DETAILED ACTION
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 .
Claims 1-20 are pending.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of U.S. Patent No. 11,886,435 (Application No. 18/210,004).
Although the claims at issue are not identical, they are not patentably distinct from each other because both are directed to similar invention with similar limitations as demonstrated in the table below:
Claims 1-10 of instant application recite similar limitations as claim 11-20, hence claims 1-10 are being used as representative for demonstration in the table below.
Similarly, claims 1-10 of U.S. Patent No. 11,886,435 recite similar to limitations as claims 11-20. Hence claims 1-10 are being used as representative for demonstration in the table below.
Instant Application
U.S. Patent No. 11,886,435
1. A method, comprising:
identifying, by one or more processors coupled to memory, a multi-dimensional sample dataset comprising a plurality of samples storing multimodal data, each of the plurality of samples comprising a first tensor identified by a respective first identifier common to each sample of the plurality of samples;
identifying, by the one or more processors, a query for the multi-dimensional sample dataset, the query specifying a transformation operation for at least a portion of the multimodal data of the multi-dimensional sample dataset, the transformation operation indicated in an expression including the respective first identifier of the first tensor, wherein the first tensor stores a subset of the portion of the multimodal data;
parsing, by the one or more processors, the query to extract the expression including the respective first identifier and the transformation operation;
executing, by the one or more processors, the query to generate a set of query results comprising a respective set of references to a result dataset generated based on applying the transformation operation to the first tensor of at least a subset of samples of the plurality of samples of the multi-dimensional sample dataset; and
2. The method of claim 1, wherein: the transformation operation is a crop operation, and the respective set of references correspond to cropped portions of the multi-dimensional sample dataset.
3. The method of claim 1, wherein: the transformation operation is a normalization operation, and the respective set of references correspond to normalized portions of the multi-dimensional sample dataset.
providing, by the one or more processors, the set of query results as input to one or more machine-learning models.
10. The method of claim 1, wherein the query comprises a plurality of transformation operations, and further comprising: executing, by the one or more processors, the query based on the respective first identifier to generate the set of query results comprising the respective set of references to portions of the result dataset generated based on the plurality of transformation operations.
4. The method of claim 1, wherein executing the query comprises generating, by the one or more processors, one or more functors based on the respective first identifier specified in the query, a condition specified in the query, or a requested shape specified in the query.
5. The method of claim 4, further comprising generating, by the one or more processors, a computational graph based on the one or more functors.
6. The method of claim 1, wherein the query comprises at least one structured query language (SQL) keyword.
7. The method of claim 1, wherein: the query specifies a shuffle operation, and the respective set of references is randomly ordered based on the shuffle operation.
8. The method of claim 1, wherein the first tensor comprises a plurality of dimensions.
9. The method of claim 1, wherein: one or more tensors of each sample of the multi-dimensional sample dataset are stored in one or more binary chunks, and the respective first identifier of the first tensor corresponds to a column in the multi-dimensional sample dataset.
1. A method, comprising:
maintaining, by one or more processors coupled to memory, a plurality of samples of a multi-dimensional sample dataset, each of the plurality of samples comprising a plurality of tensors, the plurality of samples comprising:
a first sample comprising (i) a first tensor having a first value stored in a first column of the multi-dimensional sample dataset identified by a first column identifier, and (ii) a second tensor having a first plurality of values and stored in a second column of the multi-dimensional sample dataset identified by a second column identifier, and a second sample comprising (i) a third tensor having a second value and stored in the first column, and (ii) a fourth tensor having a second plurality of values and stored in the second column;
identifying, by the one or more processors coupled to memory, a query for the multi-dimensional sample dataset, the query specifying a group operation identifying the first column identifier, and (ii) a range of values to extract from tensors identified by the second column identifier;
parsing, by the one or more processors, the query to extract the group operation and the range of values;
executing, by the one or more processors, the query based on the group operation for the first column identifier to: select the first sample for a first group and the second sample for a second group based on the first value being different from the second value, extract, based on the range of values, a first subset of the first plurality of values of the second tensor for inclusion in the first group and a second subset of the second plurality of values of the fourth tensor for inclusion in the second group, and
generate a set of query results comprising a first identifier of the first group and a second identifier of the second group; and
providing, by the one or more processors, as output, the set of query results.
