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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 03/26/2026 has been entered.
Response to Amendment
The Amendments to the Claims filed 03/26/2026 have been entered. Claims 1-3 and 5-11 are pending in the application. Claim 4 has been canceled.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1-3, 5, 7, and 9-11 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kawai et al. (US 20230264355 A1) in view of Oka et al. (US 20230213927 A1) and Friedlander et al. (US 20180031618 A1).
Regarding Claims 1 and 10, Kawai teaches:
A diagnostic method causing a processor to execute a process, the diagnostic method comprising executing on the processor the steps of:
a first measurement data acquisition step of acquiring first measurement data based on a time-series signal obtained by a physical quantity sensor detecting a physical quantity generated by a motor repeating an operation and an operation stop as a predetermined operation pattern in a first period the physical quantity including at least one of an acceleration, a velocity, an angular velocity, and a displacement (See Fig. 2, para[0009], para[0054], para[0060], para[0069], para[0190]: The time-series data acquirer acquires time-series data of state signals for a period of time from the timing of an acquisition start signal indicating a start of acquisition of a state signal reflecting a state of the robot to the timing of an acquisition end signal indicating an end of acquisition of the state signal. However, it is also possible with the robot 1 to perform the playback operation for multiple times, including the first time, calculate the average time-series data by averaging the time-series data of the multiple times, and use the average time-series data as the reference data. The above multiple times may be consecutive times. The time-series data is data related to at least one of … rotational position deviation, or actual rotational position of the servo motor that drives the robot 1. );
a reference data generation step of generating reference data based on the first measurement data (See Fig. 4, Fig. 15, para[0068] – para[0071]: The reference data, the time-series data acquired by the time-series data acquisition unit 51 is used when the robot 1 is taught an operation and the operation is played back for the first time. The reference data may be the time-series data of the first time or the filtered time-series data of the average of multiple times including the first time.);
a second measurement data acquisition step of acquiring second measurement data based on the time-series signal obtained by the physical quantity sensor detecting the physical quantity generated by the motor repeating the predetermined operation pattern in a second period (See para[0009], para[0068], and para[0072]: Comparison data, which is the data collected later.); and
a diagnosis step of diagnosing a state of the motor based on the reference data and the second measurement data (See Fig. 4, para[0009], para[0068], and para[0076]: The time-series data evaluator 53 compares the reference data with the comparison data to evaluate the comparison data. Based on the results of the evaluation, the time-series data evaluator 53 outputs an evaluation quantity for evaluating the state of the robot 1.), wherein
the reference data generation step includes
extracting, from the first measurement data, a plurality of pieces of first period unit data corresponding to at least a part of the predetermined operation pattern (See Fig. 15, para[0067] – para[0071]: it is also possible with the robot 1 to perform the playback operation for multiple times, including the first time, calculate the average time-series data by averaging the time-series data of the multiple times, and use the average time-series data as the reference data. The above multiple times may be consecutive times, such as the first, second, third.),
each first period unit data of the plurality of pieces of first period unit data being a time-series segment defined with respect to an operation-start timing of the predetermined operation pattern or an operation included in the predetermined operation pattern (See Fig. 15, para[0059], para[0067] – para[0071]: The controller 90 outputs an acquisition start signal to the state monitoring device 5 (and thus to the time-series data acquisition unit 51) after the brake is released and shortly before the servo motor starts to rotate.), and
the processor is further configured to cause the motor to operate based on the diagnosed state of the motor (See Fig. 19, para[0002], para[0009], para[0131], para[0132], para[0175], and para[0177]: The robot state evaluator uses the dissimilarity calculated by the dissimilarity calculator as an evaluation quantity for evaluating the state of the robot. a warning message indicating that an abnormality has occurred may be displayed in the display 55. The operator can use this information to properly plan for future maintenance.).
Kawai is silent as to the language of:
generating the reference data by executing synchronization processing on the plurality of pieces of first period unit data and calculating a representative value of the plurality of pieces of first period unit data subjected to the synchronization processing,
the synchronization processing is processing in which a timing at which an amplitude of predetermined data in the plurality of pieces of first period unit data is maximized matches timings at which amplitudes of the other pieces of data in the plurality of pieces of first period unit data are maximized.
