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 .
*Examiner Note: Claim language is bolded. Cited References are italicized. Examiner interpretations are preceded with an asterisk *.
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 1-13 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
101 Analysis – Step 1
Regarding Step 1 of the Revised Guidance, it must be considered whether the claims are directed to one of the four statutory classes of invention. In the instant case, claims 1-11 are directed to sensor data processing method (i.e., a method); claim 12 is directed to an electronic device (i.e., a machine) and claim 13 is directed to a computer readable storage medium.
Therefore, claims 1-12 are within at least one of the four statutory categories (processes, machines, manufactures and compositions of matter.
In the instant case, claim 13 is directed to A computer-readable storage medium. However, claim 13 is not directed to a non-transitory computer-readable storage medium (i.e., a storage device). Therefore, claim 13 is not within at least one of the four statutory categories (system, method and device) and is ineligible under 35 USC §101. Perhaps, Applicant will consider amending the claims to recite a “non-transitory” computer-readable storage medium.
Even though claim 13 is not within at least one of the four statutory categories, Examiner has continued with the analysis of claim 13 to consider subject matter eligibility in the interest of compact prosecution.
101 Analysis – Step 2A, Prong 1
Regarding Prong 1 of the Step 2A analysis in the 2019 PEG, the claims are to be analyzed to determine whether they recite a judicial exception (subject matter that falls within one of the follow groups of abstract ideas: a) mathematical concepts, b) mental processes, and/or c) certain methods of organizing human activity).
Independent claim 1 includes limitations that recite an abstract idea (bolded below) and will be used as a representative claim for the remainder of the 101 rejection. Claim 1 recites:
A sensor data processing method, comprising:
acquiring a sensor data set, wherein the sensor data set comprises M pieces of sensor data, and each of the M pieces of sensor data comprises a sensor input value and a sensor output value;
determining N sensor input sampling values, wherein M>N>1; and
determining, based on each of the N sensor input sampling values, a sensor output sampling value corresponding to each of the N sensor input sampling values in the sensor data set, to obtain N sampling data, wherein the sensor output sampling value is the sensor output value corresponding to the sensor input value adjacent to the sensor input sampling value in the sensor data set.
The Examiner submits that the foregoing bolded limitations constitute a judicial exception in terms of “mental process” because under its broadest reasonable interpretation, the claim limitations can be “performed in the human mind, or by a human using a pen and paper”. See MPEP 2106.04(a)(2)(III).
The independent claim 1 recites the limitations of determining, … wherein the sensor output sampling value is the sensor output value corresponding to the sensor input value adjacent to the sensor input sampling value in the sensor data set. The determining that the sensor output sampling value is the sensor output value corresponding to the sensor input value adjacent to the sensor input sampling value in the sensor data set, as drafted, is a process that, under the broadest reasonable interpretation, encompass identifying sensor output values corresponding to selected sensor input sampling values in a sensor data set. Such activities are capable of practical performance in the human mind or by a human using pen and paper. For example, these limitations encompass evaluating sensor data and identifying output values corresponding to selected input values within a data set. The claim recites the result of determining values and comparing, without reciting any specific technological mechanisms for performing those determinations.
Accordingly, the claim recites at least one abstract idea. The additional recitation of “acquiring a sensor data set, wherein the sensor data set comprises M pieces of sensor data, and each of the M pieces of sensor data comprises a sensor input value and a sensor output value” , standing alone, does not provide a specific technological mechanism for improving sensor operation.
Thus, the claim recites a mental process.
101 Analysis – Step 2A, Prong 2 evaluation: Practical Application - No
In Step 2A, Prong two of the 2019 PEG, a claim is to be evaluated whether, as a whole, it integrates the recited judicial exception into a practical application. As noted in MPEP 2106.04(d), it must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the judicial exception. The courts have indicated that additional elements such as: merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application.”
The Office submits that the foregoing underlined limitation(s) recite additional elements that do not integrate the recited judicial exception into a practical application. In the instant application, the additional limitations beyond the above-noted abstract ideas are as follows (where the underlined portions are the “additional limitations” while the bolded portions continue to represent the “abstract idea”):
In Claim 1:
A sensor data processing method, comprising:
acquiring a sensor data set, wherein the sensor data set comprises M pieces of sensor data, and each of the M pieces of sensor data comprises a sensor input value and a sensor output value;
determining N sensor input sampling values, wherein M>N>1; and
determining, based on each of the N sensor input sampling values, a sensor output sampling value corresponding to each of the N sensor input sampling values in the sensor data set, to obtain N sampling data, wherein the sensor output sampling value is the sensor output value corresponding to the sensor input value adjacent to the sensor input sampling value in the sensor data set.
The claim recites the additional element of “acquiring a sensor data set, wherein the sensor data set comprises M pieces of sensor data, and each of the M pieces of sensor data comprises a sensor input value and a sensor output value”.
The method recites the additional elements of acquiring a sensor data set with M pieces of sensor data which is merely gathering and organizing information for use in the recited abstract sensor data processing method. The claim does not recite any specialized sensor architecture, particular acquisition technique or unconventional hardware implementation.
Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
101 Analysis – Step 2B evaluation: Inventive Concept: - No
In Step 2B of the 2019 PEG, the claim(s) is to be evaluated as to whether the claim, as a whole, amounts to significantly more than the recited exception, i.e., whether any additional element, or combination of additional elements, adds an inventive concept to the claim. See MPEP 2106.05.
As discussed with respect to Step 2A Prong Two, the additional element amounts to conventional data gathering for use by the recited abstract evaluation process and does not provide an inventive concept sufficient to transform the judicial exception into patent eligible subject matter. The same analysis applies here in 2B, i.e., mere data gathering to generate a basic model cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B, MPEP 2106.05(f).
