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
1. This Final Office action is in reply to the Applicant amendment filed on 05 November 2025.
2. Claims 21, 30, 31, 33, 37 have been amended.
3. Claims 21-27, 29-31, 33-35, 37, 49-51 are currently pending and have been examined.
Response to Amendment
In the previous office action, Claims 21-27, 29-31, 33-35, 37, 49-51 were rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter (abstract idea). Applicants have not amended Claims 21-27, 29-31, 33-35, 37, 49-51 to provide statutory support and the rejection is maintained.
In the previous office action, Claims were rejected under 35 U.S.C. 112, second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which Applicant regards as the invention rejected as being prolix claims. Applicants have amended Claims 21-27, 29-31, 33-35, 37, 49-51 and this rejection is withdrawn. However Claims 21-27, 29-31, 33-35, 37, 49-51 are still rejected for the following indefinite claim limitations: “impact; to be (also considered intended use); causing; efficiency; and other; impact theme(s) selection; selection input; area(s); possible; objectives; equal to or less; quantity; foci; five pro rata percentiles; log base 100 of the stored KII delivery; overridden impact efficiency; from 0 to 0.0015 for the impact efficiency, etc.”. For these indefinite terms there is no additional definite and specific recitation and definition for these claim limitations. Additionally, these terms are also considered non-functional and descriptive labels. Examiner notes that the claimed contents of Claims 21-27, 29-31, 33-35, 37, 49-51 amount to non-functional descriptive material that do not functionally alter the claimed method. The recited method steps would be performed in the same manner regardless of what data is contained in these indefinite and broadly recited limitations. Thus, the prior art and the claimed invention have identical structure and the claimed descriptive material is insufficient to distinguish the claimed invention over the prior art. For at least these reasons, the rejection is maintained. Clarification is required.
Response to Arguments
Applicant’s arguments filed 05 November 2025 have been fully considered but they are not persuasive. In the remarks regarding the 35 USC § 101 rejection for Claims 21-27, 29-31, 33-35, 37, 49-51, Applicant argues that: (1) the claims are not directed to an abstract idea, and even if they were, they would amount to significantly more than the abstract idea. Examiner respectfully disagrees. Still commensurate to the two-part subject matter eligibility framework decision in the Federal court decision in Alice Corp. Pty. Ltd. V. CLS Bank International et al., (Alice), 2019 revised patent subject matter eligibility guidance (2019 PEG) and the October 2019 Update: Subject Matter Eligibility (“October 2019 Update), and the new “July 2024 Guidance Update on Patent Subject Matter Eligibility Examples, including on Artificial Intelligence”, and the Examiner details the maintained rejection under 35 U.S.C. 101 in the below rejection with further explanation. Applicant argues that as amended, Applicant states: for Step 2A, Prong Two: “The amended claims provide an improvement in the form of a new technique, the new technique includes datasets that constrain the claim elements as follows: (1) impact area sub-categories, (2) KIIs and (3) impact metrics to enumerated items. Together, these claim elements generated new data, namely the calculation of the iRR.; the amended claims provide significantly more than the cited judicial exceptions to patentability of mathematical concepts, methods of organizing human activity, and mental processes” (see Remarks/Arguments with no specific page numbering). However the Examiner respectfully disagrees. The Examiner reproduces the previous rejection under an abstract idea with additional clarification for Applicant as:
Step 2A: Prong One: Claims 21-27, 29-31, 33-35, 37, 49-51 recite limitations that set forth the abstract ideas, namely, the claims as a whole recite the claimed invention is directed to an abstract idea without significantly more. The claims recite steps for, generally, “quantitatively calculating the impact rate of return of an activity”. There is no additional and specific information disclosed within the claims as to specifically how the “transmitting; receiving; processing; providing” steps are specifically defined. This judicial exception is not integrated into a practical application because the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the claims encompass processing information by:
the claims describe a series of steps: receiving inputs, selecting impact themes/sub-categories, storing them, and transmitting outputs based on these selections and as a whole recite certain groupings characteristic of:
Certain methods of organizing human activity –managing business relations and personal behavior or relationships or interactions between people (managing investment, selecting criteria including social activities, teaching, and following rules or instructions); fundamental economic Practice: The process relates to financial investment, specifically calculating Internal Rate of Return (IRR). Specifically, it involves collecting information, analyzing that information, and selecting business-related criteria (impact themes).
Mental processes – concepts performed in the human mind (evaluating and selecting impact themes), as these are actions that could be performed mentally or with pen and paper by an analyst including an observation, evaluation, judgment, opinion).
See MPEP § 2106.04(a) II C. Hence, the claims are ineligible under Step 2A Prong one. Furthermore, the dependent claims are merely directed to the particulars of the abstract idea and likewise do not add significantly more to the above-identified judicial exception. The limitations of the claims do not transform the abstract idea that they recite into patent-eligible subject matter because the claims simply instruct the practitioner to implement the abstract idea using generally-recited computer components (first/second/third computer system).
Prong Two: Claims 21-27, 29-31, 33-35, 37, 49-51: With regard to this step of the analysis (as explained in MPEP § 2106.04(d)), the judicial exception is not integrated into a practical application. Independent Claims 21, 33, 37 recite additional elements directed to “first/second/third computer system; network; non-transitory computer-readable medium”. Therefore, the claims contain computer components that are cited at a high level of generality and are merely invoked as a tool to perform the abstract idea. Simply implementing an abstract idea on a computer is not a practical application of the abstract idea. Furthermore, the dependent claims are merely directed to the particulars of the abstract idea and likewise do not add significantly more to the above-identified judicial exception. The limitations of the claims do not transform the abstract idea that they recite into patent-eligible subject matter because the claims simply instruct the practitioner to implement the abstract idea using generally-recited computer components, and furthermore do not amount to an improvement to a computer or any other technology, and thus are ineligible.
Step 2B: (As explained in MPEP § 2106.05), Claims 21-27, 29-31, 33-35, 37, 49-51 do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements when considered both individually and as an ordered combination do not amount to significantly more than the abstract idea nor recites additional elements that integrate the judicial exception into a practical application. The additional elements of “first/second/third computer system; network; non-transitory computer-readable medium”, etc. are generically-recited computer-related elements that amount to a mere instruction to “apply it” (the abstract idea) on the computer-related elements (see MPEP § 2106.05 (f) – Mere Instructions to Apply an Exception). These additional elements in the claims are recited at a high level of generality and are merely limiting the field of use of the judicial exception (see MPEP §2106.05 (h) – Field of Use and Technological Environment). There is no indication that the combination of elements improves the function of a computer or improves any other technology.
Furthermore, the dependent claims are merely directed to the particulars of the abstract idea and likewise do not add significantly more to the above-identified judicial exception. The limitations of the claims do not transform the abstract idea that they recite into patent-eligible subject matter because the claims simply instruct the practitioner to implement the abstract idea using generally-recited computer components, and furthermore do not amount to an improvement to a computer or any other technology, and thus are ineligible. In summary as indicated below through Steps 1-2B, the recitation of a computer (one or more processors) to perform the claim limitations amount to no more than mere instruction to apply the exception using a generic computer components. Even when considered in combination, these additional elements represent mere instructions to implement an abstract idea or other exception on a computer and insignificant extra-solution activity, which do not provide an inventive concept. For at least these reasons, the rejection is maintained.
