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
This Office Action is sent in response to Applicant’s Communication received on 02 April 2026 for application number 18/916,959.
Claims 1, 5, 6, 11, 15, and 16 are currently amended.
Claims 1 – 20 are presented for examination.
Response to Amendment
Applicant’s amendment filed 02 April 2026 is sufficient to overcome the 112 rejection of claim 6 based upon the currently amended claim and 103 rejection of claims 1 – 5, 7 – 15, and 17 – 20 based upon the currently amended independent claims.
Response to Arguments
Applicant’s arguments, filed 02 April 2026, with respect to the rejection of claim(s) 1 – 5, 7 – 15, and 17 – 20 under 35 USC § 103 have been fully considered and are persuasive based upon the currently amended independent claims and arguments. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Wiggins, US Pub. No. 2025/0355947 A1.
Wiggins, in combination with the prior art of record, reads on the claim limitations based on the current claim language. Please see the new grounds of rejection below.
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, 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, 7 – 9, 11 – 14, and 17 – 19 are rejected under 35 U.S.C. 103 as being unpatentable over Shekhar [hereafter as Shekhar], US Pub. No. 2024/0143593 A1 in view of Laprise et al. [hereafter as Laprise], US Pub. No. 2024/0419906 A1 and further in view of Wiggins [hereafter as Wiggins], US Pub. No. 2025/0355947 A1.
As per claim 1, Shekhar discloses in a local area network that includes a model and query server (MQS) that includes a model manager and a cache manager [“The cache manager 100 may be implemented as part of the data system and/or server and thus has access to the queries submitted to the database system.”] [para. 0082] [“The cache manager is configured with a machine learning (ML) model trained to identify query patterns associated with one or more query heuristics.”] [para. 0006] [Examiner in interpreting the model manager as part of the cache manager which is configured with a machine learning model] [“Networks with which the computer 500 may interact include, but are not limited to, a LAN, a WAN, and other networks.”] [para. 0111], a method comprising:
receiving a query from a client connected to the local area network at the model manager [“A client may submit queries to the server to retrieve data specified by the query.”] [para. 0024] [“The cache manager is configured with a machine learning (ML) model trained to identify query patterns associated with one or more query heuristics.”] [para. 0006] [Examiner in interpreting the model manager as part of the cache manager which is configured with a machine learning model] [“Networks with which the computer 500 may interact include, but are not limited to, a LAN, a WAN, and other networks.”] [para. 0111], wherein the cache manager manages a cache at the MQS and wherein the cache is configured to store [“The cache manager 100 may learn query patterns of server queries by training the ML model using various heuristics in order to prefetch data from a database and store the data into a cache”] [para. 0067] [“The cache manager 100 may be implemented as part of the data system and/or server and thus has access to the queries submitted to the database system.”] [para. 0082] [“The cache manager is configured with a machine learning (ML) model trained to identify query patterns associated with one or more query heuristics.”] [para. 0006] [Examiner in interpreting the model manager as part of the cache manager which is configured with a machine learning model];
determining a model for answering the query [“For a given monitored query, parsing the monitored query to identify one or more query heuristics and determine whether the one or more query heuristics matches one or more trigger heuristics from a set of query predictions, wherein the set of query predictions are predicted by a machine learning model.”] [Abstract] [para. 0034]; and
generating an answer to the query using the model at the MQS without sending the query outside of the local area network, wherein the answer is provided to the client [“As used herein, a “trigger heuristic” includes, but is not limited to, particular values of one or more heuristics, or combination of heuristics, that the machine learning (ML) model has identified to likely trigger a certain query or query pattern to be issued to the system. A trigger heuristic may be viewed as answering the question, what causes a query pattern to be issued? For example, based on the query patterns identified by the ML model, the ML model generates an output that includes trigger heuristics that correspond to and seem to cause particular query patterns. 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. The cache manager may then pre-fetch query results for the predicted query pattern and store the query results in the cache prior to the queries actually being issued.”] [para. 0018] [“Thus, the clients may obtain the query results from the cache in milliseconds rather than in ten minutes when the results are not prefetched.”] [para. 0033] [“Networks with which the computer 500 may interact include, but are not limited to, a LAN, a WAN, and other networks.”] [para. 0111].
However, Shekhar does not explicitly disclose wherein the cache is configured to store models for performing inference;
determining, by the model manager, a model based on a semantic analysis of the query including an intent or topic of the query.
