any new additional elements other than those mentioned in the independent claims, which amount to no more than mere instructions to apply the exception using a generic computer component (see MPEP 2106.05(f)). As such, these claims are not patent eligible.
Claim Rejections - 35 USC § 102
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.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-3, 5-7 and 10 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by von Groll et al., U.S. Publication No. 2012/0059771 [hereinafter von Groll].
Regarding Claim 1, von Groll anticipates …A market explanation event extraction method comprising: a step of acquiring, by a market explanation event extraction system, stock price index data and unstructured data, wherein the unstructured data is of a text type for predicting a stock price (von Groll, ¶ 2, The disclosed method concerns making predictions in respect of media coverage, product sales or stock price performance, following material business developments, positive or negative, such as the win/loss of a major contract, a corporate crisis, an accidents, the discovery of a side-effect, or a significant court ruling. The method proceeds from the recognition that factorial analysis of the event (development) can help predict how the news will be reported, which in turn can be used to predict the impact on stock price, or products sales, and other significant aspect of business performance), (Id., ¶ 43, Data collection and preparation (101 in FIG. 1 and entire FIG. 2): There are various aspects to the task of data preparation, such as data gathering, storage & indexing, cleansing, and enrichment. These tasks are typically carried out by a computer system (discloses market explanation event extraction system) with features such as: [0044] Data gathering consists of retrieving media and financial data (discloses acquiring stock price index data) (201 and 202, respectively) from different providers. An example of an embodiment of the gathering process is as follows: a human being specifies in a graphical user interface (GUI) of the computer system the details of the desired data sets, such as date ranges and companies. The computer system transforms the user entry to send an HTTP request to the service provider, where the URL used in the HTTP request contains the query details such as the date ranges and desired objects. The service provider responds with delivering (also via HTTP) an XML or comma separated value (CSV) file (the news or financial feed)), (Id., ¶ 45, Data storage & indexing (203) consists of parsing the retrieved information and storing it in a structured way such as a computer database. The computer database is either linked by a computer network to or is part of the computer system that gathered the data. The database needs to ensure by its design the connection between various data sub-sets organized in tables. For reasons set out in the description of the data processing in the latter section of this document, it is beneficial to store and index articles according to such data elements as article title, the publication it appeared in, the date of publication, as well as a summary or article "snippet") (discloses acquiring unstructured text data));
a step of extracting, by the market explanation event extraction system, an event that influences a stock market based on the acquired unstructured data, wherein the step of extracting the event comprises inputting the acquired unstructured data to an unsupervised learning model, and identifying the event based on the acquired unstructured data (Id., ¶ 43, Data collection and preparation (101 in FIG. 1 and entire FIG. 2): There are various aspects to the task of data preparation, such as data gathering, storage & indexing, cleansing, and enrichment. These tasks are typically carried out by a computer system (discloses market explanation event extraction system) with features such as: [0044] Data gathering consists of retrieving media and financial data (201 and 202, respectively) from different providers. An example of an embodiment of the gathering process is as follows: a human being specifies in a graphical user interface (GUI) of the computer system the details of the desired data sets, such as date ranges and companies. The computer system transforms the user entry to send an HTTP request to the service provider, where the URL used in the HTTP request contains the query details such as the date ranges and desired objects. The service provider responds with delivering (also via HTTP) an XML or comma separated value (CSV) file (the news or financial feed)), (Id., ¶ 31, FIG. 3 illustrates exemplary process for event identification, (discloses extracting and identifying events) classification, and parameterization of an exemplary embodiment of the disclosed method), (Id., ¶ 37, The process is presented in the flowchart given in FIG. 1. The detailed description of the individual steps is presented herein. The process begins with the data collection and preparation (101). Afterwards the events are categorized into event classes and the parameters specific to each of the particular event classes are determined (102). In the current example the event class is "product withdrawal" and the event parameters are given in Table 1 (A-J)), (Id., ¶ 50, It is straight forward that significant events can strongly impact the price of a related financial instrument. The example provided herein shows how a particular product withdrawal event can be defined for the purposes of this methodology with the parameters specified in Table 2. This categorization of events 303 and extraction of event parameters 304 may be carried out by a human expert or alternatively/additionally by computer-aided clustering mechanisms such as self-organizing maps (SOM, single-layer rectangular-shaped neural networks with unsupervised learning). (discloses unsupervised learning) Where the human expert is judging out of experience and business insight which parameters are relevant, the computer-aided mechanisms look for commonalities of media coverage features in each event class to extract parameters as promising candidates. The definition of the event classes and the event parameters can be refined in a feedback loop via sub-process 105);
a step of predicting, by the market explanation event extraction system, a future direction of the stock price by analyzing the extracted event (Id., ¶ 2, The disclosed method concerns making predictions in respect of media coverage, product sales or stock price performance, following material business developments, positive or negative, such as the win/loss of a major contract, a corporate crisis, an accidents, the discovery of a side-effect, or a significant court ruling. The method proceeds from the recognition that factorial analysis of the event (development) can help predict how the news will be reported, which in turn can be used to predict the impact on stock price (discloses predicting a direction of a stock price), or products sales, and other significant aspect of business performance), (Id., ¶ 23, Determine event parameters (603) [0024] Model execution: Forecasts the short term and/or long term impact of the event with particular consideration of the effect on media coverage and the subsequent effect on the short term price movement of the financial instrument(s) (604)), (Id., ¶ 43, Data collection and preparation (101 in FIG. 1 and entire FIG. 2): There are various aspects to the task of data preparation, such as data gathering, storage & indexing, cleansing, and enrichment. These tasks are typically carried out by a computer system (discloses market explanation event extraction system) with features such as: [0044] Data gathering consists of retrieving media and financial data (201 and 202, respectively) from different providers. An example of an embodiment of the gathering process is as follows: a human being specifies in a graphical user interface (GUI) of the computer system the details of the desired data sets, such as date ranges and companies. The computer system transforms the user entry to send an HTTP request to the service provider, where the URL used in the HTTP request contains the query details such as the date ranges and desired objects. The service provider responds with delivering (also via HTTP) an XML or comma separated value (CSV) file (the news or financial feed));
