DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application is being examined under the pre-AIA first to invent provisions.
Response to Arguments
Argument:
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Response:
The examiner disagrees. At best only the use of a database itself would be considered an additional element. The calculations use to manipulate/derive/update the network map are an abstract idea (mathematical concept, mental process).
Argument:
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Response:
The examiner disagrees. MPEP 2106.04(a)(2) states:
In contrast, claims do recite a mental process when they contain limitations that can practically be performed in the human mind, including for example, observations, evaluations, judgments, and opinions. Examples of claims that recite mental processes include:
• 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);
Instant claims recite similar high level data collection/analysis/display of results.
Furthermore, the above quote only relates to the mental process grouping. The examiner has doubly many of the steps as reciting mathematical concepts. Applicant has not addressed this aspect of the rejection at all.
Argument:
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Response:
The examiner disagrees.
Data gathering does not provide a practical application or significantly more. See MPEP 2106.05(g).
Using matrix calculations to refine a network map is an improved abstract idea and not an improvement in technology.
Argument:
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Response:
The examiner disagrees. The claims at best provide an improved abstract idea, which is not an improvement in technology. See MPEP 2106.05(a).
Argument:
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Response:
The examiner does not feel like the claims as recited represents a close call.
MPEP 2106.04(a)(2)(I) is clear that mathematical calculations are an abstract idea. MPEP 2106.04(a)(2)(III) is clear that collecting data, analyzing the data and presenting the analysis is an abstract idea.
The computer as recited in the claims is used in its ordinary capacity and is not improved by performing the claimed calculations.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1, 3-5, 7-20 rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claims 1, 3-5, 7-20 all recite subject matter falling within one of the four categories of invention (step 1).
Claims 1, 3-5, 7-13 recite:
1. A method implemented on a computer having a computer monitor in relation to a database comprising using one or more processors of the computer to perform the steps of: (A) storing in the database, a network map relating to economic activity between a plurality of entities; (B) collecting data from a plurality of sources, said data relating to relationships between a plurality of entities, wherein the plurality of sources includes public information about each entity of the plurality of entities; (C) analyzing the data to identify (i) known and unknown entities, (ii) known and unknown relationships between known entities, and (iii) known and unknown economic activities between the known relationships; (D) updating the database-stored network map with the known entities, and known values corresponding to the known relationships between the known entities, wherein the known values are derived from the known economic activities between the known relationships between the known entities; (E) creating one or more placeholder entities to perform the role of one of consumer, labor market, profit balancer, and any unknown entities in the database-stored network map; (F) estimating unknown relationships and unknown values relating to the data to provide estimations regarding transactions that never actually occurred and stored in the database-stored network map; (G) updating the database-stored network map by (i) using a simulation method of perturbing the data; or (ii) using a closed-form solution, wherein the update improves the database-stored network map; (GA) calculating a matrix of standard deviations or variances corresponding to the estimated values in the economic relationship matrix to quantify uncertainty in the estimates; (GB) using the matrix of standard deviations to identify relationships or entities with high uncertainty and directing subsequent data-gathering or refinement efforts as part of a continuous process of improvement; (GC) replacing the unknown values with estimation values using a continuous iterative process to improve the database-stored map over time; and and generating for a display, a visually perceptible output based on the improved database-stored network map.
3. The method of claim 1, (G’) wherein the simulation method includes a Monte- Carlo process.
4. The method of claim 1, (G’) wherein a closed-form solution is used to directly calculate probabilistic methods.
5. The method of claim 1, further (J) comprising the step of gathering data to find lists of relationships with uncertain first estimates and repeating gathering data to find second estimates to generate a new output.
7. The method of claim 1, wherein the (K) step includes running a continuous optimization routine having a scaled variance for multiple companies that ranges between 0.0 and 1.0.
8. The method of claim 1, wherein (L) the step includes comparison of the connections between two or more companies that each have a scaled variance between 0.0 and 1.0.
9. The method of claim 1, (M) wherein calculating the matrix includes calculating an economic relationship matrix using estimates for the remaining internal values on the economic relationship matrix given partial advance knowledge of relationships and their strength resulting in a best-case estimate.
10. The method of claim 9, (M) wherein data types are considered including qualified and unqualified relationships between entities, financial statements, accounting or industry types, financials by division, geography, market, product, channel and a variety of industry specific data.
11. The method of claim 9, (M) wherein a convergence process is provided including one of an iterative proportional fitting (IPF) and parameter fitting for uncertain models (PARFUM).
12. The method of claim 1, (N) wherein an output of the data is a standard error of an estimate that provides a confidence interval around the estimate.
13. The method of claim 1, (O) wherein the method includes repeatedly running steps that comprise the method as a continuous optimization routine having a scaled variance for a company 1.0 to 0.1.
But for the recitation of the underlined additional elements, claims 1-13 recite concepts that can be performed in the human mind, or by a human using a pen and paper. A person can mentally read various data (step B, J), perform an analysis of the data (step A, C, D, E, F, G, GA, GB, GC, K, L, M, N, O) and render a judgement basis on the analysis (step A, D, H, I, N). Additionally certain steps are all considered to recite subject matter which is also considered to be mathematical concepts (step G, GA, GB, GC, K, L, M, N, O). Thus, claims 1, 3-5, 7-13 recite an abstract idea (step 2A_1).
The recited additional elements are a 1) computer with a monitor and one or more processors (claim 1+), a database providing storage (claims 1+) and visually perceptible output (claim 1+).
The processor-based computer and database are recited a high level such that they amount to mere instructions to implement an abstract idea, which per MPEP 2106.05(f) means that they do not provide a practical application or significantly more.
