DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claims 1-20 are presented for examination based on the amended claims in the application filed on June 6, 2026.
Claims 16-17 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.
Claims 1-20 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to judicial exception, an abstract idea, it has not been integrated into practical application.
Claims 1-2, 4-13, and 15-19 are rejected under 35 U.S.C. § 103 as being unpatentable over CN 111817805 A Zhang, Xiao-ying [herein “Zhang”] in view of Ning, Chao, Rui Li, and Kejiong Li. "Outdoor location estimation using received signal strength-based fingerprinting." Wireless Personal Communications 89, no. 2 (2016): 365-384 [herein “Ning”].
Claims 3 and 20 are rejected under 35 U.S.C. § 103 as being unpatentable over Zhang and Ning as applied to claim 1 above, and further in view of Wu, Jingbang, Huimei Lu, Yong Xiang, Rui Wu, and Feng Wang. “MBR: A map-based relaying algorithm for reliable data transmission through intersection in VANETs.” IEEE Transactions on Intelligent Transportation Systems 20, no. 10 (2018): 3661-3674 [herein “Wu”].
Claim 14 is rejected under 35 U.S.C. § 103 as being unpatentable over Zhang and Ning as applied to claim 1 above, and further in view of US 10,959,109 B1 Liu, Danielle et al. [herein “Liu”].
This action is made Non-Final.
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 June 6, 2026 has been entered.
Response to Amendment
The amendment filed June 6, 2026 has been entered. Claims 1-20 remain pending in the application. Applicant’s amendments to the Specification have overcome each and every objection previously set forth in the Final Office Action mailed March 10, 2026.
Claim Objections
Claims 1-20 are objected to because of the following informalities:
Claim 1, which cites “a timing advance value” in Ln. 14, is improper because there has been a previous recitation of “a timing advance value” in Ln. 12. For the purpose of examination, “a timing advance value” in Ln. 14 will be interpreted as “the timing advance value”. Claim 18, having similar limitations of claim 1, is also objected. Claims 2-17 and 19-20 are also objected to for incorporating the deficiency of their dependent claims 1 and 18, respectively.
Applicant is advised that should claim 1 be found allowable, claim 4 will be objected to under 37 CFR 1.75 as being a substantial duplicate thereof of claim 1. When two claims in an application are duplicates or else are so close in content that they both cover the same thing, despite a slight difference in wording, it is proper after allowing one claim to object to the other as being a substantial duplicate of the allowed claim. See MPEP § 608.01(m). The claims 5, 11, and 12 will also be objection to under the same rationale as being a substantial duplicate thereof of claim 1.
Appropriate correction is required.
Claim Rejections - 35 U.S.C. § 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 16-17 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.
Claim 16 recites the phrase “where the second operational data is associated with the second area” in Ln. 5. This phrase renders the claim indefinite, because it is unclear what “the second operational data” in the phrase is referring to. This instance of “the second operational data”] could be referring to either the instance of “obtain second operational data” in Claim 16 Ln. 3 or “the predicted second operational data” in Claim 1 Ln. 21 and 24. Therefore, it is unclear which is being referred to and the scope of the claim is unclear (See MPEP § 2173.05(h)). For examination purposes, the examiner has interpreted that “the second operational data” in this phrase to be the instance of “obtain second operational data” in Claim 16 Ln. 3. The examiner recommends that applicant amend the claim 16 language from “the second operational data” in Ln. 5 to “obtained second operational data” or similar, as supported by the specification, when referring to the second operational data that was obtained to distinguish over the predicted second operational data from Claim 1. Claim 17, which is dependent on claim 16, is similarly rejected.
Claim Rejections - 35 U.S.C. § 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.
Claims 1-20 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to judicial exception, an abstract idea, it has not been integrated into practical application and the claims further do not recite significantly more than the judicial exception. Examiner has evaluated the claims under the framework provided in the 2019 Patent Eligibility Guidance published in the Federal Register 01/07/2019 and has provided such analysis below.
Step 1:
Claims 1-17 are directed to a device and fall within the statutory category of a machine, and claims 18-20 are directed to a method and fall within the statutory category of process. Therefore, “Are the claims to a process, machine, manufacture or composition of matter?” Yes.
In order to evaluate the Step 2A inquiry “Is the claim directed to a law of nature, a natural phenomenon or an abstract idea?” we must determine, at Step 2A Prong 1, whether the claim recites a law of nature, a natural phenomenon or an abstract idea and further whether the claim recites additional elements that integrate the judicial exception into a practical application.
Step 2A Prong 1:
Claims 1 and 18: The limitations of:
“use the second model to predict second operational data based on the sensor data and/or the radio parameter data”,
“determine a model performance parameter based on the first performance parameter and the second performance parameter”,
“determine whether the model performance parameter satisfies a first criterion”,
“when the model performance parameter does not satisfy the first criterion: determine whether the second performance parameter satisfies a second criterion”, as drafted, is an operation that, but for the recitation of generic computing components, under its broadest reasonable interpretation, covers performance of the limitation of mathematical evaluations. For example, the limitations can be conducting as the following:
calculating the position of a target can be accomplished using the radar range equation of second model using the obtained operational data such as power received (Pg. 6 Ln. 28-30 references a transmission power model as an example of the models produced and a transmission power model of a radio system is based on the radar range equation, which can be found: https://www.ll.mit.edu/sites/default/files/outreach/doc/2018-07/lecture%202.pdf, and further see Pg. 10 Ln. 28 - Pg. 11 Ln. 2, Pg. 11 Ln. 26 – Pg. 12 Ln. 4 Pg. 19 Ln. 8-20),
calculating a performance metric between the performance of the first and second models can be conducted by subtracting the difference between the performance metric of the first model from the performance metric of the second model (Pg. 13 Ln. 19-23 gives a similar example for calculating the model performance parameter such as the mathematical difference, i.e., subtraction, between the first and second performance parameters),
calculating that the performance metric of the performance of the first and second models satisfies a first criterion can be conducted if the performance metric between the performance of the first and second models is greater than a first threshold value (Pg. 14 Ln. 10-14 gives a similar example for calculating if the first criterion is met), and
calculating that the performance of the second model satisfies a second criterion can be conducted if the performance of the second model is greater than a second threshold value when the performance metric between the performance of the first and second models is below the first threshold (Pg. 15 Ln. 15-24 gives a similar example for calculating if the second criterion is met).
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation of mathematic operation but for the recitation of generic computer components, then it falls within the “Mathematical Operation” grouping of abstract ideas. Accordingly, the claim recites an abstract idea under Prong I step 2A.
Furthermore, claims 1 and 18: The limitations of:
“obtain a first model for a first area”,
“update the first model for the first area based on the first operational data associated with the first area to determine a second model”,
“use the second model to predict second operational data based on the sensor data and/or the radio parameter data”,
“obtain a first performance parameter indicative of a performance of the first model”,
“obtain a second performance parameter based on the predicted second operational data, the second performance parameter being indicative of a performance of the second model”,
“determine a model performance parameter based on the first performance parameter and the second performance parameter”,
“determine whether the model performance parameter satisfies a first criterion”,
“when the model performance parameter does not satisfy the first criterion: determine whether the second performance parameter satisfies a second criterion”,
“when the second performance parameter does not satisfy the second criterion: determine a second area, the second area being a division of the first area and smaller than the first area, by applying clustering of one or more elements of the first area based on parameters indicative of one or more of: a spatial complexity of the second area; a geometric complexity of the second area; a number of electronic devices associated with the second area; data traffic in the second area; and/or vehicle traffic in the second area”, and
“update the second model to obtain a third model for the second area, wherein the first, second, and third models are configured to provide one or more configurations of one or more operational parameters for one or more electronic devices located in an area”, as drafted, is a process that, but for the recitation of generic computing components, under its broadest reasonable interpretation, covers performance of the limitation in the mind or with pen and paper. For example, the limitations can be conducted as the following:
a person can mentally create or draw with pen and paper a first model for an area by using the radar range equation with the current parameters and specifications for a radar system currently being utilized in the area,
a person can mentally update or draw with pen and paper the radar range equation of the first model for an area by updating the first model parameters such as frequency and using the obtained operational data such as power received to form a second model,
a person can mentally determine or draw with pen and paper the position of a target using the radar range equation of the second model using the obtained operational data such as power received,
a person can mentally determine or draw with pen and paper the accuracy of the first model using a mean square error equation to determine the performance of the model,
a person can mentally determine or draw with pen and paper the accuracy of the second model using a mean square error equation to determine the performance of the model with the determined the position of a target,
a person can mentally determine or draw with pen and paper a performance metric between the performance of the first and second models using simple arithmetic such as by subtracting the difference between the performance metric of the first model from the performance metric of the second model,
a person can mentally determine or draw with pen and paper that the performance metric between the performance of the first and second models satisfies a first criterion using simple arithmetic such as comparing if the performance metric between the performance of the first and second models is greater than a first threshold value,
a person can mentally determine or draw with pen and paper that the performance of the second model satisfies a second criterion using simple arithmetic such as comparing if the performance of the second model is greater than a second threshold value when the performance metric between the performance of the first and second models is below the first threshold,
a person can mentally determine or draw with pen and paper a second area by clustering the elements of the first area together, such as geographic and geometric attributes of the first area, to identify boundaries for where the first area is to be split into smaller portions and selecting one of the smaller portions of the first area as a second area, and
a person can mentally update or draw with pen and paper the radar range equation of the second model for the second area by updating the second model parameters such as an new operating frequency of the electronic device.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind or with pen and paper but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea under Prong I step 2A.
Therefore, yes, claims 1 and 18 recite judicial exceptions. The claims have been identified to recite judicial exceptions, Step 2A Prong 2 will evaluate whether the claims are directed to the judicial exception.
Step 2A Prong 2:
Claims 1 and 18: The judicial exception is not integrated into a practical application. In particular, the claims recite the following additional elements: “A server device comprising memory circuitry, one or more interfaces, and processor circuitry, wherein the server device is configured to”, “the processor circuitry is configured to”, and “performed by a server device” which are merely a recitation of generic computing components and functions being used as a tool to implement the judicial exception (see MPEP § 2106.05(f)) with the broadest reasonable interpretation, which does not integrate a judicial exception into elements. Further, the following additional element of “obtain first operational data associated with the first area, from the one or more first electronic devices in the first area, wherein the first operational data comprises one or more of sensor data related to the one or more first electronic devices and/or radio parameter data related to the one or more first electronic devices, wherein the sensor data comprises one or more of position data, tuples of coordinates, movement data, temperature data, and/or light data, wherein the radio parameter data comprises one or more of sensed radio conditions comprising signal strength and/or transmission power sensed by the one or more first electronic devices in the first area a channel quality indicator (CQI), a timing advance value, an error rate parameter, a difference in signal strength, a signal-to-interference-plus-noise ratio (SINR), a reference signal received power (RSRP), a reference signal received quality (RSRQ), a timing advance value (TA), a received signal strength indication (RSSI), channel state information (CSI), an error rate parameter comprising a bit error rate (BER), a frame error rate (FER), and/or a block error rate (BLER)” which is merely a recitation of insignificant extra-solution data gathering activity (see MPEP § 2106.05(g)) and/or a field of use/technological environment (see MPEP § 2106.05(h)) which does not integrate a judicial exception into practical application. The insignificant extra-solution activities are further addressed below under step 2B as also being Well-Understood, Routine, and Conventional (WURC).