Applicant submits that: (2) Riggs et al. (Riggs) (US 2019/0370308) does not teach or suggest in amended representative Claim 1: “Riggs lacks the iRR of applicants amended claims, as Riggs does not disclose calculating a single quantitative value for the qualitative and quantification effects of an activity within categories of social or planetary benefits or detriments” [see Remarks/Arguments with no specific page numbering]. With regard to argument (2), the Examiner respectfully disagrees. First this specific claim limitation is not specifically recited as such in the claims. Second Applicant's arguments fail to comply with 37 CFR 1.111(b) because they amount to a general allegation that the claims define a patentable invention without specifically pointing out how the language of the claims patentably distinguishes them from the references. Third, Applicant's arguments do not comply with 37 CFR 1.111(c) because they do not clearly point out the patentable novelty which he or she thinks the claims present in view of the state of the art disclosed by the references cited or the objections made. Further, they do not show how the amendments avoid such references or objections. Due to the current amendments, the Examiner has provided addition clarification and additional citations from the maintained prior art of Riggs. It is noted that any citations to specific, pages, columns, paragraphs, lines, or figures in the prior art references and any interpretation of the reference should not be considered to be limiting in any way. A reference is relevant for all it contains and may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art. See MPEP 2123. The Examiner has a duty and responsibility to the public and to Applicant to interpret the claims as broadly as reasonably possible during prosecution. In re Prater, 415 F.2d 1 393, 1404-05, 162 USPQ 541, 550-51 (CCPA 1969). For at least these reasons, the rejection is maintained.
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 21-27, 29-31, 33-35, 37, 49-51 are rejected under 35 U.S.C. §101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, natural phenomenon, or an abstract idea) because the claimed invention is directed to a judicial exception (i.e., a law of nature, natural phenomenon, or an abstract idea) without significantly more. The claims as a whole recite certain grouping of an abstract idea and are analyzed in the following step process:
Step 1: Claims 21-27, 29-31, 33-35, 37, 49-51 are each focused to a statutory category of invention, namely “method; non-transitory computer-readable medium” sets.
Step 2A: Prong One: Claims 21-27, 29-31, 33-35, 37, 49-51 recite limitations that set forth the abstract ideas, namely, the claims as a whole recite the claimed invention is directed to an abstract idea without significantly more. The claims recite steps for, generally, “quantitatively calculating the impact rate of return of an activity”. There is no additional and specific information disclosed within the claims as to specifically how the “transmitting; receiving; processing; providing” steps are specifically defined. This judicial exception is not integrated into a practical application because the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the claims encompass processing information by:
“receiving an iRR calculation input at a first computer system, where the iRR calculation input comprising a request to begin calculation of the iRR;
transmitting an impact theme/sub-category selection output from the first computer system where the impact theme selection output is configured for causing the impact theme selection to be provided to the second computer system;
receiving an impact theme/sub-category selection input at the first computer system where the impact theme selection input comprises a selected impact theme and is configured for storing the selected impact theme by the first computer system;
based upon the stored selected impact theme/sub-category, transmitting an impact area selection output to the second computer system, the impact area selection output configured for causing the impact area selection to be provided to the second computer system;
receiving an impact area/sub-category selection input at the first computer system where the impact area selection input comprises a selected impact area and is configured for storing the selected impact area by the first computer system;
based upon the stored selected impact area/sub-category, transmitting an impact area sub-category selection output to the second computer system the impact area sub-category selection output configured for causing the impact area sub-category selection to be provided to the second computer system;
receiving an impact area sub-category selection input, at first computer system where the impact area sub-category selection input comprises a selected impact area sub-category and configured for storing the selected impact area sub-category by the first computer system;
based upon the stored selected impact area, transmitting a KII selection output to the second computer system the KII selection output configured for causing the KII selection to be provided to the second computer system;
receiving a KII selection input, at the first computer system where the KII selection input comprises a selected KII and is configured for storing the selected KII by the first computer system;
based upon the stored selected impact area sub-category, transmitting an impact metric selection output to the second computer system, the impact metric selection output configured for causing an impact metric selection to be provided to the second computer system;
receiving an impact metric selection input, at the first computer system where the impact metric selection input comprises a selected impact metric and is configured for storing the selected impact metric by the first computer system;
processing an impact metric value, the impact metric value being processed by the first computer system, the processing of the impact metric value configured to assign an impact metric value to the impact metric and store the impact metric value;
processing an impact metric weight, the impact metric weight being processed by the first computer system, the processing of the impact metric weight configured to assign an impact metric weight to the impact metric and store the impact metric weight;
processing an impact metric possible point value, the impact metric possible point value being processed by the first computer system, the processing of the impact metric possible point value configured to assign an impact metric possible point value to the impact metric and to store the impact metric possible point value in association with the impact metric, wherein the impact metric possible point value is the product of the stored impact metric value and the impact metric weight;
based upon the stored selected impact metrics, transmitting an impact metric score range selection output to the second computer system, the impact metric score range selection output configured for causing an impact metric score range selection to be provided to the second computer system;
receiving an impact metric score range selection input at the first computer system where the impact metric score range selection input comprises an impact metric score range for the impact metric and configured for storing the impact metric score range in association with the impact metric by the first computer system;
based upon the stored impact metric score range, transmitting an impact metric score selection output to the second computer system, the impact metric score selection output configured for causing an impact metric score selection to be provided to the second computer system;
receiving an impact metric score selection input at the first computer system where the impact metric score selection input comprises a selected impact metric score for the impact metric and configured for storing the selected impact metric score in association with the impact metric by the first computer system;
processing an impact metric score rate, the impact metric score rate being processed by the first computer system, where the processing is configured to assign an impact metric score rate to the impact metric based upon the stored impact metric score and to store the impact metric score rate by the first computer system;
processing an impact metric point value, the impact metric point value being processed by the first computer system, where the processing is configured to assign the impact metric with an impact metric point value by multiplying the stored impact metric score rate and the stored impact metric possible point value and to store the impact metric point value by the first computer system;
processing an impact multiplier value, the impact multiplier value being processed by the first computer system, where the processing is configured to assign the impact multiplier value by dividing the impact metric point value by and to store the impact multiplier value;
based upon the stored selected KII, transmitting an impact goal output to the second computer system, the impact goal output configured for causing an impact goal to be provided to the second computer system;
receiving an impact goal input at the first computer system, where the impact goal input comprises the impact goal and configured for storing the impact goal in association with the stored selected KII by the first computer system;
based upon the stored selected KII, transmitting a KII delivery output to the second computer system via the computer network, the KII delivery output configured for causing a KII delivery to be provided to the second computer system;
receiving a KII delivery input at first computer system, where the KII delivery input comprises the KII delivery and is configured for storing the KII delivery in association with the stored selected KII by the first computer system;
based upon the stored KII delivery and the stored impact goal, processing the impact quantity, the impact quantity being processed by the first computer system, where the processing is configured to assign the impact quantity of the activity by dividing the stored KII delivery by the stored impact goal and to store the impact quantity by the first computer system;
transmitting a time period output to the second computer system, the time period output configured for causing a time period entry to be provided to the second computer system;
receiving a time period input at the first computer system, where the time period input comprises the time period and configured for storing the time period in years by the first computer system;
transmitting an activity cost output to the second computer system, the activity cost output configured for causing an activity cost entry to be provided to the second computer system;
receiving an activity cost input at the first computer system, where the activity cost input comprising the activity cost and configured for storing the activity cost by the first computer system;
based upon the stored activity cost and stored KII delivery, processing an impact efficiency, the impact efficiency being processed by the first computer system, where the processing is configured to assign the impact efficiency of the activity, and to store the impact efficiency by the first computer system;
based upon the stored impact multiplier value, the stored KII delivery, the stored time period, the stored impact quantity, and the stored impact efficiency, processing the iRR, the iRR being processed by the first computer system, where the processing is configured to assign the iRR of the activity by and to store the iRR associated with the activity by the first computer system;
based upon the stored iRR, transmitting an iRR output to the second computer system, the iRR output configured for causing the stored iRR to be provided to the second computer system”
As seen above in bolded, the claims describe a series of steps: receiving inputs, selecting impact themes/sub-categories, storing them, and transmitting outputs based on these selections and as a whole recite certain groupings characteristic of:
Certain methods of organizing human activity –managing business relations and personal behavior or relationships or interactions between people (managing investment, selecting criteria including social activities, teaching, and following rules or instructions); fundamental economic Practice: The process relates to financial investment, specifically calculating Internal Rate of Return (IRR). Specifically, it involves collecting information, analyzing that information, and selecting business-related criteria (impact themes).
