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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 6/08/2026 has been entered.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 6/08/2026 is/are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is/are being considered by the examiner.
Status of Claims
Claims 1-3, 5-7, 9-13, 15-18 and 20-24. Claims 4, 8, 14 and 19 are currently cancelled.
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.
Claim(s) 1, 11 and 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Abi-Akl et al. (US Patent No. 12,106,049; Date of Patent: Oct. 1, 2024) in view of Brown et al. (US PGPUB No. 2022/0292092; Pub. Date: Sep. 15, 2022) and Yu et al. (US Patent No.: 12,204,565; Date of Patent: Jan. 21, 2025).
Regarding independent claim 1,
Abi-Akl discloses a method for managing operation of a distributed system, the method comprising: receiving a prompt submitted for processing by a generative trained machine learning model hosted by a management system, the prompt comprising an ontology term; See Col. 7, lines 35-45, (Disclosing a system for generating natural language text. The system leverages automated techniques for customizing a natural language generation (NLG) system to provide customization of an ontology in an NLG system to adapt to a user's preferences. Environment 100 for generating natural language text (NLT) is configured to access an ontology 190 from a database 180 and a target vocabulary 120 including terminology used by a user or company of a user. The system may generate NLT using an accessed ontology 190 , target vocabulary 120 and user input 140 taken from a user 101, i.e. a method for managing operation of a distributed system (e.g. Note Col. 23 wherein system functionality may be distributed among different computers or processors in a modular fashion), the method comprising: receiving a prompt submitted for processing by a generative trained machine learning model hosted by a management system, the prompt comprising an ontology term;
determining, based on a level of content of the ontology term being below a threshold level, that the management system lacks sufficient information regarding edge devices of the distributed system to service the prompt; See Col. 5, lines 20-39, (The process may map terms of a user's terminology to entities associated with terms that are determined to be similar and indicate when no entity was found to be similar enough, which is measured by determining whether a match score does not exceed a threshold. This triggers the system to receive a user into which is used to modify the ontology. Note Col. 4, lines 24-28 wherein an entity represents a concept that may be rendered into natural language text, i.e. determining, based on a level of content of the ontology term being below a threshold level, that the management system lacks sufficient information regarding edge devices of the distributed system to service the prompt (e.g. the system cannot determine that a similar enough entity exists in a current ontology to determine a match.);)
The examiner notes that while Abi-Akl does not explicitly define an entity as "information regarding edge devices of the distributed system", one of ordinary skill in the art would recognize that an entity may refer to any concept that can be represented using natural language text.
Abi-Akl does not disclose the step of obtaining, by the management system and in response to the determining, a plurality of second prompts;
initiating, by the management system, first retrieval augmented generation (RAG) processing comprising: sending, by the management system, a copy of at least one second prompt of a plurality of second prompts to one of the edge devices;
indicating, by the management system and to the one of the edge devices, that the at least one second prompt is to be processed to obtain one first response of a plurality of first responses;
receiving, from processing of the at least one second prompt by the one of the edge devices, the one first response of the plurality of first responses to the management system in response to the at least one second prompt;
wherein the plurality of second prompts are processed by the edge devices using locally available data hosted by the edge devices as knowledge sources for the first RAG processing to obtain the plurality of first responses from the edge devices;
Brown discloses the step of obtaining, by the management system and in response to the determining, a plurality of second prompts; See Paragraph [0007], (Disclosing a system for querying multiple data sources. The method comprising distributing one or more relational query instances among a plurality of server nodes.) See FIG. 1& Paragraph [0039], (FIG. 1 illustrates a query base process comprising a query instance that is processed to generate one or more relational query instances.) See Paragraph [0062], (Server nodes 408 are configured to optimize the one or more relational query instances by distributing said instances among one or more data sources 404, i.e. obtaining, by the management system and in response to the determining, a plurality of second prompts (e.g. the sub-queries representing inputs to data sources derived from a first input, i.e. the query base process);)
initiating, by the management system, first retrieval augmented generation (RAG) processing comprising: sending, by the management system, a copy of at least one second prompt of a plurality of second prompts to one of the edge devices; See FIG. 1& Paragraph [0039], (FIG. 1 illustrates a query base process comprising a query instance that is processed to generate one or more relational query instances.) See Paragraph [0062], (Server nodes 408 are configured to optimize the one or more relational query instances by distributing said instances among one or more data sources 404, initiating, by the management system, first retrieval augmented generation (RAG) processing comprising: sending, by the management system, a copy of at least one second prompt of a plurality of second prompts to one of the edge devices (e.g. the relational query instances comprising subqueries for each required data source representing second prompts while the one or more external data sources represent edge devices in which data is stored);)
