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
Status of Claims
The following is a final office action in response to the communication filed on 04/20/2026.
Claims 1-6, 8-11, 13-26, and 28-29 are either amended directly or via a claim they depend from.
Claims 7, 12, and 27 are canceled.
Claims 30-32 are new.
Claims 1-6, 8-11, 13-26, and 28-32 are rejected.
Response to Arguments
Regarding the Objections of the Claims Directed to Minor Informalities: Applicant’s amendments to claims 13, 14, and 23 have rendered the objections moot. Accordingly, the objections have been withdrawn.
Regarding the Claim Rejections under 35 § USC 101: Applicant’s arguments and corresponding amendments, see pages 13-19 filed on 04/20/2026, with respect to traversing the rejections in the first action have been successful due to the subject matter amended into independent claim 1, particularly due to the incorporation of the steps of scanning the data to identify filtered ranges of interest which cannot be done purely mentally.
Regarding the Claim Rejections under 35 § USC 102/103: Applicant’s arguments and corresponding amendments, see pages 13-19 filed on 04/20/2026, with respect to traversing the rejections in the first action have been successful due to the subject matter amended into independent claim 1. However, after further search and consideration, new grounds of rejections have been respectfully made in response to the amendments. Applicant has respectfully argued that, (Page 18, Bottom Two Lines of Page) “even in combination, the cited references do not remedy the deficiencies of Gupta or Yuen as to amended Claim 1.” Examiner respectfully disagrees and has cited Sterling (US 2022/0366795 A1) in the subsequent, Claim Rejections - 35 USC § 103, for applicant’s consideration.
Applicant’s request that the Examiner contact the representative before issuing a subsequent action was carefully considered. Ultimately, the Examiner believes the new grounds of rejection was substantial enough to the point where it would be necessary for the Applicant to consider the written full office action in addition to the fact that no clear path to allowance was apparent to the Examiner at this stage of prosecution. Despite having held a previous interview, should Applicant request an additional interview after receiving the final action, said interview will be granted by the Examiner.
Claim Rejections - 35 USC § 103
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 text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claims 1-3, 5-6, 8-11, 13-16, 20, 26, and 28-32 are rejected under 35 U.S.C. 103 as being unpatentable over Gupta et al., (US 10,728,379 B1, hereinafter Gupta) in view of Sterling et al. (US 2022/0366795 A1, hereinafter Sterling)
Claim 1 Discloses: (Currently Amended)
“A method, comprising: obtaining, by a process, data from one or more environmentally reactive sensors of a particular device; analyzing, by the process, the data for motion characteristics associated with movement of devices during aircraft travel,”
Gupta teaches, (Abstract, Lines 1-5) “A method, a mobile device, and a computer program product for detecting if a mobile device is in an airplane environment. The method includes receiving motion sensor data from a motion sensor and calculating a velocity value based on the motion sensor data,” and that, (Page 8, Column 2, Lines 52-56) “The at least one processor executes program code of the airplane environment detection module, which enables the mobile device to receive motion sensor data from the motion sensor and calculate a velocity value based on the motion sensor data.”
“… determining, by the process and based on analyzing, that the particular device was traveling by aircraft based on a given portion of the data substantially sharing the motion characteristics associated with movement of devices during aircraft travel,”
Gupta teaches, (Page 11, Column 5, Lines 66-67 and Column 6, Lines 1-3) “Mobile device 100 further includes additional components, such as global positioning system (GPS) module 164, barometric pressure sensor 146 and short range communication device 147. (GPS) module 164 can receive location and time data from GPS satellites,” and that, (Page 11, Column 6, Lines 51-67 and Page 12, Column 7, Lines 1-4) “Gyroscope data 224 contains rotation or angular rotational velocity values for a period of time. After the period of time expires, the values are updated (e.g., written over) with new rotation or angular rotation velocity values sensed/determined over a next period of time. Velocity value 230 is a calculated value that indicates the velocity of mobile device 100. System memory 120 further includes velocity threshold 232 and GPS data 234. Velocity threshold 232 is a pre-established velocity that, when exceeded, indicates that mobile device 100 may be in an airplane or in an airplane that is taking off … In one embodiment, processor 102 can calculate a velocity of mobile device 100 based on GPS data 164.”
“… and causing, by the process, one or more air travel related software actions on the particular device in response to determining that the particular device was traveling by an aircraft at a time associated with the given portion of the data.”
Gupta teaches that after the velocity exceeds a threshold in step 416, (Page 9, Column 2, Lines 63-67 and Page 10, Column 3, Lines 1-3) “The mobile device is further enabled to determine if the barometric pressure data matches reference barometric pressure data and in response to determining that the barometric pressure data matches the reference barometric pressure data, the processor enables an airplane mode of the mobile device. The reference barometric pressure data is barometric pressure data that is indicative of the mobile device being in an airplane that is taking off.”
“ wherein analyzing comprises scanning the data to identify filtered ranges of interest to analyze and applying one or more qualifiers or disqualifiers,”
Gupta does not explicitly identify filtered ranges of interest to analyze in order to apply one or more qualifiers or disqualifiers.
However, Sterling does teach the preceding limitations.
Sterling teaches, (Paragraph [0010]) “the subject disclosure combines collected data from the sensor suite of the mobile device, knowledge of expected host aircraft motion, and knowledge of typical user motions to differentiate between sensor readings descriptive of motion of the host aircraft from that of motion imparted to the mobile device by actions of the user. The user motions are then filtered out using the approaches described herein, e.g., using a statistical filtering model programmed into memory of the mobile device,”
“and wherein the motion characteristics associated with movement of devices during aircraft travel are based on one or more of: alternation between high and low acceleration, alternation between high and low gyroscopic precessions, a difference in quantiles of acceleration, a difference in quantiles of gyroscopic precessions, surpassing high or low acceleration thresholds, surpassing high or low gyroscopic precession thresholds, a median of acceleration, a median of gyroscopic precessions, or a standard deviation of acceleration or gyroscopic precessions;”
Sterling teaches, (Paragraph [0012], Lines 8-11) “The mobile device then filters out human-induced motion of the mobile device from the synthesized data set, e.g., using any or all of a statistical filtering model, bandpass filters, thresholds, etc,” wherein, (Paragraph [0052], Lines 9-15) “A threshold standard deviation may be used to filter out human interaction. Thus, execution of computer-readable instructions by the processor 44 of FIG. 1 causes the mobile device 30 to filter out human-induced motion of the mobile device 30 from the raw flight data (FLT.sub.RAW) by calculating such a rolling standard deviation of the accelerometer readings.”
“… and based on disqualifying false positives from the data, wherein disqualifying false positives is based in part on accounting for changes in directions atypical to travel by aircraft;”
Sterling teaches, (Paragraph [0049]) “For example, within the sensor suite 40 of FIG. 2 one may find the gyroscope 48 and the multi-axis accelerometer 49, which can at times output extreme data spikes when the mobile device 30 is lifted, tilted, or dropped. The gyroscope 48 and the multi-axis accelerometers 49 in particular would tend to produce more extreme readings than those that would typically be indicated by normal aircraft operation.”
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to combine the detection of a mobile device which can execute software actions such as taught by Gupta, with the explicit data filtered range methodology as taught by Sterling, in order to yield predictable results.
