DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Drawings
The drawings are objected to as failing to comply with 37 CFR 1.84(p)(5) because they include the following reference character(s) not mentioned in the description: the numbers “100” and “190” appear in figure 1 and the number “200” appears in figure 2 but these numbers are not in the written specification. Corrected drawing sheets in compliance with 37 CFR 1.121(d), or amendment to the specification to add the reference character(s) in the description in compliance with 37 CFR 1.121(b) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
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 1-13 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 applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Regarding claims 1-12,
Claims 1 and 7 recite the limitation " the data entities" in the third and fourth lines, respectively. There is insufficient antecedent basis for this limitation in the claim. Claims 2-6 and 8-12 are rejected for this reason by virtue of their dependence on either claim 1 or 7.
Regarding claims 3 and 9,
Claims 3 and 9 recites the limitation "the second unified quantitative data set" and “the second similarity metric” in the first and second steps recited in the claims. There is insufficient antecedent basis for this limitation in the claim.
Regarding claims 13,
Claims 13 recites the limitation " the user-configured portfolio construction parameters" in step h. There is insufficient antecedent basis for this limitation in the claim.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-13 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Claims 1-6 and 13 are directed to a method and claims 7-12 are directed to a system and thus fall into at least one statutory category enumerated in 35 U.S.C. § 101.
However, claim 7 recite(s):
storing data from multiple disparate data sets, the disparate data sets including both qualitative properties and quantitative properties associated with the data entities; selecting an algorithm that renders qualitative properties quantitatively; applying the algorithm to the qualitative properties to convert the qualitative properties into quantitative data; selecting a data structure that unifies quantitative data associated with disparate data sets; applying the data structure to integrate the quantitative data with the quantitative properties associated with the data entities, thereby creating a unified quantitative data set; associating the data entities with the unified quantitative data set […], receiving index construction parameters associated with a user; selecting a generative algorithm that combines data entities associated with quantitative data; applying the generative algorithm to the unified quantitative data set to create a customized index of data entities; and storing the customized index of data entities […].
This concept falls within the grouping of abstract ideas of concepts performed in the human mind (including an observation, evaluation, judgment, opinion) because these steps can be performed in the human mind, or by a human using a pen and paper. See MPEP 2106.04(a)(2) subsection III(A):providing the example of a claim to "collecting information, analyzing it, and displaying certain results of the collection and analysis," where the data analysis steps are recited at a high level of generality such that they could practically be performed in the human mind, Electric Power Group v. Alstom, S.A., 830 F.3d 1350, 1353-54, 119 USPQ2d 1739, 1741-42 (Fed. Cir. 2016).
This judicial exception is not integrated into a practical application because the additional limitations are a system comprising a computerized processor, a database, and "a logical database structure".
The specification states at ¶[0254]:
Some embodiments described herein relate to methods. It should be understood such methods can be computer implemented methods (e.g., instructions stored in memory and executed on processors).
At ¶[0256] the specification states:
Some embodiments and/or methods described herein can be performed by software ( executed on hardware), hardware, or a combination thereof. Hardware modules may include, for example, a general-purpose processor, a field programmable gate array (FPGA), and/or an application specific integrated circuit (ASIC).
The additional elements are recited at a high-level of generality and amount to no more than mere instructions to apply the abstract idea using generic computer and computer networking components or amount to merely using a computer as a tool to perform the abstract idea. See MPEP 2106.05(f). Accordingly, these additional elements do not integrate the abstract idea into a practical application. The claim is directed to an abstract idea.
The claim does not include additional elements, individually and in combination, that are sufficient to amount to significantly more than the judicial exception because, as discussed above with respect to integration of the abstract idea into a practical application, the additional elements of using generic computer components or merely using a computer as a tool to perform the abstract ideas amount to no more than mere instructions to apply the exception using generic computer and computer network components. Mere instructions to apply an exception using generic computer components cannot provide an inventive concept. Thus, the claim is not patent-eligible.
Independent claim 1 is directed to a computer-implemented method but recite substantially the same limitations as independent claim 7 (which is directed to a system) and is rejected for the same reason.
Claims 2 and 8 recite additional computations (computing a similarity metric), using the metric as an input, selecting an algorithm, applying the algorithm to perform a conversion, creating a second data set, and computing a similarity metric. This is part of the abstract idea and does not integrate the abstract idea into a practical application or provide significantly more.
Claims 3 and 9 add additional computation of computing a similarity metric, verifying that a metric greater than another metric using a test of statistical significance. This is part of the abstract idea and does not integrate the abstract idea into a practical application or provide significantly more.
Claims 4 and 10 link the abstract idea to a field of use (specifying that the data entities represent investment securities). This is part of the abstract idea and does not integrate the abstract idea into a practical application or provide significantly more.
Claims 5 and 11 recite converting a data set into a matrix, network, or dimensional coordinate data structure. This can be done with pen and paper and is part of the abstract idea and does not integrate the exception into a practical application.
Claims 6 and 12 recite applying a data structure based on ordinal coding or interval variables as inputs to the algorithm wherein the ordinal coding or interval variables are based on relationships modeled in a system. This is part of the identified abstract idea and does not integrate the abstract idea into a practical application or provide significantly more.