indicating, by the management system and to the one of the edge devices, that the at least one second prompt is to be processed to obtain one first response of a plurality of first responses; See Paragraph [0039], (The one or more server nodes may distribute relational query instances to different data sources to access data, i.e. indicating, by the management system and to the one of the edge devices, that the at least one second prompt is to be processed to obtain one first response of a plurality of first responses (e.g. the one or more server nodes instruct the different data sources to execute the distrusted relational query instances);)
receiving, from processing of the at least one second prompt by the one of the edge devices, the one first response of the plurality of first responses to the management system in response to the at least one second prompt; See Paragraph [0063], (The one or more server nodes may determine where requested data resides and generate and send sub-queries to each data source for the relevant data, i.e. receiving, from processing of the at least one second prompt by the one of the edge devices, the one first response of the plurality of first responses to the management system in response to the at least one second prompt (e.g. server node coordinates access to external data sources to retrieve responses to sub-queries);)
wherein the plurality of second prompts are processed by the edge devices using locally available data hosted by the edge devices as knowledge sources for the first RAG processing to obtain the plurality of first responses from the edge devices; See Paragraph [0063], (Server nodes may determine where requested data resides and generate and send sub-queries to each data source for the relevant data. For example, a query may require tables t1 and t2 that reside at one or more data sources. The server node(s) may generate different sub-queries per data source and retrieves the relevant data based on the sub-queries, i.e. wherein the plurality of second prompts are processed by the edge devices using locally available data hosted by the edge devices as knowledge sources for the first RAG processing (e.g. data sources are probed for stored relevant data, i.e. the relevant data is local to the data source) to obtain the plurality of first responses from the edge devices (e.g. relevant data is retrieved from the data sources);)
Abi-Akl and Brown are analogous art because they are in the same field of endeavor, data retrieval. It would have been obvious to anyone having ordinary skill in the art before the effective filing date to modify the system of Abi-Alk to include the method of distributing subqueries to data sources for accessing relevant data as disclosed by Brown. Paragraph [0084] of Brown discloses that the system may employ a mesh structure for allowing new data sources to plug into the query base system quickly and easily, which addresses issues where data may be scattered in different places. These improvements represent operational and cost benefits and may additionally support the development and deployment of next generation analytical applications that may access, synthesize and integrate data from multiple systems in real-time.
Abi-Akl-Brown does not disclose the step of performing, by the management system, second RAG processing comprising: submitting the prompt and using the plurality of first responses as context for the prompt as input to the generative trained machine learning model to obtain a final response as output from the generative trained machine learning model;
and providing, by the management system, computer implemented services using the final response.
Yu discloses the step of performing, by the management system, second RAG processing comprising: submitting the prompt and using the plurality of first responses as context for the prompt as input to the generative trained machine learning model to obtain a final response as output from the generative trained machine learning model; See FIGs. 9A-9B & Col. 28, line 66 - Col. 29, line 16, (Disclosing a system for automatically generating artificial intelligence (AI) models based on natural language inputs received from a user that comprise instructions to analyze data. FIG. 9A illustrates a method wherein a first LLM prompt 902 may be augmented with metadata identifiers and then provided as input to an LLM 906 to generate a first intermediate output 908.) See FIG. 9B & Col. 32, line 25-37, (Prompt 3 914 may then be generated based on second prompt text 907 and the first intermediate output 908 and continuing to FIG. 9B of generating a prompt 4 916 from previous prompts and intermediate outputs until reaching a final prompt 5 922 which is provided to LLM 906 as a prompt for performing a task of generating a metadata graph, i.e. performing, by the management system, second RAG processing comprising: submitting the prompt and using the plurality of first responses as context for the prompt as input to the generative trained machine learning model (e.g. a final prompt comprising metadata information is submitted to the LLM for performing a task of generating a metadata graph) to obtain a final response as output from the generative trained machine learning model;)
and providing, by the management system, computer implemented services using the final response. See Col. 38, lines 55-61, (The system may receive requests for data, which may include requests to train, fine-tine, or test a model or to apply a model. Data processor 1220 accesses datasets using a metadata graph to identify data objects within data silos of a data repository, i.e. providing, by the management system, computer implemented services using the final response (e.g. the LLM is used to generate a metadata graph that may satisfy the request for data).)
