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 .
DETAILED ACTION
This Office Action is in response to an AMENDMENT entered September 29, 2025 for the patent application 18/469,874.
Status of Claims
Claims 1 – 20 are pending in the application.
Claims 1, 12 and 17 are currently amended in the application.
Claim Objections
Claims 1, 12 and 17 are objected to because of the following informalities: The amended limitation of claims 1, 12 and 17 states “…unfollow the a social media..”. The Examiner is interpreting this as a typographical error. Appropriate correction is required.
Response to Arguments
Examiner would like to point out that the Supreme Court in KSR International Co. v. Teleflex Inc. described seven rationales to support rejections under 35 U.S.C. 103:
Combining prior art elements according to known methods to yield predictable results;
Simple substitution of one known element for another to obtain predictable results;
Use of known technique to improve similar devices (methods, or products) in the same way;
Applying a known technique to a known device (method, or product) ready for improvement to yield predictable results;
“Obvious to try” –choosing from a finite number of identified, predictable solutions, with a reasonable expectation of success;
Known work in one field of endeavor may prompt variations of it for use in either the same field or a different one based on design incentives or other market forces if the variations would have been predictable to one of ordinary skill in the art; and
Some teaching, suggestion, or motivation in the prior art that would have led one of ordinary skill to modify the prior art reference or to combine prior art reference teachings to arrive at the claimed invention.
Prior art is not limited just to the references being applied, but includes the understanding of one of ordinary skill in the art. The prior art reference (or references when combined) need not teach or suggest all the claim limitations; however, Office personnel must explain why the difference(s) between the prior art and the claimed invention would have been obvious to one of ordinary skill in the art. The “mere existence of differences between the prior art and an invention does not establish the invention’s nonobviousness.” see Dann v. Johnson, 425 U.S. 219, 230 (1976).
Applicant's arguments filed with an Amendment on September 29, 2025 have been fully considered but they are not persuasive.
Applicant Argument:
“While the Office Action at page 4 alleges that the claims disclose a method of "fundamental economic principles or practices," the actual recitation of any fundamental economic principles or practices, including hedging, insurance, mitigating risk, is not
included in the claims.“, (see page 12 of the Remarks).
Examiner’s Response:
Examiner respectfully disagrees. The 2019 Revised Patent Subject Matter Eligibility Guidance states that a category of abstract ideas is a "Fundamental Economic Practice", which are concepts relating to the economy and commerce, such as agreements between people in the form of contracts, legal obligations, and business relations. The method for analyzing one or more user chance predictions to determine a user classification falls under the category of concepts relating to business relations and commerce. Furthermore unlike rejections under 35 U.S.C 102 and 35 U.S.C 103 which are evidenced based, 35 U.S.C 101 is not evidence based but rather is matter of law and as such, no evidence is required. Novelty and non-obviousness, have no bearing on whether a claim recites an abstract idea. Indeed, the Federal Circuit has made this clear -rejecting an argument substantially similar to Appellants' in Ultramercial, Inc. v. Hulu, LLC, 772 F.3d 709, 715 (Fed. Cir. 2014) ("We do not agree . . . that the addition of merely novel or non-routine components to the claimed idea necessarily turns an abstraction into something concrete.").
Applicant Argument:
“In addition, Applicant submits that the independent claims are not directed to an abstract idea at least because the present claims recite novel and technical features that are more than a generalized application of an abstract idea, but rather involve a
practical application of a particular technological solution.“, (see page 12 of the Remarks).
Examiner’s Response:
Examiner respectfully disagrees. Novelty and non-obviousness, have no bearing on whether a claim recites an abstract idea. Indeed, the Federal Circuit has made this clear - rejecting an argument substantially similar to Appellants' in Ultramercial, Inc. v. Hulu, LLC, 772 F.3d 709, 715 (Fed. Cir. 2014) ("We do not agree . . . that the addition of merely novel or non-routine components to the claimed idea necessarily turns an abstraction into something concrete."). Yet the prohibition against patenting an ineligible abstract idea cannot be circumvented by limiting the use of the abstract idea to a particular technological environment. See Bilski v. Kappas, 561 U.S. 593, 610-11 (2010). The Federal Circuit also has made clear, that "the addition of merely novel or non-routine components to the claimed idea [does not] necessarily tum[] an abstraction into something concrete." Ultramercial, 772 F.3d at 715.
Applicant Argument:
“Analogous to Example 37 of the 2019 PEG Examples as explained above, the independent claims as a whole integrate the alleged abstract idea into a practical
application.“, (see page 15 of the Remarks).
Examiner’s Response:
Examiner respectfully disagrees. In Example 37, the invention addresses the issue usage of icons by providing a method for rearranging icons on a graphical user interface (GUI) wherein the method moves the most used icons to a position on the GUI, specifically, closest to the “start” icon of the computer system, based on a determined amount of use. The amount of use of each icon is automatically determined by a processor that tracks the number of times each icon is selected or how much memory has been allocated to the individual processes associated with each icon over a period of time. This is not the same problem being addressed in this application.
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.
Claim(s) 1 – 20 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to an abstract idea without significantly more.
Claims 1 - 20 are either directed to a method or system or computer readable medium, which are statutory categories of invention. (Step 1: YES).
