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
The instant application having Application No. 19/230,895 has a total of 14 claims pending in the application; there are 2 independent claims and 12 dependent claims, all of which are ready for examination by the examiner.
The specification has not been checked to the extent necessary to determine the presence of all possible minor errors.
In response to this Office action, the Examiner respectfully requests that support be shown for language added to any original claims on amendment and any new claims. That is, indicate support for newly added claim language by specifically pointing to page(s) and line numbers in the specification and/or drawing figure(s). This will assist the Examiner in prosecuting this application.
Examiner cites columns 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 entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by prior art or disclosed by the examiner.
INFORMATION CONCERNING DRAWINGS
The applicant’s drawings submitted are acceptable for examination purposes.
STATUS OF CLAIM FOR PRIORITY IN THE APPLICATION
The instant application No. 19230895 filed 06/06/2025 claims foreign priority to 24180931, filed 06/07/2024.
ACKNOWLEDGEMENT OF REFERENCES CITED BY APPLICANT
As required by M.P.E.P. 609(C), the applicant’s submission of the Information Disclosure Statement(s) dated 6/6/2025 is/are acknowledged by the examiner and the cited references have been considered in the examination of the claims now pending. As required by M.P.E.P 609 C(2), a copy (copies) of the PTOL-1449(s) initialed and dated by the examiner is/are attached to the instant office action.
CLAIM CONSTRUCTION
The present application contains contingent limitations. Applicant is reminded that “the broadest reasonable interpretation of a method (or process) claim having contingent limitations requires only those steps that must be performed and does not include steps that are not required to be performed because the condition(s) precedent are not met.” See MPEP 2111.04(II).
See Ex parte Schulhauser, Appeal No. 2013-007847, 2016 WL 6277792, at *9 (PTAB, Apr. 28, 2016) (precedential) (holding "The Examiner did not need to present evidence of the obviousness of the remaining method steps of the claim that are not required to be performed under a broadest reasonable interpretation of the claim"); see also Ex parte Katz, Appeal No. 2010-006083, 2011 WL 514314, at *4-5 (BPAI Jan. 27, 2011).” Board Decision pages 5-6, emphasis in original.
Note that the limitations “when the corresponding asset data is not requested n times in succession” (claim 11) and “when the retention period expires” (claim 12) and may never be reached within the scope of the claim under the broadest reasonable interpretation since the “when” statement may never be met. Applicant is reminded that “the broadest reasonable interpretation of a method (or process) claim having contingent limitations requires only those steps that must be performed and does not include steps that are not required to be performed because the condition(s) precedent are not met.” See MPEP211.04(II).
It is suggested method claim 11 be amended to first require determining that “the corresponding asset data is not requested n times in success“ and then in response to the determining, have the request(s) “only forwarded”.
It is suggested method claim 12 be amended to first require determining that “the retention period expired“ and then in response to the determining, “removing…”.
OBJECTIONS
Claim Objections
Claim 5 is objected to because of the following informalities: The limitations “edge device (30)” appear to refer to “edge device”; thus, the numeral “(30)” should be removed from the claim. Appropriate correction is required.
REJECTIONS NOT BASED ON PRIOR ART
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.
Claim 14 is rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim(s) does/do not fall within at least one of the four categories of patent eligible subject matter.
Regarding the claimed “A computer program comprising instructions stored on tangible media,” the specification does not provide a definition for the claimed “tangible media”. See (paragraph 0071 of US 20250377787, the printed publication corresponding to the instant application) which merely recites “the computer program product(s) may be a product or products such as a data storage(s), in particular computer-readable data storage medium(s), on which the computer program(s) may be temporarily or permanently stored” . A Broadest reasonable interpretation for storage media would include both statutory embodiments and non-statutory embodiments such as signals. The words “tangible medium”, "storage" and/or "recording" are insufficient to convey only statutory embodiments to one of ordinary skill in the art absent an explicit and deliberate limiting definition or clear differentiation between storage media and transitory media in the disclosure. As such, the claim(s) is/are drawn to a form of energy. Energy is not one of the four categories of invention and therefore this/these claim(s) is/are not statutory. Energy is not a series of steps or acts and thus is not a process. Energy is not a physical article or object and as such is not a machine or manufacture. Energy is not a combination of substances and therefore not a composition of matter.
The Examiner suggests amending the claim(s) to read as “A computer program comprising instructions stored on tangible non-transitory media”.
REJECTIONS BASED ON PRIOR ART
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 1-4, 8-9 and 11-14 are rejected under 35 U.S.C. 103 as being unpatentable over Yang et al. (US 2019/0043351) in view of Ng et al. (US 20150278233).
