DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application is being examined under the pre-AIA first to invent provisions.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 6/25/2026 has been entered.
Response to Arguments
Argument: Pages 9-10 argue that the claims are not directed to an abstract idea because they are directed to an improvement in how a computer stores and operates on data.
Response: The examiner disagrees. Paragraphs 45 and 19 show that the invention relates to math for controlling what is stored in the computer i.e. database, not how the computer operates. This makes sense as paragraph 2 makes it clear that the field of the invention is using statistics to make estimates regarding economic flows.
Argument: The Examiner's reliance on Trading Technologies Int'l V. IBG, 921 F.3d 1084 (Fed. Cir. 2019), is misplaced. There, the claimed user interface "simply provided a trader with more information to facilitate market trades," improving the business process of trading rather than any technology. Here, by contrast, the amendment is not directed to any economic or business practice. It is directed to the specific manner in which the computer populates and stores a complete network map in the database. The improvement is to the database-stored data structure and to the computer's ability to operate on it, and not to the act of trading or to any other economic activity.
Response: The relevance is found in MPEP 2106.05(a), which cites to that decision as support for saying that improvements to abstract ideas are not improvements in technology (“However, it is important to keep in mind that an improvement in the abstract idea itself (e.g. a recited fundamental economic concept) is not an improvement in technology. “). The abstract idea of that decision is exemplary and “improved” abstract ideas of all types are still abstract.
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Response:
The examiner disagrees. The network map is content. It is not clear how it alters how the computer operates in any way. Querying the network map isn’t part of claim 1. Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016)
As claimed the database and other computer structures are used in their ordinary capacity.
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Response: The examiner disagrees. 101 is distinct from 102/103. MPEP 2106.05(d) is clear that examiner Berkheimer evidence is required only in relation assertions that additional elements are well-understood, routine and conventional. The examiner is not required to meet that burden for the abstract idea. MPEP2106.05(d) and MPEP 2106.05(f) and MPEP 2106.05(g) are clear that the recited generic computer structures do not provide significantly more.
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, the database-stored network map being stored as a sparse matrix having marginal row and column totals as convergence targets, and the continuous iterative process including holding each known value fixed by zeroing a corresponding cell of the sparse matrix and reducing the marginal row and column totals by the known value, assigning a positive token value to each cell of the sparse matrix that corresponds to an unknown relationship, fitting interior values of the sparse matrix to the marginal row and column totals until a convergence threshold is reached, and, after the convergence threshold is reached, restoring the known values held fixed and aggregating values accrued in cells corresponding to unknown relationships to the one or more placeholder entities; 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; (FA) 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; (GA) calculating a matrix of standard deviations or variances corresponding to the estimated values in the database-stored network map 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, the database-stored network map being stored as a sparse matrix having marginal row and column totals as convergence targets, and the continuous iterative process including holding each known value fixed by zeroing a corresponding cell of the sparse matrix and reducing the marginal row and column totals by the known value, assigning a positive token value to each cell of the sparse matrix that corresponds to an unknown relationship, fitting interior values of the sparse matrix to the marginal row and column totals until a convergence threshold is reached, and, after the convergence threshold is reached, restoring the known values held fixed and aggregating values accrued in cells corresponding to unknown relationships to the one or more placeholder entities; 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, FA, 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 FA, 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
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
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