Independent claim 13 recites:
storing financial data from multiple disparate data sets; b. extracting information concerning a set of investment securities, said information being obtained from the disparate data sets, the disparate data sets including both qualitative and quantitative properties associated with the investment securities; c. converting the qualitative properties to quantitative properties by an ordinal coding technique; d. unifying the disparate data sets into a unified data set of quantitative properties associated with the investment securities; e. associating the investment securities with the unified quantitative properties ; f. receiving user-configured index construction parameters; g. determining the composition of a subset index of investment securities; h. constructing a customized index of investment securities based on the user-configured portfolio construction parameters; and i. storing the customized index of investment securities
This concept falls within the grouping of abstract ideas of commercial or legal interactions (including marketing or sales activities or behaviors; business relations) because the claim recites extracting information concerning a set of investment securities, determining the composition of a security index of investment securities and constructing and storing a customized index of investment securities. See MPEP 2106.04(a)(2) subsection II.B providing an example of a claim reciting a commercial or legal interaction in Fort Properties, Inc. v. American Master Lease, LLC, 671 F.3d 1317, 101 USPQ2d 1785 (Fed Cir. 2012) where the patentee claimed a method of "aggregating real property into a real estate portfolio, dividing the interests in the portfolio into a number of deedshares, and subjecting those shares to a master agreement." 671 F.3d at 1322, 101 USPQ2d at 1788.
This judicial exception is not integrated into a practical application because the additional limitations are a database, "a logical database structure" and “an index database.”
The additional elements are recited at a high-level of generality and amount to no more than mere instructions to apply the abstract idea using generic computer and computer networking components or amount to merely using a computer as a tool to perform the abstract idea. See MPEP 2106.05(f). Accordingly, these additional elements do not integrate the abstract idea into a practical application. The claim is directed to an abstract idea.
The claim does not include additional elements, individually and in combination, that are sufficient to amount to significantly more than the judicial exception because, as discussed above with respect to integration of the abstract idea into a practical application, the additional elements of using generic computer components or merely using a computer as a tool to perform the abstract ideas amount to no more than mere instructions to apply the exception using generic computer and computer network components. Mere instructions to apply an exception using generic computer components cannot provide an inventive concept. Thus, the claim is not patent-eligible.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status. The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(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.
Claim(s) 1-12 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by RIGGS (US 20220138280 A1 to Riggs, R. et al).
Regarding claim(s) 1 and 7,
RIGGS discloses:
A computer-implemented method for creating a customized index, the method comprising/ A computer-implemented system for creating a customized index, the system comprising a computerized processor configured for (RIGGS: ¶[0094]: there is provided a computer-implemented method for storing a database characterization of an index, portfolio, set, aggregate, or composite of elements; ¶[0096]: there is provided a system for executing a command in a computing environment to construct a representation of an index or portfolio of investment securities in a database, the system comprising: a computerized processor; figure 11: Processing Unit and Data Storage and System Bus):
storing data from multiple disparate data sets, the disparate data sets including both qualitative properties and quantitative properties associated with the data entities (RIGGS: ¶[0070]: 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; ¶[0094]: 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; ¶[0096]: an electronic data store configured for: electronically storing the 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 storing the assigned weight in the database system; ¶[0148]: While quantitative values associated with securities are likely to exhibit significant a stationarity, qualitative attributes are likely to persist over time, driving performance characteristics with consistency and facilitating portfolio management and index construction at scale. The data systems described herein, which enable the syntactic and functional tagging of hundreds of thousands of securities and the dynamic segmentation and stratification of large sets of securities by their associated attributes, are instrumental in enabling this process. As a non-limiting example, by dividing the securities into correlation clusters, i.e., groupings formed based on attributes that correspond to risks, volatility can be controlled; ¶[0322]: In some further embodiments, non-functional attributes can be used as an input in the process of identifying or matching of entities, and wherein the functional locations can be identified semantically, syntactically, graphically, symbolically, visually, or aurally; and wherein non-functional attributes comprise an economic metric, a financial metric, demographic data, geographic data, temporal data, or experiential data.)
