DETAILED ACTION This Office Action is in response for A pplication # 1 8 / 163 , 431 filed on February 02, 2023 in which claims 1- 20 are presented for examination. 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. Status of claims Claims 1-2 0 are pending, of which claims 1-2 0 are rejected under 35 U.S.C. 103 and also claims 1-2 0 are rejected under 35 U.S.C. 101. Claims 1-20 are rejected under 35 U.S.C. 112(b). 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 appl icant regards as his invention. Claims 1-20 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. The term s “ strengths ” and “optimized” in claim s 1, 12 and 20 are a relative term which renders the claim indefinite. The term s “strengths ” and “optimized” are not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. Examiner believes the term “strengths” should be defined as a value that defines the relationship between entities and also term “optimized” should a degree of how to optimized a generated cube. Dependent claims 2-11 and 13-19 are also rejected under the same rationale as the independent claims since the dependent claims inherit the deficiencies o f the parent claims. 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 -2 0 are rejected under 35 U.S.C. 101. because the claims are directed to an abstract idea; and because the claims as a whole, considering all claim elements both individually and in combination, do not amount to significantly more than the abstract idea, see Alice Corporation Pty. Ltd. v. CLS Bank International, et al, 573 U.S. (2014). In determining whether the claims are subject matter eligible, the Examiner applies the 2019 USPTO Patent Eligibility Guidelines. (2019 Revised Patent Subject Matter Eligibility Guidance, 84 Fed. Reg. 50, Jan. 7, 2019.) Step 1: Is the claim to a process, machine, manufacture, or composition of matter? Yes—Claims 1-2 0 recite a method , device and readable medium respectively. The analysis of claim s 1 , 1 2 and 20 is as follows: Step 2A, prong one: Does claim s 1 , 1 2 and 20 recite an abstra ct idea, law of nature or natural phenomenon? Yes—the limitations of “ obtaining a collection of data queries; parsing each data query of the collection of data queries and ascertaining, from the parsing, dimension and measure information of the collection of data queries, the dimension and measure information comprising dimension tables and fact tables indicated in the collection of data queries; analyzing the dimension and measure information for relationships between the dimension tables and the fact tables, and strengths of the relationships ; segmenting dimensions and measures reflected in the dimension and measure information, including the dimension tables and the fact tables , into one or more communities based on the analyzing, each community of the one or more communities comprising a respective at least one dimension table of the dimension tables and at least one fact table of the fact tables; and generating one or more optimized data cubes , the generating the one or more optimized data cubes comprising, for each community of the one or more communities, generating an optimized data cube with a respective dimension table for each of the at least one dimension table of the respective community and a respective fact table for each of the at least one fact table of the respective community ” a s drafted, are mental steps based on various processes can be performed in a human mind of building cube ‘matrix’ to find information (acts of thinking , decision making ). These limitations, therefore fall within the human mind processes group and with a pen & paper . Step 2A, prong two: Does the claim recite additional elements that integrate the judicial exception into a practical application? No—the judicial exception is not integrated into a practical application as just stated as related to the technical field of computer science . Although the claim recites that the recited functionality includes “ method ”, “ computer ” and “readable medium” , these computer components are recited at a high-level of generality such that it amounts to no more than a mere instructions to apply the exception using generic computer component. In addition, the claim recites “ obtaining a collection of data queries; parsing each data query of the collection of data queries and ascertaining, from the parsing, dimension and measure information of the collection of data queries, the dimension and measure information comprising dimension tables and fact tables indicated in the collection of data queries; analyzing the dimension and measure information for relationships between the dimension tables and the fact tables, and strengths of the relationships ; segmenting dimensions and measures reflected in the dimension and measure information, including the dimension tables and the fact tables , into one or more communities based on the analyzing, each community of the one or more communities comprising a respective at least one dimension table of the dimension tables and at least one fact table of the fact tables; and generating one or more optimized data cubes , the generating the one or more optimized data cubes comprising, for each community of the one or more communities, generating an optimized data cube with a respective dimension table for each of the at least one dimension table of the respective community and a respective fact table for each of the at least one fact table of the respective community ” are mere gathering data and applying process steps (i.e., generating cube ‘spreadsheet’ ); the computers that perform those functions and the mental steps are recited at a high level of generality that do not impose a meaningful limitation on the judicial exception and are insufficient to integrate the mental steps into a practical application. Although the claim recites the additional functionality “ generating one or more optimized data cubes “, the gathering and determining are also recited at a high level of generality and merely generally link to respective technological environments (e.g., building data for insights ) and therefore likewise amounts to no more than a mere instructions to apply the exception using generic computer components and is insufficient to integrate the steps into a practical application. Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception? No— The recitation in the preamble is insufficient to transform a judicial exception to a patentable invention because the preamble elements are recited at a high level of generality that simply links to a field of use, see MPEP 2106.05(h). The claimed extra-solution of operation based on generating cubes from collection of queries is acknowledged to be well-understood, routine, conventional activity (see, e.g., court recognized WURC examples in MPEP 2106.05(d)(II)(i) . Similarly, the gathering and generating are also recited at a high level of generality and merely generally link to respective technological environments. The claim thus recites computing components only at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using generic computer components . Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Taken alone, their additional elements do not amount to significantly more than the above- identified judicial exception (the abstract idea). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation. For the reasons above, claim s 1 , 1 2 and 20 are rejected as being directed to non-patentable subject matter under §101. The analysis of claim s 2- 11 and 13 - 19 are as follows: Step 2A, prong one: Does claim s 2-11 and 13-19 recite an abstract idea, law of nature or natural phenomenon? Yes—the limitations of “ Claims 2 and 13, wherein the parsing further ascertains the dimensions, the dimensions corresponding to the dimension tables, metric columns corresponding to the fact tables, and metrics corresponding to the metric columns, the dimensions, the metric columns, and the metrics being indicated in the collection of data queries. Claim 3 , wherein the parsing uses a dictionary that identifies the dimensions, the dimension tables, the metrics, the metric columns, and the fact tables for correlation to query elements appearing in the collection of data queries. Claim 4 , wherein each relationship of the relationships comprises co-occurrence of a respective fact table, of the fact tables, and a respective dimension table, of the dimension tables, in the collection of data queries, wherein a strength of the respective relationship is based on a number of queries, of the collection of data queries, in which the respective fact table and the respective dimension table both appear. Claims 5 and 14 , further comprising building a community graph, wherein the building generates, in the community graph: a respective dimension table node for each dimension table of the dimension tables; a respective fact table node for each fact table of the fact tables; a respective edge between each fact table node-dimension table node pair that corresponds to a fact table-dimension table pair that co-occurs in the collection of data queries, wherein the edge is provided with a weight based on a number of queries, of the collection of data queries, in which the respective fact table and the respective data table of the respective fact table-dimension table pair both appear; a respective dimension node for each dimension of the ascertained dimensions, the respective dimension node being provided as a first child node to the respective dimension table node for the respective dimension table to which the respective dimension corresponds; a respective metric column node for each metric column of the ascertained metric columns, the respective metric column node being provided as a second child node to the respective fact table node for the respective fact table to which the respective metric column corresponds; and a respective metric node for each metric of the ascertained metrics, the respective metric node being provided as a third child node to the respective metric column node for the respective metric column to which the respective metric corresponds. Claims 6 and 15 , wherein the segmenting the dimensions and the measures into the one or more communities comprises segmenting the community graph into subgraphs, of the community graph, that each represent a community of the one or more communities, and each subgraph indicating the at least one dimension table of the respective community represented by the respective subgraph and the at least one fact table of the respective community represented by the respective subgraph. Claims 7 and 15 , wherein the segmenting the community graph comprises applying a community discovery algorithm using weights assigned to edges of the community graph. Claims 8 and 16 , wherein each edge of the edges extends between a respective dimension table node and a respective fact table node, wherein a weight assigned to the respective edge indicates a strength of a relationship between a respective dimension table represented by the respective dimension table node and a respective fact table represented by the respective fact table node, the strength of the relationship based on the number of queries, of the collection of data queries, in which the respective dimension table and the respective fact table both appear. Claims 9 and 17 , wherein, for each of the one or more communities, the generating the optimized data cube for the respective community comprises: for each dimension table of the at least one dimension table of the respective community indicated by the respective subgraph representing the respective community: generating a dimension table of the optimized data cube; and for each dimension corresponding to the dimension table indicated by the respective subgraph, setting the respective dimension as a dimension field of the generated dimension table of the optimized data cube; and for each fact table of the at least one fact table of the respective community indicated by the respective subgraph representing the community: generating a fact table of the optimized data cube; and for each metric column corresponding to the fact table indicated by the respective subgraph, setting a respective measure field of the generated fact table of the optimized data cube and, for each metric corresponding to the respective metric column, setting a respective measure of the measure field of the generated fact table of the optimized data cube. Claims 10 and 18 , wherein the collection of data queries is obtained from a query history comprising queries made against one or more initial data cubes, wherein the one or more optimized data cubes are provided in place of the one or more initial data cubes for satisfying incoming queries, and wherein the analyzing, segmenting, and generating identifies and includes in the one or more optimized data cubes at least one selected from the group consisting of: (i) at least one dimension that was not included in the one or more initial data cubes, and (ii) at least one metric that was not included in the one or more initial data cubes. Claims 11 and 19 , wherein the one or more optimized data cubes are used as an initial one or more optimized data cubes, and wherein the method further comprises iterating, one or more times: obtaining a next collection of data queries and repeating, using the next collection of data queries, the parsing, the analyzing, the segmenting, and the generating to generate the one or more optimized data cubes as a next one or more optimized data cubes, wherein the next one or more optimized data cubes are provided in place of the initial one or more data cubes for satisfying incoming queries ” a s drafted, are mental steps based on various processes can be performed in a human mind of building cube ‘matrix’ to find information (acts of thinking , decision making ). These limitations, therefore fall within the human mind processes group and with a pen & paper . Step 2A, prong two: Does the claim recite additional elements that integrate the judicial exception into a practical application? No—the judicial exception is not integrated into a practical application as just stated as related to the technical field of computer science . Although the claim recites that the recited functionality includes “ method ”, “ computer ” and “readable medium” , these computer components are recited at a high-level of generality such that it amounts to no more than a mere instructions to apply the exception using generic computer component. In addition, the claim recites “ Claims 2 and 13, wherein the parsing further ascertains the dimensions, the dimensions corresponding to the dimension tables, metric columns corresponding to the fact tables, and metrics corresponding to the metric columns, the dimensions, the metric columns, and the metrics being indicated in the collection of data queries. Claim 3 , wherein the parsing uses a dictionary that identifies the dimensions, the dimension tables, the metrics, the metric columns, and the fact tables for correlation to query elements appearing in the collection of data queries. Claim 4 , wherein each relationship of the relationships comprises co-occurrence of a respective fact table, of the fact tables, and a respective dimension table, of the dimension tables, in the collection of data queries, wherein a strength of the respective relationship is based on a number of queries, of the collection of data queries, in which the respective fact table and the respective dimension table both appear. Claims 5 and 14 , further comprising building a community graph, wherein the building generates, in the community graph: a respective dimension table node for each dimension table of the dimension tables; a respective fact table node for each fact table of the fact tables; a respective edge between each fact table node-dimension table node pair that corresponds to a fact table-dimension table pair that co-occurs in the collection of data queries, wherein the edge is provided with a weight based on a number of queries, of the collection of data queries, in which the respective fact table and the respective data table of the respective fact table-dimension table pair both appear; a respective dimension node for each dimension of the ascertained dimensions, the respective dimension node being provided as a first child node to the respective dimension table node for the respective dimension table to which the respective dimension corresponds; a respective metric column node for each metric column of the ascertained metric columns, the respective metric column node being provided as a second child node to the respective fact table node for the respective fact table to which the respective metric column corresponds; and a respective metric node for each metric of the ascertained metrics, the respective metric node being provided as a third child node to the respective metric column node for the respective metric column to which the respective metric corresponds. Claims 6 and 15 , wherein the segmenting the dimensions and the measures into the one or more communities comprises segmenting the community graph into subgraphs, of the community graph, that each represent a community