2. The method of claim 1, further comprising: identifying, by the one or more processors, a second query over the multi-dimensional sample dataset, the second query specifying an ungroup operation for the first column identifier; parsing, by the one or more processors, the second query to extract the ungroup operation; executing, by the one or more processors, the query based on the ungroup operation for the first column identifier to generate a second set of query results, the second set of query results comprising a set of indices identifying each sample included in the first group; and providing, by the one or more processors, as output, the second set of query results.
3. The method of claim 1, further comprising: identifying, by the one or more processors, a second query over the multi-dimensional sample dataset, the second query specifying a sampling operation for the multi-dimensional sample dataset and identifying a first weight for the first column identifier; parsing, by the one or more processors, the second query to extract the sampling operation and the first column identifier; executing, by the one or more processors, the second query based on the sampling operation to generate a second set of query results, the second set of query results comprising a first number of indices determined based on the first weight; and providing, by the one or more processors, as output, the second set of query results.
4. The method of claim 1, wherein: the query further specifies a mathematical operation for the first value of the first tensor, and the set of query results further comprises a respective set of references to portions of a result dataset generated based on the mathematical operation.
5. The method of claim 1, wherein executing the query comprises generating, by the one or more processors, one or more functors based on the group operation specified in the query, a condition specified in the query, or a requested shape specified in the query.
6. The method of claim 5, further comprising generating, by the one or more processors, a computational graph based on the one or more functors.
7. The method of claim 1, wherein the query comprises at least one structured query language (SQL) keyword.
8. The method of claim 1, wherein: the query specifies a shuffle operation, and at least one of the first group or the second group is randomly ordered based on the shuffle operation.
9. The method of claim 1, wherein each of the second tensor and the fourth tensor comprises a plurality of dimensions.
10. The method of claim 1, wherein the plurality of tensors of each sample of the multi-dimensional sample dataset are stored in one or more binary chunks.
As demonstrated by the mappings in the table above, U.S. Patent No 11,886,435 discloses or renders obvious all the features of the claims of the instant application.
Claims 1-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of U.S. Patent No. 12,265,541 (Application No. 18/426,272).
Although the claims at issue are not identical, they are not patentably distinct from each other because both are directed to similar invention with similar limitations as demonstrated in the table below:
Claims 1-10 of instant application recite similar limitations as claim 11-20, hence claims 1-10 are being used as representative for demonstration in the table below.
Similarly, claims 1-8 of U.S. Patent No. 12,265,541 recite similar to limitations as claims 9-20. Hence claims 1-8 are being used as representative for demonstration in the table below.
Instant Application
U.S. Patent No. 12,265,541
1. A method, comprising:
identifying, by one or more processors coupled to memory, a multi-dimensional sample dataset comprising a plurality of samples storing multimodal data, each of the plurality of samples comprising a first tensor identified by a respective first identifier common to each sample of the plurality of samples;
identifying, by the one or more processors, a query for the multi-dimensional sample dataset, the query specifying a transformation operation for at least a portion of the multimodal data of the multi-dimensional sample dataset, the transformation operation indicated in an expression including the respective first identifier of the first tensor, wherein the first tensor stores a subset of the portion of the multimodal data;
parsing, by the one or more processors, the query to extract the expression including the respective first identifier and the transformation operation;
executing, by the one or more processors, the query to generate a set of query results comprising a respective set of references to a result dataset generated based on applying the transformation operation to the first tensor of at least a subset of samples of the plurality of samples of the multi-dimensional sample dataset; and
providing, by the one or more processors, the set of query results as input to one or more machine-learning models.
2. The method of claim 1, wherein: the transformation operation is a crop operation, and the respective set of references correspond to cropped portions of the multi-dimensional sample dataset.
3. The method of claim 1, wherein: the transformation operation is a normalization operation, and the respective set of references correspond to normalized portions of the multi-dimensional sample dataset.
4. The method of claim 1, wherein executing the query comprises generating, by the one or more processors, one or more functors based on the respective first identifier specified in the query, a condition specified in the query, or a requested shape specified in the query.
5. The method of claim 4, further comprising generating, by the one or more processors, a computational graph based on the one or more functors.
6. The method of claim 1, wherein the query comprises at least one structured query language (SQL) keyword.
7. The method of claim 1, wherein: the query specifies a shuffle operation, and the respective set of references is randomly ordered based on the shuffle operation.
