Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
DETAILED ACTION
This Office Action corresponds to application 19/320,622 which was filed on 9/5/2022 and is a CON of PCT/CN2024/099630 filed 6/17/2024 and claims benefit of China 202311111908.5 filed 8/31/2023. Claims 1-20 are currently pending.
Specification
The title of the invention is not descriptive. A new title is required that is clearly indicative of the invention to which the claims are directed.
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 4 and 5 recites the limitation "…the at least two basic column combinations…" in the merging limitation. There is insufficient antecedent basis for this limitation in the claim.
Claim 6 recites the limitation "…wherein the determining first query overheads…" in the claim, however this is not referenced in the parent claim. There is insufficient antecedent basis for this limitation in the claim.
Claim 7 recites the limitations "…wherein the determining basic column combinations sets…", "…the local predicate column attribute…”, "…the join predicate column attribute…”, and "…the target predicate column attribute…” in the claim, however these are not referenced in the parent claim. There is insufficient antecedent basis for this limitation in the claim.
Claim 8 recites the limitations "…wherein the determining basic column combinations sets…”, "…the first merged combination…”, and "…the target selection column combination…” in the claim, however these are not referenced in the parent claim. There is insufficient antecedent basis for this limitation in the claim.
Claim 9 recites the limitations "…wherein the determining basic column combinations sets…”, "…the join predicate column combination…”, "…the target selection column combination…”, and “…the local predicate column combination…” in the claim, however these are not referenced in the parent claim. There is insufficient antecedent basis for this limitation in the claim.
Claim 10 recites the limitations "…wherein the determining basic column combinations sets…”, "…the join predicate column combination…”, "…the target selection column combination…”, “…the local predicate column combination…”, “…the first merging condition…”, and “…the second merging condition…” in the claim, however these are not referenced in the parent claim. There is insufficient antecedent basis for this limitation in the claim.
Claims 13 and 14 recites the limitation “… the … sparse target column combinations…” in the claim, however sparse column combinations are not referenced in the parent claim. There is insufficient antecedent basis for this limitation in the claim.
Claim 16 recites the limitation “… the data update information…” in the claim, however this is not referenced in the parent claim. 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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1
The claims recite a method (claim 1), a device (claim 17), and a non-transitory computer-readable storage medium (claim 20). These claims fall within at least one of the four categories of patentable subject matter.
Step 2A, Prong One
Claim 1 recites acquiring a plurality of queries and parsing the query to obtain column information, determining candidate column combinations related to the query requests based on the column information, determining target column combinations for the candidate column combinations belonging to the same table, and storing the table according to the target column combinations.
The recited steps for retrieving data, analyzing data, and storing data are acts of information evaluation and retrieval that can be practically performed in the human mind. The steps of acquiring the query request with column data and storing the table data are extra solution activity. The tables and storage are being interepted as generic computer components to apply the instructions of the abstract idea. For example, a person can receive request and determine a combination of documents to combine to answer the requests. Thus, these steps are an abstract idea in the “mental processes” grouping.
Dependent claims 2-15 recite additional elements of parsing the query, determining column identifiers and attributes, determining additional column combinations, merging additional columns, determining query overheads and comparing overheads, determining path overheads, adding column combination identifiers, determining if combinations satisfying merging conditions, acquiring and updating metadata, and determining sparsity, These are all further extensions of the abstract idea, the additional abstract idea of mathematical concepts, or mere extra-solution activity. For example, with claim 2 a person can parse a query and obtain identifiers and attributes of requested information; or with claim 15 a person can calculate the sparsity of a table/column.
Claim 17 recites acquiring a plurality of queries and parsing the query to obtain column information, determining candidate column combinations related to the query requests based on the column information, determining target column combinations for the candidate column combinations belonging to the same table, and storing the table according to the target column combinations.
The recited steps for retrieving data, analyzing data, and storing data are acts of information evaluation and retrieval that can be practically performed in the human mind. The steps of acquiring the query request with column data and storing the table data are extra solution activity. The memory, processor, tables, and storage are being interepted as generic computer components to apply the instructions of the abstract idea. For example, a person can receive request and determine a combination of documents to combine to answer the requests. Thus, these steps are an abstract idea in the “mental processes” grouping.
Dependent claims 18-19 recite additional elements of parsing the query, determining column identifiers and attributes, determining additional column combinations and merging additional columns. These are all further extensions of the abstract idea or mere extra-solution activity. For example, with claim 18 a person can parse a query and obtain identifiers and attributes of requested information.
Claim 20 recites acquiring a plurality of queries and parsing the query to obtain column information, determining candidate column combinations related to the query requests based on the column information, determining target column combinations for the candidate column combinations belonging to the same table, and storing the table according to the target column combinations.
The recited steps for retrieving data, analyzing data, and storing data are acts of information evaluation and retrieval that can be practically performed in the human mind. The steps of acquiring the query request with column data and storing the table data are extra solution activity. The memory, processor, tables, and storage are being interepted as generic computer components to apply the instructions of the abstract idea. For example, a person can receive request and determine a combination of documents to combine to answer the requests. Thus, these steps are an abstract idea in the “mental processes” grouping.
Step 2A, Prong Two
This judicial exception is not integrated into a practical application because the combination of additional elements includes only generic computer elements which do not add a meaningful limitation to the abstract idea because they amount to simply implementing the abstract idea on a computer.
For claims 1-16, the additional elements include the tables and storage.
For claim 17, the additional elements include memory, processor, tables, and storage.
For claim 20, the additional elements include memory, processor, tables, and storage.
The memory, processor, tables, and storage are all recited at a high-level of generality (i.e., as a generic computer function) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea.
Step 2B
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of using the memory, processor, tables, and storage to perform the steps or the additional elements from the dependent claims amounts to no more than part of the abstract idea, mere extra-solution activity, and mere instructions to apply the exception using a generic computer component. The claims are not patent eligible.
Claim Rejections - 35 USC § 103
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.
Claim(s) 1-5, 8-12, and 17-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kara et al. (US2021/0182064), hereinafter Kara, in view of Li et al. (US9430473), hereinafter Li, and Mangas et al. (US11334588), hereinafter Mangas.
Regarding Claim 1:
Kara teaches:
A data storage method, applied to an electronic device, (Kara, abstract, figure 1) and comprising:
acquiring a plurality of query requests for an application, and parsing the query requests to obtain at least one piece of column information in the query requests (Kara, figure 2, [0047-0048], note receiving inputting a query; note the user may input multiple queries; note parsing the query for column information);
determining candidate column combinations of tables related to the query requests based on the at least one piece of column information in the query requests, a query overhead for executing the query requests being minimum when table data of the tables related to the query requests is stored according to the candidate column combinations (Kara, figure 2, [0047-0048], note determining columns to combine, e.g., candidate column combinations);
determining target column combinations of corresponding tables based on a plurality of candidate column combinations belonging to the same table, a total query overhead for executing the plurality of query requests being minimum when the table data of the tables is stored according to respective target column combinations (Kara, figure 2, [0047-0048], note determining columns to combine and combining them, e.g., target column combinations); and
storing the table data of the tables according to the target column combinations (Kara, figure 2, [0047-0048], note storing combined column combinations).