of the one or more communities, and each subgraph indicating the at least one dimension table of the respective community represented by the respective subgraph and the at least one fact table of the respective community represented by the respective subgraph. Claims 7 and 15 , wherein the segmenting the community graph comprises applying a community discovery algorithm using weights assigned to edges of the community graph. Claims 8 and 16 , wherein each edge of the edges extends between a respective dimension table node and a respective fact table node, wherein a weight assigned to the respective edge indicates a strength of a relationship between a respective dimension table represented by the respective dimension table node and a respective fact table represented by the respective fact table node, the strength of the relationship based on the number of queries, of the collection of data queries, in which the respective dimension table and the respective fact table both appear. Claims 9 and 17 , wherein, for each of the one or more communities, the generating the optimized data cube for the respective community comprises: for each dimension table of the at least one dimension table of the respective community indicated by the respective subgraph representing the respective community: generating a dimension table of the optimized data cube; and for each dimension corresponding to the dimension table indicated by the respective subgraph, setting the respective dimension as a dimension field of the generated dimension table of the optimized data cube; and for each fact table of the at least one fact table of the respective community indicated by the respective subgraph representing the community: generating a fact table of the optimized data cube; and for each metric column corresponding to the fact table indicated by the respective subgraph, setting a respective measure field of the generated fact table of the optimized data cube and, for each metric corresponding to the respective metric column, setting a respective measure of the measure field of the generated fact table of the optimized data cube. Claims 10 and 18 , wherein the collection of data queries is obtained from a query history comprising queries made against one or more initial data cubes, wherein the one or more optimized data cubes are provided in place of the one or more initial data cubes for satisfying incoming queries, and wherein the analyzing, segmenting, and generating identifies and includes in the one or more optimized data cubes at least one selected from the group consisting of: (i) at least one dimension that was not included in the one or more initial data cubes, and (ii) at least one metric that was not included in the one or more initial data cubes. Claims 11 and 19 , wherein the one or more optimized data cubes are used as an initial one or more optimized data cubes, and wherein the method further comprises iterating, one or more times: obtaining a next collection of data queries and repeating, using the next collection of data queries, the parsing, the analyzing, the segmenting, and the generating to generate the one or more optimized data cubes as a next one or more optimized data cubes, wherein the next one or more optimized data cubes are provided in place of the initial one or more data cubes for satisfying incoming queries ” are mere gathering data and applying process steps (i.e., generating cube ‘spreadsheet’ ); the computers that perform those functions and the mental steps are recited at a high level of generality that do not impose a meaningful limitation on the judicial exception and are insufficient to integrate the mental steps into a practical application. Although the claim recites the additional functionality “ generating one or more optimized data cubes “, the gathering and determining are also recited at a high level of generality and merely generally link to respective technological environments (e.g., building data for insights ) and therefore likewise amounts to no more than a mere instructions to apply the exception using generic computer components and is insufficient to integrate the steps into a practical application. Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception? No— The recitation in the preamble is insufficient to transform a judicial exception to a patentable invention because the preamble elements are recited at a high level of generality that simply links to a field of use, see MPEP 2106.05(h). The claimed extra-solution of operation based on generating cubes from collection of queries is acknowledged to be well-understood, routine, conventional activity (see, e.g., court recognized WURC examples in MPEP 2106.05(d)(II)(i) . Similarly, the gathering and generating are also recited at a high level of generality and merely generally link to respective technological environments. The claim thus recites computing components only at a high-level of generality such that it amounts to no more than mere instructions t o apply the exception using generic computer components . Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Taken alone, their additional elements do not amount to significantly more than the above- identified judicial exception (the abstract idea). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation. For the reasons above, claims 2-11 and 13-19 are rejected as being directed to non-patentable subject matter under §101. Claim Rejections - 35 USC § 103 In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status. This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention. 