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 .
Claims 6 and 8 are cancelled. Claim(s) 1-5, 7 and 9-11 are pending and are rejected.
Response to Amendment
This Office Action is responsive to the amendment filed on 11/24/2025.
Claims 1, 7, and 9 are amended and are being fully considered by the examiner.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL.
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 fall within the judicial exception of an abstract idea:
Claims 1-5, 7 and 9-11 are rejected under 35 U.S.C. 101 because the claimed subject matter is directed to an abstract idea without significantly more.
Step 1:
Claims 1-5, 7 and 9-11 are directed to a method that falls within one of the four statutory categories (i.e., process, machine, manufacture, or composition of matter).
Step 2A:
The claims 1-5, 7 and 9-11 fall within the judicial exception of an abstract idea. Specifically, Mental Processes (MPEP 2106.04(A)(2)(III)) such that concepts performed in the human mind or with pen and paper including observation, evaluation, judgment, calculation, and determination, and mathematical relationships/calculations.
Step 2A – Prong 1:
Claim 1:
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;
providing a set of metadata of the segment of the production process related to the provided process data sequence, wherein the metadata includes data related to a batch production process;
determining process data sequences based on the provided set of metadata of the segment of the production process,
determining a start timestamp and end timestamp of each of the determined process data sequence, based on the first database;
calculating a similarity value of a sub interval of each of the determined process data sequences compared to the provided process data sequence, based on the data pattern of the at least one process variable;
wherein characteristic shapes of the determined process data sequences are determined to define the sub interval for each of the determined process data sequences, and
wherein the determined 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, for analyzing the process data to select a batch with a quality problem or an undesirable event.
This limitation given its broadest reasonable interpretation in light of the specification is a mental process since this procedure can be performed in the human mind and is an observation, analysis, determination, evaluation, selection and calculation.
Claim 2:
wherein a plurality of the determined process data sequences with a selected similarity value interval is visually
This limitation given its broadest reasonable interpretation in light of the specification is a mental process since this is a concept that is performed in the human mind and is an analysis, presentation and observation.
Claim 3:
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.
This limitation given its broadest reasonable interpretation in light of the specification is a mental process since this is a concept that is performed in the human mind and is an observation.
Claim 4:
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,
This limitation given its broadest reasonable interpretation in light of the specification is a mental process since this procedure can be performed in the human mind and is an analysis and determination.
Claim 5:
…. access to the set of metadata and related process variables for defining the subset of the metadata
This limitation given its broadest reasonable interpretation in light of the specification is a mental process since this is a concept that is performed in the human mind and is a determination.
Claim 7:
wherein a time span of the sub interval of each of the determined process data sequences is constant.
This limitation given its broadest reasonable interpretation in light of the specification is a mental process since this is a concept that is performed in the human mind and is a determination or observation.
Claim 9:
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.
This limitation given its broadest reasonable interpretation in light of the specification is a mental process since this is a concept that is performed in the human mind and is a mathematical calculations.
Claim 10:
wherein the calculation of the similarity value includes a multivariate analysis of a plurality of process data sequences.
This limitation given its broadest reasonable interpretation in light of the specification is a mental process since this is a concept that is performed in the human mind, and is a mathematical relationship and mathematical calculations.
Step 2A – Prong 2 and Step 2B:
This judicial exception is not integrated into a practical application because the additional elements including the following limitations “database,” and “user interface” as recited in the claims are mere instructions to implement an abstract idea on a general purpose computer (apply it; corresponding structure disclosed in the specification is a general purpose computer implementing the claimed functions characterized as abstract ideas above. See MPEP 2106.05(a)). The claim limitations are implemented on these generic elements such that the following are merely applying the abstract idea on a generic computer: determining, evaluating, analyzing, judging, selecting and reasoning etc.
Claim(s) 1 recites, “A method for analyzing process data related to a segment of a production process, comprising.” This is generally linking the use of a judicial exception to a particular technological environment or field of use. See MPEP 2106.05(h).
Claim(s) 3 recites, “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.” This is generally linking the use of a judicial exception to a particular technological environment or field of use. See MPEP 2106.05(h).