Abi-Akl, Brown and Yu are analogous art because they are in the same field of endeavor, data retrieval. It would have been obvious to anyone having ordinary skill in the art before the effective filing date to modify the system of Abi-Akl-Brown to include the method of distributing and formulating prompts from previously retrieved metadata information for constructing a model to provide a service as disclosed by Yu. Col. 37, lines 10-31 of Yu disclose that the system allows users to construct and apply models to extract insights or predictions from data even when users lack the expertise or time to build AI models and data pipelines for the data to be processed. The AI sandbox includes automated tools for deploying and managing models, ensuring they operate efficiently without requiring human intervention, including processes such as training, fine-tuning or improving existing models, deploying models, or generating pipelines of data suitable for analysis by said models.
Regarding independent claim 11,
The claim is analogous to the subject matter of independent claim 1 directed to a non-transitory, computer readable medium and is rejected under similar rationale.
Regarding independent claim 16,
The claim is analogous to the subject matter of independent claim 1 directed to a computer system and is rejected under similar rationale.
Claim(s) 2-3, 5-7, 9-13, 15-18, 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Abi-Akl in view of Brown and Yu as applied to claim 1 above, and further in view of Savaris et al. (US Patent No.: 12,177,692; Date of Patent: Dec. 24, 2024).
Regarding dependent claim 2,
As discussed above with claim 1, Abi-Akl-Brown-Yu discloses all of the limitations.
Abi-Akl-Brown-Yu does not disclose the step wherein each of the knowledge sources comprises information regarding a respective one of the edge devices.
Savaris discloses the step wherein each of the knowledge sources comprises information regarding a respective one of the edge devices. See Col. 6, lines 63-67, (A Wi-Fi probe request includes a plurality of metadata fields having associated values such as metadata fields indicating radio frequencies and/or data rates supported by the electronic devices, i.e. wherein each of the knowledge sources comprises information regarding a respective one of the edge devices (e.g. Wi-Fi probe requests from an electronic device include data describing the electronic device.)
Abi-Akl, Brown, Yu and Savaris are analogous art because they are in the same field of endeavor, data retrieval. It would have been obvious to anyone having ordinary skill in the art before the effective filing date to modify the system of Abi-Akl-Brown-Yu to include A. Col. 8, lines 13-26 of Savaris discloses that the use of a gradient-boosting decision tree may be used for solving prediction problems in both classification and regression domains, which improves the learning process by simplifying the objective and reducing the number of iterations to obtain an optimal solution.
Regarding dependent claim 3,
As discussed above with claim 2, Abi-Akl-Brown-Yu-Savaris discloses all of the limitations.
Savaris further discloses the step wherein at least one of the knowledge sources comprises a portion of telemetry information for a corresponding one of the edge devices, See Col. 6, lines 63-67, (A Wi-Fi probe request includes a plurality of metadata fields having associated values such as metadata fields indicating radio frequencies and/or data rates supported by the electronic devices, i.e. wherein at least one of the knowledge sources comprises a portion of telemetry information for a corresponding one of the edge devices (e.g. information relating to radio frequencies and/or data rates represents telemetry information).)
the portion of the telemetry information being required information for servicing of the prompt, See Col. 6, lines 63-67, (The data values extracted from the metadata fields indicate radio frequencies and/or data rates supported by the electronic devices and may be used as features or portions of a feature vector, i.e. the portion of the telemetry information being required information for servicing of the prompt (e.g. the processing of a Wi-Fi probe request is required to generate the feature vector).)
and the portion of the telemetry information not being available for use by the management system. See Col. 6, lines 63-67, (The data values extracted from the metadata fields indicate radio frequencies and/or data rates supported by the electronic devices and may be used as features or portions of a feature vector.) See Col. 5, lines 63-66, (Cloud server 132 extracts information about the unique data values of the individual probe requests which are then used to train the AI model. Therefore, cloud server 132 does not store or maintain the obtained metadata, i.e. the portion of the telemetry information not being available for use by the management system (e.g. telemetry information must be provided via the Wi-Fi probe requests. Cloud server 132 does not have access to the information unless provided by the electronic device).)
