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 .
This application is a CON of 16/110,026 08/23/2018 PAT 11661073
Status of Claims
Claims 1, 4-12, and 15-19 are currently pending and rejected.
Claims 2, 3, 13, and 14 are canceled.
Claim Rejection – 35 U.S.C. 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1, 4-12, and 15-19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Adams et al. (Pub. No.: US 2017/0021764), in view of Hayward (Patent No.: US 9,836,962), Grosso (Pub. No.: US 2014/0142989), Fernandes et al. (Pub. No.: US 2015/0363886), and Huang et al. (Patent No.: US 10,891,693).
As per claim 1 and 12, Adams teaches a system configured to monitor use conditions of a machine and provide feedback to an operator of the machine to maintain use within certain parameters, comprising:
a mobile personal communication device associated with the operator and located proximate to the machine (see paragraph 0192 and 0194), including:
a plurality of sensors, each sensor configured to monitor at least one parameter, the plurality of sensors selected from, speed, mileage, and run-time sensors, each sensor generating a sensor signal encapsulating the monitored parameter and transmitting the generated sensor signal (see paragraph 0002 and 0041, “a plurality of sensors located proximate to the vehicle, each sensor configured to monitor at least at least one vehicle parameter, the plurality of sensors selected from an accelerometer, speed, temperature, mileage, oil level, oil pressure, run-time and location sensors”),
a control unit including a control unit memory that receives and stores the transmitted sensor signals and selectively combines the sensor signals (see paragraph 0002 and 0041, “a control unit that receives the generated signal from each of a plurality of sensors, the control unit including a memory that stores the received signal and selectively combines the received signal with other signal received from others of the plurality of sensors”), and
a control unit transmitter coupled to the control unit that transmits the combined signal (see paragraph 0002 and 0041, “a first transmitter coupled to the control unit capable of transmitting the combined signal”);
a transceiver remote from the machine that receives the transmitted combined signal, and stores the combined signal in a memory unit (see paragraph 0002, “a second transceiver remote from the vehicle that receives a transmitted condition, and compares that condition to received conditions from other vehicles and provides feedback to adjust the user of the vehicle based on the comparison”; also see paragraph 0041 and 0049), and
a processor of a data processing service to reduce processing performed by the mobile personal communication device, the processor that processes the combined signal to capture the signal from each of the plurality of sensors, and compares a condition identified by the captured signals to received conditions from other mobile personal communication devices and provides an adjustment signal to the transceiver to broadcast to the mobile personal communication device providing feedback to adjust the use of the machine based on the comparison (see paragraph 0041 and 0049, “The description includes a control unit that receives the generated signal from each of a plurality of sensors, the control unit including a memory that stores the received signal and selectively combines the received signal with other signals received from others of the plurality of sensors”; see paragraph 0249-0251, “a processor configured to receive information associated with telematic data, wherein the telematic data is associated with at least one of the vehicle(s), the telematic data providing information concerning user of the at least one vehicle(s)”; the control unit and the processor in Adams are not part of the mobile personal communication device),
wherein the comparison of the condition utilizes a plurality of relativity factors, wherein each of the relativity factors is a numerical value generated based on the comparison of the condition for the machine to the conditions for other machines associated with corresponding conditions (see paragraph 0064, 0163-0167, 0171, 0176-0177, 0181-0185, 0212, 0232, 0235-0239, and in particularly 0287, “There relativity factors may be based on speeding, braking, acceleration, turns, mileage, time of day analysis, driving location risk, distracted driving, hot spot driving, and the types of weather during driving. Further these relativity factors may be numeric value(s) for a type of measured driving behavior. The relativity factor may be relative to other drivers within the same demographic, driving on the same or similar roads under the same or similar conditions, or to the posted speed limit, or driving regulations…A computer system then users a multivariate analysis to generate an adjusted risk score based on the results of this analysis”; also see paragraph 0289-0291);