Claim(s) 11 recites, “wherein based on the similarity value, a control signal for controlling a production process is provided;” This is generally linking the use of a judicial exception to a particular technological environment or field of use. The judicial exception of “providing a control signal or ” is now being linked to a technological environment (production process data analysis). See MPEP 2106.05(h).
The claim(s) recite the additional elements of,
(Claims 1 and 4) “wherein the metadata is stored in a first database”
(Claim 2) “a plurality of the determined process data sequences with a selected similarity value interval is visually displayed for providing a new process data sequence to be analyzed”
(Claim 4) “wherein a user interface provides access to the set of metadata and related process variables for defining the subset of the metadata”
(Claim 11) “based on the similarity value, a warning signal for warning operators of the production process is provided”
These additional elements are recited at a high level of generality and as a form of insignificant extra solution activity recognized by court as well-understood, routine, conventional activity.
The storing of data are claimed in a merely generic manner (e.g., at a high level of generality) and as an insignificant extra solution activity for data storing as computer function such that storing of data are recognized by the court as well-understood, routine, and conventional (MPEP 2106.05(d)(ii)).
The displaying, providing warning signal, and providing access to data feature(s) are claimed in a merely generic manner (e.g., at a high level of generality) and as an insignificant extra solution activity for data inputting and outputting as computer function such that data inputting and outputting are recognized by the court as well-understood, routine, and conventional (MPEP 2106.05(d)(ii)(i)).
Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Therefore, the claims are directed to an abstract idea.
The additional elements in the claim amount to no more than insignificant extra solution activity and do not amount to significantly more than the judicial exception because storing is mere data storing, and displaying, providing warning signal, providing access to data are data inputting/outputting (MPEP 2106.05(d)(ii)). As explained above, the claim limitations are implemented on these generic elements such that the following are merely applying the abstract idea on a generic computer: determining, evaluating, analyzing, calculating, selecting and mathematical calculation steps be done by at least one processor. Further, the use of the claimed invention in a production process field is simply an attempt to limit the use of the abstract idea to a particular technological environment (MPEP 2106.05(h)). The claim does not include any further additional elements that are sufficient to amount to significantly more than the judicial exception.
Even when combined with all of the claim limitations as a whole, it is still directed to the abstract idea of mental process. Therefore, the claims are not patent eligible.
Dependent claim(s) when analyzed as a whole are held to be patent ineligible under 35 U.S.C. 101 because the additional recited limitation(s) fail(s) to establish that the claim(s) is/are not directed to an abstract idea, as they recite further embellishment of the judicial exception.
Viewed as a whole, these additional claim element(s) do not provide meaningful limitation(s) to transform the abstract idea into a patent eligible application of the abstract idea such that the claim(s) amounts to significantly more than the abstract idea itself. Claims 1-5, 7 and 9-11 do not include any further additional elements that are sufficient to amount to significantly more than the judicial exception.
Therefore, the claim(s) 1-5, 7 and 9-11 are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
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) 1-4, 7, 9 and 11 is/are rejected under 35 U.S.C. 103 as being unpatentable over WATANABE et al. (US20170357694A1) [hereinafter WATANABE], and further in view of Patel et al. (US20100063611A1) [hereinafter Patel].
Regarding claim 1:
WATANABE disclose(s), A method for analyzing process data related to a segment of a production process, comprising: [(¶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”];
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 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 process data sequence, based on the first database; and [(¶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 star 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 of each of the determined process data sequences compared to the provided process data sequence, 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 characteristic shapes of the determined process data sequences are determined to define the sub interval for each of the determined process data sequences, [(¶111) “The real time data acquisition section 181 cut outs a data stream including corresponding key information according to a received query.” “in a case where the query corresponds to a motion analysis of a specific machine type, the real time data acquisition section 181 infers key information coincident with ontology data of basic motions included in the context and selects a data stream including the key information obtained by inference. When the key 1 and the key 4 of the RAW data are regarded as corresponding key information, the real time data acquisition section 181 selects the key 1/data stream 1 and the key 4/data stream 4 from the Redis data store 16. In FIG. 3, the selected RAW data is represented as data 17.”