Mental processes – concepts performed in the human mind (evaluating and selecting impact themes), as these are actions that could be performed mentally or with pen and paper by an analyst including an observation, evaluation, judgment, opinion).
See MPEP § 2106.04(a) II C. Hence, the claims are ineligible under Step 2A Prong one. Furthermore, the dependent claims are merely directed to the particulars of the abstract idea and likewise do not add significantly more to the above-identified judicial exception. The limitations of the claims do not transform the abstract idea that they recite into patent-eligible subject matter because the claims simply instruct the practitioner to implement the abstract idea using generally-recited computer components (first/second/third computer system).
Prong Two: Claims 21-27, 29-31, 33-35, 37, 49-51: With regard to this step of the analysis (as explained in MPEP § 2106.04(d)), the judicial exception is not integrated into a practical application. Independent Claims 21, 33, 37 recite additional elements directed to “first/second/third computer system; network; non-transitory computer-readable medium”. Therefore, the claims contain computer components that are cited at a high level of generality and are merely invoked as a tool to perform the abstract idea. Simply implementing an abstract idea on a computer is not a practical application of the abstract idea. Furthermore, the dependent claims are merely directed to the particulars of the abstract idea and likewise do not add significantly more to the above-identified judicial exception. The limitations of the claims do not transform the abstract idea that they recite into patent-eligible subject matter because the claims simply instruct the practitioner to implement the abstract idea using generally-recited computer components, and furthermore do not amount to an improvement to a computer or any other technology, and thus are ineligible.
Step 2B: (As explained in MPEP § 2106.05), Claims 21-27, 29-31, 33-35, 37, 49-51 do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements when considered both individually and as an ordered combination do not amount to significantly more than the abstract idea nor recites additional elements that integrate the judicial exception into a practical application. The additional elements of “first/second/third computer system; network; non-transitory computer-readable medium”, etc. are generically-recited computer-related elements that amount to a mere instruction to “apply it” (the abstract idea) on the computer-related elements (see MPEP § 2106.05 (f) – Mere Instructions to Apply an Exception). These additional elements in the claims are recited at a high level of generality and are merely limiting the field of use of the judicial exception (see MPEP §2106.05 (h) – Field of Use and Technological Environment). There is no indication that the combination of elements improves the function of a computer or improves any other technology.
Furthermore, the dependent claims are merely directed to the particulars of the abstract idea and likewise do not add significantly more to the above-identified judicial exception. The limitations of the claims do not transform the abstract idea that they recite into patent-eligible subject matter because the claims simply instruct the practitioner to implement the abstract idea using generally-recited computer components, and furthermore do not amount to an improvement to a computer or any other technology, and thus are ineligible.
The Examiner interprets that the steps of the claimed invention both individually and as an ordered combination result in Mere Instructions to Apply a Judicial Exception (see MPEP §2106.05 (f)). These claims recite only the idea of a solution or outcome with no restriction on how the result is accomplished and no description of the mechanism used for accomplishing the result. Here, the claims utilize a computer or other machinery (e.g., see Applicants’ published Specification ¶’s 20-62, 123-146) regarding using existing computer processors as well as program products comprising machine-readable media for carrying or having machine-executable instructions or data structures stored. “first computer system 202, a second computer system 204, and a third computer system 208” in its ordinary capacity for performing tasks (e.g., to receive, analyze, transmit and display data) and/or use computer components after the fact to an abstract idea (e.g., a fundamental economic practice and certain methods of organization human activities) and does not provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016)). Software implementations are accomplished with standard programming techniques with logic to perform connection steps, processing steps, comparison steps and decisions steps. These claims are directed to being a commonplace business method being applied on a general-purpose computer (see Alice Corp. Pty, Ltd. V. CLS Bank Int’l, 134 S. Ct. 2347, 1357, 110 USPQ2d 1976, 1983 (2014)); Versata Dev. Group, Inc., v. SAP Am., Inc., 793 D.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015)) and require the use of software such as via a server to tailor information and provide it to the user on a generic computer. Based on all these, Examiner finds that when viewed either individually or in combination, these additional claim element(s) do not provide meaningful limitation(s) that raise to the high standards of eligibility to transform the abstract idea(s) into a patent eligible application of the abstract idea(s) such that the claim(s) amounts to significantly more than the abstract idea(s) itself. Accordingly, Claims 21-27, 29-31, 33-35, 37, 49-51 are rejected under 35 U.S.C. §101 because the claimed invention is directed to a judicial exception (i.e. abstract idea exception) without significantly more.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 21-27, 29-31, 33-35, 37, 49-51 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, or for pre-AIA the applicant regards as the invention.
Claims 21-27, 29-31, 33-35, 37, 49-51 are rejected for the following indefinite claim limitations: “impact; to be (also considered intended use); causing; efficiency; and other; impact theme(s) selection; selection input; area(s); possible; objectives; equal to or less; quantity; foci; five pro rata percentiles; log base 100 of the stored KII delivery; overridden impact efficiency; from 0 to 0.0015 for the impact efficiency, etc.”. For these indefinite terms there is no additional definite and specific recitation and definition for these claim limitations. Additionally, these terms are also considered non-functional and descriptive labels. Examiner notes that the claimed contents of Claims 21-27, 29-31, 33-35, 37, 49-51 amount to non-functional descriptive material that do not functionally alter the claimed method. The recited method steps would be performed in the same manner regardless of what data is contained in these indefinite and broadly recited limitations. Thus, the prior art and the claimed invention have identical structure and the claimed descriptive material is insufficient to distinguish the claimed invention over the prior art. see In re Gulack, 703 F.2d 1381, 1385, 217 USPQ 401, 404 (Fed. Cir. 1983); In re Lowry, 32 F.3d 1579, 32 USPQ2d 1031 (Fed. Cir. 1994); MPEP 2106. Clarification is required.
Claim Rejections - 35 USC § 102
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)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 21-27, 29-31, 33-35, 37, 49-51 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Riggs et al. (Riggs) (US 2019/0370308).
