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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 5/27/2026 has been entered.
Specification
The title of the invention is objected to because of the following informalities: “METHOD OF SYNCRONISING DATASETS” appears to be a typographical error and should read “METHOD OF SYNCHRONIZING DATASETS” or “METHOD OF SYNCHRONISING DATASETS” to improve clarity.
Drawings
The drawings are objected to because Figures 3-4 and 8A-8B are illegible and/or difficult to read. Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Claim Interpretation
“Position receiver” will be understood as a receiver/sensor for receiving satellite navigation signals (e.g. a GPS receiver, GNSS receiver, or GLONASS receiver) in light of at least page 4 lines 9-16 of the specification.
“The data” is interpreted as having antecedent basis to both the first data and second data. That is, “the data” is shorthand for “the first data and/or second data”.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 11-12 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the enablement requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to enable one skilled in the art to which it pertains, or with which it is most nearly connected, to make and/or use the invention. Claim 9 states that
the first data and/or second data is collected at a particular frequency, and wherein the data is processed using the particular frequency in which the data is collected.
However, claim 11 states that
the data is processed using a lower frequency than the frequency in which the data is collected.
And claim 12 recites that
when the first data and the second data are collected at different frequencies, the data is processed at a same frequency.
Since claims 11-12 depend on claim 9, they should contain all the limitations of claim 9, but instead, they appear to be alternatives to claim 9. In claim 9, one or both data sets are collected and processed at the particular frequency. Claim 11 contradicts this by saying that the processing frequency is lower than the collecting frequency. Claim 12 also contradicts claim 9 by saying that when the two data sets are not collected at the same particular frequency the data is processed at the same frequency (i.e. the first and second data are processed as the same frequency as each other). There is no enablement within the disclosure that allows for the situations of 9 and 11 or 9 and 12 to occur at the same time. Instead, page 4 line 24 to page 5 line 4 pose these as alternatives:
“Normally, the data is collected at a particular frequency. Any frequency may be chosen. The data can be processed using the particular frequency in which the data is collected. The data can be processed using a frequency which is different to, typically less than the frequency in which the data is collected. In an embodiment, the data is collected at a frequency of 100 Hz. The data may be processed using this frequency. A lesser (or greater) frequency may be used to reduce the amount of processing required. For example, the data may be collected at a frequency of 100 Hz but processed at a
frequency of 1 Hz. If the data in respective dataset is collected at different frequencies,
then the data will typically be processed at the same frequency. Conversion may be required. The conversion may take place on any basis such as for example an average may be used. It is important that the same parameter be used as the basis for the
correction in each of the datasets and that the datasets have the same time unit basis.”
Since the disclosure does not provide direction to the inventor on how to make or use the invention as claimed in claim 11 or claim 12 nor provides explanation of a working example as detailed in claim 11 or claim 12, one of ordinary skill in the art would be formed to perform undue experimentation (e.g. by adding more sensors, by attempting additional processing, and so on) to attempt to make a system that satisfactorily can perform claims 11-12. Therefore, the claims are not enabling.
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 11-12 and 24 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.
Regarding claims 11-12, as detailed in the 112(a) rejection above, both claims 11 and 12 contradict claim 9 which they depend upon. It is unclear how, when the first data and second data are collected at different frequencies, the first data and/or second data can be collected at the same frequency (the particular frequency) as it is processed and, simultaneously, be processed at a different frequency such that the first data and/or second data are processed at the same frequency. Likewise, it is unclear how the data can be both processed as the same frequency (the particular frequency) as the data was collected and also simultaneously at a lower frequency. The claims as written provide a contradiction that makes the metes and bounds of the claims unclear. For the purpose of examination, claims 11-12 will be interpreted as being dependent upon claim 1. Examiner recommends amending claims 11-12 to be dependent upon claim 1 to overcome both 112(a) and 112(b) rejections.