Therefore, “Do the claims recite additional elements that integrate the judicial exception into a practical application?” No, these additional elements do not integrate the abstract idea into a practical application and they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
After having evaluated the inquires set forth in Steps 2A Prong 1 and 2, it has been concluded that claims 1 and 18 not only recite a judicial exception but that the claims are directed to the judicial exception as the judicial exception has not been integrated into practical application.
Step 2B:
Claims 1 and 18: The claims do not include additional elements, alone or in combination, that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements amount to no more than generic computing components and a field of use/technological environment (see MPEP § 2106.05(h)) which do not amount to significantly more than the abstract idea. Further, the insignificant extra-solution data gathering, record update, and data transmission activities are also Well-Understood, Routine and Conventional (see MPEP § 2106.05(d)(II), “The courts have recognized the following computer functions as well understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity. i. Receiving or transmitting data over a network, ii. Performing repetitive calculations, iii. Electronic recordkeeping, iv. Storing and retrieving information in memory” and MPEP § 2106.05(g), “Below are examples of activities that the courts have found to be insignificant extra-solution activity: Mere Data Gathering: Performing clinical tests on individuals to obtain input for an equation, In re Grams, 888 F.2d 835, 839-40; 12 USPQ2d 1824, 1827-28 (Fed. Cir. 1989); Testing a system for a response, the response being used to determine system malfunction, In re Meyers, 688 F.2d 789, 794; 215 USPQ 193, 196-97 (CCPA 1982)”).
Therefore, “Do the claims recite additional elements that amount to significantly more than the judicial exception?” No, these additional elements, alone or in combination, do not amount to significantly more than the judicial exception. Having concluded the analysis within the provided framework, claims 1 and 18 do not recite patent eligible subject matter under 35 U.S.C. § 101.
Regarding claims 2 and 19, they recite an additional element recitation of “wherein the determination of the second model is based on a training and/or re-training of the first model” which is merely a recitation of generic computing components and functions being used as a tool to implement the judicial exception (see MPEP § 2106.05(f)) which does not integrate a judicial exception into practical application. Further, these claims do not recite any further additional elements and for the same reasons as above with regard to integration into practical application and whether additional elements amount to significantly more, these claims also fail both Step 2A prong 2, thus the claims are directed to the judicial exception as they have not been integrated into practical application, and fail Step 2B as not amounting to significantly more. Therefore, claims 2 and 19 do not recite patent eligible subject matter under 35 U.S.C. § 101.
Regarding claims 3 and 20, they recite an additional limitation of “wherein the determination of the second area comprises encoding the second area into an alphanumeric string by applying a geohashing function” as drafted, is a process that, but for the recitation of generic computing components, under its broadest reasonable interpretation, covers performance of the limitation of mathematical evaluations. For example, calculating that alphanumeric string for the second area can be conducted by geohashing the second area are based on its geographical location in the first area (see Pg. 17 Ln. 22-24, the equations and methods for encoding geographical locations through geohash can be found at: https://en.wikipedia.org/wiki/Geohash).
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation of mathematic evaluations but for the recitation of generic computer components, then it falls within the “Mathematical Operation” grouping of abstract ideas. Accordingly, the claim recites an abstract idea under Prong I step 2A.
Furthermore, regarding claims 3 and 20, they recite an additional limitation of “wherein the determination of the second area comprises encoding the second area into an alphanumeric string by applying a geohashing function”, as drafted, is a process that, but for the recitation of generic computing components, under its broadest reasonable interpretation, covers performance of the limitation in the mind or with pen and paper. For example, person can mentally or draw with pen and paper an alphanumeric string for the second area by using its geographical location and encoding the location into alphanumeric string using simple binary and logarithmic operations through the geohash method.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind or with pen and paper but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea under Prong I step 2A.
Regarding claim 4, it recites an additional element recitation of “wherein the first operational data comprises sensor data and/or radio parameter data” is merely an insignificant extra-solution data gathering activity (see MPEP § 2106.05(g)) and/or a field of use/technological environment (see MPEP § 2106.05(h)) which does not integrate a judicial exception into practical application. Further, the insignificant extra-solution data gathering, record update, and data transmission activities are also Well-Understood, Routine and Conventional (see MPEP § 2106.05(d)(II), “The courts have recognized the following computer functions as well understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity. i. Receiving or transmitting data over a network, ii. Performing repetitive calculations, iii. Electronic recordkeeping, iv. Storing and retrieving information in memory” and MPEP § 2106.05(g), “Below are examples of activities that the courts have found to be insignificant extra-solution activity: Mere Data Gathering: Performing clinical tests on individuals to obtain input for an equation, In re Grams, 888 F.2d 835, 839-40; 12 USPQ2d 1824, 1827-28 (Fed. Cir. 1989); Testing a system for a response, the response being used to determine system malfunction, In re Meyers, 688 F.2d 789, 794; 215 USPQ 193, 196-97 (CCPA 1982)”). Further, this claim does not recite any further additional elements and for the same reasons as above with regard to integration into practical application and whether additional element amounts to significantly more, this claim also fails both Step 2A prong 2, thus this claim is directed to the judicial exception as it has not been integrated into practical application, and fails Step 2B as not amounting to significantly more. Therefore, claim 4 does not recite patent eligible subject matter under 35 U.S.C. § 101.
Regarding claim 5, it recites an additional limitation of “wherein the determination of the second model is based on the sensor data and/or radio parameter data”, as drafted, is a process that, but for the recitation of generic computing components, under its broadest reasonable interpretation, covers performance of the limitation in the mind or with pen and paper. For example, a person can mentally update or draw with pen and paper the radar range equation of the first model for an area by updating the first model parameters such as frequency and using the obtained operational data such as power received to form a second model.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind or with pen and paper but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea under Prong I step 2A.
Regarding claim 6, it recites an additional limitation of “when the model performance parameter satisfies the first criterion, refrain from determining whether the second performance parameter satisfies the second criterion”, as drafted, is a process that, but for the recitation of generic computing components, under its broadest reasonable interpretation, covers performance of the limitation in the mind or with pen and paper. For example, a person can mentally determine or draw with pen and paper that when the performance metric between the performance of the first and second models is above the first threshold, then determining if the performance of the second model satisfies a second criterion does not need to be conducted since the model has been fully optimized.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind or with pen and paper but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea under Prong I step 2A.
Furthermore, regarding claim 6, it recites additional element recitation of “wherein the processor circuitry is configured” is merely a recitation of generic computing components and functions being used as a tool to implement the judicial exception (see MPEP § 2106.05(f)) which does not integrate a judicial exception into practical application. Further, this claim does not recite any further additional elements and for the same reasons as above with regard to integration into practical application and whether additional element amounts to significantly more, this claim also fails both Step 2A prong 2, thus this claim is directed to the judicial exception as it has not been integrated into practical application, and fails Step 2B as not amounting to significantly more. Therefore, claim 6 does not recite patent eligible subject matter under 35 U.S.C. § 101.
Regarding claim 7, it recites an additional limitation of “when the second performance parameter satisfies the second criterion, refrain from determining the second area”, as drafted, is a process that, but for the recitation of generic computing components, under its broadest reasonable interpretation, covers performance of the limitation in the mind or with pen and paper. For example, a person can mentally determine or draw with pen and paper that when the performance of the second model is above the second threshold, then determining of the second area by splitting the first area does not need to be conducted since the model has been fully optimized.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind or with pen and paper but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea under Prong I step 2A.
Furthermore, regarding claim 7, it recites additional element recitation of “wherein the processor circuitry is configured” is merely a recitation of generic computing components and functions being used as a tool to implement the judicial exception (see MPEP § 2106.05(f)) which does not integrate a judicial exception into practical application. Further, this claim does not recite any further additional elements and for the same reasons as above with regard to integration into practical application and whether additional element amounts to significantly more, this claim also fails both Step 2A prong 2, thus this claim is directed to the judicial exception as it has not been integrated into practical application, and fails Step 2B as not amounting to significantly more. Therefore, claim 7 does not recite patent eligible subject matter under 35 U.S.C. § 101.
Regarding claim 8, it recites an additional limitation of “determine, based on the first model and/or the second model, one or more first operation schemes for the one or more first electronic devices”, as drafted, is a process that, but for the recitation of generic computing components, under its broadest reasonable interpretation, covers performance of the limitation in the mind or with pen and paper. For example, a person can mentally update or draw with pen and paper the radar range equation of the second model for the second area by updating the second model parameters such as an new operating frequency of the electronic device.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind or with pen and paper but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea under Prong I step 2A.
Furthermore, regarding claim 8, it recites additional element recitation of “wherein the processor circuitry is configured” is merely a recitation of generic computing components and functions being used as a tool to implement the judicial exception (see MPEP § 2106.05(f)) which does not integrate a judicial exception into practical application. Further, this claim does not recite any further additional elements and for the same reasons as above with regard to integration into practical application and whether additional element amounts to significantly more, this claim also fails both Step 2A prong 2, thus this claim is directed to the judicial exception as it has not been integrated into practical application, and fails Step 2B as not amounting to significantly more. Therefore, claim 8 does not recite patent eligible subject matter under 35 U.S.C. § 101.
Regarding claim 9, it recites an additional limitation of “wherein the first criterion comprises a first threshold” as drafted, is a process that, but for the recitation of generic computing components, under its broadest reasonable interpretation, covers performance of the limitation of mathematical evaluations. For example, calculating that the performance metric of the performance of the first and second models satisfies a first criterion can be conducted if the performance metric between the performance of the first and second models is greater than a first threshold value (Pg. 14 Ln. 10-14 gives a similar example for calculating if the first criterion is met).
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation of mathematic evaluations but for the recitation of generic computer components, then it falls within the “Mathematical Operation” grouping of abstract ideas. Accordingly, the claim recites an abstract idea under Prong I step 2A.
Furthermore, regarding claim 9, it recites an additional limitation of “wherein the first criterion comprises a first threshold”, as drafted, is a process that, but for the recitation of generic computing components, under its broadest reasonable interpretation, covers performance of the limitation in the mind or with pen and paper. For example, a person can mentally determine or draw with pen and paper that the performance metric between the performance of the first and second models satisfies a first criterion using simple arithmetic such as comparing if the performance metric between the performance of the first and second models is greater than a first threshold value.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind or with pen and paper but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea under Prong I step 2A.
