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
The amendments filed on July 13, 2026 have been entered. Applicant amended claimed 1, 8, and 15. Claims 1-20 remain pending in the application.
Response to Arguments
Applicant’s arguments filed on July 13, 2026 in response to the Non-Final Office Action dated March 12, 2026 have been fully considered.
Applicant argues, in page 7 of the Remarks, “However, there is no teaching or suggestion in Bhatnagar that, based on receiving the request, the system sends a response to the user interface to obtain additional information via the user interface or receives feedback from the user interface with the additional information. The systems and methods of Bhatnagar receive the request that includes specific KPIs, time ranges, and level of detail and then directly move to retrieving data, calculating KPIs, generating an output dataset and transmitting the output dataset. Accordingly, claims 1, 8 and 15 are believed to be allowable over Bhatnagar.”.
In response, the user interface of Bhatnagar is interactive. Paragraph 017 discloses the user interface uses drill-down feature to obtain additional input from user regarding initial request. For example, for selected KPIs, the drill-down feature enables the user to provide additional input like time interval hour, minute for which the KPIs were requested.
Examiner’s Note about the Format of 35 U.S.C. 102/103 Rejections
Generally, limitations of a claim are reproduced identically and followed by examiner’s explanation with citation from prior art in Italic enclosed by a parenthesis, (), for each limitation. In examiner’s explanation, the mapping of the key elements of a limitation to the disclosed elements of prior art is shown by stating the disclosed element immediately followed by the claimed element inside a parenthesis. Specific quotation from prior art is delineated with quotation mark, ““. If primary art fails to teach a limitation or part of the limitation, the limitation or the part of the limitation is placed inside double square brackets, [[ ]], for better understandability, and appropriate secondary art(s) is/are applied later addressing the deficiency of the primary art.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-8, 10, and 13-20 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Bhatnagar et al. (WIPO Document No. WO 2025046609 A1), hereinafter, Bhatnagar.
Regarding claim 1:
Bhatnagar teaches:
A system for monitoring performance of a 5G OPEN RAN (O-RAN) communication network, the system comprising: a memory that stores one or more computer readable media that includes instructions; and one or more processor devices configured to execute the instructions of the computer readable media to (paragraph 0071 discloses monitoring performance of a 5G network. See Fig. 2 for various components of a system):
receive an input prompt from a user interface comprising a request related to performance of the communication network (paragraph 0109 discloses receiving a request from a user interface for performance data as stated “At step [404], the method [400] comprises receiving, by a receiving unit [302] from a user interface (UI) [304], a request for a set of KPIs to be determined.” );
provide a response to the user interface based on the input prompt and configured to obtain additional information via the user interface regarding the input prompt; receive feedback from the user interface comprising at least the additional information (paragraph 0109 discloses interaction is provided via the user interface to receive addition detail about the request as stated “The user interface (UI) [304] acts as the point of interaction where users can define their requirements, such as selecting specific KPIs, setting time ranges, and choosing the level of detail needed for their analysis”. Paragraph 017 discloses the user interface uses drill-down feature to obtain additional input from user regarding initial request. For example, for selected KPIs, the drill-down feature enables the user to provide additional input like time interval hour, minute for which the KPIs were requested);
determine a set of performance data and a set of infrastructure information related to at least one of the input prompt and the additional information (paragraph 0111 discloses determining data related to a set of KPIs and geographic location of users (infrastructure information) );
retrieve the set of performance data and the set of infrastructure information from at least one database (paragraph 0111 discloses retrieving performance data and geographic location of users Paragraph 0111 discloses data are retrieved from database as stated “Once the request is received by the receiving unit [302], the retrieving unit [306] utilizes the IPM module [100a] to locate and retrieve the relevant data from the system’s databases or data lakes.”);
correlate the set of performance data with the set of infrastructure information (paragraph 0111 discloses correlating KPI with geographic location of users. Also see paragraph 0067 discussing correlation engine 100n for detecting relationship among various data); and
generate a report regarding performance of the communication network based on the set of performance data and the set of infrastructure information (paragraph 0116 discloses generating a output dataset (report) regarding the performance of the network as stated “At step 410, the method [400] comprises generating, by a processing unit [312], based on the received request, an output dataset comprising the computed set of KPIs.”).
As to claim 2, the rejection of claim 1 is incorporated. Bhatnagar teaches all the limitations of claim 1 as shown above.
Bhatnagar further teaches wherein the at least one database comprises a network performance database comprising performance data for the communication network (paragraph 0111 discloses the database includes network performance data).
As to claim 3, the rejection of claim 1 is incorporated. Bhatnagar teaches all the limitations of claim 1 as shown above.
Bhatnagar further teaches wherein the at least on database comprising an infrastructure database comprising infrastructure information for the communication network (paragraph 0111 discloses the performance data is filtered using geographic location of users).
As to claim 4, the rejection of claim 1 is incorporated. Bhatnagar teaches all the limitations of claim 1 as shown above.
Bhatnagar further teaches wherein the input prompt is one of a text prompt or a voice prompt (paragraph 0110 discloses the user input is text prompt).
As to claim 5, the rejection of claim 1 is incorporated. Bhatnagar teaches all the limitations of claim 1 as shown above.