wherein the processor registers a location in real time as one of: a destination or traffic location based on: 1. a signal processed from at least one of the plurality of sensors, 2. a predetermined time period rule in which the speed sensor generates a speed signal of zero for a predetermined duration, and 3. a location assessment including identification of the location as the destination location or the high traffic location based on external real-time traffic density data, and the processor first determines the speed signal is zero for a predetermined duration and second executes the location assessment (see paragraph 0063, “The system 100 receives the telematics data, and categorizes information as destination information (step 230). For example, the system 100 may receive location updates every 10 seconds. If the vehicle 140 is stopped, for more than a predetermined time period (e.g., 15 minutes) it may register a location as destination location. The system 100 may further be configured to access external real-time data, such as traffic data to refined its information. For example, if a vehicle 140 is stopped for more than 15 minutes, and the location is determined to be a high traffic location, the system 100 may then determine that the stoppage is not a destination, but a traffic related stoppage”);
wherein the processor determines transmitted sensor signals meet a pre-determined condition of having been collected for: a predetermined period of time and predetermined number of miles, and generates a value in response to the determination (see paragraph 0115, “The telematics device may be configured to provide telematics data periodically as well as based on a trigger”, one skilled in the art would know that the trigger can be vehicle being driven over a predetermined number of miles; see paragraph 0148, 0220, 0235, “Based on evaluation (e.g., one month, 2 months, year, or time between renewals)…”, the data collection being analyzed is based on collection time meeting a predetermined period of time; see paragraph 0279 and 0288, “The telematics data may be received for a predetermined time period”; also see paragraph 0098, 0178, 0182, 0198, 0213, 0238, especially 0267, “the 16 year old driver is expected to drive 2000 miles a year. However, the measured mileage is much less…whether to adjust the rate or credit or penalize the driver in the pricing” and “the driver may be in position to receive a significant discount as soon as they system 100 determines that he will not be driving the expected 2000 miles”; also see paragraph 0289-0290);
wherein the processor predicts an operator identifier for the operator of the machine, the prediction based on the received sensor signals (see paragraph 0003, “This sensor feedback may be used to identify the driver of the vehicle, such as by sensing the radio station, ignition key used, Bluetooth connectivity of a cellular device, acceleration patterns, sensed location and destination”; also see paragraph 0140-0145); and
an operator interface for providing feedback to the operator of the machine including at least one indication associated with the adjustment signal, wherein the feedback provided by the adjustment signal is directly received by the machine avoiding manipulation and the feedback automatically causes a physical alteration of the operation of the machine including at least automatically turning from a location (see paragraph 0041, “and a user interface for providing feedback to a user including at least one of visual indication, audible indication, and physically altering the use of the vehicle”; also see claim 1, “provides a signal to the transceiver to broadcast to the vehicle providing feedback to adjust the use of the vehicle based on the comparison”), and
wherein a past automatic turning from a location affects a route selection pattern of the machine and an identification of the operator (see paragraph 0003, “The systems and methods disclosed herein identify driver signature structures, based on sensor feedback about the use of the vehicle, which characterize how the vehicle is being driven. This sensor feedback may be used to identify the driver of the vehicle”; also see paragraph 0211, “scoring route selection patterns, braking patterns, accelerating patterns, and the speed, proportionally and accuracy of the vehicle’s response to the environment, such as obstacles and changing conditions. The automated system would be treated as a unique driver with a particular signatured attached”; also see claim 4 “wherein physically altering the use of the vehicle includes at least one of…steering from a location”; prior art treats autonomous driving as an operator, thus automatic turning is recorded and analyzed as part of route selection pattern of the machine).
Examiner notes Adams appears to teach the mobile personal communication device receives telematic data from telematic device installed on the vehicle, rather than using sensors of the mobile personal communication device to capture driving data (see paragraph 0192 and 0279). However, Examiner points out that using sensors on a mobile device to capture driving data for insurance purposes is well-known. Examiner cites Hayward to support this argument.