selecting an algorithm that renders qualitative properties quantitatively; (RIGGS: ¶[0142]: economic models characterize functional-attributes, which represent risk-related properties, qualities, or characteristics. Coding for these attributes in a coordinate-based or ordered tagging system enables a computer to associate tags with specific risks and generate company groupings that share these attributes; ¶[0278]: In some embodiments, they include multivariate algorithms can be used to organize large datasets. The methods can be used to generate or identify causal connections and perform real-time analyses; ¶[0308]: creating a data structure for a set of data entities in a data structure by: generating and electronically storing in a database system a logical data model having a data structure comprising assigning an m-dimensional set of n-dimensional vectors to a set of data entities corresponding to elements of a functional system, wherein the n-dimensional vectors are assigned based on functional attributes of the elements, a plurality of the functional attributes represented as an electronic tag; wherein the functional attributes group data entities in the logical data model, and wherein the assignment of n-dimensional vectors re-organizes the groups based on their functional or non-functional attributes with respect to one or more variables within the functional system; (RIGGS: [0278]:. In some embodiments, they include multivariate algorithms can be used to organize large datasets. The methods can be used to generate or identify causal connections and perform real-time analyses )
applying the selected algorithm to the qualitative properties to convert the qualitative properties into quantitative data. (RIGGS: ¶[0142]: Endogenous economic models characterize functional-attributes, which represent risk-related properties, qualities, or characteristics. Coding for these attributes in a coordinate-based or ordered tagging system enables a computer to associate tags with specific risks and generate company groupings that share these attributes. […] Further, the computerized system described herein can be used to generate an assembly of groupings, including, as a non-limiting example, a risk-stratified portfolio consisting of stratified groupings of statistical control groups. ¶[0308]: creating a data structure for a set of data entities in a data structure by: generating and electronically storing in a database system a logical data model having a data structure comprising assigning an m-dimensional set of n-dimensional vectors to a set of data entities corresponding to elements of a functional system, wherein the n-dimensional vectors are assigned based on functional attributes of the elements, a plurality of the functional attributes represented as an electronic tag; ¶[0058]: Some embodiments of the invention can include systems and methods for using a computing environment for algorithmically determining the composition of elements in a functional system represented in n-dimensional space, the system or method comprising: electronically storing a set of data entities in a database system, the data entities corresponding to elements of a functional system, wherein the functional system comprises a group of elements ordered by their functional roles in a process converting inputs to outputs; electronically assigning one or more functional attributes to an element corresponding to a data entity in a logical data model that comprises at least two fields ordered by a set of interrelationships among at least two elements in the underlying functional system, the interrelationships corresponding to the functional properties of a process converting a set of input elements to a set of output elements; assigning an m-dimensional array of n-dimensional tensors to the data entities, wherein a plurality of entries in the array are based on the attributes of the elements and correspond to functional roles of the elements in a process converting inputs to outputs; algorithmically determining a reference distribution D, […]; selecting an instance of a target distribution D′, wherein the target distribution comprises an algorithmic proportional assignment of data entities into a finite set of categories C′; and executing a statistical test T′ to assess the relative allocation in functional space of a set of data entities according to the target distribution; [0063]:kks Further embodiments of the system or method can include: using the scoring matrix as an input to a machine learning technique to construct a probability space where a functional location of a tensor maps to a location with a corresponding probability for a plurality of categorizations; using the matrix representation of that coordinate space to predict, with a given probability, where a data entity will be placed into a category C; outputting an updated scoring matrix of dimension m′×n′..);
selecting a data structure that unifies quantitative data associated with the disparate data sets (RIGGS: ¶[0308]: creating a data structure for a set of data entities in a data structure by: generating and electronically storing in a database system a logical data model having a data structure comprising assigning an m-dimensional set of n-dimensional vectors to a set of data entities corresponding to elements of a functional system, wherein the n-dimensional vectors are assigned based on functional attributes of the elements, a plurality of the functional attributes represented as an electronic tag; ¶[0230]: A graph of a heterogeneous population of securities and their associated functional attributes, tags, and/or values may be constructed based on an underlying functional syntax in conjunction with semantic tags and attributes, geographical and temporal data, and associated measures and metrics. As a non-limiting example, a graph of data entities representing a population of investment securities or financial instruments is described below; ¶[0234]: In some embodiments, the edges may be weighted or colored based on the extent of interdependence among the nodes or the categorical relationships they reflect; as non-limiting examples, this may be derived from trade, transaction, investment, or financing data among the entities and their economic referents, commonality of semantic tags or attributes, proximity of geographic or temporal relationships, or from the underlying functional syntax; ¶[0058]: electronically assigning one or more functional attributes to an element corresponding to a data entity in a logical data model that comprises at least two fields ordered by a set of interrelationships among at least two elements in the underlying functional system, the interrelationships corresponding to the functional properties of a process converting a set of input elements to a set of output elements; assigning an m-dimensional array of n-dimensional tensors to the data entities, wherein a plurality of entries in the array are based on the attributes of the elements and correspond to functional roles of the elements in a process converting inputs to outputs; algorithmically determining a reference distribution D, wherein the reference distribution comprises the proportional allocation of elements into a finite set of categories C=c.sub.1,2 . . . p;.);