Under the 2019 PEG, a conclusion that an additional element is insignificant extra-solution activity in Step 2A should be re-evaluated in Step 2B. Here, the steps of acquiring a sensor data set, wherein the sensor data set comprises M pieces of sensor data, and each of the M pieces of sensor data comprises a sensor input value and a sensor output value were considered to be additional elements in Step 2A, and thus they are re-evaluated in Step 2B to determine if they are more than what is well-understood, routine, conventional activity in the field. MPEP 2106.05(d)(II), and the cases cited therein, including Intellectual Ventures I, LLC v. Symantec Corp., 838 F.3d 1307, 1321 (Fed. Cir. 2016), TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610 (Fed. Cir. 2016), and OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363 (Fed. Cir. 2015), indicate that mere collection or receipt of data over a network is a well‐understood, routine, and conventional function when it is claimed in a merely generic manner. Further, the Federal Circuit in Trading Techs. Int’l v. IBG LLC, 921 F.3d 1084, 1093 (Fed. Cir. 2019), and Intellectual Ventures I LLC v. Erie Indemnity Co., 850 F.3d 1315, 1331 (Fed. Cir. 2017), for example, indicated that the mere displaying of data is a well understood, routine, and conventional function. The cited cases indicate that generic information gathering, collection and processing functions performed by conventional computer technology are well-understood, routine and conventional activities. The claim merely recites obtaining sensor information in a generic manner and does not recite any specialized sensor architecture, particular acquisition technique or unconventional hardware implementation. The additional element amounts to conventional data gathering for use by the recited abstract evaluation process and does not provide an inventive concept sufficient to transform the judicial exception into patent eligible subject matter. Accordingly, a conclusion that the steps of acquiring a sensor data set, wherein the sensor data set comprises M pieces of sensor data, and each of the M pieces of sensor data comprises a sensor input value and a sensor output value constitutes well-understood, routine and conventional information gathering and information processing activities is supported under Berkheimer.
Thus, claims 1 and 12-13 are ineligible.
101 Analysis – Dependent Claims
Dependent claims 2-11 do not recite any further limitations that cause the claims to be patent eligible. Rather, the limitations of the dependent claims are directed toward additional aspects of the judicial exception and/or well-understood, routine and conventional additional elements that do not integrate the judicial exception into a practical application [these dependent claims inherit the abstract idea set forth in claim 1. No other technology or action has been recited in claims 2-11 to integrate the abstract idea into a practical application nor to amount to significantly more than the abstract idea. Thus, claims 2-11 also do not confer eligibility on the claimed invention and are ineligible for reasons stated above and for similar reasons to claim 1. Therefore, dependent claims 2-11 are not patent eligible under the same rationale as provided for in the rejection of independent claim 1.
Therefore, claims 2-11 also ineligible under 35 USC §101.
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 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.
Claims 1 and 12-13 are rejected under 35 U.S.C. 103 as being unpatentable over Martinez Heras (US 2013/0212142A1) in view of Taylor (US 2012/0173185 A1) and further in view of Koukol (US 2004/0249592A1).
Regarding claim 1, Martinez Heras discloses A sensor data processing method (see at least
claim 15 of Martinez Heras which discloses “A computer-readable storage medium storing a computer program that, in response to execution by one or more computing devices, causes the one or more computing devices to implement a method for resampling a measured time series of data according to claim 1” and see at least para. [0022] of Martinez-Heras which discloses “a processing unit, the processing unit being configured to determine a variation of the parameter value within a selected sequence of data points” and see at least para. [0062] of Martinez Heras which discloses “a processing unit 511 that conducts the resampling according to the present invention. The telemetric device 500 further comprises a storage unit 516 for storing the resampled final time series data”, *Martinez Heras discloses a computer readable storage medium storing a computer program that when executed by one or more computing devices will cause the computing devices to implement the resampling method. One of ordinary skill in the art before the effective filing date of the claimed invention would understand that the computer program, comprises executable instructions stored in memory and executed by the processing unit 511 of the telemetric device 500 to perform the disclosed data processing operations), comprising: determining N sensor input sampling values, wherein M>N>1 (see at least para. [0006] of Martinez Heras which discloses “selects certain sample data points out of an original data sample in order to reduce the overall data volume” and see at least para. [0054] of Martinez Heras which discloses “the data points P11, P13, P14, P15, P17, P18 and P19 as highlighted by the square data points in FIG. 3 are added to the resampled time series. In the simple example of FIG. 3, only the data points P12 and P16 are omitted, since the variation of the respective interpolated subsequences are smaller than the predetermined error value”, *Under the broadest reasonable interpretation, the original data sample corresponds to M pieces of sensor data. Also, under the broadest reasonable interpretation, the selected sample data points correspond to the claimed sensor input sampling values because the resampling method selects a subset of data points from the original measured data sequence for retention in the resampled data set. Because the selected sample data points are a subset of the original data sample, M>N and because multiple selected sample data points are retained in the resampled time series, N>1); and determining, based on each of the N sensor input sampling values, a sensor output sampling value corresponding to each of the N sensor input sampling values in the sensor data set, to obtain N sampling data (see at least para. [0006] of Martinez Heras which discloses “ selects certain sample data points out of an original data sample in order to reduce the overall data volume” and see at least para. [0054] of Martinez Heras which discloses “the data points P11, P13, P14, P15, P17, P18 and P19 as highlighted by the square data points in FIG. 3 are added to the resampled time series. In the simple example of FIG. 3, only the data points P12 and P16 are omitted”, *Under the broadest reasonable interpretation, the selected sample data points correspond to the N sensor input sampling values).
Martinez Heras discloses a measured data sequence comprising multiple data points
corresponding to a sensor data set with M pieces of sensor data (see at least para. [0062] of Martinez Heras which discloses “a sensor 512 for measuring time series data. The measured time series data is transmitted to the buffer unit 515 that stores at least part of the time series data”. Also, see at least para. [0006] of Martinez Heras which discloses “determining a first time series which comprises a sequence of data points” and see at least para. [0044] of Martinez Heras which discloses “the first time series 10 comprises the sequence of 9 rhombic data points P11 to P19”). Paragraph [0062] of Martinez Heras further discloses that “a predetermined number of data points (e.g. 50 data points) are required before the resampling method of the present invention can be applied to the measure sequence of data points”, *Under the broadest reasonable interpretation, the measured time series data comprising a measured sequence of data points corresponds to the claimed “sensor data set” and the individual data points of the measured sequences correspond to the claimed “M pieces of sensor data”).
Martinez Heras may not explicitly disclose acquiring a sensor data set, wherein the sensor
data set comprises M pieces of sensor data, and each of the M pieces of sensor data comprises a sensor input value and a sensor output value.
However, Taylor discloses acquiring a sensor data set (Fig. 4A, 400 and see at least para.
