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 .
Claim Objections
Claims 4, 12, and 18 are objected to because of the following informalities:
Claim 4 recites “responsive to determining that a first allocation rule in the set of allocation rules selects a first storage category and a second allocation rule in the set of allocation rules selects a second storage category, the first storage category, wherein the first storage category is a higher performance category than the second storage category”. The underlined limitation, “the first storage category”, seems to be redundant and should be removed. Claims 12 and 18 recite similar limitations and they are objected for the similar reasons.
Appropriate correction is required.
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.
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, 2, 5, 7, 10, 13, 15, 16, and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Sohail et al. (US2022/0292221), hereinafter Sohail in view of Mehta et al. (US2023/0067561), hereinafter Mehta.
Regarding claims 1, 7, and 15, taking claim 1 as exemplary, Sohail teaches a computer-implemented method comprising:
classifying, by analyzing data generated by a first edge device (Sohail, [0027], the edge computing system 114 is configured to analyze IoT data generated by the IoT devices 112 to perform certain actions, before the IoT data is sent to the cloud computing system 140 for storage; Fig.1, see Device 112), the data into a sensitivity category in a set of sensitivity categories (Sohail, [0051], The data classification module 213 implements methods that are configured to classify a data sensitivity level of IoT data … IoT data can be classified into one more of a plurality of predefined sensitivity levels);
classifying, by analyzing the data, the data into a redundancy category in a set of redundancy categories (Sohail, [0029], utilizes the classified sensitivity level of IoT data to determine minimum security requirements (e.g., type of encryption, etc.) for protecting the IoT data; [0038], the data protection services 184 perform functions such as such as … and data protection functions such as data replication (synchronous and/or asynchronous), snapshots, data backup, Reed-Solomon error correction coding, and other data protection schemes based on data striping and parity (e.g., RAID), and other types of data management functions);
classifying, into a region category in a set of region categories, a region in which the first edge device is located;
selecting, by applying a set of allocation rules, a storage category of the data generated by the first edge device, the selecting resulting in a selected storage category, the set of allocation rules applied according to the sensitivity category, the redundancy category, and the region category (Sohail, [0082], The intelligent database allocation module 540 implements methods that are configured to dynamically select a given type of repository, storage tier, memory tier, etc., to store the data which is ingested by the cloud computing system 140; [0021]; [0029], the security management system 116 performs “security tiering” management operations that are configured to determine a proper security tier of IoT data residing in the cloud computing system 140 for purposes of data storage); and
causing storage of the data generated by the first edge device in a storage device in the selected storage category (Sohail, [0038], The IoT data is stored in different databases or file repositories depending on, e.g., the type of IoT data (e.g., structured, unstructured), the encryption type/level applied to the IoT data, the type of secured data analytics that are to be applied to process the IoT data, the classified security tier of the IoT data).
Sohail does not explicitly teach classifying, into a region category in a set of region categories, a region in which the first edge device is located, and selecting the set of allocation rules applied according to region category, as claimed.
However, Sohail in view of Mehta teaches classifying, by analyzing the data, the data into a redundancy category in a set of redundancy categories (Mehta, [0007], the system selects a data protection plan that satisfies the Tenant's preferences, and uses the storage resources available to the data protection plan to key in on a Resource Pool; [0008]);
classifying, into a region category in a set of region categories, a region in which the first edge device is located (Mehta, [0352], based on determining that the data source is located within the first region), and
selecting, by applying a set of allocation rules, a storage category of the data generated by the first edge device, the selecting resulting in a selected storage category, the set of allocation rules applied according to the sensitivity category, the redundancy category, and the region category (Mehta, [0007]; [0297]; [0309]; [0324]; [0352], based on determining that the data source is located within the first region and further that the second data protection plan references data storage resources in the first region, selecting the storage pool of the first resource pool as a storage target for secondary copies of the data source; [0353], the data storage management system selects a resource pool referencing a storage pool in a same region as the given data source; Sohail, [0021]; [0029]; [0082]).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified Sohail to incorporate teachings of Mehta to determine protection plan as well as location/region of data source, and select a storage based on sensitivity, redundancy/protection, and region of the data source. A person of ordinary skill in the art would have been motivated to combine Sohail with Mehta because it improves efficiency and performance of the storage system disclosed in Sohail by having a data storage topologically close/local to a data source to reduce bandwidth and data transport cost (Mehta, [0339]).
Claims 7 and 15 have similar limitations as claim 1 and they are rejected for the similar reasons.