The Examiner has identified method claim 1 as the claim that represents the claimed invention for analysis and is similar to system claim 12 and computer readable claim 17. Claim 1 recites the limitations of:
( A ) receiving, by one or more processors, a bullish threshold, a bearish threshold, and one or more time periods from at least one user;
( B ) receiving, by the one or more processors, a plurality of predictions corresponding to at least one prediction user from one or more prediction databases, wherein each of the plurality of predictions includes a basis set identifier, a date, and a basis set prediction value;
( C ) receiving, by the one or more processors, one or more basis sets from one or more databases;
( D ) based on the plurality of predictions, determining, by the one or more processors, at least one p-score for each of the one or more time periods;
( E ) calculating, by the one or more processors, a null distribution of successful predictions based on the one or more basis sets and the one or more time periods;
( F ) determining, by the one or more processors, a prediction user p-value based on the null distribution and the at least one p-score;
( G ) selecting, by the one or more processors, a classification for the at least one prediction user based on the prediction user p-value; and
( H ) displaying, by the one or more processors, at least one graphical widget corresponding to the classification on one or more interfaces of a user device, the at least one graphical widget including a recommendation to follow or unfollow the a social media platform of the prediction user.
These limitations without the bolded limitations above, cover performance of the limitations as certain methods of organizing human activity under their broadest reasonable interpretation.
More specifically, these limitations cover performance of the limitations as a fundamental economic practice.
In summary, if claim 1 limitations, under its broadest reasonable interpretation, covers performance of the limitation as a fundamental economic practice, then it falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. Accordingly, the claim recites an abstract idea. Claims 12 and 17 are also abstract for similar reasons. (Step 2A-Prong 1: YES. The claims are abstract).
The use of the one or more processors or any of the bolded limitations in claim 1 are just applying generic computer components to the recited abstract limitations. Similar arguments apply to claims 12 and 17.
Therefore, the above mentioned judicial exception is not integrated into a practical application by merely applying generic computer components (bolded elements).
Furthermore, the “receiving”, “selecting” and “displaying” steps are recited at a high level of generality and amounts to mere data gathering/transmitting, which are forms of insignificant extra-solution activity (See MPEP 2106.05(g): CyberSource v. Retail Decisions, Inc., 654 F.3d 1366, 1375 (Fed. Cir. 2011); and OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 1363 (Fed. Cir. 2015)).
In addition, supported by specification, the computer hardware are recited at a high-level of generality (i.e., as a generic processor performing a generic computer function) such that it amounts no more than mere instructions to apply the exception using a generic computer component., see MPEP 2106.05(f), where applying a computer or using a computer is not indicative of a practical application).
Claim 1, limitation ( A ) – ( H ) above in Applicant’s specification para [0029], which discloses “In general, any process or operation discussed in this disclosure that is understood to be computer-implement able, such as the processes illustrated in FIG. 2, may be performed by one or more processors of a computer system, such any of the systems or devices in the environment 100 of FIG. 1, as described above. A process or process step performed by one or more processors may also be referred to as an operation. The one or more processors may be configured to perform such processes by having access to instructions (e.g., software or computer-readable code) that, when executed by the one or more processors, cause the one or more processors to perform the processes. The instructions may be stored in a memory of the computer system. A processor may be a central processing unit (CPU), a graphics processing unit (GPU), or any suitable types of processing unit.“.
Also, claim 1, limitation ( B ) and ( C ) above in Applicant’s specification para [0034], which discloses “The method may further include receiving, by the one or more processors, a plurality of predictions corresponding to at least one prediction user from one or more prediction databases, wherein each of the plurality of pre dictions includes a basis set identifier, a date, and a basis set prediction value (Step 204). The predictions may have been previously stored in one or more prediction databases. The prediction databases may receive the predictions from one or more social media platforms. In some embodiments, the system may receive the predictions from the social media platforms, in addition to receiving some predictions from the prediction databases. The predictions may be a subset of all predictions made by the prediction user. For example, the subset may correspond to predictions that were made during a particular period of time. In some embodiments, the user may specify the one or more time frames for the subset. Additionally, or alternatively, the subset may corresponded to a predefined range (e.g., the past week, the past 24 hours).“.
Also, claim 1, limitation ( H ) above in Applicant’s specification para [0063], which discloses “The method may include displaying, by the one or more processors, at least one graphical widget corresponding to the classification on one or more interfaces of a user device (Step 216). For example, if a user has a "skilled" classification, the user interface may display a graphical widget that includes "skilled." In some embodiments, the graphical widget may include a recommendation to follow the predication user on one or more social media platforms. In other embodiments, the graphical widget may include a recommendation to unfollow a prediction user on one or more social media platforms (e.g., when the user no longer has a "skilled" classification).“.
Also, claim 1, limitation ( H ) above in Applicant’s specification para [0034], which discloses “The method may further include receiving, by the one or more processors, a plurality of predictions corresponding to at least one prediction user from one or more prediction databases, wherein each of the plurality of pre-dictions includes a basis set identifier, a date, and a basis set prediction value (Step 204). The predictions may have been previously stored in one or more prediction databases. The prediction databases may receive the predictions from one or more social media platforms. In some embodiments, the system may receive the predictions from the social media platforms, in addition to receiving some predictions from the prediction databases. The predictions may be a subset of all predictions made by the prediction user. For example, the subset may correspond to predictions that were made during a particular period of time. In some embodiments, the user may specify the one or more time frames for the subset.“. Similar arguments apply to claims 12 and 17.
Accordingly, these additional elements, when considered separately and as an ordered combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Therefore, claims 1, 12 and 17 are directed to an abstract idea without a practical application. (Step 2A-Prong 2: NO. The additional claimed elements are not integrated into a practical application).
