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 17/958,189 which was filed on 9/30/2022.
Response to Amendment
In the reply filed 1/23/2026, claims 1-14 and 16-20 have been amended. Claim 15 has been cancelled and claim 21 has been added. Accordingly, claims 1-14 and 16-21 are currently pending.
The objection to the specification/title has been withdrawn in light of the amendment.
The 35 USC 101 rejections have been withdrawn in light of the amendments.
Response to Arguments
Applicant’s arguments filed 1/23/2026 have been fully considered but are moot in view of new grounds of rejection.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(d):
(d) REFERENCE IN DEPENDENT FORMS.—Subject to subsection (e), a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
The following is a quotation of pre-AIA 35 U.S.C. 112, fourth paragraph:
Subject to the following paragraph [i.e., the fifth paragraph of pre-AIA 35 U.S.C. 112], a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
Claim 21 is rejected under 35 U.S.C. 112(d) or pre-AIA 35 U.S.C. 112, 4th paragraph, as being of improper dependent form for failing to further limit the subject matter of the claim upon which it depends, or for failing to include all the limitations of the claim upon which it depends. Claim 21 is not further limiting because the limitation for that claim is incorporated in the corresponding parent claim 16. Applicant may cancel the claim(s), amend the claim(s) to place the claim(s) in proper dependent form, rewrite the claim(s) in independent form, or present a sufficient showing that the dependent claim(s) complies with the statutory requirements.
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) 1-6, 8-9, 11-14, 16, and 18-21 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kurian et al. (US2023/0289325, previously presented in ‘892), hereinafter Kurian, in view of Wilczynski et al. (US9760606, previously presented in ‘892), hereinafter Wilczynski, Goel et al. (US11816103), hereinafter Goel, and Chen et al. (US2009/0094258), hereinafter Chen.
Regarding Claim 1:
Kurian teaches:
A server computing device comprising: one or more processors; and a non-transitory storage medium storing instructions executable by the one or more processors (Kurian, figure 4, [0028], note memory and processor) to:
receive a query from a client device to access a data object (Kurian, figures 2A-2E and 3, [0039, 0041, 0046, 0050], note requesting and receiving data);
predict a semantic structure associated with the data object sought by the query (Kurian, abstract, figures 2A-2E and 3, [0024, 0039, 0052], note analyzing the received data to identify a type of data, e.g., semantic structure);
identify one or more relationships between the semantic structure of the data object and a semantic structure of another data object (Kurian, abstract, figures 2A-2E and 3, [0024, 0039, 0052, 0054], note identifying relationships based on data type);
determine a view of the data object based on the one or more relationships (Kurian, abstract, figures 2A-2E and 3, [0024, 0039, 0054-0055, 0071], note storing the data element and connections/relationships; note a user may receive the stored data and understand connections to the other data based on the stored connections, which is interpreted to mean the stored data element and connections is a view of the data object); and
provide the view of the data object to a user interface to consume the data (Kurian, abstract, figures 2A-2E and 3, [0024, 0039, 0054-0055, 0071], note storing the data element and connections/relationships; note a user may receive the stored data and understand connections to the other data based on the stored connections, which is interpreted as providing the view of the data object to the user).
While Kurian teaches data object views, Kurian doesn’t specifically teach the semantic structure specifying prefetching data of the data object and caching the data of the data object; select, based on the semantic structure, a collection of operators implemented in the server computing device, the collection of operators to provide offloading of operations performed by the collection of operators from the client device to the server computing device, wherein the operations comprise the prefetching of the data of the data object and the caching of the data of the data object; applying the collection of operators. However, Wilczynski is in the same field of endeavor, data management, and Wilczynski teaches:
receiving a query from a client device to access a data object (Wilczynski, column 2 lines 36-56, column 3 lines 8-61, note the user is making the request for the data object);
determine a view of the data object based on the one or more relationships and applying the collection of operators (Wilczynski, figure 5, column 2 lines 36-56, column 3 lines 8-61, column 10 lines 56-67, note determining a data object view for the request); and
provide the view of the data object to a user interface to consume the data (Wilczynski, figure 5, column 2 lines 36-56, column 3 lines 8-61, column 10 lines 56-67, note determining a data object view for the request and providing it to the user).