While Kara teaches combining columns, Kara doesn’t specifically teach determining query overhead. However, Li is in the same field of endeavor, data management and information retrieval, and Li teaches:
acquiring a plurality of query requests for an application, and parsing the query requests to obtain at least one piece of column information in the query requests (Li, abstract, column 2 lines 11-24, column 6 lines 18-39, note acquiring a plurality of queries; note collecting data regarding the columns contained in the query, e.g., parsing the query to obtain column information in the query request);
determining candidate column combinations of tables related to the query requests based on the at least one piece of column information in the query requests, a query overhead for executing the query requests being minimum when table data of the tables related to the query requests is stored according to the candidate column combinations (Li, abstract, column 1 lines 23-54, column 2 lines 11-24, column 6 lines 18-39, note using the query column data to determine the optimal combination of columns, e.g., minimum overhead);
determining target column combinations of corresponding tables based on a plurality of candidate column combinations belonging to the same table, a total query overhead for executing the plurality of query requests being minimum when the table data of the tables is stored according to respective target column combinations (Li, figures 2A-2B, abstract, column 1 lines 23-54, column 2 lines 11-24, column 6 lines 18-39, note using the query column data to determine the optimal combination of columns; note columns with higher access frequencies are maintained in current database storage and less frequently used columns are stored separately, which is interpreted to mean target column combinations are based on the optimization of the columns belonging to the same table, e.g., current database storage, when the overhead would be the minimum); and
storing the table data of the tables according to the target column combinations (Li, figures 2A-2B, abstract, column 1 lines 23-54, column 2 lines 11-24, column 12 lines 17-42, note storing the table according to optimal combination).
It would have been obvious to one of ordinary skill in the art before the effective date of filing to modify the cited references to incorporate the teachings of Li because all references are directed towards data management and information retrieval and because Li would expand upon the teachings of the previously cited references in information retrieval which would improve the efficiency by improving the storage of the column data (Li, column 1 lines 9-19, column 2 lines 11-24).
While Kara as modified teaches combining columns to optimize processing, to further support the interpretation Mangas is in the same field of endeavor, data management and information retrieval, and Mangas teaches:
acquiring a plurality of query requests for an application, and parsing the query requests to obtain at least one piece of column information in the query requests (Mangas, abstract, column 3 line 49 – column 4 line 8, column 4 lines 48-58, note determining column information in the queries);
determining candidate column combinations of tables related to the query requests based on the at least one piece of column information in the query requests, a query overhead for executing the query requests being minimum when table data of the tables related to the query requests is stored according to the candidate column combinations (Mangas, abstract, figure 4, column 3 line 49 – column 4 line 8, column 4 lines 48-58, column 7 lines 23-43, note determining candidate column combinations and the benefit of processing queries using the pre-combined dataset, e.g., the query overhead being minimum);
determining target column combinations of corresponding tables based on a plurality of candidate column combinations belonging to the same table, a total query overhead for executing the plurality of query requests being minimum when the table data of the tables is stored according to respective target column combinations (Mangas, abstract, figure 4, column 3 line 49 – column 4 line 8, column 4 lines 48-58, column 7 lines 23-67, note determining candidate column combinations and the benefit of processing queries using the pre-combined dataset, e.g., the query overhead being minimum when column combinations belong to the same table); and
storing the table data of the tables according to the target column combinations (Mangas, abstract, figure 4, column 7 lines 23-67, note pre-combining columns).
It would have been obvious to one of ordinary skill in the art before the effective date of filing to modify the cited references to incorporate the teachings of Mangas because all references are directed towards data management and information retrieval and because Mangas would expand upon the teachings of the previously cited references in information retrieval which would improve the efficiency by pre-combing data (Mangas, column 1 line 49 – column 2 line 2).
Regarding Claim 2:
Kara as modified shows the method as disclosed above;
Kara as modified further teaches:
wherein the column information comprises column identifiers and column attribute information (Kara, figure 2, [0047-0048], note parsing the query for column information) (Li, abstract, column 2 lines 11-24, column 6 lines 18-39, note collecting data regarding the columns contained in the query, e.g., parsing the query to obtain column information in the query request) (Mangas, abstract, column 3 line 49 – column 4 line 8, column 4 lines 48-58, note determining column information in the queries), and the parsing the query requests to obtain at least one piece of column information in the query requests comprises:
parsing the query requests to obtain at least one query statement (Kara, figure 2, [0047-0048], note parsing the query for column information includes identifying data sets and expressions, e.g., query statement) (Li, column 6 lines 1-39, note monitoring the query, obtaining the query statement) (Mangas, abstract, column 3 line 49 – column 4 line 8, column 4 lines 48-58, note determining column information in the query statement);
determining at least one column identifier corresponding to query statements (Kara, figure 2, [0047-0048], note parsing the query for column information includes identifying columns) (Li, abstract, column 1 lines 23-54, column 2 lines 11-24, column 6 lines 1-39, note determining columns) (Mangas, abstract, column 3 line 49 – column 4 line 8, column 4 lines 48-58, note determining columns); and
determining column attribute information corresponding to column identifiers, the column attribute information comprising one of a local predicate column attribute, a join predicate column attribute, and a target selection column attribute (Kara, figure 2, [0047-0048], note determining operations to perform on the columns, e.g., attribute information) (Li, abstract, figures 2A-2B, column 1 lines 23-54, column 2 lines 11-24, column 6 lines 1-39, note column attribute information comprises local and target columns) (Mangas, abstract, column 3 line 49 – column 4 line 8, column 4 lines 48-58, note column attribute information comprises operators such as join predicates).
It would have been obvious to one of ordinary skill in the art before the effective date of filing to modify the cited references to incorporate the teachings of Li because all references are directed towards data management and information retrieval and because Li would expand upon the teachings of the previously cited references in information retrieval which would improve the efficiency by improving the storage of the column data (Li, column 1 lines 9-19, column 2 lines 11-24).
It would have been obvious to one of ordinary skill in the art before the effective date of filing to modify the cited references to incorporate the teachings of Mangas because all references are directed towards data management and information retrieval and because Mangas would expand upon the teachings of the previously cited references in information retrieval which would improve the efficiency by pre-combing data (Mangas, column 1 line 49 – column 2 line 2).
Regarding Claim 3:
Kara as modified shows the method as disclosed above;
Kara as modified further teaches:
wherein the determining candidate column combinations of tables related to the query requests based on the at least one piece of column information in the query requests, comprises: determining basic column combination sets of tables related to the ith query request based on column attribute information corresponding to column identifiers in an it query request, wherein the basic column combination set comprises at least one basic column combination, i = 1, ..., M, and M is a total number of query requests (Kara, figure 2, [0047-0048], note determining columns to combine, e.g., candidate column combinations) (Li, abstract, column 1 lines 23-54, column 2 lines 11-24, column 6 lines 18-39, note using the query column data to determine the optimal combination of columns for the queries) (Mangas, abstract, figure 4, column 3 line 49 – column 4 line 8, column 4 lines 48-58, column 7 lines 23-43, note determining candidate column combinations and the benefit of processing queries using the pre-combined dataset and not using the pre-combined dataset); and
determining, the basic column combination as a candidate column combination of the table when a basic column combination set of a jth table related to the ith query request comprises one basic column combination, wherein j = 1, ..., N, and N is a total number of tables related to the ith query request (Kara, figure 2, [0047-0048], note determining columns to combine, e.g., candidate column combinations) (Li, abstract, column 1 lines 23-54, column 2 lines 11-24, column 6 lines 18-39, note using the query column data to determine the optimal combination of columns for the queries) (Mangas, abstract, figure 4, column 3 line 49 – column 4 line 8, column 4 lines 48-58, column 7 lines 23-43, note determining candidate column combinations and the benefit of processing queries using the pre-combined dataset and not using the pre-combined dataset).
It would have been obvious to one of ordinary skill in the art before the effective date of filing to modify the cited references to incorporate the teachings of Li because all references are directed towards data management and information retrieval and because Li would expand upon the teachings of the previously cited references in information retrieval which would improve the efficiency by improving the storage of the column data (Li, column 1 lines 9-19, column 2 lines 11-24).
It would have been obvious to one of ordinary skill in the art before the effective date of filing to modify the cited references to incorporate the teachings of Mangas because all references are directed towards data management and information retrieval and because Mangas would expand upon the teachings of the previously cited references in information retrieval which would improve the efficiency by pre-combing data (Mangas, column 1 line 49 – column 2 line 2).
