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 .
This action is in response to the commination filed on July 28, 2025.
Response to Amendment
Applicant’s amendment filed on July 28, 2025, with respect to claims 1-20 has been received, entered into the record and considered.
As a result of the amendment claim 7-8, 13 and 15 have been amended, claims 2 and 20 have been cancelled and claim 22 have been newly added.
Claims 1, 3-19 and 21-22 remain pending in this office action.
Claim Objections
As a result of the amendment to the claims, examiner withdrawn the pending objection to the claims.
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.
Claims 1, 3-19 and 21-21 are rejected under 35 U.S.C. 103 as being unpatentable over Mangas et al (US 11,334,588 B1), in view of Ypma et al (US 10539882 B2).
As per claim 1, Mangas discloses:
- a method of retrieving data (a method of querying (i.e., retrieving data) a collection of data (i.e., data store) comprising plurality of datasets, claim 4, line 1-4),
- receiving, by a hardware computer system, a query, wherein the query comprises semantic information and requests data associated with the semantic information from the at least one data store (receiving a query including performance information (i.e., semantic information), column 4, line 59-67, Fig. 1, item 124), by a hardware processor, Fig. 8, item 810, 816),
- determining, by the hardware computer system, from the semantic information whether the query can be serviced using data selected from and/or derived from one or more candidate data sets of the plurality of data sets (determining which dataset are the candidate dataset to combine and answer the query, column 3, line 49-67, column 4, line 14-30, Fig. 1, item 104-108),
- if multiple candidate data sets of the plurality of data sets can service the query, using a cost function to determine at least one candidate data set of the multiple candidate data sets to service the query, and determine a portion of each of the at least one candidate data set to service the query (determine dataset by estimating cost and estimating benefit to answer the query, column 3, line 55-67, column 5, line 30-45, Fig. 1, item 104-108, Fig. 4, item 400-412),
- and returning, by the hardware computer system, a response to the query, the response comprising data obtained using the portion of each of the at least one candidate data set (returning result of the query comprising portion of data from the dataset, Fig. 1, item 126, Column 2, line 29-35),
Mangas does not explicitly disclose data associated with a semiconductor manufacturing process; wherein the semantic information comprises at least one performance indicator of the semiconductor manufacturing process; and using the obtained data in taking a corrective action or diagnosing an out of range situation of the semiconductor manufacturing process. However, in the same filed of endeavor Ypma in an analogous art disclose data associated with a semiconductor manufacturing process (semiconductor manufacturing data, column 6, line 25-35), wherein the semantic information comprises at least one performance indicator of the semiconductor manufacturing process (performance parameter (i.e., performance indicator) of manufacturing process, column 10, line 30-45), and using the obtained data in taking a corrective action or diagnosing an out of range situation of the semiconductor manufacturing process (diagnostic information from target range (i.e., diagnosing an out of range situation), column16, line 20-35, column 1, line 20-30). Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the processing of performance data associated with a semiconductor manufacturing process taught by Ypma as the means to querying and retrieving semantic data from a plurality of datasets using a cost function, (Ypma, column 6, line 25-35, Mangas, column 4, line 59-67). Mangas and Ypma are analogous prior art since they both deal with data analysis and extraction of data from various dataset in a datastore. A person of the ordinary skill in the art would have been motivated to make aforementioned modification to improve performance efficiency. This is because one aspect of Mangas invention is to evaluating the efficiency of a query plan and selecting one plan for execution as described at least in column, 2, line 20-25. Such query plan execution is related to semiconductor manufacturing process data. However, Mangas doesn’t specify any particular manner in dataset in datastore are semiconductor manufacturing process data. This would have lead one of the ordinary skill in the art to seek and recognize the processing semiconductor manufacturing data as taught by Ypma. Ypma describes how their lithographic semiconductor manufacturing process used to maintain accurate performance of the patterning operations in the lithographic cluster as described at least in column 10, line 45-55, as desired by Mangas.
As per claim 3, rejection of claim 1 is incorporated, and further Mangas discloses:
- wherein the said determining comprises determining that the multiple candidate data sets each comprise a column corresponding to at least one performance indicator of the said at least one performance indicator (dataset with performance optimization (i.e., performance indication), column 3, line 30-50).
As per claim 4, rejection of claim 1 is incorporated, and further Mangas discloses:
- wherein the semantic information comprises a function (calculation engine (i.e., function), perform various analysis, column 3, line 35-45, column 4, line 9-15).