applying the data structure to integrate the quantitative data with the quantitative properties associated with the data entities, thereby creating a unified quantitative data set (RIGGS: [0308] the assignment of n-dimensional vectors re-organizes the groups based on their functional or non-functional attributes with respect to one or more variables; ¶[0322]: non-functional attributes comprise an economic metric, a financial metric, demographic data, geographic data, temporal data, or experiential data);
associating the data entities with the unified quantitative data set with a logical database structure (RIGGS: ¶[0308]: generating and electronically storing in a database system a logical data model having a data structure; ¶[0337]: data representing companies, products, etc. can be stored in tables in the RDBMS. The tables can have pre-defined relationships between them. The tables can also have adjuncts associated with the coordinates; figure 10: company linked to barcode id and Dictionary 1025 for barcode_id and name: Oil, Software, Airplanes.);
receiving index construction parameters associated with a user (RIGGS: [0308]: receiving a user input of common exposures associated with multiple data entities that correspond to respective multiple elements for inclusion in a projected composite unit; [0258]: In some embodiments, users may input their express preferences, whether syntactic, functional, non-syntactic, non-functional, or some combination thereof, and associated values into the system; figure 1: "creation/selection of Attribute based rules" 1121);
selecting a generative algorithm that combines data entities associated with quantitative data (RIGGS: ¶[0058]: Some embodiments of the invention can include systems and methods for using a computing environment for algorithmically determining the composition of elements in a functional system represented in n-dimensional space, the system or method comprising: electronically storing a set of data entities in a database system, the data entities corresponding to elements of a functional system, wherein the functional system comprises a group of elements ordered by their functional roles in a process converting inputs to outputs; electronically assigning one or more functional attributes to an element corresponding to a data entity in a logical data model that comprises at least two fields ordered by a set of interrelationships among at least two elements in the underlying functional system, the interrelationships corresponding to the functional properties of a process converting a set of input elements to a set of output elements; assigning an m-dimensional array of n-dimensional tensors to the data entities, wherein a plurality of entries in the array are based on the attributes of the elements and correspond to functional roles of the elements in a process converting inputs to outputs; algorithmically determining a reference distribution D, wherein the reference distribution comprises the proportional allocation of elements into a finite set of categories C=c.sub.1,2 . . . p; using a statistical test T to assess the relative allocation of a set of data entities according to the reference distribution; selecting an instance of a target distribution D′, wherein the target distribution comprises an algorithmic proportional assignment of data entities into a finite set of categories C′; and executing a statistical test T′ to assess the relative allocation in functional space of a set of data entities according to the target distribution; ¶[0063]: using the scoring matrix as an input to a machine learning technique to construct a probability space where a functional location of a tensor maps to a location with a corresponding probability for a plurality of categorizations;);
applying the generative algorithm to the unified quantitative data set to create a customized index of data entities (RIGGS: ¶[0063]: using the matrix representation of that coordinate space to predict, with a given probability, where a data entity will be placed into a category C; outputting an updated scoring matrix of dimension m′×n′; ¶[0191]: The stratified portfolio architecture (1125) can then be electronically represented and stored on a computerized data storage device.; ¶[0187]: the method can first generate a stratified portfolio architecture (1125); figure 1: stratified portfolio architecture of investment securities 1125; [0258]: In some embodiments, users may input their express preferences, whether syntactic, functional, non-syntactic, non-functional, or some combination thereof, and associated values into the system; figure 1: "creation/selection of Attribute based rules" 1121 ); and
storing the customized index of data entities in a database (RIGGS: ¶[0308]: electronically storing the assigned weights in association with data entities as the set of data entities in the data structure; ¶[0191]: The stratified portfolio architecture (1125) can then be electronically represented and stored on a computerized data storage device; ¶[0094]: method for storing a database characterization of an index).
Regarding claim(s) 2 and 8,
RIGGS discloses all of the limitations of claims 1 and 7, respectively.
RIGGS further discloses:
algorithmically computing a similarity metric between the index construction parameters and the unified quantitative data set (RIGGS: ¶[0321]: algorithmically computing the proximity among a plurality of the entities or attributes representing the entities based on their functional locations; ¶[0062]: functional distance is a measure of the relative remoteness of data entities in functional space);
providing the similarity metric as an input to the generative algorithm (RIGGS: ¶[0062]: selecting a set S=s.sub.1,2 . . . k of size k and dimension ≤m of n-dimensional tensors defined by their functional distance; wherein functional distance is a measure of the relative remoteness of data entities in functional space; computing the difference between S and the remaining set of data entities L resulting in a matrix M′ of dimension ≥(m−k)×n; ¶[0063]: sing the scoring matrix as an input to a machine learning technique to construct a probability space where a functional location of a tensor maps to a location with a corresponding probability for a plurality of categorizations);
selecting a second algorithm that renders qualitative properties quantitatively (RIGGS: ¶[0260]: using a machine learning process to improve dynamically the customized recommendations to a user based on their preferences; [0257]: The relationships, tags, attributes, and/or values, derived from one or more databases of securities and economic entities, permit a default calculation of proximity to a user. User preferences, current user holdings, and network position facilitate the customization of financial recommendations to users based on dynamic proximity calculations; ¶[0262]: the system may use machine learning techniques to improve dynamically the quality of customized recommendations based on changes in the network of users, their preferences, or the tags, attributes, values, or relationships assigned to the securities or economic entities.);
