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 .
DETAILED ACTION
This action is in response to communications filed on 5/14/2026. Claims 20 have been amended. Claim 21 has been newly added. No other claims have been amended, added, or canceled. Accordingly, claims 1-14 & 16-21 are pending.
Response to Arguments
Applicant's arguments filed 5/14/2026 have been fully considered but they are not persuasive. Applicant's representative argues, in substance, that Gu fails to teach and/or disclose: (A) determining based on the attribute value and using a non-linear model, a plurality of power values, where the power values represent an amount of power expected to be consumed by the AV along the flight path; (B) determining the energy value comprises determining an integral of the plurality of power values over the portion of the flight path.
In response to (A), the examiner respectfully disagrees. Initially the examiner would like to point out that applicant’s representative employs broad language in drafting the claims and as such the examiner reserves the right to interpret the claims broadly. All that is needed to read on the limitation is a showing of an amount of power/energy expected to be consumed by the aerial vehicle along the flight path. As such the examiner contends that Gu’s disclosure of using training data (i.e., measurement aggregation which can be a complication of data related to conditions experienced by the vehicle during a portion of the voyage) to include statistical characteristics of previous/past voyages to train a feed forward neural network model to estimate remaining operating time, state of charge or other metrics associated with operating a vehicle (i.e., power level/energy level—see Gu at least fig. 1 and Summary & col. 9 lines 17-55).
In response to (B), the examiner respectfully disagrees. Keeping in mind what was said with respect to argument (A) above. The examiner further contends that Gu’s disclosure of measurement aggregation which can be a complication of data related to conditions experienced by the vehicle during a portion of the voyage (i.e., an aggregation/compilation/integration of the data over a sampled portion of the flight path—see Gu at least fig. 1 and Summary & col. 9 lines 17-55).
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1- 14 and 16-21 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Gu et al. (hereinafter Gu, US 11592824 B2).
Gu discloses:
1: A computer-implemented method comprising:
determining a portion of a flight path of an aerial vehicle (see Gu at least fig. 1-8 in particular fig. 3-4 & 7 and Abstract and Summary; determining a vehicle is completing a portion of a voyage);
determining an attribute value representing an operating condition expected to be experienced by the aerial vehicle at the portion of the flight path (see Gu at least fig. 1-8 in particular fig. 3-4 & 7 and Abstract and Summary; operational characteristic values corresponding to the portion of the voyage);
determining, based on the attribute value and using a non-linear model, a plurality of power values, wherein each respective power value of the plurality of power values represents an amount of power expected to be consumed by the aerial vehicle at a corresponding sampled location of a plurality of sampled locations along the portion of the flight path (see Gu at least fig. 1-8 in particular fig. 3-4 & 7 and Abstract and Summary; operational characteristic values corresponding to the portion of the voyage, dynamic property of a fuel/refueling of a vehicle, utilizing historical operational characteristics at fueling/refueling points, utilizing neural network model);
determining, based on the plurality of power values, an energy value representing an amount of energy expected to be consumed by the aerial vehicle in connection with the portion of the flight path, wherein determining the energy value comprises determining an integral of the plurality of power values over the portion of the flight path (see Gu at least fig. 1-8 in particular fig. 3-4 & 7 and Abstract and Summary; operational characteristic values corresponding to the portion of the voyage, dynamic property of a fuel/refueling of a vehicle, utilizing historical operational characteristics at fueling/refueling points, utilizing neural network model);
determining the flight path based on the energy value (see Gu at least fig. 1-8 in particular fig. 3-4 & 7 and Abstract and Summary; determining range of operational characteristics, generating an estimated value of an operating variable on ability of vehicle to completed the portion of the voyage); and causing the aerial vehicle to fly along the flight path(see Gu at least fig. 1-8 in particular fig. 3-4 & 7 and Abstract and Summary; determining a vehicle is completing a portion of a voyage).
2: wherein the non-linear model comprises a fleet-wide model that has been trained using fleet-wide training data obtained from a plurality of vehicle types of aerial vehicles in an aerial vehicle fleet, wherein the plurality of vehicle types comprises a vehicle type of the aerial vehicle (see Gu at least fig. 1-8 in particular fig. 2-4 & 7 and Abstract and Summary; helicopters, drones, UAV, quadcopters, electric aircraft etc.).