Nevertheless Oka teaches:
generating the reference data by executing synchronization processing on the plurality of pieces of first period unit data (See Fig. 3, Fig. 4, Fig. 8, Fig. 11, para[0011] – para[0013] and para[0058]: synchronizes the pieces of process data based on a degree of similarity. That is, based on the degree of similarity of the time-series data, the plurality of pieces of time-series data are synchronized. In this way, it becomes possible to superimpose and display a plurality of pieces of process data in such a manner that steps in the stage performed in the plant 3 correspond in chronological order.) and
calculating a representative value of the plurality of pieces of first period unit data subjected to the synchronization processing (See Fig. 3, Fig. 4, Fig. 8, Fig. 11, para[0011] – para[0013] and para[0058]: calculates average time-series data by using pieces of process data each corresponding to a stage performed in the production facility and each having the management number that is different and synchronizes the pieces of process data based on a degree of similarity to the calculated average time-series data. Based on a plurality of pieces of time-series data such as pieces of process data in different batch processing operations, average time-series data can be calculated.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Kawai by generating the reference data by executing synchronization processing on the plurality of pieces of first period unit data and calculating a representative value of the plurality of pieces of first period unit data subjected to the synchronization processing such as that of Oka. Oka teaches, “Pieces of process data of the identical stage for the processing target each having a different management number may be synchronized as described above to calculate the abnormality degree” (See para[0012]). One of ordinary skill would have been motivated to modify Kawai, because generating reference data by synchronizing a plurality of pieces of data and calculating a representative value would have helped to determine an abnormality degree using data from different products undergoing an identical processing state, as recognized by Oka.
Oka is silent as to the language of:
the synchronization processing is processing in which a timing at which an amplitude of predetermined data in the plurality of pieces of first period unit data is maximized matches timings at which amplitudes of the other pieces of data in the plurality of pieces of first period unit data are maximized.
Nevertheless Friedlander teaches:
the synchronization processing is processing in which a timing at which an amplitude of predetermined data in the plurality of pieces of first period unit data is maximized matches timings at which amplitudes of the other pieces of data in the plurality of pieces of first period unit data are maximized (See para[0026] – para[0028]: In some embodiments, the received samples may be synchronized with the determined patterns, for example by matching the times at which the peak sampled values occurred, with the times at which the peaks occurred in the patterns determined during training, and similarly for the lowest values.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Oka wherein the synchronization processing is processing in which a timing at which an amplitude of predetermined data in the plurality of pieces of first period unit data is maximized matches timings at which amplitudes of the other pieces of data in the plurality of pieces of first period unit data are maximized such as that of Friedlander. Friedlander teaches, “The sampled values may then be analyzed against the determined patterns. During analysis, machine operational state may be identified, such as “working”, “off”, “idle”, “standby”, or the like” (See para[0028]). One of ordinary skill would have been motivated to modify Oka, because synchronizing data using timings of maximized amplitude would have helped to compare data to a pattern and identify the operational state of a machine, as recognized by Friedlander.
Regarding Claim 2, Kawai teaches:
The diagnostic method according to claim 1,
wherein the reference data generation step includes displaying waveforms of the plurality of pieces of first period unit data (See Fig. 6, Fig. 7, Fig. 8, Fig. 6, Fig. 15, para[0115], and para[0131] – para[0132]: The display 55 can display a graph corresponding to FIG. 6.).
Regarding Claim 3, Kawai is silent as to the language of:
The diagnostic method according to claim 1,
wherein the synchronization processing is processing of aligning timings so that a difference between predetermined data and the other pieces of data in the plurality of pieces of first period unit data is minimized.