As per claim 5, rejection of claim 1 is incorporated, and further Ypma discloses:
- wherein the semantic information comprises at least one piece of context information (lithographic data with context information, column12, line 25-45, column 15, line 15-35).
As per claim 6, rejection of claim 5 is incorporated, and further Ypma discloses:
- wherein the at least one piece of context information comprises a granularity level of the at least one performance indicator, and the determining comprises determining that each of the multiple candidate data sets each comprise a column corresponding to the at least one said performance indicator at the granularity level or a column from which a column corresponding to the at least one performance indicator at the granularity level can be derived (context data with wafer object data (i.e., granularity data), column 4, line 30-35, column 13, line 5-15), comprising performance indicator, (column 12, line 5-20, column 17, line 15-35), examiner broadest reasonable interpretation: according to para [0078] of applicant’s specification per wafer data is the granularity data.
As per clam 7, rejection of claim 5 is incorporated, and further Ypma discloses:
- wherein the at least one piece of context information comprises one or more selected from: a time window, at least one identifier of one or more physical machines, an identifier of a physical object, a job identifier of a job which occurred, and/or a measurement location on a physical object (measurement of overlay, critical dimension, wafer quality, column 4, line 30-35).
As per claim 8, rejection of claim 1 is incorporated and further Mangas disclose:
- determining comprises determining that each of the multiple candidate data sets each comprise: a column corresponding to the specification; or a column from which a column corresponding to the said specification can be derived (dataset with columns with semantic meaning (i.e., column corresponding to the specification), column 3, line 15-30),
Manga does not explicitly disclose wherein the semantic information comprises a performance indicator column specification, the specification comprising: (i) a performance indicator type; (ii) a function; and (iii) and a granularity level. However, in the same field of endeavor Ypma in an analogous art disclose wherein the semantic information comprises a performance indicator column specification, the specification comprising: (i) a performance indicator type; (ii) a function; and (iii) and a granularity (comprising performance indicator, (column 12, line 5-20, column 17, line 15-35), type of data, column 12, line 50-60, statistical calculation (i.e., a function), (context data with wafer object data (i.e., granularity data), column 4, line 30-35, column 13, line 5-15).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the processing of performance data associated with a semiconductor manufacturing process taught by Ypma as the means to querying and retrieving semantic data from a plurality of datasets using a cost function, (Ypma, column 6, line 25-35, Mangas, column 4, line 59-67). Mangas and Ypma are analogous prior art since they both deal with data analysis and extraction of data from various dataset in a datastore. A person of the ordinary skill in the art would have been motivated to make aforementioned modification to improve performance efficiency. This is because one aspect of Mangas invention is to evaluating the efficiency of a query plan and selecting one plan for execution as described at least in column, 2, line 20-25. Such query plan execution is related to semiconductor manufacturing process data. However, Mangas doesn’t specify any particular manner in dataset in datastore are semiconductor manufacturing process data. This would have lead one of the ordinary skill in the art to seek and recognize the processing semiconductor manufacturing data as taught by Ypma. Ypma describes how their lithographic semiconductor manufacturing process used to maintain accurate performance of the patterning operations in the lithographic cluster as described at least in column 10, line 45-55, as desired by Mangas.
As per claim 9, rejection of claim 1 is incorporated, and further Mangas discloses:
- wherein the determining from the semantic information that the query can be serviced using data selected from multiple candidate data sets of the plurality of data sets comprises (servicing query using data from various datasets, column 2, line 15-20, 30-35, column 4, line 1-15),
- determining that the semantic information comprises a function (calculation engine (i.e., function), perform various analysis, column 3, line 35-45, column 4, line 9-15),
- determining what input data to the function is being specified by the semantic information (determine which data set (i.e., what input) specified, column 3, line 55-65, column 4, line 15-20),
- determining that the query can be serviced based on identifying a candidate data set comprising a column associated with data output by the function when supplied with the input data (determining which dataset are the candidate dataset to combine and answer the query, column 3, line 49-67, column 4, line 14-30, Fig. 1, item 104-108),
As per claim 10, rejection of claim 1 is incorporated, and further Mangas discloses:
- wherein the determining from the semantic information that the query can be serviced using data derived from multiple candidate data sets of the plurality of data sets comprises (servicing query using data from various datasets, column 2, line 15-20, 30-35, column 4, line 1-15),
- determining that the semantic information comprises a function (calculation engine (i.e., function), perform various analysis, column 3, line 35-45, column 4, line 9-15),
- determining from the semantic information that the function comprises a further function as an input (calculation engine (i.e., function), perform various analysis, column 3, line 35-45, column 4, line 9-15),
- determining what input data to the further function is being specified by the semantic information (determine which data set (i.e., what input) specified, column 3, line 55-65, column 4, line 15-20),
- determining that the query can be serviced based on identifying a candidate data set comprising a column associated with data output by the further function when supplied with the input data (determining which dataset are the candidate dataset to combine and answer the query, column 3, line 49-67, column 4, line 14-30, Fig. 1, item 104-108).