Regarding claim 10, it recites an additional limitation of “wherein the second criterion comprises a second threshold” as drafted, is a process that, but for the recitation of generic computing components, under its broadest reasonable interpretation, covers performance of the limitation of mathematical evaluations. For example, calculating that the performance of the second model satisfies a second criterion can be conducted if the performance of the second model is greater than a second threshold value when the performance metric between the performance of the first and second models is below the first threshold (Pg. 15 Ln. 15-24 gives a similar example for calculating if the second criterion is met).
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation of mathematic evaluations but for the recitation of generic computer components, then it falls within the “Mathematical Operation” grouping of abstract ideas. Accordingly, the claim recites an abstract idea under Prong I step 2A.
Furthermore, regarding claim 10, it recites an additional limitation of “wherein the second criterion comprises a second threshold”, as drafted, is a process that, but for the recitation of generic computing components, under its broadest reasonable interpretation, covers performance of the limitation in the mind or with pen and paper. For example, a person can mentally determine or draw with pen and paper that the performance of the second model satisfies a second criterion using simple arithmetic such as comparing if the performance of the second model is greater than a second threshold value when the performance metric between the performance of the first and second models is below the first threshold.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind or with pen and paper but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea under Prong I step 2A.
Regarding claim 11, it recites an additional limitation of “wherein the obtaining of the first model is based on an association of the first model with the first area”, as drafted, is a process that, but for the recitation of generic computing components, under its broadest reasonable interpretation, covers performance of the limitation in the mind or with pen and paper. For example, a person can mentally create or draw with pen and paper a first model for an area by using the radar range equation with the current parameters and specifications for a radar system currently being utilized in the area.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind or with pen and paper but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea under Prong I step 2A.
Regarding claim 12, it recites an additional limitation of “wherein the second area is part of a plurality of areas included in the first area” as drafted, is a process that, but for the recitation of generic computing components, under its broadest reasonable interpretation, covers performance of the limitation in the mind or with pen and paper. For example, a person can mentally determine or draw with pen and paper a second area by splitting the first area into smaller portions to contain a plurality of smaller areas and selecting one of the smaller portions of the first area as a second area.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind or with pen and paper but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea under Prong I step 2A.
Regarding claim 13, it recites an additional limitation of “wherein one or more of the first model, the second model, and/or the third model are configured to predict one or more configurations of a corresponding operation scheme” as drafted, is a process that, but for the recitation of generic computing components, under its broadest reasonable interpretation, covers performance of the limitation of mathematical evaluations. For example, calculating the power received by each radio sensor at its respective location in the area for the adjusting of sensor configuration, such as adjusting the frequency, can be conducted using the updated transmission power model that implements the radar range equation (Pg. 6 Ln. 28-30 references a transmission power model as an example of the models produced and a transmission power model of a radio system is based on the radar range equation, which can be found: https://www.ll.mit.edu/sites/default/files/outreach/doc/2018-07/lecture%202.pdf).
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation of mathematic evaluations but for the recitation of generic computer components, then it falls within the “Mathematical Operation” grouping of abstract ideas. Accordingly, the claim recites an abstract idea under Prong I step 2A.
Furthermore, regarding claim 13, it recites an additional limitation of “wherein one or more of the first model, the second model, and/or the third model are configured to predict one or more configurations of a corresponding operation scheme”, as drafted, is a process that, but for the recitation of generic computing components, under its broadest reasonable interpretation, covers performance of the limitation in the mind or with pen and paper. For example, a person can mentally update or draw with pen and paper the radar range equation of the second model for the second area by updating the second model parameters such as an new operating frequency of the electronic device.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind or with pen and paper but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea under Prong I step 2A.
Regarding claim 14, it recites an additional element recitation of “wherein the processor circuitry is configured to transmit the first model to the one or more first electronic devices associated with the first area” is merely an insignificant extra-solution data outputting activity (see MPEP § 2106.05(g)) and/or a field of use/technological environment (see MPEP § 2106.05(h)) which does not integrate a judicial exception into practical application. Further, the insignificant extra-solution data gathering, record update, and data transmission activities are also Well-Understood, Routine and Conventional (see MPEP § 2106.05(d)(II), “The courts have recognized the following computer functions as well understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity. i. Receiving or transmitting data over a network, ii. Performing repetitive calculations, iii. Electronic recordkeeping, iv. Storing and retrieving information in memory”). Further, this claim does not recite any further additional elements and for the same reasons as above with regard to integration into practical application and whether additional element amounts to significantly more, this claim also fails both Step 2A prong 2, thus this claim is directed to the judicial exception as it has not been integrated into practical application, and fails Step 2B as not amounting to significantly more. Therefore, claim 14 does not recite patent eligible subject matter under 35 U.S.C. § 101.
Regarding claim 15, it recites an additional element recitation of “wherein the processor circuitry is configured to transmit the third model to one or more second electronic devices associated with the second area” is merely an insignificant extra-solution data outputting activity (see MPEP § 2106.05(g)) and/or a field of use/technological environment (see MPEP § 2106.05(h)) which does not integrate a judicial exception into practical application. Further, the insignificant extra-solution data gathering, record update, and data transmission activities are also Well-Understood, Routine and Conventional (see MPEP § 2106.05(d)(II), “The courts have recognized the following computer functions as well understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity. i. Receiving or transmitting data over a network, ii. Performing repetitive calculations, iii. Electronic recordkeeping, iv. Storing and retrieving information in memory”). Further, this claim does not recite any further additional elements and for the same reasons as above with regard to integration into practical application and whether additional element amounts to significantly more, this claim also fails both Step 2A prong 2, thus this claim is directed to the judicial exception as it has not been integrated into practical application, and fails Step 2B as not amounting to significantly more. Therefore, claim 15 does not recite patent eligible subject matter under 35 U.S.C. § 101.
Regarding claim 16, it recites an additional limitation of “determine a fourth model, based on the second operational data and the third model”, as drafted, is a process that, but for the recitation of generic computing components, under its broadest reasonable interpretation, covers performance of the limitation in the mind or with pen and paper. For example, a person can mentally update or draw with pen and paper the radar range equation of the third model for the second area by updating the third model parameters such as frequency and using the obtained operational data such as power received from the second model to form a fourth model.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind or with pen and paper but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea under Prong I step 2A.
Furthermore, regarding claim 16, it recites additional element recitation of “wherein the processor circuitry is configured” is merely a recitation of generic computing components and functions being used as a tool to implement the judicial exception (see MPEP § 2106.05(f)) which does not integrate a judicial exception into practical application. Further, the following additional element, “obtain second operational data, from one or more second electronic devices associated with the second area, where the second operational data is associated with the second area” and “transmit the fourth model to the one or more second electronic devices” are merely an insignificant extra-solution data gathering and data outputting activities, respectively, (see MPEP § 2106.05(g)) which do not integrate a judicial exception into practical application. Further, the insignificant extra-solution data gathering, record update, and data transmission activities are also Well-Understood, Routine and Conventional (see MPEP § 2106.05(d)(II), “The courts have recognized the following computer functions as well understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity. i. Receiving or transmitting data over a network, ii. Performing repetitive calculations, iii. Electronic recordkeeping, iv. Storing and retrieving information in memory”). Further, this claim does not recite any further additional elements and for the same reasons as above with regard to integration into practical application and whether additional element amounts to significantly more, this claim also fails both Step 2A prong 2, thus this claim is directed to the judicial exception as it has not been integrated into practical application, and fails Step 2B as not amounting to significantly more. Therefore, claim 16 does not recite patent eligible subject matter under 35 U.S.C. § 101.
Regarding claim 17, it recites an additional limitation of “determine, based on the third model and/or the fourth model, one or more second operation schemes for the one or more second electronic devices” as drafted, is a process that, but for the recitation of generic computing components, under its broadest reasonable interpretation, covers performance of the limitation of mathematical evaluations. For example, calculating the power received by each radio sensor at its respective location in the area for the adjusting of sensor configuration, such as adjusting the frequency, can be conducted using the updated transmission power model that implements the radar range equation (Pg. 6 Ln. 28-30 references a transmission power model as an example of the models produced and a transmission power model of a radio system is based on the radar range equation, which can be found: https://www.ll.mit.edu/sites/default/files/outreach/doc/2018-07/lecture%202.pdf).
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation of mathematic evaluations but for the recitation of generic computer components, then it falls within the “Mathematical Operation” grouping of abstract ideas. Accordingly, the claim recites an abstract idea under Prong I step 2A.
Furthermore, regarding claim 17, it recites an additional limitation of “determine, based on the third model and/or the fourth model, one or more second operation schemes for the one or more second electronic devices”, as drafted, is a process that, but for the recitation of generic computing components, under its broadest reasonable interpretation, covers performance of the limitation in the mind or with pen and paper. For example, a person can mentally update or draw with pen and paper the radar range equation of the third or fourth model that predict the power received by each radio sensor at its respective location in the area for the adjusting of sensor configuration, such as an new operating frequency of the electronic device.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind or with pen and paper but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea under Prong I step 2A.
Furthermore, regarding claim 17, it recites additional element recitation of “wherein the processor circuitry is configured” is merely a recitation of generic computing components and functions being used as a tool to implement the judicial exception (see MPEP § 2106.05(f)) which does not integrate a judicial exception into practical application. Further, this claim does not recite any further additional elements and for the same reasons as above with regard to integration into practical application and whether additional element amounts to significantly more, this claim also fails both Step 2A prong 2, thus this claim is directed to the judicial exception as it has not been integrated into practical application, and fails Step 2B as not amounting to significantly more. Therefore, claim 17 does not recite patent eligible subject matter under 35 U.S.C. § 101.
Therefore, having concluded the analysis within the provided framework, claims 1-20 do not recite patent eligible subject matter and are rejected under 35 U.S.C. § 101 because the claimed invention is directed to judicial exception, an abstract idea, that has not been integrated into a practical application. The claims further do not recite significantly more than the judicial exception. Claims 2-17 and 19-20 are also rejected for incorporating the deficiency of their independent claim 1 and 18, respectively.
Claim Rejections - 35 U.S.C. § 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.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. § 102(b)(2)(C) for any potential 35 U.S.C. § 102(a)(2) prior art against the later invention.
Claims 1-2, 4-13, and 15-19 are rejected under 35 U.S.C. § 103 as being unpatentable over CN 111817805 A Zhang, Xiao-ying [herein “Zhang”] in view of Ning, Chao, Rui Li, and Kejiong Li. "Outdoor location estimation using received signal strength-based fingerprinting." Wireless Personal Communications 89, no. 2 (2016): 365-384 [herein “Ning”].
