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 .
A Preliminary Amendment was filed on November 20, 2024, in which the Specification and the Abstract were amended, claims 1-15 were amended and claims 16-20 were added.
Claims 1-20 are pending, of which claims 1 and 16 are independent claims.
Priority
Applicants’ claim for the priority benefit of EPO Application No. 22196685.6 filed September 20, 2022, and EPO Application No. 22190458.4 filed August 16, 2022, are acknowledged.
Information Disclosure Statement
The references cited in the information disclosure statement (IDS) submitted on November 20, 2024, have been considered by the examiner.
Claim Objections
The following claims are objected to for lack of antecedent support or for redundancies. The Examiner recommends the following changes:
Claim 18, line 16, replace “the likelihood” with “a likelihood”.
Claim 18, line 19, replace “the likelihoods” with “the likelihood”.
Appropriate correction is respectfully requested.
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 therefore, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception without significantly more.
Independent claim 1 recites, “... clustering the KPI data to identify at least one cluster; analyzing the at least one cluster to identify a plurality of failure modes associated with the apparatus, wherein the analyzing comprises, for one or more of the at least one cluster, identifying a plurality of sub-groups of KPI data relating to a failure of a product unit, each of the plurality of subgroups of KPI data associated with a failure mode of the plurality of failure modes; and determining… a classification model comprising KPI thresholds for classifying product units by assigning, for each identified failure mode, a threshold to each KPI associated with the failure mode.”
Under their broadest reasonable interpretation and based on the description provided in the published Specification, such as paragraphs [0085]- [0113], for instance, the clustering, analyzing, and determining functions are processes that entail mental functions. Under its broadest reasonable interpretation, if a claim limitation covers performance that can be executed in the human mind, but for the recitation of generic electronic devices or generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. As recited, the clustering, analyzing, and determining functions are processes that can be performed through observation, evaluation and judgement.
Accordingly, the claim recites abstract ideas.
This judicial exception is not integrated into a practical application. Independent claim 1 recites the additional elements of, “receiving key performance indicator (KPI) data obtained as a result of a plurality of product units being subject to a process performed by an apparatus, the KPI data associated with a plurality of components of the apparatus and comprising data associated with a plurality of KPIs…a hardware apparatus…”.
The receiving function is an insignificant extra-solution activity under MPEP 2106.05(g), without imposing meaningful limits. The limitation amounts to necessary data gathering. (i.e., all uses of the recited judicial exception require such data gathering or data output). See Mayo, 566 U.S. at 79, 101 USPQ2d at 1968. In accord with MPEP 2105(g), “An example of pre-solution activity is a step of gathering data for use in a claimed process, e.g., a step of obtaining information about credit card transactions, which is recited as part of a claimed process of analyzing and manipulating the gathered information by a series of steps in order to detect whether the transactions were fraudulent.”
The additional features including “an apparatus” and “a hardware computer”, as recited in the claim that are configured to carry out the abstract idea and additional idea limitations may be tools that are used as recited in claim 1, but recited so generically that they represent no more than mere instructions “to apply” the judicial exceptions on or using generic electronic or computer components. Implementing an abstract idea on generic electronic or computer components as tools to perform an abstract idea is not indicative of integration into a practical application.
In view of the foregoing, the additional limitations, individually or combined, are not sufficient to demonstrate integration of a judicial exception into a practical application.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
The recitations recited in independent claim 1 including “receiving key performance indicator (KPI) data obtained as a result of a plurality of product units being subject to a process performed by an apparatus, the KPI data associated with a plurality of components of the apparatus and comprising data associated with a plurality of KPIs…” is a well-understood, routine, and conventional recitation. For instance, US Patent Publication No. 2024/0004355 A1 to Zhao et al. describes in Paragraph [0010] “According to an embodiment, determining one or more signature for each of the one or more stages using the partitioned standardized operating data comprises generating one or more engineering features (EFs) or Key Performance Indicators (KPIs), using the partitioned standardized operating data corresponding to the one or more stages. The generated EFs and/or KPIs are grouped into a set to form a given signature.” US Patent Publication No. 2019/0164101 A1 to Koyama et al. describes in Paragraph [0021] “FIG. 4A is a diagram illustrating a method of generating KPIs of N values by grouping units given binary KPIs thereto based on pass/fail determinations,” and describes in Paragraph [0054] “At this time, the data analysis apparatus 100 can obtain data TB1 in which the second KPIs and the group identification information NID correspond to each other by 1:1.” US Patent Publication No. 2021/0263505 A1 to Zheng et al. describes in Paragraph [0007] “The method also includes generating a current load schedule based on the load schedule profile; dispatching the product lots to the plurality of workstations using the current load schedule to complete fabrication of the product lots; obtaining a set of current key performance indicators (KPIs) of the completed fabrication of the product lots; and automatically adjusting the weight factors of the objective functions of the load-balancing model based on the current KPIs using a big-data architecture to generate a next load schedule for next cycle of fabrication.”