As per claim 11, rejection of claim 1 is incorporated, and further Mangas discloses:
- wherein the cost function determines at least one candidate data set of the multiple candidate data sets to service the query based on assessing one or more attributes of each of the multiple candidate data sets (evaluating the records in the datasets (i.e., assessing attributes), column5, line 45-55).
As per claim 12, rejection of claim 1 is incorporated, and further Mangas discloses:
- determining if retrieving data associated with the semantic information from the at least one data store will involve unnecessary computation, and if so, rewriting the user query (rewriting, rearranging or substituting query plan, column 8, line 45-55, Fig. 5, item 506).
As per claim 13, rejection of claim 1 is incorporated, and further Mangas discloses:
- combining the portion of each of the at least one candidate data set to generate the data (combining data set, column 2, line 5-20).
As per claim 14, rejection of claim 1 is incorporated, and further Mangas discloses:
- wherein the determining from the semantic information that the user query can be serviced using data selected from and/or derived from multiple candidate data sets of the plurality of data sets is performed without the user query comprising an identifier of the multiple candidate data sets (user query served by identifying a specific candidate dataset, column 4, line 50-60, column 5, line 5-20).
As per claim 15,
Claim 15 is a computer readable medium claim corresponding to the method claim 1 respectively and rejected under the same reason set forth to the rejection of claim 1 above.
As per claim 16, rejection of claim 11 is incorporated, and further Mangas discloses:
- wherein the one or more attributes comprise a number of rows of the candidate data set and/or a number of columns of the candidate data set; and wherein the semantic information comprises a performance indicator, whether the candidate data set comprises a column associated with the performance indicator and no further column associated with a further key performance indicator, or the candidate data set comprises a column associated with the performance indicator and a further column associated with a further performance indicator (dataset with rows and columns, column 3, line 5-20, and dataset with semantic information, column 5, line 5-20).
As per claim 17, rejection of claim 1 is incorporated, and further Mangas discloses:
- wherein in response to determining that only a single candidate data set of the plurality of data sets can service the query, comprising returning a response to the query, the response comprising data obtained using the single candidate data set (response to the query with the data from the dataset, column 4, line 1-10, column 2, line 25-35).
As per claim 18, rejection of claim 1 is incorporated, and further Mangas discloses:
- wherein the query is a user query (query is user query, column 2, line 35-45).
As per claim 19, rejection of claim 1 is incorporated, and further Mangas discloses:
- receiving the query from a user computing device via a computer network; and returning a response to the query comprises transmitting the response to the user computing device via the computer network (receiving query and responding to the query via a computer network, Fig. 9, item 950, Fig. 8, item 804, column 11, line 5-15).
As per claim 21,
Claim 21 is a computer readable medium claim corresponding to the method claim 1 respectively and rejected under the same reason set forth to the rejection of claim 1 above.
Claim 22 is rejected under 35 U.S.C. 103 as being unpatentable over Mangas et al (US 11,334,588 B1), in view of Ypma et al (US 10539882 B2), as appled to claim 15 above and further in view of Zinner et al (US 2017/0032016 A1).
As per claim 22, rejection of claim 22 is incorporated,
Combined method of Mangas and Ypma does not explicitly disclose wherein the instructions configured to cause the one or more processors to determine from the semantic information whether the query can be serviced are further configured to cause the one or more processors to determine that the multiple candidate data sets each comprise a column corresponding to at least one performance indicator of the at least one performance indicator. However, in the same field of endeavor Zinner in an analogous art disclose wherein the instructions configured to cause the one or more processors to determine from the semantic information whether the query can be serviced are further configured to cause the one or more processors to determine that the multiple candidate data sets each comprise a column corresponding to at least one performance indicator of the at least one performance indicator (datasets corresponding to performance indicator to serve the queries, Para [0104], [0206], [0291], [0581] – [0524]).
