DETAILED ACTION
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
2. 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.
3. Claims 1-20, filed on 12/8/2023, are pending in this office action.
Claim Rejections - 35 USC § 103
4. 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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
5. Claim(s) 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Doshi et al. (US Patent 11,966,359 B1) in view of Guha Thakurta et al. (US Publication 2024/0054391 A1)
As per claim 1, Doshi teaches A system for selectively determining which data store to use for storing data based on client-provided time-related predictions, (see Abstract)
the system comprising one or more processors and one or more non-transitory, machine-readable media storing program instructions that, when executed by the one or more processors, perform operations comprising: (column 27 lines 45-64, processing devices)
providing, to a client device, a machine learning model that outputs time-related predictions indicating a most probable time for a future event based on update data; (column 6 line 40 – column 7 line 10 a machine learning algorithm generates prediction metadata, column 8 line 48 – column 9 line 45, prediction metadata utilized to indicate usage of data storage and placement for data objects to predict when they are to be accessed, interpreted as time-related predictions)
obtaining, from the client device, initial update data and a time-related prediction, wherein the time-related prediction is generated by providing the initial update data to the machine learning model as an input; (column 6 lines 8-39, column 9 lines 5-57, prediction metadata is generated to indicate placement of data objects within data storage tiers, the prediction metadata generated based on data logs indicating frequency of access and other time-related information)
determining that the time-related prediction satisfies a set of criteria associated with a first data store of a plurality of data stores comprising the first data store and a second data store, wherein the first data store is characterized by a first throughput value, and wherein the second data store is characterized by a second throughput value; (column 9 line 59 – column 10 line 8, column 11 lines 4-60, prediction metadata is utilized along with parameters that may include constraints related to different tiers of data storage systems for optimal placement of data objects within different data storage tiers, column 4 lines 49 – column 5 lines 12, the different data storage tiers providing different rates and speed of access cost based on utilization, interpreted as throughput)
determining that the time-related prediction is associated with the first data store based on a determination that the time-related prediction satisfies the set of criteria; (column 9 line 59 – column 10 line 7, column 10 lines 27 – column 45, prediction metadata is utilized along with parameters to determine whether a data object should be placed in a standard data store, interpreted as first data store)
updating a record in the first data store based on the initial update data in response to a determination that the time-related prediction is associated with the first data store; (column 14 lines 3-46, the initial data storage tier is determined and recorded)
receiving, from the client device, additional update data after obtaining the initial update data, wherein the additional update data shares an identifier with the initial update data; (column 6 lines 8-39, column 9 lines 46-58, usage data is monitored and received, utilized to update the classification of data objects and placement within data storage tiers)
retrieving the record based on an association between the additional update data and the initial update data; (column 19 lines 1-38, an object placement engine evaluates data objects as usage data is received)
and updating the record in the first data store based on the additional update data. (column 19 lines 39 – column 20 line 11, a data log indicating data object placement is recorded)
Doshi does not explicitly indicate obtaining time-related prediction that is generated by the client device.
Guha Thakurta teaches obtaining time-related prediction that is generated by the client device. (paragraphs 0025, 0030, 0045, client-side machine learning models generate predictions based on user history).
It would have been obvious for one of ordinary skill in the art at the time the invention was made to combine Doshi’s method of providing an tier-based data object storage system utilizing machine learning algorithms generating predictions with Guha Thakurta’s ability to generate predictions based on client-side machine learning models. This gives the user the ability to generate predictions from machine-learning models on the side of clients, easing the burden on the server. The motivation for doing so would be to better utilize machine learning models to provide more personalized data (paragraph 0003).