Regarding claim 24, the term “similar devices” in claim 24 is a relative term which renders the claim indefinite. The term “similar” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. It is unclear how “similar” is similar enough to cause infringement on the claim language. Can the similar device be a server, laptop, desktop computer, or infotainment center? Does the device need to be mobile to be “similar”, or will any device with computing potential able to run an application satisfy this claim? It is unclear the metes and bounds of “similar device” and therefore the claim is indefinite.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-2, 5, 7-9, 11-12, 18-20, and 23-24 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim(s) recite(s) the following limitations:
collecting first data in a first data set over a time period, t, from at least one accelerometer as the at least one first sensor;
collecting second data in a second data set over the time period, t, from at least one position receiver as the at least one second sensor;
calculating an x-axis and y-axis acceleration magnitude from the first data set;
calculating an x-axis and y-axis acceleration magnitude from the second data set;
calculating a cross-correlation between the respective x-axis and y-axis acceleration magnitudes from the first data set and the second data set;
identifying a synchronization time offset corresponding to a maximum correlation between the respective x-axis and y-axis acceleration magnitudes from the first data set and the second data set;
applying the synchronization time offset to the second data set to synchronize the second data set with the first data set; and
making an impact detection decision based on the synchronized first dataset and second dataset
wherein the first data and the second data are collected in relation to a same vehicle.
The limitations recited above, as drafted, are a process that, under its broadest reasonable interpretation, covers performance of the limitations in the mind but for the recitation of generic computer components. That is, other than reciting an accelerometer, a position receiver, and a processing device, nothing in the claim element precludes the steps from practically being performed in the mind. For example, a person, mentally or with pen and paper, can separate received acceleration value readings into two time series of x and y axis magnitudes (c). The person can then, mentally or with pen and paper, numerically differentiate positioning data into acceleration data which can be likewise split into x and y components (d). The person can then use a cross correlation function to find a maximum cross correlation between the two sets of processed data for both x and y components wherein the maximum cross correlation can be understood as a time offset between the two data sets (e-f). The person can then adjust the original time series of the positioning data by the offset amount to synchronize the readings of the two data sets (g). The person can then examine both data sets together and determine timesteps wherein an impact may have occurred based on the data sets (h). If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the "Mental Processes" grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
This judicial exception is not integrated into a practical application because the accelerometer and position receiver is/are recited at a high level of generality such that it amounts to no more than mere instructions to apply the exception using (a) generic sensing component(s). Mere instructions to apply an exception using a generic sensing component cannot provide an inventive concept. See MPEP § 2106.05(f). The processing device and software application is/are recited at a high level of generality such that it amounts to no more than mere instructions to use the processing device and software application as a tool to perform the abstract idea. Mere instructions to apply an exception cannot provide an inventive concept. See MPEP § 2106.05(f). Further, the recitation of an abstract idea applied to a computer does not prohibit the idea from being performed mentally as detailed in MPEP 2106.04(a)(2)(III)(C) and the court cases cited therein. The limitations of collecting first and second sensor data (a-b and i) and is an insignificant extra pre-solution activity of mere data gathering. Mere data gathering cannot form an inventive concept. See MPEP § 2106.05(g).
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the accelerometer and position receiver are generically claimed as detailed above. Further, the processing device and software application are mere instructions to apply the exception using the processing device and software application as a tool as detailed above. A conclusion that an additional element is insignificant extra-solution activity in Step 2A should be re-evaluated in Step 2B to determine if they are more than what is well-understood, routine, and conventional (WURC) activity in the field. The limitation of collecting first and second sensor data (a-b and i) is a WURC activity because buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) indicated that the reception of data over a network is a WURC function. See MPEP § 2106.05(d)(II). Hence, the claims are not patent eligible.
Dependent claim(s) 2, 5, 7-9, 11-12, 18-20 and 23-24 do(es) not recite any further limitations that cause the claim(s) to be patent eligible. Rather, the limitations of the dependent claim(s) is/are directed towards additional aspects of the abstract idea, additional aspects for the WURC activity of receiving data, or provide additional details regarding the generic structural components detailed above.
Claims 7-8 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim(s) does/do not fall within at least one of the four categories of patent eligible subject matter because the claims are directed towards software per se. Particularly, claim 7 claims
The impact detection system as claimed in claim 1, implemented in a software application…
This thus is interpretable prima facie as meaning that the impact detection system as a whole is implemented in a software application. That is, all the hardware components claimed in claim 1 as being comprising components of the impact detection system are merely simulated within a software application. This further appears to read as a software simulation of hardware components as the software application of claim 7 has separate and distinct antecedent basis from the impact detection software application of claim 1. A review of the specification appears to indicate that the hardware of the system is not meant to be comprised in (i.e. implemented in) a software application but rather that the software application is intended to only perform the method steps as claimed in claim 1. In light of the specification, for the purpose of examination regarding 112(a) and 103, the software application of claim 7 will be interpretated as antecedent to the impact detection software application of claim 1 wherein the “implement in” phrase only means that the method steps are implemented in the software application. Examiner recommends cancelling claim 7 or amending it to have proper antecedent basis without indicating that the entire system is implemented in software to overcome the rejection.