1. A method for distributing asset data among multiple storage locations of a distributed data storage system for one or more industrial plants, the asset data relating to one or more assets of the one or more industrial plants, the method comprising: [Yang teaches “[0055] Indeed, this disclosure contemplates use of a potentially limitless universe of IoT devices 114 and associated sensors/actuators. IoT devices 114 may include, for example, any type of equipment and/or devices associated with any type of system 100 and/or industry, including transportation (e.g., automobile, airlines), industrial manufacturing…”. “[0087]… The groups of IoT devices may be deployed in various residential, commercial, and industrial settings (including in both private or public environments).”]
obtaining usage data indicative of a usage of the asset data; and [Yang teaches “[0210]…Moreover, analysis friendly storage formats can be used to enable data to be read faster when needed for vision processing. These various data formats may be used to form the hot, warm, and cold tiers of data that can be mapped to various heterogeneous memory and storage technologies, based on the intended use and lifetime of the data.”]
distributing the asset data among the multiple storage locations based on the obtained usage data; [Yang teaches “[0210]…For example, storage tiers can be used to represent hot, cold, and optionally warm data. Hot data is accessed frequently; warm data is accessed occasionally; and cold data is accessed rarely (if ever). Accordingly, cold data may be stored on slower hardware since low access latency for retrieval of the data is less important. In this manner, intelligent decisions can be used to determine when and which portions of visual data should remain in the hot tiers and when it should be migrated to colder tiers, and which storage format should be used. For example, regions of interest may remain in hot storage in the analysis friendly format much longer than the entire image/video.”]
wherein the asset data is distributed among storage locations of different entities of the distributed data storage system, the entities including at least one storage device of the one or more industrial plants, at least one edge device and at least one storage device of a cloud server [Yang teaches “[0209]… a multi-tier lazy data storage approach may be used to store visual data more efficiently (e.g., using long- or short-term storage in different portions of the distributed edge-to-cloud network). For example, multiple storage tiers may be used to store visual data in different locations and for varying amounts of time based on the type or importance of the visual data. In some embodiments, for example, video cameras may store all video captured within the past day (corresponding to the claimed one or more storage device of the industrial plant), gateways may store (corresponding to the claimed edge device) video with motion activities within the past week, and the cloud may store (corresponding to the claimed cloud server) video associated with certain significant events within the past year.” “[0210]…For example, storage tiers can be used to represent hot, cold, and optionally warm data. Hot data is accessed frequently; warm data is accessed occasionally; and cold data is accessed rarely (if ever). Accordingly, cold data may be stored on slower hardware since low access latency for retrieval of the data is less important. In this manner, intelligent decisions can be used to determine when and which portions of visual data should remain in the hot tiers and when it should be migrated to colder tiers, and which storage format should be used. For example, regions of interest may remain in hot storage in the analysis friendly format much longer than the entire image/video.”], where Yang does not expressly refer to the video camera memory discussed in par. 0209 as storage of the one or more industrial plants, but [Yang explains “[0055] Indeed, this disclosure contemplates use of a potentially limitless universe of IoT devices 114 and associated sensors/actuators. IoT devices 114 may include, for example, any type of equipment and/or devices associated with any type of system 100 and/or industry, including transportation (e.g., automobile, airlines), industrial manufacturing…”. “[0087]… The groups of IoT devices may be deployed in various residential, commercial, and industrial settings (including in both private or public environments).” Where the discussed IoT devices include memory (see figs. 6-7, 18 and related text)] and Ng teaches [“[0041] The industrial plant may have one or more data storage locations to be monitored and managed, wherein each data storage location may be an individual data storage device such as a hard disk drive, a Random access memory, a Flash memory and the like. It is appreciated that each data storage location includes one which is virtually created and, for instance, a partition of a hard disk drive. For instance, the data stored in these one or more data storage locations can be anything related to an industrial plant automation process or equipment and the like. Typical examples of data are messages, time series and annotation.”].
Yang and Ng are analogous art because they are from the same field of endeavor of memory access and control.
Before the effective filing date of the claimed inventions, it would have been obvious to a person of ordinary skill in the art to modify Yang to have the data handling explained with respect to a camera which is provided as an exemplary IoT device, where Yang explicitly states that IoT devices may include any type of device including industrial manufacturing devices, to explicitly have such arrangement in an industrial manufacturing device such as that discussed by Yang and taught by Ng, since doing so would provide the benefits of facilitating the collection and management of data in a storage device of an industrial plant; thus, providing efficient storage management.
Therefore, it would have been obvious to combine Yang and Ng for the benefit of creating a storage system/method to obtain the invention as specified in claim 1.
