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 .
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.
Response to Arguments
Applicant's arguments filed on 07/31/2026 with respect to claims 1 - 20 has been considered but is not persuasive.
Please refer to the following office action, which clearly sets forth the reasons for non-persuasiveness.
Applicant argues that the claims do not include “mathematical relationships, mathematical formulas, or equations, and mathematical calculations” and therefore rejection under 101 “mathematical concepts is improper.
Examiner notes that the claims are directed to method/computing/processing operations including e.g. computing, receiving, generating, causing, teaching, etc. the system to operate which rely on mathematical relationship and/or computations. Therefore, absence of expressly recited equation(s) do not preclude the claims from being considered under 35 U.S.C. 101 rejection. In fact, the claim is evaluated as a whole to determine if the recited computing operations integrate the identified judicial exception into a practical application. Since the claim does not recite limitations that integrate the method/computing/processing operations into a practical application, therefore the 35 U.S.C. 101 rejection is maintained.
Applicant argues that the specification as filed paragraphs 0022 – 0028 describe technical improvements.
Examiner notes that applicant only relies on the specification as filed paragraphs 0022 – 0028 without pointing to wherein the claim any practical application is claimed. Further, applicant does not point to specific particular practical application in the noted paragraphs 0022 – 0028, nor does applicant explain where and how such particular application is reflected in the limitations of the claims. The currently filed claims do not include any practical application and since the cited specification disclosure does not establish that the claims integrate the identified judicial exception into practical application(s), therefore the 35 U.S.C. 101 rejection is maintained.
Applicant argues that Wang fails to show AI system to the use of the aggregate[ing] and recogniz[ing] status information for radio-based network. The system appears to describe general computing functionality included in boilerplate language in Wang and is unrelated to AI system.
Examiner notes paragraphs 0005, 0032, 0063, 0330 along with the abstract clearly teaches the AI system used to analyze and control all of the system functions and processing including (but not limited to) those shown in at least figure 2-4 and dealing with radio-based networks.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claim(s) 1 - 20 are directed to statutory computer-readable mediums under Step 1 of the eligibility analysis. However, the claims are further directed toward a judicial exception under Step 2A Prong One of the eligibility analysis, namely an abstract idea. Under Step 2A Prong Two of the eligibility analysis, the claim(s) does/do not include additional elements to integrate the exception into a practical application of that exception. Under Step 2B of the eligibility analysis, the claims are not sufficient to amount to significantly more than the judicial exception because nothing in the asserted claims purports to improve the functioning of the computer itself or effect an improvement in any other technology or technical field. The claim(s) is/are directed to an abstract system and method. This is “organizing information and manipulating information through mathematical correlations, Digitech Image Techs., LLC v. Electronics for Imaging, Inc., 758 F.3d 1344, 1350, 111 USPQ2d 1717, 1721 (Fed. Cir. 2014). The patentee in Digitech claimed methods of generating first and second data by taking existing information, manipulating the data using mathematical functions, and organizing this information into a new form. The court explained that such claims were directed to an abstract idea because they described a process of organizing information through mathematical correlations, like Flook's method of calculating using a mathematical formula. 758 F.3d at 1350, 111 USPQ2d at 1721”, (see MPEP 2106.04(a)(2)(I)(A)(iv)). “A claim that recites a mathematical calculation, when the claim is given its broadest reasonable interpretation in light of the specification, will be considered as falling within the "mathematical concepts" grouping. A mathematical calculation is a mathematical operation (such as multiplication) or an act of calculating using mathematical methods to determine a variable or number, e.g., performing an arithmetic operation such as exponentiation. There is no particular word or set of words that indicates a claim recites a mathematical calculation. That is, a claim does not have to recite the word "calculating" in order to be considered a mathematical calculation. For example, a step of "determining" a variable or number using mathematical methods or "performing" a mathematical operation may also be considered mathematical calculations when the broadest reasonable interpretation of the claim in light of the specification encompasses a mathematical calculation”. (see MPEP 2106.04(a)(2)(I)(C)(v. using an algorithm for determining the optimal number of visits by a business representative to a client, In re Maucorps, 609 F.2d 481, 482, 203 USPQ 812, 813 (CCPA 1979)). Furthermore, the claim(s) fail to amount to significantly more than the abstract idea itself, (see MPEP 2106.05(f)(i). A commonplace business method or mathematical algorithm being applied on a general purpose computer, Alice Corp. Pty. Ltd. V. CLS Bank Int’l, 573 U.S. 208, 223, 110 USPQ2d 1976, 1983 (2014); Gottschalk v. Benson, 409 U.S. 63, 64, 175 USPQ 673, 674 (1972); Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015)). Therefore, the claim(s) are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
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 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)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale or otherwise available to the public before the effective filing date of the claimed invention.