The additional features including “an apparatus” and “a hardware computer”, as recited in the claim that are configured to carry out the additional and abstract idea limitations may be tools that are used for the functions recited in claim 1, but recited so generically that they represent no more than mere instructions “to apply” the judicial exceptions on or using a generic electronic or computer component. See MPEP 2106.05(f) Implementing an abstract idea on generic electronic or computer components as tools to perform an abstract idea does not amount to significantly more. See Elec. Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1355 (Fed. Cir. 2016) (“Nothing in the claims, understood in light of the specification, requires anything other than off-the-shelf, conventional computer, network, and display technology for gathering, sending, and presenting the desired information.”)
Therefore, the additional claimed features, individually or combined, do not amount to significantly more and independent claim 1 is not patent eligible.
Regarding claims 2 and 3, claim 2 recites “projecting the KPI data to a lower dimensional space prior to performing the clustering” and claim 3 recites “projecting the KPI data to a 2- dimensional space”. However, neither claim integrates the abstract ideas of independent claim 1 into a practical application and does not amount to significantly more. There are no additional limitations in the claim to apply, rely on, or use the judicial exception in a manner that would impose a meaningful limitation on the judicial exception, thus, integrating the judicial exception into a practical application. Thus, claims 2 and 3 are not patent eligible.
Regarding claims 5-9, these claims are also directed to further defining the abstract idea as recited in independent claim 1. There are no additional limitations in the claim to apply, rely on, or use the judicial exception in a manner that would impose a meaningful limitation on the judicial exception. The claims are not more than a drafting effort designed to monopolize the exception. The claims also do not include additional elements that integrate the judicial exception into a practical application and that would be sufficient to amount to significantly more than the judicial exception. Thus, claims 5-9 are not patent eligible.
Regarding claim 10, this claim recites “receiving the classification model as claimed in claim 1 to obtain a threshold for each KPI associated with at least one failure mode;…” For similar reasons as those provided in claim 1, the receiving function of claim 10 is not integrating the abstract ideas of claim 1 into a practical application and does not amount to significantly more. In addition, the additional recitation of claim 10 providing “for each product unit of the product units subject to a process performed by an apparatus: determining the likelihood of each of the at least one failure mode based on KPI data of the product unit and the threshold assigned to each KPI associated with the at least one failure mode; and performing a classification of the product unit based on the likelihoods of the at least one failure mode”, based on the description provided, at least, in paragraphs [0115]-[0125] are processes that can be performed through observation, evaluation and judgement or that can be performed through mathematical computation. Therefore, claim 10 is not patent eligible.
Regarding claims 11-14, these claims are also directed to further defining the abstract idea as recited in claim 10. There are no additional limitations in the claim to apply, rely on, or use the judicial exception in a manner that would impose a meaningful limitation on the judicial exception. The claims are not more than a drafting effort designed to monopolize the exception. The claims also do not include additional elements that integrate the judicial exception into a practical application and that would be sufficient to amount to significantly more than the judicial exception. Thus, claims 11-14 are not patent eligible.
Regarding claims 15-17, these claims are directed to further applying a generic device including a non-transitory computer-readable storage medium, lithographic apparatus, and semiconductor wafers. Therefore, claims 15-17 are not integrating the judicial exception into a practical application. There are no additional limitations in the claims to apply, rely on, or use the judicial exception in a manner that would impose a meaningful limitation on the judicial exception. The claims also do not include additional elements that amount to significantly more. Thus, claims 15-17 are not patent eligible.
Independent claim 18 recites, “... clustering… the KPI data to identify at least one cluster; analyzing, …, the at least one cluster to identify a plurality of failure modes associated with the apparatus, wherein the analyzing comprises, for one or more of the at least one cluster, identifying a plurality of sub-groups of KPI data relating to a failure of a product unit, each of the plurality of sub-groups of KPI data associated with a failure mode of the plurality of failure modes; for each identified failure mode assigning a threshold to each KPI associated with the failure mode; and for each of the plurality of product units: determining the likelihood of each of the plurality of failure modes based on KPI data of the product unit and the thresholds assigned to each KPI associated with one of the plurality of failure modes; and performing a classification of the product unit based on the likelihoods of each of the plurality of failure modes.”
