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 .
This office action is in response to claim amendment filed on Feb. 13, 2026 and wherein claims 1, 6, 10, 15, 19 have been amended and claims 9, 18 have been cancelled and claims 21, 22 are added.
In virtue of this communication claims 1-8, 10-17, 19-22 are currently pending in this Office Action.
Response to Arguments
Applicant's arguments filed on 2/13/2026 (Pages 1-3) have been fully considered but they are not persuasive.
Applicant argues “Regarding the § 102 rejections, the independent claims recite “obtaining . . . status data from . . . network components,” “ingesting the obtained status data, comprising processing the obtained status data with a machine learning model (MLM) system,” and then “storing the ingested status data.” The Yahya reference does not contain any teaching of using a MLM system to process status data obtained from a radio network component, and does not contain any teaching of storing status data that has been processed by a MLM system. The Yahya reference fails to disclose each and every element of the independent claims and the Applicant respectfully asserts that the rejections under § 102 are therefore improper.” (remarks pages 1-2). Examiner respectfully disagrees.
Yahya teaches the status data to be collected and processed as training data for an MLM. The context of the MLM is stored in a session store. The AI model uses an API caller to collect information about the network resources and data (paragraph 0125). The optimization recommendations engine (519) is trained on ingested status data using a MLM (paragraph 0121). The session state store (label 512) stores the context of the language model and information from the account data to include the network resources and data (paragraph 0124). The status data is requested by the AI assistant (paragraph 0147). The data contains information about the network components (paragraph 0148).
Applicant argues “Regarding dependent claims 7 and 16, Murthy is directed at converting natural language documents (e.g., 0035, 0060). Murthy contains no teaching or suggestion regarding embedding status data for network components, for example status data that is streamed data, live data, raw data, and so on. As such, the proposed combination of references fails to teach or suggest the features of the claims for which the combination is cited.” (remarks page 2). Examiner respectfully disagrees.
The claim language encompasses status data but the status data does not have to be streamed date, raw data or live data. Murthy teaches the sequences of text to be able to be tokenized or divided into sections and can be made of words, characters, or other portions of information (paragraph 0037).
Applicant’s arguments with respect to claim(s) 1, 6, 10, 15, 19 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.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-8, 10-15, 17, 19-22 are rejected under 35 U.S.C. 103 as being unpatentable over Grida Ben Yahya (US 20250088946 A1), hereinafter Grida Ben Yahya in view of Gupta et al. (US 20220191706 A1), hereinafter Gupta.
Regarding claim 1, Grida Ben Yahya teaches:
An automated process performed by a data management system associated with a wireless network having a plurality of network components associated with a plurality of cell sites, the data management system comprising a processor and an interface to the wireless network, the automated process comprising: (paragraph 0018 – automated process involving taking in user query through an interface to a wireless network. The inclusion of network infrastructure including network cells. Paragraph 0045 – the use of processors in the network system)
obtaining, via the network interface, a plurality of status data from the plurality of network components, wherein the plurality of network components comprises at least one each of a radio unit (RU), a distributed unit (DU), and a centralized unit (CU) associated with at least one of the plurality of cell sites; (paragraph 0032, figure 1 label 109 – describing use of radio unit for purposes sending and receiving wireless signals. Paragraph 0036, figure 1, label 112 – describing use of distributed computing devices to perform network functions. Paragraph 0037, figure 1, label 115 – describing centralized computing devices to perform network functions. Paragraph 0125 – The plurality of status data is obtained using an API.)
ingesting the obtained status data, comprising processing the obtained status data with a machine learning model (MLM) system, wherein the MLM system comprises a MLM;
(The optimization recommendations engine (519) is trained on ingested status data using a MLM (paragraph 0121). The session state store (label 512) stores the context of the language model and information from the account data to include the network resources and data (paragraph 0124).
storing the ingested status data in a data store;
(paragraph 0087, figure 4, label 415 –ingested data stored in a date store)
ingesting the obtained site closeout package document using the MLM system; (paragraph 0137, label 425 – The network controller takes in the deployment configuration of the network which is used by the AI assistant (label 427).)
receiving, via a user interface of the data management system, a user query related to the network; (paragraph 0129 – customer provides query regarding the network. Paragraph 0128 – inclusion of UI to send queries)
ingesting the received user query using the MLM system; (Figure 7, label 709, paragraph 0148 – Interpreting the prompt and using AI assistant service)
retrieving a status data result corresponding to the ingested query from the data store using the MLM system; (Figure 7, label 709, paragraph 0148 – interpreting the user prompt and obtaining the relevant status data.)