and a step of selecting, by the market explanation event extraction system, an event that matches a result of predicting the future direction by comparing the result of predicting the future direction and an actual stock price index, and extracting a market explanation event that explains a cause of a rise or fall of the stock price based on the selected event, wherein the step of extracting the market explanation event comprises: a step of selecting events that match the result of predicting the future direction by comparing the result of predicting the future direction and the actual stock price index (Id., ¶ 43, Data collection and preparation (101 in FIG. 1 and entire FIG. 2): There are various aspects to the task of data preparation, such as data gathering, storage & indexing, cleansing, and enrichment. These tasks are typically carried out by a computer system (discloses market explanation event extraction system) with features such as: [0044] Data gathering consists of retrieving media and financial data (201 and 202, respectively) from different providers. An example of an embodiment of the gathering process is as follows: a human being specifies in a graphical user interface (GUI) of the computer system the details of the desired data sets, such as date ranges and companies. The computer system transforms the user entry to send an HTTP request to the service provider, where the URL used in the HTTP request contains the query details such as the date ranges and desired objects. The service provider responds with delivering (also via HTTP) an XML or comma separated value (CSV) file (the news or financial feed)), (Id., ¶ 33, FIG. 5 illustrates exemplary processes for establishing a model for determining media influence of reported events in an exemplary embodiment of the disclosed method), (Id., ¶ 56, As initially described in the overview section of this document, after building the numerical model its operational usage is to predict the influence of the event parameters on the media impact and business impact and their influence on the price movement of a financial instrument. Moreover, predictions might be made about how much revenue will be at risk or similar types of financial impact information (e.g., in connection with an amount of a potential damage award associated with a class action lawsuit). Note that a predicted financial impact might be associated with product sales (e.g., after a safety recall of the product or a critical report on a product's quality), a stock price, a stock option price, a bond value, a credit default swap value, and/or other underlying instruments (e.g., a corporation's portfolio of products and services that generate cash flow)), (Id., ¶ 57, At the occurrence of a given event (for example a corruption case or a lawsuit filed between two corporations), (discloses selecting an event) the user will retrieve in a process 601 initial media coverage of the event, and the financial parameters in process 602. The actual retrieval processes may well be the same as described in the data collection process 101, with the distinction of course that actual data on media coverage and price movement of the financial instrument only becomes available as the event unfolds. In a following process 603 the user will then query a database of event classes and their related parameters. If the user finds a suitable event class for the given event, he will then extract the related parameter values from the news about the current event in sub-process 603. In sub-process 604, the parameter values are then applied in conjunction with the relevant communication and financial parameters to the numerical model (an example of how to apply those values for prediction is given in the next paragraphs. This process 604 is similar to the one described under model testing 503, only that output 605 is the actual prediction upon which the user can act (discloses extracting a market explanation)), (Id., ¶ 62, Subsequently, the actual price movement data will be available, and the prediction can be analyzed against this benchmark in a post-mortem analysis 606. The process of analyzing and model refining is similar as described under 504. (discloses comparing prediction with an actual price index));
and a step of determining an event whose prediction accuracy is greater than or equal to a second threshold value among the selected events as the market explanation event (Id., ¶ 50, It is straight forward that significant events can strongly impact the price of a related financial instrument. The example provided herein shows how a particular product withdrawal event can be defined for the purposes of this methodology with the parameters specified in Table 2. This categorization of events 303 and extraction of event parameters 304 may be carried out by a human expert or alternatively/additionally by computer-aided clustering mechanisms such as self-organizing maps (SOM, single-layer rectangular-shaped neural networks with unsupervised learning). (discloses unsupervised learning) Where the human expert is judging out of experience and business insight which parameters are relevant, the computer-aided mechanisms look for commonalities of media coverage features in each event class to extract parameters as promising candidates. The definition of the event classes and the event parameters can be refined in a feedback loop via sub-process 105), (Id., ¶ 26, The building (104) and execution (604) of these models--numerical in nature--require the properties of the financial instruments (and/or product sales) and media coverage to be captured in quantitative parameters. Examples of communication parameters are volume of articles published per day, the weight of the publications in which the articles are published, the tonality of the articles. Tonality is a parameter to capture the sentiment expressed in the media, for example in determining if an article on a particular issue as positive or negative from the perspective of the subject company. (discloses threshold sentiment parameters for modeling) In order to apply tonality in the disclosed methodology, it needs to be cast into quantitative terms. Further parameters may be the editorial attention time span for a specific issue (e.g. number of days until messages are subsiding to 10% of maximum coverage per day), (discloses threshold attention span parameters for modeling) any reference to stock price impact in early media coverage or any reference to other problems in the same company within the initial coverage. As used herein, the phrase "media coverage" may include any publicly available information from various types of sources, including newspapers, radio, online reporting, magazines, news bulletins or feeds, press releases, television, court filings (e.g., including a court filing initiating a lawsuit, an appeal, or a judicial decision), blog postings, tweets, etc.).
