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 .
Response to Amendment
This Office Action has been issued in response to the amendment filed on 7/1/2026. Claims 1-20 are pending. Applicants’ arguments have been carefully and respectfully considered in light of the instant amendment and are persuasive, as they relate to the claim rejections under 35 U.S.C. 102. However, after further search and consideration, Examiner has found additional prior art, as will be discussed below. Accordingly, this action has been made FINAL.
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.
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Claims 1-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of U.S. Patent No. 12,353,410. Although the claims at issue are not identical, they are not patentably distinct from each other because they are an obvious variant.
Instant Application
U.S. Patent No. 12,353,410
Claim 1:
A system comprising:
one or more processors, coupled with memory, to:
generate, using a machine learning model, a plurality of predicted requests based on one or more requests received from a client device;
compute, for each predicted request of the plurality of predicted requests, a metric associated with retrieving data for a respective predicted request from a database;
select a subset of predicted requests from the plurality of predicted requests based on the metric corresponding to each predicted request and a threshold metric;
generate, using the machine learning model, labels comprising classifications of the subset of predicted requests;
store, using the labels as cache keys, data from the database for the subset of predicted requests in a cache store; and
transmit, responsive to receiving a client request matching a predicted request of the subset of predicted requests, corresponding data from the cache store.
Claim 1:
A system, comprising:
one or more processors, coupled with a cache store, the one or more processors configured to:
predict, using a machine learning model, a plurality of requests indicative of one or more subsequent requests
identify, using the machine learning model and based on a comparison with a threshold metric, a subset of predicted requests from the plurality of requests,
construct, using the machine learning model and a set of labels comprising classifications for the subset of predicted requests;
retrieve, using the set of labels and from a database, data for the subset of predicted requests;
transmit, responsive to receipt of a subsequent request that matches one of the subset of predicted requests, a cache value from the cache store that corresponds to the subsequent request.
Claim 2 and 3:
The system of claim 1, wherein the one or more processors further:
generate a plurality of predicted request identifiers associated with the subset of predicted requests; and
construct, using the machine learning model and based on the plurality of predicted request identifiers, the labels comprising classifications for the subset of predicted requests.
The system of claim 1, wherein the one or more processors:
receive one or more identifiers and key-value pairs associated with the one or more requests; and
generate, using the machine learning model, the plurality of predicted requests based on the one or more identifiers and key-value pairs.
Claim 1:
the subset of predicted requests comprising predicted request identifiers and corresponding predicted key-value pairs;
construct, using the machine learning model and based on the predicted request identifiers and the corresponding predicted key-value pairs, a set of labels comprising classifications for the subset of predicted requests;
Claim 7:
The system of claim 1, wherein the one or more processors further:
configure, in the cache store, the labels as the cache keys and the data from the database as cache values for the subset of predicted requests.
Claim 1:
configure, in the cache store, the set of labels as cache keys and the data retrieved for the subset of predicted requests as cache values;
With regards to claims 2-20 of the instant application, these claims contain limitations similar to those found in claims 2-20 of U.S. Patent No. 12,353,410, thus, are analyzed similarly as shown above.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Shekhar (US 20240143593 A1) in view of Di Fabbrizio et al (US 20230351184 A1).
As to claim 1, Shekhar teaches a system, comprising:
one or more processors (See [0103]-[0104]), coupled with memory, to:
generate, using a machine learning model, a plurality of predicted requests based on one or more subsequent requests from a client device (Shekhar discloses using a heuristic such as an “origin ID” that the machine learning (ML) model has identified to likely trigger a certain query or query pattern to be issued to the system. Thus, when a trigger heuristic is observed or identified in real-time, the cache manager can decide that the predicted query pattern associated with the trigger heuristic is expected to be issued. See [0018]);
compute, for each predicted request of the plurality of predicted requests, a metric associated with retrieving data for a respective predicted request from a database (Shekhar discloses the machine learning (ML) model has identified to likely trigger a certain query or query pattern to be issued to the system in [0018]. Shekhar discloses based on a cache hit ratio, the cache manager can determine an effectiveness and success of the current caching mechanism (i.e. metric). If the hit ratio falls below a threshold (i.e. metric), the cache manager's ML model may re-learn (e.g., as is used in Reinforcement Learning strategies) the traffic pattern and then adjust the caching mechanism dynamically by changing the cache population strategy and/or the cache eviction strategy in [0015] and [0096]-[0099]);
select a subset of predicted requests from the plurality of predicted requests based on the metric corresponding to each predicted request and a threshold metric (Shekhar discloses the machine learning (ML) model has identified to likely trigger a certain query or query pattern to be issued to the system in [0018]. Shekhar discloses based on a cache hit ratio, the cache manager can determine an effectiveness and success of the current caching mechanism. If the hit ratio falls below a threshold (i.e. metric), the cache manager's ML model may re-learn (e.g., as is used in Reinforcement Learning strategies) the traffic pattern and then adjust the caching mechanism dynamically by changing the cache population strategy and/or the cache eviction strategy (i.e. select a subset of predicted requests) in [0015] and [0096]-[0099]);
store, using the labels as cache keys, data from the database for the subset of predicted requests in a cache store (Shekhar discloses the cache manager pre-fetches query results associated with the predicted upcoming set of queries and pre-populates a cache with the query results from the predicted queries in [0013]. Shekhar discloses a cache key (label) which is used to identify the corresponding resource in [0031]-[0032].); and
transmit, responsive to receiving a client request matching a predicted request of the subset of predicted requests, corresponding data from the cache store (Shekhar discloses if and when the predicted set of queries are actually received by the system, the query results are already in the cache (cache hit). Accordingly, the system may then return the query results to the query requestor in a much faster response time as compared to retrieving the query results from a non-cache storage location when the query results are not in the cache (cache miss). See [0013].).