Regarding Claim 4:
Kara as modified shows the method as disclosed above;
Kara as modified further teaches:
wherein the determining candidate column combinations of tables related to the query requests based on the at least one piece of column information in the query requests, comprises: merging the at least two basic column combinations of the jth table to obtain at least one merged basic column combination of the jth table when N is 1 and the basic column combination set of the jth table comprises at least two basic column combinations (Kara, figure 2, [0047-0048], note determining columns to combine and combining them) (Li, figures 2A-2B, abstract, column 1 lines 23-54, column 2 lines 11-24, column 6 lines 1-39, column 12 lines 17-42, note merging columns) (Mangas, abstract, figure 4, column 3 line 49 – column 4 line 8, column 4 lines 48-58, column 7 lines 23-67, note merging columns);
determining a first reference overhead for executing the ith query request in a case that table data of the jth table is stored according to the at least two basic column combinations (Mangas, abstract, figure 4, column 3 line 49 – column 4 line 8, column 4 lines 48-58, column 7 lines 23-67, note determining net cost includes query overhead; note determining candidate column combinations and the benefit of processing queries using the pre-combined dataset and not using the pre-combined dataset);
determining first query overheads for executing the ith query request in a case that the table data of the jth table is stored according to merged basic column combinations (Mangas, abstract, figure 4, column 3 line 49 – column 4 line 8, column 4 lines 48-58, column 7 lines 23-67, note determining net cost includes query overhead; note determining candidate column combinations and the benefit of processing queries using the pre-combined dataset and not using the pre-combined dataset);
determining a first minimum query overhead among the first query overheads (Mangas, abstract, figure 4, column 3 line 49 – column 4 line 8, column 4 lines 48-58, column 6 lines 36-53, column 7 lines 23-67, note determining net cost includes query overhead; note determining minimum net cost);
determining a merged basic column combination corresponding to the first minimum query overhead as the candidate column combination of the jth table when the first minimum query overhead is less than or equal to the first reference overhead (Mangas, abstract, figure 4, column 3 line 49 – column 4 line 8, column 4 lines 48-58, column 6 lines 36-53, column 7 lines 23-67, note determining net cost includes query overhead; note determining candidate column combinations and the benefit of processing queries using the pre-combined dataset and not using the pre-combined dataset which comprises a comparison that includes the minimum overhead and reference overhead; note determining candidate combinations and determining which ones satisfy a merging condition, e.g., threshold); and
determining the at least two basic column combinations as candidate column combinations of the jth table when the first minimum query overhead is greater than the first reference overhead (Mangas, abstract, figure 4, column 3 line 49 – column 4 line 8, column 4 lines 48-58, column 6 lines 36-53, column 7 lines 23-67, note determining net cost includes query overhead; note determining candidate combinations and determining which ones satisfy a merging condition, e.g., threshold).
It would have been obvious to one of ordinary skill in the art before the effective date of filing to modify the cited references to incorporate the teachings of Li because all references are directed towards data management and information retrieval and because Li would expand upon the teachings of the previously cited references in information retrieval which would improve the efficiency by improving the storage of the column data (Li, column 1 lines 9-19, column 2 lines 11-24).
It would have been obvious to one of ordinary skill in the art before the effective date of filing to modify the cited references to incorporate the teachings of Mangas because all references are directed towards data management and information retrieval and because Mangas would expand upon the teachings of the previously cited references in information retrieval which would improve the efficiency by pre-combing data (Mangas, column 1 line 49 – column 2 line 2).
Regarding Claim 5:
Kara as modified shows the method as disclosed above;
Kara as modified further teaches:
wherein the determining candidate column combinations of tables related to the query requests based on the at least one piece of column information in the query requests, comprises: merging the at least two basic column combinations of the jth table to obtain at least one merged basic column combination of the jth table when N is greater than 1 and the basic column combination set of the jth table comprises the at least two basic column combinations (Kara, figure 2, [0047-0048], note determining columns to combine and combining them) (Li, figures 2A-2B, abstract, column 1 lines 23-54, column 2 lines 11-24, column 6 lines 1-39, column 12 lines 17-42, note merging columns) (Mangas, abstract, figure 4, column 3 line 49 – column 4 line 8, column 4 lines 48-58, column 7 lines 23-67, note merging columns);
merging the at least two basic column combinations of the another table to obtain at least one merged basic column combination of the another table when another table comprising at least two basic column combinations exists in N tables (Kara, figure 2, [0047-0048], note determining columns to combine and combining them) (Li, figures 2A-2B, abstract, column 1 lines 23-54, column 2 lines 11-24, column 6 lines 1-39, column 12 lines 17-42, note merging columns) (Mangas, abstract, figure 4, column 3 line 49 – column 4 line 8, column 4 lines 48-58, column 7 lines 23-67, note merging columns);
determining a second reference overhead for executing the ith query request in a case that table data of the N tables is stored according to corresponding basic column combinations (Mangas, abstract, figure 4, column 3 line 49 – column 4 line 8, column 4 lines 48-58, column 7 lines 23-67, note determining net cost includes query overhead; note determining candidate column combinations and the benefit of processing queries using the pre-combined dataset and not using the pre-combined dataset);
determining second query overheads for executing the ith query request in a case that table data of tables each comprising at least two basic column combinations in the N tables is stored according to corresponding merged basic column combinations, and table data of tables each comprising one basic column combination is stored according to basic column combinations (Mangas, abstract, figure 4, column 3 line 49 – column 4 line 8, column 4 lines 48-58, column 7 lines 23-67, note determining net cost includes query overhead; note determining candidate column combinations and the benefit of processing queries using the pre-combined dataset and not using the pre-combined dataset);
determining a second minimum query overhead among the second query overheads (Mangas, abstract, figure 4, column 3 line 49 – column 4 line 8, column 4 lines 48-58, column 6 lines 36-53, column 7 lines 23-67, note determining net cost includes query overhead; note determining minimum net cost);
determining column combinations of tables corresponding to the second minimum query overhead as the candidate column combinations of the tables when the second minimum query overhead is less than or equal to the second reference overhead (Mangas, abstract, figure 4, column 3 line 49 – column 4 line 8, column 4 lines 48-58, column 6 lines 36-53, column 7 lines 23-67, note determining net cost includes query overhead; note determining candidate column combinations and the benefit of processing queries using the pre-combined dataset and not using the pre-combined dataset which comprises a comparison that includes the minimum overhead and reference overhead, e.g., cost threshold; note determining candidate combinations and determining which ones satisfy a merging condition, e.g., threshold); and
determining basic column combinations of the tables as the candidate column combinations of the tables when the second minimum query overhead is greater than the second reference overhead, (Mangas, abstract, figure 4, column 3 line 49 – column 4 line 8, column 4 lines 48-58, column 6 lines 36-53, column 7 lines 23-67, note determining net cost includes query overhead; note determining candidate column combinations and the benefit of processing queries using the pre-combined dataset and not using the pre-combined dataset which comprises a comparison that includes the minimum overhead and reference overhead, e.g., cost threshold; note determining candidate combinations and determining which ones satisfy a merging condition, e.g., threshold).
It would have been obvious to one of ordinary skill in the art before the effective date of filing to modify the cited references to incorporate the teachings of Li because all references are directed towards data management and information retrieval and because Li would expand upon the teachings of the previously cited references in information retrieval which would improve the efficiency by improving the storage of the column data (Li, column 1 lines 9-19, column 2 lines 11-24).
It would have been obvious to one of ordinary skill in the art before the effective date of filing to modify the cited references to incorporate the teachings of Mangas because all references are directed towards data management and information retrieval and because Mangas would expand upon the teachings of the previously cited references in information retrieval which would improve the efficiency by pre-combing data (Mangas, column 1 line 49 – column 2 line 2).