Regarding Claim 2, von Groll anticipates …The market explanation event extraction method of claim 1…
von Groll further anticipates …wherein the step of extracting the event comprises extracting an event whose correlation with a stock index is greater than or equal to a pre-set first threshold value (Id., ¶ 26, The building (104) and execution (604) of these models--numerical in nature--require the properties of the financial instruments (and/or product sales) and media coverage to be captured in quantitative parameters. Examples of communication parameters are volume of articles published per day, the weight of the publications in which the articles are published, the tonality of the articles. Tonality is a parameter to capture the sentiment expressed in the media, for example in determining if an article on a particular issue as positive or negative from the perspective of the subject company. In order to apply tonality in the disclosed methodology, it needs to be cast into quantitative terms. Further parameters may be the editorial attention time span for a specific issue (e.g. number of days until messages are subsiding to 10% of maximum coverage per day), (discloses threshold attention span parameter for modeling) any reference to stock price impact in early media coverage or any reference to other problems in the same company within the initial coverage. As used herein, the phrase "media coverage" may include any publicly available information from various types of sources, including newspapers, radio, online reporting, magazines, news bulletins or feeds, press releases, television, court filings (e.g., including a court filing initiating a lawsuit, an appeal, or a judicial decision), blog postings, tweets, etc.).
Regarding Claim 3, von Groll anticipates …The market explanation event extraction method of claim 1…
von Groll further anticipates …wherein the step of predicting the future direction comprises: a step of analyzing the extracted event and classifying an event type into a market event that influences an entirety or a part of the stock market, a corporation event that influences a specific corporation, or an industry event that influences a specific industry (Id., ¶ 37, The process is presented in the flowchart given in FIG. 1. The detailed description of the individual steps is presented herein. The process begins with the data collection and preparation (101). Afterwards the events are categorized into event classes and the parameters specific to each of the particular event classes are determined (discloses event classification) (102). In the current example the event class is "product withdrawal" and the event parameters are given in Table 1 (A-J)), (Id., ¶ 48, Categorization of event classes and event parameters (102 in FIG. 1 and entire FIG. 3): Given the data of historical events, and corresponding press coverage and prices movements of financial instruments gathered in process 101, with a subsequent process 102 one can categorize the events. The events of interest are related to a period of abnormal return of a financial instrument (stock price gain or loss driven primarily by the impact of the event rather than normal business operations/conditions). In a first sub-process 301 one can identify the abnormal return of a financial instrument by various filtering approaches, e.g. filtering the returns above a certain value, either in absolute terms or relative to a peer group (basket/index). In a following sub-process 302 the media coverage related to the abnormal return is being processed. Media coverage published at the time of the abnormal return is filtered for relevance to the abnormal return, for example using filter criteria such as publication date and key-word search of the object (company) experiencing the abnormal return. In a following sub-process 303, the event as covered in the media causing the abnormal return is identified and categorized into classes (e.g., in accordance with a manual or automatic assessment of risk). Examples of such events are, but are not limited to: Government Probes; Product Safety; Product Approvals/Recalls; Terrorism; Resignations; Industrial Accidents; Corruption/Bribes; Strikes; Significant Lawsuits (e.g., a class action lawsuit) (discloses classifying events at company-wide, industry-wide and market-wide levels));
a step of predicting the future direction of the stock price according to the classified event type (Id., ¶ 57, At the occurrence of a given event (for example a corruption case or a lawsuit filed between two corporations), the user will retrieve in a process 601 initial media coverage of the event, and the financial parameters in process 602. The actual retrieval processes may well be the same as described in the data collection process 101, with the distinction of course that actual data on media coverage and price movement of the financial instrument only becomes available as the event unfolds. In a following process 603 the user will then query a database of event classes and their related parameters. If the user finds a suitable event class for the given event, he will then extract the related parameter values from the news about the current event in sub-process 603. In sub-process 604, the parameter values are then applied in conjunction with the relevant communication and financial parameters to the numerical model (an example of how to apply those values for prediction is given in the next paragraphs (discloses predicting stock prices according to event class/type). This process 604 is similar to the one described under model testing 503, only that output 605 is the actual prediction upon which the user can act);
and a step of calculating a prediction accuracy of the predicted direction (Id., ¶ 62, Subsequently, the actual price movement data will be available, and the prediction can be analyzed against this benchmark in a post-mortem analysis 606. The process of analyzing and model refining is similar as described under 504. (discloses comparing prediction with an actual price index to calculate a prediction accuracy)).
