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
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 12/8/2025 has been entered.
Claims 1, 9, and 17 are amended
Claims 1-20 are pending
Examiner’s Note: Paragraph 0082 of the specification discloses that computer storage media excludes signals per se
Response to Arguments
Applicant’s amendment to claims 1, 9, and 17 filed on 12/8/2025 regarding, “orchestrating, using a constraint manager, permission [[to]] associated with executing the computations based at least on tracking application of the constraints, wherein the application of the constraints comprises satisfaction and enforcement of the constraints as data flows through the computations of the data pipeline, wherein orchestrating permission [[to]] associated with executing the computations comprises dynamically controlling how executable units of logic that perform the computations that derive collaborative data from the input dataset in a collaborative pipeline are executed.“, necessitated the new ground(s) of rejection presented in this Office action. Therefore, Applicant's arguments with respect to claims 1-20 have been considered but are moot in view of the new ground(s) of rejection.
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 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.
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 of this title, 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.
1.) Claims 1, 2, 9, 10, 17 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over US 20200067789, Khuti in view of US 20160048436, Yamazaki
In regards to claim 1, Khuti teaches a computer system comprising: one or more hardware computer processors; andcomputer memory storing computer-useable instructions that, when used by the one or more hardware processors, cause the one or more hardware computer processors to perform operations(US 20200067789, Khuti, One or more computing apparatus that are local to data sources but remote from the computing platform[i.e. note: implicitly contains a hardware processor] execute services of the cloud edge engine to (i) collect, process and aggregate data from sensors associated with the data sources, (ii) forward data from those data sources for processing by the computing platform and (iii) execute in-memory advanced data analytics.) comprising:
initiating, using a cloud service application, a data pipeline of computations that ingest an input dataset into a data trustee environment and derive collaborative data from the input dataset(US 20200067789, Khuti, para. 0326, collaborating and deploying reusable Systemic Asset Intelligence analytics and applications. Embodiments of the invention constructed and operated as discussed above and adapted for systemic asset intelligence (referred to below, as the “NAUTILIAN Foresight Engine”) comprise cloud-based software that supersedes legacy modelling tools such as Matlab and OSlsoft PI for Industrial Engineers to collaborate on data ingestion, asset models (pumps, compressors, valves etc.), analytical models (vibration, oil temperature, EWMA) using standard software libraries in R, Python, Scala etc. and a user interface where engineering communities can share, critique and deploy code to rapidly develop cloud native predictive applications.), the data pipeline of computations is associated with constraints on the computations, the constraints specified by a tenant agreement among collaborating tenants (US 20200067789, Khuti, para. 0066, Such cloud systems, moreover, can support multi-tenancy by client and asset, allowing data for multiple customers (e.g., enterprises) to be transmitted to, stored on, and/or processed within a single, cloud-based data processing system without risk of data commingling or risk to data security. Multi-tenancy further facilitates the delivery of Industrial SaaS (software as a service) application functionality by taking advantage of economies of scale, pay on usage, lower cost and re-use[e.g. note: spin up].); and Khuti does not teach orchestrating, using a constraint manager, permission associated with executing the computations based at least on tracking application of the constraints, wherein the application of the constraints comprises satisfaction and enforcement of the constraints as data flows through the computations of the data pipeline, wherein orchestrating permission associated with executing the computations comprises dynamically controlling how executable units of logic that perform the computations that derive collaborative data from the input dataset in a collaborative pipeline are executed However, Yamazaki teaches orchestrating, using a constraint manager, permission associated with executing the computations based at least on tracking application of the constraints, wherein the application of the constraints comprises satisfaction and enforcement of the constraints as data flows through the computations of the data pipeline, wherein orchestrating permission associated with executing the computations comprises dynamically controlling how executable units of logic that perform the computations(US 20160048436, Yamazaki, para. 0056,The actual operation of machines in the system may change dynamically with requests from their users, constraints on the system, and the like.) that derive collaborative data from the input dataset in a collaborative pipeline are executed(US 20160048436, Yamazaki, para. 0051, The computation unit 1b determines whether the expected total power consumption[i.e. note: collaborative data] exceeds a limit that the system 2 has to observe. More specifically, the computation unit 1b collects configuration parameters from virtual machines 4 and 4a when it detects booting of the latter virtual machine 4a, as well as consulting the storage unit 1a to retrieve constraints on the system 2 in relation to the booting of the virtual machine 4a. For example, the constraints may specify an upper bound of power consumption in each different physical machine. Then the computation unit 1b determines whether the collected configuration parameters conform[i.e. note: satisfies] to these constraints, and it controls whether to continue or discontinue[i.e. note: determines constraint enforcement] the ongoing boot process for the virtual machine 4a, depending on the determination result.). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to modify the teaching of Khuti with the teaching of Yamazaki because a user would have been motivated to use monitoring of tenant resource usage, taught by Yamazaki, in order to dynamically create and/or update the data flow in the system taught by Khuti based on current activity information(Yamazaki, para. 0067)
