DETAILED ACTION
Notice of 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 .
Claim 11 is cancelled. Claim(s) 1-5, 7 and 9-10 are pending and are rejected.
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 03/05/2026 has been entered.
Response to Amendment
This Office Action is responsive to the RCE filed on 05/18/2026.
Claims 1-2 and 7 are amended and are being fully considered by the examiner.
In response to applicant’s amendments to claim 1, all the 35 U.S.C. 101 rejections have been withdrawn.
Claim Objections
Claim 1 is objected to because of the following informalities:
Claim 1 includes the following typographical error: claim recites, “by for analyzing process data” such that “by for” is a typographical error.
For the examination purpose, in broadest reasonable interpretation, this limitation is construed as, …by analyzing process data...
Appropriate correction is required.
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 text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
Determining the scope and contents of the prior art.
Ascertaining the differences between the prior art and the claims at issue.
Resolving the level of ordinary skill in the pertinent art.
Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1-5, 7 and 9 is/are rejected under 35 U.S.C. 103 as being unpatentable over WATANABE et al. (US20170357694A1) [hereinafter WATANABE] in view of Ma et al. (US20190227504A1) [hereinafter Ma] and further in view of Patel et al. (US20100063611A1) [hereinafter Patel].
Regarding claim 1 (amended):
WATANABE disclose(s), A method for controlling operation of a production process by for analyzing process data related to a segment of a production process, [(¶83) “A data processing method” “performing data processing on the RAW data by the selected data processing routine template, and (C) combining (A) and (B).” (¶84) “in a production site may be analyzed in real time”
Examiner notes the claim objections set forth in the current office action];
providing a process data sequence of the segment of the production process exhibiting a data pattern of at least one process variable to be analyzed; [(¶56) “Data from a sensor 10-1 (sensor 1) is supplied to the sensor and actuator control module 14-1”… (¶58) “The sensor and actuator control module 14-1 registers the RAW data” “in the Redis data store 16-2.”… (¶59) “The service module 18-1 accesses the Redis data store 16-4 and performs data processing”… (¶73) “The real time data processing section 182 once converts the RAW data 17 into data capable of being subjected to inference processing and performs data processing on the data.”… (¶75) “The data window segmentation module sets a window while sequentially moving a window on a time axis and sequentially supplies data within the window to the data abstraction module.”… (¶77) “The data pattern matching module performs matching of the structured abstraction data with the ontology data acquired from the RDF store 22 to perform inference.”… (¶120) “when the assembly operation element as the sequence information is extracted from sensor data, the assembly operation element is compared with sequence information obtained from ontology data of the image forming apparatus assembly so as to make it possible to determine whether an assembly operation is performed along with ontology data or not, that is, detect a failure during the product assembly.”… (¶88) “A position of the assembly operation element is inferred using arrangement configuration-within- image forming apparatus 34 and 3D CAD data 36 and sensor data corresponding to the position is extracted. A movement of the assembly operation element is inferred using basic operation ontology 38 and sensor data corresponding to the movement is extracted.”