Regarding dependent claim 5,
As discussed above with claim 1, Abi-Akl-Brown-Yu-Savaris discloses all of the limitations.
Savaris further discloses the step wherein the one first response comprises a textual response that is based, at least in part, on a portion of telemetry information that is locally stored on the one of the edge devices and not available to the management system. See FIG. 6 & Col. 13, lines 55-63, (FIG. 6 illustrates the method comprising step 608 of extracting a feature vector from the data values obtained from the Wi-Fi probe requests. The machine learning model utilizes the feature vector to determine a number of electronic devices present in proximity to the Wi-Fi receiver, i.e. wherein the one first response comprises a textual response that is based, at least in part, on a portion of telemetry information that is locally stored on the one of the edge devices and not available to the management system (e.g. the system utilizes an AI model to determine a number of electronic devices based on metadata values relating to telemetry information (e.g. radio frequency and/or data rates of a device) of the plurality of electronic devices that is only made available to the system via Wi-Fi probe requests (e.g. cloud server 132 may only interact with a device if the device sends it a probe request).)
Regarding dependent claim 6,
As discussed above with claim 5, Abi-Akl-Brown-Yu-Savaris discloses all of the limitations.
Savaris further discloses the step wherein the textual response is distinguishable from the portion of the telemetry information. See FIG. 6 & Col. 14, lines 38-41 & 49-54, (The method of FIG. 6 concludes at step 616 wherein the number of electronic devices that is determined by the AI model is transmitted to a user device for display on an application which describes the electronic devices present in proximity to the Wi-Fi receiver, i.e. wherein the textual response is distinguishable from the portion of the telemetry information (e.g. the telemetry information is transformed into a number of devices, i.e. distinguished from telemetry information).)
Regarding dependent claim 7,
As discussed above with claim 5, Abi-Akl-Brown-Yu-Savaris discloses all of the limitations.
Savaris further discloses the step wherein the portion of the telemetry information cannot be recovered from the textual response. See FIG. 6 & Col. 14, lines 38-41 & 49-54, (The method of FIG. 6 concludes at step 616 wherein the number of electronic devices that is determined by the AI model is transmitted to a user device for display on an application which describes the electronic devices present in proximity to the Wi-Fi receiver, i.e. wherein the portion of the telemetry information cannot be recovered from the textual response (e.g. Note FIG. 7 which illustrates a detection of an unauthorized device 720 displayed in a user interface. The AI model does not expose telemetry information, instead illustrates an unknown device).)
Regarding dependent claim 9,
As discussed above with claim 1, Abi-Akl-Brown-Yu discloses all of the limitations.
Abi-Akl-Brown-Yu does not disclose the step wherein providing the computer implemented services comprises: identifying, using the final response, a state of one of the edge devices;
identifying at least one modification for the one of the edge devices based on the state;
and updating operation of the one of the edge devices based on the at least one modification.
Savaris discloses the step wherein providing the computer implemented services comprises: identifying, using the final response, a state of one of the edge devices; See FIG. 7 & Col. 15, lines 25-43, (FIG. 7 illustrates detected electronic devices, wherein some devices may be known (e.g. such as electronic device 704) or unknown (e.g. such as device 708), i.e. identifying, using the final response, a state of one of the edge devices (e.g. whether a device is known or unknown).)
identifying at least one modification for the one of the edge devices based on the state; See FIG. 7 & Col. 15, lines 37-43, (The system may provide functionality for taking action in response to the detection of an electronic device as illustrated in element 724 of FIG. 7 which may include actions such as: notifying a home security service, notifying the police, instructing a home security system to capture images, and/or instructing the software application 716 to block the electronic from access to a home Wi-Fi router, i.e. identifying at least one modification for the one of the edge devices based on the state;)
and updating operation of the one of the edge devices based on the at least one modification. See FIG. 7 & Col. 15, lines 37-43, (The system may provide functionality for taking action in response to the detection of an electronic device as illustrated in element 724 of FIG. 7 which may include actions such as: notifying a home security service, notifying the police, instructing a home security system to capture images, and/or instructing the software application 716 to block the electronic from access to a home Wi-Fi router, i.e. updating operation of the one of the edge devices based on the at least one modification (e.g. the system may block the electronic device from accessing a the home Wi-Fi router. Blocking access to the network represents a change of operation of an edge device).)