(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 – 20 are rejected under 35 U.S.C. 102(a)(1)/ (a)(2) [as best understood in view of the 35 USC § 101 above] as being anticipated by Wang (US PgPub No. 20230245651).
Regarding claim 1, Wang teaches a system (abstract; AI System), comprising: an artificial intelligence (AI) language model (paragraphs 0005, 0032, 0063, 0330) taught to aggregate and recognize status information (figure 3 item 301) for radio-based networks (paragraphs 0233, 0505, 0508) that are implemented at least partly on infrastructure of a cloud provider network (paragraph 0041); and a computing device (paragraph 0505 - 0508; computing/processing device) configured to at least: receive a prompt from a customer to provide a type of status information for at least a portion of a radio-based network (figure 3 item 302 - 303); generate, using the AI language model, a suitable query to submit to a database or a service to obtain the status information (figure 3 item 304 - 307); obtain the status information using the query (figure 3 item 307); generate, using the AI language model, a response to the prompt that includes or summarizes the type of status information using the obtained status information according to an intent expressed in the prompt (figure 3 items 308 – 309); and cause the response to be presented to the customer (figure 3 items 310 – 311).
Regarding claim 2, as mentioned above in the discussion of claim 1, Wang teaches all of the limitations of the parent claim. Additionally, Wang teaches wherein the response to the prompt further includes the query (paragraphs 0153 – 0156, 0185, 0195, 0200, 0217, 0239, 0360, 0435, 0475, and 0491; query).
Regarding claim 3, as mentioned above in the discussion of claim 1, Wang teaches all of the limitations of the parent claim. Additionally, Wang teaches wherein the response to the prompt further includes an explanation of the query (paragraphs 0153 – 0156, 0185, 0195, 0200, 0217, 0239, 0360, 0435, 0475, and 0491; explanation of the query).
Regarding claim 4, as mentioned above in the discussion of claim 1, Wang teaches all of the limitations of the parent claim. Additionally, Wang teaches wherein the response to the prompt further includes a topology graph showing one or more elements of the radio-based network (paragraphs 0101, 0117, 0157, 0194 - 0195; knowledge graph).
Regarding claim 5, as mentioned above in the discussion of claim 1, Wang teaches all of the limitations of the parent claim. Additionally, Wang teaches wherein the prompt identifies at least one of: a particular region of the cloud provider network, or a particular type of network function in the radio-based network (figure 3 item 302 – 303; domain specific concepts; also paragraphs 0049 – 0050, 0243, 0281 – 0285, and 0301 - 0305; type of network function).
Regarding claim 6, Wang teaches a computer-implemented (abstract; AI System) method (Figure 3), comprising: teaching an artificial intelligence (AI) language model (paragraphs 0005, 0032, 0063, 0330) to aggregate and recognize status information (figure 3 item 301) for radio-based networks (paragraphs 0233, 0505, 0508); receiving a prompt from a customer to provide a type of status information for at least a portion of a radio-based network (figure 3 item 302 - 303); obtaining the status information (figure 3 item 304 – 307; more particularly figure 3 item 307); and generating, using the Al language model, a response to the prompt that includes or summarizes the type of status information using the obtained status information according to an intent expressed in the prompt (figure 3 items 308 – 309).
Regarding claim 7, as mentioned above in the discussion of claim 6, Wang teaches all of the limitations of the parent claim. Additionally, Wang teaches wherein the status information includes respective locations of one or more cell sites in the radio-based network, and the response to the prompt includes a visualization depicting the respective locations of the one or more cell sites (paragraphs 0046, 0051, 0327, 0349, 0358, 0362, 0370, and 0389; object location; and/or paragraphs 0044, 0074, 0102, 0104, and 0132 area).
Regarding claim 8, as mentioned above in the discussion of claim 6, Wang teaches all of the limitations of the parent claim. Additionally, Wang teaches generating a database query to obtain the status information (paragraphs 0153 – 0156, 0185, 0195, 0200, 0217, 0239, 0360, 0435, 0475, and 0491; query); and returning the database query to the customer in conjunction with the response to the prompt (paragraphs 0153 – 0156, 0185, 0195, 0200, 0217, 0239, 0360, 0435, 0475, and 0491; returning of the query).
Regarding claim 9, as mentioned above in the discussion of claim 6, Wang teaches all of the limitations of the parent claim. Additionally, Wang teaches teaching the Al language model to recognize configuration information for the radio-based networks (figure 3 item 301); receiving a subsequent prompt from the customer to improve a configuration of the radio-based network (figure 3 item 307); and generating, using the Al language model, a configuration modification according to an intent to improve the configuration expressed in the prompt (figure 3 items 308 - 309).