Combining the reference would yield the data accuracy benefits of filtering out motion induced by a user, rather than motion created by the typical operation of an aircraft. As Sterling describes, (Paragraph [0010]) “the subject disclosure combines collected data from the sensor suite of the mobile device, knowledge of expected host aircraft motion, and knowledge of typical user motions to differentiate between sensor readings descriptive of motion of the host aircraft from that of motion imparted to the mobile device by actions of the user. The user motions are then filtered out using the approaches described herein, e.g., using a statistical filtering model programmed into memory of the mobile device.”
Claim 2 Discloses:
“The method as in claim 1, wherein causing the one or more air travel related software actions occurs while traveling by aircraft in response to a takeoff.”
Gupta teaches, (Page 9, Column 2, Lines 63-67 and Page 10, Column 3, Lines 1-3) “The mobile device is further enabled to determine if the barometric pressure data matches reference barometric pressure data and in response to determining that the barometric pressure data matches the reference barometric pressure data, the processor enables an airplane mode of the mobile device. The reference barometric pressure data is barometric pressure data that is indicative of the mobile device being in an airplane that is taking off.”
Claim 3 Discloses:
“The method as in claim 1, wherein causing the one or more air travel related software actions occurs after traveling by aircraft in response to a landing.”
Gupta teaches, (Page 12, Column 7, Lines 53-67 and Column 8, Line 1) “FIG. 3A illustrates an example of mobile device 100 within airplane cabin 310 … As shown, mobile device user 320 is looking at and/or otherwise interfacing with a user interface of display 130 of mobile device 100, while airplane 300 is physically in one of take-off status, in-flight status, landing status, or landed/grounded status. Regulatory agencies prohibit the use of cellular communications (i.e., communication via radios 142a-n) during airplane flight, and the disclosure enables mobile device 100 to autonomously detect or determine when to transition mobile device 100 into airplane mode (during take-off) and out of airplane mode (following landing),” and that, (Page 12, Column 7, Lines 5-7) “System memory 120 also includes barometric pressure data 270, take-off reference barometric pressure data 272, and landing reference barometric pressure data 274.”
Claim 5 Discloses: (Original)
“The method as in claim 1, wherein analyzing comprises: applying a machine learning model trained to classify the data as matching the motion characteristics associated with movement of devices during aircraft travel.”
Gupta does not explicitly use a machine learning model to classify the data as matching the motion characteristics associated with movement of devices during aircraft travel.
Sterling does teach the value of using a machine learning model in relation to a substantially similar invention.
Sterling teaches, (Paragraph [0010]) “the subject disclosure combines collected data from the sensor suite of the mobile device, knowledge of expected host aircraft motion, and knowledge of typical user motions to differentiate between sensor readings descriptive of motion of the host aircraft from that of motion imparted to the mobile device by actions of the user. The user motions are then filtered out using the approaches described herein, e.g., using a statistical filtering model programmed into memory of the mobile device,” and that,” (Paragraph [0034], Lines 1-3) Hardware capabilities of the mobile device 30 are specially designed for artificial intelligence/machine learning computations that rely on large matrix operations.”
Sterling additionally teaches, (Paragraph [0038]) “Further regarding the sensor suite 40, representative sensors housed within the mobile device 30 include a barometer 47, which is configured to generate, as the raw flight data (FDRAW), a set of barometric pressure readings indicative of cabin pressure within the host aircraft 10, as opposed to atmospheric pressure readings. As explained below, GPS capabilities of the mobile device 30 enable the mobile device 30 to generate and output time-stamped filtered flight data (FDFILT) using the raw flight data (FDRAW), inclusive of such barometric pressure readings,” and that, (Paragraph [0041], Lines 8-11) “evolving systems-on-a-chip (SoCs) used to implement the processing, modeling, machine learning, and other functions of the mobile device 30.”
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to combine the system of Gupta which can to classify the data as matching the motion characteristics associated with movement of devices during aircraft travel, with a machine learning model solution in light of Sterling, in order to yield predictable results.
Combining the references would yield the well-known processing benefits of using a machine learning model top assist with flight data processing, which when recited at an extremely high level of generality such as in the claim set, is made obvious in light of Sterling.
Claim 6 Discloses: (Currently Amended)
“The method as in claim 1, wherein the one or more qualifiers or disqualifiers comprise
Gupta does not wherein the one or more qualifiers or disqualifiers comprise one or both of: qualifying the data; or disqualifying false positives from the data.
However, Sterling does teach the preceding limitations.
Sterling teaches, (Paragraph [0012], Lines 8-11) “The mobile device then filters out human-induced motion of the mobile device from the synthesized data set, e.g., using any or all of a statistical filtering model, bandpass filters, thresholds, etc,” wherein, (Paragraph [0052], Lines 9-15) “A threshold standard deviation may be used to filter out human interaction. Thus, execution of computer-readable instructions by the processor 44 of FIG. 1 causes the mobile device 30 to filter out human-induced motion of the mobile device 30 from the raw flight data (FLT.sub.RAW) by calculating such a rolling standard deviation of the accelerometer readings.”
Sterling additionally teaches, (Paragraph [0049]) “For example, within the sensor suite 40 of FIG. 2 one may find the gyroscope 48 and the multi-axis accelerometer 49, which can at times output extreme data spikes when the mobile device 30 is lifted, tilted, or dropped. The gyroscope 48 and the multi-axis accelerometers 49 in particular would tend to produce more extreme readings than those that would typically be indicated by normal aircraft operation.”
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to combine the detection of a mobile device which can execute software actions such as taught by Gupta, with the explicit data filtered range methodology as taught by Sterling, in order to yield predictable results.
Combining the reference would yield the data accuracy benefits of filtering out motion induced by a user, rather than motion created by the typical operation of an aircraft. As Sterling describes, (Paragraph [0010]) “the subject disclosure combines collected data from the sensor suite of the mobile device, knowledge of expected host aircraft motion, and knowledge of typical user motions to differentiate between sensor readings descriptive of motion of the host aircraft from that of motion imparted to the mobile device by actions of the user. The user motions are then filtered out using the approaches described herein, e.g., using a statistical filtering model programmed into memory of the mobile device.”
Claim 8 Discloses: (Original)
“The method as in claim 1, wherein the particular device is selected from a group consisting of: a smart phone, a smart watch, a fitness tracker, a smart ring, a tablet, and a laptop.”
Gupta teaches, Page 10, Column 4, Lines 38-43) “FIG. 1 depicts example mobile device 100 within which various aspects of the disclosure can be implemented, according to one or more embodiments. Examples of such mobile devices include, but are not limited to, a laptop computer, a notebook computer, a mobile phone, a digital camera, a tablet computer/device, and a smart-watch etc.”
Claim 9 Discloses: (Original)
“The method as in claim 1, wherein the one or more environmentally reactive sensors comprise one or more of hardware, software, or firmware sensors on one or both of the particular device or an associated sibling device.”
Gupta teaches, (Page 9, Column 2, Lines 26-29) “The method includes receiving, via a processor of a mobile device, motion sensor data from a motion sensor and calculating a velocity value based on the motion sensor data,” and that, (Page 14, Column 12, Lines 33-41) “As will be further appreciated, the processes in embodiments of the present disclosure may be implemented using any combination of software, firmware, or hardware. Accordingly, aspects of the present disclosure may take the form of an entirely hardware embodiment or an embodiment combining software (including firmware, resident software, micro-code, etc.) and hardware aspects that may all generally be referred to herein as a “circuit,” “module,” or “system.””