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. Claims 1-2 0 are rejected under 35 U.S.C. 103 as being unpatentable over Alberg et al. US 20 17 / 0116228 A1 (hereinafter ‘ Alberg ’) in view of Riscutia et al. US 20 21 / 0406263 A1 (hereinafter ‘ Riscutia ’). As per claim 1, Alberg disclose, A computer-implemented method comprising ( Alberg : paragraph 0005: disclose a method for automatic inference of a cube schema from a tabular data for use in a multidimensional database environment and paragraph 0016: disclose physical computer for implementation ) : obtaining a collection of data queries ( Alberg : paragraph 0056 and Fig. 2 Element 205: disclose t abular data received from a tabular data storage and tabular data can consist of or can be presented in columns or tables . Examiner equates tabular data to data queries. However, examiner concedes that the art is silent on collection of data queries and examiner would discuss this is secondary art below ) ; analyzing the dimension and measure information for relationships between the dimension tables and the fact tables ( Alberg : paragraph 0025: disclose a dimension represents the highest consolidation level in the database outline. Standard dimensions may be chosen to represent components of a business plan that relate to departmental functions (e.g., Time, Accounts, Product Line, Market, Division). Attribute dimensions, that are associated with standard dimensions ‘fact tables’ , enable a user to group and analyze members of standard dimensions based on member attributes or characteristics. Members (e.g., Product A, Product B, Product C) are the individual components of a dimension ) , and strengths of the relationships ( Alberg : paragraph 0026: disclose a multidimensional database uses family (parents, children, siblings; descendants and ancestors); and hierarchical (generations and levels; roots and leaves) terms, to describe the roles and relationships of the members within a database outline . Examiner argues that the prior art teaching of relationships are inherent ly have strengths of the relationships ) ; segmenting dimensions and measures reflected in the dimension and measure information ( Alberg : paragraph 0090: disclose a cube schema or database outline, the system needs to be able to map each column ‘dimensions’ on the directed hierarchy graph to a particular element (e.g., a measure column, a top-level column, a base-level column, or an attribute column) of a cube defined by the cube schema ) , including the dimension tables and the fact tables ( Alberg : paragraph 0092: disclose one or more cube elements, for example, one or more measure columns and/or one or more flat dimension columns ) , into one or more communities based on the analyzing ( Alberg : paragraph 0023: disclose a cube that might be used in a sales-oriented business application . Examiner argues that the sales-oriented business application is equated to sales community ) , each community of the one or more communities comprising a respective at least one dimension table of the dimension tables and at least one fact table of the fact tables ( Alberg : paragraph 0091: disclose identify cube elements from a plurality of columns on the directed hierarchy graph, for use in constructing a cube schema/ star schema . Examiner argues that the star schema to build cube inherently includes dimension and fact tables as disclosed in paragraph 0023 of sales community ) ; and generating one or more optimized data cubes ( Alberg : paragraph 0046: disclose cube schema to create ‘generate’ a cube in the multidimensional database environment for loading the tabular data ) , the generating the one or more optimized data cubes comprising, for each community of the one or more communities ( Alberg : paragraph 0023: disclose a cube that might be used in a sales-oriented business application . Examiner argues that the sales-oriented business application is equated to sales community ) , generating an optimized data cube with a respective dimension table for each of the at least one dimension table of the respective community and a respective fact table for each of the at least one fact table of the respective community ( Alberg : paragraph 0091: disclose identify cube elements from a plurality of columns on the directed hierarchy graph, for use in constructing a cube schema/ star schema . Examiner argues that the star schema to build cube inherently includes dimension and fact tables as disclosed in paragraph 0023 of sales community ) . It is noted, however, Alberg did not specifically detail the aspects of obtaining a collection of data queries ; parsing each data query of the collection of data queries and ascertaining, from the parsing, dimension and measure information of the collection of data queries, the dimension and measure information comprising dimension tables and fact tables indicated in the collection of data queries as recited in claim 1. On the other hand, Riscutia achieved the aforementioned limitations by providing mechanisms of obtaining a collection of data queries ( Riscutia : paragraph 0050 and Fig 1. Element 118a: disclose The queries be database queries that the management systems can interpret to obtain specific information stored in the data storage systems ) ; parsing each data query of the collection of data queries and ascertaining, from the parsing, dimension and measure information of the collection of data queries ( Riscutia : paragraph 0035: disclose graph builder may extract the tables and parse the queries to determine connections between the tables and the queries . Examiner equates tables to dimension and measure information ) , the dimension and measure information comprising dimension tables and fact tables indicated in the collection of data queries ( Riscutia : paragraph 0040: disclose q uery parser may determine that the query uses a first table ‘dimension table’ and a second