In regards to claim 2, the combination of Khuti and Yamazaki teach the computer system claim of 1, wherein the constraints specified by the tenant agreement include an aggregation constraint, and the constraint manager is configured to orchestrate applying the aggregation constraint on a first computation of the computations and not applying the aggregation constraint on subsequent computations of the computations(US 20200067789, Khuti, para. 0012, Still further related aspects of the invention provide such systems and methods, e.g., as described above, in which the aforesaid computing apparatus translate protocols, aggregate, filter, standardize, learn, store and forward, data received from plant sensors, devices, in the plant or other facility.).
In regards to claim 9, Khuti teaches one or more computer storage media storing computer-useable instructions that, when used by one or more computing devices, cause the one or more computing devices to perform operations comprising:
initiating a data pipeline of computations that ingest an input dataset into a data trustee environment and derives collaborative data from the input dataset(US 20200067789, Khuti, para. 0326, collaborating and deploying reusable Systemic Asset Intelligence analytics and applications. Embodiments of the invention constructed and operated as discussed above and adapted for systemic asset intelligence (referred to below, as the “NAUTILIAN Foresight Engine”) comprise cloud-based software that supersedes legacy modelling tools such as Matlab and OSlsoft PI for Industrial Engineers to collaborate on data ingestion, asset models (pumps, compressors, valves etc.), analytical models (vibration, oil temperature, EWMA) using standard software libraries in R, Python, Scala etc. and a user interface where engineering communities can share, critique and deploy code to rapidly develop cloud native predictive applications.), the data pipeline of computations is associated with constraints on the computations, the constraints specified by a tenant agreement among tenants(US 20200067789, Khuti, para. 0066, Such cloud systems, moreover, can support multi-tenancy by client and asset, allowing data for multiple customers (e.g., enterprises) to be transmitted to, stored on, and/or processed within a single, cloud-based data processing system without risk of data commingling or risk to data security. Multi-tenancy further facilitates the delivery of Industrial SaaS (software as a service) application functionality by taking advantage of economies of scale, pay on usage, lower cost and re-use[e.g. note: spin up].); and
Khuti does not teach orchestrating permission associated with executing the computations based at least on tracking whether or not each of the constraints has been satisfied, wherein the application of the constraints comprises satisfaction and enforcement of the constraints as data flows through the computations of the data pipeline, wherein orchestrating permission associated with executing the computations comprises dynamically controlling how executable units of logic that perform the computations that derive collaborative data from the input dataset in a collaborative pipeline are executed However, Yamazaki teaches orchestrating permission associated with executing the computations based at least on tracking whether or not each of the constraints has been satisfied, wherein the application of the constraints comprises satisfaction and enforcement of the constraints as data flows through the computations of the data pipeline, wherein orchestrating permission associated with executing the computations comprises dynamically controlling how executable units of logic that perform the computations that derive collaborative data from the input dataset in a collaborative pipeline are executed(US 20160048436, Yamazaki, para. 0051, The computation unit 1b determines whether the expected total power consumption[i.e. note: collaborative data] exceeds a limit that the system 2 has to observe. More specifically, the computation unit 1b collects configuration parameters from virtual machines 4 and 4a when it detects booting of the latter virtual machine 4a, as well as consulting the storage unit 1a to retrieve constraints on the system 2 in relation to the booting of the virtual machine 4a. For example, the constraints may specify an upper bound of power consumption in each different physical machine. Then the computation unit 1b determines whether the collected configuration parameters conform[i.e. note: satisfies] to these constraints, and it controls whether to continue or discontinue[i.e. note: determines constraint enforcement] the ongoing boot process for the virtual machine 4a, depending on the determination result.). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to modify the teaching of Khuti with the teaching of Yamazaki because a user would have been motivated to use monitoring of tenant resource usage, taught by Yamazaki, in order to dynamically create and/or update the data flow in the system taught by Khuti based on current activity information(Yamazaki, para. 0067)
In regards to claim 10, the combination of Khuti and Yamazaki teach the one or more computer storage media of claim 9, wherein the constraints specified by the tenant agreement include an aggregation constraint, the operations further comprising orchestrating applying the aggregation constraint on a first computation of the computations and not applying the aggregation constraint on subsequent computations of the computations(US 20200067789, Khuti, para. 0012, Still further related aspects of the invention provide such systems and methods, e.g., as described above, in which the aforesaid computing apparatus translate protocols, aggregate, filter, standardize, learn, store and forward, data received from plant sensors, devices, in the plant or other facility.).