Per MPEP 2106.05(g) and MPEP2106.05(d) (relying on OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1092-93 (Fed. Cir. 2015)), providing data output via a display such as a monitor can be considered to be conventional extra-solution activity that does not provide a practical application or significantly more.
Regarding the “subsequent data gathering” in claim 1, this is performing necessary data gathering which per MPEP 2106.05(g) is regarded as insignificant extra-solution activity that does not provide a practical application or significantly more. Also data gathering using a computer is regarded as not significantly more because it is well-understood routine and conventional. See MPEP 2106.05(d) citing Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016).
Therefore claims 1, 3-5, 7-13 are considered to be directed to an abstract idea without a practical application or significantly more (step 2A_2 and step 2B) and are considered ineligible.
Claims 14-20 recite:
14. A method implemented on a computer in relation to a database, the method comprising using one or more processors of the computer to perform the steps of: (A) storing in the database, a network map relating to economic activity between a plurality of entities; (B) collecting data from a plurality of sources, said data relating to relationships between a plurality of entities, wherein the plurality of sources includes public information about each entity of the plurality of entities; (C) analyzing the data to identify (i) known and unknown entities, (ii) known and unknown relationships between known entities, and (iii) known and unknown economic activities between the known relationships; (D) updating the database-stored network map with the known entities, and known values corresponding to the known relationships between the known entities, wherein the known values are derived from the known economic activities between the known relationships between the known entities; (E) running a continuous optimization routine having a scaled variance for a company of between 1.0 to 0.1; (F) estimating unknown relationships and unknown values relating to the data to provide estimations regarding transactions that never actually occurred and stored in the database-stored network map; (GA) calculating a matrix of standard deviations or variances corresponding to the estimated values in the economic relationship matrix to quantify uncertainty in the estimates;
(GB) using the matrix of standard deviations to identify relationships or entities with high uncertainty and directing subsequent data-gathering or refinement efforts as part of a continuous process of improvement;
(GC) replacing the unknown values with estimation values using a continuous iterative process to improve the database-stored network map over time; and
Generating, for a display, a visually perceptible output based on the improved database-stored network map.
15. The method of claim 14 further (H) comprising the step of updating the database-stored network map by (i) using a simulation method of perturbing the data; or (ii) using a closed-form solution to improve the database-stored network map.
16. The method of claim 14 further (I) comprising the step of creating one or more placeholder entities to perform the role of one of consumer, labor market, profit balancer, and any unknown entities in the database-stored network map.
17. The method of claim 14, wherein (J) the step includes running a continuous optimization routine having a scaled variance for a first company of 0.8; a scaled variance for a second company of 0.1; a scaled variance for a third company of 0.5; and a scaled variance for a fourth company of 0.3.
18. The method of claim 14, wherein (K) the step includes comparison of the connections between Company A to Company B having a scaled variance of 1.0; Company A to Company C having a scaled variance of 0.8; Company A to Company D having a scaled variance of 0.4; and Company B to Company E having a scaled variance of 0.1.
19. The method of claim 14, (L) wherein an economic relationship matrix is calculated using estimates for the remaining internal values on the economic relationship matrix given partial advance knowledge of relationships and their strength resulting in a best-case estimate.
20. The method of claim 19, (M) wherein a convergence process is provided including one of an iterative proportional fitting (IPF) and parameter fitting for uncertain models (PARFUM).
But for the recitation of the underlined additional elements, claims 14-20 recite concepts that can be performed in the human mind, or by a human using a pen and paper. A person can mentally read various data (step B), perform an analysis of the data (step A, C, D, E, F, GA, GB, GC, H, I, J, K, L, M) and render a judgement basis on the analysis (step A, D, F, GA, GB, GC). Additionally certain steps are all considered to recite subject matter which is also considered to be mathematical concepts (step GA, GB, GC, H, J, K, L, M). Thus, claims 14-20 recite an abstract idea (step 2A_1).
The recited additional elements are a 1) computer with one or more processors (claim 14+) and a database providing storage (claims 14+) and providing visually perceptible output (claim 14+)..
The processor-based computer and database are recited a high level such that they amount to mere instructions to implement an abstract idea, which per MPEP 2106.05(f) means that they do not provide a practical application or significantly more.
Per MPEP 2106.05(g) and MPEP2106.05(d) (relying on OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1092-93 (Fed. Cir. 2015)), providing data output via a display such as a monitor can be considered to be conventional extra-solution activity that does not provide a practical application or significantly more.
Regarding the “subsequent data gathering” in claim 1, this is performing necessary data gathering which per MPEP 2106.05(g) is regarded as insignificant extra-solution activity that does not provide a practical application or significantly more. Also data gathering using a computer is regarded as not significantly more because it is well-understood routine and conventional. See MPEP 2106.05(d) citing Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016).
Therefore, claims 14-20 are considered to be directed to an abstract idea without a practical application or significantly more (step 2A_2 and step 2B) and are considered ineligible.
Claim Status.
Claims 1, 3-5, 7-20 are considered to distinguish over the prior art of record. Steier (US 20050222929 A1) discloses characterized financial flow through directed graphs (fig. 12). Megdal (US 20100250469 A1) discloses computer-based modeling of behaviors of different entities. Yamamoto (JP 2011028454 A) discloses analyzing relationships of companies based on network maps. Pendergrafft (US 8249903 B2) discloses a system for determining and evaluating business relationships.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to NATHAN A MITCHELL whose telephone number is (571)270-3117. The examiner can normally be reached M-F 9-5.
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/NATHAN A MITCHELL/Primary Examiner, Art Unit 3627