table ‘fact table’ . The knowledge graph may then store information connecting the report to the first table and the second table. To automatically generate the knowledge graph the query parser may access all the queries used to generate reports and determine, for each query, the tables that the query uses ) . Alberg and Riscutia are analogous art because they are from the “same field of endeavor” and both from the same “problem-solving area”. Namely, they are both from the field of “Data Processing Systems”. It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to combine the systems of Alberg and Riscutia because they are both directed to data processing systems and both are from the same field of endeavor. The skilled person would therefore regard it as a normal option to include the restriction features of Riscutia with the method described by Alberg in order to solve the problem posed. The motivation for doing so would have been to identify ways to improve the organization. For example, a database may include information about a business's product sales. The business may use the database to generate reports to identify successful products and weak products ( Riscutia : paragraph 0002 ) . Therefore, it would have been obvious to combine Riscutia with Alberg t o obtain the invention as specified in instant claim 1 . A s per claim 2 , most of the limitations of this claim have been noted in the rejection of claim 1 above. It is noted, however, Alberg did not specifically detail the aspects of wherein the parsing further ascertains the dimensions, the dimensions corresponding to the dimension tables, metric columns corresponding to the fact tables, and metrics corresponding to the metric columns, the dimensions, the metric columns, and the metrics being indicated in the collection of data queries as recited in claim 2. On the other hand, Riscutia achieved the aforementioned limitations by providing mechanisms of wherein the parsing further ascertains the dimensions ( Riscutia : paragraph 0035: disclose graph builder may extract the tables and parse the queries to determine connections between the tables and the queries . Examiner equates tables to dimension and measure information) , the dimensions corresponding to the dimension tables, metric columns corresponding to the fact tables, and metrics corresponding to the metric columns, the dimensions, the metric columns, and the metrics being indicated in the collection of data queries ( Riscutia : paragraph 0040: disclose q uery parser may determine that the query uses a first table ‘dimension table’ and a second table ‘fact table’ . The knowledge graph may then store information connecting the report to the first table and the second table. To automatically generate the knowledge graph the query parser may access all the queries used to generate reports and determine, for each query, the tables that the query uses . Examiner argues that the queries are parsed to create knowledge graph and the limitation lists the knowledge from the queries ) . A s per claim 3 , most of the limitations of this claim have been noted in the rejection of claim s 1 and 2 above. It is noted, however, Alberg did not specifically detail the aspects of wherein the parsing uses a dictionary that identifies the dimensions, the dimension tables, the metrics, the metric columns, and the fact tables for correlation to query elements appearing in the collection of data queries as recited in claim 3. On the other hand, Riscutia achieved the aforementioned limitations by providing mechanisms of wherein the parsing uses a dictionary that identifies the dimensions, the dimension tables, the metrics, the metric columns, and the fact tables for correlation to query elements appearing in the collection of data queries ( Riscutia : paragraph 0055: disclose the knowledge graph ‘dictionary’ contains information describing a business significance of the data and discrete subparts of the data. For example, the knowledge graph may include information describing the meaning of one or more tables in the tables ) . A s per claim 4 , most of the limitations of this claim have been noted in the rejection of claim s 1 and 2 above. It is noted, however, Alberg did not specifically detail the aspects of wherein each relationship of the relationships comprises co-occurrence of a respective fact table, of the fact tables, and a respective dimension table, of the dimension tables, in the collection of data queries, wherein a strength of the respective relationship is based on a number of queries, of the collection of data queries, in which the respective fact table and the respective dimension table both appear as recited in claim 4. On the other hand, Riscutia achieved the aforementioned limitations by providing mechanisms of wherein each relationship of the relationships comprises co-occurrence of a respective fact table, of the fact tables, and a respective dimension table, of the dimension tables, in the collection of data queries, wherein a strength of the respective relationship is based on a number of queries, of the collection of data queries, in which the respective fact table and the respective dimension table both appear ( Riscutia : paragraph 0055: disclose the knowledge graph ‘dictionary’ contains information describing a business significance of the data and discrete subparts of the data. For example, the knowledge graph may include information describing the meaning of one or more tables in the tables ) . A s per claim 5 , most of the limitations of this claim have been noted in the rejection of claim s 1 and 2 above. In addition, Alberg disclose, building a community graph, wherein the building generates, in the community graph: a respective dimension table node for