generating, using the MLM system, a summary of the retrieved status data result; (Figure 7, label 712, paragraph 0149 – generating a response that includes status information to include information about network functions that is summarized for the user.)
and
presenting the generated summary via the user interface. (Paragraph 0149 – response can be presented to user via network page or application user interface)
Grida Ben Yahya fails to teach:
obtaining a site closeout package document for the at least one cell site, wherein the site closeout package document comprises at least one of a network component setting at a time of installation of the network component at the at least one cell site, a network component test result at the time of installation, a revision information of the network component at the time of installation, or an antenna setup information at the time of installation;
storing the ingested site closeout package document in the data store;
Gupta teaches:
obtaining a site closeout package document for the at least one cell site, wherein the site closeout package document comprises at least one of a network component setting at a time of installation of the network component at the at least one cell site, a network component test result at the time of installation, a revision information of the network component at the time of installation, or an antenna setup information at the time of installation; (paragraph 0094, figure 4, label 457 – The antenna configuration data contains the settings for the antenna at the time of installation by the customer.)
storing the ingested site closeout package document in the data store; (Paragraph 0086 Figure 4, label 415 – The data store contains the antenna configuration data)
It would have been prima facie obvious to one of ordinary skill in the art before the effective
filing date of the claimed invention to have modified Grida Ben Yahya to incorporate the site closeout package teachings of Gupta. The purpose of doing so would be to optimize performance and management of the network by using the specific configurations provided during initial installation (paragraph 0015).
Regarding claim 2, Grida Ben Yahya teaches:
The automated process of claim 1, wherein the RU status data, DU status data, and CU status data are repeatedly obtained and ingested at a predetermined time interval. (paragraph 0112 – describing AI training model that can ingest data periodically or intermittently. Paragraph 0117 – the model may compose a code generator that is trained on customer data including radio-based network health metrics.)
Regarding claim 3, Grida Ben Yahya teaches:
The automated process of claim 2, wherein the RU status data, DU status data, and CU status data are obtained asynchronously from receiving the user query. (figure 4, label 422, paragraph 0091 – The network function sends periodic health status checks about the radio network that can be used to detect unresponsiveness or network failure.)
Regarding claim 4, Grida Ben Yahya teaches:
The automated process of claim 2, wherein the obtained status data comprises a RU health check, a DU health check, and a CU health check. (paragraph 0092 – network health service collects information from the network to analyze functionality and responsiveness.)
Regarding claim 5, Grida Ben Yahya teaches:
The automated process of claim 3, further comprising:
receiving, via the user interface, a status data document; (paragraph 0020 – user may send API query to transmit desired data)
ingesting the status data document using the MLM system; and (paragraph 0146 – AI assistant takes in status and configuration for a network and uses an AI language model to process it)
storing the ingested status data document in the data store. (paragraph 0087, figure 4, label 415 –ingested data stored in a date store)
Regarding claim 6, Grida Ben Yahya teaches:
The automated process of claim 1, wherein generating the summary comprises using, by the MLM system, the ingested site closeout document package as context. (paragraphs 0146-150 - The AI assistant queries the databases where the information about the network is stored which would include the ingested site closeout package to generate a response to the prompt from the customer.)
Regarding claim 8, Grida Ben Yahya teaches:
The automated process of claim 1, wherein the MLM is trained to summarize the retrieved status data result as a table. (paragraph 0021 – the information generated by the AI assistant may be in the form of a table)
Regarding claim 21 Grida Ben Yahya teaches:
The automated process of claim 1, further comprising:
storing an original obtained status data in the data store, wherein the original obtained status data comprises the obtained status data prior to the processing of the obtained status data with the MLM system; (paragraph 0117 – The code generator may be trained on existing customer data to include RBN health metrics which are stored in a data store which is data that has not been processed by the MLM).
and wherein generating the summary comprises using, by the MLM system, the original obtained status data and the ingested status data as context. (paragraph 0117 - The code generator uses the existing customer data as the original status data. Paragraphs 0117-0120 and 0104 - The NFTO templates may be customer data that is formatted and used to train the model and generate additional outputs/summaries).