[0050] of Taylor which discloses “a calibration data set 400 stored in calibration data repository 320. Calibration data set 400 may include a plurality of calibration data points 402 collected by range sensor 102”), wherein the sensor data set comprises M pieces (Fig. 4A, 402 and see at least para. [0050] of Taylor which discloses “Calibration data set 400 may include a plurality of calibration data points 402 collected by range sensor 102”)”) of sensor data (see at least para. [0052] of Taylor which discloses “Range 410 may be a value indicating the distance (e.g., in meters) from range sensor 102 to the point 402 at the time range sensor 102 determined the location of the point 402. Range 410 may be used to determine an expected error of the point 402 for calibration purposes. For example, the accuracy of range sensor 102 may decrease as the distance from range sensor 102 to a point 402 increases”, *Under the broadest reasonable interpretation, calibration data set 400 corresponds to the claimed sensor data set and calibration data points 402 correspond to the claimed M pieces of sensor data) each of the M pieces of sensor data comprises a sensor input value (Fig. 4A, 410 and see at least para. [0052] of Taylor which discloses “a range 410 for each calibration data point 402. Range 410 may be a value indicating the distance (e.g., in meters) from range sensor 102 to the point 402 at the time range sensor 102 determined the location of the point 402. Range 410 may be used to determine an expected error of the point 402 for calibration purposes”) and a sensor output value (Fig. 4A, 404, 406, 408 and see at least para. [0050] of Taylor which discloses “a determined x-coordinate 404, a determined y-coordinate 406, and a determined z-coordinate 408 of the point 402 in the sensor coordinate frame S (or other desired coordinate frame)” and see at least para. [0053] of Taylor which discloses “Error factors 409 may also include a sensor time 412 associated with each calibration data point 402. Sensor time 412 may be the time indicated by range sensor clock 360 at the moment range sensor 102 determined the location of the point 402”, *Under the broadest reasonable interpretation, the range value and associated coordinate values are paired sensor measurement values associated with each calibration data point and therefore correspond to the claimed sensor input value and sensor output value). Also, the combined teachings of Martinez Heras and Taylor illustrate each selected sampling point corresponds to a calibration data point having associated values, such that a corresponding sensor output value is determined for each selected sampling point.
It would have been obvious to one of ordinary skill in the art before the effective filing date
of the claimed invention to modify the sensor data processing method of Martinez Heras to include acquiring a sensor data set, wherein the sensor data set comprises M pieces of sensor data, as taught in Taylor with a reasonable expectation of success in order to evaluate and process range sensor measurement data (including calibration data) more efficiently by resampling a dense set of input/output measurement points to a reduced set of representative samples suitable for storage and subsequent calibration/error analysis. See para. [0052]-[0053] of Taylor for motivation.
Martinez Heras, as modified by Taylor, may not explicitly disclose wherein the sensor
output sampling value is the sensor output value corresponding to the sensor input value adjacent to the sensor input sampling value in the sensor data set.
However, Koukol discloses “wherein the sensor output sampling value is the sensor output
value corresponding to the sensor input value adjacent to the sensor input sampling value in the sensor data set” (see at least para. [0057] of Koukol which discloses “the transmitter calibration logic at each additional calibration point interpolates between the correction values provided by the first (default) table at adjacent temperature points”, *Koukol discloses determining values based on output values associated with adjacent input entries. The adjacent temperature points correspond to adjacent sensor input values, while the correction values associated with those temperature points correspond to sensor output values associated with the adjacent sensor input values. Under the broadest reasonable interpretation, the adjacent temperature points correspond to adjacent sensor input values and the correction values associated with the adjacent temperature points correspond to sensor output values associated with the adjacent sensor input values. Therefore, Koukol teaches determining a value based on output values corresponding to adjacent input values in a stored calibration data set).
It would have been obvious to one of ordinary skill in the art before the effective filing date
of the claimed invention to modify the sampling technique of Martinez Heras and the sensor calibration data set of Taylor to utilize interpolation between values associated with adjacent input entries as taught by Koukol in order to improve the accuracy, continuity and reliability of sampled sensor data by determining values from neighboring calibration points while maintaining a reduced sampled data set.
Regarding claim 12, Martinez Heras discloses An electronic device (Fig. 5, 500 and see at
least para. [0062] of which discloses “a telemetric device for resampling time series data. The telemetric device 500 comprises a sensor 512 for measuring time series data. The measured time series data is transmitted to the buffer unit 515 that stores at least part of the time series data”), comprising: a processor (Fig. 5, 511 and see at least para. [0062] of Martinez Heras which discloses “a processing unit 511 that conducts the resampling” and see at least para. [0022] of Martinez-Heras which discloses “a processing unit, the processing unit being configured to determine a variation of the parameter value within a selected sequence of data points, and wherein the variation is determined by linearly interpolating the selected sequence of the data points and by determining the maximum absolute difference between the parameter value and the corresponding interpolated parameter value within the selected sequence”, *This processing unit 511 corresponds to a processor because it executes the resampling operations recited by the method); a memory (Fig. 5, 516 and see at least para. [0062] of Martinez which discloses “a storage unit 516 for storing the resampled final time series data. The storage unit 516 may also be used to store the measured time series data until the resampling technique of the invention is applied to the data at a later point in time”, *This storage unit 516 corresponds to a memory); and a computer program (see at least claim 15 of Martinez Heras which discloses “a computer program that, in response to execution by one or more computing devices, causes the one or more computing devices to implement a method for resampling a measured time series of data”, *The computer readable storage medium corresponds to the claimed memory and the stored computer program corresponds to the claimed computer program) stored in the memory, wherein the computer program (see at least claim 15 of Martinez Heras which discloses “a computer program) comprises instructions, and when the instructions are executed by the processor, the electronic device executes a sensor data processing method (see at least claim 15 of Martinez Heras which discloses “A computer-readable storage medium storing a computer program that, in response to execution by one or more computing devices, causes the one or more computing devices to implement a method for resampling a measured time series of data according to claim 1” and see at least para. [0022] of Martinez-Heras which discloses “a processing unit, the processing unit being configured to determine a variation of the parameter value within a selected sequence of data points” and see at least para. [0062] of Martinez Heras which discloses “a processing unit 511 that conducts the resampling according to the present invention. The telemetric device 500 further comprises a storage unit 516 for storing the resampled final time series data”, *Martinez Heras discloses a computer readable storage medium storing a computer program that when executed by one or more computing devices will cause the computing devices to implement the resampling method. One of ordinary skill in the art before the effective filing date of the claimed invention would understand that the computer program, comprises executable instructions stored in memory and executed by the processing unit 511 of the telemetric device 500 to perform the disclosed data processing operations. Also see at least claim 15 of Martinez Heras which discloses “A computer-readable storage medium storing a computer program that, in response to execution by one or more computing devices, causes the one or more computing devices to implement a method for resampling a measured time series of data”), comprising: determining N sensor input sampling values, wherein M>N>1 (see at least para. [0006] of Martinez Heras which discloses “selects certain sample data points out of an original data sample in order to reduce the overall data volume” and see at least para. [0054] of Martinez Heras which discloses “the data points P11, P13, P14, P15, P17, P18 and P19 as highlighted by the square data points in FIG. 3 are added to the resampled time series. In the simple example of FIG. 3, only the data points P12 and P16 are omitted, since the variation of the respective interpolated subsequences are smaller than the predetermined error value”, *Under the broadest reasonable interpretation, the original data sample corresponds to M pieces of sensor data. Also, under the broadest reasonable interpretation, the selected sample data points correspond to the claimed sensor input sampling values because the resampling method selects a subset of data points form the original measured data sequence for retention in the resampled data set. Because the selected sample data points are a subset of the original data sample, M>N and because multiple selected sample data points are retained in the resampled time series, N>1); and determining, based on each of the N sensor input sampling values, a sensor output sampling value corresponding to each of the N sensor input sampling values in the sensor data set, to obtain N sampling data (see at least para. [0006] of Martinez Heras which discloses “ selects certain sample data points out of an original data sample in order to reduce the overall data volume” and see at least para. [0054] of Martinez Heras which discloses “the data points P11, P13, P14, P15, P17, P18 and P19 as highlighted by the square data points in FIG. 3 are added to the resampled time series. In the simple example of FIG. 3, only the data points P12 and P16 are omitted”, *Under the broadest reasonable interpretation, the selected sample data points correspond to the N sensor input sampling values).