Dependent claim(s) 8 do(es) not recite any further limitations that cause the claim(s) to be patent eligible.
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.
Claim(s) 1-2, 5, 7-9, 11-12, 18-20, and 23-24 is/are rejected under 35 U.S.C. 103 as being unpatentable over Gleixner et al. EP 3716196 A1 (hereinafter Gleixner; a translated copy has been provided which the examiner relies upon) in view of Slatcher et al. GB 2584272 A (hereinafter Slatcher) and Pal et al. US 20170053461 A1 (hereinafter Pal).
Regarding claim 1,
Gleixner teaches
An impact detection system including:
an in-vehicle information capture device comprising at least one first sensor (¶ 0020 discloses a sensor 120 in a vehicle 110) and at least one second sensor (¶ 0020 discloses sensors of a mobile device 130; Figure 1 shows the mobile device 130 may be included in the vehicle 110), and
a processing device in communication with the in-vehicle information capture device (¶ 0020 discloses a mobile device 130 that receives sensor 120 data and data from other sensors on the mobile device 130 and a telematics service 140 and cloud server 160 that aid in processing the data) that operates an impact detection software application (¶ 0020 discloses the mobile device 130 runs an app 135 to perform the method which communicates with the server 160 and telematics service 140) for implementing a method of synchronizing a dataset of data from at least one first sensor using data from at least one second sensor (for example ¶ 0032), the method comprising:
collecting first data in a first data set over a time period, t (¶ 0020 discloses receiving data from the first sensor), from at least one accelerometer as the at least one first sensor (¶ 0020 discloses that the sensor 120 collects acceleration data);
collecting second data in a second data set over the time period, t (¶ 0020 discloses receiving data from other sensors), from at least one position receiver as the at least one second sensor (¶ 0020 discloses that the other sensor data may be GPS coordinate position data);
calculating an x-axis and y-axis acceleration magnitude from the first data set (¶ 0032 discloses that forward acceleration from the acceleration sensor is known implying processing to obtain the forward acceleration);
calculating an x-axis and y-axis acceleration magnitude from the second data set (¶ 0032 discloses differentiating GPS data to obtain a time series that can be cross correlated with the acceleration sensor data implying that acceleration data is obtained from differentiation of position data);
calculating a cross-correlation between the respective x-axis and y-axis acceleration magnitudes from the first data set and the second data set (¶ 0032 discloses calculating a cross correlation between the two time series of the processed sensor data; while Gleixner focuses on forward acceleration, one of ordinary skill in the art would understand the triviality of performing the same method steps in context of side-to-side acceleration, including the acceleration calculations above); and
identifying a synchronization time offset corresponding to a maximum correlation between the respective x-axis and y-axis acceleration magnitudes from the first data set and the second data set (¶ 0032 discloses obtaining a time shift between the measurements based on a maximum of the cross-correlation);
wherein the first data and the second data are collected in relation to a same vehicle (Figure 1 shows the sensor 120 and mobile device 130, containing the other sensors, are on the same vehicle 110).
Gleixner does not teach
applying the synchronization time offset to the second data set to synchronize the second data set with the first data set.
Slatcher teaches
collecting first data in a first data set over a time period, t (page 1 line 30 to page 2 line 4 discloses receiving second sensor data, second motion data, and second timing information), from at least one accelerometer as the at least one first sensor (page 7 lines 13-25 disclose that the motion data may be from an accelerometer);
collecting second data in a second data set over the time period, t (page 1 line 30 to page 2 line 4 discloses receiving first sensor data, first motion data, and first timing information; page 2 lines 5-11 disclose the sensor data may include position data); and
applying the synchronization time offset to the second data set to synchronize the second data set with the first data set (page 2 line 35 to page 3 line 11 discloses synchronizing the first and second sensor data based on an identified relation between the first and second motion data at a common time frame and an identified time difference);
wherein the first data and the second data are collected in relation to a same vehicle (Figure 2b shows the sensors 204 and 202 on the same device).