Bhatnagar further teaches wherein the one or more computer readable media further include at least one machine learning model configured to generate the report regarding performance of the communication network based on the set of performance data and the set of infrastructure information (paragraphs 0120 and 0121 disclose the machine learning model is used to generate the output dataset).
As to claim 6, the rejection of claim 5 is incorporated. Bhatnagar teaches all the limitations of claim 5 as shown above.
Bhatnagar further teaches wherein the at least one machine learning model is a large language model (LLM) (paragraph 0120 discloses the machine leaning model user natural language model. paragraph 0104 discloses the ML model are trained on large dataset. Therefore, the machine learning model is a LLM).
As to claim 7, the rejection of claim 1 is incorporated. Bhatnagar teaches all the limitations of claim 1 as shown above.
Bhatnagar further teaches wherein the report comprises an analysis of the set of performance data and the set of infrastructure information (paragraph 0116 discloses the output dataset comprises KPIs with various another parameter. As mentioned in paragraph 0111, the output dataset can include KPI for particular geographic location of users ).
Regarding claim 8:
Claim 8 is directed towards a method performed by the system of the claim 1. Accordingly, it is rejected under similar rationale.
Claim 10 is directed towards a method performed by the system of the claim 4. Accordingly, it is rejected under similar rationale.
Claim 13 is directed towards a method performed by the system of the claim 5. Accordingly, it is rejected under similar rationale.
Claim 14 is directed towards a method performed by the system of the claim 6. Accordingly, it is rejected under similar rationale.
Regarding claim 15:
Claim 15 is directed towards a non-transitory, computer-readable medium storing instructions that, when executed by a processor, performs the method of claim 8. Accordingly, it is rejected under similar rationale.
Claim 16 is directed towards a non-transitory, computer-readable medium performing the method of claim 10. Accordingly, it is rejected under similar rationale.
Claim 17 is directed towards a non-transitory, computer-readable medium performing the method of claim 11. Accordingly, it is rejected under similar rationale.
Claim 18 is directed towards a non-transitory, computer-readable medium performing the method of claim 13. Accordingly, it is rejected under similar rationale.
Claim 19 recites similar limitations as claim 7. Accordingly, it is rejected under similar rationale.
Claim 20 recites similar limitations as claims 2 and 3. Accordingly, it is rejected under similar rationale.
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 9 is rejected under 35 U.S.C. 103 as being unpatentable over Bhatnagar in view of Sethu et al. (US 20230072123 A1), hereinafter, Sethu.
As to claim 9, the rejection of claim 8 is incorporated. Bhatnagar teaches all the limitations of claim 8 as shown above.
Bhatnagar does not teach further comprising: receiving report feedback regarding the report regarding performance of the communication network and comprising an indication that the report is not correct; and generating a second report regarding performance of the communication network based on the report feedback.
Sethu teaches further comprising: receiving report feedback regarding the report regarding performance of the communication network and comprising an indication that the report is not correct; and generating a second report regarding performance of the communication network based on the report feedback (paragraph 0068 discloses receiving feedback about erroneous report .Paragraph 0072 discloses learning from user feedback and rectifying error in report).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Bhatnagar to incorporate the teaching of Sethu about receiving feedback about erroneous report and learning from user feedback. One would be motivated to do that to improve the reliability of the report generation by machine learning model (see paragraph 0047 of Sethu).
Claims 11 and 12 are rejected under 35 U.S.C. 103 as being unpatentable over Bhatnagar in view of Brenna et al. (US 20260067113 A1), hereinafter, Brenna.
As to claim 11, the rejection of claim 8 is incorporated. Bhatnagar teaches all the limitations of claim 8 as shown above.
Bhatnagar does not explicitly teach wherein determining a set of performance data and a set of infrastructure information related to at least one of the input prompt and the additional information comprises comparing the input prompt to a definitions database.
Brenna teaches wherein determining a set of performance data and a set of infrastructure information related to at least one of the input prompt and the additional information comprises comparing the input prompt to a definitions database (paragraph 0075 disclose comparing input prompts to database to extract context vector).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Bhatnagar to incorporate the teaching of Brenna about comparing input prompts to database to extract context vector. One would be motivated to do that to improve the execution of the input prompt (see paragraph 0075 of Brenna).
As to claim 12, the rejection of claim 11 is incorporated. Bhatnagar in view of Brenna teach all the limitations of claim 11 as shown above.
Bhatnagar does not explicitly teach wherein the definition database is a vector database.
Brenna teaches wherein the definition database is a vector database (paragraph 0075 disclose comparing input prompts to database to extract context vector).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Bhatnagar to incorporate the teaching of Brenna about comparing input prompts to database to extract context vector. One would be motivated to do that to improve the execution of the input prompt (see paragraph 0075 of Brenna).
Conclusion
THIS ACTION IS MADE FINAL. 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 KAMAL M HOSSAIN whose telephone number is (571)270-3070. The examiner can normally be reached 9:30-5:30 M-F.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, John Follansbee can be reached at (571)272-3964. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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July 21, 2026
/KAMAL M HOSSAIN/Primary Examiner, Art Unit 2444
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