Martinez Heras discloses a measured data sequence comprising multiple data points
corresponding to a sensor data set with M pieces of sensor data (see at least para. [0062] of Martinez Heras which discloses “a sensor 512 for measuring time series data. The measured time series data is transmitted to the buffer unit 515 that stores at least part of the time series data”. Also, see at least para. [0006] of Martinez Heras which discloses “determining a first time series which comprises a sequence of data points” and see at least para. [0044] of Martinez Heras which discloses “the first time series 10 comprises the sequence of 9 rhombic data points P11 to P19”). Paragraph [0062] of Martinez Heras further discloses that “a predetermined number of data points (e.g. 50 data points) are required before the resampling method of the present invention can be applied to the measure sequence of data points”, *Under the broadest reasonable interpretation, the measured time series data comprising a measured sequence of data points corresponds to the claimed “sensor data set” and the individual data points of the measured sequences correspond to the claimed “M pieces of sensor data”).
Martinez Heras may not explicitly disclose acquiring a sensor data set, wherein the sensor
data set comprises M pieces of sensor data, and each of the M pieces of sensor data comprises a sensor input value and a sensor output value.
However, Taylor discloses acquiring a sensor data set (Fig. 4A, 400 and see at least para.
[0050] of Taylor which discloses “a calibration data set 400 stored in calibration data repository 320. Calibration data set 400 may include a plurality of calibration data points 402 collected by range sensor 102”), wherein the sensor data set comprises M pieces (Fig. 4A, 402 and see at least para. [0050] of Taylor which discloses “Calibration data set 400 may include a plurality of calibration data points 402 collected by range sensor 102”)”) of sensor data (see at least para. [0052] of Taylor which discloses “Range 410 may be a value indicating the distance (e.g., in meters) from range sensor 102 to the point 402 at the time range sensor 102 determined the location of the point 402. Range 410 may be used to determine an expected error of the point 402 for calibration purposes. For example, the accuracy of range sensor 102 may decrease as the distance from range sensor 102 to a point 402 increases”, *Under the broadest reasonable interpretation, calibration data set 400 corresponds to the claimed sensor data set and calibration data points 402 correspond to the claimed M pieces of sensor data) each of the M pieces of sensor data comprises a sensor input value (Fig. 4A, 410 and see at least para. [0052] of Taylor which discloses “a range 410 for each calibration data point 402. Range 410 may be a value indicating the distance (e.g., in meters) from range sensor 102 to the point 402 at the time range sensor 102 determined the location of the point 402. Range 410 may be used to determine an expected error of the point 402 for calibration purposes”) and a sensor output value (Fig. 4A, 404, 406, 408 and see at least para. [0050] of Taylor which discloses “a determined x-coordinate 404, a determined y-coordinate 406, and a determined z-coordinate 408 of the point 402 in the sensor coordinate frame S (or other desired coordinate frame)” and see at least para. [0053] of Taylor which discloses “Error factors 409 may also include a sensor time 412 associated with each calibration data point 402. Sensor time 412 may be the time indicated by range sensor clock 360 at the moment range sensor 102 determined the location of the point 402”.
Under the broadest reasonable interpretation, the range value and associated coordinate
values are paired sensor measurement values associated with each calibration data point and therefore correspond to the claimed sensor input value and sensor output value). Also, the combined teachings of Martinez Heras and Taylor illustrate each selected sampling point corresponds to a calibration data point having associated values, such that a corresponding sensor output value is determined for each selected sampling point.
It would have been obvious to one of ordinary skill in the art before the effective filing date
of the claimed invention to modify the sensor data processing method of Martinez Heras to include acquiring a sensor data set, wherein the sensor data set comprises M pieces of sensor data, as taught in Taylor with a reasonable expectation of success in order to evaluate and process range sensor measurement data (including calibration data) more efficiently by resampling a dense set of input/output measurement points to a reduced set of representative samples suitable for storage and subsequent calibration/error analysis. See para. [0052]-[0053] of Taylor for motivation.
Martinez Heras, as modified by Taylor, may not explicitly disclose wherein the sensor
output sampling value is the sensor output value corresponding to the sensor input value adjacent to the sensor input sampling value in the sensor data set.
However, Koukol discloses “wherein the sensor output sampling value is the sensor output
value corresponding to the sensor input value adjacent to the sensor input sampling value in the sensor data set” (see at least para. [0057] of Koukol which discloses “the transmitter calibration logic at each additional calibration point interpolates between the correction values provided by the first (default) table at adjacent temperature points”, *Koukol discloses determining values based on output values associated with adjacent input entries. The adjacent temperature points correspond to adjacent sensor input values, while the correction values associated with those temperature points correspond to sensor output values associated with the adjacent sensor input values. Under the broadest reasonable interpretation, the adjacent temperature points correspond to adjacent sensor input values and the correction values associated with the adjacent temperature points correspond to sensor output values associated with the adjacent sensor input values. Therefore, Koukol teaches determining a value based on output values corresponding to adjacent input values in a stored calibration data set).
It would have been obvious to one of ordinary skill in the art before the effective filing date
of the claimed invention to modify the sampling technique of Martinez Heras and the sensor calibration data set of Taylor to utilize interpolation between values associated with adjacent input entries as taught by Koukol in order to improve the accuracy, continuity and reliability of sampled sensor data by determining values from neighboring calibration points while maintaining a reduced sampled data set.