Regarding claim 10, as mentioned above in the discussion of claim 9, Wang teaches all of the limitations of the parent claim. Additionally, Wang teaches automatically deploying an additional network function in the radio-based network according to the configuration modification (figure 3 items 309-310).
Regarding claim 11, as mentioned above in the discussion of claim 6, Wang teaches all of the limitations of the parent claim. Additionally, Wang teaches wherein the prompt identifies a particular region of a cloud provider network in which the radio-based network is at least partly implemented (paragraphs 0046, 0051, 0327, 0349, 0358, 0362, 0370, and 0389; object location; and/or paragraphs 0044, 0074, 0102, 0104, and 0132 area).
Regarding claim 12, as mentioned above in the discussion of claim 6, Wang teaches all of the limitations of the parent claim. Additionally, Wang teaches wherein the prompt identifies a particular type of network function in the radio-based network (figure 3 item 302 – 303; domain specific concepts; also paragraphs 0049 – 0050, 0243, 0281 – 0285, and 0301 - 0305; type of network function).
Regarding claim 13, as mentioned above in the discussion of claim 6, Wang teaches all of the limitations of the parent claim. Additionally, Wang teaches wherein obtaining the status information further comprises querying a plurality of application programming interfaces (APIs) to obtain the status information (paragraphs 0059, 0070, 0182, 0195, 0214 – 0223, 0298, 0324, and 0412; APIs used).
Regarding claim 14, as mentioned above in the discussion of claim 6, Wang teaches all of the limitations of the parent claim. Additionally, Wang teaches wherein the type of status information includes security audit information (paragraphs 0044, 0084 – 0086, 0098, 0183, 0218- 0240, and 0273; security and privacy).
Regarding claim 15, as mentioned above in the discussion of claim 6, Wang teaches all of the limitations of the parent claim. Additionally, Wang teaches wherein the type of status information includes network function health information (paragraphs 0231, 0345, 0354, 0365 – 0372; health info).
Regarding claim 16, as mentioned above in the discussion of claim 6, Wang teaches all of the limitations of the parent claim. Additionally, Wang teaches wherein the type of status information includes network function topology information (paragraphs 0101, 0117, 0157, 0194 - 0195; knowledge graph).
Regarding claim 17, as mentioned above in the discussion of claim 6, Wang teaches all of the limitations of the parent claim. Additionally, Wang teaches wherein the Al language model is taught based at least in part on at least one of: retrieval augmented generation (RAG) or fine-tuning (paragraph 0081, 0120, 0244, and 0442; fine-tuned).
Regarding claim 18, Wang teaches a computer-implemented (abstract; AI System) method (Figure 3), comprising: teaching an artificial intelligence (Al) language model (paragraphs 0005, 0032, 0063, 0330) to aggregate and recognize status information (figure 3 item 301) for radio-based networks (paragraphs 0233, 0505, 0508); receiving a prompt from a customer to determine a plurality of infrastructure components in a radio-based network that are associated with a particular type of status (figure 3 item 302 - 303); obtaining status information for the radio-based network (figure 3 item 304 – 307; more particularly figure 3 item 307); and generating, using the Al language model and the status information, a response to the prompt that includes a visualization of the plurality of infrastructure components that are determined to be associated with the particular type of status (figure 3 items 308 – 310).
Regarding claim 19, as mentioned above in the discussion of claim 18, Wang teaches all of the limitations of the parent claim. Additionally, Wang teaches wherein the visualization comprises a map of a plurality of physical locations corresponding to the plurality of infrastructure components (paragraphs 0046, 0051, 0327, 0349, 0358, 0362, 0370, and 0389; object location; and/or paragraphs 0044, 0074, 0102, 0104, and 0132 area).
Regarding claim 20, as mentioned above in the discussion of claim 18, Wang teaches all of the limitations of the parent claim. Additionally, Wang teaches wherein the visualization comprises a topology graph showing logical or physical connections among the plurality of infrastructure components (paragraphs 0101, 0117, 0157, 0194 - 0195; knowledge graph).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Kedalagudde (US PgPub No. 20230199868) teaches techniques related to an artificial intelligence application function (AI AF) and an artificial intelligence function (AIF) in a cellular network.
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 extension fee 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 Usman A Khan whose telephone number is (571)270-1131. The examiner can normally be reached on M - Th 5:30 AM - 2 PM, F 5:30 AM - Noon.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Sinh Tran can be reached on (571)272-7564. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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Usman Khan
/USMAN A KHAN/Primary Examiner, Art Unit 2637
09/11/2026
Prosecution Insights