Regarding Claim 8:
Kara as modified shows the method as disclosed above;
Kara as modified further teaches:
wherein the determining basic column combination sets of tables related to the ith query request based on column attribute information corresponding to column identifiers in an ith query request comprises: determining the first merged combination and the target selection column combination as basic column combinations of the jth table when the first merged combination and the target selection column combination do not satisfy the second merging condition (Mangas, abstract, figure 4, column 3 line 49 – column 4 line 8, column 4 lines 48-58, column 6 lines 36-53, column 7 lines 23-67, note determining net cost includes query overhead; note determining candidate column combinations and the benefit of processing queries using the pre-combined dataset and not using the pre-combined dataset; note determining candidate combinations and determining which ones satisfy a merging condition, e.g., threshold); and
adding the basic column combinations of the jth table to the basic column combination set of the jth table (Kara, figure 2, [0047-0048], note determining columns to combine and combining them) (Li, figures 2A-2B, abstract, column 1 lines 23-54, column 2 lines 11-24, column 6 lines 1-39, column 12 lines 17-42, note merging columns) (Mangas, abstract, figure 4, column 3 line 49 – column 4 line 8, column 4 lines 48-58, column 6 lines 36-53, column 7 lines 23-67, note determining net cost includes query overhead; note determining candidate column combinations and the benefit of processing queries using the pre-combined dataset and not using the pre-combined dataset; note determining candidate combinations and determining which ones satisfy a merging condition, e.g., threshold).
It would have been obvious to one of ordinary skill in the art before the effective date of filing to modify the cited references to incorporate the teachings of Li because all references are directed towards data management and information retrieval and because Li would expand upon the teachings of the previously cited references in information retrieval which would improve the efficiency by improving the storage of the column data (Li, column 1 lines 9-19, column 2 lines 11-24).
It would have been obvious to one of ordinary skill in the art before the effective date of filing to modify the cited references to incorporate the teachings of Mangas because all references are directed towards data management and information retrieval and because Mangas would expand upon the teachings of the previously cited references in information retrieval which would improve the efficiency by pre-combing data (Mangas, column 1 line 49 – column 2 line 2).
Regarding Claim 9:
Kara as modified shows the method as disclosed above;
Kara as modified further teaches:
wherein the determining basic column combination sets of tables related to the ith query request based on column attribute information corresponding to column identifiers in an ith query request, comprises: Merging the join predicate column combination and the target selection column combination to obtain a second merged combination when the local predicate column combination and the join predicate column combination do not satisfy the first merging condition, and the join predicate column combination and the target selection column combination satisfy the second merging condition (Mangas, abstract, figure 4, column 3 line 49 – column 4 line 8, column 4 lines 48-58, column 6 lines 36-53, column 7 lines 23-67, note determining net cost includes query overhead; note determining candidate column combinations and the benefit of processing queries using the pre-combined dataset and not using the pre-combined dataset; note determining candidate combinations and determining which ones satisfy a merging condition, e.g., threshold);
determining the local predicate column combination and the second merged combination as basic column combinations of the jth table (Kara, figure 2, [0047-0048], note determining columns to combine and combining them) (Li, figures 2A-2B, abstract, column 1 lines 23-54, column 2 lines 11-24, column 6 lines 1-39, column 12 lines 17-42, note merging columns) (Mangas, abstract, figure 4, column 3 line 49 – column 4 line 8, column 4 lines 48-58, column 6 lines 36-53, column 7 lines 23-67, note determining net cost includes query overhead; note determining candidate column combinations and the benefit of processing queries using the pre-combined dataset and not using the pre-combined dataset; note determining candidate combinations and determining which ones satisfy a merging condition, e.g., threshold); and
adding the basic column combinations of the jth table to the basic column combination set of the jth table (Kara, figure 2, [0047-0048], note determining columns to combine and combining them) (Li, figures 2A-2B, abstract, column 1 lines 23-54, column 2 lines 11-24, column 6 lines 1-39, column 12 lines 17-42, note merging columns) (Mangas, abstract, figure 4, column 3 line 49 – column 4 line 8, column 4 lines 48-58, column 6 lines 36-53, column 7 lines 23-67, note determining net cost includes query overhead; note determining candidate column combinations and the benefit of processing queries using the pre-combined dataset and not using the pre-combined dataset; note determining candidate combinations and determining which ones satisfy a merging condition, e.g., threshold).
It would have been obvious to one of ordinary skill in the art before the effective date of filing to modify the cited references to incorporate the teachings of Li because all references are directed towards data management and information retrieval and because Li would expand upon the teachings of the previously cited references in information retrieval which would improve the efficiency by improving the storage of the column data (Li, column 1 lines 9-19, column 2 lines 11-24).
It would have been obvious to one of ordinary skill in the art before the effective date of filing to modify the cited references to incorporate the teachings of Mangas because all references are directed towards data management and information retrieval and because Mangas would expand upon the teachings of the previously cited references in information retrieval which would improve the efficiency by pre-combing data (Mangas, column 1 line 49 – column 2 line 2).
Regarding Claim 10:
Kara as modified shows the method as disclosed above;
Kara as modified further teaches:
wherein the determining, basic column combination sets of tables related to the ith query request based on column attribute information corresponding to column identifiers in an ith query request comprises: determining the local predicate column combination, the join predicate column combination, and the target selection column combination as basic column combinations of the jth table when the local predicate column combination and the join predicate column combination do not satisfy the first merging condition, and the join predicate column combination and the target selection column combination do not satisfy the second merging condition (Mangas, abstract, figure 4, column 3 line 49 – column 4 line 8, column 4 lines 48-58, column 6 lines 36-53, column 7 lines 23-67, note determining net cost includes query overhead; note determining candidate column combinations and the benefit of processing queries using the pre-combined dataset and not using the pre-combined dataset; note determining candidate combinations and determining which ones satisfy a merging condition, e.g., threshold); and
adding the basic column combinations of the jth table to the basic column combination set of the jth table (Kara, figure 2, [0047-0048], note determining columns to combine and combining them) (Li, figures 2A-2B, abstract, column 1 lines 23-54, column 2 lines 11-24, column 6 lines 1-39, column 12 lines 17-42, note merging columns) (Mangas, abstract, figure 4, column 3 line 49 – column 4 line 8, column 4 lines 48-58, column 6 lines 36-53, column 7 lines 23-67, note determining net cost includes query overhead; note determining candidate column combinations and the benefit of processing queries using the pre-combined dataset and not using the pre-combined dataset; note determining candidate combinations and determining which ones satisfy a merging condition, e.g., threshold).
It would have been obvious to one of ordinary skill in the art before the effective date of filing to modify the cited references to incorporate the teachings of Li because all references are directed towards data management and information retrieval and because Li would expand upon the teachings of the previously cited references in information retrieval which would improve the efficiency by improving the storage of the column data (Li, column 1 lines 9-19, column 2 lines 11-24).
It would have been obvious to one of ordinary skill in the art before the effective date of filing to modify the cited references to incorporate the teachings of Mangas because all references are directed towards data management and information retrieval and because Mangas would expand upon the teachings of the previously cited references in information retrieval which would improve the efficiency by pre-combing data (Mangas, column 1 line 49 – column 2 line 2).
