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 .
Status of Claims
This communication is in response to application No. 18/918,063, filed on 10/17/2024. Claims 1-5 are currently pending and have been examined. Claims 1-5 have been rejected as follows.
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Information Disclosure Statement
The information disclosure statement (IDS) filed on 10/17/2024 has been acknowledged.
Specification
The disclosure is objected to because of the following informalities: in par. 21, “The memory 162 stores (records) predetermined acceleration threshold value, predetermined time threshold value, video, signal and the like acquired by the acquisition unit 163A to be described later, program for realizing the function of the processor 163 and the like” should be changed to “The memory 162 stores (records) the predetermined acceleration threshold value, predetermined time threshold value, video, signal and the like acquired by the acquisition unit 163A to be described later, and the program for realizing the function of the processor 163 and the like,” or changed to something similar.
Appropriate correction is required.
Claim Rejections - 35 USC § 112
Claim 1 and 3-5 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claims 1 and 4-5 recite the limitation "at the time of an ignition power supply of a vehicle being turned off". There is insufficient antecedent basis for this limitation in the claim. This should either be changed to “a time”. (Examiner would also like to mention that the phrase could be read as “the moment a vehicle is turned off” rather than “while the vehicle is in a turned off state”, and it may be beneficial to the applicant to be clearer).
Claim 3 recites the limitation "the accident". There is insufficient antecedent basis for this limitation in the claim. This should either be changed to “an accident” or the dependency should be changed to be dependent on claim 2.
Additionally, the meaning of “the impact associated with occurrence of the accident among the impact detected by the acceleration sensor during the learning period” is unclear. “Among the impact” would need to be changed to “among the impacts” in order to be grammatically correct. However, claim 1 claims only one impact (“an impact of a predetermined acceleration threshold value or more”), so the meaning of “the impact associated with occurrence of the accident among the impacts detected” would still be unclear.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1-2 and 4-5 is/are rejected under 35 U.S.C. 103 as being unpatentable over Jun (US 20210012589) in view of Hong (KR 20230061711).
Regarding claim 1, Jun teaches a drive recorder device (Fig. 1, vehicle image recording apparatus 100) comprising a processor (processor 140) configured to:
start video recording (Fig. 3, S205 perform impact image recording) when an impact of a predetermined acceleration threshold value or more is detected (par. 43, "The processor 140 may determine whether an impact of a predetermined reference value or more that is applied to the vehicle corresponds to an impact caused by an accident") by an acceleration sensor (Fig. 1 sensing device 200; par. 50, “the sensing device 200 may include an impact sensor (e.g., a 3-axis G-sensor) that senses the impact applied to the vehicle”) at the time of an ignition power supply of a vehicle being turned off (Fig. 2 S100, vehicle enters parking recording mode);
determine whether a door of the vehicle changes from an open state to a closed state (Fig. 2 S300, monitor door communication signal);
Jun fails to teach to learn the acceleration threshold value using the impact detected by the acceleration sensor during a learning period between a first time point at which the door changes from the open state to the closed state and a second time point which is a predetermined time before the first time point when the door changes from the open state to the closed state. Jun instead only teaches checking whether the impact is a predetermined reference value or more, and does not teach how the reference value is determined.
However, identifying a collision using an acceleration threshold value, where the threshold value is indicative of the expected value of a non-collision event and a collision event, is common in the field. (Examiner would like to mention that the current claim language includes the case that an acceleration of a door slam is measured only once, “learns” what that value is by reading the measurement, and uses that as the acceleration threshold).
Hong teaches learning to identify an impact caused by closing a door using the impact detected by the acceleration sensor (par. 16, "the learning process of the machine learning model used to estimate that an impact caused by opening or closing a door or an impact other than door opening and closing occurs in the parked vehicle"; par. 16, “a characteristic element obtained from the collected acceleration data and air pressure data, which is used as an input of the machine learning model, and an impact corresponding to each of the acceleration data and each air pressure data among the types of recorded impacts It may include a step of setting the type as an output of the machine learning model, whereby the machine learning model can learn appropriate parameters for the input and the corresponding output”; par. 30, “In example embodiments, the characteristic elements for estimating the type of impact may include an effective value (RMS) of acceleration for each axis measured by the accelerometer, a maximum acceleration, and an increase/decrease time”) during a learning period between a first time point at which the door changes from the open state to the closed state and a second time point which is a predetermined time before the first time point when the door changes from the open state to the closed state (par. 16, “An impact test caused by opening or closing a vehicle door and an impact test other than door opening and closing are performed to collect acceleration data and air pressure data of the vehicle before and after the occurrence of each impact, and record the type of each impact”).