As per claim 1, Zhang teaches “A server device comprising memory circuitry, one or more interfaces, and processor circuitry”. (Pg. 4, “The embodiment of the present invention also provides a channel propagation model parameter adjustment device including: Memory, used to store computer programs” [e.g., a server device comprising memory circuitry]. “The processor is configured to execute the computer program to implement the steps of the method for adjusting the channel propagation model parameters as described in any one of the above” [processor circuitry]. Pg. 4, “The acquiring unit is used to acquire initial feature data between the transceiver nodes in the modeling area” [one or more interfaces]. Further see Pg. 4. The examiner has interpreted that a channel propagation model parameter adjustment device including: Memory, a processor, and an acquiring unit to acquire data as a server device comprising memory circuitry, one or more interfaces, and processor circuitry.)
Zhang teaches “obtain a first model for a first area”. (Pg. 6, “The wireless propagation model is used to predict the path loss value in the modeling area”. Further see Pg. 5-6. The examiner has interpreted that predicting path loss in a modeling area using a wireless propagation model as obtain a first model for a first area.)
Zhang teaches “obtain first operational data associated with the first area, from the one or more first electronic devices in the first area, wherein the first operational data comprises one or more of sensor data related to the one or more first electronic devices and/or radio parameter data related to the one or more first electronic devices, wherein the sensor data comprises one or more of position data, tuples of coordinates, movement data, temperature data, and/or light data, wherein the radio parameter data comprises one or more of sensed radio conditions comprising signal strength and/or transmission power sensed by the one or more first electronic devices in the first area a channel quality indicator (CQI), a timing advance value, an error rate parameter, a difference in signal strength, a signal-to-interference-plus-noise ratio (SINR), a reference signal received power (RSRP), a reference signal received quality (RSRQ), a timing advance value (TA), a received signal strength indication (RSSI), channel state information (CSI), an error rate parameter comprising a bit error rate (BER), a frame error rate (FER), and/or a block error rate (BLER)”. (Pg. 6, “Taking a modeling area as an example, in practical applications, multiple groups of receiving and sending nodes can be set in the modeling area, and initial characteristic data such as frequency value, distance value, and height value between each group of receiving and sending nodes can be measured” [obtain first operational data associated with the first area, from the one or more first electronic devices in the first area, wherein the first operational data comprises one or more of sensor data related to the one or more first electronic devices, wherein the sensor data comprises one or more of position data]. Furthermore, Pg. 6, “Table 1 records the specific types of initial feature data between the base station and the measurement point, where the feature type refers to the terrain corresponding to the modeling area where the base station and the measurement point are located” [position data]. Pg. 8, “The intelligent wireless propagation model refers to the wireless propagation model in the initial state. The initial temperature corresponding to the intelligent wireless propagation model can be represented by T(0), and the optimization times can be represented by k” [wherein the sensor data comprises temperature data]. Further see Pg. 5-6 and 8. The examiner has interpreted that measuring initial characteristic data from receiving and sending nodes such as distance and height values, location, and initial temperature as obtain first operational data associated with the first area, from the one or more first electronic devices in the first area, wherein the first operational data comprises one or more of sensor data related to the one or more first electronic devices, wherein the sensor data comprises one or more of position data or temperature data.)
Zhang teaches “update the first model for the first area based on the first operational data associated with the first area to determine a second model”. (Pg. 11, “The training unit is used to train the intelligent wireless propagation model by using the target input feature set and the initial feature data to obtain the target wireless propagation model”. Further see Pg. 11. The examiner has interpreted that obtaining a target wireless propagation model by training the wireless propagation model using the initial feature data as update the first model for the first area based on the first operational data associated with the first area to determine a second model.)
Zhang teaches “use the second model to predict second operational data based on the sensor data and/or the radio parameter data”. (Pg. 9, “the performance of the target wireless propagation model obtained according to the method for adjusting the channel propagation model parameters provided in the embodiment of the present invention can be verified by means of simulation”. Further see Pg. 9. The examiner has interpreted that simulating the target wireless propagation model by adjusting the channel propagation model parameters to obtain the performance of the model as use the second model to predict second operational data based on the sensor data and/or the radio parameter data.)
Zhang teaches “obtain a first performance parameter indicative of a performance of the first model”. (Pg. 8, “Calculate the input feature set and the generalization error value of the new input feature set in the intelligent wireless propagation model” [obtain a first performance parameter indicative of a performance of the model]. “The intelligent wireless propagation model refers to the wireless propagation model in the initial state” [e.g., the first model]. Further see Pg. 8. The examiner has interpreted that calculating the generalization error value of the wireless propagation model as obtain a first performance parameter indicative of a performance of the first model.)
Zhang teaches “obtain a second performance parameter based on the predicted second operational data, the second performance parameter being indicative of a performance of the second model”. (Pg. 9, “the performance of the target wireless propagation model obtained according to the method for adjusting the channel propagation model parameters provided in the embodiment of the present invention can be verified by means of simulation” and Pg. 8, “The feature set is trained as the input layer parameter of the intelligent wireless propagation model to obtain the second generalization error value”. Further see Pg. 8. The examiner has interpreted that obtaining the second generalization error value on the trained input parameters of the wireless propagation model using to evaluate the performance of the target wireless propagation model as obtain a second performance parameter based on the predicted second operational data, the second performance parameter being indicative of a performance of the second model.)
Zhang teaches “determine a model performance parameter based on the first performance parameter and the second performance parameter”. (Pg. 7, “Adjust each coded data contained in the input feature set, mainly to remove irrelevant and redundant input features in the input feature set, and automatically screen out suitable target input feature sets for the modeling area to avoid overfitting the wireless propagation model to irrelevant The data leads to an increase in prediction errors” [e.g., the first and second generalization error values, based on the first performance parameter and the second performance parameter] “which improves the prediction accuracy of the wireless propagation model in different scenarios” [determine a model performance parameter]. Further see Pg. 7. The examiner has interpreted that improving the prediction accuracy of the wireless propagation model by avoiding overfitting the model to irrelevant data which leads to the prediction errors as determine a model performance parameter based on the first performance parameter and the second performance parameter.)
Zhang teaches “determine whether the model performance parameter satisfies a first criterion”. (Pg. 7, “Adjust each coded data contained in the input feature set, mainly to remove irrelevant and redundant input features in the input feature set, and automatically screen out suitable target input feature sets for the modeling area to avoid overfitting the wireless propagation model to irrelevant The data leads to an increase in prediction errors which improves the prediction accuracy of the wireless propagation model in different scenarios”. Further see Pg. 7. The examiner has interpreted that improving the prediction accuracy of the wireless propagation model as determine whether the model performance parameter satisfies a first criterion.)
Zhang teaches “when the model performance parameter does not satisfy the first criterion: determine whether the second performance parameter satisfies a second criterion”. (Pg. 7, “Adjust each coded data contained in the input feature set, mainly to remove irrelevant and redundant input features in the input feature set, and automatically screen out suitable target input feature sets for the modeling area to avoid overfitting the wireless propagation model to irrelevant The data leads to an increase in prediction errors which improves the prediction accuracy of the wireless propagation model in different scenarios” [when accuracy of the model needs to be improved, e.g., when the model performance parameter does not satisfy the first criterion]. Pg. 8, “when the second generalization error value is less than or equal to the first generalization error value” [determine whether the second performance parameter satisfies a second criterion]. Further see Pg. 7-8. The examiner has interpreted that improving the prediction accuracy of the wireless propagation model and comparing if the second generalization error value is less than or equal to the first generalization error value as when the model performance parameter does not satisfy the first criterion: determine whether the second performance parameter satisfies a second criterion.)
Zhang teaches “when the second performance parameter does not satisfy the second criterion, [the processor circuitry is configured to: determine a second area, the second area being smaller than the first area, by applying clustering of one or more elements of the first area based on parameters indicative of one or more of: a spatial complexity of the second area; a geometric complexity of the second area; a number of electronic devices associated with the second area; data traffic in the second area; and/or vehicle traffic in the second area]”. (Pg. 8, “when the second generalization error value is greater than the first generalization error value”. Further see Pg. 8. The examiner has interpreted that comparing if the second generalization error value greater to the first generalization error value as when the second performance parameter does not satisfy the second criterion.)
Zhang teaches “wherein the first, second, [and third] models are configured to provide one or more configurations of one or more operational parameters for one or more electronic devices located in an area.” (Pg. 2, “The wireless propagation model, that is, predicting the path transmission loss of radio waves by modeling electromagnetic wave propagation, has attracted more and more attention. Because the wireless propagation model plays an important supporting role in the estimation of communication rate, signal interference and signal coverage, it is widely used in many communication scenarios” [wherein the first model and the second model are configured to provide one or more configurations of one or more operational parameters for one or more electronic devices located in an area]. Further see Pg. 2. The examiner has interpreted that predicting path transmission loss of radio wave by modeling propagation to estimate communication rate, signal interference and signal coverage of communication scenarios as wherein the first and second models are configured to provide one or more configurations of one or more operational parameters for one or more electronic devices located in an area.)
Zhang does not specifically teach “determine a second area, the second area being a division of the first area and being smaller than the first area, by applying clustering of one or more elements of the first area based on parameters indicative of one or more of: a spatial complexity of the second area; a geometric complexity of the second area; a number of electronic devices associated with the second area; data traffic in the second area; and/or vehicle traffic in the second area” and “update the second model to obtain a third model for the second area, wherein third models are configured to provide one or more configurations of one or more operational parameters for one or more electronic devices located in an area.”