Therefore, it would have been obvious to a person of the ordinary skill in the art before the effective filing date of the claimed invention to combine the teaching of Mangas, as previously modified with Ypma, with the teaching of Zinner et al by modifying Mangas such that performance indication for a dataset is detected based on query execution cost. The motivation for doing so would be detecting more specific information from a large volume to data in a most effective and advantageous manner, (Zinner, Para [0050]).
Response to Arguments
Applicant's arguments filed on July, 28, 2025 with respect to claims 1-22 have been fully considered but they are not deemed to be persuasive.
In response to applicant’s argument in page 9, applicants argued that, Mangas fail to disclose, determining from the semantic information whether the query can be serviced using data selected from and/or derived from one or more candidate data sets of the plurality of data sets; and if multiple candidate data sets of the plurality of data sets can service the query, using a cost function to determine at least one candidate data set of the multiple candidate data sets to service the query, and determine a portion of each of the at least one candidate data set to service the query of claim 1.
Examiner disagree and respectfully response that, Mangas teaches this limitation because of the following reason:
According to applicant’s specification Para [00010], the semantic information may comprise at least one performance indicator. … the determining may comprise determining that the multiple candidate data sets each comprise a column corresponding to at least one of said at least one performance indicators.
According to Para [00011], the semantic information may comprise a function, Para [00018], line 1-3 describes a cost function. The cost function determines candidate data set to service the query. Para [00088] – [00090], of applicant’s specification describes CPU uses a cost function to determine which table or which column or row of the table or which portion of the table to select and determine how to combine the data to serve the query most efficiently.
Applicant’s Specification Para [00088] – [00090]:
[00088] If at step S1008 the CPU determines that multiple tables stored in the data store(s) 410 are candidates for servicing the query, the process 1000 proceeds to step S1010. At step S1010 the CPU uses a cost function to determine at least one table of the multiple candidate tables to service the query, and determines a portion (e.g. column and/or rows) of each of the determined tables to service the query. 30 That is, the CPU uses the cost function to determine which parts of which tables (of the multiple candidate tables) to use to service the query. [00089] The cost function may determine the at least one table from the multiple candidate tables to service the query based on assessing one or more attributes of each of the multiple candidate tables. The attributes may comprise a number of rows of the candidate table, and/or a number of columns of 35 the candidate table. In embodiments whereby the semantic information comprises a KPI, the attributes may relate to whether the candidate table comprises a column associated with the performance indicator and no further column associated with a further performance indicator, or the candidate table comprises a column associated with the performance indicator and a further column associated with a further performance indicator. [00090] At step S1010 the CPU may use a further cost function (e.g. a SQL engine) to determine how to combine the data contained in the determined at least one table of the multiple candidate tables in the most computationally efficient way.
According to Para [00012], the semantic information may comprise at least one piece of context information.
Accordingly Mangas teaches after receiving a query, it identify the candidate dataset to combine and analyze and estimate cost and benefits to process and response to the query (i.e., service the query), see Mangas, column 3, line 49-67, column 4, line 1-6, 9-12, 15-25, Fig. 4, column 5, line 32-40, column 7, line 25-45, … the analysis may be in view of 1) the time and expense of performing the combination each time a query is evaluated … 4) the benefit of processing queries using the pre-combined dataset.
Mangas also teaches a data analysis system which combine, pre-combine dataset by identifying related columns, relationship and benefits for query performance optimization (i.e., semantic information), see Mangas column 3, line 35-45, 49-67, column 4, line 5-9-67, column 5, line 24-31.
Mangas also teaches queries with metadata, customer id, name, identifying column with zip code, phone etc., (i.e., semantic information with context information), in column 5, line 1-22
Therefore, in light of applicant’s specification (as described the semantic information), examiner firmly believe that, Mangas (along with Ypma and newly found reference Zinner et al) reasonably teaches the argued limitation, as claimed.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MOHAMMED R UDDIN whose telephone number is (571)270-3138. The examiner can normally be reached M-F: 9:00 AM-5:00 PM.
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/MOHAMMED R UDDIN/Primary Examiner, Art Unit 2167