With regard to Claims 21, 33, 37, Riggs teaches a method/non-transitory computer-readable medium having computer-executable instructions (the present embodiments are embodied in machine-executable instructions. The instructions can be used to cause a processing device, for example a general-purpose or special-purpose processor, which is programmed with the instructions, to perform the steps of the present invention. Alternatively, the steps of the present invention can be performed by specific hardware components that contain hardwired logic for performing the steps, or by any combination of programmed computer components and custom hardware components. For example, the present invention can be provided as a computer program product, as outlined above. In this environment, the embodiments can include a machine-readable medium having instructions stored on it. The instructions can be used to program any processor or processors (or other electronic devices) to perform a process or method according to the present exemplary embodiments. In addition, the present invention can also be downloaded and stored on a computer program product. Here, the program can be transferred from a remote computer (e.g., a server) to a requesting computer (e.g., a client) by way of data signals embodied in a carrier wave or other propagation medium via a communication link (e.g., a modem or network connection) and ultimately such signals may be stored on the computer systems for subsequent execution)) for quantitatively calculating the impact rate of return (iRR) of an activity via a computer network (to manage default risk in an investment-grade corporate debt portfolio provides an illustration of one embodiment. Each debt security has a level of risk that is directly tied to the value in liquidation of the underlying assets of the company. This risk is distinctly separate from financial market risks associated with the supply and demand of the debt security itself, as well as from financial market factors that may impact the rate of return needed for a given investment security at a given point in time, such as the risk free rate at that point in time) (see at least paragraphs 67, 295, 333-352), the method/non-transitory computer-readable medium having computer-executable instructions comprising:
receiving an iRR calculation input at a first computer system (associating two or more numerical values with two or more groups, tags, attributes, risk exposures or relationships, wherein the numerical values relate to economic, financial, or capital markets-based data; associating a statistical property, selected from among mean, variance, standard deviation, skew, kurtosis, correlation, semivariance, and semideviation, with those groups, tags, attributes, risk exposures, or relationships based on the numerical values; calculating two or more statistical values associated with the statistical property; determining the statistical significance of the calculated statistical values of each group, tag, attribute, risk exposure, or relationship; validating that the statistical values are significant at a predetermined level; and if the values are not significant, reassigning groups, tags, attributes, risk exposures, or relationships), where the iRR calculation input comprising a request to begin calculation of the iRR, (Proximity may also be derived from the empirical relationships among the securities and economic entities, which can be aggregated, stored, and assigned to the data entities and their referents. In some embodiments, these may include [supplier-customer], [investor-entrepreneur], [impact investor-social enterprise], [intermediary-customer], [customer-customer of customer], [lender-borrower], [input-output], [employer-employee], [company-department], [general partner-limited partner], [service provider-client], [department-work group], [subject-activity-direct object-indirect object], [parent company-subsidiary], [raw material-basic component], [basic component-complex component], or [complex component-final product]) (see at least paragraphs 67-98, 252, 295, 333-352);
transmitting an impact theme/subcategory selection output (transmitting, sending, or relaying information regarding one or more data entities and one or more weights to an exchange, index provider, index calculator, brokerage, asset manager, investment advisor, investment manager, specialist, broker-dealer, authorized participant, trader, financial professional, investment professional, investor, general partner, limited partner, private equity investor, venture capital investor, hedge fund investor, conglomerate manager, executive, pension fund advisor, endowment manager, fund manager, or securities trading platform) from the first computer system, where the impact theme selection output is configured for causing the impact theme/subcategory selection to be provided to the second computer system (see at least paragraphs 67-98, 295, 333-352);
receiving an impact theme/subcategory selection input at the first computer system, where the impact theme selection input comprises a selected impact theme and is configured for storing the selected impact theme by the first computer system (The specific functional attributes associated with the investment securities can be used to segment, stratify, and weight the holdings of investment securities in a portfolio by assigning specific weights to the risk groups in which the underlying securities are held in order to meet the engineered risk objectives of the overall portfolio. As a non-limiting example, one of the goals in segmenting or stratifying risk groups may be to reduce the impact of attribute-specific volatility drag on the portfolio as a whole. As non-limiting examples, the systems and methods described herein can be used in investment management by controlling for specific types of random events that impact the overall randomness of risk, return, skewness, and kurtosis in large portfolios or groups of investment securities) (see at least paragraphs 67-98, 145, 295, 333-352);
based upon the stored selected impact theme/subcategory selection, transmitting an impact area selection output to the second computer system, the impact area selection output configured for causing the impact area selection to be provided to the second computer system, (transmitting, sending, or relaying information regarding one or more data entities and one or more weights to an exchange, index provider, index calculator, brokerage, asset manager, investment advisor, investment manager, specialist, broker-dealer, authorized participant, trader, financial professional, investment professional, investor, general partner, limited partner, private equity investor, venture capital investor, hedge fund investor, conglomerate manager, executive, pension fund advisor, endowment manager, fund manager, or securities trading platform)) (see at least paragraphs 67-98, 140-150, 295, 333-352);
receiving an impact area selection input, at the first computer system where the impact area selection input comprises a selected impact area and is configured for storing the selected impact area by the first computer system (Economic entities with one or more common functional attributes correlate with events that are associated with that attribute or set of attributes. The measure of correlation will vary by the level of importance of that attribute in a specific business. For example, if all network equipment companies share the same customers, the loss of a major customer like Cisco, a giant network company, will impact all the companies. The impact, however, will be greater if Cisco is the company's sole customer than if Cisco is less than 5% of a company's business. In this way, grouping companies in risk groups that are defined by attributes provides a method for portfolio managers to organize, segment, or stratify securities in groups that correlate with specific attribute-related events. In addition, most attributes are, in turn, part of larger attribute groups. When the large telecommunications company Nortel went bankrupt, all the companies that shared it as a customer were also part of a network equipment group which in turn was part of a communication equipment group which in turn was part of a larger digital technology group, all of which were exposed to the bankruptcy. In this way, using functional attributes enables a portfolio manager to group securities by both broad and narrow categories and by the importance of these categories in determining the performance of individual securities) (see at least paragraphs 56-98, 140-150, 295, 333-352);
based upon the stored selected impact area, transmitting an impact area sub-category selection output to the second computer system via the computer network, the impact area sub-category selection output configured for causing the impact area sub-category selection to be provided to the second computer system, wherein providing the impact area sub-category selection includes causing the webpage having impact area sub-category selections to be displayed via the display of the second computer system; receiving an impact area sub-category selection input, the impact area sub-category selection input having been transmitted from the second computer system to the first computer system via the computer network, the impact area sub-category selection input comprising a selected impact area sub-category and configured for storing the selected impact area sub-category by the first computer system (The specific functional attributes associated with the investment securities can be used to segment, stratify, and weight the holdings of investment securities in a portfolio by assigning specific weights to the risk groups in which the underlying securities are held in order to meet the engineered risk objectives of the overall portfolio. As a non-limiting example, one of the goals in segmenting or stratifying risk groups may be to reduce the impact of attribute-specific volatility drag on the portfolio as a whole. As non-limiting examples, the systems and methods described herein can be used in investment management by controlling for specific types of random events that impact the overall randomness of risk, return, skewness, and kurtosis in large portfolios or groups of investment securities) (see at least paragraphs 56-98, 140-150, 295, 333-352);