Abi-Akl, Brown, Yu and Savaris are analogous art because they are in the same field of endeavor, data retrieval. It would have been obvious to anyone having ordinary skill in the art before the effective filing date to modify the system of Abi-Akl-Brown-Yu to include A. Col. 8, lines 13-26 of Savaris discloses that the use of a gradient-boosting decision tree may be used for solving prediction problems in both classification and regression domains, which improves the learning process by simplifying the objective and reducing the number of iterations to obtain an optimal solution.
Regarding dependent claim 10,
As discussed above with claim 9, Abi-Akl-Brown-Yu-Savaris discloses all of the limitations.
Savaris further discloses the step wherein the at least one modification comprises at least one selected from a list of modification consisting of: modifying a configuration of a first software component and/or a first hardware component; disabling a second software component and/or a second hardware component; and installing a third software component. See FIG. 7 & Col. 15, lines 37-43, (The system may provide functionality for taking action in response to the detection of an electronic device as illustrated in element 724 of FIG. 7 which may include actions such as: notifying a home security service, notifying the police, instructing a home security system to capture images, and/or instructing the software application 716 to block the electronic from access to a home Wi-Fi router, i.e. wherein the at least one modification comprises disabling a second software component (e.g. the method of Savaris may disable the electronic device's ability to connect to the Wi-Fi router).)
Regarding dependent claim 12,
The claim is analogous to the subject matter of dependent claim 2 directed to a non-transitory, computer readable medium and is rejected under similar rationale.
Regarding dependent claim 13,
The claim is analogous to the subject matter of dependent claim 3 directed to a non-transitory, computer readable medium and is rejected under similar rationale.
Regarding dependent claim 15,
The claim is analogous to the subject matter of dependent claim 5 directed to a non-transitory, computer readable medium and is rejected under similar rationale.
Regarding dependent claim 17,
The claim is analogous to the subject matter of dependent claim 2 directed to a computer system and is rejected under similar rationale.
Regarding dependent claim 18,
The claim is analogous to the subject matter of dependent claim 3 directed to a computer system and is rejected under similar rationale.
Regarding dependent claim 20,
The claim is analogous to the subject matter of dependent claim 5 directed to a computer system and is rejected under similar rationale.
Claim(s) 21-23 is/are rejected under 35 U.S.C. 103 as being unpatentable over Abi-Akl in view of Brown and Yu as applied to claim 16 above, and further in view of DEVAUX et al. (US PGPUB No. 2024/0370691; Pub. Date: Nov. 7, 2024).
Regarding dependent claim 21,
As discussed above with claim 16, Abi-Akl-Brown-Yu discloses all of the limitations.
Abi-Akl-Brown-Yu does not disclose the step determining, based on a minimal level of content of a term, that the management system lacks sufficient information to service the prompt using the plurality of first responses;
and repeating the first RAG processing using an updated at least one second prompt based on the term.
DEVAUX discloses the step determining, based on a minimal level of content of a term, that the management system lacks sufficient information to service the prompt using the plurality of first responses; See FIG. 4 & Paragraphs [0096]-[0098], (Disclosing a system for searching that can determining first and second sets of canonical parameters and searching a master data domain to extract data. FIG. 4 illustrates method 400 comprising step 420 of determining whether there is sufficient information to complete a structured travel query. If the system is unable to build the structured travel query from previous inputs, the method moves to step 416 of iterating a natural language conversation via the LLM engine 120 until the system may successfully generate the structured travel query, i.e. determining, based on a minimal level of content of a term (e.g. the determination that the system has sufficient information to construct a travel query), that the management system lacks sufficient information to service the prompt using the plurality of first responses (e.g. the process iteratively receives conversation inputs which are used to generate a query.);)
and repeating the first RAG processing using an updated at least one second prompt based on the term. See FIG. 13 & Paragraph [140], (FIG. 13 illustrates method 1300 for itinerary searching comprising step 1312 wherein the system may determine that more parameters need to be gathered after determining a first set of parameters at step 1304 and deriving additional parameters at step 1308, i.e. repeating the first RAG processing using an updated at least one second prompt based on the term (e.g. the set of parameters us updated by the process of gathering).)
