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 .
Claims 1-20 are presented for examination.
This Office action is Non-Final.
Information Disclosure Statement
The information disclosure statement (IDS) filed on 08/06/2025 has been considered by the Examiner and made of record in the application file.
Obviousness Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-18 of U.S. Patent No. 12,405,956 (‘956). Although the claims at issue are not identical, they are not patentably distinct from each other because the claims of ‘956 recite determining, using a machine-learning technique and an expression of a use case, a TTL value for satisfying the use case and configuring a data cache according to the TTL value, and further expressly recite that the machine-learning technique is trained to use a cost-benefit analysis to determine the TTL value for the use case. The presently claimed determination of acceptable data staleness by evaluating the tradeoff between data freshness and resource consumption or monetary cost merely further specifies the cost-benefit analysis already claimed by the ’956 patent. It would have been obvious to evaluate such freshness-related costs and benefits when performing the expressly claimed cost-benefit analysis in order to determine a TTL appropriate for the particular use case. Accordingly, the presently claimed subject matter is not patentably distinct from the claims of the ’956 patent.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
With respect to claims 1 and 11, the claims recite “…an acceptable level of data staleness for the use case…” It is not clear what is considered to be an “acceptable” mark and it could be construed to have many ranges of interpretation.
With respect to claim 4, the claims recites “…sufficiency of data freshness” Similarly it is not clear what is considered sufficient and it can also be construed to have many ranges of interpretation.
With respect to claims 2-10 and 12-20, they would be rejected for being dependents of independent claims 1 and 11.
Claim Rejections - 35 USC § 103
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.
Claims 1, 3, 6, 9, 11, 14, 16 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Antani et al. (US 2013/0073809 A1) hereinafter “Antani”, further in view of McConnell et al. (US 10,216,631 B1) hereinafter “McConnell”.
With respect to claims 1, 6 and 11, the Antani reference discloses an electronic system, a method and non-transitory machine-readable medium [see Abstract, disclosing a TTL value for a data object stored in-memory in a data grid is dynamically adjusted], comprising:
a processor subsystem [see ¶0005, disclosing a processor]; and
a memory including instructions, which when executed by the processor subsystem, cause the processor subsystem to [see ¶0005, disclosing memory connected to the processor; The memory is encoded with instructions that when executed comprise instructions for setting a stale data tolerance policy; The instructions also comprise instructions for calculating metrics to report a cost to re-create and re-store the data object, and to adjust the TTL value based on the metrics]:
perform a cost-benefit analysis to determine an acceptable level of data staleness [see Abstract, disclosing a stale data tolerance policy is set. Low toleration for staleness would mean that eviction is certain, no matter the cost, and high toleration would mean that the TTL value would be set based on total cost] for the use case, wherein the cost-benefit analysis evaluates a tradeoff between data freshness and at least one of resource consumption or monetary cost [see Abstract, disclosing metrics to report a cost to re-create and re-store the data object are calculated, and the TTL value is adjusted based on calculated metrics; Further factors, such as, cleanup time to evict data from a storage site, may be considered in the total cost]; (emphasis added)
determine, based on the cost-benefit analysis, a time-to-live (TTL) value for data to satisfy the use case [see ¶0031, disclosing a decision to dynamically increase TTL (or decrease or leave it unaltered) of the object in a cache is based on the total cost of repopulating the data in the grid]; and (emphasis added)
configure a data cache to store data received from a data source with the TTL value [see ¶0031, disclosing a decision to dynamically increase TTL (or decrease or leave it unaltered) of the object in a cache is based on the total cost of repopulating the data in the grid].
Antani discloses the electronic system, method and non-transitory machine-readable medium, as referenced above.
Antani does not explicitly disclose receive an expression of a use case.
However, McConnell disclose it receive, at the electronic online system, an expression of a use case [see col. 6, lines 35-48, disclosing track attributes of a number of queries of the database 108; Attributes of a number of queries, as used herein, can include properties, characteristics, and/or features of the number of queries; The attributes can be collected over a period of time, such as a day, a week, a month, and/or a year, among other periods of time; The attributes of the number of queries can include data size of the query, information associated with re-caching data associated with the query (e.g., write page amount), logs of when the number of queries are received (e.g., time, day of a week, date), and/or whom requested the queries, among other attributes; That is, each query can have a history of the last time the query was requested (e.g., hit) and how often the query was requested].
It would have been obvious before the effective filing date of the invention to a person having ordinary skill in the art to which said subject matter pertains to modify the cost- and staleness-based TTL determination techniques as taught by Antani with the tracked attributes pertaining to queries as taught by McConnell, because doing so would permit cache expiration to reflect the freshness associated with the particular queries while avoiding unnecessary retrieval, refresh, and cache-update operations.