In regards to claim 17, Khuti teaches a method comprising:
executing a data pipeline of computations that ingest an input dataset into a data trustee environment and derives collaborative data from the input dataset(US 20200067789, Khuti, para. 0326, collaborating and deploying reusable Systemic Asset Intelligence analytics and applications. Embodiments of the invention constructed and operated as discussed above and adapted for systemic asset intelligence (referred to below, as the “NAUTILIAN Foresight Engine”) comprise cloud-based software that supersedes legacy modelling tools such as Matlab and OSlsoft PI for Industrial Engineers to collaborate on data ingestion, asset models (pumps, compressors, valves etc.), analytical models (vibration, oil temperature, EWMA) using standard software libraries in R, Python, Scala etc. and a user interface where engineering communities can share, critique and deploy code to rapidly develop cloud native predictive applications.), the data pipeline of computations is associated with constraints on the computations, the constraints specified by a tenant agreement among tenants(US 20200067789, Khuti, para. 0066, Such cloud systems, moreover, can support multi-tenancy by client and asset, allowing data for multiple customers (e.g., enterprises) to be transmitted to, stored on, and/or processed within a single, cloud-based data processing system without risk of data commingling or risk to data security. Multi-tenancy further facilitates the delivery of Industrial SaaS (software as a service) application functionality by taking advantage of economies of scale, pay on usage, lower cost and re-use[e.g. note: spin up].); and Khuti does not teach orchestrating permission associated with executing the computations based at least on tracking whether or not each of the constraints has been satisfied , wherein the application of the constraints comprises satisfaction and enforcement of the constraints as data flows through the computations of the data pipeline, wherein orchestrating permission associated with executing the computations comprises dynamically controlling how executable units of logic that perform the computations that derive collaborative data from the input dataset in a collaborative pipeline are executed However, Yamazaki teaches orchestrating permission associated with executing the computations based at least on tracking whether or not each of the constraints has been satisfied , wherein the application of the constraints comprises satisfaction and enforcement of the constraints as data flows through the computations of the data pipeline, wherein orchestrating permission associated with executing the computations comprises dynamically controlling how executable units of logic that perform the computations that derive collaborative data from the input dataset in a collaborative pipeline are executed (US 20160048436, Yamazaki, para. 0051, The computation unit 1b determines whether the expected total power consumption[i.e. note: collaborative data] exceeds a limit that the system 2 has to observe. More specifically, the computation unit 1b collects configuration parameters from virtual machines 4 and 4a when it detects booting of the latter virtual machine 4a, as well as consulting the storage unit 1a to retrieve constraints on the system 2 in relation to the booting of the virtual machine 4a. For example, the constraints may specify an upper bound of power consumption in each different physical machine. Then the computation unit 1b determines whether the collected configuration parameters conform[i.e. note: satisfies] to these constraints, and it controls whether to continue or discontinue[i.e. note: determines constraint enforcement] the ongoing boot process for the virtual machine 4a, depending on the determination result.). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to modify the teaching of Khuti with the teaching of Yamazaki because a user would have been motivated to use monitoring of tenant resource usage, taught by Yamazaki, in order to dynamically create and/or update the data flow in the system taught by Khuti based on current activity information(Yamazaki, para. 0067)
In regards to claim 18, the combination of Khuti and Yamazaki teach the method of claim 17, wherein the constraints specified by the tenant agreement include an aggregation constraint, the method further comprising orchestrating applying the aggregation constraint on a first computation of the computations and not applying the aggregation constraint on subsequent computations of the computations(US 20200067789, Khuti, para. 0012, Still further related aspects of the invention provide such systems and methods, e.g., as described above, in which the aforesaid computing apparatus translate protocols, aggregate, filter, standardize, learn, store and forward, data received from plant sensors, devices, in the plant or other facility.).