Regarding claim 13, Martinez Heras discloses A computer-readable storage medium,
comprising a program stored on the computer- readable storage medium, wherein when the program is executed, a device (Fig. 5, 500 and see at least para. [0062] of which discloses “a telemetric device for resampling time series data. The telemetric device 500 comprises a sensor 512 for measuring time series data. The measured time series data is transmitted to the buffer unit 515 that stores at least part of the time series data”) where the computer- readable storage medium is located (see at least claim 15 of Martinez Heras which discloses “A computer-readable storage medium storing a computer program that, in response to execution by one or more computing devices, causes the one or more computing devices to implement a method for resampling a measured time series of data”) is controlled to execute a sensor data processing method (see at least claim 15 of Martinez Heras which discloses “A computer-readable storage medium storing a computer program that, in response to execution by one or more computing devices, causes the one or more computing devices to implement a method for resampling a measured time series of data according to claim 1” and see at least para. [0022] of Martinez-Heras which discloses “a processing unit, the processing unit being configured to determine a variation of the parameter value within a selected sequence of data points” and see at least para. [0062] of Martinez Heras which discloses “a processing unit 511 that conducts the resampling according to the present invention. The telemetric device 500 further comprises a storage unit 516 for storing the resampled final time series data”, *Martinez Heras discloses a computer readable storage medium storing a computer program that when executed by one or more computing devices will cause the computing devices to implement the resampling method. One of ordinary skill in the art before the effective filing date of the claimed invention would understand that the computer program, comprises executable instructions stored in memory and executed by the processing unit 511 of the telemetric device 500 to perform the disclosed data processing operations), comprising: determining N sensor input sampling values, wherein M>N>1 (see at least para. [0006] of Martinez Heras which discloses “selects certain sample data points out of an original data sample in order to reduce the overall data volume” and see at least para. [0054] of Martinez Heras which discloses “the data points P11, P13, P14, P15, P17, P18 and P19 as highlighted by the square data points in FIG. 3 are added to the resampled time series. In the simple example of FIG. 3, only the data points P12 and P16 are omitted, since the variation of the respective interpolated subsequences are smaller than the predetermined error value”, *Under the broadest reasonable interpretation, the original data sample corresponds to M pieces of sensor data. Also, under the broadest reasonable interpretation, the selected sample data points correspond to the claimed sensor input sampling values because the resampling method selects a subset of data points from the original measured data sequence for retention in the resampled data set. Because the selected sample data points are a subset of the original data sample, M>N and because multiple selected sample data points are retained in the resampled time series, N>1); and determining, based on each of the N sensor input sampling values, a sensor output sampling value corresponding to each of the N sensor input sampling values in the sensor data set, to obtain N sampling data (see at least para. [0006] of Martinez Heras which discloses “ selects certain sample data points out of an original data sample in order to reduce the overall data volume” and see at least para. [0054] of Martinez Heras which discloses “the data points P11, P13, P14, P15, P17, P18 and P19 as highlighted by the square data points in FIG. 3 are added to the resampled time series. In the simple example of FIG. 3, only the data points P12 and P16 are omitted”, *Under the broadest reasonable interpretation, the selected sample data points correspond to the N sensor input sampling values).
Martinez Heras discloses a measured data sequence comprising multiple data points
corresponding to a sensor data set with M pieces of sensor data (see at least para. [0062] of Martinez Heras which discloses “a sensor 512 for measuring time series data. The measured time series data is transmitted to the buffer unit 515 that stores at least part of the time series data”. Also, see at least para. [0006] of Martinez Heras which discloses “determining a first time series which comprises a sequence of data points” and see at least para. [0044] of Martinez Heras which discloses “the first time series 10 comprises the sequence of 9 rhombic data points P11 to P19”). Paragraph [0062] of Martinez Heras further discloses that “a predetermined number of data points (e.g. 50 data points) are required before the resampling method of the present invention can be applied to the measure sequence of data points”, *Under the broadest reasonable interpretation, the measured time series data comprising a measured sequence of data points corresponds to the claimed “sensor data set” and the individual data points of the measured sequences correspond to the claimed “M pieces of sensor data”).
Martinez Heras may not explicitly disclose acquiring a sensor data set, wherein the sensor
data set comprises M pieces of sensor data, and each of the M pieces of sensor data comprises a sensor input value and a sensor output value. However, Taylor discloses acquiring a sensor data set (Fig. 4A, 400 and see at least para. [0050] of Taylor which discloses “a calibration data set 400 stored in calibration data repository 320. Calibration data set 400 may include a plurality of calibration data points 402 collected by range sensor 102”), wherein the sensor data set comprises M pieces (Fig. 4A, 402 and see at least para. [0050] of Taylor which discloses “Calibration data set 400 may include a plurality of calibration data points 402 collected by range sensor 102”)”) of sensor data (see at least para. [0052] of Taylor which discloses “Range 410 may be a value indicating the distance (e.g., in meters) from range sensor 102 to the point 402 at the time range sensor 102 determined the location of the point 402. Range 410 may be used to determine an expected error of the point 402 for calibration purposes. For example, the accuracy of range sensor 102 may decrease as the distance from range sensor 102 to a point 402 increases”, *Under the broadest reasonable interpretation, calibration data set 400 corresponds to the claimed sensor data set and calibration data points 402 correspond to the claimed M pieces of sensor data) each of the M pieces of sensor data comprises a sensor input value (Fig. 4A, 410 and see at least para. [0052] of Taylor which discloses “a range 410 for each calibration data point 402. Range 410 may be a value indicating the distance (e.g., in meters) from range sensor 102 to the point 402 at the time range sensor 102 determined the location of the point 402. Range 410 may be used to determine an expected error of the point 402 for calibration purposes”) and a sensor output value (Fig. 4A, 404, 406, 408 and see at least para. [0050] of Taylor which discloses “a determined x-coordinate 404, a determined y-coordinate 406, and a determined z-coordinate 408 of the point 402 in the sensor coordinate frame S (or other desired coordinate frame)” and see at least para. [0053] of Taylor which discloses “Error factors 409 may also include a sensor time 412 associated with each calibration data point 402. Sensor time 412 may be the time indicated by range sensor clock 360 at the moment range sensor 102 determined the location of the point 402”, *Under the broadest reasonable interpretation, the range value and associated coordinate values are paired sensor measurement values associated with each calibration data point and therefore correspond to the claimed sensor input value and sensor output value). Also, the combined teachings of Martinez Heras and Taylor illustrate each selected sampling point corresponds to a calibration data point having associated values, such that a corresponding sensor output value is determined for each selected sampling point.
It would have been obvious to one of ordinary skill in the art before the effective filing date
of the claimed invention to modify the sensor data processing method of Martinez Heras to include acquiring a sensor data set, wherein the sensor data set comprises M pieces of sensor data, as taught in Taylor with a reasonable expectation of success in order to evaluate and process range sensor measurement data (including calibration data) more efficiently by resampling a dense set of input/output measurement points to a reduced set of representative samples suitable for storage and subsequent calibration/error analysis. See para. [0052]-[0053] of Taylor for motivation.