Regarding Claim 11:
Kara as modified shows the method as disclosed above;
Kara as modified further teaches:
wherein the determining target column combinations of corresponding tables based on a plurality of candidate column combinations belonging to the same table comprises: merging at least two candidate column combinations belonging to the same table to obtain at least one merged candidate column combination of the corresponding tables (Kara, figure 2, [0047-0048], note determining columns to combine, e.g., candidate column combinations) (Li, abstract, column 1 lines 23-54, column 2 lines 11-24, column 6 lines 18-39, note using the query column data to determine the optimal combination of columns for the queries) (Mangas, abstract, figure 4, column 3 line 49 – column 4 line 8, column 4 lines 48-58, column 7 lines 23-67, note determining candidate column combinations and the benefit of processing queries using the pre-combined dataset, e.g., the query overhead being minimum when column combinations belong to the same table; note determining candidate combinations and determining which ones satisfy a merging condition, e.g., threshold);
determining total query overheads for executing the plurality of query requests in a case that the table data of the tables is stored according to corresponding merged candidate column combinations (Mangas, abstract, figure 4, column 3 line 49 – column 4 line 8, column 4 lines 48-58, column 7 lines 23-67, note determining net cost includes query overhead; note determining candidate column combinations and the benefit of processing queries using the pre-combined dataset and not using the pre-combined dataset; note determining candidate combinations and determining which ones satisfy a merging condition, e.g., threshold);
determining a total reference overhead for executing the plurality of query requests in a case that the table data of the tables is stored according to corresponding candidate column combinations (Mangas, abstract, figure 4, column 3 line 49 – column 4 line 8, column 4 lines 48-58, column 7 lines 23-67, note determining net cost includes query overhead; note determining candidate column combinations and the benefit of processing queries using the pre-combined dataset and not using the pre-combined dataset; note determining candidate combinations and determining which ones satisfy a merging condition, e.g., threshold);
determining a minimum total query overhead among the total query overheads (Mangas, abstract, figure 4, column 3 line 49 – column 4 line 8, column 4 lines 48-58, column 7 lines 23-67, note determining net cost includes query overhead; note determining candidate column combinations and the benefit of processing queries using the pre-combined dataset and not using the pre-combined dataset; note determining candidate combinations and determining which ones satisfy a merging condition, e.g., threshold); and
determining merged candidate column combinations of tables corresponding to the minimum total query overhead as the target column combinations of the tables when the minimum total query overhead is less than or equal to the total reference overhead (Mangas, abstract, figure 4, column 3 line 49 – column 4 line 8, column 4 lines 48-58, column 7 lines 23-67, note determining net cost includes query overhead; note determining candidate column combinations and the benefit of processing queries using the pre-combined dataset and not using the pre-combined dataset; note determining candidate combinations and determining which ones satisfy a merging condition, e.g., threshold).
It would have been obvious to one of ordinary skill in the art before the effective date of filing to modify the cited references to incorporate the teachings of Li because all references are directed towards data management and information retrieval and because Li would expand upon the teachings of the previously cited references in information retrieval which would improve the efficiency by improving the storage of the column data (Li, column 1 lines 9-19, column 2 lines 11-24).
It would have been obvious to one of ordinary skill in the art before the effective date of filing to modify the cited references to incorporate the teachings of Mangas because all references are directed towards data management and information retrieval and because Mangas would expand upon the teachings of the previously cited references in information retrieval which would improve the efficiency by pre-combing data (Mangas, column 1 line 49 – column 2 line 2).
Regarding Claim 12:
Kara as modified shows the method as disclosed above;
Kara as modified further teaches:
wherein the determining total query overheads for executing the plurality of query requests in a case that the table data of the tables is stored according to corresponding merged candidate column combinations, comprises: determining third query overheads for executing the query requests in a case that the table data of the tables is stored according to the corresponding merged candidate column combinations (Mangas, abstract, figure 4, column 3 line 49 – column 4 line 8, column 4 lines 48-58, column 7 lines 23-67, note determining net cost includes query overhead; note determining candidate column combinations and the benefit of processing queries using the pre-combined dataset and not using the pre-combined dataset; note determining candidate combinations and determining which ones satisfy a merging condition, e.g., threshold);
acquiring the number of execution times of the query requests (Mangas, abstract, figure 4, column 3 line 49 – column 4 line 8, column 4 lines 19-58, column 7 lines 23-67, note determining query execution plans, which is interpreted as acquiring the number of execution times of the query requests); and
determining, the total query overheads for executing the plurality of query requests based on the number of execution times of the query requests and the third query overheads corresponding to the query requests (Mangas, abstract, figure 4, column 3 line 49 – column 4 line 8, column 4 lines 19-58, column 7 lines 23-67, note determining query execution plans, which is interpreted as acquiring the number of execution times of the query requests; note determining net cost includes query overhead; note determining candidate column combinations and the benefit of processing queries using the pre-combined dataset and not using the pre-combined dataset; note determining candidate combinations and determining which ones satisfy a merging condition, e.g., threshold).
It would have been obvious to one of ordinary skill in the art before the effective date of filing to modify the cited references to incorporate the teachings of Mangas because all references are directed towards data management and information retrieval and because Mangas would expand upon the teachings of the previously cited references in information retrieval which would improve the efficiency by pre-combing data (Mangas, column 1 line 49 – column 2 line 2).
Claim 17 discloses substantially the same limitations as claim 1 respectively, except claim 17 is directed to a device comprising memory and a processor (Kara, figure 1, note memory and processor) while claim 1 is directed to a method. Therefore claim 17 is rejected under the same rationale set forth for claim 1.
Claim 18 discloses substantially the same limitations as claim 2 respectively, except claim 18 is directed to a device comprising memory and a processor (Kara, figure 1, note memory and processor) while claim 2 is directed to a method. Therefore claim 18 is rejected under the same rationale set forth for claim 2.
Claim 19 discloses substantially the same limitations as claim 3 respectively, except claim 19 is directed to a device comprising memory and a processor (Kara, figure 1, note memory and processor) while claim 3 is directed to a method. Therefore claim 19 is rejected under the same rationale set forth for claim 3.
Claim 20 discloses substantially the same limitations as claim 1 respectively, except claim 20 is directed to a non-transitory computer-readable storage medium comprising memory and a processor (Kara, figure 1, note memory and processor) while claim 1 is directed to a method. Therefore claim 20 is rejected under the same rationale set forth for claim 1.
Claim Rejections - 35 USC § 103
Claim(s) 6 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kara in view of Li, Mangas, and Zilio et al. (US2023/0082446), hereinafter Zilio.
Regarding Claim 6:
Kara as modified shows the method as disclosed above;
Kara as modified further teaches:
wherein the determining first query overheads for executing the ith query request in a case that the table data of the jth table is stored according to merged basic column combinations, comprises: Determining corresponding path overheads for executing the ith query request according to a plurality of different access paths, wherein k = 1, ..., P, and P is a total number of merged basic column combinations of the jth table in a case that the table data of the jth table is stored according to a kth merged basic column combination (Mangas, abstract, figure 4, column 3 line 49 – column 4 line 8, column 4 lines 48-58, column 7 lines 23-67, note determining net cost includes query overhead; note determining candidate column combinations and the benefit of processing queries using the pre-combined dataset and not using the pre-combined dataset); and
determining a minimum path overhead in the path overheads corresponding to the merged basic column combination as the first query overhead for executing the ith query request when the table data of the jth table is stored according to the kth merged basic column combination (Mangas, abstract, figure 4, column 3 line 49 – column 4 line 8, column 4 lines 48-58, column 7 lines 23-67, note determining net cost includes query overhead; note determining candidate column combinations and the benefit of processing queries using the pre-combined dataset and not using the pre-combined dataset; note determining if thresholds are satisfied. When combined with Zilio below the net cost teachings of Mangas would include the path overhead teachings of Zilio).
It would have been obvious to one of ordinary skill in the art before the effective date of filing to modify the cited references to incorporate the teachings of Mangas because all references are directed towards data management and information retrieval and because Mangas would expand upon the teachings of the previously cited references in information retrieval which would improve the efficiency by pre-combing data (Mangas, column 1 line 49 – column 2 line 2).
While Kara as modified teaches combining columns and determining overheads, Kara as modified doesn’t specifically state determining path overheads as part of the determination. However, Zilio is in the same field of endeavor, data management and information retrieval, and Zilio teaches:
Determining corresponding path overheads for executing the ith query request according to a plurality of different access paths, wherein k = 1, ..., P, and P is a total number of merged basic column combinations of the jth table in a case that the table data of the jth table is stored according to a kth merged basic column combination (Zilio, abstract, figure 2, [0027, 0032-0033, 0051-0054], note evaluation multiple access paths. When combined with the other references this would be for the column combinations as taught by Kara, Li, and Mangas);
determining a minimum path overhead in the path overheads corresponding to the merged basic column combination as the first query overhead for executing the ith query request when the table data of the jth table is stored according to the kth merged basic column combination (Zilio, abstract, figure 2, [0027, 0032-0033, 0051-0054], note evaluation multiple access paths. When combined with the other references this would be for the column combinations as taught by Kara, Li, and Mangas).