The claims 1 , 12 and 17 do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, when considered separately and as an ordered combination, they do not add significantly more (also known as an “inventive concept”) to the exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements (bolded elements above) amount to no more than mere instructions to apply the abstract idea using generic computer components. In conclusion, merely "applying" the exception using generic computer components cannot provide an inventive concept. Therefore, the claims 1, 12, and 17 are not patent eligible under 35 USC 101. (Step 2B: NO. The claims do not provide significantly more).
Dependent Claims
Dependent claims 2 – 11, 13 - 16 and 18 - 20 are also rejected under 35 U.S.C. 101. Dependent claims 2 – 11, 13 - 16 and 18 - 20 are further define the abstract idea or further define the extra-solution activities that are present in independent claim 1 thus abstract idea correspond to certain methods of organizing human activity as presented above. Claims 2 – 11, 13 - 16 and 18 - 20 clearly further define the abstract idea as stated above and further define extra-solution activities such as presenting data and transmitting/receiving data.
Furthermore, dependent claims 2 – 11, 13 - 16 and 18 - 20 do not include any additional elements that integrate the abstract idea into a practical application or are sufficient to amount to significantly more than the judicial exception when considered both individually and as an ordered combination.
Regarding claims 2, 13 and 18, these claims merely recite additional steps that amount to no more than insignificant extra-solution activity. Specifically, claim 2 states “wherein the at least one p-score includes a p-bullish score and a p-bearish score for each of the one or more time periods.”. These steps amount to no more than mere data gathering/analysis, which is a form of insignificant extra- solution activity (See M PEP 2016.05(g): CyberSource v. Retail Decisions, Inc., 654 F.3d 1366, 1375 (Fed. Cir. 2011); and GIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 1363 (Fed. Cir. 2015)). Such limitations do not integrate the abstract idea into a practical application, or amount to significantly than the abstract idea, because the courts have found the concept of data gathering to be well-understood, routine, and conventional activity (See MPEP 2106.05(d): GIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363 (Fed. Cir. 2015); and buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, (Fed. Cir. 2014)). Similar arguments can be made for claims 13 and 18.
Regarding claims 3, 14 and 19, these claims merely recite, "for each of the one or more time periods, determining, by the one or more processors, a basis set bullish comparison value corresponding to the one or more basis sets that have a higher value than the bullish threshold; and recording, by the one or more processors, the basis set bullish comparison value as the p-bullish score for the corresponding one or more time periods.“. These limitation merely recites basis set bullish comparison value corresponding to the one or more basis sets which amounts to no more than gathering/storing data which is a form of insignificant extra-solution activity (See MPEP 2106.0S(g)(3)(iii): GIP Technologies, 788 F.3d at 1363). This does not integrate the abstract idea into a practical application because it has been determined, by the courts, that the concept of storing data is well-understood, routine, and conventional activity (See MPEP 2106.0S(d)(II): Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334 (Fed. Cir. 2015)). Similar arguments can be made for claims 14 and 19.
Regarding claim 4, 15 and 20, these claims merely recite,, “for each of the one or more time periods, determining, by the one or more processors, a basis set bearish comparison value corresponding to the one or more basis sets that have a lower value than the bearish threshold; and recording, by the one or more processors, the basis set bearish comparison value as the p-bearish score for the corresponding one or more time periods.”. These limitation merely recites a basis set bearish comparison value corresponding to the one or more basis sets which amounts to no more than gathering/storing data which is a form of insignificant extra-solution activity (See MPEP 2106.0S(g)(3)(iii): GIP Technologies, 788 F.3d at 1363). This does not integrate the abstract idea into a practical application because it has been determined, by the courts, that the concept of storing data is well-understood, routine, and conventional activity (See MPEP 2106.0S(d)(II): Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334 (Fed. Cir. 2015)). . Similar arguments can be made for claims 15 and 20.
Regarding claim 5, this claim merely recite, "for each of the plurality of predictions, comparing, by the one or more processors, each of the plurality of predictions to the bullish threshold; and in response to determining that the prediction of the plurality of predictions is above the bullish threshold, determining, by the one or more processors, the p-bullish score for the one or more time periods and appending the p-bullish score to the at least one p-score for the corresponding time period.“. These limitation merely recites storing data in a server which amounts to no more than gathering/storing data which is a form of insignificant extra-solution activity (See MPEP 2106.0S(g)(3)(iii): GIP Technologies, 788 F.3d at 1363). This does not integrate the abstract idea into a practical application because it has been determined, by the courts, that the concept of storing data is well-understood, routine, and conventional activity (See MPEP 2106.0S(d)(II): Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334 (Fed. Cir. 2015)).
Regarding claim 6, this claim merely recite, “for each of the plurality of predictions, comparing, by the one or more processors, each of the plurality of predictions to the bearish threshold; and in response to determining that the prediction of the plurality of predictions is below the bearish threshold, determining, by the one or more processors, the p-bearish score for the one or more time periods and appending the p-bearish score to the at least one p-score for the corresponding time period.". These limitation merely recites storing data in a server which amounts to no more than gathering/storing data which is a form of insignificant extra-solution activity (See MPEP 2106.0S(g)(3)(iii): GIP Technologies, 788 F.3d at 1363). This does not integrate the abstract idea into a practical application because it has been determined, by the courts, that the concept of storing data is well-understood, routine, and conventional activity (See MPEP 2106.0S(d)(II): Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334 (Fed. Cir. 2015)).