applying the second algorithm to the qualitative index construction parameters to convert the qualitative properties into a second set of quantitative data (RIGGS: [0257]: The relationships, tags, attributes, and/or values, derived from one or more databases of securities and economic entities, permit a default calculation of proximity to a user. User preferences, current user holdings, and network position facilitate the customization of financial recommendations to users based on dynamic proximity calculations; ¶[0262]: the system may use machine learning techniques to improve dynamically the quality of customized recommendations based on changes in the network of users, their preferences, or the tags, attributes, values, or relationships assigned to the securities or economic entities;¶[0258]: these preferences, or filters, may be inputted or modified at any time, either through a separate module or by indicating a preference for or against data entities associated with securities and economic entities. The filters may be absolute, in that they will permit the user to exclude or include certain relationships, attributes, tags, or values, or they may be relative, in that they enable the user to indicate the extent of a preference for or against certain relationships, attributes, tags, or values );
selecting a second data structure that unifies quantitative data associated with disparate data sets (RIGGS:[0308]: a data structure comprising assigning an m-dimensional set of n-dimensional vectors to a set of data entities corresponding to elements of a functional system, wherein the n-dimensional vectors are assigned based on functional attributes of the elements; [0259]: these filters may enable the user to express absolute or relative preferences for [market capitalization], [asset class], [asset allocation], [funds], [expected return], [risk], [geography], [supplier], [investor], [customer], [lender-borrower], [issuer-investor], [...], [portfolio], [composite], [stratified structure], or one or more of any of the other relationships, tags, attributes, or values assigned to the securities and economic entities; [0250]: The method described herein can be used to recommend securities, composites, and portfolios to users. These recommendations are derived from the securities and their referent economic entities' syntactic and empirical relationships; the functional, syntactic, semantic, temporal, geographic, financial, or economic tags, attributes, and values assigned to the economic entities; the express and revealed preferences of the users of the database or software; and the relationships of the users in the network.; [0230] A graph of a heterogeneous population of securities and their associated functional attributes, tags, and/or values may be constructed based on an underlying functional syntax in conjunction with semantic tags and attributes, geographical and temporal data, and associated measures and metrics; [0261] Network position may facilitate proximity calculations and dynamic, customized recommendations. The system may track connections among users and the extent of their interactions; [0262]:the system may use machine learning techniques to improve dynamically the quality of customized recommendations based on changes in the network of users, their preferences, or the tags, attributes, values, or relationships assigned to the securities or economic entities.);
applying the second data structure to integrate the second set of quantitative data with the quantitative index construction parameters, thereby creating a second unified quantitative data set (RIGGS: [0260]: preferences will be revealed by tracking user accounts, monitoring clicks, screen time, portfolio construction, and/or transactions executed, and using a machine learning process to improve dynamically the customized recommendations to a user based on their preferences. In some embodiments, users may upload their portfolios to the system, whose constituents also may be used to guide customized recommendations; ¶[0257]: The relationships, tags, attributes, and/or values, derived from one or more databases of securities and economic entities, permit a default calculation of proximity to a user. User preferences, current user holdings, and network position facilitate the customization of financial recommendations to users based on dynamic proximity calculations.; [0259]: these filters may enable the user to express absolute or relative preferences for [market capitalization], [asset class], [asset allocation], [funds], [expected return], [risk], [geography], [supplier], [investor], [customer], [lender-borrower], [issuer-investor], [...], [portfolio], [composite], [stratified structure], or one or more of any of the other relationships, tags, attributes, or values assigned to the securities and economic entities; [0262]:the system may use machine learning techniques to improve dynamically the quality of customized recommendations based on changes in the network of users, their preferences, or the tags, attributes, values, or relationships assigned to the securities or economic entities.); and
algorithmically computing a second similarity metric between the first unified quantitative data set and the second unified quantitative data set (RIGGS: ¶[0321]: […] wherein the elements represent parts, processes, and interactions of an underlying system; receiving a set of functional locations from a database, wherein a function represents a conversion from inputs to outputs or a role or property in the conversion from inputs to outputs in the underlying system and a functional location comprises a position of an entity as an input, output, intermediate, relationship, or process associated with inputs, intermediates or outputs; algorithmically computing the proximity among a plurality of the entities or attributes representing the entities based on their functional locations; and identifying or matching the elements, the data entities, or the attributes representing the data entities based on the proximity of their functional locations).
Regarding claim(s) 3 and 9,
RIGGS discloses all of the limitations of claims 1 and 7, respectively.
RIGGS further discloses:
algorithmically computing a third similarity metric between the customized index of data entities and the second unified quantitative data set (RIGGS: ¶[0062]: [0062] Further embodiments of the system or method can include: selecting a set S=s.sub.1,2 . . . k of size k and dimension ≤m of n-dimensional tensors defined by their functional distance; wherein functional distance is a measure of the relative remoteness of data entities in functional space;.);
and verifying that the third similarity metric is greater than the second similarity metric under fluctuations in parameters associated with the qualitative properties and quantitative properties, as determined by a test of statistical significance (RIGGS: ¶[0062]: computing the difference between S and the remaining set of data entities L resulting in a matrix M′ of dimension ≥(m−k)×n; wherein the set of data entities in C are more functionally related than an arbitrary sample of data entities in S, as determined by a test of statistical significance; and using a statistical measure of relatedness on M′ to determine correspondence among functional and non-functional attributes in M′, thereby increasing the analytical performance compared to a non-filtered test on L; ¶[0083]: 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.; figure 7:score accepted?; ¶[0219]: The composite can be tested against the target score (7035). If the target score is accepted, the process can reach completion.)
Regarding claim(s) 4 and 10,
RIGGS discloses all of the limitations of claims 1 and 7, respectively.