8. The method of claim 1, wherein the first tensor comprises a plurality of dimensions.
9. The method of claim 1, wherein: one or more tensors of each sample of the multi-dimensional sample dataset are stored in one or more binary chunks, and the respective first identifier of the first tensor corresponds to a column in the multi-dimensional sample dataset.
10. The method of claim 1, wherein the query comprises a plurality of transformation operations, and further comprising: executing, by the one or more processors, the query based on the respective first identifier to generate the set of query results comprising the respective set of references to portions of the result dataset generated based on the plurality of transformation operations.
1. A method, comprising:
identifying, by one or more processors coupled to memory, a multi-dimensional sample dataset comprising a plurality of samples, each of the plurality of samples comprising a first tensor identified by a respective first identifier;
identifying, by the one or more processors, a query for the multi-dimensional sample dataset, the query specifying a sampling operation for the multi-dimensional sample dataset, the sampling operation of the query indicating an expression including the respective first identifier of the first tensor and a first weight for a probability distribution of query results to select from the plurality of samples of the multi-dimensional sample dataset;
parsing, by the one or more processors, the query to extract the sampling operation, the expression, and the first weight;
executing, by the one or more processors, the query based on the sampling operation to randomly select a subset of samples from the plurality of samples as a set of query results, the subset of samples selected to include a first number of samples that satisfy the expression for the first tensor, the first number of samples determined based on the first weight; and
providing, by the one or more processors, as output, the set of query results.
2. The method of claim 1, wherein: the expression of the query further specifies a mathematical operation for a value of the first tensor, and the set of query results further comprises a respective set of references to portions of a result dataset generated based on the mathematical operation.
3. The method of claim 1, wherein executing the query comprises generating, by the one or more processors, one or more functors based on the sampling operation specified in the query, the expression specified in the query, or a requested shape specified in the query.
4. The method of claim 3, further comprising generating, by the one or more processors, a computational graph based on the one or more functors.
5. The method of claim 1, wherein the query comprises at least one structured query language (SQL) keyword.
6. The method of claim 1, wherein: the query specifies a shuffle operation, and the set of query results is randomly ordered based on the shuffle operation.
7. The method of claim 1, wherein the first tensor comprises a plurality of dimensions.
8. The method of claim 1, wherein: one or more tensors of each sample of the multi-dimensional sample dataset are stored in one or more binary chunks, and a respective identifier of each tensor of the one or more tensors corresponds to a column in the respective dataset.
As demonstrated by the mappings in the table above, U.S. Patent No 12,265,541 discloses or renders obvious all the features of the claims of the instant application.
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 without significantly more.
Claims 1 and 11 each recites a mental process in the limitations of “…identifying a multi-dimensional sample dataset… identify a query for the multi-dimensional sample dataset…parsing the query to extract the expression….executing the query to generate result…providing the set of query results….” These limitations could be done mentally with data evaluations based on gathered information. Mental process is directed to one of the abstract ideas groups as set forth by Prong One in Step 2A of the 2019 Patent Subject Matter Eligibility Guidance.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. Additional elements (e.g. samples, tensor, first identifier, multimodal data) are directed to types of information materials, which do not impose a meaningful limit on the judicial exception, such that the claims are more than a drafting effort design to monopolize exception, because the claimed steps could be performed in a same manner to achieve the same outcome with other types of information other than the ones being used in the claims.
Hence, the claims do not include additional elements or the combination of the elements are sufficient to amount to significantly more than the judicial exception and fail to integrate the judicial exception into practical application according to Prong Two in Step 2A of the 2019 Patent Subject Matter Eligibility Guidance because the claimed elements or their combination do not impose any meaningful limits on practicing the abstract idea.
Further, in view of Step 2B of the 2019 Patent Subject Matter Eligibility Guidance, it is determined that the computing elements (such as processor, memory) in the claims amount to no more than usage of a generic computing system having a generic computing components, which fails to provide an inventive concept or significantly more than abstract idea because the elements do not necessary improve the functional of a computing system or an improvement to a technical field since network computing is well known.
Dependent claims 2-3, 6-9 and 12-13, 16-19 each further recites additional elements (e.g. crop operation, set of references, normalization operation, normalization portions, structured query language keyword, shuffle operation, dimensions, binary chunks) in the limitations are directed to types of information materials that are being manipulated, which do not impose a meaningful limit on the judicial exception.