It would have been obvious to one of ordinary skill in the art before the effective date of filing to modify the cited references to incorporate the teachings of Zilio because all references are directed towards data management and information retrieval and because Zilio would expand upon the teachings of the previously cited references in information retrieval which would improve the efficiency by utilizing the most efficient path.
Claim Rejections - 35 USC § 103
Claim(s) 7 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kara in view of Li, Mangas, and Williams et al. (US2019/0179941), hereinafter Williams.
Regarding Claim 7:
Kara as modified shows the method as disclosed above;
Kara as modified further teaches:
wherein the determining basic column combination sets of tables related to the ith query request based on column attribute information corresponding to column identifiers in an ith query request, comprises: a column whose column attribute information is the local predicate column attribute in the jth table to a local predicate column combination corresponding to the jth table (Kara, [0047-0049], note the use of SQL queries, note determining local predicate columns and join operations for selected target columns) (Li, abstract, figures 2A-2B, column 1 lines 23-54, column 2 lines 11-24, column 6 lines 1-39, note column attribute information comprises local and target columns) (Mangas, figure 4, column 2 lines 56-63; column 7 lines 12-67, note the use of SQL expressions; note column combinations may be identified based on join clauses or other expression elements which cause datasets to be combined, e.g., local predicates, join predicates, and target selection attributes);
a column whose column attribute information is the join predicate column attribute in the jth table to a join predicate column combination corresponding to the jth table (Kara, [0047-0049], note the use of SQL queries, note determining local predicate columns and join operations for selected target columns) (Li, abstract, figures 2A-2B, column 1 lines 23-54, column 2 lines 11-24, column 6 lines 1-39, note column attribute information comprises local and target columns) (Mangas, figure 4, column 2 lines 56-63; column 7 lines 12-67, note the use of SQL expressions; note column combinations may be identified based on join clauses or other expression elements which cause datasets to be combined, e.g., local predicates, join predicates, and target selection attributes);
a column whose column attribute information is the target selection column attribute in the jth table to a target selection column combination corresponding to the jth table (Kara, [0047-0049], note the use of SQL queries, note determining local predicate columns and join operations for selected target columns) (Li, abstract, figures 2A-2B, column 1 lines 23-54, column 2 lines 11-24, column 6 lines 1-39, note column attribute information comprises local and target columns) (Mangas, figure 4, column 2 lines 56-63; column 7 lines 12-67, note the use of SQL expressions; note column combinations may be identified based on join clauses or other expression elements which cause datasets to be combined, e.g., local predicates, join predicates, and target selection attributes);
merging the local predicate column combination and the join predicate column combination to obtain a first merged combination when the local predicate column combination and the join predicate column combination satisfy a first merging condition (Mangas, abstract, figure 4, column 3 line 49 – column 4 line 8, column 4 lines 48-58, column 6 lines 36-53, column 7 lines 23-67, note determining net cost of column combinations; note determining candidate column combinations and the benefit of processing queries using the pre-combined dataset and not using the pre-combined dataset; note determining candidate combinations and determining which ones satisfy a merging condition, e.g., threshold; note merging column combinations if thresholds are satisfied, e.g., merging condition);
merging the first merged combination and the target selection column combination to obtain a basic column combination of the jth table when the first merged combination and the target selection column combination satisfy a second merging condition (Mangas, abstract, figure 4, column 3 line 49 – column 4 line 8, column 4 lines 48-58, column 6 lines 36-53, column 7 lines 23-67, note determining net cost of column combinations; note determining candidate column combinations and the benefit of processing queries using the pre-combined dataset and not using the pre-combined dataset; note determining candidate combinations and determining which ones satisfy a merging condition, e.g., threshold; note merging column combinations if thresholds are satisfied, e.g., merging condition); and
adding the basic column combination of the jth table to the basic column combination set of the jth table (Mangas, abstract, figure 4, column 3 line 49 – column 4 line 8, column 4 lines 48-58, column 6 lines 36-53, column 7 lines 23-67, note determining net cost of column combinations; note determining candidate column combinations and the benefit of processing queries using the pre-combined dataset and not using the pre-combined dataset; note merging column combinations if thresholds are satisfied, e.g., merging condition).
It would have been obvious to one of ordinary skill in the art before the effective date of filing to modify the cited references to incorporate the teachings of Li because all references are directed towards data management and information retrieval and because Li would expand upon the teachings of the previously cited references in information retrieval which would improve the efficiency by improving the storage of the column data (Li, column 1 lines 9-19, column 2 lines 11-24).
It would have been obvious to one of ordinary skill in the art before the effective date of filing to modify the cited references to incorporate the teachings of Mangas because all references are directed towards data management and information retrieval and because Mangas would expand upon the teachings of the previously cited references in information retrieval which would improve the efficiency by pre-combing data (Mangas, column 1 line 49 – column 2 line 2).
While Kara as modified teaches merging column combinations that satisfy a merging condition, Kara as modified doesn’t specifically teach labeling the combination. However, Williams is in the same field of endeavor, data management and information retrieval, and Williams teaches:
adding a column identifier of a column whose column attribute information is the local predicate column attribute in the jth table to a local predicate column combination corresponding to the jth table (Williams, [0037], note adding an identifier, e.g., label, to each column combination, e.g., materialized view. When combined with the previously cited references this would be for the column combinations as taught by Kara, Li, and Mangas)
adding a column identifier of a column whose column attribute information is the join predicate column attribute in the jth table to a join predicate column combination corresponding to the jth table (Williams, [0037], note adding an identifier, e.g., label, to each column combination, e.g., materialized view. When combined with the previously cited references this would be for the column combinations as taught by Kara, Li, and Mangas);
adding a column identifier of a column whose column attribute information is the target selection column attribute in the jth table to a target selection column combination corresponding to the jth table (Williams, [0037], note adding an identifier, e.g., label, to each column combination, e.g., materialized view. When combined with the previously cited references this would be for the column combinations as taught by Kara, Li, and Mangas).
It would have been obvious to one of ordinary skill in the art before the effective date of filing to modify the cited references to incorporate the teachings of Williams because all references are directed towards data management and information retrieval and because Williams would expand upon the teachings of the previously cited references in information retrieval which would improve the efficiency and processing by adding labels/identifiers to the combinations.
Claim Rejections - 35 USC § 103
Claim(s) 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kara in view of Li, Mangas, Li et al. (US2023/0267121), hereinafter Li 2, and Li et al. (US2023/0073666), hereinafter Li 3.