3: wherein determining the plurality of power value comprises: determining, based on the attribute value and using the fleet-wide model, a first power value representing a first amount of power expected to be consumed by the aerial vehicle in connection with the portion of the flight path; determining, based on the attribute value and using a vehicle-type-specific model corresponding to the vehicle type of the aerial vehicle, a correction value representing an error of the fleet-wide model in determining the first power value for the vehicle type of the aerial vehicle, wherein the vehicle-type-specific model has been trained using vehicle-type-specific training data that is a proper subset of the fleet-wide training data; and determining, based on the first power value and the correction value, a second power value representing a second amount of power expected to be consumed by the aerial vehicle in connection with the portion of the flight path (see Gu at least fig. 1-8 in particular fig. 2-4 & 7 and Abstract and Summary; vehicle operational data, components [234]).
4: wherein the attribute value comprises a plurality of attribute values representing a plurality of operating conditions expected to be experienced by the aerial vehicle at the portion of the flight path, wherein the fleet-wide model is configured to determine the first power value based on the plurality of attribute values, and wherein the vehicle-type-specific model is configured to determine the correction value based on a proper subset of the plurality of attribute values (see Gu at least fig. 1-8 in particular fig. 2-4 & 7 and Abstract and Summary; vehicle operational data, components [234]).
5: wherein the vehicle-type-specific model is configured to determine the correction value further based on the first power value (see Gu at least fig. 1-8 in particular fig. 3-4 & 7 and Abstract and Summary; determining range of operational characteristics, generating an estimated value of an operating variable on ability of vehicle to completed the portion of the voyage).
6: wherein the vehicle-type-specific model comprises a linear model (see Gu at least fig. 1-8 in particular fig. 1-4 & 7 and Abstract and Summary; linear objective function, linear regression, multiple linear regression).
7: wherein each respective vehicle type of the plurality of vehicle types has a physical configuration that differs by at least one physical component from respective physical configurations of other vehicle types of the plurality of vehicle types (see Gu at least fig. 1-8 in particular fig. 2-4 & 7 and Abstract and Summary; helicopters, drones, UAV, quadcopters, electric aircraft etc.).
8: wherein the non-linear model comprises a vehicle-type-specific model that has been trained using vehicle-type-specific training data that is a proper subset of fleet-wide training data obtained from a plurality of vehicle types of aerial vehicles in an aerial vehicle fleet, wherein the plurality of vehicle types comprises a vehicle type of the aerial vehicle, and wherein the plurality of power value is specific to the vehicle type of the aerial vehicle (see Gu at least fig. 1-8 in particular fig. 2-4 & 7 and Abstract and Summary and ¶150-151; helicopters, drones, UAV, quadcopters, electric aircraft etc., utilizing various algorithmic analysis methods).
9: wherein the non-linear model comprises a vehicle-specific model that has been trained using vehicle-specific training data obtained from the aerial vehicle, and wherein the plurality of power value is specific to the aerial vehicle (see Gu at least fig. 1-8 in particular fig. 2-4 & 7 and Abstract and Summary and ¶150-151; helicopters, drones, UAV, quadcopters, electric aircraft etc., utilizing various algorithmic analysis methods).
10: wherein the portion of the flight path is a first portion of the flight path, wherein the attribute value is a first attribute value, and wherein the method further comprises: causing the aerial vehicle to traverse the first portion of the flight path; determining a second attribute value representing an operating condition that has actually been experienced by the aerial vehicle at the first portion of the flight path; determining a third attribute value representing an operating condition expected to be experienced by the aerial vehicle at a second portion of the flight path that follows the first portion of the flight path; determining, using a flight-specific model and based on the second attribute value and the third attribute value, a second power value representing an amount of power expected to be consumed by the aerial vehicle in connection with the second portion of the flight path; and updating the flight path based on the second power value (see Gu at least fig. 1-8 in particular fig. 2-4 & 7 and Abstract and Summary and ¶150-151; trained neural network models and utilizing various algorithmic analysis methods).
11: wherein determining the second power value comprises: training the flight-specific model based on the second attribute value and a third power value representing an amount of power that has actually been consumed by the aerial vehicle in connection with traversing the first portion of the flight path; and determining the second power value by processing the third attribute value using the flight-specific model (see Gu at least fig. 1-8 in particular fig. 2-4 & 7 and Abstract and Summary and ¶150-151; trained neural network models and utilizing various algorithmic analysis methods).