based upon the stored selected impact area, transmitting a KII selection output to the second computer system, the KII selection output configured for causing the KII selection to be provided to the second computer system, (In one aspect of the disclosure, there is provided a computer-implemented method for storing a representation in a database of an index or portfolio of investment securities, the method comprising electronically storing one or more data entities in a database system, each of the data entities representing the identity of an investment security, the investment security associated with a corresponding economic entity; electronically tagging each data entity with one or more functional attributes of the corresponding economic entities; wherein the functional attributes characterize the roles of each of the economic entities in one or more processes converting inputs to outputs; selecting multiple investment securities represented by the data entities for inclusion in an index or portfolio of investment securities; defining at least a first group and a second group of investment securities based on the electronic tags or the functional attributes associated with the corresponding economic entities; segmenting the selected investment securities into the two or more groups based on the electronic tags or the functional attributes; wherein the investment securities in the first segmented group share a first common or proximate functional attribute, and the investment securities in the second segmented group share a second common or proximate functional attribute; electronically accessing the database representation of the segmented groups; electronically iterating through the accessed representations to compute a negative or positive weight for one or more of the investment securities based on the one or more segmented groups into which the investment securities are segmented; and assigning the negative or positive weight to the one or more of the investment securities; and electronically storing the assigned weight in the database system) (see at least paragraphs 56-98, 140-150, 295, 333-352);
receiving a KII selection input,at the first computer system where the KII selection input comprises a selected KII and configured for storing the selected KII by the first computer system (Further embodiments comprise electronically storing a computerized representation of an economic systems syntax, wherein the economic systems syntax can be applied by a computer processor to establish the validity of expressions of elements of the system based on one or more functional properties of the economic entities) (see at least paragraphs 56-98, 140-150, 295, 333-352);
based upon the stored selected impact area sub-category, transmitting an impact metric selection output to the second computer system the impact metric selection output configured for causing an impact metric selection to be provided to the second computer system (transmitting, sending, or relaying information regarding one or more data entities and one or more weights to an exchange, index provider, index calculator, brokerage, asset manager, investment advisor, investment manager, specialist, broker-dealer, authorized participant, trader, financial professional, investment professional, investor, general partner, limited partner, private equity investor, venture capital investor, hedge fund investor, conglomerate manager, executive, pension fund advisor, endowment manager, fund manager, or securities trading platform)) (see at least paragraphs 67-98, 140-150, 295, 333-352);
receiving an impact metric selection input, at the first computer system where the impact metric selection input comprises a selected impact metric and is configured for storing the selected impact metric by the first computer system (Further embodiments comprise selecting a financial or economic metric to measure with respect to one or more of the groups, indices, or portfolios, wherein: the distribution of expected or realized values of the metric for the index, portfolio, or group is relatively more normal than the distribution of expected or realized values of the metric for an alternative index, portfolio, or group; or the value of the metric is more stable or predictable for the index or portfolio than it is for the group, as measured by a mathematical test of stability or predictability; or the value of the metric is more stable or predictable for the group than it is for an investment security, as measured by the mathematical test of stability or predictability. In some further embodiments, the normality of the distribution is assessed using Cramér-von Mises criterion, Kolmogorov-Smirnov test, Shapiro-Wilk test, Anderson-Darling test, Jarque-Bera test, Siegel-Tukey test, Kuiper test, p-value test, a Q-Q plot, a test of skewness, or a test of kurtosis. As non-limiting examples, stability may be assessed through a test of variance or a test of heteroscedasticity) (see at least paragraphs 67-98, 140-150, 295, 333-352);
processing an impact metric value, the impact metric value being processed by the first computer system, the processing of the impact metric value configured to assign an impact metric value to the impact metric and store the impact metric value (Further embodiments comprise selecting a financial or economic metric to measure with respect to one or more of the groups, indices, or portfolios, wherein: the distribution of expected or realized values of the metric for the index, portfolio, or group is relatively more normal than the distribution of expected or realized values of the metric for an alternative index, portfolio, or group; or the value of the metric is more stable or predictable for the index or portfolio than it is for the group, as measured by a mathematical test of stability or predictability; or the value of the metric is more stable or predictable for the group than it is for an investment security, as measured by the mathematical test of stability or predictability. In some further embodiments, the normality of the distribution is assessed using Cramér-von Mises criterion, Kolmogorov-Smirnov test, Shapiro-Wilk test, Anderson-Darling test, Jarque-Bera test, Siegel-Tukey test, Kuiper test, p-value test, a Q-Q plot, a test of skewness, or a test of kurtosis. As non-limiting examples, stability may be assessed through a test of variance or a test of heteroscedasticity) (see at least paragraphs 67-98, 140-150, 295, 333-352);
processing an impact metric weight, the impact metric weight being processed by the first computer system, the processing of the impact metric weight configured to assign an impact metric weight to the impact metric and store the impact metric weight (Further embodiments comprise selecting a financial or economic metric to measure with respect to one or more of the groups, indices, or portfolios, wherein: the distribution of expected or realized values of the metric for the index, portfolio, or group is relatively more normal than the distribution of expected or realized values of the metric for an alternative index, portfolio, or group; or the value of the metric is more stable or predictable for the index or portfolio than it is for the group, as measured by a mathematical test of stability or predictability; or the value of the metric is more stable or predictable for the group than it is for an investment security, as measured by the mathematical test of stability or predictability. In some further embodiments, the normality of the distribution is assessed using Cramér-von Mises criterion, Kolmogorov-Smirnov test, Shapiro-Wilk test, Anderson-Darling test, Jarque-Bera test, Siegel-Tukey test, Kuiper test, p-value test, a Q-Q plot, a test of skewness, or a test of kurtosis. As non-limiting examples, stability may be assessed through a test of variance or a test of heteroscedasticity) (see at least paragraphs 67-98, 140-150, 295, 333-352);
processing an impact metric possible point value, the impact metric possible point value being processed by the first computer system, the processing of the impact metric possible point value configured to assign an impact metric possible point value to the impact metric and to store the impact metric possible point value in association with the impact metric, wherein the impact metric possible point value is the product of the stored impact metric value and the impact metric weight (Further embodiments comprise selecting a financial or economic metric to measure with respect to one or more of the groups, indices, or portfolios, wherein: the distribution of expected or realized values of the metric for the index, portfolio, or group is relatively more normal than the distribution of expected or realized values of the metric for an alternative index, portfolio, or group; or the value of the metric is more stable or predictable for the index or portfolio than it is for the group, as measured by a mathematical test of stability or predictability; or the value of the metric is more stable or predictable for the group than it is for an investment security, as measured by the mathematical test of stability or predictability. In some further embodiments, the normality of the distribution is assessed using Cramér-von Mises criterion, Kolmogorov-Smirnov test, Shapiro-Wilk test, Anderson-Darling test, Jarque-Bera test, Siegel-Tukey test, Kuiper test, p-value test, a Q-Q plot, a test of skewness, or a test of kurtosis. As non-limiting examples, stability may be assessed through a test of variance or a test of heteroscedasticity) (see at least paragraphs 67-98, 140-150, 295, 333-352);
based upon the stored selected impact metrics, transmitting an impact metric score range selection output to the second computer system, the impact metric score range selection output configured for causing an impact metric score range selection to be provided to the second computer system (Further embodiments comprise selecting a financial or economic metric to measure with respect to one or more of the groups, indices, or portfolios, wherein: the distribution of expected or realized values of the metric for the index, portfolio, or group is relatively more normal than the distribution of expected or realized values of the metric for an alternative index, portfolio, or group; or the value of the metric is more stable or predictable for the index or portfolio than it is for the group, as measured by a mathematical test of stability or predictability; or the value of the metric is more stable or predictable for the group than it is for an investment security, as measured by the mathematical test of stability or predictability. In some further embodiments, the normality of the distribution is assessed using Cramér-von Mises criterion, Kolmogorov-Smirnov test, Shapiro-Wilk test, Anderson-Darling test, Jarque-Bera test, Siegel-Tukey test, Kuiper test, p-value test, a Q-Q plot, a test of skewness, or a test of kurtosis. As non-limiting examples, stability may be assessed through a test of variance or a test of heteroscedasticity) (see at least paragraphs 67-98, 140-170, 295, 333-352);