Examiner notes that, in broadest reasonable interpretation, the meaning of characteristic shapes of the determined process data sequences can be any data that describes any characteristics.
As such, Watanabe discloses, determination of characteristic data such as key data to define the cut out intervals.];
wherein the determined 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, for analyzing the process data. [(¶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
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 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:
WATANABE and Patel disclose all the elements of claim 1, and
WATANABE further discloses, wherein a plurality of the determined 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 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 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 7 (amended):
WATANABE 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 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 (amended):
WATANABE and Patel disclose all the elements of claims 1, and
Patel 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. [(¶65) “Thereafter, steps 330, 332, and 334 are performed. In step 330, the Euclidean distances between the projections of archived batch profiles on BFDA space (ZBFDA) to each centroid C902, . . . , C918 are computed. In step 332, a statistical control limit Dc is computed based on a threshold analysis of the distances. If the cluster prototypes are used in place of the centriods, then the Euclidean distances are calculated between the BFDA projections of archived batch profiles and cluster prototypes. The mean distance for all clusters is calculated and taken as a distance measure of the batch profiles to that cluster. The Euclidean distances are used to measure the closeness of a point to the centroid.”].
Regarding Claim 11:
WATANABE and Patel disclose all the elements of claim 1, and
Patel further discloses, wherein based on the similarity value, a control signal for controlling a production process is provided; and/or based on the similarity value, a warning signal for warning operators of the production process is provided. [(¶65) “steps 330, 332, and 334 are performed. In step 330, the Euclidean distances between the projections of archived batch profiles on BFDA space (ZBFDA) to each centroid C902, . . . , C918 are computed. In step 332, a statistical control limit Dc is computed based on a threshold analysis of the distances.”
Examiner notes that claim requires only one of based on the similarity value, a control signal for controlling a production process is provided; or based on the similarity value, a warning signal for warning operators of the production process is provided.
Patel discloses, based on the similarity value, control signal for controlling a production process is provided].
Claim(s) 5 is/are rejected under 35 U.S.C. 103 as being unpatentable over WATANABE and Patel, and further in view of Sharma et al. (US20170277521A1) [hereinafter Sharma]
Regarding Claim 5:
WATANABE and Patel disclose all the elements of claims 1 and 4, but they do not explicitly disclose, and
Sharma discloses, wherein a user interface provides access to the set of metadata and related process variables for defining the subset of the metadata. [(¶16) “platform includes a graphical user interface that is displayed on a screen of a computer.”… (¶274) “a declarations page 2405 where the user can declares things that statically exist.”… (¶275) “On the declaration page, some examples of declarations include stream definitions (e.g., representing sensors), patterns (e.g., declaring pattern or patterns to match, specifying TFR(1)), timers, user-defined functions, complex data types, and so forth. There is tool bar an icon and button for each of the definition types, stream 2422, constants 2426, user-defined types 2430, timer 2434, user-defined functions 2438, and patterns 2442.”].
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 user interface for user to access metadata and related process variables and capability of defining subset of metadata in order to provide efficient data processing taught by Sharma with the method taught by WATANABE and Patel as discussed above in order to have a reasonable expectation of success such as providing an efficient and highly scalable edge analytics platform that enables real-time, on-site stream processing of sensor data from industrial machines [Sharma: (¶75) “provides an efficient and highly scalable edge analytics platform that enables real-time, on-site stream processing of sensor data from industrial machines”].
Claim(s) 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over WATANABE and Patel, and further in view of Mueen et al. (US10853372B1) [hereinafter Mueen].
Regarding Claim 10:
WATANABE 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 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 filed 11/24/2025 have been fully considered but they are not persuasive.
Applicant responds
(a) Rejections under 35 U.S.C. § 101
it is respectfully submitted that claims 1-11 are not directed to an abstract idea.. claims 1- 11 recite features, which alone and in combination, provide improvements to similarity searches in production process data for identifying deviations, e.g., quality issues or unwanted behavior like unexpected high pressures or unreached target flow rates.
Specifically, claims 1-11 are not directed to a mathematical concept, a certain method of organizing human activity, or a mental process.