Regarding claims 2, 10, and 16, taking claim 2 as exemplary, the combination of Sohail teaches all the features with respect to claim 1 as outlined above. The combination of Sohail further teaches the computer-implemented method of claim 1, wherein an allocation rule in the set of allocation rules comprises selecting, responsive to determining that the data is in a highest sensitivity category in the set of sensitivity categories, a highest-performance storage category in the set of storage categories (Sohail, [0083], As another example, a heartbeat detection and cardiac monitoring system can be implemented using IoT medical sensor devices and associated medical analysis applications to track cardiac conditions of patients. In such instances, there may be a need for real-time monitoring and analysis of the cardiac functions of individuals that have undergone surgery or are known to be at risk for cardiac failure. In such instances, these high priority devices and applications can have data stored in low latency, high performance memory and/or storage tiers; Note -life critical, time sensitive).
Claims 10 and 16 have similar limitations as claim 2 and they are rejected for the similar reasons.
Regarding claims 5, 13, and 19, taking claim 5 as exemplary, the combination of Sohail teaches all the features with respect to claim 1 as outlined above. The combination of Sohail further teaches the computer-implemented method of claim 1, wherein the storage device is in the region category (Mehta, [0353], the data storage management system selects a resource pool referencing a storage pool in a same region as the given data source).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified Sohail to incorporate teachings of Mehta to determine location/region of data source and select a storage based on sensitivity, redundancy/protection, and region of the data source. A person of ordinary skill in the art would have been motivated to combine Sohail with Mehta because it improves efficiency and performance of the storage system disclosed in Sohail by having a data storage topologically close/local to a data source to reduce bandwidth and data transport cost (Mehta, [0339]).
Claims 13 and 19 have similar limitations as claim 5 and they are rejected for the similar reasons.
Claim(s) 3, 11, and 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over the combination of Sohail and Mehta as applied to claims 1, 7, and 15 respectively above, and further in view of Vankamamidi et al. (US2024/0028267), hereinafter Vankamamidi.
Regarding claim 3, the combination of Sohail teaches all the features with respect to claim 1 as outlined above. The combination of Sohail does not explicitly teach the computer-implemented method of claim 1, wherein an allocation rule in the set of allocation rules comprises selecting, responsive to determining that the data is in a highest redundancy category in the set of redundancy categories, a lowest-performance storage category in the set of storage categories, as claimed.
However, the combination of Sohail in view of Vankamamidi teaches the computer-implemented method of claim 1, wherein an allocation rule in the set of allocation rules comprises selecting, responsive to determining that the data is in a highest redundancy category in the set of redundancy categories, a lowest-performance storage category in the set of storage categories (Vankamamiki, [0065], the RAID engine may choose a RAID configuration with higher redundancy such as RAID6 over RAID5 if the RAID configuration has slower storage drives).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified the combination of Sohail to incorporate teachings of Vankamamidi to match a lowest-performance storage device to a highest redundancy level. A person of ordinary skill in the art would have been motivated to combine the teachings of the combination of Sohail with Vankamamidi because it improves efficiency of the storage system disclosed in the combination of Sohail by balancing performance and redundancy to optimize cost effectiveness.
Claims 11 and 17 have similar limitations as claim 3 and they are rejected for the similar reasons.
Claim(s) 4, 12, and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over the combination of Sohail and Mehta as applied to claims 1, 7, and 15 respectively above, and further in view of Todd et al. (US8,935,474), hereinafter Todd.
Regarding claims 4, 12, and 18, taking claim 4 as exemplary, the combination of Sohail teaches all the features with respect to claim 1 as outlined above. The combination of Sohail does not explicitly teach the computer-implemented method of claim 1, wherein applying the set of allocation rules comprises selecting, responsive to determining that a first allocation rule in the set of allocation rules selects a first storage category and a second allocation rule in the set of allocation rules selects a second storage category, the first storage category, wherein the first storage category is a higher performance category than the second storage category, as claimed.
However, the combination of Sohail in view of Todd teaches the computer-implemented method of claim 1, wherein applying the set of allocation rules comprises selecting, responsive to determining that a first allocation rule in the set of allocation rules selects a first storage category and a second allocation rule in the set of allocation rules selects a second storage category, the first storage category, wherein the first storage category is a higher performance category than the second storage category (Todd, col.2, lines 32-43, lines, One tier may provide fast access to data and may serve as a transactional storage tier; col.5, lines 45-54, such a rule may also include information from which a tier of a multi-tier OAS system can be identified and used for storing the fragment when those metadata criteria are met; col.5, line 61 – col.6, line 6, if a first rule specifies a storage capability that is not available on one of the plurality of storage tiers, even if the predicate portion of the first rule is satisfied, a second rule may be applied to select a target tier.).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified the combination of Sohail to incorporate teachings of Todd to obtain one or more results from one or more applied rules and one of the results is a higher performance/fast access storage category. A person of ordinary skill in the art would have been motivated to combine the teachings of the combination of Sohail with Todd because it improves efficiency of the storage system disclosed in the combination of Sohail by obtaining multiple results from different rules/policies to select the most appropriate one.