Regarding claim 7, this claim merely add further description to the process of “wherein the basis set prediction value includes at least one bullish prediction or at least one bearish prediction.”, which amounts to no more than gathering/storing data which is a form of insignificant extra-solution activity (See MPEP 2106.0S(g)(3)(iii): GIP Technologies, 788 F.3d at 1363). This does not integrate the abstract idea into a practical application because it has been determined, by the courts, that the concept of storing data is well-understood, routine, and conventional activity (See MPEP 2106.0S(d)(II): Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334 (Fed. Cir. 2015)).
Regarding claim 8, this claim merely add further description to the process of “wherein the at least one bullish prediction or the at least one bearish prediction is expressed as a ratio.”, which amounts to no more than gathering/storing data which is a form of insignificant extra-solution activity (See MPEP 2106.0S(g)(3)(iii): GIP Technologies, 788 F.3d at 1363). This does not integrate the abstract idea into a practical application because it has been determined, by the courts, that the concept of storing data is well-understood, routine, and conventional activity (See MPEP 2106.0S(d)(II): Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334 (Fed. Cir. 2015)).
Regarding claims 9 and 16, this claim merely recite, “refining, by the one or more processors, the plurality of predictions for each of the at least one prediction user, the refining including removing at least one redundant predication from the plurality of predictions, wherein the at least one redundant prediction includes a similar basis set identifier and a similar date that is similar to at least one other prediction of the plurality of predictions for the at least one prediction user.". This does not integrate the abstract idea into a practical application because it does not impose any meaningful limitation on practicing the abstract idea. Similar arguments can be made for claim 16.
Regarding claim 10, this claim merely add further description to the process of “further refining, by the one or more processors, the plurality of predictions by a basis set type.”, which amounts to no more than gathering/storing data which is a form of insignificant extra-solution activity (See MPEP 2106.0S(g)(3)(iii): GIP Technologies, 788 F.3d at 1363). This does not integrate the abstract idea into a practical application because it has been determined, by the courts, that the concept of storing data is well-understood, routine, and conventional activity (See MPEP 2106.0S(d)(II): Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334 (Fed. Cir. 2015)).
Regarding claim 11, this claim merely add further description to the process of “determining, by the one or more processors, at least one low p-value that is lower than a p-value threshold; and flagging, by the one or more processors, the at least one prediction user that corresponds to the at least one low p-value.”, which amounts to no more than gathering/storing data which is a form of insignificant extra-solution activity (See MPEP 2106.0S(g)(3)(iii): GIP Technologies, 788 F.3d at 1363). This does not integrate the abstract idea into a practical application because it has been determined, by the courts, that the concept of storing data is well-understood, routine, and conventional activity (See MPEP 2106.0S(d)(II): Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334 (Fed. Cir. 2015)).
As a result, such limitations do not overcome the requirements as described above. Therefore, claims 2 – 11, 13 - 16 and 18 - 20 are directed to an abstract idea. Thus, claims 1 - 20 are not patent eligible.
Claim Rejections – 35 USC §103
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 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 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102 of this title, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1 – 20 are rejected under 35 U.S.C. 103 as being obvious over John L. Foley et al. (Pub. # US 2021/0056629 A1 – herein referred to as Foley) in view of Ben-Elazar et al. (Pub. # US 2023/0050034 A1 – herein referred to as Ben-Elazar) and further in view of Leslie R. Fine et al. (Pat. # US 8,583,470 B1 – herein referred to as Fine).
Re: Claim 1, Foley discloses a computer-implemented method for analyzing one or more user chance predictions to determine a user classification, the method comprising:
receiving, by one or more processors, a bullish threshold, a bearish threshold, and one or more time periods from at least one user (Foley, [0050], [0052] – As described above, visualization tool 200 displays a range of data for a given security via a set of interactive charts. These may include, for example, historic securities prices, current securities prices, the bullish and bearish consensus and corporate actions such as earning events. The user interacts with visualization tool 200 to, e.g., explore future price targets, identify the likelihood of a security reaching a given target, set a target price and date in the future. For a specified trade, visualization tool 200 is operable by a user to identify and display zones of profit and loss, and levels that identify trade breakeven and maximum profit or loss. For post-trade management, visualization tool 200 is operable by a user to identify and display zones of profit and loss, and levels that identify trade breakeven and maximum profit or loss in conjunction with the updated stock price and updated expected move, as determined by the options prices. In one embodiment, visualization tool 200 communicates with market data access module 1142 to retrieve historical and current price data, likelihoods of future price targets, and corporate events. Visualization tool 200 may also communicate with consensus module 1132 and expected move module 1141 to generate the expected move and future likelihoods for a given security. Visualization tool 200 may also communicate with target trade generation module 1143 to transmit the user's selected price and date target and zone information.);
receiving, by the one or more processors, a plurality of predictions corresponding to at least one prediction user from one or more prediction databases, wherein each of the plurality of predictions includes a basis set identifier, a date, and a basis set prediction value (Foley, [0051] – When provided with a target (symbol, price, date), target trade generation module 1143 determines the most suitable trades and their variants. For each trade/variant, target trade generation module 1143 computes instructions and attributes for each (legs, strikes, etc.) In one embodiment, visualization tool 200 transmits a packet of information to target trade generation module 1143 including security ticker identifier, target price, target date, and target zone, as selected in full or part by the user. In one embodiment, expected move module 1141 validates the target zone and communicates the validation or lack of validation to target trade generation module 1143. If validated, target trade generation module 1143 then generates a list of one or more appropriate trades (e.g. "variants") and corresponding details appropriate for each trade (e.g., date and strikes). Target trade generation module 1143 then communicates the attributes of the generated trade(s) (price, cost, gain at target, and likelihood) to trade analysis module 1144.);
receiving, by the one or more processors, one or more basis sets from one or more databases (Foley, [0053], [0064] – Server application 1140 may be configured for bulk download or upload of data, such as bulk download of index data on a second-by-second, minute-by-minute, or daily basis from market data service 1147. Such data may be furnished such as via a spreadsheet file or via suitable xml documents, by way of example. Data may be exchanged between server application 1140 and one or more legacy systems via suitable middleware systems. One or more modules may be configured to perform data validation steps prior to storing bulk data in one or more databases 1149. Server application 1140 may further be configured to permit bulk upload or download of data, such as portfolio data of clients of a broker or financial services retailer, to a device of suitably-authorized user.);
based on the plurality of predictions, determining, by the one or more processors, at least one p-score for each of the one or more time periods (Foley, [0030] – Visualization tool 200 further includes expected move selector 205, which permits a user to select from a plurality of pre-populated time periods for which to calculate stock price "expected move" or "implied move," and represent the results in visualization tool 200, e.g., as lines on a graph or similar chart. In one embodiment, the expected move is derived from options prices. In the example shown, the expected move selector 205 is set to "3D," or three days. Other options may be eight days, fifteen days, or any number of days, or one, two or three or more months, or another time period pre-populated and/or selected by a user.).