Dependent claims 4 and 14 each further recites an additional mental process in a limitation of “…generating, by the one or more processors, one or more functors” which correspond to generate an output functor based on gathered data information, which could be performed mentally based on the gathered information. The additional elements (e.g. first identifier specified in the query, a condition specified in the query, a requested shape) in the limitation are directed to types of information materials that are being manipulated, which do not impose a meaningful limit on the judicial exception.
Dependent claims 5 and 15 each further recites an additional mental process in a limitation of “generating…a computational graph based on the one or more functors” which correspond to generate an output of a computational graph based on gathered data information, which could be performed mentally based on the gathered information. The additional elements (e.g. computation graph) in the limitation are directed to types of information materials that are being manipulated, which do not impose a meaningful limit on the judicial exception.
Dependent claims 10 and 20 each further recites an additional mental process in a limitation of “…executing the query based on the respective first identify to generate the set of query result.. ” which correspond to determine an output of a query result based on gathered data information, which could be performed mentally based on the gathered information. The additional elements (e.g. first identifier, references) in the limitation are directed to types of information materials that are being manipulated, which do not impose a meaningful limit on the judicial exception.
Thus, for at least the reasoning above, the pending claims are not patent eligible.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Acharya et al (Patent No. US 6,519,604, hereinafter Acharya) in view of Interlandi et al (Pub No. US 2023/0244662, hereinafter Interlandi).
Acharya is cited in IDS filed on 1/7/2026, and Interlandi is cited in the IDS filed on 1/7/2026.
With respect to claim 1, Acharya discloses a method (abstract), comprising:
identifying, by one or more processors coupled to memory, a multi-dimensional sample dataset comprising a plurality of samples storing multimodal data, each of the plurality of samples comprising a first tensor identified by a respective first identifier common to each sample of the plurality of samples (Col. 4, lines 62-67: identify a dimensional sample dataset represented by groups of sample data storing multimodal data-which is merely data--in a data warehouse, each sample represent by a group of data includes first tensor-- which is a identified group of data with respective identifier as set forth by an attribute-- such as but not limited to a tuple with attribute identifier that is in common to the each group since each group has a group identifier, as further disclosed in Col. 5, lines 1-13);
identifying, by the one or more processors, a query for the multi-dimensional sample dataset, the query specifying a transformation operation for at least a portion of the multimodal data of the multi-dimensional sample dataset (Col. 4, lines 25-52: identify a query for a multidimensional sample dataset as set forth by the groups of data. Th query specifying a transformation operation—which is merely an operation that transforms or processes data—for a portion of the data the groups data, as further described in Col. 5, lines 8-20), the transformation operation indicated in an expression including the respective first identifier of the first tensor, wherein the first tensor stores a subset of the portion of the multimodal data (Col. 5, lines 34-42: the operation indicated an expression including the tensor or group identifier in which the group has a subset of data, e.g. select A, B, sum (Q*SF)… group A,B as shown in Fig 7-10 and further described in Col. 12, lines 17-24);
parsing, by the one or more processors, the query to extract the expression including the respective first identifier and the transformation operation (Col. 5, lines 9-16, Fig 7-10: parse the query to extract expression and the transformation operation, such as an expression select SR.A, SR.B with identifier that identifiers a subset of data, and a SUM correspond to the transformation operation, as further described in Col. 11, lines 18-35, Col. 12, lines 1-12);
executing, by the one or more processors, the query to generate a set of query results comprising a respective set of references to a result dataset generated based on applying the transformation operation to the first tensor of at least a subset of samples of the plurality of samples of the multi-dimensional sample dataset (Col. 11, lines 41-43: execute the query to generate result based on the references to a result data generated—such as based on the groups A and B dataset—using or apply the transformation operation such a SUM to the sample dataset, as further described in Col. 12, lines 5-20, Col. 13, lines 45-46); and
providing, by the one or more processors, the set of query results (Col. 11, lines 43-45, , : provide the results as output, as further described in Col. 13, lines 46-47).
Acharya does not explicitly disclose the set of query results is provided as input to one or more machine-learning models as claimed.
However, Interlandi discloses providing, by the one or more processors, the set of query results as input to one or more machine-learning models ([0014], Fig 1: provide the set of query results represented by the responses to one or more learning models in a deep neural network with machine leaning, which is further described in [0028-0029]).