2.) Claims 3, 11 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over US 20200067789, Khuti in view of US 20160048436, Yamazaki and further in view of US 20140373016, Ruggiero
In regards to claim 3, the combination of Khuti and Yamazaki teach the computer system claim of 1. The combination of Khuti and Yamazaki do not teach wherein the constraints specified by the tenant agreement include different constraints applicable to different input datasets, one of the computations specified by the tenant agreement is a merging operation that merges the different input datasets, and the constraint manager instruct application of a stricter constraint of the different constraints during the merging operation However, Ruggiero teaches wherein the constraints specified by the tenant agreement include different constraints applicable to different input datasets, one of the computations specified by the tenant agreement is a merging operation that merges the different input datasets, and the constraint manager instruct application of a stricter constraint of the different constraints during the merging operation (US 20140373016, Ruggiero, para. 0039, In some embodiments, when a node completes processing, it merges its set of objects or data with the set of objects or data calculated by the other nodes processing partitions from the same input data set. In some embodiments, when a set of new objects calculated by a node is merged with a set objects previously calculated by other nodes processing partitions from the same input data set, the set of new objects is checked to verify that it does not contain any objects already written to the previously calculated set. Any repeated elements found are eliminated.). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to modify the teaching of the combination of Khuti and Yamazaki with the teaching of Ruggiero because a user would have been motivated to expedite processing for multiple tenants in the system taught by the combination of Khuti and Yamazaki by employing sequentially and parallel flow of operations, taught by Ruggiero, in order to complete each job task in a reasonable amount of time(Ruggiero, para. 0003 and 0015)
In regards to claim 11, the combination of Khuti and Yamazaki teach the one or more computer storage media of claim 9. The combination of Khuti and Yamazaki do not teach wherein the constraints specified by the tenant agreement include different constraints applicable to different input datasets, one of the computations specified by the tenant agreement is a merging operation that merges the different input datasets, and the operations further comprise instructing application of a stricter constraint of the different constraints during the merging operation However, Ruggiero teaches wherein the constraints specified by the tenant agreement include different constraints applicable to different input datasets, one of the computations specified by the tenant agreement is a merging operation that merges the different input datasets, and the operations further comprise instructing application of a stricter constraint of the different constraints during the merging operation (US 20140373016, Ruggiero, para. 0039, In some embodiments, when a node completes processing, it merges its set of objects or data with the set of objects or data calculated by the other nodes processing partitions from the same input data set. In some embodiments, when a set of new objects calculated by a node is merged with a set objects previously calculated by other nodes processing partitions from the same input data set, the set of new objects is checked to verify that it does not contain any objects already written to the previously calculated set. Any repeated elements found are eliminated.). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to modify the teaching of the combination of Khuti and Yamazaki with the teaching of Ruggiero because a user would have been motivated to expedite processing for multiple tenants in the system taught by the combination of Khuti and Yamazaki by employing sequentially and parallel flow of operations, taught by Ruggiero, in order to complete each job task in a reasonable amount of time(Ruggiero, para. 0003 and 0015)
In regards to claim 19, the combination of Khuti and Yamazaki teach the method of claim 17. The combination of Khuti and Yamazaki do not teach wherein the constraints specified by the tenant agreement include different constraints applicable to different input datasets, one of the computations specified by the tenant agreement is a merging operation that merges the different input datasets, and the method further comprises instructing application of a stricter constraint of the different constraints during the merging operation However, Ruggiero teaches wherein the constraints specified by the tenant agreement include different constraints applicable to different input datasets, one of the computations specified by the tenant agreement is a merging operation that merges the different input datasets, and the method further comprises instructing application of a stricter constraint of the different constraints during the merging operation (US 20140373016, Ruggiero, para. 0039, In some embodiments, when a node completes processing, it merges its set of objects or data with the set of objects or data calculated by the other nodes processing partitions from the same input data set. In some embodiments, when a set of new objects calculated by a node is merged with a set objects previously calculated by other nodes processing partitions from the same input data set, the set of new objects is checked to verify that it does not contain any objects already written to the previously calculated set. Any repeated elements found are eliminated.). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to modify the teaching of the combination of Khuti and Yamazaki with the teaching of Ruggiero because a user would have been motivated to expedite processing for multiple tenants in the system taught by the combination of Khuti and Yamazaki by employing sequentially and parallel flow of operations, taught by Ruggiero, in order to complete each job task in a reasonable amount of time(Ruggiero, para. 0003 and 0015)
3.) Claims 4, 12, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over US 20200067789, Khuti in view of US 20160048436, Yamazaki and further in view of US 20200311294, Sim-Tang
In regards to claim 4, the combination of Khuti and Yamazaki teach the computer system claim of 1. The combination of Khuti and Yamazaki do not teach wherein the data pipeline associate attribution metadata indicating ownership or providence of the data generated by the computations of the data pipeline as the data flows through the computations of the data pipeline However, Sim-Tang teaches wherein the data pipeline associate attribution metadata indicating ownership or providence of the data generated by the computations of the data pipeline as the data flows through the computations of the data pipeline (US 20200311294, Sim-Tang, para. 0171 and 0176: [0171]- As shown in FIG. 6B, a sample Data Server object contains an association to a Corporate Data Connector, a data source name (such as a directory or a database name), metadata associated with the data server (e.g., owner identity, security classification, properties, and attributes),[0176]- A Project Container object manages job scheduling and execution, and track execution history and results. A Project Container object is linked to its Data User Environment (0312), and contains a project ID, a project name, the dates of its creation and updates, metadata, registered Datasets (0330-a, b, c, z), data pipelines and programs, jobs, job scheduling, and execution history and results.). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to modify the teaching of the combination of Khuti and Yamazaki with the teaching of Sim-Tang because a user would have been motivated to implement role-based security and privacy access controls, taught by Sim-Tang, in order to preserve data security of the data shared by the multiple tenants in the system taught by the combination of Khuti and Yamazaki(Sim-Tang, para. 0070)
In regards to claim 12 the combination of Khuti and Yamazaki teach the one or more computer storage media of claim 9. The combination of Khuti and Yamazaki do not teach wherein the data pipeline associate attribution metadata indicating ownership or providence of the data generated by the computations of the data pipeline as the data flows through the computations of the data pipeline However, Sim-Tang teaches wherein the data pipeline associate attribution metadata indicating ownership or providence of the data generated by the computations of the data pipeline as the data flows through the computations of the data pipeline (US 20200311294, Sim-Tang, para. 0171 and 0176: [0171]- As shown in FIG. 6B, a sample Data Server object contains an association to a Corporate Data Connector, a data source name (such as a directory or a database name), metadata associated with the data server (e.g., owner identity, security classification, properties, and attributes),[0176]- A Project Container object manages job scheduling and execution, and track execution history and results. A Project Container object is linked to its Data User Environment (0312), and contains a project ID, a project name, the dates of its creation and updates, metadata, registered Datasets (0330-a, b, c, z), data pipelines and programs, jobs, job scheduling, and execution history and results.). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to modify the teaching of the combination of Khuti and Yamazaki with the teaching of Sim-Tang because a user would have been motivated to implement role-based security and privacy access controls, taught by Sim-Tang, in order to preserve data security of the data shared by the multiple tenants in the system taught by the combination of Khuti and Yamazaki(Sim-Tang, para. 0070)