each dimension table of the dimension tables; a respective fact table node for each fact table of the fact tables; a respective edge between each fact table node-dimension table node pair that corresponds to a fact table-dimension table pair that co-occurs in the collection of data queries, wherein the edge is provided with a weight based on a number of queries, of the collection of data queries, in which the respective fact table and the respective data table of the respective fact table-dimension table pair both appear; a respective dimension node for each dimension of the ascertained dimensions, the respective dimension node being provided as a first child node to the respective dimension table node for the respective dimension table to which the respective dimension corresponds; a respective metric column node for each metric column of the ascertained metric columns, the respective metric column node being provided as a second child node to the respective fact table node for the respective fact table to which the respective metric column corresponds; and a respective metric node for each metric of the ascertained metrics, the respective metric node being provided as a third child node to the respective metric column node for the respective metric column to which the respective metric corresponds ( Alberg : paragraph 0064: disclose the hierarchy directed graph can also be a data structure that comprise a finite set of vertices (nodes) and a finite set of ordered pairs of edges. The hierarchy directed graph can be parsed by the hierarchy directed graph parser, which can transform the graph into an XML-based cube schema, for example, an Essbase database outline, which can be used to create a cube on the database server ) . A s per claim 6 , most of the limitations of this claim have been noted in the rejection of claim s 1 , 2 and 5 above. In addition, Alberg disclose, wherein the segmenting the dimensions and the measures into the one or more communities comprises segmenting the community graph into subgraphs, of the community graph, that each represent a community of the one or more communities, and each subgraph indicating the at least one dimension table of the respective community represented by the respective subgraph and the at least one fact table of the respective community represented by the respective subgraph ( Alberg : paragraph 0026: disclose a multidimensional database uses family (parents, children, siblings; descendants and ancestors); and hierarchical (generations and levels; roots and leaves) terms, to describe the roles and relationships of the members within a database outline . Examiner argues that this prior art teaching related to subgraph related to community ) . A s per claim 7 , most of the limitations of this claim have been noted in the rejection of claim s 1 , 2, 5 and 6 above. In addition, Alberg disclose, wherein the segmenting the community graph comprises applying a community discovery algorithm using weights assigned to edges of the community graph ( Alberg : paragraph 0086: disclose a hierarchy directed graph can be constructed from the cross correlation matrix. The directed graph includes a plurality of nodes and an edge between each pair of nodes except between node A and node C. Each node represents an attribute or column in the tabular data, and each edge represents the relationship between a pair of nodes. The label on each edge indicates information gain ratios ‘weights’ between the two adjacent nodes ) . A s per claim 8 , most of the limitations of this claim have been noted in the rejection of claim s 1 , 2, 5, 6 and 7 above. In addition, Alberg disclose, wherein each edge of the edges extends between a respective dimension table node and a respective fact table node, wherein a weight assigned to the respective edge indicates a strength of a relationship between a respective dimension table represented by the respective dimension table node and a respective fact table represented by the respective fact table node, the strength of the relationship based on the number of queries, of the collection of data queries, in which the respective dimension table and the respective fact table both appear ( Alberg : paragraph 0086: disclose a hierarchy directed graph can be constructed from the cross correlation matrix. The directed graph includes a plurality of nodes and an edge between each pair of nodes except between node A and node C. Each node represents an attribute or column in the tabular data, and each edge represents the relationship between a pair of nodes. The label on each edge indicates information gain ratios ‘weights’ between the two adjacent nodes . Examiner believes this limitation is an algorithmic logic that the invention is implemented ) . A s per claim 9 , most of the limitations of this claim have been noted in the rejection of claim s 1 , 2 and 5 above. In addition, Alberg disclose, wherein, for each of the one or more communities, the generating the optimized data cube for the respective community comprises: for each dimension table of the at least one dimension table of the respective community indicated by the respective subgraph representing the respective community: generating a dimension table of the optimized data cube; and for each dimension corresponding to the dimension table indicated by the respective subgraph, setting the respective dimension as a dimension field of the generated dimension table of the optimized data cube; and for each fact table of the at least one fact table of the respective community indicated by the respective subgraph representing the community: generating a fact table of the optimized data cube; and for each metric column corresponding to the fact table indicated by the respective