receiving an impact metric score range selection input at the first computer system where the impact metric score range selection input comprises an impact metric score range for the impact metric and configured for storing the impact metric score range in association with the impact metric by the first computer system (Any expression or sub-expression of the syntax, containing elements which have a range of potential values, may be stratified, in which case that expression or sub-expression describes a dimension which consists of regions and successive sub-regions within the multi-dimensional space. As a default, elements of syntax which are designated as stratified are interpreted from left to right according to their position within the expression, as successive levels from top to bottom within the architecture) (see at least paragraphs 67-98, 140-170, 295, 333-352);
based upon the stored impact metric score range, transmitting an impact metric score selection output to the second computer system, the impact metric score selection output configured for causing an impact metric score selection to be provided to the second computer system (Any expression or sub-expression of the syntax, containing elements which have a range of potential values, may be stratified, in which case that expression or sub-expression describes a dimension which consists of regions and successive sub-regions within the multi-dimensional space. As a default, elements of syntax which are designated as stratified are interpreted from left to right according to their position within the expression, as successive levels from top to bottom within the architecture) (see at least paragraphs 67-98, 140-170, 295, 333-352);
receiving an impact metric score selection input, at the first computer system where the impact metric score selection input comprises a selected impact metric score for the impact metric and configured for storing the selected impact metric score in association with the impact metric by the first computer system (Any expression or sub-expression of the syntax, containing elements which have a range of potential values, may be stratified, in which case that expression or sub-expression describes a dimension which consists of regions and successive sub-regions within the multi-dimensional space. As a default, elements of syntax which are designated as stratified are interpreted from left to right according to their position within the expression, as successive levels from top to bottom within the architecture) (see at least paragraphs 67-98, 140-170, 295, 333-352);
processing an impact metric score rate, the impact metric score rate being processed by the first computer system, where the processing is configured to assign an impact metric score rate to the impact metric based upon the stored impact metric score and to store the impact metric score rate by the first computer system (Any expression or sub-expression of the syntax, containing elements which have a range of potential values, may be stratified, in which case that expression or sub-expression describes a dimension which consists of regions and successive sub-regions within the multi-dimensional space. As a default, elements of syntax which are designated as stratified are interpreted from left to right according to their position within the expression, as successive levels from top to bottom within the architecture) (see at least paragraphs 67-98, 140-170, 295, 333-352);
processing an impact metric point value, the impact metric point value being processed by the first computer system (Syntax elements may be considered to have a proximate syntactic position if they are relatively close to other elements based on either their specialization or serial positions. These relationships allow for comparison of values across syntactic positions. This property supports applications including but not limited to the complex structures, population sorting, autoclassification, and integration with prior art temporal and spatial classification systems), where the processing is configured to assign the impact metric with an impact metric point value by multiplying the stored impact metric score rate and the stored impact metric possible point value and to store the impact metric point value by the first computer system (Any expression or sub-expression of the syntax, containing elements which have a range of potential values, may be stratified, in which case that expression or sub-expression describes a dimension which consists of regions and successive sub-regions within the multi-dimensional space. As a default, elements of syntax which are designated as stratified are interpreted from left to right according to their position within the expression, as successive levels from top to bottom within the architecture) (see at least paragraphs 67-98, 140-170, 295, 333-352);
processing an impact multiplier value, the impact multiplier value being processed by the first computer system, where the processing is configured to assign the impact multiplier value by dividing the impact metric point value by 100 (parent weight = 100) and to store the impact multiplier value (see at least paragraphs 67-98, 140-202, 217, 295, 333-352);
based upon the stored selected KII, transmitting an impact goal output to the second computer system, the impact goal output configured for causing an impact goal to be provided to the second computer system (After population stratification or segmentation, the metrics are identified that will be used to evaluate the portfolio. The metrics used can depend on the population that is being stratified. For example, the metrics used for an investment-grade debt portfolio may be expected yield and volatility, while the metrics of an equity portfolio may be expected risk and return. Once the metrics have been identified, a target score can be established (7010). The target score is the goal that the user would like to see the portfolio achieve, the goal being measured by the identified metrics. For example, the target score of an investment grade debt portfolio can be an expected yield and expected volatility that an investor would like the portfolio to achieve) (see at least paragraphs 67-98, 140-202, 215-223, 295, 333-352);
receiving an impact goal input at the first computer system, where the impact goal input comprises the impact goal and configured for storing the impact goal in association with the stored selected KII by the first computer system (After population stratification or segmentation, the metrics are identified that will be used to evaluate the portfolio. The metrics used can depend on the population that is being stratified. For example, the metrics used for an investment-grade debt portfolio may be expected yield and volatility, while the metrics of an equity portfolio may be expected risk and return. Once the metrics have been identified, a target score can be established (7010). The target score is the goal that the user would like to see the portfolio achieve, the goal being measured by the identified metrics. For example, the target score of an investment grade debt portfolio can be an expected yield and expected volatility that an investor would like the portfolio to achieve) (see at least paragraphs 67-98, 140-202, 215-223, 295, 333-352);
based upon the stored selected KII, transmitting a KII delivery output to the second computer system, the KII delivery output configured for causing a KII delivery to be provided to the second computer system (After population stratification or segmentation, the metrics are identified that will be used to evaluate the portfolio. The metrics used can depend on the population that is being stratified. For example, the metrics used for an investment-grade debt portfolio may be expected yield and volatility, while the metrics of an equity portfolio may be expected risk and return. Once the metrics have been identified, a target score can be established (7010). The target score is the goal that the user would like to see the portfolio achieve, the goal being measured by the identified metrics. For example, the target score of an investment grade debt portfolio can be an expected yield and expected volatility that an investor would like the portfolio to achieve) (see at least paragraphs 67-98, 140-202, 215-223, 295, 333-352);
receiving an impact goal input at the first computer system, where the impact goal input comprises the impact goal and configured for storing the impact goal in association with the stored selected KII by the first computer system (After population stratification or segmentation, the metrics are identified that will be used to evaluate the portfolio. The metrics used can depend on the population that is being stratified. For example, the metrics used for an investment-grade debt portfolio may be expected yield and volatility, while the metrics of an equity portfolio may be expected risk and return. Once the metrics have been identified, a target score can be established (7010). The target score is the goal that the user would like to see the portfolio achieve, the goal being measured by the identified metrics. For example, the target score of an investment grade debt portfolio can be an expected yield and expected volatility that an investor would like the portfolio to achieve) (see at least paragraphs 67-98, 140-202, 215-223, 295, 333-352)