Claim 10 Discloses: (Original)
“The method as in claim 1, wherein the one or more environmentally reactive sensors are selected from a group consisting of: inertial sensors, an inertial measurement unit, an accelerometer, a barometric pressure sensor, a gyroscope, and a magnetometer.”
Gupta teaches, (Page 11, Column 5, Lines 31-34) “Mobile device 100 further includes proximity sensor 160 and motion sensor(s) 161 that are communicatively coupled to processor 102. Motion sensor(s) 161 can include one or more accelerometers 162 and gyroscope 163.”
Claim 11 Discloses: (Original)
“The method as in claim 1, wherein the data is selected from a group consisting of: acceleration, magnetic field moment measurement, magnetic dipole moment measurement, spatial axis gyroscopic data, altitude, and barometric pressure.”
Gupta teaches, (Page 11, Column 5, Lines 66-67 and Column 6, Lines 1-2) “Mobile device 100 further includes additional components, such as global positioning system (GPS) module 164, barometric pressure sensor 146 and short range communication device 147.”
Claim 13 Discloses: (Currently Amended)
“The method as in claim 1, wherein the motion characteristics associated with movement of devices during aircraft travel are based on changes to one or more of acceleration, altitude, angle, rotation, direction, or barometric pressure in a manner reflective of either takeoff, landing, or rapid changed of altitude of the aircraft.”
Gupta teaches, (Page 9, Column 2, Lines 63-67 and Page 10, Column 3, Lines 1-3) “The mobile device is further enabled to determine if the barometric pressure data matches reference barometric pressure data and in response to determining that the barometric pressure data matches the reference barometric pressure data, the processor enables an airplane mode of the mobile device. The reference barometric pressure data is barometric pressure data that is indicative of the mobile device being in an airplane that is taking off.”
Claim 14 Discloses: (Currently Amended)
“The method as in claim 1, further comprising: augmenting the analyzing process by
Applicant’s specification provides non-limiting example of “non-sensor factors” within the third paragraph of page 30 which reads, “In the pursuit of precision, in one embodiment herein, in step 1430 the determination may optionally be augmented with “non-sensor factors” (implying unrelated to the environmentally reactive sensors herein). These factors may encompass a spectrum of considerations, including internet and cellular connectivity, network associations, calendared plans, stored transit tickets, alterations in time zone information provided by the operating system, radio-frequency-derived location data including that from satellite-based radio navigation systems (e.g., GPS), among others such as externally-obtained location information.”
Therefore, Gupta teaches, (Page 11, Column 5, Lines 66-67 and Column 6, Lines 1-3) “Mobile device 100 further includes additional components, such as global positioning system (GPS) module 164, barometric pressure sensor 146 and short range communication device 147. (GPS) module 164 can receive location and time data from GPS satellites,” and that, (Page 11, Column 6, Lines 51-67 and Page 12, Column 7, Lines 1-4) “Gyroscope data 224 contains rotation or angular rotational velocity values for a period of time. After the period of time expires, the values are updated (e.g., written over) with new rotation or angular rotation velocity values sensed/determined over a next period of time. Velocity value 230 is a calculated value that indicates the velocity of mobile device 100. System memory 120 further includes velocity threshold 232 and GPS data 234. Velocity threshold 232 is a pre-established velocity that, when exceeded, indicates that mobile device 100 may be in an airplane or in an airplane that is taking off … In one embodiment, processor 102 can calculate a velocity of mobile device 100 based on GPS data 164.”
Claim 15 Discloses: (Original)
“The method as in claim 14, wherein the one or more non-sensor factors are selected from a group consisting of: internet connectivity, cellular connectivity, network associations, calendared plans, stored transit tickets, alterations in time zone information provided by an operating system, radio-frequency-derived location determinations; and externally-obtained location information.”
Gupta teaches, (Page 11, Column 5, Lines 66-67 and Column 6, Lines 1-3) “Mobile device 100 further includes additional components, such as global positioning system (GPS) module 164, barometric pressure sensor 146 and short range communication device 147. (GPS) module 164 can receive location and time data from GPS satellites,” and that, (Page 11, Column 6, Lines 51-67 and Page 12, Column 7, Lines 1-4) “Gyroscope data 224 contains rotation or angular rotational velocity values for a period of time. After the period of time expires, the values are updated (e.g., written over) with new rotation or angular rotation velocity values sensed/determined over a next period of time. Velocity value 230 is a calculated value that indicates the velocity of mobile device 100. System memory 120 further includes velocity threshold 232 and GPS data 234. Velocity threshold 232 is a pre-established velocity that, when exceeded, indicates that mobile device 100 may be in an airplane or in an airplane that is taking off … In one embodiment, processor 102 can calculate a velocity of mobile device 100 based on GPS data 164.”
Claim 16 Discloses: (Original)
“The method as in claim 1, wherein the one or more air travel related software actions comprises notifying a user of a detected transportation journey to prompt user behavior.”
Gupta teaches, (Page 13, Column 10, Lines 39-48) “Concurrently with the activation of, and while airplane mode is enabled, radios 142a-n that enable cellular communications (i.e., communication via radios 142a-n) are disabled. Also, concurrently with the activation of airplane mode, airplane mode icon 192 can be shown on display 130 which informs the user when mobile device 100 is in airplane mode (non-transmitting mode). In an optional embodiment, the user interface may also provide prompts to the user to enable the user to agree to activate the airplane mode, such as by using accept airplane mode icon 194.”
Claim 20 Discloses: (Original)
“The method as in claim 1, wherein the one or more air travel related software actions comprise one of either: requesting whether a user would like the particular device to either enable or disable an Airplane Mode or Flight Mode; or auto-adjusting the particular device to either enable or disable the Airplane Mode or Flight Mode.”
Gupta teaches, (Page 13, Column 10, Lines 39-48) “Concurrently with the activation of, and while airplane mode is enabled, radios 142a-n that enable cellular communications (i.e., communication via radios 142a-n) are disabled. Also, concurrently with the activation of airplane mode, airplane mode icon 192 can be shown on display 130 which informs the user when mobile device 100 is in airplane mode (non-transmitting mode). In an optional embodiment, the user interface may also provide prompts to the user to enable the user to agree to activate the airplane mode, such as by using accept airplane mode icon 194.”
Claim 26 Discloses: (Original)
“The method as in claim 1, wherein the data is obtained from an operating system application programming interface or directly from the one or more environmentally reactive sensors, or both.”
Gupta teaches, (Page 9, Column 2, Lines 43-54) “a mobile device includes a memory having stored thereon an airplane environment detection module for detecting the presence of an airplane environment. The mobile device also includes a motion sensor that detects movement of the mobile device, a barometric pressure sensor that detects ambient barometric pressure, at least one radio that enables wireless communication, and at least one processor communicatively coupled to the memory, the motion sensor, the barometric pressure sensor, and the at least one radio. The at least one processor executes program code of the airplane environment detection module.”
Claim 28 Discloses: (Currently Amended)
“An apparatus, comprising: one or more network interfaces to communicate with a computer network; a processor coupled to the one or more network interfaces and adapted to execute one or more processes; and a memory configured to store a process that is executable by the processor,”
Gupta teaches, (Page 10, Column 3, Lines 4-11) “According to an additional embodiment, a computer program product includes a computer readable storage device and program code on the computer readable storage device. When executed within a processor associated with a device, the program code enables the device to provide the various functionality presented in the above-described method processes,” and that, (Page 10, Column 4, Lines 49-54) “System memory 120 may be a combination of volatile and non-volatile memory, such as random access memory (RAM) and read-only memory (ROM). System memory 120 can store program code or similar instructions associated with firmware 128, an operating system 124, applications 122, and airplane environment detection module 136.”