subgraph, setting a respective measure field of the generated fact table of the optimized data cube and, for each metric corresponding to the respective metric column, setting a respective measure of the measure field of the generated fact table of the optimized data cube ( Alberg : paragraph 0086: disclose a hierarchy directed graph can be constructed from the cross correlation matrix. The directed graph includes a plurality of nodes and an edge between each pair of nodes except between node A and node C. Each node represents an attribute or column in the tabular data, and each edge represents the relationship between a pair of nodes. The label on each edge indicates information gain ratios ‘weights’ between the two adjacent nodes . Examiner believes this limitation is an algorithmic logic that the invention is implemented ) . A s per claim 10 , most of the limitations of this claim have been noted in the rejection of claim 1 above. In addition, Alberg disclose, wherein the collection of data queries is obtained from a query history comprising queries made against one or more initial data cubes, wherein the one or more optimized data cubes are provided in place of the one or more initial data cubes for satisfying incoming queries, and wherein the analyzing, segmenting, and generating identifies and includes in the one or more optimized data cubes at least one selected from the group consisting of: (i) at least one dimension that was not included in the one or more initial data cubes, and (ii) at least one metric that was not included in the one or more initial data cubes ( Alberg : paragraph 0096: disclose a column can be identified as a flat hierarchy dimension column if the column is a text column that does not have a relationship with any other column in the plurality of columns, or the column is a numeric or text column that has a one-to-many or many-to-one relationship with all other columns. A numeric column with no duplicate values can be identified as a flat hierarchy dimension column by default ) . A s per claim 11 , most of the limitations of this claim have been noted in the rejection of claim 1 above. In addition, Alberg disclose, wherein the one or more optimized data cubes are used as an initial one or more optimized data cubes, and wherein the method further comprises iterating, one or more times: obtaining a next collection of data queries and repeating, using the next collection of data queries, the parsing, the analyzing, the segmenting, and the generating to generate the one or more optimized data cubes as a next one or more optimized data cubes, wherein the next one or more optimized data cubes are provided in place of the initial one or more data cubes for satisfying incoming queries ( Alberg : paragraph 0091: disclose identify cube elements from a plurality of columns on the directed hierarchy graph, for use in constructing a cube schema/ star schema . Examiner argues that the star schema to build cube inherently includes dimension and fact tables as disclosed in paragraph 0023 of sales community ) . As per claim 12, Alberg disclose, A computer system ( Alberg : paragraph 0016: disclose physical computer ) comprising: a memory ( Alberg : paragraph 0016: disclose physical memory ) ; and a processor in communication with the memory ( Alberg : paragraph 0016: disclose microprocessor ) , wherein the computer system is configured to perform a method comprising: remaining limitations in this claim 12 are similar to the limitations in claim 1. Therefore, examiner rejects these remaining limitations under the same rationale as limitations rejected under claim 1. As per claim 13 , li mitations of this claim are similar to claim 2 . Therefore, examiner rejects claim 13 limitations und er the same rationale as claim 2 . As per claim 14 , li mitations of this claim are similar to claim 5 . Therefore, examiner rejects claim 14 limitations und er the same rationale as claim 5 . As per claim 15 , li mitations of this claim are similar to claims 6 and 7 . Therefore, examiner rejects claim 15 limitations und er the same rationale as claims 6 and 7 . As per claim 16, li mitations of this claim are similar to claim 8 . Therefore, examiner rejects claim 16 limitations und er the same rationale as claim 8 . As per claim 17 , li mitations of this claim are similar to claim 9 . Therefore, examiner rejects claim 17 limitations und er the same rationale as claim 9 . As per claim 18 , li mitations of this claim are similar to claim 10 . Therefore, examiner rejects claim 18 limitations und er the same rationale as claim 10 . As per claim 19 , li mitations of this claim are similar to claim 11 . Therefore, examiner rejects claim 19 limitations und er the same rationale as claim 11 . As per claim 20, Alberg disclose, A computer program product comprising: a computer readable storage medium readable ( Alberg : paragraph 0124: disclose computer readable storage ) by a processing circuit and storing instructions for execution by the processing circuit for performing a method comprising: remaining limitations in this claim 20 are similar to the limitations in claim 1. Therefore, examiner rejects these remaining limitations under the same rationale as limitations rejected under claim 1. Conclusion The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. US Pub. US 2010 / 0235345 A1 disclose “ INDIRECT DATABASE QUERIES WITH LARGE OLAP CUBES ” US Pub. US 2008 / 0091634 A1 disclose “ CONTENT ENHANCEMENT SYSTEM AND METHOD AND APPLICATIONS THEREOF ” Any inquiry concerning this communication or earlier communications from the examiner should be directed to FILLIN "Enter examiner's name" \* MERGEFORMAT PAVAN MAMILLAPALLI whose telephone number is FILLIN "Phone number" \* MERGEFORMAT (571)270-3836 . The