Examiner notes that, applicant’s specification ¶17 describes “process data sequence of the segment of the production process” as a set of data such as data set 115 or 120 that are in consideration for analysis. Therefore, in broadest reasonable interpretation in light of the specification these limitations are interpreted as a dataset of any segment of production process that is being analyzed
As such Watanabe teaches, a dataset of a segment of production process that is in consideration, data segments/sets from the production segment are provided for analysis that shows a data pattern of any variable such as movement related variable sensed by the sensors of the actuator];
providing a set of metadata of the segment of the production process related to the provided process data sequence, [(¶58) “The sensor and actuator control module 14-1 registers the RAW data” “in the Redis data store 16-2 and the sensor and actuator control module 14-2 registers the RAW data,” “in the Redis data store 16-3.”… (¶44) “The Redis data store 16 sequentially stores data (RAW data), which is converted into a key/value format, from the sensor and actuator control module 14.”… (¶45) “The meta data extraction module 20 extracts meta data from RAW data of a key/value and structures the RAW data.”];
determining historical process data sequences based on the provided set of metadata of the segment of the production process, wherein the metadata is stored in a first database; [(¶44) “The Redis data store 16 sequentially stores data (RAW data), which is converted into a key/value format, from the sensor and actuator control module 14.”… (¶45) “The meta data extraction module 20 extracts meta data from RAW data of a key/value and structures the RAW data. The meta data extraction module 20 structures the RAW data into subject information, subject attribute information, object information, object attribute information, and environment information. The subject information is key information which is uniquely given. The structured data is stored in the RDF store 22.”];
determining a start timestamp and end timestamp of each of the determined historical process data sequence, based on the first database; [(¶92) “RAW data from the sensor 10 is transmitted to the sensor and actuator hubs 141 and the sensor and actuator hubs 141 give a timestamp and an ID (address of sensor and actuator hubs 141) to the RAW data and converts the RAW data into a piece of key-value type data”… (¶75) “The data window segmentation module sets a window while sequentially moving a window on a time axis and sequentially supplies data within the window to the data abstraction module.”… (¶111) “processing of a comparison of sensor data sequentially obtained from the sensor 10 with learning data 192. Similar to teacher data 190, sensor data is cut out into pieces of sensor data having a predetermined time width by causing the window W which is set in advance to be slid on the time axis. The piece of cut out sensor data is compared with learning data 192 to generate vector data.”… figure 3
Examiner notes that, Watanabe teaches, based on the data stored in the redis database, determines start and end time such as data window in a time axis that includes start and end time stamps making that data window such as moving a window on a time axis and sequentially supplies data within the window for processing];
calculating a similarity value of a sub interval…based on the data pattern of the at least one process variable; [(¶102) “when the key value is inferred, the real time data acquisition section 181 cuts out data stream having the corresponding key value from the Redis data store 16 and supplies data stream to the real time data processing section 182.”… (¶103) “FIG. 13 and FIG. 14 schematically illustrate processing in the real time data processing section 182.”… (¶107) “as vector data, a degree of similarity of operation elements with each classification result of operation elements is calculated. For example, operation elements are respectively classified into classification 1, classification 2, and classification 3, and the classification 1 is,”… (¶108) “a degree of similarity with the assembly operation element 1 is represented as a vector [0.1, 0.2]. The degrees of similarity with the assembly operation elements 2 and 3 are similar.”… (¶111) “The piece of cut out sensor data is compared with learning data 192 to generate vector data.”… (¶112) “the piece of cut out sensor data is compared with the learning data 192 and when a rate of each classification is”… (¶113) “[:classification 1 0.3:classification 2 0.1:classification 3 0.6], the piece of cut out sensor data is calculated as, [:assembly operation element 1[0.3, 0.2]:assembly operation element 2[0.1, 0.2]:assembly operation element 3[0.6, 0.7]] or the like using the degree of similarity with the assembly operation element in each classification.”… (¶114) “the vector data is generated and vector data is sequentially generated with respect to the pieces of cut out sensor data to generate a matrix as a set of the pieces of cut out sensor data.”];
wherein the determined historical process data sequences for the calculation are provided, based on the related start timestamps and end timestamps, by accessing a second database comprising the process data sequences, [(¶102) “when the key value is inferred, the real time data acquisition section 181 cuts out data stream having the corresponding key value from the Redis data store 16 and supplies data stream to the real time data processing section 182.”… (¶111) “processing of a comparison of sensor data sequentially obtained from the sensor 10” “sensor data is cut out into pieces of sensor data having a predetermined time width by causing the window W which is set in advance to be slid on the time axis. The piece of cut out sensor data is compared with learning data 192 to generate vector data.”… (¶114) “the vector data is generated and vector data is sequentially generated with respect to the pieces of cut out sensor data to generate a matrix as a set of the pieces of cut out sensor data.”], but doesn’t explicitly disclose, and
Ma discloses, calculating a similarity value of a sub interval of each of the determined historical process data sequences compared to the provided process data sequence, based on the data pattern of the at least one process variable; [(¶9) “monitor trends in movement (time series) of a KPI that are associated with an operating event during execution of the online plant process”… “deploy one or more pattern models from the library, in iterations, to the online execution of the plant process….apply the corresponding set of event signatures to the trends in KPI movement (time series) in the online plant process.” “For each applied event signature,…compare the KPI time series pattern contained in the event signature, as a whole, to a range of the online KPI time series to determine similarity….account for variations in amplitude, offset, shape, and time in determining the similarity between the online KPI time series range and time series pattern model….calculate a distance (similarity) score based on the comparison,”…
(¶7) “to define an event signature of an event pattern model in several ways, e.g., (i) use a known time series event-pattern from past events;”];
wherein characteristic shapes of the determined historical process data sequences are determined to define the sub interval for each of the determined historical process data sequences, and [(¶14) “defines one or more pattern shapes representing pattern characteristics of an abnormal operating condition. The defined pattern shapes being stored in a shape library. The supervised pattern discovery method selects a pattern shape from the shape library for the operating event.”