RIGGS further discloses:
the customized index is a financial index (RIGGS: ¶[0004]: Financial indices are often used to benchmark the performance of a financial instrument; ¶[0070]: 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.);
the data entities represent investment securities (RIGGS: ¶[0070]: 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,);
a plurality of the quantitative properties are selected from among market metrics, financial metrics, financial ratios, and economic metrics (RIGGS: ¶[0326]: economic or financial metric comprising liquidity, transparency, price discovery, efficiency, a financial statement metric, a market metric, or a financial ratio, as compared to a third-party method, as determined by a test of statistical significance.); and
a plurality of the qualitative properties are selected from among sector, industry, geography, theme, environmental sustainability, social sustainability, governance, and economic properties (RIGGS: ¶[0116]: In some embodiments, the functional attributes can be qualities, features, properties, or inherent characteristics of the underlying entity or assets with which an investment security is associated. Functional attributes may define relationships throughout the value chain and structure of an economic entity, including, as non-limiting examples: (a) what a company does, such as manufacturing or transportation; (b) aspects of the company's product, such as specific utility provided by the car, computer or couch; (c) what the company's customer does, such as consumer sales or business intelligence; (d) what the customer's customer does; (e) the products and materials a company uses to provide its product; (f) the multivariate industries or industry segments in which a company may operate; (g) the structure of a company's business, such as integrated, non-integrated, forward integrated, backward integrated or networked; (h) risks based on a company's management, including its decisions and strategies; (i) risks based on the internal operations of a company.; ¶[0248]: the contextual subset could be defined, as non-limiting examples, as a sector, industry, geographic region, […].).
Regarding claim(s) 5 and 11,
RIGGS discloses all of the limitations of claims 1 and 7, respectively.
RIGGS further discloses:
further comprising algorithmically converting the unified quantitative data set into a matrix, network, or high-dimensional coordinate data structure (RIGGS: ¶[0060]: outputting a matrix of dimension ≤(m+k)×n; wherein the entries of the matrix comprise updated scores of the tensors and dimensions; ¶[0063]: using the scoring matrix as an input to a machine learning technique to construct a probability space where a functional location of a tensor maps to a location with a corresponding probability for a plurality of categorizations; using the matrix representation of that coordinate space to predict, with a given probability, where a data entity will be placed into a category C; outputting an updated scoring matrix of dimension m′×n′.; ¶[0194]: the resultant portfolio architecture (1125) can comprise an electronic representation of a set of attributes arranged, as non-limiting examples, in graphical, segmented, stratified, or network form, according to the defined attribute rules.; ¶[0058]: assigning an m-dimensional array of n-dimensional tensors to the data entities)
Regarding claim(s) 6 and 12,
RIGGS discloses all of the limitations of claims 1 and 7, respectively.
RIGGS further discloses:
further comprising applying a data structure based on ordinal coding or interval variables as an input to the algorithm that renders qualitative properties quantitatively (RIGGS: ¶[0142]: Coding for these attributes in a coordinate-based or ordered tagging system enables a computer to associate tags with specific risks and generate company groupings that share these attributes.; ¶[0309]: electronically assigning functional locations in n-dimensional space to data entities associated with the elements, wherein the n dimensions are ordered; [0157]: Any element of the syntax that has a range of potential values describes a dimension in a discrete multidimensional space consisting of the dimensions associated with all such elements.; ¶[0062]: n-dimensional tensors defined by their functional distance; wherein functional distance is a measure of the relative remoteness of data entities in functional space; ¶[0142]: Coding for these attributes in a coordinate-based or ordered tagging system enables a computer to associate tags with specific risks ),
wherein the ordinal coding or interval variables are based on relationships modeled in an underlying system (RIGGS: ¶[0058]: electronically storing a set of data entities in a database system, the data entities corresponding to elements of a functional system, wherein the functional system comprises a group of elements ordered by their functional roles in a process converting inputs to outputs; electronically assigning one or more functional attributes to an element corresponding to a data entity in a logical data model that comprises at least two fields ordered by a set of interrelationships among at least two elements in the underlying functional system; ¶[0321]: an electronic representation of a systems syntax, wherein the systems syntax comprises a logical data model that can be applied by a computer processor to evaluate or generate expressions of elements, wherein the elements represent parts, processes, and interactions of an underlying system; ¶[0136]: a functional proximity algorithm can be configured to compute correspondence based on the magnitude and category of relationships among a plurality of the data entities).
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 (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim 13 is rejected under 35 U.S.C. 103 as being unpatentable over RIGGS (US 20220138280 A1 to Riggs, R. et al) in view of KAHRAMAN (US 20220292239 A1 to Kahraman; Aykut et al.).
