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 .
This action is responsive to the communication filed 5/22/2024.
Claims 1-20 are presented for examination.
Examiner Notes
Examiner cites particular columns, paragraphs, figures and line numbers in the references as applied to the claims below for the convenience of the applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested that, in preparing responses, the applicant fully consider the references in entirely as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the examiner.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 5/22/2024. The submissions are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 16-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
Regarding to Claim 16, Claim 16 recites “A computer program product comprising: a set of one or more computer readable storage media” that store program instructions. Such claimed “a set of one or more computer readable storage media” under BRI is possible to include signals per se (note: [0013] from the specification does include statement like “A computer-readable storage medium, as that term is used in the present disclosure, is not to be construed as storage in the form of transitory signals per se”; however, such statement is defining “computer-readable storage medium” while the claim 16 recites “computer readable storage media”. In this way, the statement from [0013] of specification does not necessarily guarantee that the claimed “computer readable storage media” under BRI to exclude signal per se). Signals are directed to a non-statutory subject matter. Thus, Claim 16 is rejected under 35 U.S.C. 101 for directing to a non-statutory subject matter. Examiner suggests amend the claim elements above as:
A computer program product comprising:
a set of one or more non-transitory computer readable storage media;
in order to draw the claim to non-transitory subject matter.
Claims 17-20 are rejected for failing to cure the deficiency from their respective parent claim by dependency.
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.
Claims 1-3, 5, 8, 10-12 and 14-20 are rejected under 35 U.S.C. 103 as being unpatentable over Fukatani et al. (US 20190332261 A1, Fukatani) in view of Sen et al. (US 20190042126 A1, hereafter Sen).
Regarding to claim 1, Fukatani discloses: A computer-implemented method (see [0286]) comprising:
building a vector database having properties of storage devices of a network environment (see Figs. 1, 6, [0044], [0066], [0104]-[0109]; “hosts 101-1 to 101-m using storage regions of the storage nodes 103-1 to 103-n are connected via a network 102”, “The global node table 71 is a management table of the storage node 103 referred to by the global node management 61 … a node ID 711 …a memory capacity 714, a drive ID 715, a drive type 716, a chunk capacity 717, and an allocated chunk capacity 718 in one entry”. Also see Fig. 14, [0142]-[0146]; “In the monitor information collection table 75, the global monitor 65 of the management node 104 stores monitor information collected regularly (for example, every hour) from the local monitor 34 of each storage node 103 … In the volume ID 751, an identifier of the volume 200 is stored. The identifier is a unique value in the global pool 24 … IOPH (IO per hour) 753, an IO count per hour for the logical page is stored”. Note: it is understood that tables as shown by Figs. 6 and 14 are reasonable to be considered as vector database);
associating the storage devices with storage classes using the properties of the storage devices, each storage device of the storage devices being associated with a storage class of the storage classes (see Figs. 5, 6, [0096]-[0097], [0106]; “The node type 712 stores a type of the storage node 103. In the present embodiment, any one of “high performance”, “standard”, and “high cost performance” is used”. Also see Fig. 7 and [0111]-[0112]; “In the volume ID 731, an identifier of the volume 200 is stored. The identifier is a unique value in the global pool 24. In the volume type 732, a type of the volume 200 is set. In the first embodiment, any one of “high performance”, “standard”, and “high cost performance” is used”);
receiving a request for provisioning a storage volume to support a workload, wherein the request indicates application requirements associated with servicing the workload (see [0177], [0187], “In the command of the volume creation from the storage administrator, the necessary capacity and volume type are specified”, “receiving an instruction of the volume creation from the user”. Also see [0010]; “the performance problems really occur in user applications or services actually accessing the storage system”. According to the description from [0010], it is understood that “the necessary capacity and volume type” from [0177] represents application requirements associated with servicing application workload);
performing a semantic search on the vector database and determining, based on the semantic search, a storage class for the requested storage volume; and provisioning the storage volume on a storage device, of the storage devices, associated with the determined storage class for the requested storage volume (see [0067], [0089]-[0090], [0101], [0180]-[0184]; “In the storage node 103-1 of the high-performance node, the high-performance volume 200-1 is created with the logical pages of the local tier 1 (27-1) and is provided to the host 101 … the high cost-performance volume 200-3 is created with the logical pages of the local tier 3 (27-3) and is provided to the host 101”, “the global volume management 63 determines the node type 712 that can newly generate the volume 200. These processing searches a node type having a free capacity capable of generating the new volume 200” and “The global volume management 63 refers to the global node table 71 and the global volume table 73 …. the global volume management 63 determines that a new volume 200 can be created for the corresponding node type … the global volume management 63 selects the storage node 103 newly generating the volume 200”).