Hong does not explicitly teach learning an acceleration threshold. Hong teaches that it takes an acceleration measurement as input to the machine learning algorithm, and outputs an impact type (par. 19, “time series data of the barometer measurement value and time series data of the accelerometer measurement value may be detected by deep It is used as an input of a deep neural network model, and from the output of the deep neural network model, it can be estimated that an impact caused by opening or closing a door in the parked vehicle or an impact other than door opening or closing has occurred”). However, since the acceleration measurement is used in this decision making, it would intrinsically learn some threshold value for the acceleration in order to categorize the impact, and use that threshold information (along with other information) to come to a conclusion.
Hong teaches a method for estimating the type of impact of a vehicle using machine learning. Hong’s system learns by collecting measurement data from different impacts, including impacts from closing a door. Jun and Hong are analogous art because both relate to determining whether an impact was caused from a collision or from closing the vehicle door.
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Jun to incorporate the teachings of Hong in order to estimate the cause of an impact, and to not falsely indicate a door closing event as a crash event (par. 102).
Regarding claim 2, the combination of Jun in view of Hong teaches the drive recorder device according to claim 1. Jun further teaches (par. 43, "The processor 140 may determine whether an impact of a predetermined reference value or more that is applied to the vehicle corresponds to an impact caused by an accident").
Although it is not explicitly taught that the reference value is a value greater than the impact the impact detected by the acceleration sensor when the door changes from the open state to the closed state, and less than or equal to the impact detected by the acceleration sensor when an accident occurs, it can be reasonably assumed that the predetermined reference value is determined in order to distinguish between door closing events and crash events.
Hong teaches the processor is configured to learn the acceleration threshold value so that the acceleration threshold value is greater than the impact detected by the acceleration sensor when the door changes from the open state to the closed state, and less than or equal to the impact detected by the acceleration sensor when an accident occurs (par. 16, "the learning process of the machine learning model used to estimate that an impact caused by opening or closing a door or an impact other than door opening and closing occurs in the parked vehicle"; par. 16, “a characteristic element obtained from the collected acceleration data and air pressure data, which is used as an input of the machine learning model, and an impact corresponding to each of the acceleration data and each air pressure data among the types of recorded impacts It may include a step of setting the type as an output of the machine learning model, whereby the machine learning model can learn appropriate parameters for the input and the corresponding output”; par. 30, “In example embodiments, the characteristic elements for estimating the type of impact may include an effective value (RMS) of acceleration for each axis measured by the accelerometer, a maximum acceleration, and an increase/decrease time”). The machine learning model learns how to estimate an impact by using the acceleration value. Therefore, it would learn a threshold acceleration value that is greater than a door slam, and less than or equal to an accident in order to distinguish between the two.
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Jun to incorporate the teachings of Hong in order to estimate the cause of an impact, and to not falsely indicate a door closing event as a crash event (par. 102).
Regarding claim 4, Jun teaches a video recording method comprising:
starting video recording (Fig. 3, S205 perform impact image recording) when an impact of a predetermined acceleration threshold value or more is detected (par. 43, "The processor 140 may determine whether an impact of a predetermined reference value or more that is applied to the vehicle corresponds to an impact caused by an accident") by an acceleration sensor (Fig. 1 sensing device 200; par. 50, “the sensing device 200 may include an impact sensor (e.g., a 3-axis G-sensor) that senses the impact applied to the vehicle”) at the time of an ignition power supply of a vehicle being turned off (Fig. 2 S100, vehicle enters parking recording mode);
determining whether a door of the vehicle changes from an open state to a closed state (Fig. 2 S300, monitor door communication signal);
Jun fails to teach learning the acceleration threshold value using the impact detected by the acceleration sensor during a learning period between a first time point at which the door changes from the open state to the closed state and a second time point which is a predetermined time before the first time point when the door changes from the open state to the closed state. Jun instead only teaches checking whether the impact is a predetermined reference value or more, and does not teach how the reference value is determined.