Regarding claim(s) 13,
RIGGS discloses:
A computer-implemented method for creating a customized index of investment securities, comprising the steps of (RIGGS: ¶[0094]: there is provided a computer-implemented method for storing a database characterization of an index, portfolio, set, aggregate, or composite of elements; ¶[0096]: there is provided a system for executing a command in a computing environment to construct a representation of an index or portfolio of investment securities in a database, the system comprising: a computerized processor; figure 11: Processing Unit and Data Storage and System Bus; RIGGS: ¶[0004]: Financial indices are often used to benchmark the performance of a financial instrument; ¶[0070]: 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):
a. storing financial data from multiple disparate data sets (RIGGS: ¶[0070]: 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; ¶[0094]: 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; ¶[0096]: an electronic data store configured for: electronically storing the 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 storing the assigned weight in the database system; ¶[0148]: While quantitative values associated with securities are likely to exhibit significant a stationarity, qualitative attributes are likely to persist over time, driving performance characteristics with consistency and facilitating portfolio management and index construction at scale. The data systems described herein, which enable the syntactic and functional tagging of hundreds of thousands of securities and the dynamic segmentation and stratification of large sets of securities by their associated attributes, are instrumental in enabling this process. As a non-limiting example, by dividing the securities into correlation clusters, i.e., groupings formed based on attributes that correspond to risks, volatility can be controlled; ¶[0322]: In some further embodiments, non-functional attributes can be used as an input in the process of identifying or matching of entities, and wherein the functional locations can be identified semantically, syntactically, graphically, symbolically, visually, or aurally; and wherein non-functional attributes comprise an economic metric, a financial metric, demographic data, geographic data, temporal data, or experiential data.);
b. extracting information concerning a set of investment securities from a database, said information being obtained from the disparate data sets, the disparate data sets including both qualitative and quantitative properties associated with the investment securities (RIGGS: ¶[0257]: The relationships, tags, attributes, and/or values, derived from one or more databases of securities and economic entities, permit a default calculation of proximity to a user. User preferences, current user holdings, and network position facilitate the customization of financial recommendations to users based on dynamic proximity calculations; ¶[0070]: electronically accessing the database representation of the segmented groups; [0230] A graph of a heterogeneous population of securities and their associated functional attributes, tags, and/or values may be constructed based on an underlying functional syntax in conjunction with semantic tags and attributes, geographical and temporal data, and associated measures and metrics.; ¶[0148]: While quantitative values associated with securities are likely to exhibit significant a stationarity, qualitative attributes are likely to persist over time, driving performance characteristics with consistency and facilitating portfolio management and index construction at scale. The data systems described herein, which enable the syntactic and functional tagging of hundreds of thousands of securities and the dynamic segmentation and stratification of large sets of securities by their associated attributes, are instrumental in enabling this process; ¶[0242]: the model of the system characterized by the field may be […] qualitative, or quantitative, or some combination thereof);
c. converting the qualitative properties to quantitative properties […] (RIGGS: ¶[0142]: Endogenous economic models characterize functional-attributes, which represent risk-related properties, qualities, or characteristics. Coding for these attributes in a coordinate-based or ordered tagging system enables a computer to associate tags with specific risks and generate company groupings that share these attributes. […] Further, the computerized system described herein can be used to generate an assembly of groupings, including, as a non-limiting example, a risk-stratified portfolio consisting of stratified groupings of statistical control groups. ¶[0308]: creating a data structure for a set of data entities in a data structure by: generating and electronically storing in a database system a logical data model having a data structure comprising assigning an m-dimensional set of n-dimensional vectors to a set of data entities corresponding to elements of a functional system, wherein the n-dimensional vectors are assigned based on functional attributes of the elements, a plurality of the functional attributes represented as an electronic tag; ¶[0058]: Some embodiments of the invention can include systems and methods for using a computing environment for algorithmically determining the composition of elements in a functional system represented in n-dimensional space, the system or method comprising: electronically storing a set of data entities in a database system, the data entities corresponding to elements of a functional system, wherein the functional system comprises a group of elements ordered by their functional roles in a process converting inputs to outputs; electronically assigning one or more functional attributes to an element corresponding to a data entity in a logical data model that comprises at least two fields ordered by a set of interrelationships among at least two elements in the underlying functional system, the interrelationships corresponding to the functional properties of a process converting a set of input elements to a set of output elements; assigning an m-dimensional array of n-dimensional tensors to the data entities, wherein a plurality of entries in the array are based on the attributes of the elements and correspond to functional roles of the elements in a process converting inputs to outputs; algorithmically determining a reference distribution D, […]; selecting an instance of a target distribution D′, wherein the target distribution comprises an algorithmic proportional assignment of data entities into a finite set of categories C′; and executing a statistical test T′ to assess the relative allocation in functional space of a set of data entities according to the target distribution; [0063]: Further embodiments of the system or method can include: using the scoring matrix as an input to a machine learning technique to construct a probability space where a functional location of a tensor maps to a location with a corresponding probability for a plurality of categorizations; using the matrix representation of that coordinate space to predict, with a given probability, where a data entity will be placed into a category C; outputting an updated scoring matrix of dimension m′×n′.)