Regarding Claim 5, von Groll anticipates …The market explanation event extraction method of claim 1…
von Groll further anticipates …further comprising a step of analyzing, by the market explanation event extraction system, a plurality of market explanation events, and grouping market explanation events of the plurality of market explanation events whose correlation with one another is greater than or equal to a pre-set third threshold value (Id., ¶ 43, Data collection and preparation (101 in FIG. 1 and entire FIG. 2): There are various aspects to the task of data preparation, such as data gathering, storage & indexing, cleansing, and enrichment. These tasks are typically carried out by a computer system (discloses market explanation event extraction system) with features such as: [0044] Data gathering consists of retrieving media and financial data (201 and 202, respectively) from different providers. An example of an embodiment of the gathering process is as follows: a human being specifies in a graphical user interface (GUI) of the computer system the details of the desired data sets, such as date ranges and companies. The computer system transforms the user entry to send an HTTP request to the service provider, where the URL used in the HTTP request contains the query details such as the date ranges and desired objects. The service provider responds with delivering (also via HTTP) an XML or comma separated value (CSV) file (the news or financial feed)), (Id., ¶ 48, Categorization of event classes and event parameters (discloses groping events) (102 in FIG. 1 and entire FIG. 3): Given the data of historical events, and corresponding press coverage and prices movements of financial instruments gathered in process 101, with a subsequent process 102 one can categorize the events. The events of interest are related to a period of abnormal return of a financial instrument (stock price gain or loss driven primarily by the impact of the event rather than normal business operations/conditions). In a first sub-process 301 one can identify the abnormal return of a financial instrument by various filtering approaches, e.g. filtering the returns above a certain value, either in absolute terms or relative to a peer group (basket/index). In a following sub-process 302 the media coverage related to the abnormal return is being processed. Media coverage published at the time of the abnormal return is filtered for relevance to the abnormal return, for example using filter criteria such as publication date and key-word search of the object (company) experiencing the abnormal return. In a following sub-process 303, the event as covered in the media causing the abnormal return is identified and categorized into classes (e.g., in accordance with a manual or automatic assessment of risk). Examples of such events are, but are not limited to: Government Probes; Product Safety; Product Approvals/Recalls; Terrorism; Resignations; Industrial Accidents; Corruption/Bribes; Strikes; Significant Lawsuits (e.g., a class action lawsuit)), (Id., ¶ 26, The building (104) and execution (604) of these models--numerical in nature--require the properties of the financial instruments (and/or product sales) and media coverage to be captured in quantitative parameters. Examples of communication parameters are volume of articles published per day, the weight of the publications in which the articles are published, the tonality of the articles. Tonality is a parameter to capture the sentiment expressed in the media, for example in determining if an article on a particular issue as positive or negative from the perspective of the subject company. (discloses threshold sentiment parameters for modeling) In order to apply tonality in the disclosed methodology, it needs to be cast into quantitative terms. Further parameters may be the editorial attention time span for a specific issue (e.g. number of days until messages are subsiding to 10% of maximum coverage per day), (discloses threshold attention span parameters for modeling) any reference to stock price impact in early media coverage or any reference to other problems in the same company within the initial coverage. As used herein, the phrase "media coverage" may include any publicly available information from various types of sources, including newspapers, radio, online reporting, magazines, news bulletins or feeds, press releases, television, court filings (e.g., including a court filing initiating a lawsuit, an appeal, or a judicial decision), blog postings, tweets, etc.).
Regarding Claim 6, von Groll anticipates …The market explanation event extraction method of claim 5…
von Groll further anticipates …The market explanation event extraction method of claim 5, wherein the step of grouping comprises grouping market explanation events of the plurality of market explanation events whose correlation with one another is greater than or equal to the pre-set third threshold value with reference to a prediction accuracy, a range of a target influenced, and a range of stock price fluctuation caused by an event (Id., ¶ 48, Categorization of event classes and event parameters (discloses groping events) (102 in FIG. 1 and entire FIG. 3): Given the data of historical events, and corresponding press coverage and prices movements of financial instruments gathered in process 101, with a subsequent process 102 one can categorize the events. The events of interest are related to a period of abnormal return of a financial instrument (stock price gain or loss driven primarily by the impact of the event rather than normal business operations/conditions). In a first sub-process 301 one can identify the abnormal return of a financial instrument by various filtering approaches, e.g. filtering the returns above a certain value, either in absolute terms or relative to a peer group (basket/index). In a following sub-process 302 the media coverage related to the abnormal return is being processed. Media coverage published at the time of the abnormal return is filtered for relevance to the abnormal return, for example using filter criteria such as publication date and key-word search of the object (company) experiencing the abnormal return. In a following sub-process 303, the event as covered in the media causing the abnormal return is identified and categorized into classes (e.g., in accordance with a manual or automatic assessment of risk). Examples of such events are, but are not limited to: Government Probes; Product Safety; Product Approvals/Recalls; Terrorism; Resignations; Industrial Accidents; Corruption/Bribes; Strikes; Significant Lawsuits (e.g., a class action lawsuit)), (Id., ¶ 26, The building (104) and execution (604) of these models--numerical in nature--require the properties of the financial instruments (and/or product sales) and media coverage to be captured in quantitative parameters. Examples of communication parameters are volume of articles published per day, the weight of the publications in which the articles are published, the tonality of the articles. Tonality is a parameter to capture the