It would have been prima facie obvious to one of ordinary skill in the art at the time of filing to have modified Gleixner to incorporate the teachings of Slatcher such that following determination of a time offset based on max cross correlation as taught by Gleixner, the sensor data of Gleixner can be resynchronized using the determined time offset according to the method of Slatcher. This modification would be made with a reasonable expectation of success to reduce data loss and improve robustness of data collection through temporal drift.
Gleixner does not teach
making an impact detection decision based on the synchronized first dataset and second dataset.
Pal teaches
making an impact detection decision based on the synchronized first dataset and second dataset (Figure 1 shows that a mobile device detects that an accident has occurred S140 based on received data; see also Abstract wherein data includes position and acceleration data).
It would have been prima facie obvious to one of ordinary skill in the art at the time of filing to have further modified Gleixner to incorporate the teachings of Pal such that GPS and acceleration data synchronized from the combined method of Gleixner and Slatcher can be used in detection of vehicle accidents according to the teachings of Hanson. This modification would be made with a reasonable expectation of success to improve response time to an accident by allowing automatic accident detection followed by automatic suggested accident response as suggested in Pal (see at least Figure 2).
Regarding claim 2, the modified Gleixner reference teaches all of claim 1 as detailed above.
Gleixner further teaches that
the first data is acceleration data (¶ 0020 discloses that the sensor 120 collects acceleration data) and the second data is position data to correct for any smoothing in the position data (¶ 0020 discloses that the other sensor data may be GPS coordinate position data).
Regarding claim 5, the modified Gleixner reference teaches all of claim 1 as detailed above.
Gleixner further teaches that
the at least one first sensor and at least one second sensor capture information in relation to different parameters but the x-axis and y-axis acceleration magnitudes used to synchronize the respective datasets is at least derivable from the different parameters sensed by the at least one first sensor and at least one second sensor (see ¶ 0032 for example wherein GPS data is differentiated to obtain data that can be cross correlated with the acceleration data of the acceleration sensor).
Regarding claim 7, the modified Gleixner reference teaches all of claim 1 as detailed above.
Gleixner further teaches
The impact detection system as claimed in claim 1, implemented in a software application wherein the software application identifies and/or isolates the x-axis and y-axis acceleration magnitude from the first data set and/or the second data set (¶ 0020 discloses the mobile device runs an app 135 to perform the method which communicates with the server 160 and telematics service 140 for processing support; see acceleration derivation for synchronization in ¶ 0032).
Regarding claim 8, the modified Gleixner reference teaches all of claim 1 as detailed above.
Gleixner further teaches that
the x-axis and y-axis acceleration magnitude is identified/isolated with a related time such that each time point in the time period has a value for the x-axis and y-axis acceleration magnitude which is to be used as a basis for the synchronization (¶ 0032 discloses that cross correlation occurs between the two determined time series).
Examiner suggests that if Gleixner’s time series comparison is not satisfactory to the reader for covering this limitation, the rejection of this limitation under Slatcher as presented in the Office Action dated 11/28/2025 is still applicable and thus this limitation cannot be considered allowable.
Regarding claim 9, the modified Gleixner reference teaches all of claim 1 as detailed above.
The sampling of data at a frequency and processing of data at a frequency are inherent to data reception and data processing as known in the art. The limitations of
the first data and/or second data is collected at a particular frequency, and wherein the data is processed using the particular frequency in which the data is collected
are thus a matter of mere design choice such that the sampling frequencies and processing frequencies of one or both of the data sets use the same frequency. Therefore, it would have been prima facie obvious to one of ordinary skill in the art at the time of filing to try a method wherein the sampling and processing frequencies of one or both of the data sets are the same and incorporate it into the teachings of Gleixner since there is a finite number of identified, predictable potential solutions (i.e. either sampling and processing frequencies can be equivalent or not equivalent) to the recognized need (data sampling and processing). One of ordinary skill in the art could have pursued the known potential solutions with a reasonable expectation of success.
Regarding claim 11, the modified Gleixner reference teaches all of claim 1 as detailed above.
