DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: “detection component configured to detect parameters” and “machine learning component configured to provide an aircraft analysis” in claim 14.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 19 is 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 19 recites “the aircraft inputting data”. It’s not clear as to what structure of the aircraft inputs data.
Claim 19 is a product claim, but recites only method steps. It’s not clear as to if applicant is trying to claim method or apparatus for claim 19.
Claim Rejections - 35 USC § 102
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, 3-7, 14-15 and 18 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Ibrahim (US 11597519).
Regarding claim 1, Ibrahim teaches a method for automated aircraft management, the method comprising:
collecting sensor data from sensors (via sensor interface 120) associated with an aircraft (col 1 line 17, “within a commercial aircraft”);
inputting information including the sensor data into a machine learning model (see fig 1, sensor interface 120 is in communication with artificial intelligence processing unit 110. Also col 3 line 21-22, “learning by the artificial intelligence assistant”) for analysis;
outputting, via the machine learning model, an aircraft analysis (to end effector 150, fig 1) associated with the sensor data and the aircraft, where the aircraft analysis includes adjustments to mechanisms within the aircraft (col 4 lines 21-24, “the artificial intelligence processing unit 110 is configured to direct the end effector 150 to perform at least one task responsive to information acquired from one or more of the sensor interface 120”); and
applying, at least in part, the adjustments (col 4 lines 21-24, “the artificial intelligence processing unit 110 is configured to direct the end effector 150 to perform at least one task responsive to information acquired from one or more of the sensor interface 120”. The adjustments is done by end effector) from the aircraft analysis to the aircraft.
Regarding claim 3, Ibrahim teaches the adjustments include adjustments to environmental conditions (col 7 lines 17-18, “a control system that adjusts temperature or other ambient condition”) within an aircraft cabin of the aircraft.
Regarding claim 4, Ibrahim teaches receiving user input (from crew input 132 via flight crew interface 130, or from passenger input 142 via passenger interface 140, fig 1) from within an aircraft cabin of the aircraft; and combining the user input into the information including the sensor data prior to inputting the information (see fig 1, sensor input and human inputs are sent to controller 110) into the machine learning model.
Regarding claim 5, Ibrahim teaches the user input adjusts a component (components of end effectors 250 such as ECS 255, fig 2) in the aircraft cabin.
Regarding claim 6, Ibrahim teaches detecting, via at least one sensor from the sensors (from human interface 202, fig 1), an activity (an activity would be movement of fingers under human interface 202, fig 2. Also col 4 lines 55-57, “The memory 112 may also store historical data, for example, relating to desired or preferred actions to be taken responsive to information”. Historical data implies that activities were taken previously) performed in an aircraft cabin of the aircraft; and combining the activity into the information (see fig 2) including the sensor data prior to inputting the information into the machine learning model (see fig 3, input 304 and sensor data from 302 are combined).
Regarding claim 7, Ibrahim teaches the activity is an individual using an aircraft cabin component (col 6 lines 27-28, “the flight crew interface 130 may include a microphone to receive audible information from a flight crew member”).
Regarding claim 14, Ibrahim teaches a system for automated aircraft management, the system comprising:
a detection component (sensor 122, fig 1) configured to detect parameters associated with an aircraft;
a machine learning component (artificial intelligence processing unit 110, fig 1) configured to provide an aircraft analysis of at least the parameters detected by the detection component (AI processing unit inherently perform analysis); and
an adjustment component configured to adjust aircraft mechanisms associated with at least the aircraft analysis (col 4 lines 21-24, “the artificial intelligence processing unit 110 is configured to direct the end effector 150 to perform at least one task responsive to information acquired from one or more of the sensor interface 120”).
Regarding claim 15, Ibrahim teaches a storage component (memory 112, fig 1) stores the aircraft analysis in a profile.
Regarding claim 17, Ibrahim teaches the detection component include pressure (col 5 line 51, “pressure sensor”), capacitive (col 6 line 23-24, “touch screen”), temperature (col 5 line 51, “thermometer”), and switch sensing instruments (col 5 line 50-54 “light meter”, “camera”, Also col 5 line 33-34, “a request from an individual passenger”).
Regarding claim 18, Ibrahim teaches a computer program product for automated aircraft management, the computer program product comprising a computer readable storage medium having computer readable instructions stored therein, wherein the computer readable instructions, when executed on a computing device (col 2 lines 9-12, “a tangible and non-transitory computer-readable medium having instructions stored thereon. The instructions, when executed by a computer, causing the computer to”), causes the computing device to:
receive data (via sensor interface 120) associated with an aircraft, wherein the data includes sensor data from sensors (122, fig 1) associated with the aircraft;
analyzing the aircraft by inputting the data into a machine learning model (see fig 1, sensor interface 120 is in communication with artificial intelligence processing unit 110. Also col 3 line 21-22, “learning by the artificial intelligence assistant”), wherein analyzing the aircraft includes adjustments to aircraft mechanisms (to end effector 150, fig 1); and
implementing the adjustments (adjustment to different components of 250, fig 2) to the aircraft.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim 2 is rejected under 35 U.S.C. 103 as being unpatentable over Ibrahim (US 11597519) in view of Katz (US 20220203996).