Laprise teaches wherein the cache is configured to store models [“In at least one embodiment, to process a request, a request may be entered into a database, a machine learning model may be located from model registry 1324 if not already in a cache, a validation step may ensure appropriate machine learning model is loaded into a cache (e.g., shared storage), and/or a copy of a model may be saved to a cache.”] [para. 0214].
Shekhar and Laprise are analogous art aimed to improve memory performance in storage systems.
It would have been obvious to one with ordinary skill in the art before the effective filing date of the claimed invention to combine Shekhar with Laprise in order to modify Shekhar where “wherein the cache is configured to store models” as taught by Laprise. One of ordinary skill in the art would be motivated to combine Shekhar with Laprise before the effective filing date of the claimed invention to improve a system by providing for the ability where “Processing efficiency can be further improved through replacing manual labor with machine learning model-based automation.” [Laprise, para. 0052].
However, Shekhar and Laprise do not explicitly disclose models for performing inference;
determining, by the model manager, a model based on a semantic analysis of the query including an intent or topic of the query.
Wiggins models for performing inference [“The disclosed system utilizes the one or more digital content analysis models to determine an intended use of the artificial intelligence system.”] [Abstract];
determining, by the model manager [intent analysis system], a model based on a semantic analysis of the query including an intent or topic of the query [“In one or more aspects, the intent analysis system 102 determines a machine-learning model 400 of an AI system. An AI system can have any number of machine-learning models for various purposes in connection with performing one or more operations.”] [para. 0057] [“determining the intent of the one or more queries by utilizing a text processing neural network or one or more image processing neural networks to perform a semantic analysis on the one or more text strings or the one or more digital images.”] [para. 0092].
Shekhar, Laprise, and Wiggins are analogous art aimed to improve memory performance in storage systems.
It would have been obvious to one with ordinary skill in the art before the effective filing date of the claimed invention to combine Shekhar and Laprise with Wiggins in order to modify Shekhar and Laprise for “models for performing inference;
determining, by the model manager, a model based on a semantic analysis of the query including an intent or topic of the query” as taught by Wiggins. One of ordinary skill in the art would be motivated to combine Shekhar and Laprise with Wiggins before the effective filing date of the claimed invention to improve a system by providing for the ability where “the intent analysis system utilizes intelligent intent analysis to improve the flexibility and efficiency of artificial intelligence systems in computing systems.” [Wiggins, para. 0024]
Claim 11 is rejected with like reasoning.
As per claim 2, Shekhar in view of Laprise and further in view of Wiggins discloses the method of claim 1, Laprise teaches further comprising determining whether the model is present in the cache [“… if not already in a cache, a validation step may ensure appropriate machine learning model is loaded into a cache …”] [para. 0214].
Claim 12 is rejected with like reasoning.
As per claim 3, Shekhar in view of Laprise and further in view of Wiggins discloses the method of claim 1,Wiggins teaches wherein the query identifies the model or wherein the model manager determines the model based on an intent or topic of the query [“In one or more aspects, the intent analysis system 102 determines a machine-learning model 400 of an AI system. An AI system can have any number of machine-learning models for various purposes in connection with performing one or more operations.”] [para. 0057] [“determining the intent of the one or more queries by utilizing a text processing neural network or one or more image processing neural networks to perform a semantic analysis on the one or more text strings or the one or more digital images.”] [para. 0092].
As per claim 4, Shekhar in view of Laprise and further in view of Wiggins discloses the method of claim 2, Laprise teaches further comprising acquiring the model from an external source [model registry] when the model is not present in the cache and storing the acquired model in the cache [“a machine learning model may be located from model registry 1324 if not already in a cache, a validation step may ensure appropriate machine learning model is loaded into a cache (e.g., shared storage), and/or a copy of a model may be saved to a cache.”] [para. 0214].
Claim 14 is rejected with like reasoning.
As per claim 7, Shekhar in view of Laprise and further in view of Wiggins discloses the method of claim 1, Laprise teaches further comprising determining that the client is authorized [authorized users] to access the model [“In at least one embodiment, access to APIs in cloud 1426 may be restricted to authorized users through enacted security measures or protocols. In at least one embodiment, a security protocol may include web tokens that may be signed by an authentication (e.g., AuthN, AuthZ, Gluecon, etc.) service and may carry appropriate authorization. In at least one embodiment, APIs of virtual instruments (described herein), or other instantiations of system 1400, may be restricted to a set of public IPs that have been vetted or authorized for interaction.”] [para. 0201] [“UI 1414 (or a different user interface) may be used for selecting models for use in deployment system 1306, for selecting models for training, or retraining, in training system 1304, and/or for otherwise interacting with training system 1304.”] [para. 0209].