Regarding Claim 13:
Kara as modified shows the method as disclosed above;
Kara as modified further teaches:
wherein the storing the table data of the tables according to the target column combinations comprises: Merging the at least two sparse target column combinations to obtain at least one merged sparse target column combination when there are at least two sparse target column combinations in target column combinations of a Sth table, wherein s = 1, 2, ..., Q, and Q is a total number of tables that need to be optimized (Kara, figure 2, [0047-0048], note determining columns to combine and combining them. When combined with the other cited references this would be for the sparse columns as taught by Li 2) (Li, figures 2A-2B, abstract, column 1 lines 23-54, column 2 lines 11-24, column 6 lines 1-39, column 12 lines 17-42, note merging columns. When combined with the other cited references this would be for the sparse columns as taught by Li 2) (Mangas, abstract, figure 4, column 3 line 49 – column 4 line 8, column 4 lines 48-58, column 7 lines 23-67, note merging columns. When combined with the other cited references this would be for the sparse columns as taught by Li 2);
determining new metadata information corresponding to sparse target column identifiers in the at least one merged sparse target column combination, the new metadata information comprising a storage location of a storage file corresponding to column data (Li, column 9 lines 55-67, column 12 lines 17-42, note metadata comprises pointers to the location of columns, e.g., storage location. When combined with the other cited references this would be for the sparse columns as taught by Li 2) (Mangas, figure 2, column 4 lines 14-33, column 4 line 48 - column 5 line 5, note collecting metadata corresponding to the column identifiers and combinations. When combined with the other cited references this would be for the sparse columns as taught by Li 2)
storing column data corresponding to the sparse target column identifiers into a corresponding storage file based on metadata information corresponding to the sparse target column identifiers (Kara, figure 2, [0047-0048], note storing combined column combinations. When combined with the other cited references this would be for the sparse columns as taught by Li 2) (Li, figures 2A-2B, abstract, column 1 lines 23-54, column 2 lines 11-24, column 12 lines 17-42, note storing the table according to optimal combination. When combined with the other cited references this would be for the sparse columns as taught by Li 2) (Mangas, abstract, figures 4 and 7, column 3 line 49 – column 4 line 8, column 4 lines 48-58, column 6 lines 36-53, column 7 lines 23-67, note pre-combining columns. When combined with the other cited references this would be for the sparse columns as taught by Li 2);
updating the metadata information corresponding to the sparse target column identifiers in a system table to the new metadata information corresponding to the sparse target column identifiers (Li, column 6 lines 18-39, column 9 lines 55-67, column 12 lines 17-42, note metadata comprises pointers to the location of columns, e.g., storage location; note updating the data. When combined with the other cited references this would be for the sparse columns as taught by Li 2) (Mangas, figure 2, column 4 lines 14-33, column 4 line 48 - column 5 line 5, column 8 lines 18-23; note collecting metadata corresponding to the column identifiers and combinations; note updating data. When combined with the other cited references this would be for the sparse columns as taught by Li 2);
determining new metadata information corresponding to other target column identifiers in the another target column combination when the sth table further comprises another target column combination except the at least two sparse target column combinations (Li, column 6 lines 18-39, column 9 lines 55-67, column 12 lines 17-42, note metadata comprises pointers to the location of columns, e.g., storage location; note updating the data. When combined with the other cited references this would be for the sparse columns as taught by Li 2) (Mangas, figure 2, column 4 lines 14-33, column 4 line 48 - column 5 line 5, column 8 lines 18-23; note collecting metadata corresponding to the column identifiers and combinations; note updating data. When combined with the other cited references this would be for the sparse columns as taught by Li 2);
storing column data corresponding to the other target column identifiers into the corresponding storage file based on the new metadata information corresponding to the other target column identifiers in the another target column combination (Li, column 6 lines 18-39, column 9 lines 55-67, column 12 lines 17-42, note metadata comprises pointers to the location of columns, e.g., storage location; note updating the data. When combined with the other cited references this would be for the sparse columns as taught by Li 2) (Mangas, figure 2, column 4 lines 14-33, column 4 line 48 - column 5 line 5, column 8 lines 18-23; note collecting metadata corresponding to the column identifiers and combinations; note updating data. When combined with the other cited references this would be for the sparse columns as taught by Li 2); and
updating metadata information corresponding to the other target column identifiers in the system table to the new metadata information corresponding to the other target column identifiers (Li, column 6 lines 18-39, column 9 lines 55-67, column 12 lines 17-42, note metadata comprises pointers to the location of columns, e.g., storage location; note updating the data. When combined with the other cited references this would be for the sparse columns as taught by Li 2) (Mangas, figure 2, column 4 lines 14-33, column 4 line 48 - column 5 line 5, column 8 lines 18-23; note collecting metadata corresponding to the column identifiers and combinations; note updating data. When combined with the other cited references this would be for the sparse columns as taught by Li 2).
It would have been obvious to one of ordinary skill in the art before the effective date of filing to modify the cited references to incorporate the teachings of Li because all references are directed towards data management and information retrieval and because Li would expand upon the teachings of the previously cited references in information retrieval which would improve the efficiency by improving the storage of the column data (Li, column 1 lines 9-19, column 2 lines 11-24).
It would have been obvious to one of ordinary skill in the art before the effective date of filing to modify the cited references to incorporate the teachings of Mangas because all references are directed towards data management and information retrieval and because Mangas would expand upon the teachings of the previously cited references in information retrieval which would improve the efficiency by pre-combing data (Mangas, column 1 line 49 – column 2 line 2).
While Kara as modified teaches merging column combinations, Kara as modified doesn’t specifically sparse columns. However, Li 2 is in the same field of endeavor, data management and information retrieval, and Li 2 teaches:
Merging the at least two sparse target column combinations to obtain at least one merged sparse target column combination when there are at least two sparse target column combinations in target column combinations of a Sth table, wherein s = 1, 2, ..., Q, and Q is a total number of tables that need to be optimized (Li 2, [0015], note merging sparse columns. When combined with the other cited references this would be for the column combinations as taught by Kara, Li, and Mangas).
It would have been obvious to one of ordinary skill in the art before the effective date of filing to modify the cited references to incorporate the teachings of Li 2 because all references are directed towards data management and information retrieval and because Li 2 would expand upon the teachings of the previously cited references in information retrieval which would improve the performance (Li 2, [0015]).
While Kara as modified teaches merging column combinations, Kara as modified doesn’t specifically teach metadata comprising file identifier and format. However, Li 3 is in the same field of endeavor, data management and information retrieval, and Li 3 teaches:
determining new metadata information corresponding to target column identifiers in the at least one merged target column combination, the new metadata information comprising a file identifier, a file format, and a storage location of a storage file corresponding to column data (Li 3, [0060-0061], note metadata comprises file name and format. When combined with the other cited references this would be for the column identifiers and combinations as taught by Kara, Li, and Mangas).
It would have been obvious to one of ordinary skill in the art before the effective date of filing to modify the cited references to incorporate the teachings of Li 3 because all references are directed towards data management and information retrieval and because Li 3 would expand upon the teachings of the previously cited references in information retrieval which would improve the efficiency and response speed (Li 3 [0005]).
Claim Rejections - 35 USC § 103
Claim(s) 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kara in view of Li, Mangas, and Li 2.
Regarding Claim 14:
Kara as modified shows the method as disclosed above;
Kara as modified further teaches:
wherein the storing the table data of the tables according to the target column combinations comprises: determining new metadata information corresponding to target column identifiers in the target column combinations when the target column combinations of the sth table do not comprise the at least two target column combinations (Li, column 6 lines 18-39, column 9 lines 55-67, column 12 lines 17-42, note metadata comprises pointers to the location of columns, e.g., storage location; note updating the data) (Mangas, figures 2 and 4, abstract, column 3 line 49 - column 4 line 33, column 4 line 48 - column 5 line 5, column 6 lines 36-53, column 7 lines 23-67, column 8 lines 18-23; note collecting metadata corresponding to the column identifiers and combinations; note updating data; note determining net cost includes query overhead; note determining candidate column combinations and the benefit of processing queries using the pre-combined dataset and not using the pre-combined dataset; note determining candidate combinations and determining which ones satisfy a merging condition, e.g., threshold);
storing column data corresponding to the target column identifiers into the corresponding storage file based on metadata information corresponding to the target column identifiers (Li, column 6 lines 18-39, column 9 lines 55-67, column 12 lines 17-42, note metadata comprises pointers to the location of columns, e.g., storage location; note updating the data) (Mangas, figures 2 and 4, abstract, column 3 line 49 - column 4 line 33, column 4 line 48 - column 5 line 5, column 6 lines 36-53, column 7 lines 23-67, column 8 lines 18-23; note collecting metadata corresponding to the column identifiers and combinations; note updating data; note determining net cost includes query overhead; note determining candidate column combinations and the benefit of processing queries using the pre-combined dataset and not using the pre-combined dataset; note determining candidate combinations and determining which ones satisfy a merging condition, e.g., threshold); and
updating the metadata information corresponding to the target column identifiers in the system table to the new metadata information corresponding to the target column identifiers (Li, column 6 lines 18-39, column 9 lines 55-67, column 12 lines 17-42, note metadata comprises pointers to the location of columns, e.g., storage location; note updating the data) (Mangas, figure 2, column 4 lines 14-33, column 4 line 48 - column 5 line 5, column 8 lines 18-23; note collecting metadata corresponding to the column identifiers and combinations; note updating data).