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 Wilczynski because all references are directed towards data management and because Wilczynski would expand upon the teachings of the previously cited references in data object management which would improve the performance and flexibility of the system by determining and providing data object views (Wilczynski, column 1 lines 12-58, column 2 lines 36-56).
While Kurian as modified teaches data object views, Kurian as modified doesn’t specifically teach the semantic structure specifying prefetching data of the data object and caching the data of the data object; to provide offloading of operations performed by the collection of operators from the client device to the server computing device. However, Goel is in the same field of endeavor, data management, and Goel teaches:
predict a semantic structure associated with the data object sought by the query, the semantic structure specifying prefetching data of the data object and caching the data of the data object (Goel, column 2 lines 1-44, column 4 lines 15-27, column 10 lines 34-64, note prefetching data includes prefetching/predicting a data structure and associated data; note prefetching data into a buffer is interpreted as caching the data of the data object);
select, based on the semantic structure, a collection of operators implemented in the server computing device, the collection of operators to provide offloading of operations performed by the collection of operators from the client device to the server computing device, wherein the operations comprise the prefetching of the data of the data object and the caching of the data of the data object (Goel, column 2 lines 1-44, column 3 line 57 - column 4 line 27, column 10 lines 34-64, note prefetching data includes prefetching/predicting a data structure and associated data; note prefetching data includes selecting operations based on the semantic structure, such as a join operation for a query representing a join on two tables; note the join operation is a part of the prefetching operations and prefetching data into a buffer is interpreted as caching the data of the data object);
identify one or more relationships between the semantic structure of the data object and a semantic structure of another data object (Goel, column 2 lines 1-44, column 3 line 57 - column 4 line 27, column 10 lines 34-64, note prefetching data includes prefetching/predicting a data structure and associated data; note prefetching data includes selecting operations based on the semantic structure, such as a join operation for a query representing a join on two tables; note the join operation is interpreted as identifying a relationship between data objects);
determine a view of the data object based on the one or more relationships and applying the collection of operators (Goel, figure 1 and 6, column 2 lines 1-44, column 3 line 57 - column 4 line 27, column 10 lines 34-64, note prefetching data includes prefetching/predicting a data structure and associated data; note prefetching data includes selecting operations based on the semantic structure, such as a join operation for a query representing a join on two tables; note performing the join operation and the query);
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 Goel because all references are directed towards data management and because Goel would expand upon the teachings of the previously cited references in data object management which would improve the performance of the system by reducing the time to process queries (Goel, column 2 lines 1-44).
While Kurian as modified teaches data object views, Kurian as modified doesn’t specifically teach to provide offloading of operations performed by the collection of operators from the client device to the server computing device. However, Chen is in the same field of endeavor, data management, and Chen teaches:
to provide offloading of operations performed by the collection of operators from the client device to the server computing device (Chen, abstract, figure 1, [0006, 0018, 0021], note offloading operations from a host to a server. When combined with the previously cited references this would be for the prefetching operations as taught by Goel).
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 Chen because all references are directed towards data management and because Chen would expand upon the teachings of the previously cited references in data object management which would improve the performance of the system by reducing CPU consumption on the host system (Chen, [0008]).
Regarding Claim 2:
Kurian as modified shows the computing device as disclosed above;
Kurian as modified further teaches:
wherein the semantic structure further specifies a precomputation of information for accessing the data object, and the operations provided by the collection of operators further comprise the precomputation of the information for accessing the data object (Kurian, abstract, figures 2A-2E and 3, [0024, 0039, 0052], note using machine learning to identify a type of data, e.g., semantic structure, which is interpreted as predicting the semantic structure) (Goel, column 2 lines 1-44, column 3 line 57 - column 4 line 27, column 10 lines 34-64, note prefetching data includes prefetching/predicting a data structure and associated data; note prefetching data includes selecting operations based on the semantic structure, such as a join operation for a query representing a join on two tables; note the join operation is interpreted as specified precomputation of information for accessing the data object).