2. The method of claim 1, wherein asset data comprises one or more operational data of the one or more industrial plants and/or application data of an application of the one or more industrial plants [Yang teaches “[0054] IoT devices 114 may include various types of sensors for monitoring, detecting, measuring, and generating sensor data and signals associated with characteristics of their environment. In some embodiments, for example, certain IoT devices 114 may include visual sensors 120 (e.g., cameras) for capturing visual representations and data associated with their surroundings. IoT devices 114 may also include other types of sensors configured to detect characteristics such as movement, weight, physical contact, temperature, wind, noise, light, position, humidity, radiation, liquid, specific chemical compounds, battery life, wireless signals, computer communications, and bandwidth, among other examples. Sensors can include physical sensors (e.g., physical monitoring components) and virtual sensors (e.g., software-based monitoring components). IoT devices 114 may also include actuators to perform various actions in their respective environments. For example, an actuator may be used to selectively activate certain functionality, such as toggling the power or operation of a security system (e.g., alarm, camera, locks) or household appliance (e.g., audio system, lighting, HVAC appliances, garage doors), among other examples.” “[0055] Indeed, this disclosure contemplates use of a potentially limitless universe of IoT devices 114 and associated sensors/actuators. IoT devices 114 may include, for example, any type of equipment and/or devices associated with any type of system 100 and/or industry, including transportation (e.g., automobile, airlines), industrial manufacturing…”. “[0087]… The groups of IoT devices may be deployed in various residential, commercial, and industrial settings (including in both private or public environments).”; thus, captured sensor day may be operational data and/or application data of industrial manufacturing when using IoT device in industrial manufacturing as suggested by Yang. Ng further teaches “[0040] FIG. 1 is a block diagram of a data storage management apparatus 1000 for an industrial plant in some embodiments of the present invention. The industrial plant covers plants or factories of large scale that require handling of a high volume of data. The high volume of data may be due to a large number of machinery, equipment, and any other electronic apparatuses.
[0041] The industrial plant may have one or more data storage locations to be monitored and managed, wherein each data storage location may be an individual data storage device such as a hard disk drive, a Random access memory, a Flash memory and the like. It is appreciated that each data storage location includes one which is virtually created and, for instance, a partition of a hard disk drive. For instance, the data stored in these one or more data storage locations can be anything related to an industrial plant automation process or equipment and the like. Typical examples of data are messages, time series and annotation.”].
3. The method of claim 1, wherein the asset data is transformed for distributing the asset data among the multiple storage locations, the transformation comprising compression of the asset data and/or aggregation of the asset data [Yang teaches “[0082] Three types of IoT devices 302 are shown in this example, gateways 304, data aggregators 326, and sensors 328, although any combinations of IoT devices 302 and functionality may be used. The gateways 304 may be edge devices that provide communications between the cloud 300 and the fog 320, and may also provide the backend process function for data obtained from sensors 328, such as motion data, flow data, temperature data, and the like. The data aggregators 326 may collect data from any number of the sensors 328, and perform the back-end processing function for the analysis. The results, raw data, or both may be passed along to the cloud 300 through the gateways 304. The sensors 328 may be full IoT devices 302, for example, capable of both collecting data and processing the data. In some cases, the sensors 328 may be more limited in functionality, for example, collecting the data and allowing the data aggregators 326 or gateways 304 to process the data.” “[0216] Moreover, storage architecture 1800 is also capable of storing visual data on data storage 1810 using an analytic image format designed to aid in visual processing. In the illustrated embodiment, for example, visual compute library (VCL) 1806 of storage architecture 1800 is designed to handle processing on analytic image formats 1807 in addition to traditional formats 1808. For example, visual compute library 1806 can implement an analytic image format 1807 using an array-based data management system such as TileDB, as described further with respect to FIG. 22. The analytic image format 1807 provides fast access to image data and regions of interest within an image. Moreover, since the analytic image format 1807 stores image data as an array, the analytic image format 1807 enables visual compute library 1806 to perform computations directly on the array of image data. Visual compute library 1806 can also convert images between the analytic image format 1807 and traditional image formats 1808 (e.g., JPEG and PNG). Similarly, videos may be stored using a machine-friendly video format designed to facilitate machine-based analysis. For example, videos are typically encoded, compressed, and stored under the assumption that they will be consumed by humans.”].
4. The method of claim 3, wherein the transformation of the asset data is executed on one or more of the at least one storage device of the one or more industrial plants, the at least one edge device and/or the at least one storage device of the cloud server [Yang teaches “[0082] Three types of IoT devices 302 are shown in this example, gateways 304, data aggregators 326, and sensors 328, although any combinations of IoT devices 302 and functionality may be used. The gateways 304 may be edge devices that provide communications between the cloud 300 and the fog 320, and may also provide the backend process function for data obtained from sensors 328, such as motion data, flow data, temperature data, and the like. The data aggregators 326 may collect data from any number of the sensors 328, and perform the back-end processing function for the analysis. The results, raw data, or both may be passed along to the cloud 300 through the gateways 304. The sensors 328 may be full IoT devices 302, for example, capable of both collecting data and processing the data. In some cases, the sensors 328 may be more limited in functionality, for example, collecting the data and allowing the data aggregators 326 or gateways 304 to process the data.” “[0251] In distributed visual analytics systems, image and video is often compressed before transmission (e.g., from the pixel domain to a compressed domain), and subsequently decompressed after transmission (e.g., back to the pixel domain) before any processing can be performed, such as deep learning using neural networks. As an example, image and video captured by edge devices may be compressed and transmitted to the cloud, and then decompressed by the cloud before any further processing begins.”].