based upon the stored KII delivery and the stored impact goal, processing the impact quantity, the impact quantity being processed by the first computer system, where the processing is configured to assign the impact quantity of the activity by dividing the stored KII delivery by the stored impact goal and to store the impact quantity by the first computer system (After population stratification or segmentation, the metrics are identified that will be used to evaluate the portfolio. The metrics used can depend on the population that is being stratified. For example, the metrics used for an investment-grade debt portfolio may be expected yield and volatility, while the metrics of an equity portfolio may be expected risk and return. Once the metrics have been identified, a target score can be established (7010). The target score is the goal that the user would like to see the portfolio achieve, the goal being measured by the identified metrics. For example, the target score of an investment grade debt portfolio can be an expected yield and expected volatility that an investor would like the portfolio to achieve) (see at least paragraphs 67-98, 140-202, 215-223, 295, 333-352);
transmitting a time period output to the second computer system, the time period output configured for causing a time period entry to be provided to the second computer system (to create a stratified architecture or segmented sets of specific risk groups, allocating the securities in a portfolio across these stratified or segmented risk groups and selecting the desired exposure to the risk groups by applying calculated or user-provided weights for identified non-systematic risks. Thus, stratification or segmentation can be used to systematically control exposure to non-systematic risks. These exposures can then be managed over time by creating rebalancing rules that reset on an appropriate periodic schedule a portfolio's exposure to these identified non-systematic risks. In this way, a large-scale securities portfolio's exposure to a set of non-systematic risks can be systematically determined and managed) (see at least paragraphs 67-98, 139-202, 215-224, 295, 333-352);
receiving a time period input at the first computer system where the time period input comprises the time period and configured for storing the time period in years by the first computer system (for any given security s, its return r over a time period t can be described as
k∫∫∫f.sub.j(a)dwdadt+∫n.sub.m(t)dt
where a.sub.1, 2 . . . n are the attributes in a given time period that influence the return of the security, w.sub.1, 2 . . . n are the weights to be assigned to each of those attributes, k is a constant, and n is a set of equations modeling stochastic components) (see at least paragraphs 67-98, 139-202, 215-224, 295, 333-352);
transmitting an activity cost output to the second computer system, the activity cost output configured for causing an activity cost entry to be provided to the second computer system (associating two or more numerical values with two or more groups, tags, attributes, risk exposures or relationships, wherein the numerical values relate to economic, financial, or capital markets-based data; associating a statistical property, selected from among mean, variance, standard deviation, skew, kurtosis, correlation, semivariance, and semideviation, with those groups, tags, attributes, risk exposures, or relationships based on the numerical values; calculating two or more statistical values associated with the statistical property; determining the statistical significance of the calculated statistical values of each group, tag, attribute, risk exposure, or relationship; validating that the statistical values are significant at a predetermined level; and if the values are not significant, reassigning groups, tags, attributes, risk exposures, or relationships) (see at least paragraphs 67-98, 139-202, 215-224, 295, 333-352);
receiving an activity cost input (the management of the synthetic conglomerate can be effectuated in real-time by the data systems described herein, by permitting the dynamic aggregation of the financial statements of each of the constituents of large portfolios and the calculation and display of their consolidated balance sheets, income statements, and cash flow statements. The technologies described herein permit customized identification and selection of exposures within large-scale portfolios across functional, temporal, and geographic space; the data systems described herein enable the creation of streams of earnings, dividends, and cash flows at the portfolio level that are more stable, consistent, and predictable than those at the group level. In other embodiments, the streams at the group level will be more stable, consistent, and predictable than those at the security level. In other embodiments, the streams at the portfolio level will be more stable, consistent, and predictable than those at the security level. In some embodiments, the engineered composite or synthetic conglomerate can be considered a benchmark that delivers more consistent, stable, and predictable returns that more reliably attain the rates of risk and liquidity-adjusted return predicted by financial theory than other commercially available or widely held indices or benchmarks) at the first computer system where, the activity cost input comprises the activity cost and is configured for storing the activity cost by the first computer system (the management of the synthetic conglomerate can be effectuated in real-time by the data systems described herein, by permitting the dynamic aggregation of the financial statements of each of the constituents of large portfolios and the calculation and display of their consolidated balance sheets, income statements, and cash flow statements) (see at least paragraphs 67-98, 139-202, 215-228, 295, 333-352);
based upon the stored activity cost and stored KII delivery, processing an impact efficiency, the impact efficiency being processed by the first computer system, where the processing is configured to assign the impact efficiency of the activity, and to store the impact efficiency by the first computer system (estimating a correlation value for each segment in the third stratum of a stratified heterogeneous 900 security portfolio or index and assigning the relevant value to each constituent of a group can reduce the number of correlations necessary to estimate pairwise correlations by a factor of over 200, facilitating the construction of an index or portfolio that will more consistently and predictably approximate the efficient frontier than other methods of portfolio or index construction) (see at least paragraphs 67-98, 139-202, 215-228, 276, 295, 333-352);
based upon the stored impact multiplier value, the stored KII delivery, the stored time period, the stored impact quantity, and the stored impact efficiency, processing the iRR, the iRR being processed by the first computer system, where the processing is configured to assign the iRR of the activity by and to store the iRR associated with the activity by the first computer system (there is provided a computer-implemented method for storing a representation in a database of an index or portfolio of investment securities, the method comprising electronically storing one or more data entities in a database system, each of the data entities representing the identity of an investment security, the investment security associated with a corresponding economic entity; electronically tagging each data entity with one or more functional attributes of the corresponding economic entities; wherein the functional attributes characterize the roles of each of the economic entities in one or more processes converting inputs to outputs; selecting multiple investment securities represented by the data entities for inclusion in an index or portfolio of investment securities; defining at least a first group and a second group of investment securities based on the electronic tags or the functional attributes associated with the corresponding economic entities; segmenting the selected investment securities into the two or more groups based on the electronic tags or the functional attributes; wherein the investment securities in the first segmented group share a first common or proximate functional attribute, and the investment securities in the second segmented group share a second common or proximate functional attribute; electronically accessing the database representation of the segmented groups; electronically iterating through the accessed representations to compute a negative or positive weight for one or more of the investment securities based on the one or more segmented groups into which the investment securities are segmented; and assigning the negative or positive weight to the one or more of the investment securities; and electronically storing the assigned weight in the database system) (see at least paragraphs 67-98, 139-202, 215-228, 276, 295, 333-352);
based upon the stored iRR, transmitting an iRR output to the second computer system the iRR output configured for causing the stored iRR to be provided to the second computer system (there is provided a computer-implemented method for storing a database characterization of an index, portfolio, set, aggregate, or composite of elements of a functional system, or of a representation of those elements, the method comprising: electronically storing a set of data entities in a database system, each of the data entities corresponding to an element of a functional system; wherein the functional system comprises a group of elements ordered by their functional roles in converting inputs to outputs, or as the inputs, or as the outputs; electronically assigning each data entity associated with an element one or more functional attributes represented as an electronic tag; wherein the functional attributes characterize the roles of each of the elements in a process of converting inputs to outputs; selecting multiple elements, or a representation of those elements, characterized by data entities for inclusion in a portfolio, index, set, aggregate, or composite; segmenting the selected elements, or a representation of those elements, into two or more defined groups based on the electronic tags representing the functional attributes associated with the corresponding elements; wherein the first group shares a first common functional attribute, and the second group shares a second common functional attribute; electronically accessing the database representation of the segmented groups; electronically iterating through the accessed representations to compute a negative or positive weight for one or more of the elements, or a representation of those elements, based on the one or more segmented groups; and assigning the negative or positive weight to the one or more of the elements, or a representation of those elements; and electronically storing the assigned weight in the database system) (see at least paragraphs 67-98, 139-202, 215-228, 276, 295, 333-352).