Martinez Heras, as modified by Taylor, may not explicitly disclose wherein the sensor
output sampling value is the sensor output value corresponding to the sensor input value adjacent to the sensor input sampling value in the sensor data set.
However, Koukol discloses “wherein the sensor output sampling value is the sensor output
value corresponding to the sensor input value adjacent to the sensor input sampling value in the sensor data set” (see at least para. [0057] of Koukol which discloses “the transmitter calibration logic at each additional calibration point interpolates between the correction values provided by the first (default) table at adjacent temperature points”, *Koukol discloses determining values based on output values associated with adjacent input entries. The adjacent temperature points correspond to adjacent sensor input values, while the correction values associated with those temperature points correspond to sensor output values associated with the adjacent sensor input values. Under the broadest reasonable interpretation, the adjacent temperature points correspond to adjacent sensor input values and the correction values associated with the adjacent temperature points correspond to sensor output values associated with the adjacent sensor input values. Therefore, Koukol teaches determining a value based on output values corresponding to adjacent input values in a stored calibration data set).
It would have been obvious to one of ordinary skill in the art before the effective filing date
of the claimed invention to modify the sampling technique of Martinez Heras and the sensor calibration data set of Taylor to utilize interpolation between values associated with adjacent input entries as taught by Koukol in order to improve the accuracy, continuity and reliability of sampled sensor data by determining values from neighboring calibration points while maintaining a reduced sampled data set.
Claims 2-9 are rejected under 35 U.S.C. 103 as being unpatentable over Martinez Heras (US 2013/0212142A1) in view of Taylor (US 2012/0173185 A1) and further in view of Koukol (US 2004/0249592A1) and further in view of Liu (US 2023/0296458 A1).
Regarding claim 2, Martinez Heras, as modified by Taylor and Koukol discloses acquiring the
sensor data set (Fig. 4A, 400 and see at least para. [0050] of Taylor which discloses “a calibration data set 400 stored in calibration data repository 320. Calibration data set 400 may include a plurality of calibration data points 402 collected by range sensor 102”).
Martinez Heras, as modified by Taylor and Koukol may not explicitly disclose, subsequently
determining, from the sensor data set, forward stroke sensor data and reverse stroke sensor data.
However, Liu discloses determining, from the sensor data set, forward stroke sensor data
and reverse stroke sensor data (see at least para. [0102] of Liu which discloses “of three cycles of forward stroke of the sensor at the i-th calibration point, and b1 is an average value of the output wavelengths of three cycles of reverse stroke of the sensor at the i-th calibration point”, *Under the broadest reasonable interpretation, the output wavelength data obtained during the forward stroke of the sensor correspond to the claimed forward stroke sensor data and the output wavelength data obtained during the reverse stroke of the sensor correspond to the claimed reverse stroke sensor data).
It would have been obvious to one of ordinary skill in the art before the effective filing date
of the claimed invention to further modify the data processing method of Martinez Heras, as modified by Taylor and Koukol to include determining, from the sensor data set, forward stroke sensor data and reverse stroke sensor data as taught in Liu with a reasonable expectation of success in order to compensate for sensor hysteresis and improve the accuracy, repeatability and reliability of sensor calibration and sampled sensor data.
Regarding claim 3, Martinez Heras, as modified by Taylor, Koukol and Liu discloses wherein
said determining, from the sensor data set, forward stroke sensor data and reverse stroke sensor data (see at least para. [0102] of Liu which discloses “of three cycles of forward stroke of the sensor at the i-th calibration point, and b1 is an average value of the output wavelengths of three cycles of reverse stroke of the sensor at the i-th calibration point”, *Under the broadest reasonable interpretation, the output wavelength data obtained during the forward stroke of the sensor correspond to the claimed forward stroke sensor data and the output wavelength data obtained during the reverse stroke of the sensor correspond to the claimed reverse stroke sensor data) comprises: determining, from the sensor data set, the forward stroke sensor data and the reverse stroke sensor data based on a change trend of sensor output values in the sensor data set (see at least para. [0109] of Liu which discloses “Sensitivity is an indicator of sensing characteristics. It reflects the degree of wavelength change of the fiber grating under the action of environmental physical quantity. Taking the forward stroke and reverse stroke as one measurement cycle, according to the measurement results of three cycles, the least squares method is used to calculate the reference straight line formula”).
Regarding claim 4, Martinez Heras, as modified by Taylor, Koukol and Liu discloses
wherein said determining, from the sensor data set, the forward stroke sensor data and the reverse stroke sensor data (see at least para. [0102] of Liu which discloses “of three cycles of forward stroke of the sensor at the i-th calibration point, and b1 is an average value of the output wavelengths of three cycles of reverse stroke of the sensor at the i-th calibration point”, *Under the broadest reasonable interpretation, the output wavelength data obtained during the forward stroke of the sensor correspond to the claimed forward stroke sensor data and the output wavelength data obtained during the reverse stroke of the sensor correspond to the claimed reverse stroke sensor data) based on a change trend of sensor output values in the sensor data set comprises: determining, from the sensor data set, sensor data whose sensor output values are within an increasing interval as the forward stroke sensor data; and determining, from the sensor data set, sensor data whose sensor output values are within a decreasing interval as the reverse stroke sensor data (see at least para. [0101] of Liu which discloses “The return error h refers to the difference between the calibration characteristics of the optical fiber sensor in the forward and reverse process of the stretching test under the same experimental environment, also called the hysteresis error. It represents the difference of output wavelength indications of fiber grating located at the same calibration point. When the forward and reverse paths of a cycle of the optical fiber sensor reach the calibration point, there is a significant difference in the feedback wavelength values, which indicates that there is a problem in the adaptability between the components inside the sensor, and the design criteria in the development stage are not met”, *Under the broadest reasonable interpretation, the output wavelength values obtained during the forward stroke correspond to sensor output values within an increasing interval and the output wavelength values obtained during the reverse stroke correspond to sensor output values within a decreasing interval).