d. unifying the disparate data sets into a unified data set of quantitative properties associated with the investment securities (RIGGS: [0308] the assignment of n-dimensional vectors re-organizes the groups based on their functional or non-functional attributes with respect to one or more variables; ¶[0322]: non-functional attributes comprise an economic metric, a financial metric, demographic data, geographic data, temporal data, or experiential data; ¶[0230]: A graph of a heterogeneous population of securities and their associated functional attributes, tags, and/or values may be constructed based on an underlying functional syntax in conjunction with semantic tags and attributes, geographical and temporal data, and associated measures and metrics. As a non-limiting example, a graph of data entities representing a population of investment securities or financial instruments is described below);
e. associating the investment securities with the unified quantitative properties within a logical database structure (RIGGS: ¶[0308]: generating and electronically storing in a database system a logical data model having a data structure; ¶[0337]: data representing companies, products, etc. can be stored in tables in the RDBMS. The tables can have pre-defined relationships between them. The tables can also have adjuncts associated with the coordinates; figure 10: company linked to barcode id and Dictionary 1025 for barcode_id and name: Oil, Software, Airplanes.);
f. receiving user-configured index construction parameters (RIGGS: [0308]: receiving a user input of common exposures associated with multiple data entities that correspond to respective multiple elements for inclusion in a projected composite unit; [0258]: In some embodiments, users may input their express preferences, whether syntactic, functional, non-syntactic, non-functional, or some combination thereof, and associated values into the system; figure 1: "creation/selection of Attribute based rules" 1121);
g. determining the composition of a subset index of investment securities (RIGGS: ¶[0058]: Some embodiments of the invention can include systems and methods for using a computing environment for algorithmically determining the composition of elements in a functional system represented in n-dimensional space, the system or method comprising: electronically storing a set of data entities in a database system, the data entities corresponding to elements of a functional system, wherein the functional system comprises a group of elements ordered by their functional roles in a process converting inputs to outputs; electronically assigning one or more functional attributes to an element corresponding to a data entity in a logical data model that comprises at least two fields ordered by a set of interrelationships among at least two elements in the underlying functional system, the interrelationships corresponding to the functional properties of a process converting a set of input elements to a set of output elements; assigning an m-dimensional array of n-dimensional tensors to the data entities, wherein a plurality of entries in the array are based on the attributes of the elements and correspond to functional roles of the elements in a process converting inputs to outputs; algorithmically determining a reference distribution D, wherein the reference distribution comprises the proportional allocation of elements into a finite set of categories C=c.sub.1,2 . . . p; using a statistical test T to assess the relative allocation of a set of data entities according to the reference distribution; selecting an instance of a target distribution D′, wherein the target distribution comprises an algorithmic proportional assignment of data entities into a finite set of categories C′; and executing a statistical test T′ to assess the relative allocation in functional space of a set of data entities according to the target distribution; ¶[0063]: using the scoring matrix as an input to a machine learning technique to construct a probability space where a functional location of a tensor maps to a location with a corresponding probability for a plurality of categorizations;);
h. constructing a customized index of investment securities based on the user-configured portfolio construction parameters (RIGGS: ¶[0063]: using the matrix representation of that coordinate space to predict, with a given probability, where a data entity will be placed into a category C; outputting an updated scoring matrix of dimension m′×n′; ¶[0191]: The stratified portfolio architecture (1125) can then be electronically represented and stored on a computerized data storage device.; ¶[0187]: the method can first generate a stratified portfolio architecture (1125); figure 1: stratified portfolio architecture of investment securities 112; [0258]: In some embodiments, users may input their express preferences, whether syntactic, functional, non-syntactic, non-functional, or some combination thereof, and associated values into the system; figure 1: "creation/selection of Attribute based rules" 1121); and
i. storing the customized index of investment securities in an index database (RIGGS: ¶[0308]: electronically storing the assigned weights in association with data entities as the set of data entities in the data structure; ¶[0191]: The stratified portfolio architecture (1125) can then be electronically represented and stored on a computerized data storage device; ¶[0094]: method for storing a database characterization of an index).
RIGGS does not expressly disclose the following limitations, which KAHRAMAN however, teaches:
by an ordinal coding technique (KAHRAMAN: ¶[0008]: Supervised Learning algorithms are the most common algorithms used in the industry where a target can be ordinal, nominal or integer and algorithms predict the target based on available independent features (variables) in the data.; ¶[0019]: and the feature encoding step further comprising using a categorical data encoding technique when the categorical variables are ordinal, producing labels through label encoding, ordinal coding or one hot encoding, and converting the labels into numeric values via multiple statistical techniques; [0052] Step 4 represents an Auto-Feature Encoding Process step. Converting categorical data is an important activity in modeling. It not only elevates the model quality but also helps in better feature engineering. Better encoding leads to a better model and most of the algorithms cannot handle the categorical variables unless they are converted into numerical values. The STSA software uses categorical data encoding technique when the categorical feature is ordinal. In this case, retaining the order is important. Hence encoding should reflect the sequence. Software uses in label encoding; each label is converted into a numeric value via multiple statistical techniques.; ¶[0065]: MLWay offers multiple encoding techniques such as ordinal encoding, One Hot Encoding and Label encoding. Ordinal encoding technique is used for ordinal variables where retaining the order is important. In label encoding, each label is converted into a numeric value via multiple statistical techniques. In One Hot Encoding, each categorical value is represented by a binary flag);
It would have been obvious to one of ordinary skill in the art before the time of filing to combine/modify the system/method of RIGGS, which discloses coding for attributes in an ordered tagging system or coordinate based tagging system (RIGGS [142]) with the technique of KAHRAMAN, in order to model categorical data to improve model quality and retain order where retaining the order is important (KAHRAMAN ¶[0065]).
Conclusion
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BOLKO HAMERSKI
Examiner
Art Unit 3694
/BOLKO M HAMERSKI/ Examiner, Art Unit 3694
/BENNETT M SIGMOND/ Supervisory Patent Examiner, Art Unit 3694