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 Goel because all references are directed towards data management and because Goel would expand upon the teachings of the previously cited references in data object management which would improve the performance of the system by reducing the time to process queries (Goel, column 2 lines 1-44).
Regarding Claim 3:
Kurian as modified shows the computing device as disclosed above;
Kurian as modified further teaches:
wherein the identifying of the one or more relationships comprises using a machine learning model. (Kurian, abstract, figures 2A-2E and 3, [0024, 0039, 0052, 0054], note using machine learning models to identify relationships based on data type).
Regarding Claim 4:
Kurian as modified shows the computing device as disclosed above;
Kurian as modified further teaches:
wherein the machine learning model comprises a recurrent neural network. (Kurian, [0023, 0033-0035, 0061], note the use of artificial neural network algorithms, which include recurrent neural networks).
Regarding Claim 5:
Kurian as modified shows the computing device as disclosed above;
Kurian as modified further teaches:
wherein the precomputation comprises one or more of filtering the data of the data object or matching the data of the data object to an expression (Kurian, abstract, figures 2A-2E and 3, [0024, 0039, 0054-0055, 0071], note storing the data element and connections/relationships) (Goel, column 2 lines 1-44, column 3 line 57 - column 4 line 27, column 10 lines 34-64, note prefetching data includes prefetching/predicting a data structure and associated data; note prefetching data includes selecting operations based on the semantic structure, such as a join operation for a query representing a join on two tables; note the join operation includes matching data objects to an expression to correctly perform the join which also filters out none matched data objects).
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 Goel because all references are directed towards data management and because Goel would expand upon the teachings of the previously cited references in data object management which would improve the performance of the system by reducing the time to process queries (Goel, column 2 lines 1-44).
Regarding Claim 6:
Kurian as modified shows the computing device as disclosed above;
Kurian as modified further teaches:
wherein the precomputation comprises precomputing an index of a database (Goel, column 2 lines 1-44, column 3 line 28 - column 4 line 27, column 10 lines 34-64, note prefetched elements may include pages of an index and since this is occurring during the prefetched stage it is interpreted as a precomputation of an index).
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 Goel because all references are directed towards data management and because Goel would expand upon the teachings of the previously cited references in data object management which would improve the performance of the system by reducing the time to process queries (Goel, column 2 lines 1-44).
Regarding Claim 8:
Kurian as modified shows the computing device as disclosed above;
Kurian as modified further teaches:
wherein the one or more relationships comprise a data type, a data size, or an access protocol (Kurian, abstract, figures 2A-2E and 3, [0024, 0039, 0052, 0054], note identifying relationships based on data type. It is also noted that this limitation is nonfunctional descriptive material as explained in section 2111.05 of the MPEP and does not hold patentable weight).
Claim 9 discloses substantially the same limitations as claim 1 respectively, except claim 9 is directed to a method while claim 1 is directed to a computing device. Therefore claim 9 is rejected under the same rationale set forth for claim 1.
Regarding Claim 11:
Kurian as modified shows the method as disclosed above;
Kurian as modified further teaches:
wherein the identifying of the semantic structure is based on use of a machine learning model that has learned a data transformation applied to the data object (Kurian, abstract, figures 2A-2E and 3, [0024, 0039, 0052, 0054], note using machine learning models to identify relationships based on data type; note the machine learning model may identifying relationships based on learned data transformations such as mortgage payment data and transformation to the payment data during payment).
Claim 12 discloses substantially the same limitations as claim 5 respectively, except claim 12 is directed to a method while claim 5 is directed to a computing device. Therefore claim 12 is rejected under the same rationale set forth for claim 5.
Claim 13 discloses substantially the same limitations as claim 6 respectively, except claim 13 is directed to a method while claim 6 is directed to a computing device. Therefore claim 13 is rejected under the same rationale set forth for claim 6.
Claim 14 discloses substantially the same limitations as claim 2 respectively, except claim 14 is directed to a method while claim 2 is directed to a computing device. Therefore claim 14 is rejected under the same rationale set forth for claim 2.