12: wherein the operating condition expected to be experienced by the aerial vehicle at the portion of the flight path comprises one or more of: (i) a motion expected to be performed by the aerial vehicle at the portion of the flight path, (ii) a physical property that the aerial vehicle is expected to have at the portion of the flight path, or (iii) an environmental condition expected to be experienced by the aerial vehicle at the portion of the flight path, wherein the physical property comprises at least one of a vehicle type of the aerial vehicle, a weight of the aerial vehicle, a cross-section of the aerial vehicle, or a property of a payload carried by the aerial vehicle (see Gu at least fig. 1-8 in particular fig. 2-4 & 7 and Abstract and Summary and ¶150-151; trained neural network models and utilizing various algorithmic analysis methods).
13: wherein the non-linear model comprises a neural network (see Gu at least fig. 1-8 in particular fig. 2-4 & 7 and Abstract and Summary and ¶150-151; trained neural network models and utilizing various algorithmic analysis methods).
14: wherein determining the plurality of power value using the non-linear model comprises: determining a feature vector by applying a non-linear function to the attribute value; and multiplying the feature vector by a weight vector that represents a relative weight assigned to the attribute value by the non-linear model (see Gu at least fig. 1-8 in particular fig. 2-4 & 7 and Abstract and Summary and ¶150-151; trained neural network models, optimization criterion, and utilizing various algorithmic analysis methods).
16: wherein the energy value is a first energy value, and wherein the method further comprises: causing the aerial vehicle to traverse the flight path; determining a second energy value representing an amount of energy that has actually been consumed by the aerial vehicle in connection with traversing the portion of the flight path; determining that the second energy value differs from the first energy value by more than a threshold energy value; and based on determining that the second energy value differs from the first energy value by more than the threshold energy value, updating the non-linear model (see Gu at least fig. 1-8 in particular fig. 2-4 & 7 and Abstract and Summary and ¶150-151; trained neural network models, optimization criterion, and utilizing various algorithmic analysis methods).
17: wherein the plurality of power value comprises a first power value, wherein the attribute value is a first attribute value, and wherein the method further comprises: causing the aerial vehicle to traverse the flight path; determining a second attribute value representing an operating condition that has actually been experienced by the aerial vehicle at the portion of the flight path; determining a second power value representing an amount of power that has been consumed by the aerial vehicle in connection with traversing the portion of the flight path; determining, based on the second attribute value and using the non-linear model, a third power value representing an amount of power that the non-linear model expects to have been consumed by the aerial vehicle in connection with traversing the portion of the flight path; and updating the non-linear model based on a difference between the second power value and the third power value (see Gu at least fig. 1-8 in particular fig. 2-4 & 7 and Abstract and Summary and ¶150-151; trained neural network models, optimization criterion, and utilizing various algorithmic analysis methods).
18: wherein: determining the portion of the flight path comprises determining a plurality of candidate portions of the flight path; determining the attribute value comprises determining, for each respective candidate portion of the plurality of candidate portions, a corresponding attribute value representing an operating condition expected to be experienced by the aerial vehicle at the respective candidate portion of the flight path; determining the plurality of power values comprises, for each respective candidate portion of the plurality of candidate portions, determining, based on the corresponding attribute value and using the non-linear model, a corresponding one or more power values, wherein each respective power value of the corresponding one or more power values represents an amount of power expected to be consumed by the aerial vehicle at a corresponding sampled location of one or more sampled locations along the respective candidate portion of the flight path; determining the energy value comprises, for each respective candidate portion of the plurality of candidate portions, determining, based on the corresponding power value, a corresponding energy value representing an amount of energy expected to be consumed by the aerial vehicle in connection with the respective candidate portion of the flight path; and determining the flight path comprises selecting, from the plurality of candidate portions and based on the corresponding energy values thereof, two or more candidate portions to define the flight path(see Gu at least fig. 1-8 in particular fig. 2-4 & 7 and Abstract and Summary and ¶150-151; trained neural network models, optimization criterion, and utilizing various algorithmic analysis methods).