8. The method of claim 1, wherein the method further comprises obtaining current distribution data indicative of a current distribution of the asset data among the multiple storage locations; wherein distributing of the asset data among the multiple storage locations is further based on the obtained current distribution data [Yang teaches “[0210] Similarly, intelligent placement and aging of visual data across the storage tiers may further improve the data storage efficiency (e.g., determining where to store the visual data within the distributed edge-to-cloud system, when the data should be moved from hot to warm to cold storage, and so forth). For example, visual data and metadata can be distinguished and segregated based on data access patterns. Moreover, analysis friendly storage formats can be used to enable data to be read faster when needed for vision processing. These various data formats may be used to form the hot, warm, and cold tiers of data that can be mapped to various heterogeneous memory and storage technologies, based on the intended use and lifetime of the data. For example, storage tiers can be used to represent hot, cold, and optionally warm data. Hot data is accessed frequently; warm data is accessed occasionally; and cold data is accessed rarely (if ever). Accordingly, cold data may be stored on slower hardware since low access latency for retrieval of the data is less important. In this manner, intelligent decisions can be used to determine when and which portions of visual data should remain in the hot tiers and when it should be migrated to colder tiers, and which storage format should be used. For example, regions of interest may remain in hot storage in the analysis friendly format much longer than the entire image/video.” Where before moving or migrating data, data is in a current distribution and the determination to move or migrate data among the storage tiers is based on the current data distribution or current data locations].
9. The method of claim 8, wherein the method further comprises comparing the current distribution data with the usage data to generate a storage request for moving the asset data from one storage location to another and/or to generate a deletion request among the multiple storage locations for deleting data from a storage location [Yang teaches [0210] Similarly, intelligent placement and aging of visual data across the storage tiers may further improve the data storage efficiency (e.g., determining where to store the visual data within the distributed edge-to-cloud system, when the data should be moved from hot to warm to cold storage, and so forth). For example, visual data and metadata can be distinguished and segregated based on data access patterns. Moreover, analysis friendly storage formats can be used to enable data to be read faster when needed for vision processing. These various data formats may be used to form the hot, warm, and cold tiers of data that can be mapped to various heterogeneous memory and storage technologies, based on the intended use and lifetime of the data. For example, storage tiers can be used to represent hot, cold, and optionally warm data. Hot data is accessed frequently; warm data is accessed occasionally; and cold data is accessed rarely (if ever). Accordingly, cold data may be stored on slower hardware since low access latency for retrieval of the data is less important. In this manner, intelligent decisions can be used to determine when and which portions of visual data should remain in the hot tiers and when it should be migrated to colder tiers, and which storage format should be used. For example, regions of interest may remain in hot storage in the analysis friendly format much longer than the entire image/video.” Where before moving or migrating data, data is in a current distribution and the determination to move or migrate data among the storage tiers is based on the current data distribution or current data locations and whether data access patterns compared to current data locations indicate data should be moved among tiers. Ng teaches “[0070] When the writing tool administration unit 112 receives a notification from the monitoring tool to implement a data retention policy 142, the writing tool administration unit 112 sends instructions to the other units in the writing tool 110 to identify stored data having a storage duration the same as or greater than the data retention policy. The identified stored data is deleted. The storage duration is calculated from a storage start date of the stored data to the current date.”].
11. The method of claim 8, wherein the storage request and/or deletion request is only forwarded when the corresponding asset data is not requested n times in succession, wherein n is a positive absolute number which represents a configurable threshold [See claim construction section above, wherein the when statement in claim 11 may not be met according to the broadest reasonable interpretation of the claim; thus, not requiring the storage request and/or deletion request to be forwarded. However, Yang teaches moving data from hot to warm or from warm to cold storage where data is moved as its access frequency decreases or is no longer frequently accessed or hot, which may correspond to data not being accesses n times in succession (see pars. 0209 and 0210)].
12. The method of claim 1, the method further comprising: obtaining a retention period for storing the asset data in any one of the multiple storage locations; and removing the asset data from the storage location, in which it is stored for the retention period when the retention period expires [See claim construction section above, wherein the when statement in claim 12 may not be met according to the broadest reasonable interpretation of the claim; thus, as claimed the retention period may not expire and not requiring removing the asset data. However, Ng teaches “[0070] When the writing tool administration unit 112 receives a notification from the monitoring tool to implement a data retention policy 142, the writing tool administration unit 112 sends instructions to the other units in the writing tool 110 to identify stored data having a storage duration the same as or greater than the data retention policy. The identified stored data is deleted. The storage duration is calculated from a storage start date of the stored data to the current date.” “[0080] In some embodiments, a data management policy includes a data retention policy 140. The data retention policy 140 has one or more periods. In a typical example, a first period is data retention period 144 and a second period is data erase period 146, different from the data retention period 144. The writing tool administration unit 112 uses the data retention period 144 to delete stored data. The writing tool administration unit 112 uses the data erase period 146 to delete the stored data, when there is no available storage location 116a, 116b, 116c after deleting the stored data by using the data retention period 144.”].
