DETAILED ACTION
1. Claims 1-20 are pending in this examination.
Notice of Pre-AIA or AIA Status
2. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
3. In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
Claim Rejections - 35 USC § 103
5.1. 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.
Independent Claims 1, 8 and 15
5.2. Claims 1-2, 6-9, 15-16 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over US Patent Application No. 20180293283 to Litoiu et al (“Litoiu”) in view of US Patent No. 11269876 issued to Basavaiah et al (“Basavaiah”).
As per claim 1, Litoiu discloses a computer-implemented method for enforcing data security constraints in a data pipeline, wherein the data pipeline takes one or more source datasets as input and performs one or more data transformations on the one or more source datasets, the method comprising (Abstract, a data provider defines one or more data policies and allows access to data to one or more data consumers. Each data consumer submits analytics tasks (jobs) that include two phases: data transformation and data mining. The data provider verifies that data is trans-formed (e.g., anonymized) according to the data policies. Upon verification, the data consumer is provided with access to the results of the data mining phase):
within a stage of the data pipeline, generate a transformed dataset by performing a data transformation on a first subset of entries of the one or more source datasets, determine one or more validation constraints to be applied to the transformed dataset based at least in part on a security level of the stage ([0048] The data context policies module 122 is a service that manages privacy and access policies on specific data types (e.g. SIN, name, address, age, etc.) and can be specific to a data provider's attributes or group settings. For instance, the access policies may require that a data consumer may have access only to cities and movies… [0063], [0065], The verifier module verifies that the transformation part 401 is compliant with the context policies and, in one example, enhances the transformation to comply with the context policies. The enhanced transformation is then returned to the job controller module 116 which then forwards it to the preprocessor module 112. The preprocessor module 112 transforms the data and requires a data stream, at 214, from the data sharing service module 124, The stream, at 216, is returned to the job controller module 116 which submits the analytics part 402 through a request, at 222. The data sharing service module 124 starts processing the analytics part 402 and returns a job tracker id at 224 to the REST API 110. The data consumer server 104 can now query the progress of the analytics part 402 through a request, at 226, and can get back the status through an output URL at 228. Finally, when the data sharing service module finishes processing the analytics job (402), it closes the data stream at 232, and after the anonymization is verified at 234, the results are returned to the client at 240.),,
Litoiu do not explicitly disclose however, In the same field of endeavor, Basavaiah discloses the one or more validation constraints indicate constraints to be satisfied for the transformed dataset to be propagated to a subsequent stage of the data pipeline; selectively propagate the transformed dataset to the subsequent stage of the data pipeline based on the determined propagation permission level; and
selectively implement the subsequent stage of the data pipeline based on the selectively propagated transformed dataset (co. 166, lines 44-67, the routine 6700 may be modified such that blocks 6710 and 6712 represent a first and second element of loop 6716, such that transformation occurs with respect to one or more graphs representing all instances of a journey (e.g., a single graph representing all instances, individual graphs for each instance, etc.). Thereafter, block 6708 can be applied to a result of that transformation, and such that filter criteria are applied to one or more transformed graphs. In this manner, filter criteria may be specified with respect to attributes of transformed graphs, as opposed to attributes of a journey prior to transformation. …, the interface 5900 of FIG. 59 may be modified to support designation of transformations, and such that on designating a transformation, the elements of the filter panel 5910 are updated to reflect any modifications made via the transformation. Illustratively, if a user specifies a transformation that renames a node, panel object 5916 may be modified such that a step is identified by the new node name. Thus, filtering may be applied either prior or subsequent transformations. In some instances, a combination of pre- and post-transformation filters may be specified. For example, a first filter may be specified with respect to pre-transformation graphs, and a second filter may be specified with respect to post-transformation graphs. Thus, the routine 6700 may be modified, for example, to both implement block 6708 as shown in FIG. 67 and also to apply a subsequent set filter of filter criteria after block 6712 to determine one or more transformed graphs that match the subsequent set filter of filter criteria, also see col. 167, lines 1-45).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teaching of Litoiu with the teaching of Basavaiah by including the feature of second transformation in order for Litoiu’s system for reducing resources uses because loop 6716 can occur repeatedly with respect to each set of filter criteria, only a single structured data set is needed to support multiple graph transformations, and the computing cost required to generate the single structured data set (e.g., to query unstructured data and build from events within that data a set of journey instances) is minimized. Implementation of routine 6700 may therefore be superior in terms of resource usage to alternatives for implementing graph transformations, such as modification of the query used to build the journey instances at block 6704. The routine 6700 therefore represents an improvement on such techniques (Basavaiah, col. 167, lines 45-55).
Claims 8 and 15 rejected for similar reasons as stated above, and claim 1.
As per claim 2, the combination of Litoiu, and Basavaiah discloses the method of claim 1, wherein the stage comprises a first stage, the subsequent stage comprises a second stage, the transformed dataset comprises a first transformed dataset, the validation constraints comprise first validation constraints; and the selectively implementing of the subsequent stage comprises: generating a second transformed dataset by performing a second data transformation on the first subset of entries or a second subset of entries of the first transformed dataset; determining one or more second validation constraints to be applied to the second transformed dataset based at least in part on a second security level of the second stage selectively propagating the second transformed dataset to a third stage of the data pipeline based on the determined second propagation permission level; and selectively implement the third stage of the data pipeline based on the selectively propagated second transformed dataset. (Litoiu, [0048], [0052], [0063], [0065] Note: applied to a second data, and is rejected by the same rationales as claim 1) and wherein the verification is performed on one or more of the second transformed datasets (Litoiu, [0063] [0065], Basavaiah discloses selectively propagated transformed dataset (co. 166, lines 44-67, also see col. 167, lines 1-45). Note: system/method my repeat the steps, applied to a second data). The motivation regarding the obviousness of claim 1 is also applied to claim 2.