The sampling of data at a frequency and processing of data at a frequency are inherent to data reception and data processing as known in the art. The limitations of
the data is processed using a lower frequency than the frequency in which the data is collected
are thus a matter of mere design choice such that the data sampling frequency is greater than the processing frequency for one or both of the data sets. Therefore, it would have been prima facie obvious to one of ordinary skill in the art at the time of filing to try a method wherein the sampling frequency for both data sets is larger than the processing frequency for both data sets and incorporate it into the teachings of Gleixner since there is a finite number of identified, predictable potential solutions (i.e. sampling frequencies can only be equivalent, greater than, or less than processing frequencies) to the recognized need (data sampling and processing). One of ordinary skill in the art could have pursued the known potential solutions with a reasonable expectation of success.
Regarding claim 12, the modified Gleixner reference teaches all of claim 1 as detailed above.
The sampling of data at a frequency and processing of data at a frequency are inherent to data reception and data processing as known in the art. The limitations of
when the first data and the second data are collected at different frequencies, the data is processed at a same frequency
are thus a matter of mere design choice such that the data of both data sets is sampled at different frequencies but is processed at the same frequency. Therefore, it would have been prima facie obvious to one of ordinary skill in the art at the time of filing to try a method wherein the sampling frequency for both data sets are not equivalent but the processing frequency for both data sets are equivalent and incorporate it into the teachings of Gleixner since there is a finite number of identified, predictable potential solutions (i.e. frequencies can only be equivalent or not equivalent) to the recognized need (data sampling and processing). One of ordinary skill in the art could have pursued the known potential solutions with a reasonable expectation of success.
Regarding claim 18, the modified Gleixner reference teaches all of claim 1 as detailed above.
Gleixner further teaches that
position data is collected using at least one position sensor (¶ 0020 discloses that the other sensor data may be GPS coordinate position data and speed data) and the impact detection software application uses speed information and course information provided from the at least one position sensor to calculate an acceleration magnitude in the x-axis and y-axis acceleration magnitude (¶ 0032 discloses both speed and position data is differentiated for cross correlation with acceleration sensor data; while Gleixner focuses on forward acceleration, one of ordinary skill in the art would understand the triviality of performing the same method steps in context of side-to-side acceleration).
Regarding claim 19, the modified Gleixner reference teaches all of claim 18 as detailed above.
Gleixner further teaches that
the impact detection software application can then calculate a cross-correlation between the first dataset and second dataset using acceleration magnitude in the x-axis and y-axis, in the respective datasets (¶ 0032 discloses calculating a cross correlation between the three time series of processed sensor data; while Gleixner focuses on forward acceleration, one of ordinary skill in the art would understand the triviality of performing the same method steps in context of side-to-side acceleration).
Regarding claim 20, the modified Gleixner reference teaches all of claim 19 as detailed above.
Gleixner further teaches that
the impact detection software application calculates a cross-correlation between first data in the first dataset and second data in the second dataset at a number of different time offsets of between plus or minus 5 seconds and 10 seconds to identify the synchronization time offset providing maximum cross-correlation (¶ 0032 discloses processing the cross correlation to find the maximum cross correlation which is used as the time offset; examiner understands that finding maximum cross correlation with no specified bounds will result in searching every time offset possible including those found within the time range specified by the claimed invention).
Regarding claim 23, the modified Gleixner reference teaches all of claim 1 as detailed above.
Gleixner further teaches that
The impact detection system according to claim 1 wherein the processing device is a mobile computing device operating the impact detection software application (¶ 0020 discloses the mobile device runs an app 135).
Regarding claim 24, the modified Gleixner reference teaches all of claim 1 as detailed above.
Gleixner further teaches that
the mobile computing device is a smartphone (¶ 0020 “smartphone”) or tablet (¶ 0020 “tablet computer”) or similar device.
Response to Arguments
Applicant's arguments filed 5/27/2026 have been fully considered but they are not persuasive.
On pages 7-12, applicant argues that the amendment to change the invention from a method to an apparatus integrates the abstract idea into a practical application. Particularly, applicant argues that this is “a significant amount of hardware” wherein “the in-vehicle capture device, sensor, processing device, and impact detection software application based operations are incapable of being practically performed in the human mind” wherein “the method is implemented with ‘a particular machine or manufacture that is integral to the claim’”.