19: A system comprising a processor configured to perform operations comprising:
determining a portion of a flight path of an aerial vehicle (see Gu at least fig. 1-8 in particular fig. 3-4 & 7 and Abstract and Summary; determining a vehicle is completing a portion of a voyage);
determining an attribute value representing an operating condition expected to be experienced by the aerial vehicle at the portion of the flight path (see Gu at least fig. 1-8 in particular fig. 3-4 & 7 and Abstract and Summary; operational characteristic values corresponding to the portion of the voyage);
determining, based on the attribute value and using a non-linear model, a plurality of power values, wherein each respective power value of the plurality of power values represents an amount of power expected to be consumed by the aerial vehicle at a corresponding sampled location of a plurality of sampled locations along the portion of the flight path (see Gu at least fig. 1-8 in particular fig. 3-4 & 7 and Abstract and Summary; operational characteristic values corresponding to the portion of the voyage, dynamic property of a fuel/refueling of a vehicle, utilizing historical operational characteristics at fueling/refueling points, utilizing neural network model);
determining, based on the plurality of power values, an energy value representing an amount of energy expected to be consumed by the aerial vehicle in connection with the portion of the flight path, wherein determining the energy value comprises determining an integral of the plurality of power values over the portion of the flight path (see Gu at least fig. 1-8 in particular fig. 3-4 & 7 and Abstract and Summary; operational characteristic values corresponding to the portion of the voyage, dynamic property of a fuel/refueling of a vehicle, utilizing historical operational characteristics at fueling/refueling points, utilizing neural network model);
modifying the flight path based on the energy value (see Gu at least fig. 1-8 in particular fig. 3-4 & 7 and Abstract and Summary; determining range of operational characteristics, generating an estimated value of an operating variable on ability of vehicle to completed the portion of the voyage, causing the vehicle to maneuver through another portion of the voyage); and causing the aerial vehicle to fly along the flight path (see Gu at least fig. 1-8 in particular fig. 3-4 & 7 and Abstract and Summary; determining a vehicle is completing a portion of a voyage).
20: A non-transitory computer-readable storage medium having stored thereon instructions that, when executed by a computing system, cause the computing system to perform operations comprising:
determining a portion of a flight path of an aerial vehicle (see Gu at least fig. 1-8 in particular fig. 3-4 & 7 and Abstract and Summary; determining a vehicle is completing a portion of a voyage);
determining an attribute value representing an operating condition expected to be experienced by the aerial vehicle at the portion of the flight path (see Gu at least fig. 1-8 in particular fig. 3-4 & 7 and Abstract and Summary; operational characteristic values corresponding to the portion of the voyage);
determining, based on the attribute value and using a non-linear model, a plurality of power values, wherein each respective power value of the plurality of power values represents an amount of power expected to be consumed by the aerial vehicle at a corresponding sampled location of a plurality of sampled locations along the portion of the flight path; determining, based on the plurality of power value, an energy value representing an amount of energy expected to be consumed by the aerial vehicle in connection with the portion of the flight path, wherein determining the energy value comprises determining an integral of the plurality of power values over the portion of the flight path (see Gu at least fig. 1-8 in particular fig. 3-4 & 7 and Abstract and Summary; operational characteristic values corresponding to the portion of the voyage, dynamic property of a fuel/refueling of a vehicle, utilizing historical operational characteristics at fueling/refueling points, utilizing neural network model);
modifying the flight path based on the energy value (see Gu at least fig. 1-8 in particular fig. 3-4 & 7 and Abstract and Summary; determining range of operational characteristics, generating an estimated value of an operating variable on ability of vehicle to completed the portion of the voyage, causing the vehicle to maneuver through another portion of the voyage); and causing the aerial vehicle to fly along the flight path (see Gu at least fig. 1-8 in particular fig. 3-4 & 7 and Abstract and Summary; determining a vehicle is completing a portion of a voyage).
21: wherein the non-linear model comprises a fleet-wide model that has been trained using fleet-wide training data obtained from a plurality of vehicle types of aerial vehicles in an aerial vehicle fleet, wherein the plurality of vehicle types comprises a vehicle type of the aerial vehicle (see Gu at least fig. 1 and Summary & col. 9 lines 17-55).
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MACEEH ANWARI whose telephone number is 571-272-7591. The examiner can normally be reached on Monday-Friday 7:30-5:00 PM ES.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Angela Ortiz can be reached on 571-272-1206. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MACEEH ANWARI/Primary Examiner, Art Unit 3663
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