However, Foley does not expressly disclose:
calculating, by the one or more processors, a null distribution of successful predictions based on the one or more basis sets and the one or more time periods;
determining, by the one or more processors, a prediction user p-value based on the null distribution and the at least one p-score;
selecting, by the one or more processors, a classification for the at least one prediction user based on the prediction user p-value.
In a similar field of endeavor, Ben-Elazar discloses:
calculating, by the one or more processors, a null distribution of successful predictions based on the one or more basis sets and the one or more time periods (Ben-Elazar, [0054] – Once a successful model is generated, it is important to make sure that bias is avoided. One non-limiting example of a way in which bias can be introduced into user activity analysis is by identifying a specific user account and/or class/course. To avoid bias, additional processing may be executed comprising one or more of: evaluating the impact of masking user identifiable features; retraining AI modeling without user identifiable features to quantify the contribution of personalization to success of the modeling; investigate algorithmic mitigation approaches (e.g., demographic parity) to set a discrimination threshold per user group; and execute processing that swaps out attributes of identification (e.g., user account and/or class/course) with random identifiers. Developers can apply one or more of these approaches to tune a trained AI model based on threshold for accuracy and/or precision with respect to results of a trained AI model.);
determining, by the one or more processors, a prediction user p-value based on the null distribution and the at least one p-score (Ben-Elazar, [0053] – rained AI processing of the present disclosure is further configured to implement additional trained AI modeling, in parallel with a trained attention model, for com parison evaluation and improvement of accuracy and precision of exemplary modeling. For instance, a trained AI model may be implemented based on decision trees for comparative evaluation. Implementation of AI modeling using decision trees is known to one skilled in the field of art. Success of trained AI modeling is evaluated by criteria that comprising: accuracy in classification predictions; beating simpler, baseline, approaches; identifying meaningful and interpretable behaviors captured by trained machine learning models; and robustness to bias. Using either knowledge distillation, or training from scratch, a decision tree/random forest model may be applied to develop a baseline for generating relevant predictions.);
selecting, by the one or more processors, a classification for the at least one prediction user based on the prediction user p-value (Ben-Elazar, [0077] – Additionally, or in lieu of applying an educational frame of reference designation, user activity data can be curated according to a temporal frame of reference (e.g., predetermined period of time). A temporal frame of reference is an identified period of time for which to generate a predictive classification pertaining to a user activity level of one or more users (e.g., associated with an educational frame of reference designation). In one example, a temporal frame of reference is one week, where a predictive classification of a user activity level is to be generated for student users associated with an educational class for a given week (e.g., one week period). It is to be recognized that non-limiting examples of temporal frame of reference comprise any period of time including but not limited to: days; weeks; months; and years, among other examples. In technical instances where other types of domain-specific software platforms are being evaluated, specific references as to data types of user activity data and reference designations may be adapted for a specific domain. A temporal frame of reference enables trained AI processing to generate a real-time (or near real-time) classification prediction as to a level of user activity.).
Therefore, in light of the teachings of Ben-Elazar, it would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to modify the method of Foley, motivation according to one KSR Exemplary Rationale where a known technique is used to improve similar methods and systems in the same way by applies dimensionality reduction processing to efficiently manage user activity data and further improve accuracy in generating downstream binary classifications.
However, Foley in view Ben-Elazar does not expressly disclose:
displaying, by the one or more processors, at least one graphical widget corresponding to the classification on one or more interfaces of a user device, the at least one graphical widget including a recommendation to follow or unfollow the a social media platform of the prediction user.
In a similar field of endeavor, Fine discloses:
displaying, by the one or more processors, at least one graphical widget corresponding to the classification on one or more interfaces of a user device, the at least one graphical widget including a recommendation to follow or unfollow the a social media platform of the prediction user (Fine, col. 15, lines 31 - 47 – A second block 505 is labeled "API, authentication, session management, etc." and represents software that manages the database after initialization, i.e., software used for rendering images of the database, as well as for widget functionality, including commands to open up a spreadsheet associated with a location, to print and to enter prediction/wager. As indicated earlier, one implementation of the system provides a spreadsheet view where a participant may "click" on individual entries, to invoke a widget associated with the individual entry. The widget in tum invokes one or more functional commands associated with authenticating the particular participant, determining group membership (and any associated permissions) and selectively allowing the particular participant to enter a forecast or wager or print a newly displayed page. The second block 505 represents the software that performs this functionality, as well as subroutines for invoking others of the functions represented in FIG. 5.).