Regarding claim 9, Grida Ben Yahya teaches:
The automated process of claim 5, further comprising asynchronously receiving, from the network, a second status data. (paragraph 0053 – a secondary status data can be a backup of the primary data that is received asynchronously and stored on a server)
Regarding claim 10, Grida Ben Yahya teaches:
A data management system comprising a processor, non-transitory storage, and an interface to a wireless network having a plurality of network components associated with a plurality of cell sites, wherein the non-transitory storage comprises computer-executable instructions that, when executed by the processor, perform an automated process that comprises: (paragraph 0018 – automated process involving taking in user query through an interface to a wireless network. The inclusion of network infrastructure including network cells. Paragraph 0045 – the use of processors in the network system)
obtaining, via the network interface, a plurality of status data from the plurality of network components, wherein the plurality of network components comprises at least one each of a radio unit (RU), a distributed unit (DU), and a centralized unit (CU) associated with at least one of the plurality of cell sites; (paragraph 0032, figure 1 label 109 – describing use of radio unit for purposes sending and receiving wireless signals. Paragraph 0036, figure 1, label 112 – describing use of distributed computing devices to perform network functions. Paragraph 0037, figure 1, label 115 – describing centralized computing devices to perform network functions. Paragraph 0125 – The plurality of status data is obtained using an API.)
ingesting the obtained status data, comprising processing the obtained status data with a machine learning model (MLM) system, wherein the MLM system comprises a MLM;
(The optimization recommendations engine (519) is trained on ingested status data using a MLM (paragraph 0121). The session state store (label 512) stores the context of the language model and information from the account data to include the network resources and data (paragraph 0124).
storing the ingested status data in a data store;
(paragraph 0087, figure 4, label 415 –ingested data stored in a date store)
ingesting the obtained site closeout package document using the MLM system; (paragraph 0137, label 425 – The network controller takes in the deployment configuration of the network which is used by the AI assistant (label 427).)
receiving, via a user interface of the data management system, a user query related to the network; (paragraph 0129 – customer provides query regarding the network. Paragraph 0128 – inclusion of UI to send queries)
ingesting the received user query using the MLM system; (Figure 7, label 709, paragraph 0148 – Interpreting the prompt and using AI assistant service)
retrieving a status data result corresponding to the ingested query from the data store using the MLM system; (Figure 7, label 709, paragraph 0148 – interpreting the user prompt and obtaining the relevant status data.)
generating, using the MLM system, a summary of the retrieved status data result; (Figure 7, label 712, paragraph 0149 – generating a response that includes status information to include information about network functions that is summarized for the user.)
and
presenting the generated summary via the user interface. (Paragraph 0149 – response can be presented to user via network page or application user interface)
Grida Ben Yahya fails to teach:
obtaining a site closeout package document for the at least one cell site, wherein the site closeout package document comprises at least one of a network component setting at a time of installation of the network component at the at least one cell site, a network component test result at the time of installation, a revision information of the network component at the time of installation, or an antenna setup information at the time of installation;
storing the ingested site closeout package document in the data store;
Gupta teaches:
obtaining a site closeout package document for the at least one cell site, wherein the site closeout package document comprises at least one of a network component setting at a time of installation of the network component at the at least one cell site, a network component test result at the time of installation, a revision information of the network component at the time of installation, or an antenna setup information at the time of installation; (paragraph 0094, figure 4, label 457 – The antenna configuration data contains the settings for the antenna at the time of installation by the customer.)
storing the ingested site closeout package document in the data store; (Paragraph 0086 Figure 4, label 415 – The data store contains the antenna configuration data)
It would have been prima facie obvious to one of ordinary skill in the art before the effective
filing date of the claimed invention to have modified Grida Ben Yahya to incorporate the site closeout package teachings of Gupta. The purpose of doing so would be to optimize performance and management of the network by using the specific configurations provided during initial installation (paragraph 0015).
Regarding claim 11, Grida Ben Yahya teaches:
The data management system of claim 10, wherein the RU status data, DU status data, and CU status data are repeatedly obtained and ingested at a predetermined time interval. (paragraph 0112 – describing AI training model that can ingest data periodically or intermittently. Paragraph 0117 – the model may compose a code generator that is trained on customer data including radio-based network health metrics.)