As per claim 2, Doshi teaches A method comprising: (see Abstract)
providing, to a client device, a machine learning model; (column 6 line 40 – column 7 line 10 a machine learning algorithm generates prediction metadata)
obtaining, from the client device, a first set of update data and a time-related prediction generated by providing the machine learning model with at least one value of the first set of update data; (column 8 line 48 – column 9 line 45, prediction metadata utilized to indicate usage of data storage and placement for data objects to predict when they are to be accessed, interpreted as time-related predictions, column 6 lines 8-39, column 9 lines 5-57, prediction metadata is generated to indicate placement of data objects within data storage tiers, the prediction metadata generated based on data logs indicating frequency of access and other time-related information)
determining a result indicating that the time-related prediction is associated with a first data store of a plurality of data stores comprising the first data store and a second data store, wherein the first data store is characterized by a first throughput value, and wherein the second data store is characterized by a second throughput value; (column 9 line 59 – column 10 line 8, column 11 lines 4-60, prediction metadata is utilized along with parameters that may include constraints related to different tiers of data storage systems for optimal placement of data objects within different data storage tiers, column 4 lines 49 – column 5 lines 12, the different data storage tiers providing different rates and speed of access cost based on utilization, interpreted as throughput)
updating a record of the first data store based on the first set of update data in response to the result indicating that the time-related prediction is associated with the first data store; (column 9 line 59 – column 10 line 7, column 10 lines 27 – column 45, prediction metadata is utilized along with parameters to determine whether a data object should be placed in a standard data store, interpreted as first data store, column 14 lines 3-46, the initial data storage tier is determined and recorded)
and updating the record of the first data store based on a second set of update data by retrieving the record of the first data store based on an association between the second set of update data and the first set of update data. (column 6 lines 8-39, column 9 lines 46-58, usage data is monitored and received, utilized to update the classification of data objects and placement within data storage tiers, column 19 lines 1-38, an object placement engine evaluates data objects as usage data is received, column 19 lines 39 – column 20 line 11, a data log indicating data object placement is recorded)
Doshi does not explicitly indicate client-side version of the machine learning model.
Guha Thakurta teaches client-side version of the machine learning model (paragraphs 0025, 0030, 0045, client-side machine learning models generate predictions based on user history).
It would have been obvious for one of ordinary skill in the art at the time the invention was made to combine Doshi’s method of providing an tier-based data object storage system utilizing machine learning algorithms generating predictions with Guha Thakurta’s ability to generate predictions based on client-side machine learning models. This gives the user the ability to generate predictions from machine-learning models on the side of clients, easing the burden on the server. The motivation for doing so would be to better utilize machine learning models to provide more personalized data (paragraph 0003).
As per claim 3, Doshi teaches obtaining, from the client device, a third set of update data, wherein the third set of update data comprises a timestamp indicating a transaction time; (column 6 lines 8-15, time at which data was accessed)
determining a result indicating that the transaction time is not within a first time range associated with the first data store; (column 24 lines 20-48, periods of inactivity)
updating training data based on the third set of update data in response to the result indicating that the transaction time is not within the first time range; (column 9 lines 46=58, update prediction metadata)
and updating a server-side version of the machine learning model based on the training data after the training data is updated with the third set of update data. column 9 line 59 – column 10 line 7, update)
As per claim 4, Doshi teaches updating the record in the first data store comprises: determining an available memory of the first data store; and determining a result indicating that the available memory satisfies a set of memory-related criteria, wherein updating the record comprises updating the record in response to the result indicating that the available memory satisfies the set of memory-related criteria. (column 10 lines 8-26, column 12 line 63 – column 13 line 58, availability of data storage)
As per claim 5, Doshi teaches the time-related prediction is a first time value, and wherein the record is a first record, further comprising: obtaining, from the client device, a third set of update data in association with a second time value; determining a second result indicating that the second time value is associated with the second data store; and updating a second record of the second data store based on the third set of update data in response to the second result indicating that the second time value is associated with the second data store. (column 6 lines 8-39, column 9 lines 46-58, usage data is monitored and received, column 19 lines 39 – column 20 line 11, a data log indicating data object placement is recorded)