As per claim 6, the combination of Litoiu, and Basavaiah discloses the method of claim 1, wherein the data transformation is a pre-existent data transformation of the data pipeline (Litoiu, [0042]).
As per claim 7, the combination of Litoiu, and Basavaiah discloses the method of claim 1, further comprising communicating the transformed dataset to an external entity (Litoiu, [0068], [0029]).
Claims 9 and 16 are rejected for similar reasons as stated above, and claim 2.
Claim 20 is rejected for similar reasons as stated above, and claim 6.
5.3. Claims 3-5, 10-14 and 17-19 are rejected under 35 U.S.C. 103 as being unpatentable over Litoiu, and Basavaiah as applied to claim above, and in view of US Patent Application No. 20200012584 to Walters et al (“Walters”).
As per claim 3, the combination of Litoiu, and Basavaiah discloses the invention as described above. Litoiu, and Basavaiah do not explicitly disclose however, In the same field of endeavor, Walter discloses the method of claim 1, wherein the validation constraints comprise data security constraints which define one or more conditions based on which the entry or a different entry in the one or more source datasets is either accepted or rejected for inclusion in the first subset of entries (Walters, [0139]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teaching of Litoiu with the teaching of Walter by including the feature of values, in order for Litoiu’s system for retrieving an input dataset based on the identifier and receiving an input model to perform a desired outcome. An automated system for optimizing a model is disclosed, the system comprising at least one processor and at least one non-transitory memory storing instructions. The system may be configured to perform operations including receiving a data input comprising a desired outcome and an input dataset identifier. The operations may include retrieving an input dataset based on the identifier and receiving an input model to perform a desired outcome. The operations may include using a data synthesis model to create a synthetic dataset based on the input dataset and a similarity metric. The operations may comprise debugging the input model to create a debugged model. The operations may include selecting an actual dataset based on the input dataset and the desired outcome. In some aspects, the operations may include optimizing the debugged model using the actual dataset and storing the optimized model (Walter).
As per claim 4, the combination of Litoiu, Basavaiah and Walter discloses the method of claim 3, wherein the data security constraints define one or more acceptable values for entries of a certain type, and wherein the entry is accepted or rejected based on whether the entry matches the one or more acceptable values (Walters, [0139]). The motivation regarding the obviousness of claim 3 is also applied to claim 4.
As per claim 5, the combination of Litoiu, Basavaiah and Walter discloses the method of claim 3, wherein the data security constraints are defined based on one or more configuration datasets (Litoiu, [0042], also see ([0048], [0063]).
Claims 10 and 17 are rejected for similar reasons as stated above, and claim 3.
Claims 11 and 18 are rejected for similar reasons as stated above, and claim 4.
Claims 12 and 19 are rejected for similar reasons as stated above, and claim 5.
Claim 13 is rejected for similar reasons as stated above, and claim 6.
Claim 14 is rejected for similar reasons as stated above, and claim 7.
6.1. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure as the prior art discloses many of the claim features (See PTO-form 892).
6.2. a). US Patent Application No. 20220121689 to James et al., discloses systems and methods are disclosed for supporting transformations of a graph generated from a query to event data. The event data may be unstructured event data, from which instances of a journey can be identified that represent sequences of related events describing actions performed in a computing environment. When evaluating journey instances, it can be helpful to visualize the instances as a graph. Depending on the instances viewed, a user may desire different modifications to the graph. While such modifications can be made when initially building instances from the unstructured event data, this can limit reuse of the resulting instances (since the modification would also be present when evaluating other subsets). To address this, embodiments of the present disclosure enable graph modifications to be applied to subsets of journey instances after building those instances from unstructured event data, increasing reuse of instances built from a query against the unstructured data.
b). US Patent Application No. 20070260602 to Taylor et al., discloses a system and method for inspecting a data stream for data segments matching one or more patterns each having a predetermined allowable error, which includes filtering a data stream for a plurality of patterns of symbol combinations with a plurality of parallel filter mechanisms, detecting a plurality of potential pattern piece matches, identifying a plurality of potentially matching patterns, reducing the identified plurality of potentially matching patterns to a set of potentially matching patterns with a reduction stage, providing associated data and the reduced set of potentially matching patterns, each having an associated allowable error, to a verification stage, and verifying presence of a pattern match in the data stream from the plurality of patterns of symbol combinations and associated allowable errors with the verification stage.
c). US Patent Application No. 20080229428 to Camiel et al., discloses an autonomous data storage device for storing data files via an external file interface, the external file interface being controllable from an external device, the device comprising: a physical file storage for homogenous storage of files; the external file interface configured to allow sector level access to at least part of the physical file storage to support standard operating file system calls; an internal sector policy management unit located in between the external file interface and the physical file storage for sector level policy enforcement of the physical file storage, for one or more of the sector level managed sectors, the unit having an input for receiving instructions from the external file interface for sector oriented operations, and being configured to carry out sector policy management operations in accordance with.
Conclusion
7.. Any inquiry concerning this communication or earlier communications from the examiner should be directed to HARUNUR RASHID whose telephone number is (571)270-7195. The examiner can normally be reached 9 AM to 5PM.
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HARUNUR . RASHID
Primary Examiner
Art Unit 2497
/HARUNUR RASHID/Primary Examiner, Art Unit 2497
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