In regards to claim 20, the combination of Khuti and Yamazaki teach the method of claim 17. The combination of Khuti and Yamazaki do not teach wherein the data pipeline associate attribution metadata indicating ownership or providence of the data generated by the computations of the data pipeline as the data flows through the computations of the data pipeline However, Sim-Tang teaches wherein the data pipeline associate attribution metadata indicating ownership or providence of the data generated by the computations of the data pipeline as the data flows through the computations of the data pipeline (US 20200311294, Sim-Tang, para. 0171 and 0176: [0171]- As shown in FIG. 6B, a sample Data Server object contains an association to a Corporate Data Connector, a data source name (such as a directory or a database name), metadata associated with the data server (e.g., owner identity, security classification, properties, and attributes),[0176]- A Project Container object manages job scheduling and execution, and track execution history and results. A Project Container object is linked to its Data User Environment (0312), and contains a project ID, a project name, the dates of its creation and updates, metadata, registered Datasets (0330-a, b, c, z), data pipelines and programs, jobs, job scheduling, and execution history and results.). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to modify the teaching of the combination of Khuti and Yamazaki with the teaching of Sim-Tang because a user would have been motivated to implement role-based security and privacy access controls, taught by Sim-Tang, in order to preserve data security of the data shared by the multiple tenants in the system taught by the combination of Khuti and Yamazaki(Sim-Tang, para. 0070)
4.) Claims 5 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over US 20200067789, Khuti in view of US 20160048436, Yamazaki and further in view of US 20180219674, Mullins
In regards to claim 5, the combination of Khuti and Yamazaki teach the computer system claim 1. The combination of Khuti and Yamazaki do not teach wherein the data pipeline delete intermediate data generated by the computations of the data pipeline upon generating the collaborative data However, Mullins teaches wherein the data pipeline delete intermediate data generated by the computations of the data pipeline upon generating the collaborative data (US 20180219674, Mullins, para. 0042 and 0086: [0042]- If, at determination operation 112, it is determined that there are not additional users, flow branches NO to operation 114, where the second encrypted resource is stored as a successively encrypted resource. Intermediate data generated during the successive encryption process (e.g. the resource and the first encrypted resource) may not be retained and may instead be deleted.[0086]- Data/information generated or captured by the mobile computing device 800 and stored via the system 802 may be stored locally on the mobile computing device 800, as described above, or the data may be stored on any number of storage media that may be accessed by the device via the radio interface layer 872 or via a wired connection between the mobile computing device 800 and a separate computing device associated with the mobile computing device 800,….such data/information may be readily transferred between computing devices for storage and use according to well-known data/information transfer and storage means, including electronic mail and collaborative data /information sharing systems.[i.e. note: generated intermediate data may be deleted, wherein the intermediate data is associated with collaborative data]). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to modify the teaching of the combination of Khuti and Yamazaki with the teaching of Mullins because a user would have been motivated to multiply encrypt data using a plurality of keys, taught by Mullins, in order to enhance data transmission security in the system taught by the combination of Khuti and Yamazaki(Mullins, para. 0035)
In regards to claim 13, the combination of Khuti and Yamazaki teach the one or more computer storage media of claim 9. The combination of Khuti and Yamazaki do not teach wherein the data pipeline delete intermediate data generated by the computations of the data pipeline upon generating the collaborative data However, Mullins teaches wherein the data pipeline delete intermediate data generated by the computations of the data pipeline upon generating the collaborative data (US 20180219674, Mullins, para. 0042 and 0086: [0042]- If, at determination operation 112, it is determined that there are not additional users, flow branches NO to operation 114, where the second encrypted resource is stored as a successively encrypted resource. Intermediate data generated during the successive encryption process (e.g. the resource and the first encrypted resource) may not be retained and may instead be deleted.[0086]- Data/information generated or captured by the mobile computing device 800 and stored via the system 802 may be stored locally on the mobile computing device 800, as described above, or the data may be stored on any number of storage media that may be accessed by the device via the radio interface layer 872 or via a wired connection between the mobile computing device 800 and a separate computing device associated with the mobile computing device 800,….such data/information may be readily transferred between computing devices for storage and use according to well-known data/information transfer and storage means, including electronic mail and collaborative data /information sharing systems.[i.e. note: generated intermediate data may be deleted, wherein the intermediate data is associated with collaborative data]). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to modify the teaching of the combination of Khuti and Yamazaki with the teaching of Mullins because a user would have been motivated to multiply encrypt data using a plurality of keys, taught by Mullins, in order to enhance data transmission security in the system taught by the combination of Khuti and Yamazaki(Mullins, para. 0035)