Since both Acharya and Interlandi are from the same field of endeavor because both are directed to query processing with multidimensional data, which is in the same field of endeavor as the claimed invention, it would have been obvious to one skilled in the art before the effective filing date of the claimed invention to modify and combine their teachings by incorporate machine learning model utilization of Interlandi into Acharya for query processing as claimed. The motivation to combine is to improve query processing by provide fast highly-accurate results to queries (Acharya, Col. 2, lines 50-53; Interlandi., [0014]).
With respect to claim 11, Acharya discloses a system (abstract), comprising:
one or more processors coupled to memory (Col. 4, lines 33-40), the one or more processors configured to:
identify a multi-dimensional sample dataset comprising a plurality of samples storing multimodal data, each of the plurality of samples comprising a first tensor identified by a respective first identifier common to each sample of the plurality of samples (Col. 4, lines 62-67: identify a dimensional sample dataset represented by groups of sample data storing multimodal data-which is merely data--in a data warehouse, each sample represent by a group of data includes first tensor-- which is a identified group of data with respective identifier as set forth by an attribute-- such as but not limited to a tuple with attribute identifier that is in common to the each group since each group has a group identifier, as further disclosed in Col. 5, lines 1-13);
identify a query for the multi-dimensional sample dataset, the query specifying a transformation operation for at least a portion of the multimodal data of the multi-dimensional sample dataset (Col. 4, lines 25-52: identify a query for a multidimensional sample dataset as set forth by the groups of data. Th query specifying a transformation operation—which is merely an operation that transforms or processes data—for a portion of the data the groups data, as further described in Col. 5, lines 8-20), the transformation operation indicated in an expression including the respective first identifier of the first tensor, wherein the first tensor stores a subset of the portion of the multimodal data (Col. 5, lines 34-42: the operation indicated an expression including the tensor or group identifier in which the group has a subset of data, e.g. select A, B, sum (Q*SF)… group A,B as shown in Fig 7-10 and further described in Col. 12, lines 17-24);
parse the query to extract the expression including the respective first identifier and the transformation operation (Col. 5, lines 9-16, Fig 7-10: parse the query to extract expression and the transformation operation, such as an expression select SR.A, SR.B with identifier that identifiers a subset of data, and a SUM correspond to the transformation operation, as further described in Col. 11, lines 18-35, Col. 12, lines 1-12);
execute the query to generate a set of query results comprising a respective set of references to a result dataset generated based on applying the transformation operation to the first tensor of at least a subset of samples of the plurality of samples of the multi-dimensional sample dataset (Col. 11, lines 41-43: execute the query to generate result based on the references to a result data generated—such as based on the groups A and B dataset—using or apply the transformation operation such a SUM to the sample dataset, as further described in Col. 12, lines 5-20, Col. 13, lines 45-46); and
provide the set of query results Col. 11, lines 43-45, , : provide the results as output, as further described in Col. 13, lines 46-47).
Acharya does not explicitly disclose the set of query results is provided as input to one or more machine-learning models as claimed.
However, Interlandi discloses provide the set of query results as input to one or more machine-learning models ([0014], Fig 1: provide the set of query results represented by the responses to one or more learning models in a deep neural network with machine leaning, which is further described in [0028-0029]).
Since both Acharya and Interlandi are from the same field of endeavor because both are directed to query processing with multidimensional data, which is in the same field of endeavor as the claimed invention, it would have been obvious to one skilled in the art before the effective filing date of the claimed invention to modify and combine their teachings by incorporate machine learning model utilization of Interlandi into Acharya for query processing as claimed. The motivation to combine is to improve query processing by provide fast highly-accurate results to queries (Acharya, Col. 2, lines 50-53; Interlandi., [0014]).
With respect to claims 2 and 12, the combined teachings of Acharya and Interlandi further disclose wherein: the transformation operation is a crop operation, and the respective set of references correspond to cropped portions of the multi-dimensional sample dataset (the limitations are directed to non-functional descriptive materials describing transformation operation and the references, and neither is being used to impact the outcome of the claimed steps; Acharya, Col. 11, lines 18-20 & Col. 12, lines 18-30, Fig 7-10; Interlandi, [0019], [0028]: the transformation operation is a crop operation with references corresponding to the cropped portion since groupings or portions of the date involved in the operation as set forth by the SQL).