13. The method of claim 1, wherein the distributing of the asset data among the plurality of storage locations further comprises: storing asset data that is frequently accessed in the at least one storage device of the one or more industrial plants and/or at least one edge device; storing asset data that is accessed less frequently in the at least one storage device of a cloud server [wherein the asset data is distributed among storage locations of different entities of the distributed data storage system, the entities including at least one storage device of the one or more industrial plants, at least one edge device and at least one storage device of a cloud server [Yang teaches “[0209]… a multi-tier lazy data storage approach may be used to store visual data more efficiently (e.g., using long- or short-term storage in different portions of the distributed edge-to-cloud network). For example, multiple storage tiers may be used to store visual data in different locations and for varying amounts of time based on the type or importance of the visual data. In some embodiments, for example, video cameras may store all video captured within the past day, gateways may store (corresponding to the claimed edge device) video with motion activities within the past week, and the cloud may store (corresponding to the claimed cloud server) video associated with certain significant events within the past year.” “[0210]…For example, storage tiers can be used to represent hot, cold, and optionally warm data. Hot data is accessed frequently; warm data is accessed occasionally; and cold data is accessed rarely (if ever). Accordingly, cold data may be stored on slower hardware since low access latency for retrieval of the data is less important. In this manner, intelligent decisions can be used to determine when and which portions of visual data should remain in the hot tiers and when it should be migrated to colder tiers, and which storage format should be used. For example, regions of interest may remain in hot storage in the analysis friendly format much longer than the entire image/video…”].
14. A computer program comprising instructions stored on tangible media that, when executed by a distributed data storage system, cause the distributed data storage system to carry out a method for distributing asset data among multiple storage locations of a distributed data storage system for one or more industrial plants, the asset data relating to one or more assets of the one or more industrial plants, the method comprising: instructions for obtaining usage data indicative of a usage of the asset data; and instructions for distributing the asset data among the multiple storage locations based on the obtained usage data; wherein the asset data is distributed among storage locations of different entities of the distributed data storage system, the entities including at least one storage device of the one or more industrial plants, at least one edge device and at least one storage device of a cloud server [The rationale in the rejection of claim 1 is herein incorporated].
Claims 6-7 are rejected under 35 U.S.C. 103 as being unpatentable over Yang et al. (US 2019/0043351) in view of Ng et al. (US 20150278233) as applied above in the rejection of claim 1, and further in view of Thrane et al. (US 10572159).
6. The combination of Yang and Ng teaches The method of claim 1, but does not expressly disclose wherein the usage data is based on access logs and/or cached access requests of the distributed data storage system; however, regarding these limitations, [Thrane teaches “In an embodiment, the access evaluation engine 206 utilizes machine learning techniques, such as supervised learning techniques to determine optimal placement of a data object over time. For instance, the access evaluation engine 206 may evaluate a data log corresponding to a data object 222 from a logical data container 212 corresponding to the standard data storage tier 208 to identify a frequency at which the data object 222 has been accessed and used by the customer 202 and other users. Further, the access evaluation engine 206 may utilize the machine learning techniques to evaluate the cost efficiency of maintaining the data object 222 within the standard data storage tier 208 versus transferring the data object 222 to a logical data container 214 corresponding to the infrequent access data storage tier 210 or to the archival data storage service 220.” (col. 11, lines 4-36; see col. 21, lines 4-22)].
Yang, Ng and Thrane are analogous art because they are from the same field of endeavor of memory access and control.
Before the effective filing date of the claimed inventions, it would have been obvious to a person of ordinary skill in the art to modify the combination of Yang and Ng to have the usage data determinations based on access logs as taught by Thrane since doing so would provide the benefits of allowing for the determination of data usage to [determine optimal placement of the data (col. 11, lines 4-36)].
Therefore, it would have been obvious to combine Yang and Ng with Thrane for the benefit of creating a storage system/method to obtain the invention as specified in claim 6.