Regarding claim 5, Martinez Heras, as modified by Taylor, Koukol and Liu discloses
wherein said determining, from the sensor data set, the forward stroke sensor data and the reverse stroke sensor data (see at least para. [0102] of Liu which discloses “of three cycles of forward stroke of the sensor at the i-th calibration point, and b1 is an average value of the output wavelengths of three cycles of reverse stroke of the sensor at the i-th calibration point”, *Under the broadest reasonable interpretation, the output wavelength data obtained during the forward stroke of the sensor correspond to the claimed forward stroke sensor data and the output wavelength data obtained during the reverse stroke of the sensor correspond to the claimed reverse stroke sensor data) based on a change trend of sensor output values in the sensor data set comprises: determining, from the sensor data set, sensor data whose sensor output values are within an increasing interval as the forward stroke sensor data; and determining, from the sensor data set, sensor data whose sensor output values are within a decreasing interval as the reverse stroke sensor data (see at least para. [0101] of Liu which discloses “The return error h refers to the difference between the calibration characteristics of the optical fiber sensor in the forward and reverse process of the stretching test under the same experimental environment, also called the hysteresis error. It represents the difference of output wavelength indications of fiber grating located at the same calibration point. When the forward and reverse paths of a cycle of the optical fiber sensor reach the calibration point, there is a significant difference in the feedback wavelength values, which indicates that there is a problem in the adaptability between the components inside the sensor, and the design criteria in the development stage are not met”, *Under the broadest reasonable interpretation, the output wavelength values obtained during the forward stroke correspond to sensor output values within an increasing interval and the output wavelength values obtained during the reverse stroke correspond to sensor output values within a decreasing interval).
Regarding claim 6, Martinez Heras, as modified by Taylor, Koukol and Liu discloses wherein
said determining, based on each of the N sensor input sampling values, a sensor output sampling value corresponding to each of the N sensor input sampling values in the sensor data set, to obtain N sampling data (see at least para. [0006] of Martinez Heras which discloses “ selects certain sample data points out of an original data sample in order to reduce the overall data volume” and see at least para. [0054] of Martinez Heras which discloses “the data points P11, P13, P14, P15, P17, P18 and P19 as highlighted by the square data points in FIG. 3 are added to the resampled time series. In the simple example of FIG. 3, only the data points P12 and P16 are omitted”, *Under the broadest reasonable interpretation, the selected sample data points correspond to the N sensor input sampling values) comprises: determining, based on each of the N sensor input sampling values, a positive stroke sensor output sampling value and a reverse stroke sensor output sampling value (see at least para. [0109] of Liu which discloses “Sensitivity is an indicator of sensing characteristics. It reflects the degree of wavelength change of the fiber grating under the action of environmental physical quantity. Taking the forward stroke and reverse stroke as one measurement cycle, according to the measurement results of three cycles, the least squares method is used to calculate the reference straight line formula”) corresponding to each of the N sensor input sampling values in the sensor data set, to obtain N positive stroke sampling data and N reverse stroke sampling data, wherein the forward stroke sensor output sampling value is a forward stroke sensor output value corresponding to a forward stroke sensor input value adjacent to the sensor input sampling value in the sensor data set, and the reverse stroke sensor output sampling value is a reverse stroke sensor output value corresponding to a reverse stroke sensor input value adjacent to the sensor input sampling value in the sensor data set (Under the broadest reasonable interpretation, The forward-stroke wavelength values of Liu correspond to forward-stroke sensor output values and the reverse stroke wavelength values of Liu correspond to reverse stroke sensor output values. The application of Koukol’s interpolation between adjacent calibration points separately to the forward stroke data and reverse stroke data results in determining forward stroke output values corresponding to adjacent forward stroke input values and reverse stroke output values corresponding to adjacent reverse stroke input values.
Regarding claim 7, Martinez Heras, as modified by Taylor, Koukol and Liu discloses wherein
said determining, based on each of the N sensor input sampling values, a sensor output sampling value corresponding to each of the N sensor input sampling values in the sensor data set, to obtain N sampling data (see at least para. [0006] of Martinez Heras which discloses “ selects certain sample data points out of an original data sample in order to reduce the overall data volume” and see at least para. [0054] of Martinez Heras which discloses “the data points P11, P13, P14, P15, P17, P18 and P19 as highlighted by the square data points in FIG. 3 are added to the resampled time series. In the simple example of FIG. 3, only the data points P12 and P16 are omitted”, *Under the broadest reasonable interpretation, the selected sample data points correspond to the N sensor input sampling values) comprises: determining, based on each of the N sensor input sampling values, a positive stroke sensor output sampling value and a reverse stroke sensor output sampling value (see at least para. [0109] of Liu which discloses “Sensitivity is an indicator of sensing characteristics. It reflects the degree of wavelength change of the fiber grating under the action of environmental physical quantity. Taking the forward stroke and reverse stroke as one measurement cycle, according to the measurement results of three cycles, the least squares method is used to calculate the reference straight line formula”) corresponding to each of the N sensor input sampling values in the sensor data set, to obtain N positive stroke sampling data and N reverse stroke sampling data, wherein the forward stroke sensor output sampling value is a forward stroke sensor output value corresponding to a forward stroke sensor input value adjacent to the sensor input sampling value in the sensor data set, and the reverse stroke sensor output sampling value is a reverse stroke sensor output value corresponding to a reverse stroke sensor input value adjacent to the sensor input sampling value in the sensor data set (Under the broadest reasonable interpretation, The forward-stroke wavelength values of Liu correspond to forward-stroke sensor output values and the reverse stroke wavelength values of Liu correspond to reverse stroke sensor output values. The application of Koukol’s interpolation between adjacent calibration points separately to the forward stroke data and reverse stroke data results in determining forward stroke output values corresponding to adjacent forward stroke input values and reverse stroke output values corresponding to adjacent reverse stroke input values.
Regarding claim 8, Martinez Heras, as modified by Taylor, Koukol and Liu discloses wherein
said determining, based on each of the N sensor input sampling values, a sensor output sampling value 2 corresponding to each of the N sensor input sampling values in the sensor data set, to obtain N sampling data comprises: determining, based on each of the N sensor input sampling values, a positive stroke sensor output sampling value and a reverse stroke sensor output sampling value corresponding to each of the N sensor input sampling values in the sensor data set, to obtain N positive stroke sampling data and N reverse stroke sampling data, wherein the forward stroke sensor output sampling value is a forward stroke sensor output value corresponding to a forward stroke sensor input value adjacent to the sensor input sampling value in the sensor data set, and the reverse stroke sensor output sampling value is a reverse stroke sensor output value corresponding to a reverse stroke sensor input value adjacent to the sensor input sampling value in the sensor data set As discussed above, Liu discloses forward-stroke output wavelength values and reverse stroke output wavelength values associated with calibration points (see at least para. [0101]-[0102] of Liu). Under the broadest reasonable interpretation, the forward-stroke output wavelength values correspond to positive stroke sensor output sampling values and the reverse stroke output wavelength values correspond to reverse stroke sensor output sampling values. Additionally, Koukol discloses determining values using correction values associated with adjacent calibration points (see at least para. [0057] of Koukol). Under the broadest reasonable interpretation, the adjacent calibration points correspond to adjacent forward stroke and reverse stroke sensor input values and the associated correction values correspond to the claimed forward stroke and referees stroke sensor output values corresponding to those adjacent input values.