Therefore, in light of the teachings of Fine, it would have been obvious to one of ordinary skill in the art at the time the invention was made to modify the method of Foley in view Ben-Elazar, motivation according to one KSR Exemplary Rationale where a known technique is used to improve similar methods and systems in the same way by providing a prediction market, used for making forecasts based on the views of a group of individuals,
Re: Claim 2, Foley discloses the computer-implemented method of claim 1,
wherein the at least one p-score includes a p-bullish score and a p-bearish score for each of the one or more time periods (Foley, [0031] – In this embodiment, visualization tool 200 presents a "real- time” bullish 210 and bearish 211 "expected move" consensus derived from real-time options prices for several dates, shown on date axis 206.).
Re: Claim 3, Foley discloses the computer-implemented method of claim 2, the method further comprising:
for each of the one or more time periods, determining, by the one or more processors, a basis set bullish comparison value corresponding to the one or more basis sets that have a higher value than the bullish threshold (Foley, [0030] – Visualization tool 200 further includes expected move selector 205, which permits a user to select from a plurality of pre-populated time periods for which to calculate stock price "expected move" or "implied move," and represent the results in visualization tool 200, e.g., as lines on a graph or similar chart. In one embodiment, the expected move is derived from options prices. In the example shown, the expected move selector 205 is set to "3D," or three days. Other options may be eight days, fifteen days, or any number of days, or one, two or three or more months, or another time period pre-populated and/or selected by a user.); and
recording, by the one or more processors, the basis set bullish comparison value as the p-bullish score for the corresponding one or more time periods (Foley, [0031] – In this embodiment, visualization tool 200 presents a "real- time” bullish 210 and bearish 211 "expected move" consensus derived from real-time options prices for several dates, shown on date axis 206.).
Re: Claim 4, Foley discloses the computer-implemented method of claim 2, the method further comprising:
for each of the one or more time periods, determining, by the one or more processors, a basis set bearish comparison value corresponding to the one or more basis sets that have a lower value than the bearish threshold (Foley, [0030] – Visualization tool 200 further includes expected move selector 205, which permits a user to select from a plurality of pre-populated time periods for which to calculate stock price "expected move" or "implied move," and represent the results in visualization tool 200, e.g., as lines on a graph or similar chart. In one embodiment, the expected move is derived from options prices. In the example shown, the expected move selector 205 is set to "3D," or three days. Other options may be eight days, fifteen days, or any number of days, or one, two or three or more months, or another time period pre-populated and/or selected by a user.); and
recording, by the one or more processors, the basis set bearish comparison value as the p-bearish score for the corresponding one or more time periods (Foley, [0031] – In this embodiment, visualization tool 200 presents a "real- time” bullish 210 and bearish 211 "expected move" consensus derived from real-time options prices for several dates, shown on date axis 206.).
Re: Claim 5, Foley discloses the computer-implemented method of claim 2, wherein determining the at least one p-score for each of the one or more time periods further comprises:
for each of the plurality of predictions, comparing, by the one or more processors, each of the plurality of predictions to the bullish threshold (Foley, [0043] – Turning to FIG. 10, a flow diagram illustrating a method of constructing and comparing options trade strategies (spreads) from a single user input according to an embodiment of the invention is shown. In step 1010, a user selects a company or stock and accesses visualization tool 200. In step 1020, the user sets a price target by selecting a location on the graph of expected move rendered on visualization tool 200. In step 1030, suitable options spread strategies are calculated reflecting at least one user input by iteratively mapping current stock price; expected price move and the user price target to nearest options contracts in the options chain. In one embodiment, respective payout out comes are mapped back to the expected move chart. In step 1040, suitable options spread strategies are presented to the user for comparison and evaluation. In step 1050, the user selects one or more suitable options spread strategies. In step 1060, a pre-populated order ticket is generated. In step 1070, the user confirms that the order be executed. In step 1080, the order is executed. In step 1090, an executed trade visualization is generated. Additional steps may further be available in some embodiments, including viewing, com paring, canceling and/or changing a position via the executed trade visualization. In one embodiment, a user may optimally return to an earlier screen to, e.g., select a company or stock and accesses visualization tool 200 (e.g., step 1010) at any stage in the process by selecting a "back." "return" or "start over" option in a search screen or menu screen.); and
in response to determining that the prediction of the plurality of predictions is above the bullish threshold, determining, by the one or more processors, the p-bullish score for the one or more time periods and appending the p-bullish score to the at least one p-score for the corresponding time period (Foley, [0041] – Turning now to FIG. 8, following successful trade execution, the user is able to view and interact with executed trade visualization 800, which illustrates dynamically, in real-time, the evolving payout and probability outcome of the user's executed strategy, and is operable to engage in post-trade management. In one embodiment, a user is able to review an open position in the context of real-time updated probabilities and risk/reward, in the context of the evolving stock price and expected move. Using the executed trade visualization 800 and probability updates as signals for "taking profits" or "cutting losses," provides the potential for overall outcome improvement and reduction of irrational human sentiment. Executed trade visualization 800 may include trade summary information 810, which may in turn include several items of information concerning the executed trade, including creation date, target price/date, trade profile, and/or trade description. Executed trade visualization 800 may also include comparison summary 820, which may include comparison data for various parameters concerning the executed trade at the time it was created, and currently. These parameters may include, for example, days to expiry, trade price, current r returns, likelihood of breakeven, and likelihood of target.).