Regarding claim 12, Grida Ben Yahya teaches:
The data management system of claim 11, wherein the RU status data, DU status data, and CU status data are obtained asynchronously from receiving the user query. (figure 4, label 422, paragraph 0091 – The network function sends periodic health status checks about the radio network that can be used to detect unresponsiveness or network failure.)
Regarding claim 13, Grida Ben Yahya teaches:
The data management system of claim 11, wherein the obtained status data comprises a RU health check, a DU health check, and a CU health check. (paragraph 0092 – network health service collects information from the network to analyze functionality and responsiveness.)
Regarding claim 14, Grida Ben Yahya teaches:
The data management system of claim 12, wherein the automated process further comprises: receiving, via the user interface, a status data document; (paragraph 0020 – user may send API query to transmit desired data)
ingesting the status data document using the MLM system; (paragraph 0146 – AI assistant takes in status and configuration for a network and uses an AI language model to process it)
and storing the ingested status data document in the data store. (paragraph 0087, figure 4, label 415 –ingested data stored in a date store)
Regarding claim 15, Grida Ben Yahya teaches:
The data management system of claim 13, wherein the obtained status data further comprises a site closeout package document and Key Performance Indicators (KPIs) for the at least one cell site. (Paragraph 0097 – data stored in the data store may include RBN health metrics and device data. Paragraph 0098 – the RBN metrics include KPIs which contains latency and packet loss of wireless devices. Paragraph 0101 – the device data includes a closeout package document which contains information about the IMEI, serial numbers and, network address of wireless devices.)
Regarding claim 17, Grida Ben Yahya teaches:
The data management system of claim 10, wherein the MLM is trained to summarize the retrieved status data result as a table. (paragraph 0021 – the information generated by the AI assistant may be in the form of a table)
Regarding claim 22, Grida Ben Yahya teaches:
The data management system of claim 10, wherein the automated process further comprises: storing an original obtained status data in the data store, wherein the original obtained status data comprises the obtained status data prior to the processing of the obtained status data with the MLM system; and wherein generating the summary comprises using, by the MLM system, the original obtained status data and the ingested status data as context. (paragraph 0117 – The code generator may be trained on existing customer data to include RBN health metrics which are stored in a data store which is data that has not been processed by the MLM). (paragraph 0117 - The code generator uses the existing customer data as the original status data. Paragraphs 0117-0120 and 0104 - The NFTO templates may be customer data that is formatted and used to train the model and generate additional outputs/summaries).
Regarding claim 19 Grida Ben Yahya teaches:
An automated process performed by a data management system associated with a wireless network having a plurality of network components associated with one or more cell sites, the data management system comprising a processor and an interface to the wireless network, the automated process comprising: (paragraph 0018 – automated process involving taking in user query through an interface to a wireless network. The inclusion of network infrastructure including network cells. Paragraph 0045 – the use of processors in the network system)
periodically obtaining, via the network interface, a plurality of status data from a subset of the plurality of network components; (paragraph 0032, figure 1 label 109 – describing use of radio unit for purposes sending and receiving wireless signals. Paragraph 0036, figure 1, label 112 – describing use of distributed computing devices to perform network functions. Paragraph 0037, figure 1, label 115 – describing centralized computing devices to perform network functions. Paragraph 0125 – The plurality of status data is obtained using an API.)
ingesting the obtained status data, comprising processing the obtained status data with a machine learning model (MLM) system, wherein the MLM system comprises a MLM; (The optimization recommendations engine (519) is trained on ingested status data using a MLM (paragraph 0121). The session state store (label 512) stores the context of the language model and information from the account data to include the network resources and data (paragraph 0124).
storing the ingested status data in a data store; (paragraph 0087, figure 4, label 415 –ingested data stored in a date store)
receiving an automated query related to the network; (paragraph 0148 – The AI assistant may automatically generate a database query based on the network)
ingesting the received automated query using the MLM system; (Figure 7, label 709, paragraph 0148 – Interpreting the prompt and using AI assistant service)
retrieving a status data result corresponding to the ingested automated query from the data store using the MLM system; (Figure 7, label 709, paragraph 0148 – interpreting the user prompt and obtaining the relevant status data.)
generating, using the MLM system, a summary of the retrieved status data result; (Figure 7, label 712, paragraph 0149 – generating a response that includes status information to include information about network functions that is summarized for the user.)
and storing the generated summary in a second database. (paragraph 0124 – the session state is stored in the session state store which includes the context of the language model and other information retrieved from the customer account data.)