Claims 12 and 18 have similar limitations as claim 4 and they are rejected for the similar reasons.
Claim(s) 6, 14, and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over the combination of Sohail and Mehta as applied to claims 1, 7, and 15 respectively above, and further in view of Harmon et al. (US2018/0241569), hereinafter Harmon.
Regarding claims 6, 14, and 20, taking claim 6 as exemplary, the combination of Sohail teaches all the features with respect to claim 1 as outlined above. The combination of Sohail does not explicitly teach the computer-implemented method of claim 1, wherein the storage device is in a second region category, the second region category selected according to a number of edge devices classified into the second region category relative to a number of edge devices classified into the region category, as claimed.
However, the combination of Sohail in view of Harmon teaches the computer-implemented method of claim 1, wherein the storage device is in a second region category, the second region category selected according to a number of edge devices classified into the second region category relative to a number of edge devices classified into the region category (Harmon, [0029], Since the geographic locations of the servers are also known, a server can be selected that is central to the geographic locations of the users, within a predetermined threshold distance of the most number of users, or based on other geographic location-based selection criteria).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified the combination of Sohail to incorporate teachings of Harmon to select a storage server based on number of users in a region. A person of ordinary skill in the art would have been motivated to combine the combination of Sohail with Harmon because it improves flexibility of the storage system disclosed in the combination of Sohail by allowing the storage system to select a storage server based on different geographic selection criteria.
Claims 14 and 20 have similar limitations as claim 6 and they are rejected for the similar reasons.
Claim(s) 8 and 9 is/are rejected under 35 U.S.C. 103 as being unpatentable over the combination of Sohail and Mehta as applied to claim 7 above, and further in view of Lei et al. (US2022/0237446), hereinafter Lei.
Regarding claim 8, the combination of Sohail teaches all the features with respect to claim 7 as outlined above. The combination of Sohail does not explicitly teach the computer program product of claim 7, wherein the stored program instructions are stored in a computer readable storage device in a data processing system, and wherein the stored program instructions are transferred over a network from a remote data processing system, as claimed.
However, the combination of Sohail in view of Lei teaches the computer program product of claim 7, wherein the stored program instructions are stored in a computer readable storage device in a data processing system, and wherein the stored program instructions are transferred over a network from a remote data processing system (Lei, [0110]; claim 15).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified the combination of Sohail to incorporate teachings of Lei to download program instructions in response to a request over network. A person of ordinary skill in the art would have been motivated to combine the teachings of the combination of Sohail with Lei because it improves efficiency of the storage system disclosed in the combination of Sohail by providing flexibility to access program instructions across a network.
Regarding claim 9, the combination of Sohail teaches all the features with respect to claim 7 as outlined above. The combination of Sohail does not explicitly teach the computer program product of claim 7, wherein the stored program instructions are stored in a computer readable storage device in a server data processing system, and wherein the stored program instructions are downloaded in response to a request over a network to a remote data processing system for use in a computer readable storage device associated with the remote data processing system, further comprising: program instructions to meter use of the program instructions associated with the request; and program instructions to generate an invoice based on the metered use, as claimed.
However, the combination of Sohail in view of Lei teaches the computer program product of claim 7, wherein the stored program instructions are stored in a computer readable storage device in a server data processing system, and wherein the stored program instructions are downloaded in response to a request over a network to a remote data processing system for use in a computer readable storage device associated with the remote data processing system, further comprising: program instructions to meter use of the program instructions associated with the request; and program instructions to generate an invoice based on the metered use (Lei, [0110], Computer readable program instructions described herein can be downloaded to respective computing/processing devices from a computer readable storage medium or to an external computer or external storage device via a network; claim 16).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified the combination of Sohail to incorporate teachings of Lei to download program instructions in response to a request over network. A person of ordinary skill in the art would have been motivated to combine the teachings of the combination of Sohail with Lei because it improves efficiency of the storage system disclosed in the combination of Sohail by providing flexibility to access program instructions across a network.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Cherubini et al. (US2016/0070492) teaches classifying ingested data and making storage decisions based on the classification.
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/NANCI N WONG/Primary Examiner, Art Unit 2137
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