5.) Claims 6 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over US 20200067789, Khuti in view of US 20160048436, Yamazaki and further in view of US 20150319192, Cabrera
In regards to claim 6, the combination of Khuti and Yamazaki teach the computer system claim of 1. The combination of Khuti and Yamazaki do not teach wherein the data pipeline determine whether to store the collaborative data in the data trustee environment or output the collaborate data from the data trustee environment based on a corresponding one of the constraints specified by the tenant agreement However, Cabrera teaches wherein the data pipeline determine whether to store the collaborative data in the data trustee environment or output the collaborate data from the data trustee environment based on a corresponding one of the constraints specified by the tenant agreement (US 20150319192, Cabrera, para. 0044 and 0079: [0044]- The tenant computing environment 11 includes a tenant secrets database 110, a tenant secrets policy 111, and a policy execution engine 112, according to one embodiment. The tenant secrets database 110 also includes secrets 113, e.g., one or more passwords, encryption keys, and digital certificates that are different than the secrets 103, according to one embodiment.[0079]- If the cross-tenant policies 147 do not permit the requested secrets sharing, the process 300 proceeds to: block 310, where the service provider policy manager 141 issues a request rejection; block 312, where the multi-tenant asset 135 issues a request rejection; and block 314, where the application 136 receives notification of the request rejection and notifies the first tenant, e.g., the tenant computing environment 10, according to one embodiment.[i.e. note: when secret is prevented from being shared[i.e. no output], the secret remains stored in a database]). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to modify the teaching of the combination of Khuti and Yamazaki with the teaching of Cabrera because a user would have been motivated to compare the multiple-tenant security policies with the required system policy, taught by Cabrera, in order to enhance the overall system security for the system taught by the combination of Khuti and Yamazaki(Cabrera, para. 0004)
In regards claim 14, the combination of Khuti and Yamazaki teach the one or more computer storage media of claim 9. The combination of Khuti and Yamazaki do not teach wherein the data pipeline determine whether to store the collaborative data in the data trustee environment or output the collaborate data from the data trustee environment based on a corresponding one of the constraints specified by the tenant agreement However, Cabrera teaches wherein the data pipeline determine whether to store the collaborative data in the data trustee environment or output the collaborate data from the data trustee environment based on a corresponding one of the constraints specified by the tenant agreement (US 20150319192, Cabrera, para. 0044 and 0079: [0044]- The tenant computing environment 11 includes a tenant secrets database 110, a tenant secrets policy 111, and a policy execution engine 112, according to one embodiment. The tenant secrets database 110 also includes secrets 113, e.g., one or more passwords, encryption keys, and digital certificates that are different than the secrets 103, according to one embodiment.[0079]- If the cross-tenant policies 147 do not permit the requested secrets sharing, the process 300 proceeds to: block 310, where the service provider policy manager 141 issues a request rejection; block 312, where the multi-tenant asset 135 issues a request rejection; and block 314, where the application 136 receives notification of the request rejection and notifies the first tenant, e.g., the tenant computing environment 10, according to one embodiment.[i.e. note: when secret is prevented from being shared[i.e. no output], the secret remains stored in a database]). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to modify the teaching of the combination of Khuti and Yamazaki with the teaching of Cabrera because a user would have been motivated to compare the multiple-tenant security policies with the required system policy, taught by Cabrera, in order to enhance the overall system security for the system taught by the combination of Khuti and Yamazaki(Cabrera, para. 0004)
6.) Claims 7, 8, 15, and 16 are rejected under 35 U.S.C. 103 as being unpatentable over US 20200067789, Khuti in view of US 20160048436, Yamazaki and further in view of US 20190370370, Wittern
In regards to claim 7, the combination of Khuti and Yamazaki teach the computer system claim of 1. The combination of Khuti and Yamazaki do not teach wherein the computations of the data pipeline are associated with a corresponding schema specified by the tenant agreement However, Wittern teaches wherein the computations of the data pipeline are associated with a corresponding schema specified by the tenant agreement (US 20190370370, Wittern, para. 0002, In one or more embodiments described herein, systems, computer-implemented methods, apparatuses and/or computer program products that can autonomously generate a schema for one or more graph query language wrappers that can translate one or more queries to one or more requests against a target application programming interface are described.). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to modify the teaching of the combination of Khuti and Yamazaki with the teaching of Wittern because a user would have been motivated to enhance data security of sensitive information, taught by the combination of Khuti and Poras, by performing a sanitation procedure, taught by Wittern(Wittern, para. 0045)