(¶93) “the executed pattern discovery technique will attempt to discover the selected one or more pattern shapes in the time series of the process variable”
(¶95) “The method 530, at step 540, initiates the guided pattern discovery technique to perform pattern clustering based on the selected pattern shape…repeat steps 531-540 to conduct guided pattern discovery technique on different time series for different or the same process variable (KPI).”]
based at least in part on the calculated similarity value of the process data sequence to at least one of the determined historical process data sequences associated with a quality problem or an undesirable event, outputting a control signal to adjust operation of the production process to address the quality problem or undesirable event and/or outputting a warning signal for warning operators of the quality problem or undesirable event. [Examiner notes that claim requires only one of the elements separated by “or.”
Accordingly, Ma discloses, outputting a control signal to adjust operation of the production process to address the quality problem or undesirable event as described below along with the rest of the other limitations of the claim…
(¶9) “For each applied event signature,…compare the KPI time series pattern contained in the event signature, as a whole, to a range of the online KPI time series to determine similarity….account for variations in amplitude, offset, shape, and time in determining the similarity between the online KPI time series range and time series pattern model….calculate a distance (similarity) score based on the comparison,”…
(¶7) “to define an event signature of an event pattern model in several ways, e.g., (i) use a known time series event-pattern from past events;”
(¶83) “If the total distance score meets the threshold event level, the modeler engine 403 may also automatically provide input (adjust parameters/variables /constraints) at the Instrumentation, Control, Operation Computer 405 in the Distributed Control System (DCS) 404 in response to the occurrence of the operating event of interest. The Instrumentation, Control, Operation Computer 405, based on the input, may then automatically adjust (via network 408) physical valves, actuators, heaters, and the like 409A-4091 to prevent, stop, or alter the occurrence of the operating event of interest.”];
Therefore, it would have been obvious to one of ordinary skill in the art before the filing date of the claimed invention to have combined the capability of calculating a similarity value of a sub interval of each of the determined historical process data sequences compared to the provided process data sequence, based on the data pattern of the at least one process variable; outputting a control signal to adjust operation of the production process to address the quality problem or undesirable event and/or outputting a warning signal for warning operators of the quality problem or undesirable event based at least in part on the calculated similarity value of the process data sequence to at least one of the determined historical process data sequences associated with a quality problem or an undesirable event and combined the characteristic shapes of the determined historical process data sequences are determined to define the sub interval for each of the determined historical process data sequences in order to speed up the pattern search process, reduce the number of non-event patterns, and result in the most meaningful pattern models for online deployment taught by Ma with the data processing method taught by WATANABE as discussed above in order to have a reasonable expectation of success such as to speed up the pattern search process, reduce the number of non-event patterns, and result in the most meaningful pattern models for online deployment [Ma: (¶8) “to speed up the pattern search process, reduce the number of non-event patterns, and result in the most meaningful pattern models for online deployment”], but doesn’t explicitly disclose, and
Patel discloses, wherein the metadata includes data related to a batch production process;
analyzing the process data to select a batch with a quality problem or an undesirable event. [(¶11) “archived data includes stored process data obtained during runs of the batch process. The archived data also includes information defining at least one batch quality attribute for the runs of the batch process.”… “forming clusters by classifying the archived data for a pre-selected subset of batch runs into classes based on the batch attribute(s).”… (¶26) “characterize operations of a batch process in terms of batch attributes or quality indicators (e.g., a yield of a run of a batch process and a total duration of a run of a batch process). It should be noted that the batch attributes or quality indicators represent the overall batch performance.”… (¶27) “The batch attribute or quality indicator predictions enable the initiation of performance enhancement steps so as to correct (in real time) the batch runs that have a potential to evolve as relatively lower quality batches.”].