However, identifying a collision using an acceleration threshold value, where the threshold value is indicative of the expected value of a non-collision event and a collision event, is common in the field. (Examiner would like to mention that the current claim language includes the case that an acceleration of a door slam is measured only once, “learns” what that value is by reading the measurement, and uses that as the acceleration threshold).
Hong teaches learning the acceleration threshold value using the impact detected by the acceleration sensor (par. 16, "the learning process of the machine learning model used to estimate that an impact caused by opening or closing a door or an impact other than door opening and closing occurs in the parked vehicle"; par. 16, “a characteristic element obtained from the collected acceleration data and air pressure data, which is used as an input of the machine learning model, and an impact corresponding to each of the acceleration data and each air pressure data among the types of recorded impacts It may include a step of setting the type as an output of the machine learning model, whereby the machine learning model can learn appropriate parameters for the input and the corresponding output”; par. 30, “In example embodiments, the characteristic elements for estimating the type of impact may include an effective value (RMS) of acceleration for each axis measured by the accelerometer, a maximum acceleration, and an increase/decrease time”) during a learning period between a first time point at which the door changes from the open state to the closed state and a second time point which is a predetermined time before the first time point when the door changes from the open state to the closed state (par. 16, “An impact test caused by opening or closing a vehicle door and an impact test other than door opening and closing are performed to collect acceleration data and air pressure data of the vehicle before and after the occurrence of each impact, and record the type of each impact”).
Hong does not explicitly teach learning an acceleration threshold. Hong teaches that it takes an acceleration measurement as input to the machine learning algorithm, and outputs an impact type (par. 19, “time series data of the barometer measurement value and time series data of the accelerometer measurement value may be detected by deep It is used as an input of a deep neural network model, and from the output of the deep neural network model, it can be estimated that an impact caused by opening or closing a door in the parked vehicle or an impact other than door opening or closing has occurred”). However, since the acceleration measurement is used in this decision making, it would intrinsically learn some threshold value for the acceleration in order to categorize the impact, and use that threshold information along with other information to come to a conclusion about the impact type.
Hong teaches a method for estimating the type of impact of a vehicle using machine learning. Hong’s system learns by collecting measurement data from different impacts, including impacts from closing a door. Jun and Hong are analogous art because both relate to determining whether an impact was caused from a collision or from closing the vehicle door.
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Jun to incorporate the teachings of Hong in order to estimate the cause of an impact, and to not falsely indicate a door closing event as a crash event (par. 102).
Regarding claim 5, Jun teaches a non-transitory recording medium (Fig. 1, storage 120) having recorded thereon a computer program for causing a processor (processor 140) to execute a processing comprising:
starting video recording (Fig. 3, S205 perform impact image recording) when an impact of a predetermined acceleration threshold value or more is detected (par. 43, "The processor 140 may determine whether an impact of a predetermined reference value or more that is applied to the vehicle corresponds to an impact caused by an accident") by an acceleration sensor (Fig. 1 sensing device 200; par. 50, “the sensing device 200 may include an impact sensor (e.g., a 3-axis G-sensor) that senses the impact applied to the vehicle”) at the time of an ignition power supply of a vehicle being turned off (Fig. 2 S100, vehicle enters parking recording mode);
determining whether a door of the vehicle changes from an open state to a closed state (Fig. 2 S300, monitor door communication signal);
Jun fails to teach learning the acceleration threshold value using the impact detected by the acceleration sensor during a learning period between a first time point at which the door changes from the open state to the closed state and a second time point which is a predetermined time before the first time point when the door changes from the open state to the closed state. Jun instead only teaches checking whether the impact is a predetermined reference value or more, and does not teach how the reference value is determined.
However, identifying a collision using an acceleration threshold value, where the threshold value is indicative of the expected value of a non-collision event and a collision event, is common in the field. (Examiner would like to mention that the current claim language includes the case that an acceleration of a door slam is measured only once, “learns” what that value is by reading the measurement, and uses that as the acceleration threshold).