Shekhar fails to teach generate, using the machine learning model, labels comprising classifications of the subset of predicted requests.
However, Di Fabbrizio teaches generate, using the machine learning model, labels comprising classifications of the subset of predicted requests (Di Fabbrizio Abstract, [0003]-[0009], and [0035]-[0046] discloses Data is received characterizing a plurality of search queries including user provided natural language representations of the plurality of search queries of an item catalogue and first labels associated with the plurality of search queries. Label weights characterizing a frequency of occurrence of the first labels within the received data is determined using the received data. Second labels are determined. The determining of the second labels includes removing or changing the first labels from the received data to reduce a total number of allowed labels for at least one search query. A classifier is trained using the plurality of search queries, the second labels, and the determined weights. The classifier is trained to predict, from an input search query, a prediction weight and at least one prediction label associated with the prediction weight.).
Before the effective filing date, it would have been obvious to one of ordinary skill in the art, to modify the teachings of Shekhar to incorporate Query Classification With Sparse Soft Labels as taught by Di Fabbrizio for the purpose of increasing the accuracy and relevancy with which content is returned to the user.
Claims 11 and 20 are method and medium claims, respectively, and contain limitations similar to those found in claim 1 above. Thus, they are rejected similarly to claim 1, as shown above.
As to claims 2 and 12, Shekhar teaches generate a plurality of predicted request identifiers associated with the subset of predicted requests (Shekhar discloses real-time transactions and queries being received in [0013]. Shekhar also discloses an origin ID such as a particular username, a certain type of user (e.g., role of a user), an application that submits query requests such as a dashboard application, a function within an application such as a particular report, etc. is used as a trigger in [0019].); and construct, using the machine learning model and based on the plurality of predicted request identifiers, the labels comprising classifications for the subset of predicted requests (Shekhar discloses a cache key (label) which is used to identify the corresponding resource in [0031]-[0032].).
As to claims 4 and 14, Shekhar teaches predict, responsive to receiving a second request, using the machine learning model, a second plurality of predicted requests based on the first request and the second request; identify, using the machine learning model and based on a comparison with the threshold metric, a second subset of predicted requests from the second plurality of predicted requests indicative of one or more subsequent requests; and transmit, responsive to receipt of a subsequent request that matches one of a group of predicted requests comprising the second subset of predicted requests and one or more of the subset of predicted requests that are below a cache store threshold, a cache value from the cache store that corresponds to the subsequent request (Please see claim 1 analysis above. Claims 2, 13, and 20 recite the same steps but for subsequent (second) request(s). The process disclosed in Shekhar would be applicable to not only the initial request but any subsequent requests.).
As to claim 7 and 17, Shekhar teaches configure, in the cache store, the labels as the cache keys and the data from the database as cache values for the subset of predicted requests (Shekhar discloses the cache manager pre-fetches query results associated with the predicted upcoming set of queries and pre-populates a cache with the query results from the predicted queries in [0013]. Shekhar discloses a cache key (label) which is used to identify the corresponding resource in [0031]-[0032].).
Claim(s) 3 and 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Shekhar (US 20240143593 A1), in view of Di Fabbrizio et al (US 20230351184 A1), and further in view of Kanefsky (US 20120023120 A1).
As to claims 3 and 13, Shekhar teaches receive one or more identifiers (Shekhar discloses real-time transactions and queries being received in [0013]. Shekhar also discloses an origin ID such as a particular username, a certain type of user (e.g., role of a user), an application that submits query requests such as a dashboard application, a function within an application such as a particular report, etc. is used as a trigger in [0019].); and generate, using the machine learning model, the plurality of predicted requests (Shekhar discloses using a heuristic such as an “origin ID” that the machine learning (ML) model has identified to likely trigger a certain query or query pattern to be issued to the system. Thus, when a trigger heuristic is observed or identified in real-time, the cache manager can decide that the predicted query pattern associated with the trigger heuristic is expected to be issued. See [0018]).
Shekhar fails to teach a set of key-value pairs associated with query requests.
However, Kanefsky teaches provide the initial query suggestions and additional query suggestions in the form of key value pairs, the key value pairs being pairs of query input sequences and query suggestions having initial query characters that match the query input sequences (See [0038] and Claim 8).