Therefore, it would have been obvious to one of ordinary skill in the art before the filing date of the claimed invention to have used data related to a batch production process and to have combined the capability of analyzing the process data to select a batch with a quality problem or an undesirable event in order to provide earlier detection of abnormal batch conditions to facilitate early correction of batch process operations before the abnormal batch condition becomes incurable taught by Patel with the data processing method taught by Ma and WATANABE as discussed above in order to have a reasonable expectation of success such as to provide earlier detection of abnormal batch conditions to facilitate early correction of batch process operations before the abnormal batch condition becomes incurable [Patel: (¶28) “generally provide earlier detection of abnormal batch conditions as compared to the detection provided by conventional methods for online batch process monitoring. Earlier detection of abnormal conditions can facilitate a correction of batch process operations before the abnormal batch condition becomes incurable.”].
Regarding claim 2 (amended):
WATANABE, Ma and Patel disclose all the elements of claim 1, and
WATANABE further discloses, wherein a plurality of the determined historical process data sequences with a selected similarity value interval is visually displayed for providing a new process data sequence to be analyzed. [(¶35) “generating a knowledge information visualization module 74 by extraction of context (various information needed to execute a program),” (¶127) “the assembly operation element obtained from sensor data is compared with the assembly operation element obtained from ontology data so as to make it possible to evaluate whether an assembly worker assembles a product in a correct procedure or in desired required time and output the result of evaluation. Such an output is fed back to an assembly worker to enable more efficient and reliable product assembly.”
Examiner notes that, WATANABE teaches visualization module 74 in figure 7 that provides visual representations of data such as outputting (can be visual) the result of evaluation to the operator such as the operator is able to view various evaluation results such as selected/determined similarity results as described above in claim 1].
Regarding claim 3:
WATANABE, Ma and Patel disclose all the elements of claim 1, and
WATANABE further discloses, wherein the metadata includes a batch process and/or batch process interval and/or product type and/or an operation type and/or a phase of an operation and/or a production recipe and/or a process variable. [(¶45) “The meta data extraction module 20 extracts meta data from RAW data of a key/value and structures the RAW data. The meta data extraction module 20 structures the RAW data into subject information, subject attribute information, object information, object attribute information, and environment information.”… (¶76) “Plural pieces of abstraction data may be generated from a single piece of RAW data.” “structures the abstraction data into quaternary fact data based on the extracted data. The quaternary fact data is specifically time information/subject information/attribute information/sensor data. The time information allocates time stamp information.”… (¶124) “When sensor data is cut out into the window W, a timestamp may be given to each window to give time information to each piece of cut out sensor data.”
Examiner notes that, claim requires metadata includes only one of a batch process or batch process interval or product type or an operation type or a phase of an operation or a production recipe or a process variable
WATANABE teaches, metadata includes a phase of an operation such as The time information allocates time stamp information].