Hong teaches learning the acceleration threshold value using the impact detected by the acceleration sensor (par. 16, "the learning process of the machine learning model used to estimate that an impact caused by opening or closing a door or an impact other than door opening and closing occurs in the parked vehicle"; par. 16, “a characteristic element obtained from the collected acceleration data and air pressure data, which is used as an input of the machine learning model, and an impact corresponding to each of the acceleration data and each air pressure data among the types of recorded impacts It may include a step of setting the type as an output of the machine learning model, whereby the machine learning model can learn appropriate parameters for the input and the corresponding output”; par. 30, “In example embodiments, the characteristic elements for estimating the type of impact may include an effective value (RMS) of acceleration for each axis measured by the accelerometer, a maximum acceleration, and an increase/decrease time”) during a learning period between a first time point at which the door changes from the open state to the closed state and a second time point which is a predetermined time before the first time point when the door changes from the open state to the closed state (par. 16, “An impact test caused by opening or closing a vehicle door and an impact test other than door opening and closing are performed to collect acceleration data and air pressure data of the vehicle before and after the occurrence of each impact, and record the type of each impact”).
Hong does not explicitly teach learning an acceleration threshold. Hong teaches that it takes an acceleration measurement as input to the machine learning algorithm, and outputs an impact type (par. 19, “time series data of the barometer measurement value and time series data of the accelerometer measurement value may be detected by deep It is used as an input of a deep neural network model, and from the output of the deep neural network model, it can be estimated that an impact caused by opening or closing a door in the parked vehicle or an impact other than door opening or closing has occurred”). However, since the acceleration measurement is used in this decision making, it would intrinsically learn some threshold value for the acceleration in order to categorize the impact, and use that threshold information along with other information to come to a conclusion about the impact type.
Hong teaches a method for estimating the type of impact of a vehicle using machine learning. Hong’s system learns by collecting measurement data from different impacts, including impacts from closing a door. Jun and Hong are analogous art because both relate to determining whether an impact was caused from a collision or from closing the vehicle door.
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Jun to incorporate the teachings of Hong in order to estimate the cause of an impact, and to not falsely indicate a door closing event as a crash event (par. 102).
Claim(s) 3 is/are rejected under 35 U.S.C. 103 as being unpatentable over Jun in view of Hong, and further in view of DiCarlo (US 20200406727).
Regarding claim 3, the combination of Jun in view of Hong teaches the drive recorder device according to claim 1. Both Jun and Hong fail to teach the processor is not configured to use the impact associated with occurrence of the accident among the impact detected by the acceleration sensor during the learning period to learn the acceleration threshold value.
Hong only teaches using both a door slamming impact and an accident impact (par. 16, “An impact test caused by opening or closing a vehicle door and an impact test other than door opening and closing are performed to collect acceleration data and air pressure data of the vehicle before and after the occurrence of each impact, and record the type of each impact”).
However, DiCarlo teaches the processor is not configured to use the impact associated with occurrence of the accident among the impact detected by the acceleration sensor during the learning period to learn the acceleration threshold value (column 18 line 18, “In some examples, a machine learning model may be applied to the event signature methods independently or in a combined manner. The machine learning model may be developed by providing training sets of known gyroscope and/or sound level data associated with door closing events to the model”).
DiCarlo teaches a method for detecting vehicle door closing events by only using door closing data. Hong teaches a method for detecting vehicle door closing events and collision events by using door closing data and collision data. It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the combination of Jun in view of Hong to incorporate the teachings of DiCarlo in order to detect vehicle door closing events using only door closing data. Learning how to identify a door slam would also include learning an expected acceleration range for a door slam. Detecting an abnormal value (for example, a non-door slam acceleration value) to determine a possible crash is well-known in the art (see Agata US 20220410675), and would have been an obvious modification.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Agata (US 20220410675) teaches detecting an abnormality to determine an accident
Tammali (US 11961339 B2) teaches using a machine learning algorithm to learn to identify a collision
Kasuya (US 20220217298) teaches a recording control apparatus that detects an event using vehicle sensors
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MINATO LEE HORNER whose telephone number is (571)272-5425. The examiner can normally be reached M-F 8-5.
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/M.L.H./Examiner, Art Unit 3665 /CHRISTIAN CHACE/Supervisory Patent Examiner, Art Unit 3665