Hayward teaches a mobile personal communication device associated with the operator and located proximate to the machine, including a plurality of sensors, each sensor configured to monitor at least one parameter, the plurality of sensors selected from speed, mileage, and run-time sensors (see abstract and col 2 line 55-61, “The telematics data is generated by an originating mobile device (i) having a Telematics Application (or ‘App’), and (ii) associated with a second driver/vehicle, the telematics data including acceleration, braking, speed, heading, and location data associated with an originating vehicle”; see col 3 line 12-31, “The telematics data may (1) be generated and/or collected by the originating mobile device”; see col 7 line 14-34, “For instance, the mobile device may be equipped with (i) various sensors and/or meters capable of generating telematics data”; also see col 4 line 28-51, “(ii) monitoring, by the one or more processors associated with the server, a time amount and/or mileage amount that the insured customer drives an insured vehicle with the risk mitigation or prevention functionality discussed herein, or the Telematics App, enabled and/or executing on their mobile device”),
wherein each sensor generates the encrypted sensor signal in response to an event trigger” (see col 21 line 46-61 and col 22 line 23-37, “mobile computing device 300 may begin to capture data upon detecting that it has been placed in a cradle, and otherwise not capture data in such a manner…mobile computing device 300 may utilize one or more sensors (e.g., an accelerometer that is part of sensor array 326) to determine that the mobile computing device 300 has changed orientation to horizontal (as is common when docked in a vehicle), that mobile computing device 300 is communicating via BLUETOOTH with the vehicle, that the vehicle is moving above a threshold speed, etc. Aspects including any suitable number of conditions, upon being satisfied, triggering mobile computing device 300 to start collecting telematics data, images, audio, video, etc., vis sensor array 326”; Hayward suggests any suitable number of conditions could be programmed as the triggering events which cause the mobile device sensors to start collecting telematics data),
each sensor generating a sensor signal encapsulating the monitored parameter and transmitting the generated encrypted sensor signal (see col 26 line 4-16, “Telematics collection routine 354 may include instructions, that when executed by controller 340, facilitate sampling, monitoring, measuring, collecting, quantifying, storing, encrypting, transmitting, and/or broadcasting of telematics data”).
It would have been obvious to one of ordinary skill in the art at the time of effective filing of the present application to modify Adams with Hayward to include using sensors on mobile device to monitor at least one parameter, the plurality of sensors selected from acceleration, speed, mileage, run-time and location sensors, wherein each sensor generates the encrypted sensor signal in response to an event trigger. The modification would have been obvious, because it is merely applying a known technique (i.e., using sensors on mobile device to capture driving data) to a known system (i.e., system configured to monitor use conditions of a machine) ready to provide predictable result (i.e., allow the system to be used on older machine/vehicle which does not have built-in sensors).
Examiner notes the combination of Adams and Hayward does not teach the processor transmits a location-based alert indicating the machine is traveling on an unusual path based on the comparison.
Grosso teaches wherein the processor determines transmitted sensor signals meet a pre-determined condition of having been collected for: a predetermined period of time and predetermined number of miles, and generates a value in response to the determination (see paragraph 0030, “a customer can receive a data collection device 104 from the insurance company, couple the device 104 to his car for a set period of time or number of miles, and then mail the device 104 with the collected data to the insurance company system 108 or extract or send the collected data to the insurance company system 108 via mail, email, or through a website”; also see paragraph 0060, “a final discount value may not be determined until data has been collected for a predetermined period of time and/or a predetermined number of miles”);
the processor transmits a location-based alert indicating the machine is traveling on an unusual path based on the comparison (see paragraph 0023, “The data processing service 106 can perform additional monitoring functions, such as vehicle security monitoring or providing location-based alerts (e.g., alerting a parent or employer when a vehicle travels an unusual path) and/or speed alerts”).