7. The method of claim 6, wherein the distributing of the asset data among the multiple storage locations is further based on one or more predictions of potential changes in a future usage of the asset data, wherein the one or more predictions are determined using a machine learning algorithm trained based on the access logs and/or the cached access requests [Thrane teaches “In an embodiment, the object-based data storage service 104 utilizes machine learning techniques, such as supervised learning techniques to determine optimal placement of the data object 114 over time and to create prediction metadata, which may specify an optimal data storage tier for the data object 114. A machine learning algorithm may utilize, as input, usage data for a particular data object garnered from one or more data usage logs for the data object 114. Further, in some instances, the machine learning algorithm may also utilize usage data for other data objects of the customer 102 and/or of other similar data objects of the customer 102 or of other customers (e.g., same type, stored in a logical data container corresponding to the same data storage tier of the data object 114, etc.). In some examples, the machine learning algorithm may utilize prediction metadata for other similar data objects as input. “ (col. 6, lines 40-55) “ In an embodiment and as described above, the output from the machine learning algorithm includes prediction metadata for the particular data object 114. The prediction metadata may specify an optimal data storage tier for the data object 114 based at least in part on the usage data. Additionally, or alternatively, the prediction metadata may provide information regarding storage of the data object 114 in accordance with the various data storage tiers based at least in part on other parameters (e.g., cost of maintaining the data object 114 in each data storage tier in accordance with a frequency of use, etc.). The prediction metadata may be stored along with the data object 114 in a corresponding logical data container. Alternatively, the object-based data storage service 104 may store the prediction metadata of the data object 114 in a centralized repository of the object-based data storage service 104, in a database of the customer's account profile, or in another location. In an embodiment, programmatic access to the prediction metadata is provided to other computer systems authorized to access the data object 114. This may enable these other computer systems to utilize the prediction metadata to make programmatic decisions regarding transitions of the data object 114 among the various data storage tiers.” (col. 8, lines 49-4)].
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Yang et al. (US 2019/0043351) in view of Ng et al. (US 20150278233) as applied above in the rejection of claim 1, and further in view of Liu et al. (US 2022/0283710)
10. The combination of Yang and Ng teaches The method of claim 8, but does not expressly disclose wherein the storage request and/or the deletion request is only forwarded among adjacent storage locations of the different entities of the multiple storage locations; however, regarding these limitations, [Liu teaches “[0004] In a first aspect of the present disclosure, a method for managing a storage device is provided. The method includes: determining, based on the frequency of data access to the storage device, whether a data access component of the storage device will move; determining, if it is determined that the data access component will move, a first storage unit in the storage device based on a storage location of previously accessed data in the storage device, wherein the data access component is located at a first spatial location corresponding to the first storage unit; and sending a read request for data in a second storage unit in the storage device that is adjacent to the first storage unit, so as to cause the data access component to move from the first spatial location to a second spatial location corresponding to the second storage unit.”].
Yang, Ng and Liu are analogous art because they are from the same field of endeavor of memory access and control.
Before the effective filing date of the claimed inventions, it would have been obvious to a person of ordinary skill in the art to modify the combination of Yang and Ng to have the storage request and/or the deletion request is only forwarded among adjacent storage locations of the different entities of the multiple storage locations as taught by Lie since doing so would provide the benefits of faster data move operations among the storage units/locations.
Therefore, it would have been obvious to combine Yang and Ng with Liu for the benefit of creating a storage system/method to obtain the invention as specified in claim 10.
RELEVANT ART CITED BY THE EXAMINER
The following prior art made of record and not relied upon is cited to establish the level of skill in the applicant’s art and those arts considered reasonably pertinent to applicant’s disclosure. See MPEP 707.05(c). Kim et al. (US 2019/0121899) teaches “[0118] The backend storage management unit 140 provides the function of automatically distributing and storing data in various tiers of storage. Here, based on the characteristics of the provided storage, various types of storage may be tiered. Initially, created data is stored in high-speed storage, data is moved to a lower tier of storage when the data is less frequently accessed, and the data is finally stored in the public cloud storage. The backend storage management unit 140 may automatically move data between various tiers of storage depending on the characteristics of the data.” See figs. 1, 6 and 10-11 and related text.
Dinkel et al. (US 2024/0411621) teaches “[0070] The data insight layer 220 includes one or more components for time series databases (TDSB), relational/document databases, data lakes, blob, files, images, and videos, and/or an API for data query. According to various embodiments, when raw data is received at the IoT platform 125, the raw data is stored as time series tags or events in warm storage (e.g., in a TSDB) to support interactive queries and to cold storage for archive purposes. According to various embodiments, data is sent to the data lakes for offline analytics development. According to various embodiments, the data pipeline layer 215 accesses the data stored in the databases of the data insight layer 220 to perform analytics, as detailed above.” “[0075] In one or more embodiments, the event data structure 306 is related to the edge devices 161a-161n. In one or more embodiments, the edge devices 161a-161n are associated with a portfolio of assets. For instance, in one or more embodiments, the edge devices 161a-161n include one or more assets in a portfolio of assets. The edge devices 161a-161n include, in one or more embodiments, one or more databases, one or more assets (e.g., one or more machines, equipment, one or more tools, one or more industrial assets, one or more warehouse assets, one or more building assets, etc.), one or more IoT devices (e.g., one or more industrial IoT devices)…” “[0079] In one or more embodiments, the set of event processors 310 include an HTTP connector processor configured to redirect event data structures to a particular HTTP endpoint of the network 110, a cold-storage processor configured to store event data structure in a datastore associated with data archiving functionality for event data structures, a hot-storage processor configured to store event data structures in a relational database associated with data querying functionality for event data structures, a stream processing processor configured to allocate event data structure to an event stream for rendering of visualization data associated with the event data structure via an electronic interface of a mobile device, and/or one or more other types of event processors…”.