Fukatani does not disclose: storage devices of a cloud environment;
However, Sen discloses: building a [vector] database having properties of storage devices of a cloud environment (see [0028] and [0068]; “predicts resource utilization for different types of workloads based on past resource utilization” and “the storage device allocation manager 1430 receives storage device data from the storage device 1230 … one or more storage device performance parameters (e.g., latency, IOPS, throughput, bandwidth, response time, application workload, or the like)”. Also see [0049]; “The system 1200 may be located in a data center and provide storage and compute services (e.g., cloud services) to a compute sled 1204 that is in communication with the system 1200 through a network 1290. The orchestrator server 1202 may support a cloud operating environment”).
It would have been obvious to one with ordinary skill, in the art before the effective filing date of the claim invention, to modify collecting monitored information associated with each storage nodes of a network environment from Fukatani by including collecting tested information associated with each storage devices of a cloud environment from Sen, and thus the combination of Fukatani and Sen would disclose the missing limitations from Fukatani, since a cloud environment is a specified type of network environment to provide on-demand access to computing resources and services over the internet.
Regarding to Claim 2, the rejection of Claim 1 is incorporated and further the combination of Fukatani and Sen discloses: wherein the properties of the storage devices comprise historic performance metrics of the storage devices for different workload types, the historic performance metrics comprising metrics related to input/output per unit of time (IOPS), bandwidth, latency, and response times of the storage devices (see [0028] and [0068] from Sen; “predicts resource utilization for different types of workloads based on past resource utilization” and “the storage device allocation manager 1430 receives storage device data from the storage device 1230 … one or more storage device performance parameters (e.g., latency, IOPS, throughput, bandwidth, response time, application workload, or the like)”).
Regarding to Claim 3, the rejection of Claim 2 is incorporated and further the combination of Fukatani and Sen discloses: wherein the properties of the storage devices further comprise metrics of storage device availability, reliability, durability, environmental impact, or sustainability (see at least “chunk capacity 717” from Fig. 6, [0105], [0109] from Fukatani; “In the chunk capacity 717, a total capacity of chunks for each drive 20 is stored”. Such “chunk capacity 717” is reasonable to be considered as claimed metrics of storage device availability).
Regarding to Claim 5, the rejection of Claim 1 is incorporated and further the combination of Fukatani and Sen discloses: wherein the performing the semantic search uses the indicated application requirements to identify candidate storage classes for the requested storage volume by identifying vectors of the vector database that semantically match to the application requirements (see [0177] and [0180]-[0184] from Fukatani; “In the command of the volume creation from the storage administrator, the necessary capacity and volume type are specified” and “the global volume management 63 determines the node type 712 that can newly generate the volume 200. These processing searches a node type having a free capacity capable of generating the new volume 200 … for the volume type designated by the storage administrator. When a plurality of node types are designated as the available owner node type 784, searching is performed from the node type with high performance” and “The global volume management 63 refers to the global node table 71 and the global volume table 73 …. the global volume management 63 determines that a new volume 200 can be created for the corresponding node type … the global volume management 63 selects the storage node 103 newly generating the volume 200”).
Regarding to Claim 8, the rejection of Claim 1 is incorporated and further the combination of Fukatani and Sen discloses: maintaining the vector database, the maintaining comprising performing a refresh of a model for building the vector database, the performing the refresh being based on: changes in characteristics of workloads supported by the storage devices, the characteristics comprising types or number of applications, usage patterns, or performance requirements; changes in storage infrastructure, the changes in storage infrastructure comprising introduction of a new storage class, removal of storage class of the storage classes, or changes in network configuration of the cloud environment; or lapse of a time interval for updating the vector database (see [0145] from Fukatani; “The global monitor 65 of the management node 104 collects the monitor information from the local monitor 34 of each storage node 103 at a predetermined interval (for example, one hour) and updates the monitor information collection table 75”).
Regarding to Claim 10, Claim 10 is a system claim corresponds to method Claim 1 and is rejected for the same reason set forth in the rejection of Claim 1 above (note: also see [0054] and [0065] from Fukatan for claimed computer system).
Regarding to Claim 11, Claim 11 is a system claim corresponds to method Claim 2 and is rejected for the same reason set forth in the rejection of Claim 2 above.