The human mind is incapable of implementing a process for calculating a similarity value of a sub interval of each of the determined process data sequences when dealing with such a large amount of data for a production process. Moreover, the human mind is incapable of defining for each sub interval of the determined process data sequences characteristic shapes which can be used to determine calculation of a similarity value. It is respectfully submitted that these steps are incapable of being performed in the human mind with or without the aid of pen and paper as the human mind is incapable of implementing the calculations and determining steps to ultimately select a batch with a quality problem or an undesirable event as specified in amended claim 1 when dealing with a large amount of data that is inherent in the field of production processes.
(Page: 4-7)
With respect to (a) above, Examiner appreciates the interpretative description given by Applicant in response.
As described in the current office action, limitations recited in the claims are mere instructions to implement an abstract idea on a general purpose computer such that corresponding structure disclosed in the specification is a general purpose computer implementing the claimed functions characterized as abstract ideas above. See MPEP 2106.05(a)).
Examiner notes that the additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea and the use of the claimed invention in a production process field is simply an attempt to limit the use of the abstract idea to a particular technological environment (MPEP 2106.05(h)). The claim does not include any further additional elements that are sufficient to amount to significantly more than the judicial exception.
The most claims recite regarding practical application is, claim 11 recites providing a control signal for controlling a production process based on the similarity value. In broadest reasonable interpretation, providing a control signal is broad and doesn’t specifically state how this control signal is used to improve the batch production process of claim 1.
Applicant may consider adding concrete practical application that is significantly more than the abstract idea such as how this processed data is used to control/adjust/optimize the batch process to provide improvement to the technology.
Applicant’s arguments are fully considered, but for the above described reasons, they are not persuasive; therefore, the 35 USC § 101 rejections of claims 1-5, 7 and 9-11 are maintained and are updated based on the amendments.
Applicant’s arguments with respect to claim(s) 1-2, 4-15, and 17-31 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
(b) Rejections under 35 U.S.C. § 102:
Watanabe does not describe that the similarity value of the determined process data sequences is calculated, compared to the provided process data sequence, based on the data pattern of the at least one process variable, for analyzing the process data to select a batch with a quality problem or an undesirable event, as required by amended claim 1.
(Pages: 7-8)
With respect to (b) 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 Patel has been introduced in the current office action.
WATANABE 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 9-11 are rejected under 35 U.S.C. 103 in view of the references as presented in the current office action.
Applicant's arguments filed 11/24/2025 have been fully considered but they are not persuasive.
Applicant responds
(c) Rejections under 35 U.S.C. § 102/103:
This means that Watanabe determines a similarity degree by classification using neural networks and not by calculating a similarity value.
Watanabe does not describe that the similarity value of sub interval for each of the determined process data sequences is calculated, wherein characteristic shapes of the determined process data sequences are determined to define the sub interval for each of the determined process data sequences.
Because the combination of Watanabe, Sharma, Mueen, and Kanbe fails to disclose or suggest at least the above-recited features of amended independent claim 1, the combination of Watanabe, Sharma, Mueen, and Kanbe cannot render claim 1 or any of its dependent claims obvious. Accordingly, reconsideration and withdrawal of the rejection of claims 5, 9, 10, and 11 under 35 U.S.C. § 103 based on Watanabe, Sharma, Mueen, and Kanbe is respectfully requested.
(Page: 10)
With respect to (c) above, Examiner appreciates the interpretative description given by Applicant in response.
In broadest reasonable interpretation, calculation of similarity value is any calculation, and as described in the current office action Watanabe discloses, a similarity value of the data acquisition sections (sub intervals) are calculated.
The limitation, characteristic shapes of the determined process data sequences are determined to define the sub interval for each of the determined process data sequences is broad. As described in the current office action Watanabe discloses, sub intervals are defined such that system selects and determines characteristics shapes of collected process data sequence such as assigning characteristics key information to data stream sections.
Applicant’s arguments are fully considered, but for the above described reasons, the arguments are not persuasive; therefore, claims 1-5, 7 and 9-11 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.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/M.S./
Patent Examiner,
Art Unit 2116
/KENNETH M LO/Supervisory Patent Examiner, Art Unit 2116