Under their broadest reasonable interpretation and based on the description provided in the published Specification, such as paragraphs [0085]- [0113], for instance, the clustering, analyzing, determining, and performing a classification functions are processes that entail mental functions. Under its broadest reasonable interpretation, if a claim limitation covers performance that can be executed in the human mind, but for the recitation of generic electronic devices or generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. As recited, the clustering, analyzing, and determining functions are processes that can be performed through observation, evaluation and judgement.
Accordingly, the claim recites abstract ideas.
This judicial exception is not integrated into a practical application. Independent claim 18 recites the additional elements of, “receiving key performance indicator (KPI) data obtained as a result of a plurality of product units being subject to a process by an apparatus, the KPI data associated with a plurality of components of the apparatus and comprising data associated with a plurality of KPIs…a hardware computer system…”.
The receiving function is an insignificant extra-solution activity under MPEP 2106.05(g), without imposing meaningful limits. The limitation amounts to necessary data gathering. (i.e., all uses of the recited judicial exception require such data gathering or data output). See Mayo, 566 U.S. at 79, 101 USPQ2d at 1968. In accord with MPEP 2105(g), “An example of pre-solution activity is a step of gathering data for use in a claimed process, e.g., a step of obtaining information about credit card transactions, which is recited as part of a claimed process of analyzing and manipulating the gathered information by a series of steps in order to detect whether the transactions were fraudulent.”
The additional features including “a hardware computer”, as recited in the claim that are configured to carry out the abstract idea and additional idea limitations may be tools that are used as recited in claim 18, but recited so generically that they represent no more than mere instructions “to apply” the judicial exceptions on or using generic electronic or computer components. Implementing an abstract idea on generic electronic or computer components as tools to perform an abstract idea is not indicative of integration into a practical application.
In view of the foregoing, the additional limitations, individually or combined, are not sufficient to demonstrate integration of a judicial exception into a practical application.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
The recitations recited in independent claim 18 including “receiving key performance indicator (KPI) data obtained as a result of a plurality of product units being subject to a process by an apparatus, the KPI data associated with a plurality of components of the apparatus and comprising data associated with a plurality of KPIs” is a well-understood, routine, and conventional recitation. For instance, US Patent Publication No. 2024/0004355 A1 to Zhao et al. describes in Paragraph [0010] “According to an embodiment, determining one or more signature for each of the one or more stages using the partitioned standardized operating data comprises generating one or more engineering features (EFs) or Key Performance Indicators (KPIs), using the partitioned standardized operating data corresponding to the one or more stages. The generated EFs and/or KPIs are grouped into a set to form a given signature.” US Patent Publication No. 2019/0164101 A1 to Koyama et al. describes in Paragraph [0021] “FIG. 4A is a diagram illustrating a method of generating KPIs of N values by grouping units given binary KPIs thereto based on pass/fail determinations,” and describes in Paragraph [0054] “At this time, the data analysis apparatus 100 can obtain data TB1 in which the second KPIs and the group identification information NID correspond to each other by 1:1.” US Patent Publication No. 2021/0263505 A1 to Zheng et al. describes in Paragraph [0007] “The method also includes generating a current load schedule based on the load schedule profile; dispatching the product lots to the plurality of workstations using the current load schedule to complete fabrication of the product lots; obtaining a set of current key performance indicators (KPIs) of the completed fabrication of the product lots; and automatically adjusting the weight factors of the objective functions of the load-balancing model based on the current KPIs using a big-data architecture to generate a next load schedule for next cycle of fabrication.”
The additional features including “an apparatus” and “a hardware computer”, as recited in the claim that are configured to carry out the additional and abstract idea limitations may be tools that are used for the functions recited in claim 18, but recited so generically that they represent no more than mere instructions “to apply” the judicial exceptions on or using a generic electronic or computer component. See MPEP 2106.05(f) Implementing an abstract idea on generic electronic or computer components as tools to perform an abstract idea does not amount to significantly more. See Elec. Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1355 (Fed. Cir. 2016) (“Nothing in the claims, understood in light of the specification, requires anything other than off-the-shelf, conventional computer, network, and display technology for gathering, sending, and presenting the desired information.”)