It would have been obvious to one of ordinary skill in the art at the time of effective filing of the present application to modify the combination of Adams and Hayward with teaching from Grosso to include wherein the processor determines transmitted sensor signals meet a pre-determined condition of having been collected for: a predetermined period of time and predetermined number of miles, and generates a value in response to the determination; and the processor transmits a location-based alert indicating the machine is traveling on an unusual path based on the comparison. The modification would have been obvious, because it is merely applying a known technique (i.e., collecting data over a predetermined period of time and miles, and providing alert to indicate that the vehicle is traveling on unusual path) to a known system (i.e., system configured to monitor use conditions of a machine) ready to provide predictable result (i.e., ensure monitoring period is sufficient for calculating insurance risk, and provide security monitoring).
Examiner further notes the combination of Adams, Hayward, and Grosso still does not teach the operator displays a location of each of one or more safety events.
Fernandes teaches the operator displays a location of each of one or more safety events (see claim 1, “receive data indicative of safety events…and a map display associated with the first vehicle that displays a plurality of indications of a plurality of the identified safety events involving the first vehicle, comprising safety events occurring on a plurality of trips, and indications of one or more safety events associated with other vehicles; also see FIG. 7 and FIG. 8, the figures of the prior art are nearly identical to FIG. 19 and FIG. 20 of the present application).
It would have been obvious to one of ordinary skill in the art at the time of effective filing of the present application to modify the combination of Adams, Hayward, and Grosso with teaching from Fernandes to include the operator displays a location of each of one or more safety events. The modification would have been obvious, because it is merely applying a known technique (i.e., displaying safety events on a map) to a known system (i.e., system configured to monitor use conditions of a machine) ready to provide predictable result (i.e., allow user to see where safety events occurred).
Examiner further notes the combination of Adams, Hayward, Grosso, and Fernandes still does not teach a safety event score for the operator of the machine for each of two or more safety event types, the safety event types including intensity-based braking events, hard braking events, excessive speeding, and rapid de-acceleration, and a comparison of the safety event score for the operator of the machine for each of two or more safety event types to a safety event score for one or more operators of other machines for respective safety event types.
Huang teaches a safety event score for the operator of the machine for each of two or more safety event types, the safety event types including intensity-based braking events, hard braking events, excessive speeding, and rapid de-acceleration, and a comparison of the safety event score for the operator of the machine for each of two or more safety event types to a safety event score for one or more operators of other machines for respective safety event types (see claim 1, “receiving trip data under a Usage Based Insurance (UB) model for a driver; determining, using a processor on a computer, tough contextual incidents in said trip data; calculating scoring metrics for said driver during said tough contextual incidents; comparing the scoring metrics of said driver during said tough contextual incidents with scoring metrics of other drivers during same tough contextual incidents”; also see column 3 line 19-62, “some contextual data, such as driving behavior during any specific trip, can be ascertained from the reported driving data for that trip, by for example associating present locations with report data for speed, acceleration, braking, etc.” and “reported trip data can be used to calculate one or more categories of driving behavior 206, such as speed-related behavior (e.g., anxious accelerations, harsh braking, over-speeding, etc.)”).
It would have been obvious to one of ordinary skill in the art at the time of effective filing of the present application to modify the combination of Adams, Hayward, Grosso, and Fernandes with teaching from Huang to include a safety event score for the operator of the machine for each of two or more safety event types, the safety event types including intensity-based braking events, hard braking events, excessive speeding, and rapid de-acceleration, and a comparison of the safety event score for the operator of the machine for each of two or more safety event types to a safety event score for one or more operators of other machines for respective safety event types. The modification would have been obvious, because it is merely applying a known technique (i.e., comparing event safety score of a driver to other drivers in similar event) to a known system (i.e., system configured to monitor use conditions of a machine) ready to provide predictable result (i.e., normalize behavior data and improve risk assessment).
Claims 2-3 and 13-14 are cancelled.