Examiner respectfully reminds the applicant that hardware components are analyzed in steps 2A-2B and are not considered when determining if an abstract idea exists. In step 1, an abstract idea is present since the method steps of calculating acceleration magnitudes, calculating cross correlation, identifying a synchronization time offset based on maximizing the cross correlation, applying the synchronization time offset to the data, and determining that an impact has occurred based on the synchronized data can be practically performed in the mind or at least with pen and paper. For example, the acceleration and position data may be a simple collection of data written in a table on a piece of paper with corresponding x and y position and acceleration measurements and corresponding timestamps. A person can easily numerically differentiate the position data to obtain acceleration data and further can easily separate the obtained and calculated acceleration data sets into their respective x and y coordinates. From there, a maximum cross correlation function, which is a mere mathematical function, can be calculated by hand/mentally to obtain the time offset which can then be used to synchronize the two datasets. Finally, mere observation of the two synchronized data sets with mental inferences can be utilized to determine when an impact has occurred in the data sets. Thus, the method can be understood as a routine pen and paper utilization of mathematical formulae to synchronize two data sets wherein a mental observation of the two data sets after synchronization can be performed to determine if an impact has occurred. None of the method steps prohibit the method from being performed in the mind. For example, no real time processing is detailed, nor do any of the method steps detail aspects that cannot be practically performed in the mind or with pen and paper beyond what is well understood, routine, and conventional.
Further, examiner respectfully disagrees that the listed hardware is significant enough to be considered as a particular machine. Firstly, the sensors are generically claimed. Yes, types of sensors are listed: an accelerometer and a position receiver; however, these are still generic sensors. The claims provide no specificity as to the accuracy, size, location, data collection speed, brand, or other details that may define the sensors as highly specific components. Further, the processing device is generically claimed and only defined further in claims 23-24. Even in claims 23-24, the processing device is still generically claimed, not providing sufficient detail to avoid the realm of generality. For example, the processing speed, memory size, input/output capability, screen resolution, manufacturer, chip composition, or other elements that may define a specific processing device are not detailed within the claims. Instead, the claimed method is applied to a random system comprising any acceleration sensor, any position receiver, and any processing device. Merely indicating that the processing device is in communication with the sensors is not sufficient to provide structural detail as being “in communication” with the sensors may be as simple as receiving data from the sensors over a network or retrieving collected sensor data from a memory which are both WURC functions as determined in buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) and Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015) and OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93 respectively. Furthermore, no meaningful difference in the performance of the method would occur if the method was instead performed on a remote server using simulated acceleration and position data. The method would still be adequately performed on a materially different device such that the abstract idea cannot be considered as being implemented into a practical application by a particular machine.
On pages 11-12, applicant further argues that the limitation of collecting data from the sensors integrates the abstract idea into a practical application. As detailed above, the limitations of collecting data are an insignificant extra pre-solution activity of mere data gather that can be practically performed using the WURC activity of data reception over a network as detailed in buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014). WURC activities cannot integrate the abstract idea into a practical application.
On pages 12-13, applicant argues that “these additional elements amount to significantly more than the judicial exception because they enable the impact detection system to identify specific artifacts, which occur at or around the same time within two different datasets, in order to remove the effects of smoothing to see whether the same artifacts occur at substantially the same time in each dataset. For example, these additional elements integrate the method into a practical application as they allow a user to detect whether an impact has occurred.” Examiner respectfully asserts that this is a mere expression of the functional method steps within the abstract idea. As the method steps can be practically performed in the mind and/or with pen and paper as detailed above and the additional elements are WURC and/or generically claimed, they aren’t understood, even as a whole, as being significantly more than the abstract idea.
Applicant’s arguments, see pages 14-17, filed 5/27/2026, with respect to the rejection(s) of claim(s) 1 under 103 have been fully considered and are persuasive in light of the claim amendments filed 5/27/2026. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Gleixner, Slatcher, and Pal.
Documents Considered but not Relied Upon
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Hanson US 9773281 B1 teaches a mobile device that detects occurrence of an accident based on GPS and acceleration data.
Basir et al. US 20130081442 A1 teaches sampling GPS and accelerometer data at different speed and then interpolating GPS data such that its sample frequency matches the accelerometer data sample frequency for further data processing.
Conclusion
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/A.T.S./Examiner, Art Unit 3669
/Erin M Piateski/Supervisory Patent Examiner, Art Unit 3669
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