Therefore, the additional claimed features, individually or combined, do not amount to significantly more and independent claim 18 is not patent eligible.
Regarding claims 19 and 20, these claims are directed to further applying a generic device including a lithographic apparatus, semiconductor wafers, and a non-transitory computer-readable storage medium. Therefore, claims 19 and 20 are not integrating the judicial exception into a practical application. There are no additional limitations in the claims to apply, rely on, or use the judicial exception in a manner that would impose a meaningful limitation on the judicial exception. The claims also do not include additional elements that amount to significantly more. Thus, claims 19 and 20 are not patent eligible.
Allowable Subject Matter and Relevant Prior Art cited by Examiner
Provided that the non-statutory subject matter rejection to independent claims 1 and 18 and related dependent claims is overcome, the following prior art is made of record:
Independent claim 1
Zhao et al. (US Patent Publication No. 2024/0004355 A1) (“Zhao”) teaches:
A method comprising: Zhao: Paragraph [0052] (“FIG. 2 is a flow chart of one such example method embodiment 220 that provides functionality that creates a machine learning predictive model for real-world batch production industrial process monitoring and optimization. Embodiments of the method 220 can be configured to control any batch production process in any industrial settings known in the art, such as the system 100 described hereinabove in relation to FIG. 1.”)
receiving key performance indicator (KPI) data obtained as a result of a plurality of product units being subject to a process performed by an apparatus, the KPI data associated with a plurality of components of the apparatus and comprising data associated with a plurality of KPIs; Zhao: Paragraph [0010] (“According to an embodiment, determining one or more signature for each of the one or more stages using the partitioned standardized operating data comprises generating one or more engineering features (EFs) or Key Performance Indicators (KPIs), using the partitioned standardized operating data corresponding to the one or more stages. The generated EFs and/or KPIs are grouped into a set to form a given signature. In an embodiment, a set forming a signature is an expandable set. In such an embodiment, grouping the generated one or more EFs or KPIs into the expandable set to form the given signature includes (i) adding the one or more EFs or KPIs into the expandable set over time as the one or more EFs or KPIs are generated and (ii) assigning a weight to each of the generated one or more EFs or KPIs in the expandable set. According to one such embodiment, the expandable set increases in size over time with progress of a given batch production run.”) Zhao: Paragraph [0058] (“According to an embodiment determining one or more signature for each of the one or more stages using the partitioned standardized operating data at step 223 comprises generating one or more engineering features (EFs) or Key Performance Indicators (KPIs), using the partitioned standardized operating data corresponding to the one or more stages. In such an embodiment the generated EFs and/or KPIs are grouped into a set to form a given signature. In an embodiment, each signature is defined for a specific point in time in a batch process. Returning to the example system 100, a signature may be dryer temperature maximum (peak value) (item 10) at the end of phase-2 of the batch process (332a and 332b in FIG. 3). Before the end of phase-2, the dryer temperature maximum measurement would not be a valid signature. Consequently, the signature of dryer temperature maximum is not be available until the end of phase-2 and the value of the signature is fixed at that point in time.”) Zhao: Paragraph [0069] (“This analysis includes at least one of (i) diagnosing one or more operational problems in the batch run of the industrial process and (ii) providing prescriptive guidance to a plant operator with one or more recommended corrective actions. According to an embodiment, diagnosing one or more problems and providing prescriptive guidance to a plant operator includes identifying one or more contributing KPIs and outputting an alert to the plant operator with an associated risk assessment report. Risk assessment reports may indicate statistical probability that output of the current batch will not conform with operational standards, a root-cause analysis that lists features, e.g., measured sensor values, with significant deviations from standard golden-batch features, and recommendations of corrective actions, amongst other examples.”) [The KPI data associated with list of features including dryer temperature of the system or plant reads on “the KPI data associated with a plurality of components of the apparatus and comprising data associated with a plurality of KPIs”.]