Regarding to Claim 12, Claim 12 is a system claim corresponds to method Claim 3 and is rejected for the same reason set forth in the rejection of Claim 3 above.
Regarding to Claim 14, Claim 14 is a system claim corresponds to method Claim 5 and is rejected for the same reason set forth in the rejection of Claim 5 above.
Regarding to Claim 15, Claim 15 is a system claim corresponds to method Claim 8 and is rejected for the same reason set forth in the rejection of Claim 8 above.
Regarding to Claim 16, Claim 16 is a product claim corresponds to method Claim 1 and is rejected for the same reason set forth in the rejection of Claim 1 above (note: also see [0054] and [0065] from Fukatan for claimed “a set of one or more computer readable storage media”).
Regarding to Claim 17, Claim 17 is a product claim corresponds to method Claim 2 and is rejected for the same reason set forth in the rejection of Claim 2 above.
Regarding to Claim 18, Claim 18 is a product claim corresponds to method Claim 3 and is rejected for the same reason set forth in the rejection of Claim 3 above.
Regarding to Claim 19, Claim 19 is a product claim corresponds to method Claim 5 and is rejected for the same reason set forth in the rejection of Claim 5 above.
Regarding to Claim 20, Claim 20 is a product claim corresponds to method Claim 8 and is rejected for the same reason set forth in the rejection of Claim 8 above.
Claims 4 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Fukatani et al. (US 20190332261 A1, Fukatani) in view of Sen et al. (US 20190042126 A1, hereafter Sen) and further in view of Wang (CN 115617280 B, publication date: 3/3/2023, English translation provided by Google Patents) and Lu et al. (US 20180101486 A1, hereafter Lu).
Regarding to Claim 4, the rejection of Claim 2 is incorporated, the combination of Fukatani and Sen does not disclose: wherein the application requirements comprise requirements for IOPS, bandwidth, latency, and response times for the workload.
However, Wang discloses: wherein the application requirements comprise requirements for IOPS, bandwidth, latency for the workload (see [0133]-[0134]; “The optimization management module embeds the basic requirements of various host service temporary directories for performance (such as IOPS (Input/Output Operations Per Second, the number of read and write operations per second), bandwidth, delay, etc.), which can be recorded as basic requirements A”. Note: it is understood that the feature or service of “temporary directories” is related to storage type resource usage, i.e., the basic requirements discussed from [0133]-[0134] are application requirements for storage type resource).
It would have been obvious to one with ordinary skill, in the art before the effective filing date of the claim invention, to modify the specified requirements for a volume creation process from the combination of Fukatani and Sen by including application workload requirements including IOPS, bandwidth and latency from Wang, since it would it would provide a mechanism to specify details for performance requirements (see [0068] from Sen and [0134] from Wang).
In addition, Lu discloses: wherein the application requirements comprise requirements for latency, and response times for the workload (see [0010]; “providing the low-latency and fast response times required by virtual machine (VM) telecommunication (telco) workloads … telco workloads are one example of latency-sensitive VMs that are network input/output (I/O) intensive, but it should be understood that techniques disclosed herein may be applied to any workload that has similar network, storage, or other I/O intensive characteristics and latency/quality-of-service requirements”).
It would have been obvious to one with ordinary skill, in the art before the effective filing date of the claim invention, to modify the specified requirements for a volume creation process from the combination of Fukatani, Sen and Wang by including latency and response time requirements for IO workloads from Lu, and thus the combination of Fukatani, Sen, Wang and Lu would disclose the combination of Fukatani and Sen, since it would provide a mechanism to specify more detail delay requirements for workloads to be executed (see [0010] from Lu).
Regarding to Claim 13, Claim 13 is a system claim corresponds to method Claim 4 and is rejected for the same reason set forth in the rejection of Claim 4 above.
Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Fukatani et al. (US 20190332261 A1, Fukatani) in view of Sen et al. (US 20190042126 A1, hereafter Sen) and further in view of Kumar et al. (US 20200019614 A1, hereafter Kumar).
Regarding to Claim 6, the rejection of Claim 5 is incorporated and further the combination of Fukatani and Sen discloses: wherein the semantic search comprises an embedding-based semantic search that represents vectors of the vector database as embeddings in a [continuous] vector space, and wherein the performing the semantic search identifies one or more vectors, of the vector database, embedded in the [continuous] vector space nearest [a vector built] based on the indicated application requirements (see [0177] and [0180]-[0184] from Fukatani; “In the command of the volume creation from the storage administrator, the necessary capacity and volume type are specified” and “the global volume management 63 determines the node type 712 that can newly generate the volume 200. These processing searches a node type having a free capacity capable of generating the new volume 200 … for the volume type designated by the storage administrator. When a plurality of node types are designated as the available owner node type 784, searching is performed from the node type with high performance” and “The global volume management 63 refers to the global node table 71 and the global volume table 73 …. the global volume management 63 determines that a new volume 200 can be created for the corresponding node type … the global volume management 63 selects the storage node 103 newly generating the volume 200”).