Claim 17 is rejected with like reasoning.
As per claim 8, Shekhar in view of Laprise and further in view of Wiggins discloses the method of claim 1, Laprise teaches further comprising managing the cache in response to a trigger, wherein managing the cache includes one or more of reducing a size of at least one model stored in the cache in a lossless manner, in a lossy manner, and/or by eviction from the cache [“In at least one embodiment, to retrain, or update, initial model 1504, output or loss layer(s) of initial model 1504 may be reset, deleted, and/or replaced with an updated or new output or loss layer(s). In at least one embodiment, initial model 1504 may have previously fine-tuned parameters (e.g., weights and/or biases) that remain from prior training, so training or retraining 1514 may not take as long or require as much processing as training a model from scratch. In at least one embodiment, during model training, by having reset or replaced output or loss layer(s) of initial model 1504, parameters may be updated and re-tuned for a new data set based on loss calculations associated with accuracy of output or loss layer(s) at generating predictions on new, customer dataset 1506.”] [para. 0223] [“In at least one embodiment, to process a request, a request may be entered into a database, a machine learning model may be located from model registry 1324 if not already in a cache, a validation step may ensure appropriate machine learning model is loaded into a cache (e.g., shared storage), and/or a copy of a model may be saved to a cache.”] [para. 0214].
Claim 18 is rejected with like reasoning.
As per claim 9, Shekhar in view of Laprise and further in view of Wiggins discloses the method of claim 1, Shekhar discloses wherein the model manager has model capabilities awareness and is configured to recommend to address the query, and wherein the model manager is configured to perform lifecycle management [a specified date and/or time (timestamp) that reoccurs] [“In one embodiment, the following examples are a few heuristics that may be used to represent a learning parameter(s) for the ML model of the cache manager 100. The cache manager 100 may learn query patterns of server queries by training the ML model using various heuristics in order to prefetch data from a database and store the data into a cache before the actual queries are issued.”] [para. 0067] [“This may include a set of queries being triggered and issued by a particular origin ID at a specified date and/or time (timestamp) that reoccurs, or queries being automatically issued by a particular application when a user opens the application.”] [para. 0019].
Laprise teaches manager has semantic awareness and other models to address the query, to perform model management [“In some embodiments, multiple language models may be used. For example, a language model might be used to determine the semantics, topology, and geometry of an environment that are then to be fed as input to another language model.”] [para. 0069] [para. 0080] [para. 0193].
Claim 19 is rejected with like reasoning.
Claims 5 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Shekhar [hereafter as Shekhar], US Pub. No. 2024/0143593 A1 in view of Laprise et al. [hereafter as Laprise], US Pub. No. 2024/0419906 A1 and further in view of Wiggins [hereafter as Wiggins], US Pub. No. 2025/0355947 A1 as applied to claims 2 and 12 above, and further in view of Chen et al. [hereafter as Chen], US Pub. No. 2023/0153286 A1.
As per claim 5, Shekhar in view of Laprise and further in view of Wiggins discloses the method of claim 2, however Shekhar, Laprise, and Wiggins do not explicitly disclose further comprising determining a mode associated with the query.
Chen teaches determining a mode associated with the query [“and determining a query mode corresponding to the meta-information based on a comparison result”] [Abstract].
Shekhar, Laprise, Wiggins and Chen are analogous art aimed to improve memory performance in storage systems.
It would have been obvious to one with ordinary skill in the art before the effective filing date of the claimed invention to combine Shekhar, Laprise, and Wiggins with Chen in order to modify Shekhar, Laprise, and Wiggins for “determining a mode associated with the query” as taught by Chen. One of ordinary skill in the art would be motivated to combine Shekhar, Laprise, and Wiggins with Chen before the effective filing date of the claimed invention to improve a system by providing for the ability where “the system will consider that constructing an aggregate index for the query of the mode in advance can improve the overall performance”. [Chen, para. 0172].
Claim 15 is rejected with like reasoning.