Gupta additionally teaches, (Page 12, Column 7, Lines 43-47) “System memory 120 also includes high-speed vehicle or airline services application 280. High-speed vehicle or airline services application 280 can be an application that functions with short range communication device 147 to enable communications with airplane wireless network 190.”
“the process, when executed, operable to perform a method comprising: obtaining data from one or more environmentally reactive sensors of a particular device; analyzing the data for motion characteristics associated with movement of devices during aircraft travel,”
Gupta teaches, (Abstract, Lines 1-5) “A method, a mobile device, and a computer program product for detecting if a mobile device is in an airplane environment. The method includes receiving motion sensor data from a motion sensor and calculating a velocity value based on the motion sensor data,” and that, (Page 8, Column 2, Lines 52-56) “The at least one processor executes program code of the airplane environment detection module, which enables the mobile device to receive motion sensor data from the motion sensor and calculate a velocity value based on the motion sensor data.”
“… determining, based on analyzing, that the particular device was traveling by aircraft based on a given portion of the data substantially sharing the motion characteristics associated with movement of devices during aircraft travel,”
Gupta teaches, Column 2, Lines 56-63) “The mobile device is further enabled to determine if the velocity value is greater than a velocity value threshold, and in response to determining that the velocity value is greater than the velocity value threshold, receive barometric pressure data from the barometric pressure sensor. The barometric pressure data is associated with a current environment of the mobile device.”
“… and causing one or more air travel related software actions on the particular device in response to determining that the particular device was traveling by an aircraft at a time associated with the given portion of the data.”
Gupta teaches, (Page 9, Column 2, Lines 63-67 and Page 10, Column 3, Lines 1-3) “The mobile device is further enabled to determine if the barometric pressure data matches reference barometric pressure data and in response to determining that the barometric pressure data matches the reference barometric pressure data, the processor enables an airplane mode of the mobile device. The reference barometric pressure data is barometric pressure data that is indicative of the mobile device being in an airplane that is taking off.”
Gupta additionally teaches, (Page 11, Column 5, Lines 66-67 and Column 6, Lines 1-3) “Mobile device 100 further includes additional components, such as global positioning system (GPS) module 164, barometric pressure sensor 146 and short range communication device 147. (GPS) module 164 can receive location and time data from GPS satellites,” and that, (Page 12, Column 7, Lines 1-4) “GPS data 234 includes location and time information received from GPS satellites. In one embodiment, processor 102 can calculate a velocity of mobile device 100 based on GPS data 164.”
“ wherein analyzing comprises scanning the data to identify filtered ranges of interest to analyze and applying one or more qualifiers or disqualifiers,”
Gupta does not explicitly identify filtered ranges of interest to analyze in order to apply one or more qualifiers or disqualifiers.
However, Sterling does teach the preceding limitations.
Sterling teaches, (Paragraph [0010]) “the subject disclosure combines collected data from the sensor suite of the mobile device, knowledge of expected host aircraft motion, and knowledge of typical user motions to differentiate between sensor readings descriptive of motion of the host aircraft from that of motion imparted to the mobile device by actions of the user. The user motions are then filtered out using the approaches described herein, e.g., using a statistical filtering model programmed into memory of the mobile device,”
“ and wherein the motion characteristics associated with movement of devices during aircraft travel are based on one or more of: alternation between high and low acceleration, alternation between high and low gyroscopic precessions, a difference in quantiles of acceleration, a difference in quantiles of gyroscopic precessions, surpassing high or low acceleration thresholds, surpassing high or low gyroscopic precession thresholds, a median of acceleration, a median of gyroscopic precessions, or a standard deviation of acceleration or gyroscopic precessions;”
Sterling teaches, (Paragraph [0012], Lines 8-11) “The mobile device then filters out human-induced motion of the mobile device from the synthesized data set, e.g., using any or all of a statistical filtering model, bandpass filters, thresholds, etc,” wherein, (Paragraph [0052], Lines 9-15) “A threshold standard deviation may be used to filter out human interaction. Thus, execution of computer-readable instructions by the processor 44 of FIG. 1 causes the mobile device 30 to filter out human-induced motion of the mobile device 30 from the raw flight data (FLT.sub.RAW) by calculating such a rolling standard deviation of the accelerometer readings.”
“… and based on disqualifying false positives from the data, wherein disqualifying false positives is based in part on accounting for changes in directions atypical to travel by aircraft;”
Sterling teaches, (Paragraph [0049]) “For example, within the sensor suite 40 of FIG. 2 one may find the gyroscope 48 and the multi-axis accelerometer 49, which can at times output extreme data spikes when the mobile device 30 is lifted, tilted, or dropped. The gyroscope 48 and the multi-axis accelerometers 49 in particular would tend to produce more extreme readings than those that would typically be indicated by normal aircraft operation.”
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to combine the detection of a mobile device which can execute software actions such as taught by Gupta, with the explicit data filtered range methodology as taught by Sterling, in order to yield predictable results.
Combining the reference would yield the data accuracy benefits of filtering out motion induced by a user, rather than motion created by the typical operation of an aircraft. As Sterling describes, (Paragraph [0010]) “the subject disclosure combines collected data from the sensor suite of the mobile device, knowledge of expected host aircraft motion, and knowledge of typical user motions to differentiate between sensor readings descriptive of motion of the host aircraft from that of motion imparted to the mobile device by actions of the user. The user motions are then filtered out using the approaches described herein, e.g., using a statistical filtering model programmed into memory of the mobile device.”
Claim 29 Discloses: (Currently Amended)
“A tangible, non-transitory, computer-readable medium having computer-executable instructions stored thereon that, when executed by a processor on a computer, cause the computer to perform a method comprising:”
Gupta teaches, (Page 10, Column 3, Lines 4-11) “According to an additional embodiment, a computer program product includes a computer readable storage device and program code on the computer readable storage device. When executed within a processor associated with a device, the program code enables the device to provide the various functionality presented in the above-described method processes,” and that, (Page 10, Column 4, Lines 49-54) “System memory 120 may be a combination of volatile and non-volatile memory, such as random access memory (RAM) and read-only memory (ROM). System memory 120 can store program code or similar instructions associated with firmware 128, an operating system 124, applications 122, and airplane environment detection module 136.”
“obtaining data from one or more environmentally reactive sensors of a particular device; analyzing the data for motion characteristics associated with movement of devices during aircraft travel,”
Gupta teaches, (Abstract, Lines 1-5) “A method, a mobile device, and a computer program product for detecting if a mobile device is in an airplane environment. The method includes receiving motion sensor data from a motion sensor and calculating a velocity value based on the motion sensor data,” and that, (Page 8, Column 2, Lines 52-56) “The at least one processor executes program code of the airplane environment detection module, which enables the mobile device to receive motion sensor data from the motion sensor and calculate a velocity value based on the motion sensor data.”
“… determining, based on analyzing, that the particular device was traveling by aircraft based on a given portion of the data substantially sharing the motion characteristics associated with movement of devices during aircraft travel,”
Gupta teaches, Column 2, Lines 56-63) “The mobile device is further enabled to determine if the velocity value is greater than a velocity value threshold, and in response to determining that the velocity value is greater than the velocity value threshold, receive barometric pressure data from the barometric pressure sensor. The barometric pressure data is associated with a current environment of the mobile device.”