Before the effective filing date, it would have been obvious to one of ordinary skill in the art, to modify the teachings of Shekhar and Di Fabbrizio to incorporate the PREDICTIVE QUERY SUGGESTION CACHING as taught by Kanefsky for the purpose of speeding up the process by which the browser accesses and displays suggestions based on the user's current partial query.
Claim(s) 5, 8, 9, 15, 18, and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Shekhar (US 20240143593 A1), in view of Di Fabbrizio et al (US 20230351184 A1), and further in view of Langseth et al (US 20180173705 A1).
As to claim 5 and 15, Shekhar and Di Fabbrizio fail to teach the metric associated with retrieving data for the respective predicted request comprises at least one of a latency of the plurality of predicted requests and a frequency of the plurality of predicted requests.
However, Langseth teaches the metric associated with retrieving data for the respective predicted request comprises at least one of a latency of the plurality of predicted requests and a frequency of the plurality of predicted requests (See [0018]).
Before the effective filing date, it would have been obvious to one of ordinary skill in the art, to modify the teachings of Shekhar and Di Fabbrizio to incorporate the SYSTEM AND METHOD FOR FACILITATING QUERIES VIA REQUEST-PREDICTION-BASED TEMPORARY STORAGE OF QUERY RESULTS as taught by Langseth for the purpose of significantly decrease latency or other delays for sufficiently responding to requests and improve efficiency of temporary data storage or other computer resource usage.
As to claim 8 and 18, Langseth teaches generate a ranking of the plurality of predicted requests based on the metric corresponding to each predicted request; and select the subset of predicted requests based on the ranking (Langseth discloses obtainment and/or temporary storage of the subset of results (or the lack thereof with respect to the other subsets of results) may be based on cost information, frequency information, preference information, or other information. The selectiveness of the obtainment and/or temporary storage of results (prior to particular requests occurring) may significantly decrease latency or other delays for sufficiently responding to requests and improve efficiency of temporary data storage or other computer resource usage. See [0018]. It would have been obvious to use the cost information as a ranking metric to aid in selectiveness of the obtainment and/or temporary storage of results.).
As to claim 9 and 19, Langseth teaches determine the metric for each of the plurality of predicted requests by calculating a product of a latency associated with each of the plurality of predicted requests and a frequency of receiving each of the plurality of predicted requests (Langseth discloses obtainment and/or temporary storage of the subset of results (or the lack thereof with respect to the other subsets of results) may be based on cost information, frequency information, preference information, or other information. The selectiveness of the obtainment and/or temporary storage of results (prior to particular requests occurring) may significantly decrease latency or other delays for sufficiently responding to requests and improve efficiency of temporary data storage or other computer resource usage. See [0018]. It would have been obvious to use the cost information as a metric to aid in selectiveness of the obtainment and/or temporary storage of results.).
Claim(s) 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Shekhar (US 20240143593 A1), in view of Di Fabbrizio et al (US 20230351184 A1), and further in view of Heimendinger (US 20110055202 A1).
As to claim 10, Shekhar and Di Fabbrizio fail to teach train the machine learning model based on historical data associated with a plurality of profiles received from a plurality of entities; receive an entity identifier and a profile associated with each of the one or more requests; and use the entity identifier and the profile associated with each of the one or more requests to generate the plurality of predicted requests.
However, Heimendinger teaches train the machine learning model based on historical data associated with a plurality of profiles received from a plurality of entities; receive an entity identifier and a profile associated with each of the one or more requests; and use the entity identifier and the profile associated with each of the one or more requests to generate the plurality of predicted requests (Heimendinger discloses the predictive model may be formed/adjusted based on user or organization profiles, usage history, and similar factors in [0005] and [0021].)
Before the effective filing date, it would have been obvious to one of ordinary skill in the art, to modify the teachings of Shekhar and Di Fabbrizio to incorporate the PREDICTIVE DATA CACHING as taught by Heimendinger for the purpose of significantly decrease latency or other delays for sufficiently responding to requests and improve efficiency of temporary data storage or other computer resource usage.
Allowable Subject Matter
Claims 6 and 16 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
The prior art fails to teach/suggest or make obvious “generate the plurality of predicted requests, the one or more processors further: generate a hash from data associated with each of the one or more requests based on a hash function; concatenate each hash with a value derived from the data associated with each of the one or more requests to generate input feature encoding; and generate the plurality of predicted requests by identifying requests associated with the input feature encoding”, as recited in claims 6 and 16.
Response to Arguments
Applicant’s arguments with respect to claim(s) 1-20 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Applicant’s arguments, see pages 8-14, filed 7/1/2026, with respect to the rejection(s) of claim(s) 1-2, 4, 7, 11-12, 14, 17, and 20 under 35 U.S.C. 102 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Di Fabbrizio et al (US 20230351184 A1).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JARED M BIBBEE whose telephone number is (571)270-1054. The examiner can normally be reached Monday-Thursday 8AM-6PM.
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/JARED M BIBBEE/ Primary Examiner, Art Unit 2161
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