Nevertheless Oka teaches:
The diagnostic method according to claim 1,
wherein the synchronization processing is processing of aligning timings so that a difference between predetermined data and the other pieces of data in the plurality of pieces of first period unit data is minimized (See para[0058]: For individual values which are elements included in the pieces of time-series data of the batch processing operations having different product serial numbers, the shortest distances between values included in different pieces of time-series data are obtained in a round-robin manner, and then alignment is performed by sliding the time-series data in a time-axis direction in such a manner that an integrated value of the shortest distances becomes smallest.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Kawai wherein the synchronization processing is processing of aligning timings so that a difference between predetermined data and the other pieces of data in the plurality of pieces of first period unit data is minimized such as that of Oka. Oka teaches, “Pieces of process data of the identical stage for the processing target each having a different management number may be synchronized as described above to calculate the abnormality degree” (See para[0012]). One of ordinary skill would have been motivated to modify Kawai, because generating reference data by synchronizing a plurality of pieces of data would have helped to determine an abnormality degree using data from different products undergoing an identical processing state, as recognized by Oka.
Regarding Claim 5, Kawai teaches:
The diagnostic method according to claim 1,
wherein the diagnosis step includes
extracting, from the second measurement data, diagnosis target data corresponding to at least a part of the predetermined operation pattern (See Fig. 2, para[0009], para[0054], para[0060], para[0069], para[0176], and para[0190]: The time-series data acquirer acquires time-series data of state signals for a period of time from the timing of an acquisition start signal indicating a start of acquisition of a state signal reflecting a state of the robot to the timing of an acquisition end signal indicating an end of acquisition of the state signal. the state monitoring device 5 monitors the state of an industrial robot capable of playing back predetermined operations.), and
diagnosing a state of the motor based on a difference between the reference data and the diagnosis target data subjected to the synchronization processing (See Fig. 4, para[0009], para[0068], and para[0076]: The time-series data evaluator 53 compares the reference data with the comparison data to evaluate the comparison data. Based on the results of the evaluation, the time-series data evaluator 53 outputs an evaluation quantity for evaluating the state of the robot 1. The dissimilarity calculator calculates dissimilarity between reference data based on the time-series data acquired in at least one playback operation and comparison data.).
Kawai is silent as to the language of:
executing synchronization processing between the reference data and the diagnosis target data.
Nevertheless Oka teaches:
executing synchronization processing between the reference data and the diagnosis target data (See Fig. 3, Fig. 4, Fig. 8, Fig. 11, para[0011] – para[0013] and para[0058]: synchronizes the pieces of process data based on a degree of similarity. That is, based on the degree of similarity of the time-series data, the plurality of pieces of time-series data are synchronized. In this way, it becomes possible to superimpose and display a plurality of pieces of process data in such a manner that steps in the stage performed in the plant 3 correspond in chronological order.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Kawai by executing synchronization processing between the reference data and the diagnosis target data such as that of Oka. Oka teaches, “Pieces of process data of the identical stage for the processing target each having a different management number may be synchronized as described above to calculate the abnormality degree” (See para[0012]). One of ordinary skill would have been motivated to modify Kawai, because generating reference data by synchronizing a plurality of pieces of data would have helped to determine an abnormality degree using data from different products undergoing an identical processing state, as recognized by Oka.
Regarding Claim 7. Kawai teaches:
The diagnostic method according to claim 1,
wherein the representative value is an average value (See para[0069]: calculate the average time-series data by averaging the time-series data of the multiple times, and use the average time-series data as the reference data.).
Regarding Claim 9. Kawai teaches:
The diagnostic method according to claim 1,
wherein the predetermined operation pattern is a pattern in which each time the motor executes each of the plurality of different types of the operations and, thereafter, the each of a plurality of different types of operations is stopped (See para[0009], para[0043] – para[0044], para[0060], and para[0065]: By switching the program to be executed between multiple programs, the operation to be performed by the robot 1 can be changed. When all the series of operations taught to the robot 1 are completed, the servo motor is controlled to stop rotating.), and
the plurality of pieces of first period unit data are data corresponding to any one of the plurality of operations (See para[0044], para[0069] – para[0070], and para[0075]: The reference data is prepared for each playback operation of the robot 1 (in other words, for each program).).