Grida Ben Yahya fails to teach:
obtaining a site closeout package document for the at least one cell site, wherein the site closeout package document comprises at least one of a network component setting at a time of installation of the network component at the at least one cell site, a network component test result at the time of installation, a revision information of the network component at the time of installation, or an antenna setup information at the time of installation;
storing the ingested site closeout package document in the data store;
Gupta teaches:
obtaining a site closeout package document for the at least one cell site, wherein the site closeout package document comprises at least one of a network component setting at a time of installation of the network component at the at least one cell site, a network component test result at the time of installation, a revision information of the network component at the time of installation, or an antenna setup information at the time of installation; (paragraph 0094, figure 4, label 457 – The antenna configuration data contains the settings for the antenna at the time of installation by the customer.)
storing the ingested site closeout package document in the data store; (Paragraph 0086 Figure 4, label 415 – The data store contains the antenna configuration data)
It would have been prima facie obvious to one of ordinary skill in the art before the effective
filing date of the claimed invention to have modified Grida Ben Yahya to incorporate the site closeout package teachings of Gupta. The purpose of doing so would be to optimize performance and management of the network by using the specific configurations provided during initial installation (paragraph 0015).
Regarding claim 20, Grida Ben Yahya teaches:
The automated process of claim 19, further comprising:
receiving and ingesting a user query;
retrieving a status data result corresponding to the ingested user query using the MLM system; (Figure 7, label 709, paragraph 0148 – interpreting the user prompt and obtaining the relevant status data.)
and generating, using the MLM system, a summary of the retrieved status data result. (Figure 7, label 712, paragraph 0149 – generating a response that includes status information to include information about network functions that is summarized for the user.)
Claims 7 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over,
Grida Ben Yahya-Gupta in view of Murthy et al (US 20250322167 A1, hereinafter Murthy).
Regarding claim 7, Grida Ben Yahya-Gupta teaches all of the elements of the current invention as stated above, Murthy teaches:
The automated process of claim 1, wherein:
the MLM system comprises a large language model (LLM) and an embedding model (EM), wherein: (paragraph 0011 – LLM, paragraph 0026 – embedding model)
ingesting the obtained status data comprises creating vector embeddings, via the EM, for the obtained status data, wherein: (paragraph 0035 – creating vector embeddings via EM)
the data store comprises a vector database; and (paragraph 0040 – vector database)
storing the ingested status data in the data store comprises storing the vector embeddings in the vector database; and (paragraph 0040 – storing in vector database)the LLM generates the summary of the retrieved status data result based on the user query and a context corresponding to the retrieved status data result. (paragraph - 0064 output can be in relation to user input and summary is generated by LLM)
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing data of the claimed invention to have modified Grida Ben Yahya-Gupta to incorporate the teachings of Murthy. Incorporating the use of a large language model and embedding model capable of creating and storing vector embeddings would reduce the amount of information processed by the machine learning model and would speed up the processing of requests from a user (paragraph 0003 Murthy).
Regarding claim 16, Grida Ben Yahya-Gupta teaches all of the elements of the current invention as stated above, Murthy teaches:
The data management system of claim 10, wherein:
the MLM system comprises a large language model (LLM) and an embedding model (EM), wherein: (paragraph 0011 – LLM, paragraph 0026 – embedding model)
ingesting the obtained status data comprises creating vector embeddings, via the EM, for the obtained status data, wherein: (paragraph 0035 – creating vector embeddings via EM for ingesting data)
the data store comprises a vector database; and (paragraph 0040 – vector database in a data store)
storing the ingested status data in the data store comprises storing the vector embeddings in the vector database; and (paragraph 0040 – storing in vector database vector embeddings)the LLM generates the summary of the retrieved status data result based on the user query and a context corresponding to the retrieved status data result. (paragraph - 0064 output can be in relation to user input and summary is generated by LLM)
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing data of the claimed invention to have modified Grida Ben Yahya-Gupta to incorporate the teachings of Murthy. Incorporating the use of a large language model and embedding model capable of creating and storing vector embeddings would reduce the amount of information processed by the machine learning model and would speed up the processing of requests from a user (paragraph 0003 Murthy).
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.
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/RYAN ALEXANDER CRIGLER/Examiner, Art Unit 2472
/NICHOLAS A JENSEN/Supervisory Patent Examiner, Art Unit 2472