Claims 10 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Shekhar [hereafter as Shekhar], US Pub. No. 2024/0143593 A1 in view of Laprise et al. [hereafter as Laprise], US Pub. No. 2024/0419906 A1 and further in view of Wiggins [hereafter as Wiggins], US Pub. No. 2025/0355947 A1 as applied to claims 1 and 11 above, and further in view of Furuhashi et al. [hereafter as Furuhashi], US Patent No. 7,502,904 B2.
As per claim 10, Shekhar in view of Laprise and further in view of Wiggins discloses the method of claim 1, Laprise teaches further comprising storing models in the cache based on model usage by clients in the network [“In at least one embodiment, to process a request, a request may be entered into a database, a machine learning model may be located from model registry 1324 if not already in a cache, a validation step may ensure appropriate machine learning model is loaded into a cache (e.g., shared storage), and/or a copy of a model may be saved to a cache.”] [para. 0214] [para. 0099].
Shekhar discloses local area network [“Networks with which the computer 500 may interact include, but are not limited to, a LAN, a WAN, and other networks.”] [para. 0111].
However, Shekhar, Laprise, and Wiggins do not explicitly disclose storing in a predictive manner based on telemetry collected relative to usage.
Furuhashi teaches storing in a predictive manner based on telemetry collected relative to usage [“wherein the storage system migrates data to the physical storage region responsive to the instruction, wherein the requirement information has plural patterns corresponding to type of data, wherein the type of data is a name of the data, when the management computer receives a request for assigning a logical volume for data used by the host computer, wherein the management computer receives the name of the data related to the request and time of the event related data, wherein the management computer specifies the pattern of temporal change corresponding to the received name of the data and the received time of related data, selects a physical storage region corresponding to the specified the pattern”] [claim 8] [“In a case of this kind, desirably, the data transmitted by the host computer and located hypothetically in a logical storage region is stored in the physical storage region provided by the most suitable disk device, on the basis of indicators relating to access frequency and access patterns, and the like.”] [col. 1, lines 45-50].
Shekhar, Laprise, Wiggins, and Furuhashi are analogous art aimed to improve memory performance in storage systems.
It would have been obvious to one with ordinary skill in the art before the effective filing date of the claimed invention to combine Shekhar, Laprise, and Wiggins with Furuhashi in order to modify Shekhar, Laprise, and Wiggins for “storing in a predictive manner based on telemetry collected relative to usage” as taught by Furuhashi. One of ordinary skill in the art would be motivated to combine Shekhar, Laprise, and Wiggins with Furuhashi before the effective filing date of the claimed invention to improve a system by providing for the ability where “desirably, the data transmitted by the host … is stored in the physical storage region …, on the basis of indicators relating to access frequency and access patterns, and the like.” [Furuhashi, col. 1, lines 45-50].
Conclusion
STATUS OF CLAIMS IN THE APPLICATION
CLAIMS REJECTED IN THE APPLICATION
Per the instant office action, claims 1 – 20 have received a second action on the merits and are subject of a second action final. Claim 1 – 5, 7 – 15, and 17 – 20 are rejected under a 103 rejection.
Allowable Subject Matter
Claims 6 and 16 are objected to as being dependent upon a rejected based claim, but are considered as containing allowable subject matter. These claims would be allowable if rewritten or amended to overcome the rejection(s) under 35 U.S.C. 112(b) set forth in this Office action and to include all of the limitations of the base claim and any intervening claims in independent form.
The following is a statement of reasons for the indication of allowable subject matter: for dependent claims 6 and 16 the prior art of record, neither anticipates, nor renders obvious pushing a model to the client in response to operating the model and query server in a first mode where the answer to the query is inferred at the client or executing the model in response to the model and query server operating in a second mode where the answer is generated at the model and query server using the model.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Manandise et al., US Pub. No. 2024/0127026 A1 – teaches “For some queries, shallow parsing or intent classification is enough; for others, deep semantic analysis is needed to surface the most likely interpretation to trigger appropriate actions by the chatbot executor.” [para. 0118]
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to EDWARD WADDY JR whose telephone number is (571)272-5156. The examiner can normally be reached M-Th 8am-5pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jared Rutz can be reached at (571)272-5535. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/EW/Examiner, Art Unit 2135
/JARED I RUTZ/Supervisory Patent Examiner, Art Unit 2135
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