Re: Claim 6, Foley discloses the computer-implemented method of claim 2, wherein determining the at least one p-score for each of the one or more time periods further comprises:
for each of the plurality of predictions, comparing, by the one or more processors, each of the plurality of predictions to the bearish threshold (Foley, [0043] – Turning to FIG. 10, a flow diagram illustrating a method of constructing and comparing options trade strategies (spreads) from a single user input according to an embodiment of the invention is shown. In step 1010, a user selects a company or stock and accesses visualization tool 200. In step 1020, the user sets a price target by selecting a location on the graph of expected move rendered on visualization tool 200. In step 1030, suitable options spread strategies are calculated reflecting at least one user input by iteratively mapping current stock price; expected price move and the user price target to nearest options contracts in the options chain. In one embodiment, respective payout out comes are mapped back to the expected move chart. In step 1040, suitable options spread strategies are presented to the user for comparison and evaluation. In step 1050, the user selects one or more suitable options spread strategies. In step 1060, a pre-populated order ticket is generated. In step 1070, the user confirms that the order be executed. In step 1080, the order is executed. In step 1090, an executed trade visualization is generated. Additional steps may further be available in some embodiments, including viewing, com paring, canceling and/or changing a position via the executed trade visualization. In one embodiment, a user may optimally return to an earlier screen to, e.g., select a company or stock and accesses visualization tool 200 (e.g., step 1010) at any stage in the process by selecting a "back." "return" or "start over" option in a search screen or menu screen.); and
in response to determining that the prediction of the plurality of predictions is below the bearish threshold, determining, by the one or more processors, the p-bearish score for the one or more time periods and appending the p-bearish score to the at least one p-score for the corresponding time period (Foley, [0041] – Turning now to FIG. 8, following successful trade execution, the user is able to view and interact with executed trade visualization 800, which illustrates dynamically, in real-time, the evolving payout and probability outcome of the user's executed strategy, and is operable to engage in post-trade management. In one embodiment, a user is able to review an open position in the context of real-time updated probabilities and risk/reward, in the context of the evolving stock price and expected move. Using the executed trade visualization 800 and probability updates as signals for "taking profits" or "cutting losses," provides the potential for overall outcome improvement and reduction of irrational human sentiment. Executed trade visualization 800 may include trade summary information 810, which may in turn include several items of information concerning the executed trade, including creation date, target price/date, trade profile, and/or trade description. Executed trade visualization 800 may also include comparison summary 820, which may include comparison data for various parameters concerning the executed trade at the time it was created, and currently. These parameters may include, for example, days to expiry, trade price, current r returns, likelihood of breakeven, and likelihood of target.).
Re: Claim 7, Foley discloses the computer-implemented method of claim 1,
wherein the basis set prediction value includes at least one bullish prediction or at least one bearish prediction (Foley, [0033] – Turing back to FIG. 2, a range of prices are illustrated on price axis 207. Visualization tool 200 further presents a graph or chart illustrating the selected stock's past performance 209, and current price, as indicated with a solid dot or maneuverable click-and-drop "pin" and a vertical line separating the graphical illustration of past performance 209 from the bullish 210 and bearish 211 expected move consensus representations.).
Re: Claim 8, Foley discloses the computer-implemented method of claim 7,
wherein the at least one bullish prediction or the at least one bearish prediction is expressed as a ratio (Foley, [0032] – FIG. 3 illustrates how the bullish 210 (upward) and bearish 211 (downward) consensus of future expected stock price movement over time can be calculated by the system of the invention from options data that would be typically represented in several options matrices 310 for each relevant expiry date. In one embodiment, at-the-money (ATM) call and put options prices are extracted in step 320. Next, an 85% "dampener" is applied to derive the expected move. For example, in one embodiment, the expected move of a stock for can be found by calculating 85% of the value of the front month at the money (ATM) straddle, adding the price of the front month ATM call and the price of the front month ATM put, then multiplying this value by 85%. Given the dynamic and fast-moving nature of options prices, it would not be possible to manually perform the number of calculations required to generate visualization tool 200's expected move analysis for several dates without the data output likely being immediately stale.).
Re: Claim 9, Foley in view of Ben-Elazar discloses the computer-implemented method of claim 1, the method further comprising:
refining, by the one or more processors, the plurality of predictions for each of the at least one prediction user, the refining including removing at least one redundant predication from the plurality of predictions, wherein the at least one redundant prediction includes a similar basis set identifier and a similar date that is similar to at least one other prediction of the plurality of predictions for the at least one prediction user (Ben-Elazar, [0076] – An educational frame of reference designation identifies a specific educational segmentation that corresponds with one or more student users (e.g., a group of users). An educational frame of reference designation aids identification of similar users (e.g., students in the same class) who have similar patterns of user activity, thereby providing the most appropriate manner by which to execute a comparative evaluation of user activity. In one example, an educational frame of reference designation is an educational class that comprises a plurality of student users, where user activity data of the plurality of student users of the educational class are evaluated. However, it is to be recognized that an educational frame of reference designation can be any type of educational segmentation of users including but not limited to: educational classes; schools; school districts; grade levels; user descriptors (e.g., age) and geographical descriptors, among other examples. In examples where trained AI processing is adapted to work with a different domain, it is to be recognized that a domain-specific frame of reference designation may be applied to group users for evaluation. In some technical instances, selection of different data types of user activity data, as input feature data, depends on the type of educational frame of reference designation selected. For instance, categorical representations of student social/emotional feedback from student specific surveys in one educational class, school, etc., for a specific assignment (or other content) may be more relevant than a comparison of students in different geographical regions and/or age ranges that might be working on different content/assignments. In one example, developers may preset data types of user activity data depending on educational frame of reference designations that are selected, where feature input data may vary dependent on which education frame of reference designation is chosen.). The rationale for support of motivation, obviousness and reason to combine see claim 1 above.