sentiment expressed in the media, for example in determining if an article on a particular issue as positive or negative from the perspective of the subject company. (discloses threshold sentiment parameters for modeling) In order to apply tonality in the disclosed methodology, it needs to be cast into quantitative terms. Further parameters may be the editorial attention time span for a specific issue (e.g. number of days until messages are subsiding to 10% of maximum coverage per day), (discloses threshold attention span parameters for modeling) any reference to stock price impact in early media coverage or any reference to other problems in the same company within the initial coverage. As used herein, the phrase "media coverage" may include any publicly available information from various types of sources, including newspapers, radio, online reporting, magazines, news bulletins or feeds, press releases, television, court filings (e.g., including a court filing initiating a lawsuit, an appeal, or a judicial decision), blog postings, tweets, etc.), (Id., ¶ 62, Subsequently, the actual price movement data will be available, and the prediction can be analyzed against this benchmark in a post-mortem analysis 606. The process of analyzing and model refining is similar as described under 504. (discloses comparing prediction with an actual price index to calculate a prediction accuracy)), (Id., ¶ 43, Data collection and preparation (101 in FIG. 1 and entire FIG. 2): There are various aspects to the task of data preparation, such as data gathering, storage & indexing, cleansing, and enrichment. These tasks are typically carried out by a computer system with features such as: [0044] Data gathering consists of retrieving media and financial data (201 and 202, respectively) from different providers. An example of an embodiment of the gathering process is as follows: a human being specifies in a graphical user interface (GUI) of the computer system the details of the desired data sets, such as date ranges and companies. (discloses a date range of a target influenced) The computer system transforms the user entry to send an HTTP request to the service provider, where the URL used in the HTTP request contains the query details such as the date ranges and desired objects. The service provider responds with delivering (also via HTTP) an XML or comma separated value (CSV) file (the news or financial feed)), (Id., ¶ 27, Financial parameters include the trading volume and price movement of the instrument itself (e.g. stocks), its derivatives (e.g. options), and benchmarks (e.g. indices) as well as the underlying quantitative factors including but not limited to products revenues, products pricing and profit margins. Additional financial parameters such as the (abnormal) return or volatility over a given period can be calculated based on the price movement and other primary financial parameters. (discloses price range/volatility parameters) Examples of further parameters related or useful as context to the financial instrument are overall market volatility, industry volatility, background economic data such as consumer confidence indices, house prices and recent interest rate developments, % stock ownership of individuals and institutions, corporate profit/earnings benchmarked against industry, long term dividend record and marketing statistics for the sales of products/services in the relevant sector).
Regarding Claim 7, von Groll anticipates …The market explanation event extraction method of claim 6…
von Groll further anticipates …wherein the step of grouping comprises, when the market event that influences the entirety or the part of the stock market or the industry event that influences the specific industry occurs, extracting information on an actual price index and a stork price direction in the past when other market explanation events of the same group as that of the occurred market event or industry event occurred (Id., ¶ 13, Securities analysts and many investors employ quantitative valuation models of financial instruments. For readability the description in this document will by way of example sometimes refer to stock prices, however, it is understood that the methodology is applicable to other sales of products, to all corporate financial instruments, such as, but not limited to, bonds, currencies, commodities contracts, or indeed derivative constructs of those asset classes. Financial instruments will typically be analyzed in conjunction with related instruments, e.g. in the case of company stock prices together with the related industry index. (discloses extracting price index of a company and a related industry) The methodology described herein is concerned with extending such models of pricing financial instruments to include the impact of sudden financially sensitive events, such as, but not limited to, industrial accidents, earnings warnings and product withdrawals. Depending on the financial instrument in question, the main media attention may not necessarily involve companies, but could just as well relate to governments or other organizations, commodities, and so forth), (Id., ¶ 27, Financial parameters include the trading volume and price movement of the instrument itself (e.g. stocks), its derivatives (e.g. options), and benchmarks (e.g. indices) as well as the underlying quantitative factors including but not limited to products revenues, products pricing and profit margins. Additional financial parameters such as the (abnormal) return or volatility over a given period can be calculated based on the price movement and other primary financial parameters. Examples of further parameters related or useful as context to the financial instrument are overall market volatility, industry volatility, background economic data such as consumer confidence indices, house prices and recent interest rate developments, % stock ownership of individuals and institutions, corporate profit/earnings benchmarked against industry, long term dividend record and marketing statistics for the sales of products/services in the relevant sector), (Id., ¶ 51, Population of event classes and event parameters (103 in FIG. 1 and entire FIG. 4): Once the event classes and event parameters have been determined, historical events can be mapped to these classes and their event parameter values can be extracted. In sub-process 401, for each historical event the relevant media coverage is filtered out, identified, and allocated to the relevant event class. In sub-process 402, the event parameter values for each historical event are being extracted. The set of event parameters is specific to the event class, although more than one event class may use a particular parameter. In a sub-process 403, the search criteria for this particular event are being identified and stored with search criteria of other historical events of the same class. (discloses occurrences of multiple events of the same class) The purpose of this sub-process 403 is to aid the development of automatic identification of media coverage of a particular event class).