The combination of Fukatani and Sen does not disclose: vectors of the vector database as embeddings in a continuous vector space, a vector build based on the indicated application requirements.
However, Kumar discloses: wherein the semantic search comprises an embedding-based semantic search that represents vectors of the vector database as embeddings in a continuous vector space, and wherein the performing the semantic search identifies one or more vectors, of the vector database, embedded in the continuous vector space nearest a vector built based on the indicated search requirements (see [0014]; “outputs the continuous output vector 107 and searches for the list of close neighbors (via, e.g., a variant of a k-nearest neighbor algorithm) of the continuous output vector 107 within the embedding table 105 to output the corresponding word(s)”).
It would have been obvious to one with ordinary skill, in the art before the effective filing date of the claim invention, to modify the searching proper storage node recorded at tables having properties of the storage node candidates according to specified requirements from the combination of Fukatani and Sen by including searching matched data recorded at table according to specified requirements in vector from Kumar, and thus the combination of Fukatani, Sen and Kumar would disclose the combination of Fukatani and Sen, since it would provide a mechanism of converting the search terms or requirements in a same data structure format as search candidates (see [0014] from Kumar).
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Fukatani et al. (US 20190332261 A1, Fukatani) in view of Sen et al. (US 20190042126 A1, hereafter Sen) and further in view of Zhang et al. (CN 112579600 A, publication date: 3/30/2021, hereafter Zhang, English translation provided by Google Patents).
Regarding to Claim 7, the rejection of Claim 5 is incorporated, the combination of Fukatani and Sen does not disclose: wherein the semantic search comprises a graph-based semantic search that builds a graph with nodes representing vectors of the vector database and edges connecting nodes that are among k-nearest neighbors based on a distance metric, wherein the performing the semantic search identifies one or more nodes, of the graph, with edges connecting to a node, of the graph, built based on the indicated application requirements.
However, Zhang discloses: wherein the semantic search comprises a graph-based semantic search that builds a graph with nodes representing vectors of the vector database and edges connecting nodes that are among k-nearest neighbors based on a distance metric, wherein the performing the semantic search identifies one or more nodes, of the graph, with edges connecting to a node, of the graph, built based on the indicated search requirements (see Fig. 2, [0050]-[0051]; “as shown in fig. 2, the names of tables and columns in the database may be used as labels to form nodes of a directed graph (i.e., a schema graph for a vehicle information database), such as nodes in a directed graph may be formed by the names of tables "geographical region" and columns "province", "city" and "region"; the edges of the directed graph may also be determined according to pre-existing database relationships in the database” and “a knowledge base for the vehicle information database for semantic search; through the organization form of the directed graph, the main information of the SQL statement can be conveniently and effectively extracted; and can carry on the reasoning calculation according to the graph calculation of the directed graph”. Also see [0056] and [0067]; “for an obtained query (i.e., a vehicle-mounted question and answer request), the data tables in the query and a vehicle information database can be linked through a database mode combined link based on a mode map … an attribute column, and an attribute value corresponding to the column may be obtained” and “obtain a directed graph, and then the semantic meaning can be enhanced by utilizing the distribution information of each node and relation of the database mode according to the preset mode map for the vehicle information database to carry out combined linkage of the database mode”).
It would have been obvious to one with ordinary skill, in the art before the effective filing date of the claim invention, to modify searching on tables recording search candidates according to specified requirements from the combination of Fukatani and Sen by including searching matched data recorded at directed graph and tables having search candidates according to search requirements or terms from Zhang, and thus the combination of Fukatani, Sen and Zhang would disclose the combination of Fukatani and Sen, since a directed graph is known as a type of data structure indicating relationships among objects (see [0067] from Zhang).
Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over Fukatani et al. (US 20190332261 A1, Fukatani) in view of Sen et al. (US 20190042126 A1, hereafter Sen) and further in view of Munteanu et al. (US 20210200387 A1, hereafter Munteanu).