With respect to claims 3, 9 and 14, the combination of Antani and McConnell discloses the electronic system, method and non-transitory machine-readable medium of claims 1, 6 and 11, as referenced above. The combination further discloses it comprises:
estimating a cost associated with obtaining fresh data from the data source [Antani, see ¶0031, disclosing factors in the cost of replicating or making copies and cleanup. The varying degrees of cost (for example, synchronous replication or copying, is more expensive than asynchronous replication/copying), and the cost of having no copy available, is not just the cost of fetching the data from a database]; and
estimating a benefit associated with providing fresher data to the use case [Antani, see ¶0003, disclosing there are also use cases where the cost to re-create the data in the cache outweighs the cost of reading stale data; For example, if the database is unreachable or the connection to the database has been saturated, then having the eviction policy temporarily disabled or increased is more beneficial than having the user wait indefinitely or suffer a connection timeout, which can cascade the database problem to the front-end systems]; and
selecting the TTL value that maximizes a net benefit metric [Antani, see ¶0003, disclosing there are also use cases where the cost to re-create the data in the cache outweighs the cost of reading stale data; For example, if the database is unreachable or the connection to the database has been saturated, then having the eviction policy temporarily disabled or increased is more beneficial than having the user wait indefinitely or suffer a connection timeout, which can cascade the database problem to the front-end systems; the cited portion suggests that the process is selecting the TTL value that maximizes a net benefit metric].
With respect to claim 16, the combination of Antani and McConnell discloses the non-transitory machine-readable medium of claim 11, as referenced above. The combination further discloses wherein the expression of the use case is formed as a query [McConnell, see col. 6, lines 35-48, disclosing track attributes of a number of queries of the database 108; Attributes of a number of queries, as used herein, can include properties, characteristics, and/or features of the number of queries; The attributes can be collected over a period of time, such as a day, a week, a month, and/or a year, among other periods of time; The attributes of the number of queries can include data size of the query, information associated with re-caching data associated with the query (e.g., write page amount), logs of when the number of queries are received (e.g., time, day of a week, date), and/or whom requested the queries, among other attributes; That is, each query can have a history of the last time the query was requested (e.g., hit) and how often the query was requested].
With respect to claim 20, the combination of Antani and McConnell discloses the non-transitory machine-readable medium of claim 11, as referenced above. The combination further discloses wherein the data source includes at least one of: a database with a SQL database structure, a database with a NoSQL database structure, or an in-memory data structure store [Antani, see ¶0004, disclosing a TTL value for a data object stored in-memory in a data grid; it is noted that throughout Antani’s disclosure it is mentioned database, however, it does not specify if it’s a SQL/NoSQL database structure, either variations is a design choice and it would have been obvious to a person of ordinary skill in the art].
Claims 2, 4, 5, 7, 8, 10, 12, 13 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Antani and McConnell, further in view of Alagumuthu (US 2020/0201773 A1) hereinafter “Alagumuthu”.
With respect to claims 2, 7 and 12, the combination of Antani and McConnell discloses the electronic system, method and non-transitory machine-readable medium of claims 1, 6 and 11, as referenced above. The combination does not discloses wherein the cost-benefit analysis is performed by a machine-learning model trained to optimize TTL values for different use cases/using a machine-learning model trained to perform the cost-benefit analysis and determine the TTL value.
However, Alagumuthu discloses wherein the cost-benefit analysis is performed by a machine-learning model trained to optimize TTL values for different use cases/using a machine-learning model trained to perform the cost-benefit analysis and determine the TTL value [see Abstract, disclosing machine learning module generates, based on access patterns to the software cache, a control value that specifies a size of the cache and generates time-to-live values for entries in the cache].
It would have been obvious before the effective filing date of the invention to a person having ordinary skill in the art to which said subject matter pertains to modify the combination with the machine-learning TTL techniques as taught by Alagumuthu since doing so would have enabled the combination to intelligently manage the data in the cache by learning the patterns, conditions and optimization via machine learning.
With respect to claims 4, 8 and 13, the combination of Antani and McConnell discloses the electronic system, method and non-transitory machine-readable medium of claims 1, 6 and 11, as referenced above. The combination does not discloses it comprises:
receive feedback from an application regarding a sufficiency of data freshness; and
update the cost-benefit analysis or retrain a machine-learning model based on the feedback.
However, Alagumuthu discloses it comprises:
receive feedback from an application regarding a sufficiency of data freshness [see ¶0043, disclosing the machine learning module may adjust how various initial scores such as the total read score and the read time score are generated based on feedback during training]; and
update the cost-benefit analysis or retrain a machine-learning model based on the feedback [see ¶0047, disclosing a training module trains the machine learning module based on access patterns to the cache by a subset of the user accounts and based on feedback information that includes: a simulated hit rate for the cache for the subset of user accounts, and simulated read access times for the cache for the subset of user accounts].
It would have been obvious before the effective filing date of the invention to a person having ordinary skill in the art to which said subject matter pertains to modify the combination with the machine-learning TTL techniques as taught by Alagumuthu since doing so would have enabled the combination to intelligently manage the data in the cache by learning the patterns, conditions and optimization via machine learning.
With respect to claims 5, 10 and 15, the combination of Antani and McConnell discloses the electronic system, method and non-transitory machine-readable medium of claims 1, 6 and 11, as referenced above. The combination does not discloses wherein the cost-benefit analysis further considers a frequency of data access and a criticality of data freshness for the use case.