Regarding Claim 10, von Groll anticipates …A market explanation event extraction system comprising: a communication system configured to acquire a stock price index data and unstructured data, wherein the unstructured data is of a text type for predicting a stock price (von Groll, ¶ 2, The disclosed method concerns making predictions in respect of media coverage, product sales or stock price performance, following material business developments, positive or negative, such as the win/loss of a major contract, a corporate crisis, an accidents, the discovery of a side-effect, or a significant court ruling. The method proceeds from the recognition that factorial analysis of the event (development) can help predict how the news will be reported, which in turn can be used to predict the impact on stock price, or products sales, and other significant aspect of business performance), (Id., ¶ 43, Data collection and preparation (101 in FIG. 1 and entire FIG. 2): There are various aspects to the task of data preparation, such as data gathering, storage & indexing, cleansing, and enrichment. These tasks are typically carried out by a computer system (discloses market explanation event extraction system) with features such as: [0044] Data gathering consists of retrieving media and financial data (discloses acquiring stock price index data) (201 and 202, respectively) from different providers. An example of an embodiment of the gathering process is as follows: a human being specifies in a graphical user interface (GUI) of the computer system the details of the desired data sets, such as date ranges and companies. The computer system transforms the user entry to send an HTTP request to the service provider, where the URL used in the HTTP request contains the query details such as the date ranges and desired objects. The service provider responds with delivering (also via HTTP) an XML or comma separated value (CSV) file (the news or financial feed)), (Id., ¶ 45, Data storage & indexing (203) consists of parsing the retrieved information and storing it in a structured way such as a computer database. The computer database is either linked by a computer network (discloses communication system) to or is part of the computer system that gathered the data. The database needs to ensure by its design the connection between various data sub-sets organized in tables. For reasons set out in the description of the data processing in the latter section of this document, it is beneficial to store and index articles according to such data elements as article title, the publication it appeared in, the date of publication, as well as a summary or article "snippet") (discloses acquiring unstructured text data));
at least one processor configured to: extract an event that influences a stock market based on the acquired unstructured data, wherein the step of extracting the event comprises inputting the acquired unstructured data to an unsupervised learning model, and identifying the event based on the acquired unstructured data (Id., ¶ 43, Data collection and preparation (101 in FIG. 1 and entire FIG. 2): There are various aspects to the task of data preparation, such as data gathering, storage & indexing, cleansing, and enrichment. These tasks are typically carried out by a computer system (discloses market explanation event extraction system) with features such as: [0044] Data gathering consists of retrieving media and financial data (201 and 202, respectively) from different providers. An example of an embodiment of the gathering process is as follows: a human being specifies in a graphical user interface (GUI) of the computer system the details of the desired data sets, such as date ranges and companies. The computer system transforms the user entry to send an HTTP request to the service provider, where the URL used in the HTTP request contains the query details such as the date ranges and desired objects. The service provider responds with delivering (also via HTTP) an XML or comma separated value (CSV) file (the news or financial feed)), (Id., ¶ 31, FIG. 3 illustrates exemplary process for event identification, (discloses extracting and identifying eventsevents) classification, and parameterization of an exemplary embodiment of the disclosed method), (Id., ¶ 37, The process is presented in the flowchart given in FIG. 1. The detailed description of the individual steps is presented herein. The process begins with the data collection and preparation (101). Afterwards the events are categorized into event classes and the parameters specific to each of the particular event classes are determined (102). In the current example the event class is "product withdrawal" and the event parameters are given in Table 1 (A-J)), (Id., ¶ 50, It is straight forward that significant events can strongly impact the price of a related financial instrument. The example provided herein shows how a particular product withdrawal event can be defined for the purposes of this methodology with the parameters specified in Table 2. This categorization of events 303 and extraction of event parameters 304 may be carried out by a human expert or alternatively/additionally by computer-aided clustering mechanisms such as self-organizing maps (SOM, single-layer rectangular-shaped neural networks with unsupervised learning). (discloses unsupervised learning) Where the human expert is judging out of experience and business insight which parameters are relevant, the computer-aided mechanisms look for commonalities of media coverage features in each event class to extract parameters as promising candidates. The definition of the event classes and the event parameters can be refined in a feedback loop via sub-process 105);
predict a future direction of the stock price by analyzing the extracted event, select an event that matches a result of predicting the future direction by comparing the result of predicting the future direction and an actual stock price index, and extract a market explanation event that explains a cause of a rise or fall of the stock price based on the selected event, wherein the one processor is further configured to: select events that match the result of predicting the future direction by comparing the result of predicting the future direction and the actual stock price index (Id., ¶ 2, The disclosed method concerns making predictions in respect of media coverage, product sales or stock price performance, following material business developments, positive or negative, such as the win/loss of a major contract, a corporate crisis, an accidents, the discovery of a side-effect, or a significant court ruling. The method proceeds from the recognition that factorial analysis of the event (development) can help predict how the news will be reported, which in turn can be used to predict the impact on stock price (discloses predicting a direction of a stock price), or products sales, and other significant aspect of business performance), (Id., ¶ 23, Determine event parameters (603) [0024] Model execution: Forecasts the short term and/or long term impact of the event with particular consideration of the effect on media coverage and the subsequent effect on the short term price movement of the financial instrument(s) (604)), (Id., ¶ 43, Data collection and preparation (101 in FIG. 1 and entire FIG. 2): There are various aspects to the task of data preparation, such as data gathering, storage & indexing, cleansing, and enrichment. These tasks are typically carried out by a computer system (discloses market explanation event extraction system) with features such as: [0044] Data gathering consists of retrieving media and financial data (201 and 202, respectively) from different providers. An example of an embodiment of the gathering process is as