Regarding Claim 11. Kawai teaches:
A diagnostic system comprising:
the diagnostic device according to claim 10; and
the physical quantity sensor attached to the object (See Fig. 2, para[0042]: An encoder detecting its rotational position, not shown in the figure, is attached to each servo motor.).
Claim(s) 6 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kawai et al. (US 20230264355 A1) in view of Oka et al. (US 20230213927 A1) and Friedlander et al. (US 20180031618 A1) as applied to claim 1 above, and further in view of Tsuduki (US 20220276130 A1).
Regarding Claim 6, Kawai teaches:
The diagnostic method according to claim 1,
diagnosing a state of the motor based on a difference between the reference data and the diagnosis target data subjected to the synchronization processing (See Fig. 4, para[0009], para[0068], and para[0076]: The time-series data evaluator 53 compares the reference data with the comparison data to evaluate the comparison data. Based on the results of the evaluation, the time-series data evaluator 53 outputs an evaluation quantity for evaluating the state of the robot 1. The dissimilarity calculator calculates dissimilarity between reference data based on the time-series data acquired in at least one playback operation and comparison data.).
Kawai is silent as to the language of:
wherein the diagnosis step includes extracting, from the second measurement data, a plurality of pieces of second period unit data corresponding to at least a part of the predetermined operation pattern, each second period unit data of the plurality of pieces of second period unit data being the time-series segment defined with respect to the operation- start timing of the predetermined operation pattern or the operation included in the predetermined operation pattern,
generating diagnosis target data by executing synchronization processing on the plurality of pieces of second period unit data and calculating a representative value of the plurality of pieces of second period unit data subjected to the synchronization processing, and
executing synchronization processing between the reference data and the diagnosis target data.
Nevertheless Oka teaches:
extracting, from the second measurement data, a plurality of pieces of second period unit data corresponding to at least a part of the predetermined operation pattern (See para[0009] – para[0013], para[0039], para[0041], and para[0052]: In the batch stage 31, processing targets are sequentially processed on a transaction-to-transaction basis by using a predetermined transaction. In a row of the extraction timing, information indicating a timing of extracting a value to be used for the abnormality determination among output values of sensors is registered. The timing in the batch processing, for example, may be defined by a step indicating a phase of processing in each stage, a certain period of time, a certain time point, or the like.),
each second period unit data of the plurality of pieces of second period unit data being the time-series segment defined with respect to the operation-start timing of the predetermined operation pattern or the operation included in the predetermined operation pattern (See Fig. 3, para[0010] – para[0013]: The processing target is processed through a plurality of stages in the production facility. Pieces of process data of the identical stage for the processing target each having a different management number may be synchronized. The process data may be associated with a step indicating a phase of processing in a stage performed in the production facility to be stored in the storage device.),
calculating a representative value of the plurality of pieces of second period unit data subjected to the synchronization processing (See Fig. 3, Fig. 4, Fig. 8, Fig. 11, para[0011] – para[0013] and para[0058]: calculates average time-series data by using pieces of process data each corresponding to a stage performed in the production facility and each having the management number that is different and synchronizes the pieces of process data based on a degree of similarity to the calculated average time-series data. Based on a plurality of pieces of time-series data such as pieces of process data in different batch processing operations, average time-series data can be calculated.), and
executing synchronization processing between the reference data and the diagnosis target data (See Fig. 3, Fig. 4, Fig. 8, Fig. 11, para[0011] – para[0013] and para[0058]: synchronizes the pieces of process data based on a degree of similarity. That is, based on the degree of similarity of the time-series data, the plurality of pieces of time-series data are synchronized. In this way, it becomes possible to superimpose and display a plurality of pieces of process data in such a manner that steps in the stage performed in the plant 3 correspond in chronological order.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Kawai by extracting, from the second measurement data, a plurality of pieces of second period unit data corresponding to at least a part of the predetermined operation pattern, each second period unit data of the plurality of pieces of second period unit data being the time-series segment defined with respect to the operation- start timing of the predetermined operation pattern or the operation included in the predetermined operation pattern, calculating a representative value of the plurality of pieces of second period unit data subjected to the synchronization processing, and executing synchronization processing between the reference data and the diagnosis target data such as that of Oka. Oka teaches, “Pieces of process data of the identical stage for the processing target each having a different management number may be synchronized as described above to calculate the abnormality degree” (See para[0012]). One of ordinary skill would have been motivated to modify Kawai, because generating reference data by synchronizing a plurality of pieces of data would have helped to determine an abnormality degree using data from different products undergoing an identical processing state, as recognized by Oka.