“… and causing one or more air travel related software actions on the particular device in response to determining that the particular device was traveling by an aircraft at a time associated with the given portion of the data.”
Gupta teaches, (Page 9, Column 2, Lines 63-67 and Page 10, Column 3, Lines 1-3) “The mobile device is further enabled to determine if the barometric pressure data matches reference barometric pressure data and in response to determining that the barometric pressure data matches the reference barometric pressure data, the processor enables an airplane mode of the mobile device. The reference barometric pressure data is barometric pressure data that is indicative of the mobile device being in an airplane that is taking off.”
Gupta additionally teaches, (Page 11, Column 5, Lines 66-67 and Column 6, Lines 1-3) “Mobile device 100 further includes additional components, such as global positioning system (GPS) module 164, barometric pressure sensor 146 and short range communication device 147. (GPS) module 164 can receive location and time data from GPS satellites,” and that, (Page 12, Column 7, Lines 1-4) “GPS data 234 includes location and time information received from GPS satellites. In one embodiment, processor 102 can calculate a velocity of mobile device 100 based on GPS data 164.”
“wherein analyzing comprises scanning the data to identify filtered ranges of interest to analyze and applying one or more qualifiers or disqualifiers,”
Gupta does not explicitly identify filtered ranges of interest to analyze in order to apply one or more qualifiers or disqualifiers.
However, Sterling does teach the preceding limitations.
Sterling teaches, (Paragraph [0010]) “the subject disclosure combines collected data from the sensor suite of the mobile device, knowledge of expected host aircraft motion, and knowledge of typical user motions to differentiate between sensor readings descriptive of motion of the host aircraft from that of motion imparted to the mobile device by actions of the user. The user motions are then filtered out using the approaches described herein, e.g., using a statistical filtering model programmed into memory of the mobile device.”
“and wherein the motion characteristics associated with movement of devices during aircraft travel are based on one or more of: alternation between high and low acceleration, alternation between high and low gyroscopic precessions, a difference in quantiles of acceleration, a difference in quantiles of gyroscopic precessions, surpassing high or low acceleration thresholds, surpassing high or low gyroscopic precession thresholds, a median of acceleration, a median of gyroscopic precessions, or a standard deviation of acceleration or gyroscopic precessions;”
Sterling teaches, (Paragraph [0012], Lines 8-11) “The mobile device then filters out human-induced motion of the mobile device from the synthesized data set, e.g., using any or all of a statistical filtering model, bandpass filters, thresholds, etc,” wherein, (Paragraph [0052], Lines 9-15) “A threshold standard deviation may be used to filter out human interaction. Thus, execution of computer-readable instructions by the processor 44 of FIG. 1 causes the mobile device 30 to filter out human-induced motion of the mobile device 30 from the raw flight data (FLT.sub.RAW) by calculating such a rolling standard deviation of the accelerometer readings.”
“… and based on disqualifying false positives from the data, wherein disqualifying false positives is based in part on accounting for changes in directions atypical to travel by aircraft;”
Sterling teaches, (Paragraph [0049]) “For example, within the sensor suite 40 of FIG. 2 one may find the gyroscope 48 and the multi-axis accelerometer 49, which can at times output extreme data spikes when the mobile device 30 is lifted, tilted, or dropped. The gyroscope 48 and the multi-axis accelerometers 49 in particular would tend to produce more extreme readings than those that would typically be indicated by normal aircraft operation.”
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to combine the detection of a mobile device which can execute software actions such as taught by Gupta, with the explicit data filtered range methodology as taught by Sterling, in order to yield predictable results.
Combining the reference would yield the data accuracy benefits of filtering out motion induced by a user, rather than motion created by the typical operation of an aircraft. As Sterling describes, (Paragraph [0010]) “the subject disclosure combines collected data from the sensor suite of the mobile device, knowledge of expected host aircraft motion, and knowledge of typical user motions to differentiate between sensor readings descriptive of motion of the host aircraft from that of motion imparted to the mobile device by actions of the user. The user motions are then filtered out using the approaches described herein, e.g., using a statistical filtering model programmed into memory of the mobile device.”
Claim 30 Discloses: (New)
“The method as in claim 1, wherein scanning the data to identify filtered ranges of interest to analyze comprises one of either scanning the data using one or more rolling windows or searching for one or more hot zones within the data.”
Gupta does not explicitly teach either scanning the data using one or more rolling windows or searching for one or more hot zones within the data.
However, Sterling does teach the preceding limitations.
Sterling teaches, (Paragraph [0012], Lines 8-11) “The mobile device then filters out human-induced motion of the mobile device from the synthesized data set, e.g., using any or all of a statistical filtering model, bandpass filters, thresholds, etc,” wherein, (Paragraph [0052], Lines 9-15) “A threshold standard deviation may be used to filter out human interaction. Thus, execution of computer-readable instructions by the processor 44 of FIG. 1 causes the mobile device 30 to filter out human-induced motion of the mobile device 30 from the raw flight data (FLT.sub.RAW) by calculating such a rolling standard deviation of the accelerometer readings,” and further that, (Paragraph [0061], Lines 4-11) “As described above, block B57 can entail calculating a rolling standard deviation and applying cutoff thresholds, calculating a rolling average sum of absolute values, and applying thresholds or cutoffs to the rolling average sum, or using other model-based statistical analysis to separate what is likely to be motion of the host aircraft 10 from motion that is likely to be user-generated by interactions with the mobile device 30.”
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to combine the detection of a mobile device which can execute software actions such as taught by Gupta, with the explicit data filtered range methodology as taught by Sterling, in order to yield predictable results.
Combining the reference would yield the data accuracy benefits of filtering out motion induced by a user, rather than motion created by the typical operation of an aircraft. As Sterling describes, (Paragraph [0010]) “the subject disclosure combines collected data from the sensor suite of the mobile device, knowledge of expected host aircraft motion, and knowledge of typical user motions to differentiate between sensor readings descriptive of motion of the host aircraft from that of motion imparted to the mobile device by actions of the user. The user motions are then filtered out using the approaches described herein, e.g., using a statistical filtering model programmed into memory of the mobile device.”
Claim 31 Discloses: (New)
“The method as in claim 1, wherein the data comprises acceleration data and, when available, spatial axis gyroscopic data.”
Gupta teaches, (Page 11, Column 5, Lines 31-47) “Mobile device 100 further includes proximity sensor 160 and motion sensor(s) 161 that are communicatively coupled to processor 102. Motion sensor(s) 161 can include one or more accelerometers 162 and gyroscope 163. Proximity sensor 160 can be an infrared (IR) sensor that detects the presence of a nearby object. Motion sensor(s) 161 can detect movement of mobile device 100 and provide motion data to processor 102 that indicate the spatial orientation and movement of mobile device 100. Accelerometers 162 measure linear acceleration of movement of mobile device 100 in multiple axes (X, Y and Z). For example, accelerometers 162 can include three accelerometers, where one accelerometer measures linear acceleration in the X axis, one accelerometer measures linear acceleration in the Y axis, and one accelerometer measures linear acceleration in the Z axis. Gyroscope 163 measures rotation or angular rotational velocity of mobile device 100.”