As per claim 4 and 15, Adams teaches wherein the machine is a vehicle and physically altering operation of the vehicle includes at least one of: (i) applying brakes, and (ii) turning from a location (see paragraph 0003, “The vehicle may also be controlled to apply the brakes”; also see paragraph 0055 and claim 4 for applying brakes autonomously; see paragraph 0101, “the vehicle may provide autonomous features where the vehicle is connected to weather data and based on the weather data moves into the garage…the vehicle may move to a safer location based on the weather data…upon receipt of information that requires movement, may turn itself on and move as appropriate to aid in protecting the vehicle”, prior art clearly teaches transmitting location-based alert to vehicle/machine so that it can move itself to a safer location).
As per claim 5 and 16, Adams teaches wherein the processor that processes the combined signal also automatically predicts an operator identifier associated with received machine information, wherein said prediction utilizes a prediction algorithm based on a pattern detected via a machine learning analysis of past operator usage of the machine (see paragraph 0003, “This sensor feedback may be used to identify the driver of the vehicle, such as by sensing the radio station, ignition key used, Bluetooth connectivity of a cellular device, acceleration patterns, sensed location and destination, and the like”; also see paragraph 0060 and 0113, “The DPU 170 may use a software based algorithm to analyze the telematics data to identify driving segments wherein each driving segment is associated with a driver signature”).
As per claim 6 and 17, Adams teaches wherein the predicted operator identifier and the combined signal are used to calculate a risk score (see paragraph 0093).
As per claim 7, Adams teaches wherein a record for the operator of the machine is updated with the risk score (see paragraph 0064 and 0287).
As per claim 8, Adams teaches wherein the calculated risk score is also based on at least one machine safety feature associated with a machine identifier (see paragraph 0093-0099).
As per claim 9, Adams teaches wherein the machine safety feature is at least one of adaptive headlights, an autonomous operation feature, a camera, a sensor, an automatic braking feature, a brake warning feature, a parking feature and a lane departure warning (see paragraph 0073, 0084-0088, and 0092).
As per claim 10, Adams teaches wherein at least one of the plurality of relativity factors is based on a predetermined road segment (see paragraph 0164-0165 and 0237).
As per claim 11, Adams teaches wherein at least one of the plurality of relativity factors is a braking relativity factor, a speeding relativity factor, and a time of day relativity factor (see paragraph 0171, 0176-0177, 0181, 0183-0184, and 0240).
As per claim 18, Adams teaches wherein the feedback provided by the adjustment signal is directly received by the machine and automatically causes a physical altercation of operation of the machine (see paragraph 0041, “and a user interface for providing feedback to a user including at least one of visual indication, audible indication, and physically altering the use of the vehicle”; see paragraph 0003, “The vehicle may also be controlled to apply the brakes”; also see paragraph 0055 and claim 4 for applying brakes autonomously; see paragraph 0101, “the vehicle may provide autonomous features where the vehicle is connected to weather data and based on the weather data moves into the garage…the vehicle may move to a safer location based on the weather data…upon receipt of information that requires movement, may turn itself on and move as appropriate to aid in protecting the vehicle”).
As per claim 19, Adams teaches wherein the processor corrects a prediction of the operator identifier for the machine based on third party data (see paragraph 0003, “This sensor feedback may be used to identify the driver of the vehicle, such as by sensing the radio station, ignition key used, Bluetooth connectivity of a cellular device, acceleration patterns, sensed location and destination”; also see paragraph 0140-0145; some of the data used to identify or correct identity of the driver is third party data).
Response to Remarks
Rejection under 35 U.S.C. 103
In the response filed on 04/01/2026, Applicant amended independent claims 1 and 12 by adding a few limitations. Examiner cites new paragraphs in Adams and Grosso, and adds a new prior art, Huang et al. (Patent No.: US 10,891,693), to address the added limitations.
Updated rejection is provided in this Office Action.
Conclusion
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/HAO FU/Primary Examiner, Art Unit 3697
May-2026
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