Regarding claim 4:
WATANABE, Ma and Patel disclose all the elements of claim 1, and
WATANABE further discloses, wherein the determination of the process data sequences is based on a subset of the provided set of metadata of the segment of the production process,… [(¶43) “Redis API which converts received data into data of a key/value format in real time and stores the data in a Redis data store 16.”… (¶45) “The meta data extraction module 20 extracts meta data from RAW data of a key/value and structures the RAW data.”… (¶75) “The data window segmentation module sets a window while sequentially moving a window on a time axis and sequentially supplies data within the window to the data abstraction module.”… (¶76) “Plural pieces of abstraction data may be generated from a single piece of RAW data. The machine learning module structures the abstraction data into quaternary fact data based on the extracted data.”… (¶62-¶67) “In the processing, key information corresponding to a query is inferred and a data stream corresponding to the inferred key information is selected….Raw data of a key/value is stored in the Redis data store 16. In the figure, as an example of the stored RAW data,….key 1/data stream 1…key 2/data stream 2…key 3/data stream 3…key 4/data stream 4 are illustrated.”];
a subset of the provided set of metadata of the segment of the production process, which are stored in a first database. [(¶44) “The Redis data store 16 sequentially stores data (RAW data), which is converted into a key/value format, from the sensor and actuator control module 14.”… (¶45) “The meta data extraction module 20 extracts meta data from RAW data of a key/value and structures the RAW data. The meta data extraction module 20 structures the RAW data into subject information, subject attribute information, object information, object attribute information, and environment information. The subject information is key information which is uniquely given. The structured data is stored in the RDF store 22.”].
Regarding Claim 5:
WATANABE, Ma and Patel disclose all the elements of claims 1 and 4, but they do not explicitly disclose, and
Ma further discloses, wherein a user interface provides access to the set of metadata and related process variables for defining the subset of the metadata. [(¶41) “The method 100 further enabled the user to build a library of the located event patterns (signature patterns).”…
(¶42) “The method 100 begins at step 110, by enabling a human or system user to select one or more key performance indicator (KPI) of an industrial or chemical process at a subject plant (e.g. a refinery or Ethylene plant). Method 100 (step 110) enables the user to select one or more KPI from the available process variables for the plant process…step 110 may provide a user interface for the user to select the one or more KPI from the available process variables for the plant process. The user selects one or more KPI as an indicator of an undesirable (or abnormal) operating event related to the plant process,”
(¶44) “step 130 may provide a user interface that enables the user (e.g., domain expert) to review the historical data as a time series (e.g., in a graph or other such format) and identify KPI time series patterns related to the operating event of interest in the historical data. The historical data may include a known time series event-pattern from past occurrences of the operating event of interest.”].
Regarding claim 7 (amended):
WATANABE, Ma and Patel disclose all the elements of claim 1, and
WATANABE further disclose(s), wherein a time span of the sub interval of each of the determined historical process data sequences is constant. [(¶111) “The piece of cut out sensor data is compared with learning data 192 to generate vector data.”… (¶112) “the piece of cut out sensor data is compared with the learning data 192 and when a rate of each classification is”… (¶113) “[:classification 1 0.3:classification 2 0.1:classification 3 0.6], the piece of cut out sensor data is calculated as, [:assembly operation element 1[0.3, 0.2]:assembly operation element 2[0.1, 0.2]:assembly operation element 3[0.6, 0.7]] or the like using the degree of similarity with the assembly operation element in each classification.”… (¶116) “FIG. 16 schematically illustrates processing of a comparison of sensor data sequentially obtained from the sensor 10 with learning data 192. Similar to teacher data 190, sensor data is cut out into pieces of sensor data having a predetermined time width by causing the window W which is set in advance to be slid on the time axis. The piece of cut out sensor data is compared with learning data 192 to generate vector data.”
Examiner notes that Watanabe teaches time span such as shown in figure 16, the window w is the time span that is constant such as a predetermined time width for each time intervals such as the cutout pieces of sensor data].
Regarding Claim 9:
WATANABE, Ma and Patel disclose all the elements of claims 1, and
Ma further discloses, wherein the similarity value is calculated based on Euclidean distance calculation and/or based on dynamic time warping DTW of the process data sequences concerned. [(¶9) “calculate a distance (similarity) score based on the comparison,”…
(¶16) “The computer methods, systems, and program products determine the distance score based on the calculated Euclidean distance with DTW”
(¶73) “the formulation of distance scoring function in Equation 3 enables real-time calculation of the distance score (ATD(A,B)). That is, the ATD distance function being formulated based on Euclidian distance and DTW provides fast calculation of the ATD distance score,”].