Claim 32 Discloses (New)
“The method as in claim 1, wherein scanning the data to identify filtered ranges of interest to analyze comprises applying one or more optimized metrics to the data while applying the one or more qualifiers or disqualifiers.”
Gupta does not explicitly teach applying one or more optimized metrics to the data while applying the one or more qualifiers or disqualifiers.
However, Sterling does teach the preceding limitations.
Sterling teaches, (Paragraph [0012], Lines 8-11) “The mobile device then filters out human-induced motion of the mobile device from the synthesized data set, e.g., using any or all of a statistical filtering model, bandpass filters, thresholds, etc,” wherein, (Paragraph [0052], Lines 9-15) “A threshold standard deviation may be used to filter out human interaction. Thus, execution of computer-readable instructions by the processor 44 of FIG. 1 causes the mobile device 30 to filter out human-induced motion of the mobile device 30 from the raw flight data (FLT.sub.RAW) by calculating such a rolling standard deviation of the accelerometer readings.”
Sterling additionally teaches, (Paragraph [0035], Lines 12-20) “As described below with reference to FIGS. 4-6, execution of the computer-readable instructions by the processor 44 causes the mobile device 30, using a statistical filter 50F, to filter out human-induced motion of the mobile device 30 from a processed and possibly reduced set (“synthesized data set”) of the raw flight data (FD.sub.RAW) generated by the sensor suite 40, to thereby generate time-stamped filtered flight data (FD.sub.FILT),” wherein, (Paragraph [0059], Lines 10-15) “the mobile device 30 would first reduce, combine, or otherwise synthesize the collected raw flight data (FD.sub.RAW) into the synthesized data set as data in a preparatory step to isolate data that is relevant to the particular flight event(s) being detected and ultimately reported.”
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to combine the detection of a mobile device which can execute software actions such as taught by Gupta, with the explicit data filtered range with optimized metric methodology as taught by Sterling, in order to yield predictable results.
Combining the reference would yield the data accuracy benefits of isolating data that is relevant to the particular flight event which in turn saves computational resources. As Sterling describes, (Paragraph [0010]) (Paragraph [0059], Lines 10-15) “the mobile device 30 would first reduce, combine, or otherwise synthesize the collected raw flight data (FD.sub.RAW) into the synthesized data set as data in a preparatory step to isolate data that is relevant to the particular flight event(s) being detected and ultimately reported.”
Claim 22 is rejected under 35 U.S.C. 103 as being unpatentable over Gupta in view of Sterling, further in view of Messiah. (WO 2022/192939 A1, hereinafter Messiah)
Claim 22 Discloses: (Original)
“The method as in claim 1, wherein the one or more air travel related software actions comprises causing one or more software gameplay actions within an application on the particular device based on the particular device traveling by the aircraft.”
Gupta and Sterling do not explicitly teach causing one or more software gameplay actions within an application. However, Gupta does teach the following.
Gupta teaches, (Page 12, Column 7, Lines 43-52) “System memory 120 also includes high-speed vehicle or airline services application 280. High-speed vehicle or airline services application 280 can be an application that functions with short range communication device 147 to enable communications with airplane wireless network 190. For example, airline services application 280 can enable mobile device 100 to have voice, text and data services, and to receive movies, video clips, and video phone calls while mobile device 100 is inside an airplane that is airborne or within a high-speed train.”
Messiah does explicitly teach software gameplay actions when a user device is on an airplane.
Messiah teaches, (Paragraph [0006], Lines 1-4, Paragraph [0008] and Paragraph [0009]) “A first aspect of the invention provides a method for virtual interaction with an aircraft, including a step of initiating a virtual interaction with an aircraft using a computing device, wherein initiation of the virtual interaction requires location of the aircraft within a defined zone … Suitably, the computing device according to the method of the first aspect is a mobile computing device. In embodiments, the mobile computing device is a smartphone, smartwatch, tablet, or the like. Suitably, the method of the first aspect is performed using a software application installed, loaded, or running on the computing device” and that, (Paragraph [0110]) “Figures 7-10 depict screens of Play GUI element 2180, with which a user of computing device 1000 can engage in gameplay associated with method 10.”
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to combine the detection of a mobile device which can execute software actions such as receiving a movie as taught by Gupta, with the explicit gameplay application of Messiah, in order to yield predictable results.
Combining the references would yield the well know entertainment and advertisement benefits of providing a gaming application. Messiah provides the following example rationale by relaying, (Paragraphs [0193-0194]) “As well as offering an entertaining gaming experience, users of the methods and systems can be strongly incentivized to participate by the potential for prizes… Advantageously, the methods and systems have been developed to attract interest and investment from airlines and travel agencies and the like. Substantial scope is offered for involvement of such third parties in contributing prizes, such as tickets or vouchers for flights and/or holidays, in exchange for advertising or the like.”
Claims 1, 4, 17-18, 21, and 23-25 are rejected under 35 U.S.C. 103 as being unpatentable over Yuen et al., (US 8,965,730 B2, hereinafter Yuen) in view of Sterling.
Claim 1 Discloses: (Currently Amended)
“A method, comprising: obtaining, by a process, data from one or more environmentally reactive sensors of a particular device; analyzing, by the process, the data for motion characteristics associated with movement of devices during aircraft travel,”
Yuen teaches, (Abstract) “Biometric monitoring devices, including various technologies that may be implemented in such devices, are discussed herein. Additionally, techniques for utilizing altimeters in biometric monitoring devices are provided. Such techniques may, in some implementations, involve recalibrating a biometric monitoring device altimeter based on location data; using altimeter data as an aid to gesture recognition; and/or using altimeter data to manage an airplane of a biometric monitoring device.”
“… determining, by the process and based on analyzing, that the particular device was traveling by aircraft based on a given portion of the data substantially sharing the motion characteristics associated with movement of devices during aircraft travel,”
Yuen teaches, (Page 58, Column 6, Lines 57-62) “In some such implementations of the wearable biometric tracking device, the altitude sensor data may be indicative of flight in an airplane when the altitude sensor data indicates a rate of altitude change that exceeds a rate of altitude change threshold. In some such implementations the rate of altitude change threshold may be above 500 feet per minute.”
“… and causing, by the process, one or more air travel related software actions on the particular device in response to determining that the particular device was traveling by an aircraft at a time associated with the given portion of the data.”
Yuen teaches, (Page 105, Column 99, Lines 48-67, and Column 100, Lines 1-2) “the biometric tracking device may also be placed into a mode associated with air travel through external inputs. For example the biometric tracking device may be placed into the mode associated with air travel upon receiving an input signal via a wireless communications interface such as through receiving a signal from a personal computing or communications device such as a smartphone. In certain implementations, the smartphone may contain an app or functionality that would send the signal to the biometric tracking device automatically when the smartphone is placed into an airplane mode. Thus, when the smartphone is placed into the airplane mode, the biometric tracking device may also be placed into a mode associated with air travel such as an airplane mode. Similarly, when the smartphone airplane mode is deactivated, the smartphone may send a signal to the biometric monitoring device to cause the biometric monitoring device to exit airplane mode (such implementations may require that the biometric monitoring device periodically activate (or keep powered on) a receiver to scan for such a signal).”
“ wherein analyzing comprises scanning the data to identify filtered ranges of interest to analyze and applying one or more qualifiers or disqualifiers,”
Yuen does not explicitly identify filtered ranges of interest to analyze in order to apply one or more qualifiers or disqualifiers.
However, Sterling does teach the preceding limitations.