Regarding to Claim 9, the rejection of Claim 1 is incorporated and further the combination of Fukatani and Sen discloses: determining whether the indicated application requirements match to a storage class of the storage classes (see [0177] and [0180]-[0184] from Fukatani; “In the command of the volume creation from the storage administrator, the necessary capacity and volume type are specified” and “the global volume management 63 determines the node type 712 that can newly generate the volume 200. These processing searches a node type having a free capacity capable of generating the new volume 200 … for the volume type designated by the storage administrator”).
The combination of Fukatani and Sen does not disclose: based on determining that the indicated application requirements fail to match to any storage class of the storage classes, determining the storage class based on sematic similarity between the indicated application requirements and a list of candidate storage classes, provided by the sematic searching, ranked by their semantic similarity to the indicated application requirements.
However, Munteanu discloses: based on determining that the indicated search requirements fail to match to any search candidate of the search candidates, determining the search candidate based on sematic similarity between the indicated search requirements and a list of candidates, provided by the sematic searching, ranked by their semantic similarity to the indicated search requirements (see [0070]; “When the search for an exact match fails, instead of abandoning with an error message, some embodiments may re-attempt to find the runtime target using fuzzy matching instead … by inserting a ‘matching:[attribute_name]=fuzzy’ attribute and/or an associated similarity threshold into a selected tag”. Also see [0060], [0068]; “When the currently selected node is considered a fuzzy match for the target UI element … for each candidate, robot 44 further stores the value of the similarity measure(s) calculated for the respective candidate … further sort the candidate list according to similarity measure, so that when robot 44 selects candidates from the list (see e.g., step 310 in FIG. 8-A), it selects the most similar candidates first, thus accelerating the search and potentially uncovering a runtime target which is most similar to the design-time target”).
It would have been obvious to one with ordinary skill, in the art before the effective filing date of the claim invention, to modify the searching proper storage node according to specified requirements from the combination of Fukatani, Sen and Wang by including re-searching via fuzzy matching when no exact match found from Munteanu, and thus the combination of Fukatani, Sen, Wang and Munteanu would disclose the combination of Fukatani and Sen, since it would provide a mechanism of still being able to find out a search result without generating error (see [0070] from Munteanu).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Gu et al. (US 20230205582 A1) discloses: A common QoS parameter includes any one or a combination of the following: a quantity of packets per second (PPS), a success rate of a request response, a quantity of input/output operations per second (TOPS), various delays (such as a network delay and a service response delay), network bandwidth, or storage bandwidth (see [0064]).
Huang et al. (US 20240168672 A1) discloses: A characteristic parameter of a storage unit may include a configuration parameter of the storage unit, for example, an input/output operations per second (IOPS), a bandwidth, access latency, a size of an TO, a read IO ratio, and the like of the LUN. In addition, a characteristic parameter of a storage unit may further include a performance parameter of the storage unit in a running process, for example, a historical average IOPS, historical average latency, and a current free space size of a LUN (see [0143]).
Cillis et al. (US 20180165385 A1) discloses: non-functional requirements (NFRs) and target service levels (e.g., target levels for availability and fluctuations in transaction throughput over time) and how to design infrastructure to achieve the NFRs and the target service levels. NFRs are requirements associated with characteristics of the system, including the security, availability, response time, throughput, and latency) (see [0002]).
Sodagar (US 20220377389 A1) discloses: For each SC, a set of Service Class Requirements (SCR) may be defined for a 5GMS AS running the service, which may include one or more of the following aspects: connectivity requirements comprising bandwidth, latency, maximum request rate and maximum response time (see [0142]-[0160]).
Narayanan (US 20150019722 A1) discloses: QoS parameters herein referred to as a set of service requirements that need to be met to achieve a level of quality. QoS parameters may include, but not limited to, parameters such as number of users, number of concurrent users, throughput, response time, utilization level, latency etc (see [0013]).
Jin et al. (US 20250181835 A1) discloses: The query vector has a query data structure storing a semantic meaning of the query. The method also includes applying a semantic matching algorithm to both the query vector and a lookup vector. The lookup vector has a lookup data structure storing semantic meanings of entries of a lookup table. The semantic matching algorithm compares the query vector to the lookup vector and returns, as a result of comparing, a found entry in the lookup table (see [0006]).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ZHI CHEN whose telephone number is (571)272-0805. The examiner can normally be reached on M-F from 9:30AM to 5:30PM.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, April Y Blair can be reached on 571-270-1014. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Zhi Chen/
Patent Examiner, AU2196
/APRIL Y BLAIR/Supervisory Patent Examiner, Art Unit 2196
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