Regarding claim 9, Martinez Heras, as modified by Taylor, Koukol and Liu discloses wherein
said determining, based on each of the N sensor input sampling values, a sensor output sampling value corresponding to each of the N sensor input sampling values in the sensor data set, to obtain N sampling data (see at least para. [0006] of Martinez Heras which discloses “ selects certain sample data points out of an original data sample in order to reduce the overall data volume” and see at least para. [0054] of Martinez Heras which discloses “the data points P11, P13, P14, P15, P17, P18 and P19 as highlighted by the square data points in FIG. 3 are added to the resampled time series. In the simple example of FIG. 3, only the data points P12 and P16 are omitted”, *Under the broadest reasonable interpretation, the selected sample data points correspond to the N sensor input sampling values) comprises: determining, based on each of the N sensor input sampling values, a positive stroke sensor output sampling value and a reverse stroke sensor output sampling value (see at least para. [0109] of Liu which discloses “Sensitivity is an indicator of sensing characteristics. It reflects the degree of wavelength change of the fiber grating under the action of environmental physical quantity. Taking the forward stroke and reverse stroke as one measurement cycle, according to the measurement results of three cycles, the least squares method is used to calculate the reference straight line formula”) corresponding to each of the N sensor input sampling values in the sensor data set, to obtain N positive stroke sampling data and N reverse stroke sampling data, wherein the forward stroke sensor output sampling value is a forward stroke sensor output value corresponding to a forward stroke sensor input value adjacent to the sensor input sampling value in the sensor data set, and the reverse stroke sensor output sampling value is a reverse stroke sensor output value corresponding to a reverse stroke sensor input value adjacent to the sensor input sampling value in the sensor data set (Under the broadest reasonable interpretation, The forward-stroke wavelength values of Liu correspond to forward-stroke sensor output values and the reverse stroke wavelength values of Liu correspond to reverse stroke sensor output values. The application of Koukol’s interpolation between adjacent calibration points separately to the forward stroke data and reverse stroke data results in determining forward stroke output values corresponding to adjacent forward stroke input values and reverse stroke output values corresponding to adjacent reverse stroke input values.
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Martinez Heras (US 2013/0212142A1) in view of Taylor (US 2012/0173185 A1) and further in view of Koukol (US 2004/0249592A1) and further in view of Profit (US 2012/0296596 A1).
Regarding claim 10, Martinez Heras, as modified by Taylor and Koukol discloses variable
values (see at least para. [0037] of Koukol which discloses “known high and low range process variable values are applied at a current temperature, and sensor output readings are taken”, *Under the broadest reasonable interpretation, the low range process variable value of Koukol corresponds to the minimum value of the sensor range and the high range process variable value corresponds to the maximum value of the sensor range).
Martinez Heras, as modified by Taylor and Koukol may not explicitly disclose wherein a
minimum value and a maximum value of the N sensor input sampling values are a minimum value and a maximum value of a sensor range, respectively (see at least para. [0005] of Profit which discloses “The offset value is then calculated by means of a method such as averaging the maximum value and the minimum value of the output value of the magnetic sensor acquired during this operation” and see at least para. [0056] of Profit which discloses “the output value of the magnetic sensor 2 during this rotation contains both the maximum value and the minimum value. The offset value calculation section 22 calculates a value of a parameter relating to the current sensitivity of the axis of interest using the maximum value and the minimum value, and updates a parameter”).
It would have been obvious to one of ordinary skill in the art before the effective filing date
of the claimed invention to further modify the senso data processing method of Martinez Heras, as modified by Taylor and Koukol to include wherein a minimum value and a maximum value of the N sensor input sampling values are a minimum value and a maximum value of a sensor range, respectively, as taught in Profit with a reasonable expectation of success in order to ensure that the sampled sensor data span the entire operating range of the sensor, thereby improving calibration accuracy and reliability.
Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable over Martinez Heras (US 2013/0212142A1) in view of Taylor (US 2012/0173185 A1) and further in view of Koukol (US 2004/0249592A1) in view of Profit (US 2012/0296596 A1) and further in view of Gui (CN 113375853).
Regarding claim 11, Martinez Heras, as modified by Taylor and Koukol and Profit discloses
adjacent sensor input sampling values (see at least para. [0057] of Koukol which discloses “adjacent temperature points”, *Under the broadest reasonable interpretation, the adjacent temperature points correspond to adjacent sensor input sampling values).
Martinez Heras, as modified by Taylor and Koukol and Profit may not explicitly disclose
wherein differences between each two adjacent sensor input sampling values in the N sensor input sampling values are equal to each other.
However, Gui disclose wherein differences between each two adjacent sensor input
sampling values in the N sensor input sampling values are equal to each other (see at least the translation of Gui which discloses “The calibrated pressure points are preselected pressure value points, and the intervals between two connected calibrated pressure points are equal, for example, within the external pressure detection range of 0 kpa-400 kpa, the calibrated pressure points are respectively 0kpa, 50kpa, 100kpa, 150kpa, 200kpa, 250kpa, 300kpa, 350kpa and 400 kpa”).
It would have been obvious to one of ordinary skill in the art before the effective filing date
to modify the sensor data processing method of Martinez Heras, as modified by Taylor and Koukol and Profit to include wherein differences between each two adjacent sensor input sampling values in the N sensor input sampling values are equal to each other, as taught in Gui with a reasonable expectation of success in order to provide uniformly distributed sampling points across the sensor range, thereby improving calibration consistency, simplifying interpolation between adjacent sampling values and increasing measurement accuracy throughout the operating range of the sensor.
Additional Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Blaylock (US 20200058148A1) discloses derived skeletal constructions may include manipulations of depth sensor data and previously-established motion models independent and with respect to each other in order to enable efficient teaching of human movement skills. In an example, a motion model of a human movement can be a 5-dimensional object. These dimensions are the usual four dimensions of space-time (three for positioning things in space and one for time) plus an additional parameter that specifies which body segment the other four dimensions are specifying with spatial and time coordinates. Jo (US 2019/0077442 A1) discloses a sensor signal processing apparatus including a sensor signal test module configured to receive a sensor signal from each of at least three or more sensors and perform an abnormality test on the received sensor signals, and a sensor signal selection module configured to receive the sensor signals on which the abnormality test has been performed from the sensor signal test module, determine the received sensor signals as normal sensor signals and abnormal sensor signals, select a main sensor signal on the basis of the determined normal sensor signals, determine validity of the selected main sensor signal, and control an output of the selected main sensor signal.
Conclusion
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/DANA D IVEY/Examiner, Art Unit 3662
/D.D.I/June 9, 2026
/JELANI A SMITH/Supervisory Patent Examiner, Art Unit 3662
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