However, Alagumuthu discloses wherein the cost-benefit analysis further considers a frequency of data access and a criticality of data freshness for the use case [see Abstract, disclosing machine learning module generates, based on access patterns to the software cache, a control value that specifies a size of the cache and generates time-to-live values for entries in the cache; also, see ¶0047, disclosing a machine learning module generates a control value that specifies a size of the cache and generates time-to-live values for entries in the cache, based on access patterns to the cache].
It would have been obvious before the effective filing date of the invention to a person having ordinary skill in the art to which said subject matter pertains to modify the combination with the machine-learning TTL techniques as taught by Alagumuthu since doing so would have enabled the combination to intelligently manage the data in the cache by learning the patterns, conditions and optimization via machine learning.
Claims 17 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Antani and McConnell, further in view of Phillipps et al. (US 9,646,262 B2) hereinafter “Phillipps”.
With respect to claim 17, the combination of Antani and McConnell discloses the non-transitory machine-readable medium of claim 11, as referenced above. The combination does not discloses wherein the expression of the use case is formed as a business objective.
However, Phillipps discloses wherein the expression of the use case is formed as a business objective [see col. 8, lines 28-31, disclosing the extract module 202 identifies data sources 104 based on one or more declared business objectives of the data intelligence module 102].
It would have been obvious before the effective filing date of the invention to a person having ordinary skill in the art to which said subject matter pertains to modify the combination with the business objectives as taught by Phillipps to identify information and data sources relevant to the user’s stated business objective, while permitting the system to obtain and process data appropriate for satisfying the particular intended user without requiring the user to manually identify the underlying data sources.
With respect to claim 19, the combination of Antani and McConnell discloses the non-transitory machine-readable medium of claim 11, as referenced above. The combination does not discloses wherein the expression of the use case does not include the data source.
However, Phillipps discloses wherein the expression of the use case does not include the data source [see col. 8, lines 28-31, disclosing the extract module 202 identifies data sources 104 based on one or more declared business objectives of the data intelligence module 102; as understood by the Examiner, since the process automatically identifies the data source(s) based on an objective the data source does not have to be specified as part of the use case].
It would have been obvious before the effective filing date of the invention to a person having ordinary skill in the art to which said subject matter pertains to modify the combination with the business objectives as taught by Phillipps to identify information and data sources relevant to the user’s stated business objective, while permitting the system to obtain and process data appropriate for satisfying the particular intended user without requiring the user to manually identify the underlying data sources.
Claim 18 is rejected under 35 U.S.C. 103 as being unpatentable over Antani and McConnell, further in view of Feblowitz et al. (US 8,166,465 B2) hereinafter “Feblowitz”.
With respect to claim 18, the combination of Antani and McConnell discloses the non-transitory machine-readable medium of claim 11, as referenced above. The combination does not discloses wherein the expression of the use case is formed as a description of an output.
However, Feblowitz discloses wherein the expression of the use case is formed as a description of an output [see Abstract, disclosing each of the data source descriptions includes a graph pattern that semantically describes an output of a data source, each of the component descriptions includes a graph pattern that semantically describes an input of a component and a graph pattern that semantically describes an output of the component, the stream processing request includes a goal that is represented by a graph pattern that semantically describes a desired stream processing outcome and the stream processing graph includes at least one data source or at least one component that satisfies the desired processing outcome].
It would have been obvious before the effective filing date of the invention to a person having ordinary skill in the art to which said subject matter pertains to modify the combination with the techniques described in Feblowitz because doing so would permit a user to specify the desired result without requiring knowledge of the underlying data sources or processing components, while allowing the system to determine the resources appropriate for satisfying the requested result.
Prior Art Made of Record
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Deng et al. discloses generating user-specific time-to-live (TTL) values using machine learning. In various embodiments, a server system maintains a cache data store that is operable to store data for a plurality of users of a web service. In response to a cache miss for a request from a first one of the plurality of users, the server system may generate a user-specific TTL value for the first user. In various embodiments, generating the user-specific TTL value may include using a machine learning model to generate a predicted future access pattern for the first user that indicates a distribution of time periods during which the first user is expected to access the web service and, based on the predicted future access pattern, determining the user-specific TTL value for the first user.
Oliner et al. discloses a non-transitory computer readable storage medium has instructions executed by a processor to maintain a repository of machine learning directed acyclic graphs. Each machine learning directed acyclic graph has machine learning artifacts as nodes and machine learning executors as edges joining machine learning artifacts. Each machine learning artifact has typed data that has associated conflict rules maintained by the repository. Each machine learning executor specifies executable code that executes a machine learning artifact as an input and produces a new machine learning artifact as an output. A request about an object in the repository is received. A response with information about the object is supplied.
Conclusions/Points of Contacts
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JORGE A CASANOVA whose telephone number is (571)270-3563. The examiner can normally be reached M-F: 9 a.m. to 6 p.m. (EST).
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, Aleksandr Kerzhner can be reached at (571) 270-1760. 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.
/JORGE A CASANOVA/Primary Examiner, Art Unit 2165
Prosecution Insights