follows: a human being specifies in a graphical user interface (GUI) of the computer system the details of the desired data sets, such as date ranges and companies. The computer system transforms the user entry to send an HTTP request to the service provider, where the URL used in the HTTP request contains the query details such as the date ranges and desired objects. The service provider responds with delivering (also via HTTP) an XML or comma separated value (CSV) file (the news or financial feed)), (Id., ¶ 33, FIG. 5 illustrates exemplary processes for establishing a model for determining media influence of reported events in an exemplary embodiment of the disclosed method), (Id., ¶ 56, As initially described in the overview section of this document, after building the numerical model its operational usage is to predict the influence of the event parameters on the media impact and business impact and their influence on the price movement of a financial instrument. Moreover, predictions might be made about how much revenue will be at risk or similar types of financial impact information (e.g., in connection with an amount of a potential damage award associated with a class action lawsuit). Note that a predicted financial impact might be associated with product sales (e.g., after a safety recall of the product or a critical report on a product's quality), a stock price, a stock option price, a bond value, a credit default swap value, and/or other underlying instruments (e.g., a corporation's portfolio of products and services that generate cash flow)), (Id., ¶ 57, At the occurrence of a given event (for example a corruption case or a lawsuit filed between two corporations), (discloses selecting an event) the user will retrieve in a process 601 initial media coverage of the event, and the financial parameters in process 602. The actual retrieval processes may well be the same as described in the data collection process 101, with the distinction of course that actual data on media coverage and price movement of the financial instrument only becomes available as the event unfolds. In a following process 603 the user will then query a database of event classes and their related parameters. If the user finds a suitable event class for the given event, he will then extract the related parameter values from the news about the current event in sub-process 603. In sub-process 604, the parameter values are then applied in conjunction with the relevant communication and financial parameters to the numerical model (an example of how to apply those values for prediction is given in the next paragraphs. This process 604 is similar to the one described under model testing 503, only that output 605 is the actual prediction upon which the user can act (discloses extracting a market explanation)), (Id., ¶ 62, Subsequently, the actual price movement data will be available, and the prediction can be analyzed against this benchmark in a post-mortem analysis 606. The process of analyzing and model refining is similar as described under 504. (discloses comparing prediction with an actual price index));
and a step of determining an event whose prediction accuracy is greater than or equal to a second threshold value among the selected events as the market explanation event (Id., ¶ 50, It is straight forward that significant events can strongly impact the price of a related financial instrument. The example provided herein shows how a particular product withdrawal event can be defined for the purposes of this methodology with the parameters specified in Table 2. This categorization of events 303 and extraction of event parameters 304 may be carried out by a human expert or alternatively/additionally by computer-aided clustering mechanisms such as self-organizing maps (SOM, single-layer rectangular-shaped neural networks with unsupervised learning). (discloses unsupervised learning) Where the human expert is judging out of experience and business insight which parameters are relevant, the computer-aided mechanisms look for commonalities of media coverage features in each event class to extract parameters as promising candidates. The definition of the event classes and the event parameters can be refined in a feedback loop via sub-process 105), (Id., ¶ 26, The building (104) and execution (604) of these models--numerical in nature--require the properties of the financial instruments (and/or product sales) and media coverage to be captured in quantitative parameters. Examples of communication parameters are volume of articles published per day, the weight of the publications in which the articles are published, the tonality of the articles. Tonality is a parameter to capture the sentiment expressed in the media, for example in determining if an article on a particular issue as positive or negative from the perspective of the subject company. (discloses threshold sentiment parameters for modeling) In order to apply tonality in the disclosed methodology, it needs to be cast into quantitative terms. Further parameters may be the editorial attention time span for a specific issue (e.g. number of days until messages are subsiding to 10% of maximum coverage per day), (discloses threshold attention span parameters for modeling) any reference to stock price impact in early media coverage or any reference to other problems in the same company within the initial coverage. As used herein, the phrase "media coverage" may include any publicly available information from various types of sources, including newspapers, radio, online reporting, magazines, news bulletins or feeds, press releases, television, court filings (e.g., including a court filing initiating a lawsuit, an appeal, or a judicial decision), blog postings, tweets, etc.).
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 8-9 are rejected under 35 U.S.C. 103 as being unpatentable over von Groll.
Regarding Claim 8, von Groll anticipates …The market explanation event extraction method of claim 5…
While suggested in at least Fig. 2 and related Text, von Groll does not explicitly disclose …wherein the step of extracting the market explanation event comprises, when a discrepancy event that is discrepant with the result of predicting the future direction is selected as a result of comparing the result of predicting the future direction and the actual stock price, extracting, as an unexplainable event, a discrepancy event that does not provide any rationale for direction prediction of the discrepancy event among selected discrepancy events.
However, through KSR Rationale E (See MPEP 2141(III)(E)), von Groll discloses …wherein the step of extracting the market explanation event comprises, when a discrepancy event that is discrepant with the result of predicting the future direction is selected as a result of comparing the result of predicting the future direction and the actual stock price, extracting, as an unexplainable event, a discrepancy event that does not provide any rationale for direction prediction of the discrepancy event among selected discrepancy events.
First, von Groll discloses extracting an event (von Groll, ¶ 31, FIG. 3 illustrates exemplary process for event identification, (discloses extracting events) classification, and parameterization of an exemplary embodiment of the disclosed method), (Id., ¶ 37, The process is presented in the flowchart given in FIG. 1. The detailed description of the individual steps is presented herein. The process begins with the data collection and preparation (101). Afterwards the events are categorized into event classes and the parameters specific to each of the particular event classes are determined (102). In the current example the event class is "product withdrawal" and the event parameters are given in Table 1 (A-J)).