Claim(s) 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over WATANABE, Ma and Patel, and further in view of Mueen et al. (US10853372B1) [hereinafter Mueen].
Regarding Claim 10:
WATANABE, Ma and Patel disclose all the elements of claim 1, but they do not explicitly disclose, and
Mueen discloses, wherein the calculation of the similarity value includes a multivariate analysis of a plurality of process data sequences. [(col. 18, lines 20-26) “At a “Compute Distribution Strategy” block 903, DisPatch controller 802 may determine a number of processors among processors 811-815 to use for pattern matching, and DisPatch controller 802 may determine which partitions, and which patterns, are to be processed by each processor. The number of processors and distribution strategy may account for several different variables,”… (col. 18, lines 35-43) “DisPatch controller 802 may assign partitions on a window-by-window basis, wherein first partitions associated with a first window are assigned to a first processor, and second partitions associated with a second window are assigned to a second processor, and so on. In some embodiments, at block 903, DisPatch controller 802 may assign patterns of different lengths to different processors, for example, DisPatch controller 802 may assign patterns of substantially a first length to a first processor, and DisPatch controller 802 may assign patterns of substantially a second length to a second processor,”]
Therefore, it would have been obvious to one of ordinary skill in the art before the filing date of the claimed invention to have combined the capability of performing multivariate analysis of a plurality of process data sequences for calculation of the similarity value in order to accomplish pattern matching faster, bringing down the processing time to identify patterns taught by Mueen with the method taught by WATANABE, Ma and Patel as discussed above in order to have a reasonable expectation of success such as to accomplish pattern matching faster, bringing down the processing time to identify patterns [Mueen: (col. 7, lines 61-63) “Based on the distance function D which is chosen by the user, we optimize the way the worker is searching the patterns to make it faster.”].
Response to Arguments
Applicant’s arguments with respect to claim(s) 1 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Applicant responds
(a) Rejections under 35 U.S.C. § 103:
The rejections are respectfully traversed. As agreed during the interview, the amendments to claim 1 presented herein overcome the current prior art rejections.
Accordingly, Applicant respectfully submits that claim 1, and the claims depending therefrom, are in condition for allowance.
(Pages: 7-8)
With respect to (a) above, Examiner appreciates the interpretative description given by Applicant in response.
In response to applicant’s amendments to claim 1, a new grounds of rejections in view of Ma has been introduced in the current office action.
WATANABE, Ma and Patel disclose all the elements of claim 1.
Applicant’s arguments are fully considered, but for the above described reasons, the arguments are moot; therefore, claims 1-5, 7 and 9-10 are rejected under 35 U.S.C. 103 in view of the references as presented in the current office action.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure is listed in the PTO-892 Notice of Reference Cited document.
Birdwell et al. (US20100332475A1) - Method and apparatus for predicting object properties and events using similarity-based information retrieval and modeling:
(¶12): method and apparatus for predicting object properties using similarity-based information retrieval… Measurable properties of the objects may be stored in one or a plurality of databases including multi-dimensional databases… an automated search strategy may locate nearest neighbor items, or items within a specified neighborhood, with the most similar properties, from a reference collection and utilize any geographic or other information associated with these items to predict properties.
Dhollander et al. (US20150178286A1) - System and Method for Similarity Search in Process Data:
(¶40): the industrial process analysis system comprises a process data connection device for the acquisition of process data from one or more process data sources, an indexing system for indexing the process data to create a set of indexed process data,….The industrial process analysis system also includes a data processing device for processing the at least one search instruction to create a search parameter set and comparing distances of members of the search parameter set with corresponding members of the indexed process data to obtain a similarity value, and an output device to display results based on the similarity value.
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/M.S./
Patent Examiner,
Art Unit 2116
/KENNETH M LO/Supervisory Patent Examiner, Art Unit 2116
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