As per claim 6, Doshi teaches storing data from the first record in the second data store based on the second result. (column 12 lines 8-43, transfer data object to different data storage tier)
As per claim 7, Doshi teaches the plurality of data stores comprises a third data store, and wherein the third data store is characterized by a third throughput value that is greater than the first throughput value and less than the second throughput value. (column 15 lines 9-39, smart storage tier)
As per claim 8, Doshi teaches the record is a first record, and wherein the time-related prediction is a first time value, further comprising: obtaining, from the client device, a second record in association with a second time value, wherein the second time value is different from the first time value; determining, based on the second time value, a result indicating that the second time value is associated with the third data store; and storing the second record in the third data store based on the result indicating that the second time value is associated with the third data store. (column 15 lines 25 0 column 16 line 15, transfer to smart storage tier)
As per claim 9, Doshi teaches obtaining model parameters of the client-side version of the machine learning model; and updating a server-side version of the machine learning model based on the model parameters. (column 19 lines 39 – column 20 line 11, a data log indicating data object placement is recorded)
As per claim 10, Doshi teaches the first set of update data comprises location data; and the machine learning model is configured to provide the time-related prediction based on the location data. (column 11 lines 3-60, storage location)
As per claim 11, Doshi teaches One or more non-transitory, machine-readable media storing program instructions that, when executed by one or more processors, cause the one or more processors to perform operations comprising: (see Abstract)
obtaining, from a client device, a first set of update data and a time-related prediction generated by a machine learning model; (column 6 line 40 – column 7 line 10 a machine learning algorithm generates prediction metadata, column 8 line 48 – column 9 line 45, prediction metadata utilized to indicate usage of data storage and placement for data objects to predict when they are to be accessed, interpreted as time-related predictions, column 6 lines 8-39, column 9 lines 5-57, prediction metadata is generated to indicate placement of data objects within data storage tiers, the prediction metadata generated based on data logs indicating frequency of access and other time-related inforamtion)
determining that the time-related prediction is associated with a first data store of a plurality of data stores comprising the first data store and a second data store, wherein the first data store is characterized by a first throughput value, and wherein the second data store is characterized by a second throughput value; (column 9 line 59 – column 10 line 8, column 11 lines 4-60, prediction metadata is utilized along with parameters that may include constraints related to different tiers of data storage systems for optimal placement of data objects within different data storage tiers, column 4 lines 49 – column 5 lines 12, the different data storage tiers providing different rates and speed of access cost based on utilization, interpreted as throughput)
updating a record in the first data store based on the first set of update data in response to a determination that the time-related prediction is associated with the first data store; (column 9 line 59 – column 10 line 7, column 10 lines 27 – column 45, prediction metadata is utilized along with parameters to determine whether a data object should be placed in a standard data store, interpreted as first data store, column 14 lines 3-46, the initial data storage tier is determined and recorded)
and updating the record in the first data store based on a second set of update data obtained after obtaining the first set of update data, wherein the second set of update data shares an identifier with the first set of update data. (column 6 lines 8-39, column 9 lines 46-58, usage data is monitored and received, utilized to update the classification of data objects and placement within data storage tiers, column 19 lines 1-38, an object placement engine evaluates data objects as usage data is received, column 19 lines 39 – column 20 line 11, a data log indicating data object placement is recorded)
Doshi does not explicitly indicate client-side version of a machine learning model.
Guha Thakurta teaches client-side version of a machine learning model (paragraphs 0025, 0030, 0045, client-side machine learning models generate predictions based on user history).
It would have been obvious for one of ordinary skill in the art at the time the invention was made to combine Doshi’s method of providing an tier-based data object storage system utilizing machine learning algorithms generating predictions with Guha Thakurta’s ability to generate predictions based on client-side machine learning models. This gives the user the ability to generate predictions from machine-learning models on the side of clients, easing the burden on the server. The motivation for doing so would be to better utilize machine learning models to provide more personalized data (paragraph 0003).
