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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 04/08/2026 has been entered.
Drawings
The drawings were received on 10/04/2021. These drawings are acceptable.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1, 3-4, 8, 11, 14-20, and 23-30 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Regarding claim 1, 19, and 20 recites “hidden layers to use of an adversarial learning to produce…” which renders the claim incoherent. Adversarial is an adjective that describes a type of learning so when claiming it’s use it using to perform a learning activity or applied to apply an outcome of a learning activity. The current limitation fails to make grammatical sense and renders the claim indefinite as it is unclear what the intended scope of the claimed limitation should be For example ““hidden layers applied using adversarial learning to produce…” maybe the intended recitation. Applicant should clarify the intended scope, such that the intended scope is understood by a person having ordinary skill in the art.
Regarding claim 1, the limitation “wherein the recommendation accepts the prediction when the second output indicates that an influence of algorithmic bias in the prediction has been minimized” renders the claim indefinite because the limitation is incoherent. A recommendation is an output of an algorithm that has no claimed attributes that would allow it to accept data as claimed. Maybe the recommendation system or a claimed system can decide to accept a prediction/recommended prediction when a condition is met, however that is not what the claim limitation states. Applicant should clarify the intended scope, such that the intended scope is understood by a person having ordinary skill in the art.
Regarding claims 20 and 19, the limitations are similar with the ones noted in claim 1 and are rejected under the same rationale.
Regarding the claims that depend on claims 1, 19 and 20 the claims do not resolve the issues noted above and are thus rejected under the same rationale.
Allowable Subject Matter
Claims 1, 3-4, 8, 11, 14-20, and 23-30 are objected to because the claims contain allowable subject matter.
Reasons for Allowance
The following is an examiner's statement of reasons for allowance:
The noted claims above are considered allowable since when reading the claims in light of the specification, as per MPEP 2111.01. The following is an examiner’s statement of reasons for allowance: applicant arguments filed April 8, 2026 have been found to be persuasive.
The examiner has found arguments directed the 35 USC § 103 rejection, documented in the previous office action, persuasive. See pages 15-20 of remarks:
“… In particular, the Applicant submits that Larson in view of Nguyen and further in view of Chakraborty fails to disclose or suggest at least a neural network including "a first input layer comprising a first plurality of inputs including sensitive features describing attributes of individuals who live in a specified location and non-sensitive features describing attributes of the specified location which are independent of the individuals[,] ... a first plurality of hidden layers[,] and a first output layer comprising [al first output, wherein the first output comprises a prediction generated in response to the first plurality of inputs, wherein the prediction indicates whether a fifth generation network tile should be installed in the specified location," as recited in the Applicant's independent claims 1, 19, and 20.
By contrast, none of Larson, Nguyen, and Chakraborty appears to be specifically directed to predicting whether a 5G network tile should be installed in a specified location, much less basing such a prediction on attributes of individuals living in the specified location and on attributes of the specified location which are independent of the individuals.
One example of the Applicant's disclosure provides a neural network architecture that comprises an application neural network and a bias control neural network arranged in a feedback loop. The application neural network may be trained to build a model for making business decisions (e.g., whether a 5G tile should be installed in a specified location that is described by both sensitive and non-sensitive features), while the bias control ("controller") neural network may be trained to mitigate the algorithmic bias in the application neural network (See, e.g., Applicant's disclosure, paragraphs 0015, 0027, 0039). In this case, the sensitive features may describe attributes of the individuals who live in and around the specific demographic location, such as e.g., gender (including sexual orientation and identity), race, color, nationality, religion, age, disability, occupation, income level, education level such as degree or area of educational background, interests, or other characteristics deemed to be sensitive (See, e.g., Applicant's disclosure, paragraph 0040). The non-sensitive features may describe attributes of the specific geographic location which are independent of the individuals who live in and around the specific demographic location, such as average temperature, climate, topography, distance to nearest existing 5G tile, signal strength, signal impairing structures, equipment requirements, and the like (See, e.g., Applicant's disclosure, paragraph 0041). …”
In summary, the references made of record, fail to disclose the required claimed technical features as recited by the independent claim limitations as a whole.
Furthermore, the references of record alone or in combination disclose or suggest the combination of limitations found within the independent claims as a whole without hindsight reasoning.
Response to Arguments
Applicant's arguments filed 04/08/2026 have been fully considered by the examiner. The rejections made in the previous office action have been withdrawn. See current office action addressing the amended claim limitations.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Banipal et al. (US 20220358237): teaches secure data analytics provided via a process that identifies sensitive data fields of an initial dataset and mappings between the sensitive data fields and other data fields of the dataset, where analytics processing is to be performed on the initial dataset, then, based on an expectation of data fields, of the initial data set, to be used in performance of the analytics processing and on the identified sensitive data fields, selects and applies a masking method to the initial dataset to mask the sensitive data fields and produce a masked dataset, provides the masked dataset to an analytics provider with a request for the analytics processing, and receives, in response, a generated analytics function, generated based on the masked dataset, that is configured to perform the analytics processing, and invokes the generated analytics function against the initial dataset to perform the analytics processing on the initial dataset, in abstract.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to OLUWATOSIN ALABI whose telephone number is (571)272-0516. The examiner can normally be reached Monday-Friday, 8:00am-5:00pm EST..
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/OLUWATOSIN ALABI/ Primary Examiner, Art Unit 2129
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