However, in the same field of endeavor namely enhancing wire propagation prediction models, Ning teaches “determine a second area, the second area being a division of the first area and being smaller than the first area, by applying clustering of one or more elements of the first area based on parameters indicative of one or more of: a spatial complexity of the second area; a geometric complexity of the second area; a number of electronic devices associated with the second area; data traffic in the second area; and/or vehicle traffic in the second area”. (Pg. 368 Sect. 3, “the large target area can be partitioned into small ones using a clustering method” [determine a second area, the second area being a division of the first area and being smaller than the first area, by applying clustering of one or more elements of the first area]. Pg. 369 Sect. 3, “In the context of wireless networks, there are two benefits of Affinity Propagation (AP) clustering technique for this research work: (a) the clusters emerge naturally and the number of clusters is related to a pre-set ‘‘preference’’ value, rather than by setting the number of clusters in advance; (b) it allows great flexibility in the face of dynamic environments, since all clustering parameters can be changed across iterations” [clustering based on parameters indicative of complexity of the second area]. Pg. 369 Sect. 3, “Moreover, the use of RSS deviation data for calculating the similarity can eliminate the effects of distance dependent path loss attenuation to some extent. Therefore, the effects of multipath and shadowing associated mainly with the topography can be better captured. On the contrary, similarity calculation using raw RSS will be dominated by the path loss” [parameters indicative of spatial complexity of the second area]. Pg. 377 Sect. 4, “Note that as many as 160 clusters are produced in the central area due to of the complex terrain” [parameters indicative of a geometric complexity of the second area]. Pg. 378 Sect. 4, “As can be seen from the results in Fig. 9, the clusters can generally represent the features of the current geographical patterns to a certain extent” [parameters indicative of a spatial/geometric complexity of the second area]. Pg. 369 Sect. 3, “the similarity calculation in the AP clustering process is based on the Mahalanobis distance rather than the Euclidean distance in the signal space to create distinct and stable clusters. This is because Mahalanobis distance function can avoid giving too much weight to correlated RSS values in the distance function and enables both nonlinear and linear decision boundaries. Comparisons between the Euclidean distance and Mahalanobis distance on real data set are presented in Sect. 4. Let ri = (ri,1, ri,2, …, ri,q) represent the RSS tuple of MS i received from q neighbouring antennas in the area of interest” [parameters indicative of a number of electronic devices associated with the second area]. Further see Sect. 3-4. The examiner has interpreted that partitioning a larger target area into small ones through a clustering method that allows for great flexibility in the face of dynamic environments to better capture the effects of multipath and shadowing and features across the topography, complex terrain, and current geographical patterns for distances between neighboring antennas in the area of interest as determine a second area, the second area being a division of the first area and being smaller than the first area, by applying clustering of one or more elements of the first area based on parameters indicative of one or more of: a spatial complexity of the second area; a geometric complexity of the second area; or a number of electronic devices associated with the second area.)
Ning teaches “update the second model to obtain a third model for the second area, wherein the third models are configured to provide one or more configurations of one or more operational parameters for one or more electronic devices located in an area.” (Pg. 368 Sect. 3, “In the training phase, the real time RSS samples collected during the network planning stage are analysed. Based on this analysis, the large target area can be partitioned into small ones using a clustering method. The relatively homogeneous RSS distribution within each small region is then accurately modelled” [update the second model to obtain a third model for the second area]. Pg. 366 Sect. 1, “RSS data can be readily collected indoors or outdoors for most wireless systems and the data can be used to obtain either range estimates or connectivity information” [wherein the third models are configured to provide one or more configurations of one or more operational parameters for one or more electronic devices located in an area]. Further see Sect. 1 and 3. The examiner has interpreted that partitioning the large target are into smaller ones based on the collected received signal strength to accurately model received signal strength in each small region to obtain range estimates and connectivity information as update the second model to obtain a third model for the second area, wherein the third models are configured to provide one or more configurations of one or more operational parameters for one or more electronic devices located in an area.)
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to add “determine a second area, the second area being a division of the first area and being smaller than the first area, by applying clustering of one or more elements of the first area based on parameters indicative of one or more of: a spatial complexity of the second area; a geometric complexity of the second area; a number of electronic devices associated with the second area; data traffic in the second area; and/or vehicle traffic in the second area” and “update the second model to obtain a third model for the second area, wherein third models are configured to provide one or more configurations of one or more operational parameters for one or more electronic devices located in an area”, as conceptually seen from the teaching of Ning, into that of Zhang because this modification of creating a model for a second area inside the first area for the advantageous purpose of increasing the accuracy of the radio propagation models for larger amounts of data (Ning, Pg. 366 Sect. 1 & Pg.367 Sect 2). Further motivation to combine be that Zhang and Ning are analogous art to the current claim are directed to enhancing wire propagation prediction models.
As per claim 2, Zhang teaches “wherein the determination of the second model is based on a training and/or re-training of the first model.” (Pg. 11, “The training unit is used to train the intelligent wireless propagation model by using the target input feature set and the initial feature data to obtain the target wireless propagation model”. Further see Pg. 11. The examiner has interpreted that obtaining a target wireless propagation model by training the wireless propagation model as wherein the determination of the second model is based on a training and/or re-training of the first model.)
As per claim 4, Zhang teaches “wherein the first operational data comprises sensor data and/or radio parameter data.” (Pg. 6, “initial characteristic data such as frequency value, distance value, and height value between each group of receiving and sending nodes can be measured”. Further see Pg. 5-6. The examiner has interpreted that measuring initial characteristic data such as distance and height values of the receiving node wherein the first operational data comprises sensor data.)
As per claim 5, Zhang teaches “wherein the determination of the second model is based on the sensor data and/or radio parameter data.” (Pg. 6, “initial characteristic data such as frequency value, distance value, and height value between each group of receiving and sending nodes can be measured” [the sensor data]. Pg. 11, “The training unit is used to train the intelligent wireless propagation model by using the target input feature set and the initial feature data to obtain the target wireless propagation model” [the determination of the second model is based on the sensor data]. Further see Pg. 5-6 and 11. The examiner has interpreted that obtaining a target wireless propagation model by training the wireless propagation model using initial feature data such as frequency values as wherein the determination of the second model is based on the sensor data.)
As per claim 6, Zhang teaches “wherein the processor circuitry is configured to, when the model performance parameter satisfies the first criterion, refrain from determining whether the second performance parameter satisfies the second criterion.” (Pg. 7, “Adjust each coded data contained in the input feature set, mainly to remove irrelevant and redundant input features in the input feature set, and automatically screen out suitable target input feature sets for the modeling area to avoid overfitting the wireless propagation model to irrelevant The data leads to an increase in prediction errors which improves the prediction accuracy of the wireless propagation model in different scenarios” [e.g., when the prediction accuracy does not need to be improved then the prediction error not need to be evaluated, when the model performance parameter satisfies the first criterion, refrain from determining whether the second performance parameter satisfies the second criterion]. Pg. 4, “The embodiment of the present invention also provides a channel propagation model parameter adjustment device including: Memory, used to store computer programs; The processor is configured to execute the computer program to implement the steps of the method for adjusting the channel propagation model parameters as described in any one of the above” [processor circuitry is configured to]. Further see Pg. 4 and 7. The examiner has interpreted that when needing to improve the prediction accuracy of the wireless propagation model to avoid overfitting the irrelevant data which causes prediction error as executed by a processor as wherein the processor circuitry is configured to, when the model performance parameter satisfies the first criterion, refrain from determining whether the second performance parameter satisfies the second criterion.)
As per claim 7, Zhang teaches “wherein the processor circuitry is configured to, when the second performance parameter satisfies the second criterion, refrain from determining the second area.” (Pg. 8, “when the second generalization error value is less than or equal to the first generalization error value; the new input feature set is used as the iteration” [e.g., when the second performance parameter satisfies the second criterion, use the second model, i.e., refrain from determining the second area for determining the third model]. Pg. 4, “The embodiment of the present invention also provides a channel propagation model parameter adjustment device including: Memory, used to store computer programs; The processor is configured to execute the computer program to implement the steps of the method for adjusting the channel propagation model parameters as described in any one of the above” [processor circuitry is configured to]. Further see Pg. 4 and 8. The examiner has interpreted that when the second generalization error value is less than the first generalization error value and using the new input feature set as the iteration of the wireless propagation model as executed by a processor as wherein the processor circuitry is configured to, when the second performance parameter satisfies the second criterion, refrain from determining the second area.)
As per claim 8, Zhang teaches “wherein the processor circuitry is configured to determine, based on the first model and/or the second model, one or more first operation schemes for the one or more first electronic devices.” (Pg. 2, “The wireless propagation model, that is, predicting the path transmission loss of radio waves by modeling electromagnetic wave propagation, has attracted more and more attention. Because the wireless propagation model plays an important supporting role in the estimation of communication rate, signal interference and signal coverage, it is widely used in many communication scenarios” [based on the first model and the second model, one or more first operation schemes for the one or more first electronic devices]. Pg. 4, “The embodiment of the present invention also provides a channel propagation model parameter adjustment device including: Memory, used to store computer programs; The processor is configured to execute the computer program to implement the steps of the method for adjusting the channel propagation model parameters as described in any one of the above” [processor circuitry is configured to]. Further see Pg. 2 and 4. The examiner has interpreted that estimating communication rate, signal interference and signal coverage of communication scenarios as executed by a processor as wherein the processor circuitry is configured to determine, based on the first model and the second model, one or more first operation schemes for the one or more first electronic devices.)
As per claim 9, Zhang teaches “wherein the first criterion comprises a first threshold.” (Pg. 7, “Adjust each coded data contained in the input feature set, mainly to remove irrelevant and redundant input features in the input feature set, and automatically screen out suitable target input feature sets for the modeling area to avoid overfitting the wireless propagation model to irrelevant The data leads to an increase in prediction errors which improves the prediction accuracy of the wireless propagation model in different scenarios” [when the prediction accuracy needs to be improved, e.g., wherein the first criterion comprises a first threshold]. Further see Pg. 7. The examiner has interpreted that when needing to improve the prediction accuracy of the wireless propagation model as wherein the first criterion comprises a first threshold.)
As per claim 10, Zhang teaches “wherein the second criterion comprises a second threshold.” (Pg. 8, “when the second generalization error value is less than or equal to the first generalization error value” [e.g., when the second performance parameter satisfies the second criterion, use the second model, i.e., refrain from determining the second area for determining the third model]. Further see Pg. 8. The examiner has interpreted that when the second generalization error value is less than the first generalization error value as wherein the second criterion comprises a second threshold.)
As per claim 11, Zhang teaches “wherein the obtaining of the first model is based on an association of the first model with the first area.” (Pg. 6, “The wireless propagation model is used to predict the path loss value in the modeling area”. Further see Pg. 5-6. The examiner has interpreted that predicting path loss in a modeling area using a wireless propagation model as wherein the obtaining of the first model is based on an association of the first model with the first area.)
As per claim 12, Zhang does not specifically teach “wherein the second area is part of a plurality of areas included in the first area.”
Ning teaches “wherein the second area is part of a plurality of areas included in the first area.” (Pg. 368 Sect. 3, “the large target area can be partitioned into small ones using a clustering method” [wherein the second area is part of a plurality of areas included in the first area]. Further see Sect. 3. The examiner has interpreted that partitioning a larger target area into small ones as wherein the second area is part of a plurality of areas included in the first area.)
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to add “wherein the second area is part of a plurality of areas included in the first area”, as conceptually seen from the teaching of Ning, into that of Zhang because this modification of creating a model for a second area inside the first area for the advantageous purpose of increasing the accuracy of the radio propagation models for larger amounts of data that are split into smaller segments (Ning, Pg. 366 Sect. 1 & Pg. 367 Sect 2). Further motivation to combine be that Zhang and Ning are analogous art to the current claim are directed to enhancing wire propagation prediction models.