With regard to Claim 22, Riggs teaches wherein the display of the impact metric score range selections comprises five pro rata percentiles (see at least paragraphs 48-57, 67-98, 139-202, 215-228, 276-295, 333-352).
With regard to Claim 23, Riggs teaches wherein the business entity is selected from the group consisting of a government agency, a for-profit legal entity, a non-profit legal entity, and an individual user (see at least paragraphs 4, 48-57, 67-98, 111-202, 215-228, 276, 295, 333-352).
With regard to Claim 24, Riggs teaches wherein the impact theme selection output comprises a social impact theme, an environmental impact theme, an economic impact theme, and a governance impact theme (see at least paragraphs 4, 48-57, 67-98, 111-202, 215-228, 276, 295, 333-352).
With regard to Claim 25, Riggs teaches wherein the impact theme selection input comprises the social impact theme, and wherein the impact area selection comprises housing and shelter, smallholders and family farmers, education, connectivity, employment, health and human services, equity and access, financial services and inclusion, community and stakeholder engagement, safety, and other (see at least paragraphs 4, 48-57, 67-98, 111-202, 215-228, 276, 295, 333-352).
With regard to Claim 26, Riggs teaches wherein the impact theme selection input comprises the environmental impact theme, and wherein the impact area selection comprises natural resources and ecosystems, climate, energy pollution and waste, food production, materials, animals and wildlife, buildings and real assets, and other (see at least paragraphs 4, 48-57, 67-98, 111-202, 215-228, 276, 295, 333-352).
With regard to Claim 27, Riggs teaches wherein the impact theme selection input comprises the economic impact theme, and wherein the impact area selection comprises infrastructure, real estate and property value, economic indicators, community or stakeholder ownership, economic performance, economic practices, research and development, and other (see at least paragraphs 4, 48-57, 67-98, 111-202, 215-228, 276, 295, 333-352).
With regard to Claim 29, Riggs teaches wherein the impact metric selection comprises at least two selected impact metrics and is configured for storing each of the at least two selected impact metrics in association with the impact area sub-category (see at least paragraphs 4, 48-57, 67-98, 111-202, 215-228, 276, 295, 333-352);
and wherein processing the impact metric weight comprises pro-rata assignment of at least two impact metric sub-weights to the at least two impact metrics, and wherein processing the impact metric possible point value comprises assigning each of the at least two impact metric sub-weights an impact metric possible point sub-value, wherein each of the impact metric possible point sub-values is the product of the stored impact metric value and each of the respective at least two impact metric sub-weights (see at least paragraphs 4, 48-57, 67-98, 111-202, 215-228, 276, 295, 333-352);
the impact metric score range selection output includes at least two impact metric score ranges to be displayed in association with the corresponding impact metrics of the at least two impact metrics, and wherein the impact metric score range selection input comprises at least two selected impact metric score ranges and is configured for storing each of the at least two selected impact metric score ranges in association with the respective impact metrics of the at least two impact metrics (see at least paragraphs 4, 48-57, 67-98, 111-202, 215-228, 276, 295, 333-352); the impact metric score selection input comprises at least two selected impact metric scores and is configured for storing each of the at least two selected impact metric scores in association with the respective impact metrics of the at least two impact metrics (see at least paragraphs 4, 48-57, 67-98, 111-202, 215-228, 276, 295, 333-352);
and wherein processing the impact metric score rate comprises assigning each of the at least two impact metrics an impact metric score; processing the impact metric point value comprises assigning each of the at least two impact metrics an impact metric point sub-value, the processing configured to store the at least two impact metric point sub-values, wherein each of the impact metric possible point sub-values is the product of each of the stored impact metric rates and the at least two impact metric possible point sub-values for each respective impact metric of the at least two impact metrics (see at least paragraphs 4, 48-57, 67-98, 111-202, 215-228, 276, 295, 333-352).
With regard to Claim 30, Riggs teaches wherein the impact area sub-category selection input comprises at least two selected impact area sub-categories and is configured for storing the at least two selected impact area sub-categories in association with the respective impact area of the at least two impact areas; and wherein the impact metric selection output includes having the impact metric selections in association with the corresponding impact area sub-categories of the at least two impact area sub-categories (see at least paragraphs 4, 48-57, 67-98, 111-202, 215-228, 276, 295, 333-352).
With regard to Claim 31, Riggs teaches wherein the impact area selection input comprises at least two selected impact areas and is configured for storing each of the at least two selected impact areas in association with the corresponding impact theme (see at least paragraphs 4, 48-57, 67-98, 111-202, 215-228, 276, 295, 333-352);
and wherein the impact area sub-category selection output includes the impact area sub-category selections to be displayed in association with the corresponding impact area of the at least two selected impact areas and wherein providing the KII selection includes causing the webpage having the KII selections to be displayed in association with the corresponding impact area for each of the at least two impact areas (see at least paragraphs 4, 48-57, 67-98, 111-202, 215-228, 276, 295, 333-352);
and wherein the KII selection input comprises at least two selected KIIs and is configured for storing each of the at least two selected KIIs by the first computer system (see at least paragraphs 4, 48-57, 67-98, 111-202, 215-228, 276, 295, 333-352).
With regard to Claim 34, Riggs teaches wherein the processing of the impact quantity is further configured to assign the impact quantity by calculating the log base 100 of the stored KII delivery (see at least paragraphs 4, 48-57, 67-98, 111-202, 215-228, 276, 295, 333-352).
With regard to Claim 35, Riggs teaches wherein the processing of the impact quantity is further configured to assign the impact quantity by assigning the stored KII delivery as the impact quantity (see at least paragraphs 4, 48-57, 67-98, 111-202, 215-228, 276, 295, 333-352).
With regard to Claim 49, Riggs teaches wherein the impact theme selection output comprises impact themes based on a foci of the activity (see at least paragraphs 4, 48-57, 67-98, 111-202, 215-228, 276, 295, 333-352).
With regard to Claim 50, Riggs teaches wherein the impact theme selection output comprises impact themes based on assessing objectives of the activity (see at least paragraphs 4, 48-57, 67-98, 111-202, 215-228, 276, 295, 333-352).
With regard to Claim 51, Riggs teaches wherein processing the impact efficiency further comprises overriding the assigned impact efficiency when the assigned impact efficiency is equal to or less than zero, where the first computer system assigns an overridden impact efficiency from 0 to 0.0015 for the impact efficiency (see at least paragraphs 4, 48-57, 67-98, 111-202, 215-228, 276, 295, 333-352).
Conclusion
The prior art made of record and not relied upon is considered pertinent to Applicant's disclosure:
Evans (US 11,750,633)
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).
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Any inquiry concerning this communication or earlier communications from the examiner should be directed to THOMAS L MANSFIELD whose telephone number is (571)270-1904. The examiner can normally be reached M-Thurs, alt. Fri. (9-6).
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THOMAS L. MANSFIELD
Examiner
Art Unit 3623
/THOMAS L MANSFIELD/Primary Examiner, Art Unit 3624