With respect to claims 3 and 13, the combined teachings of Acharya and Interlandi further disclose wherein: the transformation operation is a normalization operation, and the respective set of references correspond to normalized portions of the multi-dimensional sample dataset(the limitations are directed to non-functional descriptive materials describing the transformation operation and the references, and neither is being used to impact the outcome of the claimed steps; Acharya, Col. 11, lines 18-20 & 40-45, Col. 12, lines 18-30, Fig 7-10; Interlandi, [0019],[0028]: the transformation operation is a normalization operation with references corresponding to the normalized portions since groupings or portions of the data involved in the operation are normalized tabular data as set forth by the SQL).
With respect to claims 4 and 14, the combined teachings of Acharya and Interlandi further disclose wherein executing the query comprises generating, by the one or more processors, one or more functors based on the respective first identifier specified in the query, a condition specified in the query, or a requested shape specified in the query (“or” indicates that only of the listed is needed to read on the limitation; Acharya, Col. 11, lines 18-20 & 40-45 & 67; Interlandi, [0019], [0024]: generate functors based on the grouping such as generate functors via rewriting query based at least query identifier)
With respect to claims 5 and 15, the combined teachings of Acharya and Interlandi further disclose generating, by the one or more processors, a computational graph based on the one or more functors (Acharya, Col. 11, lines 63-67, Fig 4 & 14; Interlandi, [0019], [0024], Fig 1: generate a computational graph, which is merely graph, based on the functors, such as and not limited to the result table).
With respect to claims 6 and 16, the combined teachings of Acharya and Interlandi further disclose wherein the query comprises at least one structured query language (SQL) keyword (the limitation is directed to non-functional descriptive materials describing the query, which does not impact the outcome of the claimed steps; Acharya, Col. 11, lines 18-20, Fig 7-10; Interlandi, [0019], [0024], Fig 1: SQL query processing).
With respect to claims 7 and 17, the combined teachings of Acharya and Interlandi further disclose wherein: the query specifies a shuffle operation, and the respective set of references is randomly ordered based on the shuffle operation (the limitations are directed to non-functional descriptive materials describing element described by the query and the references, and neither is being used to impact the outcome of the claimed steps; Acharya, Col. 11, lines 18-20, Fig 7-10; Interlandi, [0019], [0024]: the query may specify any operation including a shuffle operation that orders references/data as set forth by the SQL).
With respect to claims 8 and 18, the combined teachings of Acharya and Interlandi further disclose wherein the first tensor comprises a plurality of dimensions (the limitation is directed to non-functional descriptive materials describing the first tensor which is merely data ad does not impact the outcome of the claimed steps; Acharya, Col. 4, lines 61-67, Col. 11, lines 18-20; Interlandi, [0019]: the tensor or grouping involves multiple dimensions, as further explicitly described by Interlandi in [0028]).
With respect to claims 9 and 19, the combined teachings of Acharya and Interlandi further disclose wherein: one or more tensors of each sample of the multi-dimensional sample dataset are stored in one or more binary chunks, and the respective first identifier of the first tensor corresponds to a column in the multi-dimensional sample dataset (the limitations are directed to non-functional descriptive materials describing the tensor and identifiers of the tensor, Neither is being used to impact the outcome of the claimed steps; Acharya, Col. 4, lines 61-67, Col. 11, lines 18-20; Interlandi, [0019], [0028]: the sample data are stored in chunk as set forth by the group, and the identifier correspond to the column in the dataset)
With respect to claims 10 and 20, the combined teachings of Acharya and Interlandi further disclose wherein the query comprises a plurality of transformation operations, and further comprising: executing, by the one or more processors, the query based on the respective first identifier to generate the set of query results comprising the respective set of references to portions of the result dataset generated based on the plurality of transformation operations (Acharya, Col. 11, lines 40-45; Interlandi, [0019], [0044], Fig 1 & 3: execute the query to generate results with references to the portion of result, which is merely result data).
Examiner Note
Examiner has cited particular columns/paragraph and line numbers in the references applied to the claims above for the convenience of the applicant. Although the specified citations are representative of the teachings of the art and are applied to specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested from the applicant in preparing responses, to fully consider the references in entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the Examiner.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Michelle Owyang whose telephone number is (571)270-1254. The examiner can normally be reached Monday-Friday, 8am-6pm EST.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Charles Rones can be reached at (571)272-4085. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/MICHELLE N OWYANG/Primary Examiner, Art Unit 2168
Prosecution Insights