Further, von Groll discloses predicting a direction of a stock price and comparing it to an actual stock price (von Groll, ¶ 57, At the occurrence of a given event (for example a corruption case or a lawsuit filed between two corporations), the user will retrieve in a process 601 initial media coverage of the event, and the financial parameters in process 602. The actual retrieval processes may well be the same as described in the data collection process 101, with the distinction of course that actual data on media coverage and price movement of the financial instrument only becomes available as the event unfolds. In a following process 603 the user will then query a database of event classes and their related parameters. If the user finds a suitable event class for the given event, he will then extract the related parameter values from the news about the current event in sub-process 603. In sub-process 604, the parameter values are then applied in conjunction with the relevant communication and financial parameters to the numerical model (an example of how to apply those values for prediction is given in the next paragraphs (discloses predicting stock prices according to event class/type). This process 604 is similar to the one described under model testing 503, only that output 605 is the actual prediction upon which the user can act).
it would have been obvious to try, by one of ordinary skill in the art at the time of the invention was made, to determine unexplainable/discrepancy events since there are a finite number of identified, predictable potential solutions (i.e. explainable events or unexplainable events) to the recognized need (stock price movement predictions) and one of ordinary skill in the art could have pursued the known potential solutions with a reasonable expectation of success (the cost and benefits are known).
Thus, through KSR Rationale E (See MPEP 2141(III)(E)), von Groll discloses …wherein the step of extracting the market explanation event comprises, when a discrepancy event that is discrepant with the result of predicting the future direction is selected as a result of comparing the result of predicting the future direction and the actual stock price, extracting, as an unexplainable event, a discrepancy event that does not provide any rationale for direction prediction of the discrepancy event among selected discrepancy events.
Regarding Claim 9, von Groll anticipates …The market explanation event extraction method of claim 8…
von Groll further discloses …wherein the step of grouping comprises grouping all events that are extracted as unexplainable events and all events that are extracted as market explanation events into the same group when the event types of all the events classified in the direction prediction process for a pre-set effective period belong to the same category, and wherein, if an event belonging to the same event type as the grouped events is extracted, the market explanation event extraction system provides information on previous prediction results of the grouped events and an actual stock price index when predicting a stock price direction of the extracted event (Id., ¶ 48, Categorization of event classes and event parameters (102 in FIG. 1 and entire FIG. 3): Given the data of historical events, and corresponding press coverage and prices movements of financial instruments gathered in process 101, with a subsequent process 102 one can categorize the events. The events of interest are related to a period of abnormal return of a financial instrument (stock price gain or loss driven primarily by the impact of the event rather than normal business operations/conditions) (discloses grouping events of the same class during an effective period of abnormal return). In a first sub-process 301 one can identify the abnormal return of a financial instrument by various filtering approaches, e.g. filtering the returns above a certain value, either in absolute terms or relative to a peer group (basket/index). In a following sub-process 302 the media coverage related to the abnormal return is being processed. Media coverage published at the time of the abnormal return is filtered for relevance to the abnormal return, for example using filter criteria such as publication date and key-word search of the object (company) experiencing the abnormal return. In a following sub-process 303, the event as covered in the media causing the abnormal return is identified and categorized into classes (e.g., in accordance with a manual or automatic assessment of risk). Examples of such events are, but are not limited to: Government Probes; Product Safety; Product Approvals/Recalls; Terrorism; Resignations; Industrial Accidents; Corruption/Bribes; Strikes; Significant Lawsuits (e.g., a class action lawsuit)), (Id., ¶ 51, Population of event classes and event parameters (103 in FIG. 1 and entire FIG. 4): Once the event classes and event parameters have been determined, historical events can be mapped to these classes and their event parameter values can be extracted. (discloses previous prediction results for historic events) In sub-process 401, for each historical event the relevant media coverage is filtered out, identified, and allocated to the relevant event class. In sub-process 402, the event parameter values for each historical event are being extracted. The set of event parameters is specific to the event class, although more than one event class may use a particular parameter. In a sub-process 403, the search criteria for this particular event are being identified and stored with search criteria of other historical events of the same class. The purpose of this sub-process 403 is to aid the development of automatic identification of media coverage of a particular event class), (Id., ¶ 57, At the occurrence of a given event (for example a corruption case or a lawsuit filed between two corporations), the user will retrieve in a process 601 initial media coverage of the event, and the financial parameters in process 602. The actual retrieval processes may well be the same as described in the data collection process 101, with the distinction of course that actual data on media coverage and price movement of the financial instrument only becomes available as the event unfolds. In a following process 603 the user will then query a database of event classes and their related parameters. If the user finds a suitable event class for the given event, he will then extract the related parameter values from the news about the current event in sub-process 603. In sub-process 604, the parameter values are then applied in conjunction with the relevant communication and financial parameters to the numerical model (an example of how to apply those values for prediction is given in the next paragraphs (discloses predicting stock prices according to event class/type). This process 604 is similar to the one described under model testing 503, only that output 605 is the actual prediction upon which the user can act).
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.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Cummings, U.S. Publication No. 2021/0264620, discloses a system and method of providing and recording context-specific advice in the form of an artificial intelligence view of a hierarchical portfolio.
Krishnan, U.S. Publication No. 2020/0202436, discloses a method and system using machine learning for prediction of stocks and/or other market instruments price volatility, movements and future pricing by applying random forest based techniques.
Arazi, U.S. Patent No. 10,467,327, discloses a real-time event transcription system and method.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to NICHOLAS D BOLEN whose telephone number is (408)918-7631. The examiner can normally be reached Monday - Friday 8:00 AM - 5:00 PM PST.
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/NICHOLAS D BOLEN/ Examiner, Art Unit 3624 /PATRICIA H MUNSON/Supervisory Patent Examiner, Art Unit 3624