Oka does not explicitly recite:
generating diagnosis target data by executing synchronization processing on the plurality of pieces of second period unit data.
Nevertheless Tsuduki teaches:
generating diagnosis target data by executing synchronization processing on the plurality of pieces of second period unit data (See para[0065] - para[0068]: Additionally or alternatively, the parameter calculated by the DTW processing portion 46 may be the number of pieces of measurement data shifted in a time axis direction in the DTW path among pieces of measurement data of each point included in the target batch data.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Kawai by generating diagnosis target data by executing synchronization processing on the plurality of pieces of second period unit data such as that of Tsuduki. Tsuduki teaches, “the target batch data may be batch data included in the latest batch file 430. The reference batch data may be selected from batch data in a state where the facility 2 is good, may be, as an example, batch data selected by an operator, may be batch data having a median time width from the head to the tail among a plurality of pieces of batch data, or may be batch data having the smallest sum of DTW distances with other batch data among the plurality of pieces of batch data” (See para[0066]). One of ordinary skill would have been motivated to modify Kawai, because synchronizing a plurality of pieces of second period unit data would have helped to identify what batch the of a process the target data was from, as recognized by Tsuduki.
Claim(s) 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kawai et al. (US 20230264355 A1) in view of Oka et al. (US 20230213927 A1) and Friedlander et al. (US 20180031618 A1) as applied to claim 1 above, and further in view of Bosco (US 20210067381 A1).
Regarding Claim 8. Kawai is silent as to the language of:
The diagnostic method according to claim 1,
wherein the physical quantity sensor is an inertial sensor.
Nevertheless Bosco teaches:
wherein the physical quantity sensor is an inertial sensor (See para[0002] – para[0003]: inertial sensors.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Kawai wherein the physical quantity sensor is an inertial sensor such as that of Bosco. Bosco teaches, “processing time-series data sensed via inertial sensors such as accelerometers, e.g., for human activity recognition, gesture recognition or for the characterization of vibrations in motors for predictive maintenance” (See para[0002]). One of ordinary skill would have been motivated to modify Kawai, because using an inertial sensor would have helped to characterize vibrations of a motor for predictive maintenance, as recognized by Bosco.
Response to Arguments
Applicant's arguments filed 03/26/2026 have been fully considered but they are not persuasive.
Applicant argues that: More particularly, in Kawai, an entirety of a playback process is traced. As such, Kawai does not disclose or suggest monitoring repeated operations (cycle segmentations) as a predetermined operation pattern including start and stop of the operation.
In response to applicant's argument that the references fail to show certain features of the invention, during patent examination, the pending claims must be "given their broadest reasonable interpretation consistent with the specification." The Federal Circuit’s en banc decision in Phillips v. AWH Corp., 415 F.3d 1303, 1316, 75 USPQ2d 1321, 1329 (Fed. Cir. 2005).
As shown in further detail in the 35 USC 103 rejection of the above, Kawai teaches: “In the time-series data acquisition process, time-series data of state signals are acquired for a period of time from the timing of an acquisition start signal indicating a start of acquisition of a state signal reflecting a state of the robot to the timing of an acquisition end signal indicating an end of acquisition of the state signal” (See para[0010]); “The controller 90 outputs an acquisition start signal to the state monitoring device 5 (and thus to the time-series data acquisition unit 51) after the brake is released and shortly before the servo motor starts to rotate” (See para[0059]); “However, it is also possible with the robot 1 to perform the playback operation for multiple times, including the first time” (See para[0069]); and “The above multiple times may be consecutive times, such as the first, second, third” (See para[0069]).