Sterling teaches, (Paragraph [0010]) “the subject disclosure combines collected data from the sensor suite of the mobile device, knowledge of expected host aircraft motion, and knowledge of typical user motions to differentiate between sensor readings descriptive of motion of the host aircraft from that of motion imparted to the mobile device by actions of the user. The user motions are then filtered out using the approaches described herein, e.g., using a statistical filtering model programmed into memory of the mobile device.”
“ and wherein the motion characteristics associated with movement of devices during aircraft travel are based on one or more of: alternation between high and low acceleration, alternation between high and low gyroscopic precessions, a difference in quantiles of acceleration, a difference in quantiles of gyroscopic precessions, surpassing high or low acceleration thresholds, surpassing high or low gyroscopic precession thresholds, a median of acceleration, a median of gyroscopic precessions, or a standard deviation of acceleration or gyroscopic precessions;”
Sterling teaches, (Paragraph [0012], Lines 8-11) “The mobile device then filters out human-induced motion of the mobile device from the synthesized data set, e.g., using any or all of a statistical filtering model, bandpass filters, thresholds, etc,” wherein, (Paragraph [0052], Lines 9-15) “A threshold standard deviation may be used to filter out human interaction. Thus, execution of computer-readable instructions by the processor 44 of FIG. 1 causes the mobile device 30 to filter out human-induced motion of the mobile device 30 from the raw flight data (FLT.sub.RAW) by calculating such a rolling standard deviation of the accelerometer readings.”
“… and based on disqualifying false positives from the data, wherein disqualifying false positives is based in part on accounting for changes in directions atypical to travel by aircraft;”
Sterling teaches, (Paragraph [0049]) “For example, within the sensor suite 40 of FIG. 2 one may find the gyroscope 48 and the multi-axis accelerometer 49, which can at times output extreme data spikes when the mobile device 30 is lifted, tilted, or dropped. The gyroscope 48 and the multi-axis accelerometers 49 in particular would tend to produce more extreme readings than those that would typically be indicated by normal aircraft operation.”
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to combine the detection of a mobile device which can execute software actions such as taught by Yuen, with the explicit data filtered range methodology as taught by Sterling, in order to yield predictable results.
Combining the reference would yield the data accuracy benefits of filtering out motion induced by a user, rather than motion created by the typical operation of an aircraft. As Sterling describes, (Paragraph [0010]) “the subject disclosure combines collected data from the sensor suite of the mobile device, knowledge of expected host aircraft motion, and knowledge of typical user motions to differentiate between sensor readings descriptive of motion of the host aircraft from that of motion imparted to the mobile device by actions of the user. The user motions are then filtered out using the approaches described herein, e.g., using a statistical filtering model programmed into memory of the mobile device.”
Claim 4 Discloses: (Original)
“The method as in claim 1, wherein analyzing is based on an algorithm that applies a series of weighted, conditional formulas in a particular weighted order on the data.”
Yuen teaches, (Page 58, Column 6, Lines 57-62) “In some such implementations of the wearable biometric tracking device, the altitude sensor data may be indicative of flight in an airplane when the altitude sensor data indicates a rate of altitude change that exceeds a rate of altitude change threshold. In some such implementations the rate of altitude change threshold may be above 500 feet per minute.”
Yuen additionally teaches, (Page 90, Column 70, Lines 8-9) “Location information may be used to estimate GPS signal quality,” and that, (Page 90, Column 70, Lines 22-24) “certainty may be used later by algorithms that attempt to determine the user's track, speed, and/or elevation based on, at least in part, GPS data.”
Yuen additionally teaches, (Page 93, Column 76, Lines 54-59) “The location data may be provided by a location-determination system that is located in the same device or a different device. For example, a smart-watch type biometric monitoring device may include both a pressure sensor and a GPS receiver, and may thus obtain the location data from the GPS receiver,” and that, (Page 94, Column 77, Lines 6-11) “In block 1804, a first location may be determined from the location data. In some implementations, the first location may be a single coordinate obtained from the location data. In other implementations, however, the first location may be an average of multiple coordinates drawn from the location data, e.g., the last 20 location fixes,” as well as, (Page 94, Column 77, Lines 13-14) “In block 1806, the device may look up the first location in a historical topographic dataset.”
Yuen additionally teaches, (Page 94, Column 78, Lines 17-20) “In block 1906, the device may look up the first location in a historical topographic dataset, e.g., a topographic contour database or map set, etc., from a remote device in order to obtain a first topographic altitude, much as in block 1806,” and that, (Page 94, Column 78, Lines 29-34) “Such a dataset may be structured so as to weight altitude data from newer biometric monitoring devices to a greater extent than altitude data from older biometric monitoring devices (or to exclude altitude data from biometric monitoring devices over a certain age). Since some pressure sensors/barometric altimeters may experience drift as they age, such weighting (or exclusion) may reduce the contributions of older biometric monitoring devices (and their altimeters) to the data pool. Such techniques may be modified to treat more favorably data from biometric monitoring devices that, while older, have been recalibrated on a regular basis and that may thus be comparable in performance to newer biometric monitoring devices as compared with older biometric monitoring devices that have not been frequently recalibrated.”
Claim 17 Discloses: (Original)
“The method as in claim 1, wherein the one or more air travel related software actions comprises asking a user whether to share a new location of the particular device to a social network.”
Yuen teaches, (Page 89, Column 67, Lines 36-53) “Through one or more methods, embodiments of the biometric monitoring devices disclosed herein may have sensors that can determine or estimate the location and or context (e.g. in a bus, at home, in a car) of the biometric monitoring device. Purpose-built location sensors such as GPS, GLONASS, or other GNSS (Global Navigation Satellite System) sensors may be used. Alternatively, location may be inferred, estimated or guessed using less precise sensors. In some embodiments in which it is difficult to know the user's location, user input may aid in the determination of their location and or context. For example, if sensor data makes it difficult to determine if a user was in a car or a bus, the biometric monitoring device or a portable communication device in communication with the biometric monitoring device or a cloud server which is in communication with the biometric monitoring device may present a query to the user asking them if they took the bus today or took a car. Similar queries may occur for locations other than vehicular contexts.” Therefore, a request to confirm a user is located within an aircraft would be obvious given the disclosure is mainly directed to a, (Title) “Fitness Monitoring Device With Altimeter And Airplane Mode.”
Yuen additionally teaches, (Page 64, Column 18, Lines 60-66 and Page 65, Column 19, Lines 1-4) “The biometric monitoring device may then transmit data representative of the user's step count to an account on a web service … computer, mobile phone, or health station where the data may be stored, processed, and visualized by the user. Indeed, the biometric monitoring device may measure or calculate a plurality of other physiological metrics in addition to, or in place of, the user's step count. These include, but are not limited to, energy expenditure, e.g., calorie burn, floors climbed and/or descended, heart rate, heart rate variability, heart rate recovery, location and/or heading.”
Yuen additionally teaches, (Page 82, Column 54, Lines 1- 28) “Embodiments of biometric monitoring devices of the present disclosure may also include functionality for streaming or transmitting web content for display on the biometric monitoring device. The following are typical examples of such content … Comparisons between the aforementioned data for the user and similar data for his/her "friends" with similar devices and/or tracking methods 8. Social content such as Twitter feeds, instant messaging, and/or Facebook updates 9.”
Claim 18 Discloses: (Original)
“The method as in claim 17, further comprising: enabling a satellite-based radio navigation system or other location-resolution service or resource to determine at least a coarse location of the particular device to share to the social network.”