As per claim 13, Zhang teaches “wherein one or more of the first model, the second model, and/or the third model are configured to predict one or more configurations of a corresponding operation scheme.” (Pg. 2, “The wireless propagation model, that is, predicting the path transmission loss of radio waves by modeling electromagnetic wave propagation, has attracted more and more attention. Because the wireless propagation model plays an important supporting role in the estimation of communication rate, signal interference and signal coverage, it is widely used in many communication scenarios” [wherein the first model and the second model are configured to predict one or more configurations of a corresponding operation schemes for the one or more first electronic devices]. Further see Pg. 2. The examiner has interpreted that predicting path transmission loss of radio wave by modeling propagation to estimate communication rate, signal interference and signal coverage of communication scenarios as executed by a processor as wherein the first model and the second model are configured to predict one or more configurations of a corresponding operation scheme.)
As per claim 15, Zhang does not specifically teach “wherein the processor circuitry is configured to transmit the third model to one or more second electronic devices associated with the second area.”
However, Ning teaches “wherein the processor circuitry is configured to transmit the third model to one or more second electronic devices associated with the second area.” (Pg. 368 Sect. 3, “the large target area can be partitioned into small ones using a clustering method” [the second area]. “The relatively homogeneous RSS distribution within each small region is then accurately modelled” [a third model associated with the second area]. Pg. 366 Sect. 1, “Then in the online localisation phase the mobile location can be estimated by further analysing these clusters with the help of a refined intersection approach. The novel features that contribute to the greater accuracy include: (a) clusters are created using RSS deviations resulting from the observed path loss model which capture better the wireless topography in a complex environment” [using the third model in the actual environment, e.g., transmit the third model]. Pg. 366 Sect. 1, “RSS data can be readily collected indoors or outdoors for most wireless systems and the data can be used to obtain either range estimates or connectivity information” [to one or more second electronic devices]. Pg. 365 Sect. 1, “Localisation has become more and more popular in pervasive computing environments” [e.g., wherein the processor circuitry is configured]. Further see Sect. 1 and 3. The examiner has interpreted that modeling, using a computing environment, received signal strength distribution within a small partitioned region of the large target area for use in capturing better the wireless topography of a complex environment that is collected by wireless systems as wherein the processor circuitry is configured to transmit the third model to one or more second electronic devices associated with the second area.)
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to add “wherein the processor circuitry is configured to transmit the third model to one or more second electronic devices associated with the second area”, as conceptually seen from the teaching of Ning, into that of Zhang because this modification of sending the updated model for the advantageous purpose of increasing the accuracy of the radio propagation models for larger amounts of data (Ning, Pg. 366 Sect. 1 & Pg. 367 Sect 2). Further motivation to combine be that Zhang and Ning are analogous art to the current claim are directed to enhancing wire propagation prediction models.
As per claim 16, Zhang does not specifically teach “wherein the processor circuitry is configured to: obtain second operational data, from one or more second electronic devices associated with the second area, where the second operational data is associated with the second area”, “determine a fourth model, based on the second operational data and the third model”, and “transmit the fourth model to the one or more second electronic devices.”
However, Ning teaches “wherein the processor circuitry is configured obtain second operational data, from one or more second electronic devices associated with the second area, where the second operational data is associated with the second area.” (Pg. 368 Sect. 3, “the large target area can be partitioned into small ones using a clustering method” [the second area]. “The relatively homogeneous RSS distribution within each small region is then accurately modelled” [a third model associated with the second area]. Pg. 366 Sect. 1, “Then in the online localisation phase the mobile location can be estimated by further analysing these clusters with the help of a refined intersection approach. The novel features that contribute to the greater accuracy include: (a) clusters are created using RSS deviations resulting from the observed path loss model which capture better the wireless topography in a complex environment” [using the third model in the actual environment to collect wireless data, e.g., obtain second operational data, where the second operational data is associated with the second area]. Pg. 366 Sect. 1, “RSS data can be readily collected indoors or outdoors for most wireless systems and the data can be used to obtain either range estimates or connectivity information” [from second electronic devices]. Pg. 365 Sect. 1, “Localisation has become more and more popular in pervasive computing environments” [e.g., wherein the processor circuitry is configured]. Further see Sect. 1 and 3. The examiner has interpreted that modeling, using a computing environment, received signal strength distribution within a small partitioned region of the large target area for use in capturing better the received signal strength deviations and wireless topography of a complex environment that is collected by wireless systems as wherein the processor circuitry is configured obtain second operational data, from one or more second electronic devices associated with the second area, where the second operational data is associated with the second area.)
Ning teaches “determine a fourth model, based on the second operational data and the third model”. (Pg. 368 Sect. 3, “the large target area can be partitioned into small ones using a clustering method. The relatively homogeneous RSS distribution within each small region is then accurately modelled” [a third model associated with the second area]. “When a new MS is collected” [new received signal strength data, e.g. based on the second operational data], “these models are involved in the K-Nearest Neighbour Venn Probability Machine (KNN-VPM) algorithm in order to estimate which small region the new MS is most probably located in” [based on the third model]. “Within this region, further location estimation can be made with acceptable precision” [determine a fourth model]. Further, Fig. 2 shows that the determination of the models is an iterative process. Further see Sect. 3. The examiner has interpreted that modeling the received signal strength distribution within a small partitioned region of the large target area for use in further modeling the estimated location of the new mobile station in small regions as determine a fourth model, based on the second operational data and the third model.)
Ning teaches “transmit the fourth model to the one or more second electronic devices”. (Pg. 368 Sect. 3, “the large target area can be partitioned into small ones using a clustering method. The relatively homogeneous RSS distribution within each small region is then accurately modelled” [a third model associated with the second area]. “When a new MS is collected” [new received signal strength data, e.g. based on the second operational data], “these models are involved in the K-Nearest Neighbour Venn Probability Machine (KNN-VPM) algorithm in order to estimate which small region the new MS is most probably located in” [based on the third model]. “Within this region, further location estimation can be made with acceptable precision” [a fourth model]. Pg. 366 Sect. 1, “Then in the online localisation phase the mobile location can be estimated by further analysing these clusters with the help of a refined intersection approach. The novel features that contribute to the greater accuracy include: (a) clusters are created using RSS deviations resulting from the observed path loss model which capture better the wireless topography in a complex environment” [using the third model in the actual environment to collect wireless data, e.g., obtain second operational data, where the second operational data is associated with the second area]. Pg. 366 Sect. 1, “RSS data can be readily collected indoors or outdoors for most wireless systems and the data can be used to obtain either range estimates or connectivity information” [from second electronic devices]. Further, Fig. 2 shows that the determination of the models is an iterative process, e.g., also be conducted for the fourth model, i.e. transmit the fourth model to the one or more second electronic device. Pg. 365 Sect. 1, “Localisation has become more and more popular in pervasive computing environments” [e.g., wherein the processor circuitry is configured]. Further see Sect. 1 and 3. The examiner has interpreted that modeling, using a computing environment, received signal strength distribution within a small partitioned region of the large target area for use in capturing better the received signal strength deviations and wireless topography of a complex environment that is collected by wireless systems in an iterative process to further modeling the estimated location of the new mobile station in small regions as transmit the fourth model to the one or more second electronic devices.)
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to add “wherein the processor circuitry is configured to: obtain second operational data, from one or more second electronic devices associated with the second area, where the second operational data is associated with the second area”, “determine a fourth model, based on the second operational data and the third model”, and “transmit the fourth model to the one or more second electronic devices”, as conceptually seen from the teaching of Ning, into that of Zhang because this modification of create and sending a further updated model for the advantageous purpose of increasing the accuracy of the radio propagation models for larger amounts of data (Ning, Pg. 366 Sect. 1 & Pg. 367 Sect 2). Further motivation to combine be that Zhang and Ning are analogous art to the current claim are directed to enhancing wire propagation prediction models.
As per claim 17, Zhang does not specifically teach “wherein the processor circuitry is configured to determine, based on the third model and/or the fourth model, one or more second operation schemes for the one or more second electronic devices.”
However, Ning teaches “wherein the processor circuitry is configured to determine, based on the third model and/or the fourth model, one or more second operation schemes for the one or more second electronic devices.” (Pg. 368 Sect. 3, “the large target area can be partitioned into small ones using a clustering method. The relatively homogeneous RSS distribution within each small region is then accurately modelled” [a third model]. Pg. 366 Sect. 1, “RSS data can be readily collected indoors or outdoors for most wireless systems and the data can be used to obtain either range estimates or connectivity information” [determine second operation schemes for second electronic devices]. Pg. 365 Sect. 1, “Localisation has become more and more popular in pervasive computing environments” [e.g., wherein the processor circuitry is configured]. Further see Sect. 1 and 3. The examiner has interpreted that modeling, using a computing environment, received signal strength distribution within a small partitioned region of the large target area for use in obtaining range estimate and connectivity information from wireless systems as wherein the processor circuitry is configured to determine, based on the third model and/or the fourth model, one or more second operation schemes for the one or more second electronic devices.)
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to add “wherein the processor circuitry is configured to determine, based on the third model and/or the fourth model, one or more second operation schemes for the one or more second electronic devices”, as conceptually seen from the teaching of Ning, into that of Zhang because this modification of create and sending a further updated model for the advantageous purpose of developing ranges and connectivity information for wireless systems through increasing the accuracy of the radio propagation models for larger amounts of data (Ning, Pg. 366 Sect. 1 & Pg. 367 Sect 2). Further motivation to combine be that Zhang and Ning are analogous art to the current claim are directed to enhancing wire propagation prediction models.
Re claim 18, it is a method claim, having similar limitations of claim 1. Thus, claim 18 is also rejected under the similar rationale as cited in the rejection of claim 1.
Re claim 19, it is a method claim, having similar limitations of claim 2. Thus, claim 19 is also rejected under the similar rationale as cited in the rejection of claim 2.
Claims 3 and 20 are rejected under 35 U.S.C. § 103 as being unpatentable over Zhang and Ning as applied to claim 1 above, and further in view of Wu, Jingbang, Huimei Lu, Yong Xiang, Rui Wu, and Feng Wang. “MBR: A map-based relaying algorithm for reliable data transmission through intersection in VANETs.” IEEE Transactions on Intelligent Transportation Systems 20, no. 10 (2018): 3661-3674 [herein “Wu”].
As per claim 3, Zhang does not specifically teach “wherein the determination of the second area comprises encoding the second area into an alphanumeric string by applying a geohashing function.”