Claims 1 and 10 recite the limitation “extracting, from the first measurement data, a plurality of pieces of first period unit data corresponding to at least a part of the predetermined operation pattern, each first period unit data of the plurality of pieces of first period unit data being a time-series segment defined with respect to an operation-start timing of the predetermined operation pattern or an operation included in the predetermined operation pattern”.
In view of the state of the art, the examiner understands a broadest reasonable interpretation of “first period unit data” to include a playback operation with a start timing related to the start of an operation. Further, the limitation “a plurality of pieces of first period unit data” is interpreted to include consecutively performing a playback operation. Applicant’s specification does not rebut the presumption that the terms “first period unit data” and “a plurality of pieces of first period unit data” are to be given its broadest reasonable interpretation by clearly setting forth a different definition of the term. Kawai discloses a broadest reasonable interpretation of the recited limitation, because Kawai teaches performing a playback operation multiple consecutive times and acquiring data from the start of the operation to the end. Accordingly, applicant’s arguments regarding the recited limitation are not persuasive and the rejection is maintained.
Applicant argues that: Thus, since Kawai is silent about a phase anchor in repeated operations.
In response to applicant's argument that the references fail to show certain features of the invention, it is noted that the features upon which applicant relies (i.e., a phase anchor) are not recited in the rejected claim(s). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993).
Applicant argus that: Similarly, each of Oka and Friedlander does not disclose or suggest monitoring repeated operations (cycle segmentations) as a predetermined operation pattern including start and stop of the operation because they are directed to monitoring of a batch-type process.
In response to applicant's argument that the references fail to show certain features of the invention, during patent examination, the pending claims must be "given their broadest reasonable interpretation consistent with the specification." The Federal Circuit’s en banc decision in Phillips v. AWH Corp., 415 F.3d 1303, 1316, 75 USPQ2d 1321, 1329 (Fed. Cir. 2005).
As shown in further detail in the 35 USC 103 rejection of the above, Oka teaches: “In the batch stage 31, processing targets are sequentially processed on a transaction-to-transaction basis by using a predetermined transaction, and for example, processing operations such as receiving, holding, and emitting of a raw material with respect to each equipment are performed in order” (See para[0039]); “Furthermore, the processes are classified into a pretreatment stage, a precooling stage, and a reaction stage” (See para[0041]); and “In the pretreatment stage, pieces of time-series data are acquired from sensors with tags 001 and 002. In the precooling stage, pieces of time-series data are acquired from sensors with tags 003 and 004. In the reaction stage, pieces of time-series data are acquired from sensors with tags 005, 006, and 007” (See para[0041]).
Claim 6 recites the limitation “extracting, from the second measurement data, a plurality of pieces of second period unit data corresponding to at least a part of the predetermined operation pattern, each second period unit data of the plurality of pieces of second period unit data being the time-series segment defined with respect to the operation-start timing of the predetermined operation pattern or the operation included in the predetermined operation pattern”.
In view of the state of the art, the examiner understands a broadest reasonable interpretation of “second period unit data” to include a pretreatment stage, precooling stage, or a reaction stage of a batch stage process. Further, the limitation “a plurality of pieces of second period unit data” is interpreted to include a batch stage process. Applicant’s specification does not rebut the presumption that the terms “second period unit data” and “a plurality of pieces of second period unit data” are to be given its broadest reasonable interpretation by clearly setting forth a different definition of the term. Oka discloses a broadest reasonable interpretation of the recited limitation, because Oka teaches gathering and tagging data from substages of a batch stage. Accordingly, applicant’s arguments regarding the recited limitation are not persuasive and the rejection is maintained.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CARTER W FERRELL whose telephone number is (571)272-0551. The examiner can normally be reached Monday - Friday 10 am - 8 pm.
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/CARTER W FERRELL/ Examiner, Art Unit 2857
/Catherine T. Rastovski/ Supervisory Primary Examiner, Art Unit 2857
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