Yuen teaches, (Page 64, Column 18, Lines 51-67 and Page 65, Column 19, Lines 1-4) “Portable biometric monitoring devices may collect one or more types of physiological and/or environmental data from embedded sensors and/or external devices and communicate or relay such information to other devices, including devices capable of serving as an Internet-accessible data sources, thus permitting the collected data to be viewed, for example, using a web browser or network-based application … Indeed, the biometric monitoring device may measure or calculate a plurality of other physiological metrics in addition to, or in place of, the user's step count. These include, but are not limited to, energy expenditure, e.g., calorie burn, floors climbed and/or descended, heart rate, heart rate variability, heart rate recovery, location and/or heading, e.g., through GPS, GLONASS, or a similar system.”
Claim 21 Discloses: (Original)
“The method as in claim 1, wherein the one or more air travel related software actions comprise adjusting one or more settings on a sibling device of the particular device.”
Yuen teaches, (Page 64, Column 17, Lines 41-46) “The concepts disclosed and discussed herein may be applied to both stand-alone biometric monitoring devices as well as biometric monitoring devices that leverage sensors or functionality provided in external devices, e.g., external sensors, sensors or functionality provided by smartphones, etc.”
Yuen additionally teaches, (Page 105, Column 99, Lines 48-67, and Column 100, Lines 1-2) “the biometric tracking device may also be placed into a mode associated with air travel through external inputs. For example the biometric tracking device may be placed into the mode associated with air travel upon receiving an input signal via a wireless communications interface such as through receiving a signal from a personal computing or communications device such as a smartphone. In certain implementations, the smartphone may contain an app or functionality that would send the signal to the biometric tracking device automatically when the smartphone is placed into an airplane mode. Thus, when the smartphone is placed into the airplane mode, the biometric tracking device may also be placed into a mode associated with air travel such as an airplane mode. Similarly, when the smartphone airplane mode is deactivated, the smartphone may send a signal to the biometric monitoring device to cause the biometric monitoring device to exit airplane mode (such implementations may require that the biometric monitoring device periodically activate (or keep powered on) a receiver to scan for such a signal).”
Claim 23 Discloses: (Amended)
“The method as in claim 1, wherein causing the one or more air travel related software actions is delayed until after detecting landing of the aircraft.”
In step 3708 of Figure 37, exit mode is effectively delayed until sensor data indicates landing of an aircraft.
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Claim 24 Discloses: (Original)
“The method as in claim 23, wherein causing is further delayed until after detecting an end of a travel journey of the particular device based on detecting chained travel events.”
Examiner in interpreting chained travel events under broadest reasonable interpretation as the sequential take-off and landing sequences of an airplane present in Figure 38.
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Claim 25 Discloses: (Original)
“The method as in claim 1, further comprising: chaining additionally detected travel events into a travel journey of the particular device,”
Yuen teaches, (Page 80, Column 50, Lines 41-45) “Some biometric monitoring devices may have information about the user's calendar and/or schedule. The user's calendar information may be entered directly into the biometric monitoring device or it may be downloaded from a different device (e.g. a smartphone),” and that, (Page 89, Column 67, Lines 1-4) “In one embodiment, the day's travel requirements (to work, from work, between meetings) may be scheduled for the user based on the information in their calendar (or emails or text messages etc.).”
“wherein the one or more air travel related software actions comprise one or more journey-based software actions on the particular device.”
Yuen teaches, (Page 106, Column 101, Lines 30-34) “FIG. 38 shows a flow diagram detailing an example algorithm used by a biometric tracking device to determine whether to first place the biometric tracking device into a mode associated with airplane travel and then to determine whether to exit the mode associated with airplane travel.”
Claim 19 is rejected under 35 U.S.C. 103 as being unpatentable over Yuen in view of Sterling, further in view of Schneider. (US 11,223,591 B2, hereinafter Schneider).
Claim 19 Discloses: (Original)
“The method as in claim 17, further comprising: obfuscating precision of the new location of the particular device to share to the social network by sharing a text-only indication of the new location.”
Yuen and Sterling do not explicitly obfuscate the location of the mobile device using a text-only indication.
However, Yuen does teach the following.
Yuen teaches, (Page 93, Column 76, Lines 34-38) “In some implementations, however, the location-determination system may utilize a camera to obtain images, either automatically or culled from images taken by a user of the device in which the location-determination system resides, that may be analyzed to determine location.”
Schneider does teach the preceding limitation regarding text based resolution.
Schneider teaches, (Page 15, Column 1, Lines 9-16) “Smart devices are capable of determining and sharing a user's location. For example, individuals may use a smart device to take a photo and upload the photo to a social networking site (SNS). The photo may be automatically tagged with geocoordinates associated with the user's location, or the photo may be manually labeled with the user's location based on user input (e.g., “@Chicago” added to text about a shared photo).”
Schneider additionally teaches, (Page 19, Column 9, Lines 37-51) “FIGS. 4A-4C represent various location resolution parameters 124, in accordance with various embodiments of the present disclosure. FIG. 4A illustrates location resolution parameters 124 comprising an ontological location resolution model 400. The ontological location resolution model 400 can define variations in location specificity according to a hierarchical, language-based model. For example, in an order from most general to most specific, an example hierarchy of location resolutions from an ontological location resolution model 400 can comprise the classifications of: country, region, state, county, town, address. Advantageously, the ontological location resolution model 400 can be well-suited for Natural Language Processing (NLP) applications such as those modifying location resolutions created using text descriptors.”
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to combine the imaging system which can determine the location of a mobile device as taught by Yuen, with the ability to adjust location resolution via text based descriptors as taught by Schneider, in order to yield predictable results.
Combining the references would yield the well-known privacy benefits of general text based location descriptors. As Schneider describes, (Page 15, Column 2, Lines 8-13) “Advantageously, the aforementioned method improves privacy, security, and/or safety of a user by dynamically modifying location information associated with publicly available posts without requiring the user to alter their interactions with social networking platforms.”
RELEVANT, BUT NOT CITED ART
The prior art made of record and not relied upon is considered pertinent to Applicant’s disclosure.
Busch (US 10,733,629 B2) teaches, (Page 46, Column 30, Lines 22-26) “In some forms of the invention, the system is able to determine the user's mode of transportation. The mode of transportation may be walking, jogging, biking, riding a motorcycle, driving a car, air travel, or any other mode of transportation,” and that, (Page 46, Column 30, Lines 56-59) “If the elevation data suggests that the user is very high above the ground level, it may be determined that the user is in an aircraft,” as well as, (Page 47, Column 31, Lines 23-33) “Content is sent to a mobile device based upon at least either current location information, historical location information, or anticipated location information … the targeted content is any type of information including but not limited to advertisements, traffic information, danger warnings, or any other type of information that pertains to a geographic location,” as well as, (Page 49, Column 35, Lines 63-67) “The method is initiated at action 900 with location, speed, and heading data from a mobile device. If the mobile device only provides location data, the speed and heading data may be calculated by taking the difference in position and time between subsequent location data points.”
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any 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 ALEXANDER V. GENTILE whose telephone number is (703)756-1501. The examiner can normally be reached Monday - Friday 9-5.
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/ALEXANDER V GENTILE/Examiner, Art Unit 3664
/KITO R ROBINSON/Supervisory Patent Examiner, Art Unit 3664
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