As per claim 12, Doshi and Guha Thakurta are taught as per claim 11 above. Guha Thakurta teaches sending model parameter data to the client device, wherein the client device reconfigures one or more parameters of the client-side version of the machine learning model based on the model parameter data. (paragraphs 0025, 0030, 0045, client-side machine learning models)
As per claim 13, Doshi teaches the time-related prediction comprises a probability value indicating a likelihood that a target future event will occur within a pre-set duration. (column 6 lines 8-39, column 9 lines 5-57, prediction metadata associated with predicted utilization of data object)
As per claim 14, Doshi teaches determining that the time-related prediction is associated with the first data store comprises: determining a product based on the time-related prediction and the pre-set duration; determining that a storage duration threshold is satisfied based on the product; and determining that the time-related prediction is associated with the first data store based on a determination that the product satisfies the storage duration threshold. (column 24 lines 20-48, periods of inactivity, column 10 lines 8-26, column 12 line 63 – column 13 line 58, availability of data storage)
As per claim 15, Doshi teaches the machine learning model is a first machine learning model, and wherein the time-related prediction is a first time-related prediction, the operations further comprising: providing the client device with the first machine learning model and a second machine learning model, wherein: the first time-related prediction indicates a likelihood that a future target event occurs within a first time range associated with the first data store; a client-side version of the second machine learning model provides a second time-related prediction; and the second time-related prediction indicates a likelihood that the future target event occurs within a second time range associated with the second data store; and obtaining, from the client device, the second time-related prediction, wherein determining that the time-related prediction is associated with the first data store comprises comparing the first time-related prediction with the second time-related prediction. (column 21 lines 4-42, prediction as input into one or more machine learning models)
As per claim 16, Doshi teaches determining that the time-related prediction is associated with the first data store comprises: determining that a set of criteria associated with the first data store is satisfied by the time-related prediction; and determining that the time-related prediction is associated with the first data store based on the determination that the set of criteria associated with the first data store is satisfied by the time-related prediction; the operations further comprising: determining a prediction accuracy based on a timestamp associated with the second set of update data and the time-related prediction; and modifying a threshold of the set of criteria based on the prediction accuracy. (column 6 lines 8-39, column 9 lines 46-58, usage data is monitored and received, column 19 lines 39 – column 20 line 11, a data log indicating data object placement is recorded)
As per claim 17, Doshi teaches obtaining, from the client device, a third set of update data and a second time-related prediction generated by the client-side version of the machine learning model; determining that the third set of update data is associated with the first data store based on the machine learning model; receiving an indication that the first data store cannot be used for storage; and updating a second record stored in a third data store based on the third set of update data. (column 15 lines 9-39, smart storage tier, column 15 lines 25 0 column 16 line 15, transfer to smart storage tier)
As per claim 18, Doshi teaches determining a memory-related value associated with the first data store, wherein determining that the time-related prediction is associated with the first data store comprises determining that the time-related prediction is associated with the first data store based on the memory-related value. (column 10 lines 8-26, column 12 line 63 – column 13 line 58, availability of data storage)
As per claim 19, Doshi teaches the client device stores a set of locally accessible data, wherein the first set of update data does not comprise the set of locally accessible data; and the machine learning model is configured to provide the time-related prediction based on the set of locally accessible data. (column 25 line 61 – column 26 line 18, distributed, clustered storage system)
As per claim 20, Doshi teaches the set of locally accessible data comprises a history of locations. (column 11 lines 3-60, storage location)
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Grebenisan (US Publication 2021/0303631 A1)
Akkapeddi (US Publication 2022/0114020 A1)
Pendharkar (US Patent 11,137,926 B1)
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DANGELINO N GORTAYO whose telephone number is (571)272-7204. The examiner can normally be reached Monday-Friday 7:00am - 3:30pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Charles Rones can be reached at 571-272-4085. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/DANGELINO N GORTAYO/ Primary Examiner, Art Unit 2168
Prosecution Insights