Claim 16 discloses substantially the same limitations as claim 1 respectively, except claim 16 is directed to a non-transitory computer-readable storage medium (Kurian, figure 4, note processor and memory) while claim 1 is directed to a computing device. Therefore claim 16 is rejected under the same rationale set forth for claim 1.
Claim 18 discloses substantially the same limitations as claim 11 respectively, except claim 18 is directed to a non-transitory computer-readable storage medium (Kurian, figure 4, note processor and memory) while claim 11 is directed to a computing device. Therefore claim 18 is rejected under the same rationale set forth for claim 11.
Claim 19 discloses substantially the same limitations as claim 5 respectively, except claim 19 is directed to a non-transitory computer-readable storage medium (Kurian, figure 4, note processor and memory) while claim 5 is directed to a computing device. Therefore claim 19 is rejected under the same rationale set forth for claim 5.
Claim 20 discloses substantially the same limitations as claim 6 respectively, except claim 20 is directed to a non-transitory computer-readable storage medium (Kurian, figure 4, note processor and memory) while claim 6 is directed to a computing device. Therefore claim 20 is rejected under the same rationale set forth for claim 6.
Regarding Claim 21:
Kurian as modified shows the method as disclosed above;
Kurian as modified further teaches:
wherein the semantic structure further specifies prefetching the data of the data object, and the operations provided by the collection of operators further comprise the prefetching of the data of the data object (Goel, column 2 lines 1-44, column 3 line 57 - column 4 line 27, column 10 lines 34-64, note prefetching data includes prefetching/predicting a data structure and associated data; note prefetching data includes selecting operations based on the semantic structure, such as a join operation for a query representing a join on two tables; note the join operation is interpreted as specified precomputation of information for accessing the data object).
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 Goel because all references are directed towards data management and because Goel would expand upon the teachings of the previously cited references in data object management which would improve the performance of the system by reducing the time to process queries (Goel, column 2 lines 1-44).
Claim Rejections - 35 USC § 103
Claim(s) 7, 10, and 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kurian in view of Wilczynski, Goel, Chen, and Bedadala et al. (US2019/0250839), hereinafter Bedadala.
Regarding Claim 7:
Kurian as modified shows the computing device as disclosed above;
Kurian as modified further teaches:
wherein the predicting of the semantic structure uses a machine learning model that has learned an access pattern of the data object (Kurian, figure 1B, [0023, 0033-0035, 0061], note the machine learning models are trained on historical data, outputs or outcomes of actions, etc., which are interpreted to include the data object; note the use of one or more machine learning models).
While Kurian as modified teaches training a machine learning model, Kurian as modified doesn’t specifically teach using access patterns. However, Bedadala is in the same field of endeavor, data management, and Bedadala teaches:
wherein the predicting of the semantic structure uses a machine learning model that has learned an access pattern of the data object (Bedadala, [0007, 0009, 0309] , note training the machine learning model based on access patterns of data objects. When combined with the previously cited references this would be for the model usage and training as taught by Kurian).
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 Bedadala because all references are directed towards data management and because Bedadala would expand upon the teachings of the previously cited references in data object management which would improve the performance of the system by using machine learning to improve the speed of the system (Bedadala, [0046]).
Claim 10 discloses substantially the same limitations as claim 7 respectively, except claim 10 is directed to a method while claim 7 is directed to a computing device. Therefore claim 10 is rejected under the same rationale set forth for claim 7.
Claim 17 discloses substantially the same limitations as claim 7 respectively, except claim 17 is directed to a non-transitory computer-readable storage medium (Kurian, figure 4, note processor and memory) while claim 7 is directed to a computing device. Therefore claim 17 is rejected under the same rationale set forth for claim 7.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Subramanian et al. (US2007/0216674) teaches prefetching data;
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JOHN J MORRIS whose telephone number is (571)272-3314. The examiner can normally be reached M-F 6:00-2:00 PM EST.
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/JOHN J MORRIS/Examiner, Art Unit 2151 5/18/2026
/James Trujillo/Supervisory Patent Examiner, Art Unit 2151
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