It would have been obvious to one of ordinary skill in the art before the effective date of filing to modify the cited references to incorporate the teachings of Li because all references are directed towards data management and information retrieval and because Li would expand upon the teachings of the previously cited references in information retrieval which would improve the efficiency by improving the storage of the column data (Li, column 1 lines 9-19, column 2 lines 11-24).
It would have been obvious to one of ordinary skill in the art before the effective date of filing to modify the cited references to incorporate the teachings of Mangas because all references are directed towards data management and information retrieval and because Mangas would expand upon the teachings of the previously cited references in information retrieval which would improve the efficiency by pre-combing data (Mangas, column 1 line 49 – column 2 line 2).
While Kara as modified teaches merging column combinations, Kara as modified doesn’t specifically sparse columns. However, Li 2 is in the same field of endeavor, data management and information retrieval, and Li 2 teaches:
sparse column combinations (Li 2, [0015], note merging sparse columns. When combined with the other cited references this would be for the column combinations as taught by Kara, Li, and Mangas).
It would have been obvious to one of ordinary skill in the art before the effective date of filing to modify the cited references to incorporate the teachings of Li 2 because all references are directed towards data management and information retrieval and because Li 2 would expand upon the teachings of the previously cited references in information retrieval which would improve the performance (Li 2, [0015]).
Claim Rejections - 35 USC § 103
Claim(s) 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kara in view of Li, Mangas, Rais-Ghasem et al. (US2015/0339369), hereinafter Rais-Ghasem, and Wilton (US11514184).
Regarding Claim 15:
Kara as modified shows the method as disclosed above;
Kara as modified further teaches:
wherein the method further comprises: acquiring column identifiers in the target column combination (Kara, figure 2, [0047-0048], note receiving inputting a query; note the user may input multiple queries; note parsing the query for column information) (Li, abstract, column 2 lines 11-24, column 6 lines 18-39, note acquiring a plurality of queries; note collecting data regarding the columns contained in the query, e.g., parsing the query to obtain column information in the query request) (Mangas, abstract, column 3 line 49 – column 4 line 8, column 4 lines 48-58, note determining column information in the queries),
determining the target column combination as the sparse target column combination when columns corresponding to the column identifiers in the target column combination are sparse columns (Mangas, abstract, figure 4, column 3 line 49 – column 4 line 8, column 4 lines 48-58, column 6 lines 36-53, column 7 lines 23-67, note determining candidate column combinations and the benefit of processing queries using the pre-combined dataset and not using the pre-combined dataset; note determining candidate combinations and determining which ones satisfy a merging condition, e.g., threshold. When combined with the other cited references this would be for the sparse columns as taught by Rais-Ghasem and Wilton).
It would have been obvious to one of ordinary skill in the art before the effective date of filing to modify the cited references to incorporate the teachings of Li because all references are directed towards data management and information retrieval and because Li would expand upon the teachings of the previously cited references in information retrieval which would improve the efficiency by improving the storage of the column data (Li, column 1 lines 9-19, column 2 lines 11-24).
It would have been obvious to one of ordinary skill in the art before the effective date of filing to modify the cited references to incorporate the teachings of Mangas because all references are directed towards data management and information retrieval and because Mangas would expand upon the teachings of the previously cited references in information retrieval which would improve the efficiency by pre-combing data (Mangas, column 1 line 49 – column 2 line 2).
While Kara as modified teaches merging column combinations, Kara as modified doesn’t specifically sparse columns and sparsity values. However, Rais-Ghasem is in the same field of endeavor, data management and information retrieval, and Rais-Ghasem teaches:
acquiring the number of pieces of null data and the number of pieces of total data in column data corresponding to the column identifiers (Rais-Ghasem, [0045], note sparsity of data entries in columns is the ratio of nun-null to null values. When combined with the previously cited references this would be for the columns as taught by Kara, Li, and Mangas);
determining ratios of the number of pieces of null data to the number of pieces of total data corresponding to the column identifiers as sparsity values corresponding to the column identifiers (Rais-Ghasem, [0045], note sparsity of data entries in columns is the ratio of nun-null to null values. When combined with the previously cited references this would be for the columns as taught by Kara, Li, and Mangas);
It would have been obvious to one of ordinary skill in the art before the effective date of filing to modify the cited references to incorporate the teachings of Rais-Ghasem because all references are directed towards data management and information retrieval and because Rais-Ghasem would expand upon the teachings of the previously cited references in information retrieval which would improve the performance by utilizing optimizations based on identified classifications.
While Kara as modified teaches merging column combinations, Kara as modified doesn’t specifically teach a sparsity threshold. However, Wilton is in the same field of endeavor, data management and information retrieval, and Wilton teaches:
determining a column corresponding to a column identifier whose sparsity value is greater than a sparsity threshold as a sparse column (Wilton, column 8 lines 12-26, note sparsity threshold to determine if the table, e.g., column, is sparse).
It would have been obvious to one of ordinary skill in the art before the effective date of filing to modify the cited references to incorporate the teachings of Wilton because all references are directed towards data management and information retrieval and because Wilton would expand upon the teachings of the previously cited references in information retrieval which would improve the performance by utilizing identify optimizations (Wilton, column 2 lines 25-38).
Claim Rejections - 35 USC § 103
Claim(s) 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kara in view of Li, Mangas, and Baruch et al. (US11080242).
Regarding Claim 16:
Kara as modified shows the method as disclosed above;
Kara as modified further teaches:
storing the data update information into an update log when data update information is received in a data storage process (Li, column 6 lines 18-39, column 9 lines 55-67, column 12 lines 17-42, note metadata comprises pointers to the location of columns, e.g., storage location; note updating the data) (Mangas, figure 2, column 4 lines 14-33, column 4 line 48 - column 5 line 5, column 8 lines 18-23; note collecting metadata corresponding to the column identifiers and combinations; note updating data);
updating the table data based on the data update information in the update log to obtain updated table data after data storage is completed (Li, column 6 lines 18-39, column 9 lines 55-67, column 12 lines 17-42, note metadata comprises pointers to the location of columns, e.g., storage location; note updating the data) (Mangas, figure 2, column 4 lines 14-33, column 4 line 48 - column 5 line 5, column 8 lines 18-23; note collecting metadata corresponding to the column identifiers and combinations; note updating data);
It would have been obvious to one of ordinary skill in the art before the effective date of filing to modify the cited references to incorporate the teachings of Li because all references are directed towards data management and information retrieval and because Li would expand upon the teachings of the previously cited references in information retrieval which would improve the efficiency by improving the storage of the column data (Li, column 1 lines 9-19, column 2 lines 11-24).
It would have been obvious to one of ordinary skill in the art before the effective date of filing to modify the cited references to incorporate the teachings of Mangas because all references are directed towards data management and information retrieval and because Mangas would expand upon the teachings of the previously cited references in information retrieval which would improve the efficiency by pre-combing data (Mangas, column 1 line 49 – column 2 line 2).
While Kara as modified teaches merging column combinations, Kara as modified doesn’t specifically teach deleting the data update information from the update log. However, Baruch is in the same field of endeavor, data management and information retrieval, and Baruch teaches:
deleting the data update information from the update log (Baruch, column 12 lines 21-32, note removing entries from the log. When combined with the previously cited references this would be for the transactions as taught by Kara, Li, and Mangas).
It would have been obvious to one of ordinary skill in the art before the effective date of filing to modify the cited references to incorporate the teachings of Baruch because all references are directed towards data management and information retrieval and because Baruch would expand upon the teachings of the previously cited references in data management which would improve the efficiency by improving the storage of old data.
Conclusion
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/JOHN J MORRIS/Examiner, Art Unit 2151 8/21/2026
/James Trujillo/Supervisory Patent Examiner, Art Unit 2151
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