Case et al. (US 2025/0321566) teaches “[0039] Excessive data transmission may be costly and slow down communication within the network. Controllers and drives, in particular, produce vast amounts of data. Accordingly, a smart filter disposed on the edge device would be configured to constrain what data values are reflected to the cloud and how frequently data is sent up to the cloud. Specifically, the smart filter could be utilized at the edge (e.g., running on an edge device) to optimize the way data generated by the industrial automation device is transmitted to the cloud. For example, the smart filter could be configured to prioritize the data to be transmitted to the cloud and decide what data to actually transmit based on the bandwidth available and the capacity of the cloud. The smart filter could also use machine learning algorithms to monitor for unusual conditions in the industrial automation device, and transmit data to the cloud that is out of the ordinary. In some embodiments, the smart filter may be configured to automatically classify data into types (e.g., configuration, device state, application state, alarms, etc.) and apply policies to determine what data is transmitted to the cloud and what data, if any, receives preference. The smart filter may also be configured to optimize data transmission and/or pick/filter data to transmit to the cloud. Because data being transmitted between a controller and I/O modules might not be that helpful to applications in the cloud, the smart filter may be configured to categorize data into pre-set categories and then transmit/filter/hold data based on the assigned categories. Further, data categories may also be used figure out how quickly to sample data and how quickly to transmit data to the cloud. Some data values generated by industrial automation devices change frequently, whereas other data values do not. Accordingly, the smart filter could be configured to set sampling rates based on how quickly data values change. Further, different data collection modes may have different speed rates. For example, configuration data collection rates may be slower than I/O data collection rates. In such embodiments, the smart filter may monitor data and set the collection rates. In some cases, there may be classes of collection rates, and the smart filter may be configured to set collection rates based on different factors (e.g., learning based on how quickly it actually changes, metadata from the catalog service, how the data is being used (e.g., whether the data is displayed, slow/fast, if data is being historized, etc.), the kind of automation application it is (e.g., process vs. high-speed motion), and so forth. The smart filter may be configured to allow a compute surface of the edge device, cloud compute, bandwidth, and/or storage resources to be efficiently deployed without significant input from the customer. For example, in some embodiments, there may be a base rate of speed that all data gets updated, but the collection rate increases when data is being used, such that the smart filter tunes collection rates. In some cases, the smart filter may utilize artificial intelligence and/or machine learning to lean over time and develop rules/policies applied by the smart filter. By using the smart filter, the volume of data transmitted between the OT network and the cloud may be significantly reduced, resulting in less network traffic and lower cloud computing costs. Additional details with regard to industrial automation device twins in accordance with the techniques described above will be provided below with reference to FIGS. 1-13.”
Raut et al. (US 20190243559) teaches “[0021] In accordance with storage policy 303, storage controller 302 preferably organizes and manages data storage devices 304 in multiple tiers, which can be formed, for example, based on the access latency, storage capacity, estimated useful lifetime, data storage duration, data priority, and/or other characteristics of stored data and/or the underlying storage media. In one preferred embodiment, the data storage devices 304 forming each of the various tiers of data storage devices 304 have similar access latencies. Thus, for example, first tier storage devices 306 can include one or more data storage devices, such as flash or other non-volatile memory devices, having relatively low access latencies as well as relatively low storage capacities. Data storage devices 304 may further include second tier storage devices 308, such as magnetic disks, having higher access latencies, but also providing greater storage capacities than first tier storage devices 306. Data storage devices 304 may optionally include one or more additional lower tiers of storage, such as Nth tier storage devices 310 (e.g., magnetic tape storage), providing even greater storage capacities at even higher access latencies. Nth tier storage devices 310 may be employed, for example, to provide archival data storage.” “[0022] In accordance with storage policy 303, storage controller 302 also preferably maintains a respective heat attribute 314 for each of a plurality of file system objects 312a-312f distributed among the tiers 306, 308, . . . , 310 of data storage devices 304. The heat attribute 314 indicates a frequency and/or recency of access of the associated file system object 312 and is preferably computed by storage controller 302 in accordance with a heat formula specified by storage policy 303. In one particular example, the heat formula includes a built-in decay (e.g., an exponential decay) so that unaccessed file system objects 312 become colder as the time since the most recent access increases. In general, storage controller 302 maintains the hottest file system objects 312a-312b (e.g., those most frequently accessed) in first tier storage devices 306, the next hottest file system objects 312c-312d in second tier storage devices 308, and the coldest file system objects 312e-312f in Nth tier storage devices 310. This arrangement can be achieved, for example, by applying various heat thresholds specified by storage policy 303 to distribute file system objects 312 among the various tiers 306, 308, . . . , 310 based on the values of their heat attributes 314, while reserving appropriate amounts of unused storage capacity at one or more tiers 306, 308, . . . , 310.” “[0024] The illustrated process begins at block 400 and thereafter proceeds to block 402, which illustrates storage controller 302 waiting until a migration interval has elapsed since storage controller 302 last performed a heat-based migration of file system objects between the various tiers of storage device in data storage system 300. In various implementations, the migration interval, which is defined by the storage policy 303 presently implemented by storage controller 302, may be, for example, one day, a few days, or a week. In response to storage controller 302 determining at block 402 that a migration interval has elapsed since a heat-based migration of file system objects has been performed, the process proceeds from block 402 to block 404.