Re: Claim 10, Foley in view of Ben-Elazar discloses the computer-implemented method of claim 9, the method further comprising:
further refining, by the one or more processors, the plurality of predictions by a basis set type (Ben-Elazar, [0061] – A third linear representation 126 is further presented in process flow 120, progressing from a projection in a PCA space as shown in the second linear representation 124. The third linear representation 126 illustrates projection of feature vectors to a first principal component. A first principal component in principal component analysis identifies the direction of maximal variance in the feature space. As indicated in the foregoing description, user activity data can vary greatly on a weekly basis due to the robustness and variety of applications/services that are associated with an educational software platform. As such, executing PCA analysis in real-time (or near real-time) for each temporal frame of reference can help identify the most important weighting of features/variables of user activity data (e.g., on a weekly basis).). The rationale for support of motivation, obviousness and reason to combine see claim 1 above.
Re: Claim 11, Foley in view of Ben-Elazar discloses the computer-implemented method of claim 1, the method further comprising:
determining, by the one or more processors, at least one low p-value that is lower than a p-value threshold (Ben-Elazar, [0028] – In another technical instance, a threshold value is set to identify a high activity score set relative to the median activity score. For example, activity scores in the bottom and top fifteen percent (15%) relative to a median activity score are provided as labeled examples for supervised binary classification, where a real-time (or near real-time) evaluation of activity scores identify activity scores in the bottom and top fifteen percent metrics respectively as low activity and high activity. However, it is to be recognized that developers can set a threshold value, for threshold evaluation, to any desired metric with out departing from the spirit of the present disclosure. In some examples, threshold values for classification may be set relative to a frame of reference designation. For instance, an educational frame of reference designation may vary where rules may be set to apply a predetermined threshold (e.g., threshold values) to evaluate activity scores based on the types of educational frame of reference designation selected. That is, threshold values for evaluating an activity scores of users of an educational class may vary from threshold values for evaluating activity scores of users of a school district.); and
flagging, by the one or more processors, the at least one prediction user that corresponds to the at least one low p-value (Ben-Elazar, [0064] – An exemplary classification model is adapted, through training on evaluation of historical activity data, to generate a classification as to a level of user activity of a user based on a comparative evaluation of activity scores for users (e.g., student users associated with an educational frame of reference designation and/or a temporal frame of reference). In one example, a comparative evaluation is a threshold evaluation pertaining to an activity score for each of a plurality of users (e.g., student users). For instance, activity scores of student users may be comparatively evaluated against a median activity score for users associated with the educational frame of reference designation, where a threshold evaluation is used to identify users whose activity scores are within certain threshold values. In one technical instance, a threshold value is set to identify a low activity score relative to the median activity score. In another technical instance, a threshold value is set to identify a high activity score set relative to the median activity score. For example, activity scores in the bottom and top fifteen percent (15%) relative to a median activity score are provided as labeled examples for supervised binary classification, where a real-time (or near real-time) evaluation of activity scores identify activity scores in the bottom and top fifteen percent metrics respectively as low activity and high activity.). The rationale for support of motivation, obviousness and reason to combine see claim 1 above.
Re: Claim 12, Claim 12 is a system claim corresponding to method claim 1. Therefore, claim 12 is analyzed and rejected as previously discussed with respect to claim 1.
Re: Claim 13, Claim 13 is a system claim corresponding to method claim 2. Therefore, claim 13 is analyzed and rejected as previously discussed with respect to claim 2.
Re: Claim 14, Claim 14 is a system claim corresponding to method claim 3. Therefore, claim 14 is analyzed and rejected as previously discussed with respect to claim 3.
Re: Claim 15, Claim 15 is a system claim corresponding to method claim 4. Therefore, claim 15 is analyzed and rejected as previously discussed with respect to claim 4.
Re: Claim 16, Claim 16 is a system claim corresponding to method claim 9. Therefore, claim 16 is analyzed and rejected as previously discussed with respect to claim 9.
Re: Claim 17, Claim 17 is an apparatus claim corresponding to method claim 1 and system claim 12. Therefore, claim 17 is analyzed and rejected as previously discussed with respect to claims 1 and 12.
Re: Claim 18, Claim 18 is an apparatus claim corresponding to method claim 2 and system claim 13. Therefore, claim 18 is analyzed and rejected as previously discussed with respect to claims 2 and 13.
Re: Claim 19, Claim 19 is an apparatus claim corresponding to method claim 3 and system claim 14. Therefore, claim 19 is analyzed and rejected as previously discussed with respect to claims 3 and 14.
Re: Claim 20, Claim 20 is an apparatus claim corresponding to method claim 4 and system claim 15. Therefore, claim 20 is analyzed and rejected as previously discussed with respect to claims 4 and 15.
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 extension fee 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 date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JOHN H. HOLLY whose telephone number is (571)270-3461. The examiner can normally be reached on MON. - FRI 10 AM - 8 PM.
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/John H. Holly/Primary Examiner, Art Unit 3696
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