In regards to claim 8, the combination of Khuti and Yamazaki teach the computer system claim of 1. The combination of Khuti and Yamazaki do not teach wherein the constraint manager orchestrate enforcement of a sanitation constraint, specified by the tenant agreement and requiring sanitation of values coming from one or more fields of the input dataset, based at least on tracking transformation applied to the values by the computations of the data pipeline as the data flows through the computations of the data pipeline However, Wittern teaches wherein the constraint manager orchestrate enforcement of a sanitation constraint, specified by the tenant agreement and requiring sanitation of values coming from one or more fields of the input dataset, based at least on tracking transformation applied to the values by the computations of the data pipeline as the data flows through the computations of the data pipeline (US 20190370370, Wittern, para. 0003, The schema can comprise a sanitation map that can delineate a relation between a raw data format expected by the target application programming interface and a sanitized data format exposed by the graph query language wrapper.). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to modify the teaching of the combination of Khuti and Yamazaki with the teaching of Wittern because a user would have been motivated to enhance data security of sensitive information, taught by the combination of Khuti and Poras, by performing a sanitation procedure, taught by Wittern(Wittern, para. 0045)
In regards to claim 15, the combination of Khuti and Yamazaki teach the one or more computer storage media of claim 9. The combination of Khuti and Yamazaki do not teach wherein the computations of the data pipeline are configured to associated with a corresponding schema specified by the tenant agreement However, Wittern teaches wherein the computations of the data pipeline are configured to associated with a corresponding schema specified by the tenant agreement (US 20190370370, Wittern, para. 0002, In one or more embodiments described herein, systems, computer-implemented methods, apparatuses and/or computer program products that can autonomously generate a schema for one or more graph query language wrappers that can translate one or more queries to one or more requests against a target application programming interface are described.). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to modify the teaching of the combination of Khuti and Yamazaki with the teaching of Wittern because a user would have been motivated to enhance data security of sensitive information, taught by the combination of Khuti and Poras, by performing a sanitation procedure, taught by Wittern(Wittern, para. 0045)
In regards to claim 16, the combination of Khuti and Yamazaki teach the one or more computer storage media of claim 9. The combination of Khuti and Yamazaki do not teach the operations further comprising orchestrating enforcement of a sanitation constraint, specified by the tenant agreement and requiring sanitation of values coming from one or more fields of the input dataset, based at least on tracking transformation applied to the values by the computations of the data pipeline as the data flows through the computations of the data pipeline However, Wittern teaches the operations further comprising orchestrating enforcement of a sanitation constraint, specified by the tenant agreement and requiring sanitation of values coming from one or more fields of the input dataset, based at least on tracking transformation applied to the values by the computations of the data pipeline as the data flows through the computations of the data pipeline (US 20190370370, Wittern, para. 0003, The schema can comprise a sanitation map that can delineate a relation between a raw data format expected by the target application programming interface and a sanitized data format exposed by the graph query language wrapper.). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to modify the teaching of the combination of Khuti and Yamazaki with the teaching of Wittern because a user would have been motivated to enhance data security of sensitive information, taught by the combination of Khuti and Poras, by performing a sanitation procedure, taught by Wittern(Wittern, para. 0045)
CONCLUSION
Any inquiry concerning this communication or earlier communications from the examiner should be directed to GREGORY LANE whose telephone number is (571)270-7469. The examiner can normally be reached on 571 270 7469 from 8:00 AM to 6:00 PM.
If attempts to reach the examiner by telephone are unsuccessful, the examiner's supervisor, Taghi Arani, can be reached on 571 272 3787. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/GREGORY A LANE/Examiner, Art Unit 2438
/TAGHI T ARANI/Supervisory Patent Examiner, Art Unit 2438