[0025] Block 404 illustrates storage controller 302 migrating file system objects 312 between tiers 306, 308, . . . , 310 in accordance with their respective heat attributes 314. For example, assuming a heat attribute range of 0 . . . 100, where 0 corresponds to a minimum access frequency and 100 corresponds to a maximum access frequency, storage controller 302 may perform the necessary migrations of file system objects 312 to place file system objects 312 having a heat greater than 80 in first tier storage devices 306, file system objects 312 having a heat of 31 to 80 in second tier storage devices 308, and file system objects 312 having a heat of 30 or less in Nth tier storage devices 310. As indicated by arrows in FIG. 3, this migration may entail storage controller 302 migrating various file system objects 312 upward or downward between storage tiers to achieve the desired heat-based distribution determined by storage policy 303. Following block 404, the process of FIG. 4 returns to block 402, which has been described.“
Sanguineti et al. (US 20230185457) teaches “[0025] The temperature and lifetime predictor model 220 is configured to receive the data object 22 and/or the associated data object parameters 24 output by the parameter determiner 210 and predict the object temperature 222 and the object lifetime 224 of the data object 22. The predicted object temperature 222 represents a frequency of access for the data object 22, and the predicted object lifetime 224 represents an amount of time the data object 22 is to be stored (i.e., before deletion or garbage collection). In other words, the model 220 uses the current data object parameters 24 to generate the predictions 222, 2224 access patterns for the data object 22 and how long the data object 22 will be stored before being deleted from the data store 146. In some examples, the temperature and lifetime predictor model 220 may predict the object temperature 222 and the object lifetime 224 independently (i.e., as two separate values or data structures) or as a single combined value/data structure. In some examples, the predicted object temperature 222 may vary over the predicted object lifetime 224 of the data object 22. For example, the temperature and lifetime predictor model 220 may predict that the data object 22 will be frequently accessed early in its lifetime and rarely accessed late in its lifetime.” “[0037] FIG. 3 shows an example of a training process 300 for training the model 220 to predict the object temperature 222 and the object lifetime 224 of the data object 22. The training process 300 includes a model trainer 310 that obtains a plurality of historical temperature and lifetime training samples 322, 322a-n (also referred to herein as training samples 322) stored in a sample data store 320. The model trainer 310 trains the model 220 using the historical temperature and lifetime training samples 322. The sample data store 130 may reside on the memory hardware 142 of the remote system 140. As discussed above with respect to FIGS. 2A and 2B, the temperature and lifetime for any given data object 22 is unknown when the data object 22 is first received for storage, which makes identifying the optimal data store 146 challenging. Training the model 220 using historical temperature and lifetime training samples 322 allow the model 220 to predict an object temperature 222 and an object lifetime 224 of a data object 22 when it is received for storage at the data store 146.” (see fig. 3 and related text)].
CLOSING COMMENTS
a. STATUS OF CLAIMS IN THE APPLICATION
a(1) CLAIMS REJECTED IN THE APPLICATION
Per the instant office action, 1-4 and 6-14 have received a first action on the merits and are subject of a first action non-final.
a(2) ALLOWABLE SUBJECT MATTER
Per the instant office action, claim 5 is objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all the limitations of the base claim and any intervening claims and if the objections above are overcome.
5. The method of claim 3, wherein the one or more operational data is at least indicative of a measured value from one or more sensors, information on an energy consumption of the one or more industrial plants, information on a current status of the one or more industrial plants, information on a production process and/or setting and configuration of a controller that controls the production process; wherein the operational data is first compressed on the at least one storage device of the one or more industrial plants, then on the at least one edge device (30) and then on the least one storage device of a cloud server.
b. DIRECTION OF FUTURE CORRESPONDENCES
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August 28, 2026
/YAIMA RIGOL/
Primary Examiner, Art Unit 2135
Prosecution Insights