Regarding claim 2, Ibrahim teaches all the limitations of claim 1, but fails to teach the machine learning model is a neural network.
Katz teaches machine learning model is a neural network ([0051] “via a neural network and/or utilizing one or more machine learning techniques”)
It would have been obvious to one of ordinary skill in the art to modify Ibrahim as taught by Katz by applying the control with assistance of a neural network in order to execute multiple computations simultaneously and speed up big data analysis and workflows.
Claims 8-9, 16 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Ibrahim (US 11597519) in view of Krzywon (US 20220243608).
Regarding claim 8, Ibrahim teaches all the limitations of claim 1, and analyzing the aircraft analysis (by users reading touch screen 242, fig 2) produced by the machine learning model.
Ibrahim fails to teach detecting an anomaly associated with a component of the aircraft; and producing an alert associated with the anomaly, wherein the alert includes data associated with the component.
Krzywon teaches detecting an anomaly associated with a component (propeller 130) of the aircraft ([0037] “the controller 202 is configured to detect a failure of the feedback device 136 on the basis of the PCU command and of the input signal(s) received from the sensor(s) 204”); and producing an alert associated with the anomaly, wherein the alert includes data associated with the component ([0038] “the alert is a warning indication or message that is output for annunciation in the aircraft cockpit in order to inform the crew of the malfunction of the feedback device 136”).
It would have been obvious to one of ordinary skill in the art to modify Ibrahim as taught by Kryzwon by incorporating anomaly detection device on the aircraft in order to allow flight operator to monitor the condition of different aircraft components so that appropriate response can be taken (such as repair).
Regarding claim 9, Ibrahim in view of Krzywon teaches the anomaly includes indicators of faulty equipment (Krzywon [0037] talks about anomaly of propeller) within the aircraft.
Regarding claim 16, Ibrahim teaches all the limitations of claim 14, but fails to teach that when the aircraft analysis includes an anomaly, providing an alert associated with the anomaly.
Krzywon teaches when the aircraft analysis includes an anomaly, providing an alert associated with the anomaly ([0038] “the alert is a warning indication or message that is output for annunciation in the aircraft cockpit in order to inform the crew of the malfunction of the feedback device 136”).
It would have been obvious to one of ordinary skill in the art to modify Ibrahim as taught by Kryzwon by incorporating anomaly detection device on the aircraft in order to allow flight operator to monitor the condition of different aircraft components so that appropriate response can be taken (such as repair).
Regarding claim 19, Ibrahim teaches all the limitations of claim 18 and analyzing the aircraft inputting data into the machine learning model (by users reading touch screen 242, fig 2).
Ibrahim fails to teach detecting an anomaly associated with a component of the aircraft; and producing an alert associated with the anomaly, wherein the alert includes data associated with the component.
Krzywon teaches detecting an anomaly associated with a component (propeller 130) of the aircraft ([0037] “the controller 202 is configured to detect a failure of the feedback device 136 on the basis of the PCU command and of the input signal(s) received from the sensor(s) 204”); and producing an alert associated with the anomaly, wherein the alert includes data associated with the component ([0038] “the alert is a warning indication or message that is output for annunciation in the aircraft cockpit in order to inform the crew of the malfunction of the feedback device 136”).
It would have been obvious to one of ordinary skill in the art to modify Ibrahim as taught by Kryzwon by incorporating anomaly detection device on the aircraft in order to allow flight operator to monitor the condition of different aircraft components so that appropriate response can be taken (such as repair).
Claims 13 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Ibrahim (US 11597519) in view of Kneller (US 7878586).
Regarding claim 13, Ibrahim teaches all the limitations of claim 1, and detecting, via at least one sensor from the sensors, an individual within an aircraft cabin of the aircraft (col 5 lines 54-56, “a camera may be used to detect a person of interest, with an alert sent to the flight crew autonomously regarding the presence and/or location of the person of interest”), and retrieving a user profile associated with the individual (the term “person of interest imply that a user profile is inherently inputted into the controller).
Ibrahim fails to teach the user profile includes historical data of user preferences (67, fig 4); and combining the historical data into the information including the sensor data.
Kneller teaches that user profile includes historical data of user preferences (user preference data 67, fig 4); and combining the historical data into the information including the sensor data prior to inputting the information into the machine learning model (See fig 4, sensor data from camera is combined with user preference at “user control module 46” before information is sent to smart control module).
It would have been obvious to one of ordinary skill in the art to modify Ibrahim as taught by Kneller by incorporating user preference algorithm in order to allow the system to control the temperature in the cabin according to different passenger needs.
Regarding claim 20, Ibrahim teaches all the limitations of claim 18, but fails to teach receiving historical data of a user profile.
Kneller teaches receiving historical data of a user profile (from user ID data 63 and user preference data 67 to user profile control module 44, fig 4).
It would have been obvious to one of ordinary skill in the art to modify Ibrahim as taught by Kneller by incorporating user preference algorithm in order to allow the system to control the temperature in the cabin according to different passenger needs.
Allowable Subject Matter
Claims 10-12 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KO-WEI LIN whose telephone number is (571)270-7675. The examiner can normally be reached M-F 6:30-2:30 Eastern Time.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Helena Kosanovic can be reached at (571)272-9059. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/KO-WEI LIN/Primary Examiner, Art Unit 3762
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