Abi-Akl, Brown, Yu and DEVAUX are analogous art because they are in the same field of endeavor, data retrieval. It would have been obvious to anyone having ordinary skill in the art before the effective filing date to modify the system of Abi-Akl-Brown-Yu to include the method of retrieving travel itinerary data based on iterative conversation parsing as disclosed by DEVAUX. Paragraph [0195] of DEVAUX discloses that the system may enrich a sparse database while still keeping it lean, which provides a balance between a lean database and the need for current data and a need for data that may not be commonly searched and would be outside the sparse data domain.
Regarding dependent claim 22,
The claim is analogous to the subject matter of dependent claim 21 directed to a method or process and is rejected under similar rationale.
Regarding dependent claim 23,
The claim is analogous to the subject matter of dependent claim 21 directed to a non-transitory computer-readable medium and is rejected under similar rationale.
Claim(s) 24 is/are rejected under 35 U.S.C. 103 as being unpatentable over Abi-Akl in view of Brown and Yu as applied to claim 11 above, and further in view of PANWAR et al. (US PGPUB No. 2024/0291708; Pub. Date: Aug. 29, 2024).
Regarding dependent claim 24,
As discussed above with claim 11, Abi-Akl-Brown-Yu discloses all of the limitations.
Abi-Akl-Brown-Yu does not disclose the step wherein providing the computer implemented services comprises: identifying, using the final response, a state of one of the edge devices;
identifying at least one modification for the one of the edge devices based on the state;
and updating operation of the one of the edge devices based on the at least one modification.
PANWAR discloses the step wherein providing the computer implemented services comprises: identifying, using the final response, a state of one of the edge devices; See Paragraph [0165], (Disclosing a system for detecting occurrence of triggers related to properties of objects indicated in event data of a discoverable vent stream. The system comprises status detection component 1532 configured to determine a current state based on a discoverable event stream 1514. Note [0048] wherein the event stream may comprise events added based on query results, i.e. identifying, using the final response (e.g. stream data is used to determine a device state. Stream data includes query results), a state of one of the edge devices;)
identifying at least one modification for the one of the edge devices based on the state; See Paragraph [0159], (An activation trigger event 1516 may include an "update device state" command that identifies an API call of the management device for updating the state of the IoT devices via a management device that facilitates a transition from a current device state to a different device state, i.e. identifying at least one modification for the one of the edge devices based on the state;)
and updating operation of the one of the edge devices based on the at least one modification. See Paragraph [0165], (Status comparison component 1534 compares the current state to an indented state meant to be achieved by an activation instruction event and may attempt to achieve the intended state, i.e. and updating operation of the one of the edge devices based on the at least one modification.)
Abi-Akl, Brown, Yu and PANWAR are analogous art because they are in the same field of endeavor, data retrieval. It would have been obvious to anyone having ordinary skill in the art before the effective filing date to modify the system of Abi-Akl-Brown-Yu to include the method of managing states of edge devices as disclosed by PANWAR. Paragraph [0133] of PANWAR discloses that the system may execute queries using either a streaming or micro-batching approach wherein both types of queries offer different benefits. Executing component 1204 may determine which methodology is most appropriate for a particular set of queries.
Response to Arguments
Applicant’s arguments with respect to claim(s) 1, 11 and 16 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 amendments modify the scope of the claimed invention and therefore necessitated the new grounds of rejection presented in this Office Action.
Applicant’s cancellation of claims 4, 8, 14 and 19 are acknowledged by the examiner. The corresponding rejections are withdrawn.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Fernando M Mari whose telephone number is (571)272-2498. The examiner can normally be reached Monday-Friday 7am-4pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Ann J. Lo can be reached at (571) 272-9767. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/FMMV/Examiner, Art Unit 2159
/ANN J LO/Supervisory Patent Examiner, Art Unit 2159
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