Ning teaches “wherein the determination of the second area comprises encoding the second area into an alphanumeric string [by applying a geohashing function].” (Pg. 370 Sect. 3, “we can calculate the probability of one MS in the cluster testing set belonging to one cluster, which means the most probable cluster ID for each testing MS can be estimated and verified. According to this resultant accuracy of cluster identification and the number of clusters produced, the preference value of the Affinity Propagation method can be optimised iteratively”. Algorithm 1 K-Nearest Neighbours Venn Probability Machine as shown in Pg. 370 a variable Cluster ID as Cluster ID: {C1, C2, C3, ...,CT }, cluster ID being a variable, therefore the cluster ID is an alphanumeric string. Additionally, Pg. 373 Sect. 3, “several intersection areas are generated. This is illustrated in Fig. 4, where the intersection areas that contain at least one MS are ABCDEA and AEFGA” [encoding an alphanumeric string]. Fig. 4 shows the cluster containing the intersections labeled as ABCDEA and AEFGA, e.g., encoding the second area into an alphanumeric string. Further see Sect. 3. The examiner has interpreted that partitioning a larger target area into small ones and producing and verifying the cluster identification variables and labeling cluster intersection, as ABCDEA for example, as wherein the determination of the second area comprises encoding the second area into an alphanumeric string.)
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to add “wherein the determination of the second area comprises encoding the second area into an alphanumeric string”, as conceptually seen from the teaching of Ning, into that of Zhang because this modification of creating a model for a second area inside the first area for the advantageous purpose of increasing the accuracy of the radio propagation models for larger amounts of data that are split into smaller segments (Ning, Pg. 366 Sect. 1 & Pg. 367 Sect 2). Further motivation to combine be that Zhang and Ning are analogous art to the current claim are directed to enhancing wire propagation prediction models.
Zhang nor Ning specifically teach “wherein the determination of the second area comprises encoding the second area into an alphanumeric string by applying a geohashing function”.
However, in the same field of endeavor namely modeling a geological map for wireless propagation, Wu teaches “wherein the determination of the second area comprises encoding the second area into an alphanumeric string by applying a geohashing function”. (Pg. 3664 Sect. III, “To efficiently represent and calculate the relay zone, MBR utilizes the Geohash coding system [13], which can quickly convert coordinates from the digital map or GPS into one-dimensional code and represent a grid with arbitrary precision” [by applying a geohashing function]. Pg. 3665 Sect. III, “Geohash is an efficient geo-coding system with arbitrary precision, it uses dichotomy to hierarchically subdivide a specified area into grids, and each grid is represented by an unique code [13]. The size of the grid is determined by the length of the code and the size and the latitude of the coding area. Since road segments and intersections consist of coordinates in the digital map, the Geohash code of each road element can be calculated in the preprocessing phase. A mapping between the Geohash code and road element can be established for the road matching” [wherein the determination of the second area comprises encoding the second area into a string by applying a geohashing function]. Fig. 5 shows the grid comprises of numbers, e.g., encoding the second area into an alphanumeric string. Further see Sect. III. The examiner has interpreted that utilizing a geohash coding system to represent the relay zone by subdividing a specified area into grids that represented by a code bound in a numbered grid as wherein the determination of the second area comprises encoding the second area into an alphanumeric string by applying a geohashing function.)
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to add “wherein the determination of the second area comprises encoding the second area into an alphanumeric string by applying a geohashing function”, as conceptually seen from the teaching of Wu, into that of Zhang and Ning because this modification of applying a geohashing function to the second area for the advantageous purpose of representing portions of the area in a fast and precise manner (Wu Pg. 3664 Sect. III). Further motivation to combine be that Zhang, Ning, and Wu are analogous art to the current claim are directed to modeling a geological map for wireless propagation.
Re claim 20, it is a method claim, having similar limitations of claim 3. Thus, claim 20 is also rejected under the similar rationale as cited in the rejection of claim 3.
Claim 14 is rejected under 35 U.S.C. § 103 as being unpatentable over Zhang and Ning as applied to claim 1 above, and further in view of US 10,959,109 B1 Liu, Danielle et al. [herein “Liu”].
As per claim 14, Zhang nor Ning specifically teach “wherein the processor circuitry is configured to transmit the first model to the one or more first electronic devices associated with the first area.”
However, in the same field of endeavor namely enhancing wire propagation prediction models, Liu teaches “wherein the processor circuitry is configured to transmit the first model to the one or more first electronic devices associated with the first area”. (Col. 20 Ln. 65 – Col. 21 Ln. 1, “server computer 102 can transmit the existing coverage model 116 to one or more data source 112 (e.g., the other data source 112N) for storage” [transmit the first model to the one or more first electronic devices]. Col. 9 Ln. 47-53, “the network data stored by the network data source 112D can include, for example, geographic locations of networking equipment, performance and/or quality of service statistics measured at specific geographic locations and/or in specific geographic areas, equipment information associated with equipment at specific geographic locations and/or areas, combinations thereof, or the like” [first electronic devices associated with the first area]. Col. 10 Ln. 42-45, “The other data sources 112N can store other data that may be used in accordance with the various embodiments of the concepts and technologies disclosed herein. The other data sources 112N also can store existing coverage information” [e.g., data acquired by the electronic devices]. Col. 26 Ln. 53-58, “The processing unit 902 may be a standard central processor that performs arithmetic and logical operations, a more specific purpose programmable logic controller (“PLC”), a programmable gate array, or other type of processor known to those skilled in the art and suitable for controlling the operation of the server computer” [e.g., wherein the processor circuitry is configured]. Further see Col. 9-10, 20, and 26. The examiner has interpreted that transmitting the existing coverage model to a network data source that measures performance of the equipment at specific geographic location such as coverage information using a server computer controlled by a processor as wherein the processor circuitry is configured to transmit the first model to the one or more first electronic devices associated with the first area.)
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to add “wherein the processor circuitry is configured to transmit the first model to the one or more first electronic devices associated with the first area”, as conceptually seen from the teaching of Liu, into that of Zhang and Ning because this modification of sending the model to the devices for the advantageous purpose of accurately acquire the signal strength in the real world (Liu, Col. 1 Ln. 15-32). Further motivation to combine be that Zhang, Ning, and Liu are analogous art to the current claim are directed to enhancing wire propagation prediction models.
Response to Arguments
Applicant's arguments filed on June 10, 2026 have been fully considered but they are not persuasive.
Applicant argues that the amended claim features are patent eligible under 35 U.S.C. § 101 because the claims do not recite mental processes as the claimed subject matter goes beyond the capabilities of a human or of a human mind with or without the aid of a pen and paper. (See Applicant’s response, Pg. 13).
MPEP § 2106.04(a)(2)(III)(A) recites “claims do recite a mental process when they contain limitations that can practically be performed in the human mind, including for example, observations, evaluations, judgments, and opinions”, “claims can recite a mental process even if they are claimed as being performed on a computer”, and “in evaluating whether a claim that requires a computer recites a mental process, examiners should carefully consider the broadest reasonable interpretation of the claim in light of the specification. For instance, examiners should review the specification to determine if the claimed invention is described as a concept that is performed in the human mind and applicant is merely claiming that concept performed 1) on a generic computer, or 2) in a computer environment, or 3) is merely using a computer as a tool to perform the concept. In these situations, the claim is considered to recite a mental process.”
The examiner has provided the rational for the claim limitations that are being directed to a mental process in the rejection above. Specifically, for the argued limitation of amended claim 1 “update the first model for the first area based on the first operational data associated with the first area to determine a second model”, a person can mentally update or draw with pen and paper the radar range equation of the first model for an area by updating the first model parameters such as frequency and using the obtained operational data such as power received to form a second model. The parameters of the first model, e.g., frequency, can changed and used along the obtained operational data, e.g., received power, to update the radar range equation of the first model to create an updated first model, i.e., a second model. Since a person can update the parameters and values of an equation to create an updated model, then this limitation can by a person mentally or using a pen and paper. Furthermore, for the argued limitation of amended claim 1 “when the second performance parameter does not satisfy the second criterion: determine a second area, the second area being smaller than the first area, by applying clustering of one or more elements of the first area based on parameters indicative of one or more of: a spatial complexity of the second area; a geometric complexity of the second area; a number of electronic devices associated with the second area; data traffic in the second area; and/or vehicle traffic in the second area to obtain a third model for the second area”, a person can mentally determine or draw with pen and paper a second area by clustering the elements of the first area together, such as geographic and geometric attributes of the first area, to identify boundaries for where the first area is to be split into smaller portions and selecting one of the smaller portions of the first area as a second area and update the radar range equation of the second model to create a third model. A person mentally or using a pen and paper can identify geographic and geometric attributes of the first area, e.g., mountains or roads, and group them together by regions in the first area. Then a person can split the first area based on these attributes in the first area to create smaller areas, i.e., the second area. Further, similarly as above in the creation of the second model, since a person can update the parameters and values of an equation to create an updated model, i.e., update the second model to create the third model, then this limitation can by a person mentally or using a pen and paper.
The examiner has properly identified that the claims recite mental concepts as provided in the rejection above is proper under the framework provided in the 2019 Patent Eligibility Guidance and MPEP § 2106.04(a)(2)(III)(C). The claims are directed to judicial exception, an abstract idea.
Applicant argues that the amended claim features are patent eligible under 35 U.S.C. § 101 because the claim is integrated into a practical application as claim features recite improvements to another technology or technical field (See Applicant’s response, Pg. 13-14).
MPEP § 2106.04(d)(II) recites “examiners evaluate integration into a practical application by: (1) identifying whether there are any additional elements recited in the claim beyond the judicial exception(s); and (2) evaluating those additional elements individually and in combination to determine whether they integrate the exception into a practical application”. MPEP § 2106.05(a) also recites “It is important to note, the judicial exception alone cannot provide the improvement. The improvement can be provided by one or more additional elements.” MPEP § 2106.05(d)(II), “The courts have recognized the following computer functions as well understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity. i. Receiving or transmitting data over a network, ii. Performing repetitive calculations, iii. Electronic recordkeeping, iv. Storing and retrieving information in memory” and MPEP § 2106.05(g), “Below are examples of activities that the courts have found to be insignificant extra-solution activity: Mere Data Gathering: Performing clinical tests on individuals to obtain input for an equation, In re Grams, 888 F.2d 835, 839-40; 12 USPQ2d 1824, 1827-28 (Fed. Cir. 1989); Testing a system for a response, the response being used to determine system malfunction, In re Meyers, 688 F.2d 789, 794; 215 USPQ 193, 196-97 (CCPA 1982)”).
The examiner has provided the rational for the independent claim limitations that are being directed to mental processes and mathematical concepts in the rejection above. The additional elements are “A server device comprising memory circuitry, one or more interfaces, and processor circuitry, wherein the server device is configured to”, “the processor circuitry is configured to”, and “performed by a server device” which are merely using the generic computer components and functions being used as a tool to perform the abstract idea. Furthermore, the additional elements of “obtain first operational data associated with the first area, from the one or more first electronic devices in the first area, wherein the first operational data comprises one or more of sensor data related to the one or more first electronic devices and/or radio parameter data related to the one or more first electronic devices, wherein the sensor data comprises one or more of position data, tuples of coordinates, movement data, temperature data, and/or light data, wherein the radio parameter data comprises one or more of sensed radio conditions comprising signal strength and/or transmission power sensed by the one or more first electronic devices in the first area a channel quality indicator (CQI), a timing advance value, an error rate parameter, a difference in signal strength, a signal-to-interference-plus-noise ratio (SINR), a reference signal received power (RSRP), a reference signal received quality (RSRQ), a timing advance value (TA), a received signal strength indication (RSSI), channel state information (CSI), an error rate parameter comprising a bit error rate (BER), a frame error rate (FER), and/or a block error rate (BLER)” which is mere insignificant extra-solution activities, which are Well-Understood, Routine and Conventional, and/or a field of use/technological environment (see MPEP § 2106.05(h)). Furthermore, the insignificant extra-solution activities are not a unique arrangement of elements as it merely gathers parameter data using sensors which used to characterize the performance of model, i.e., testing a system for a response. Therefore, there are no additional element limitations in the independent claims which can integrate the abstract idea into a practical application by improvements to the technology or through the use of consideration as listed in MPEP § 2106.04(d)(I). Furthermore, the examiner has also provided the rational for the dependent claim limitations that are being directed to a mental process or a mathematical concept in the rejection above. With the exception of the additional element limitations in the dependent claims which are merely using the generic computer components and functions being used as a tool to perform the abstract idea, field of use/technological environment, and further insignificant extra-solution activities, there are no additional limitations in the dependent claims which can integrate the abstract idea into a practical application by improvements to the technology or through the use of meaningful limitations.
Therefore, the examiner has properly identified that the claims recite mental processes, mathematical concepts, and limitations that merely use the computer as a tool to perform the abstract idea and insignificant extra-solution activities.
Applicant argues that the combination of references does not teach each and every limitation in the amend claims because cited references fail to teach “obtain first operational data associated with the first area, from the one or more first electronic devices in the first area, wherein the first operational data comprises one or more of sensor data related to the one or more first electronic devices and/or radio parameter data related to the one or more first electronic devices, wherein the sensor data comprises one or more of position data, tuples of coordinates, movement data, temperature data, and/or light data, wherein the radio parameter data comprises one or more of sensed radio conditions comprising signal strength and/or transmission power sensed by the one or more first electronic devices in the first area a channel quality indicator (CQI), a timing advance value, an error rate parameter, a difference in signal strength, a signal-to-interference-plus-noise ratio (SINR), a reference signal received power (RSRP), a reference signal received quality (RSRQ), a timing advance value (TA), a received signal strength indication (RSSI), channel state information (CSI), an error rate parameter comprising a bit error rate (BER), a frame error rate (FER), and/or a block error rate (BLER)” (See Applicant’s response, Pg. 18-19).
MPEP § 2143.03 states that “All words in a claim must be considered in judging the patentability of that claim against the prior art” and “Examiners must consider all claim limitations when determining patentability of an invention over the prior art.”
In the rejection above with respect to the amended claim 1 limitation, Zhang discloses this limitation as measuring initial characteristic data from receiving and sending nodes such as distance and height values, location, and initial temperature. The following sensor data from the receiving and sensing nodes are taught by Zhang: distance and height values (position data) and initial temperature (temperature data). Thus by measuring this sensor data, the claimed limitation is taught.
Therefore, all of the limitations of the amended claims 1 and 18 are disclosed in Zhang or Ning, and the combination of these references renders the claimed invention obvious. Therefore, applicant’s arguments are not persuasive and the rejection of claim 1 and 18 as obvious over Zhang in view of Ning is maintained.
Applicant argues that the combination of references does not teach each and every limitation in the amend claims because cited references fail to teach “update the first model for the first area based on the first operational data associated with the first area to determine a second model” (See Applicant’s response, Pg. 18-19).
MPEP § 2143.03 states that “All words in a claim must be considered in judging the patentability of that claim against the prior art” and “Examiners must consider all claim limitations when determining patentability of an invention over the prior art.”
In the rejection above with respect to the amended claim 1 limitation, Zhang discloses this limitation as obtaining a target wireless propagation model by training the wireless propagation model using the initial feature data. Training the first model to obtain second model using the initial measured data is updating the first model with the first operational data to generated a second model. Thus, the claimed limitation is taught.
Therefore, all of the limitations of the amended claims 1 and 18 are disclosed in Zhang or Ning, and the combination of these references renders the claimed invention obvious. Therefore, applicant’s arguments are not persuasive and the rejection of claim 1 and 18 as obvious over Zhang in view of Ning is maintained.
Applicant argues that the combination of references does not teach each and every limitation in the amend claims because cited references fail to teach “use the second model to predict second operational data based on the sensor data and/or the radio parameter data” and “obtain a second performance parameter based on the predicted second operational data, the second performance parameter being indicative of a performance of the second model” (See Applicant’s response, Pg. 18-19).
MPEP § 2143.03 states that “All words in a claim must be considered in judging the patentability of that claim against the prior art” and “Examiners must consider all claim limitations when determining patentability of an invention over the prior art.”
In the rejection above with respect to the amended claim 1 limitation, Zhang discloses these limitation as simulating the target wireless propagation model by adjusting the channel propagation model parameters to obtain the performance of the model as use the second model to predict second operational data based on the sensor data and/or the radio parameter data and obtaining the second generalization error value on the trained input parameters of the wireless propagation model using to evaluate the performance of the target wireless propagation model, respectively. Determining the performance of the target model (second model) through a simulating and adjust the model parameters teaches the limitation of use the second model to predict second operational data based on the sensor data and/or the radio parameter data. Further, determining the second error value of the trained model using the trained input parameters to find its performance error teaches the limitation of obtain a second performance parameter based on the predicted second operational data, the second performance parameter being indicative of a performance of the second model. Thus, the claimed limitations are taught.
Therefore, all of the limitations of the amended claims 1 and 18 are disclosed in Zhang or Ning, and the combination of these references renders the claimed invention obvious. Therefore, applicant’s arguments are not persuasive and the rejection of claim 1 and 18 as obvious over Zhang in view of Ning is maintained.
Applicant argues that the combination of references does not teach each and every limitation in the amend claims because cited references fail to teach “when the second performance parameter does not satisfy the second criterion: determine a second area, the second area being a division of the first area and being smaller than the first area, by applying clustering of one or more elements of the first area based on parameters indicative of one or more of: a spatial complexity of the second area; a geometric complexity of the second area; a number of electronic devices associated with the second area; data traffic in the second area; and/or vehicle traffic in the second area” (See Applicant’s response, Pg. 18-19).
MPEP § 2143.03 states that “All words in a claim must be considered in judging the patentability of that claim against the prior art” and “Examiners must consider all claim limitations when determining patentability of an invention over the prior art.”
In the rejection above with respect to the amended claim 1 limitation, Ning discloses this limitation as partitioning a larger target area into small ones through a clustering method that allows for great flexibility in the face of dynamic environments to better capture the effects of multipath and shadowing and features across the topography, complex terrain, and current geographical patterns for distances between neighboring antennas in the area of interest in addition to modeling the received signal strength in each region. By clustering the larger area into a smaller area, this cuts the first area into small pieces, i.e., determining a second area. A smaller area that is clustered from a larger area is the second area being a division of the first area and being smaller than the first area. The clustering is based on effects of multipath and shadowing and features (clustering of one or more elements of the first area based on parameters) across the topography, complex terrain, and current geographical patterns (spatial and geometric complexity) for distances between neighboring antennas in the area of interest (a number of electronic devices). Thus, the claimed limitation is taught.
Therefore, all of the limitations of the amended claims 1 and 18 are disclosed in Zhang or Ning, and the combination of these references renders the claimed invention obvious. Therefore, applicant’s arguments are not persuasive and the rejection of claim 1 and 18 as obvious over Zhang in view of Ning is maintained.
Applicant argues that the combination of references does not teach each and every limitation in the amend claims because cited references fail to teach “wherein the first, second, and third models are configured to provide one or more configurations of one or more operational parameters for one or more electronic devices located in an area” (See Applicant’s response, Pg. 18-19).
MPEP § 2143.03 states that “All words in a claim must be considered in judging the patentability of that claim against the prior art” and “Examiners must consider all claim limitations when determining patentability of an invention over the prior art.”
In the rejection above with respect to the amended claim 1 limitation, Zhang teaches that the first and second models of this limitation as predicting path transmission loss of radio wave by modeling propagation to estimate communication rate, signal interference and signal coverage of communication scenarios. These estimates provided by the model determine where the current parameters of the devices used to calculate the path loss are acceptable or not; otherwise the model is updated, i.e., update the parameters of the devices. Thus the claimed limitation is taught for the first and second model as Zhang does not teach the third model. Ning teaches the third model of this limitation as obtaining range estimates and connectivity information for the devices using the data determined from the model. Similarly, the estimates provided by the model determine where the current parameters of the devices used to calculate the ranges and connections of the devices are acceptable or not; otherwise the model is updated, i.e., update the parameters of the devices. Thus the claimed limitation is taught for the third model. Together, the cited references teach the limitation.
Therefore, all of the limitations of the amended claims 1 and 18 are disclosed in Zhang or Ning, and the combination of these references renders the claimed invention obvious. Therefore, applicant’s arguments are not persuasive and the rejection of claim 1 and 18 as obvious over Zhang in view of Ning is maintained.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Zhao, Liang, Weiliang Zhao, Ammar Hawbani, Ahmed Y. Al-Dubai, Geyong Min, Albert Y. Zomaya, and Changqing Gong. "Novel online sequential learning-based adaptive routing for edge software-defined vehicular networks." IEEE Transactions on Wireless Communications 20, no. 5 (2020): 2991-3004 teaches a method of using geohashing to divide a geographical area into multiple grids to update machine learning models based on the density of road segments with corresponding vehicles in the area on and the communications of the vehicles to a central controller.
Wu, Rui. "GeoTAR: A map-based and traffic-aware VANET routing in urban scenario." In 2019 IEEE 11th International Conference on Communication Software and Networks (ICCSN), pp. 34-40. IEEE, 2019 teaches using radio propagation model determining propagation loss based on measurement data to divide a digital map into discrete grids using geohashing.
Examiner’s Note: The examiner has cited particular columns and line numbers in the reference that applied to the claims above for the convenience of the applicant. Although the specified citations are representative of the art and are applied to specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested from the applicant, to fully consider the references in their entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the examiner. In the case of amending the claimed invention, the applicant is respectfully requested to indicate the portion(s) of the specification which dictate(s) the structure relied on for the proper interpretation and also to verify and ascertain the metes and bound of the claimed invention.
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/SIMEON P DRAPEAU/Examiner, Art Unit 2188
/RYAN F PITARO/Supervisory Patent Examiner, Art Unit 2188
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