clustering the KPI data to identify at least one cluster; analyzing the at least one cluster to identify a plurality of failure modes associated with the apparatus, … Zhao: Paragraph [0093] (“FIG. 7 is a plot 770 of historical batch data showing different batch families with defined EFs in a PCA model. In this example of batch production data, batch feature values are represented by PCA scores, where each point represents a batch. Batches with similar “signatures” (e.g. features represented at score values of PC1 and PC2) form the closed groups (clusters) 771 a and 771 b. The shading represents batch labels, for example, the black shading (e.g., batches in cluster 771 a) represents “in-spec” batches and the lighter shading (e.g., batches in cluster 771 b) represents “out-of-spec” batches. An embodiment, e.g., method 550, is able to predict whether an online batch run will most likely end up in the “in-spec” cluster 771 a or the out-of-spec cluster 771 b. Once an online batch run is predicted most likely to be “out-of-spec”, an embodiment may identify one or more out-of-spec batches, from amongst the cluster 771 b, as “siblings” to the running batch. Such an embodiment performs an analysis on the sibling batch data, and compares the sibling batch data with a group of batches from amongst the “in-spec” batches 771 a to identify significant differences in features between the two groups (siblings and in-spec batches). Based on these identified differences, an embodiment provides recommendations of corrective actions.”)
Zhao does not expressly teach “wherein the analyzing comprises, for one or more of the at least one cluster, identifying a plurality of sub-groups of KPI data relating to a failure of a product unit, each of the plurality of subgroups of KPI data associated with a failure mode of the plurality of failure modes; and determining, by a hardware computer, a classification model comprising KPI thresholds for classifying product units by assigning, for each identified failure mode, a threshold to each KPI associated with the failure mode.”
However, Burch et al. (US Patent Publication No. 2007/0288185 A1) (“Burch”) describes a failure signal detection analysis. Burch: Paragraph [0027] (“Failure Signature Detection Analysis (FSDA) is a method for identifying yield loss mechanisms in semiconductor data, utilizing product test data, wherein a novel data organization and clustering method is applied to improve the identification of wafers with similar root cause induced failures.”) Burch: Paragraph [0031] (“At step 1104, a clustering algorithm can be applied in this N-dimensional space to identify groups or clusters of wafers with similar failing mechanisms or root causes.”) Burch: Paragraph [0032] (“At step 1106, a variety of analytical methods and tools may be used to obtain information about the cause of the problems. These tools present the data in a fashion that makes it easier to identify the problem cause(s).”) Burch: Paragraph [0033] (“At step 1108, the wafers thus identified to belong to certain groups can be further analyzed with so called drilldown techniques to identify the root cause of the failure. In this way, one can significantly improve upon the signal to noise resulting in a higher success rate of identifying the fundamental root cause of failure(s). The drill down techniques may include parametric to yield correlation analysis, defect to yield correlation (kill ratio) analysis, equipment commonality analysis, or the like.”) Burch: Paragraph [0034] (“FSDA uses a novel algorithm to group or cluster the wafers by their fail bin patterns; the type of bin failure and the spatial distribution of that failure.”) Burch: Paragraph [0035] (“Clustering of defect modes for FSDA detects/identifies clusters of failure bins and their associated spatial patterns. The failure bins can be from Fail Bit Map (FBM) data or die sort data and the spatial patterns can be constructed as per user configuration: typically a 9 zone+reticle field pattern are used but the zone definitions are not limited to these two choices and overlapping zones are permissible.”) Burch: Paragraph [0039] (“Individual clusters of wafers and/or lots are identified by their failure mode (bin and pattern) and are compared against the "background" cluster group, which is the largest constituent cluster or a user-selected cluster. This approach uses a more generalized clustering approach based on the failure bin mode and the spatial distribution of that failure mode...The differences in local distance between groups of wafers versus their distance from other groups can have a statistical threshold applied to it, so that a significance test can be used to determine if a given wafer is part of a cluster and whether a given cluster is discernible from other clusters. Given that many clusters can be generated, many of which are insignificant or spurious, some filtering using Principal Component Analysis is applied to identify the “natural” major cluster groups on which additional drilldown analysis can be performed. Also, engineering discretion may be applied such that grouping of clusters into larger groups is done subjectively if the automated algorithm appears to have excessive differentiation or if the user feels that the subsequent analysis is more appropriately done in larger groupings.”) Burch: Paragraph [0146] (“Thus, a method has been described for organizing semiconductor wafer data and its spatial variability such that N-dimensional vectors can be constructed that represent each wafer as a single point in this aforementioned N-dimensional space. A wafer zone map is prescribed with or without overlapping regions. A data-zone vector is constructed for each wafer. The semiconductor data may be bin data such as die sort, multi-probe, and fail bit map data. A portion of the resulting constructed data points in the N-dimensional space can be defined as “clustered” according to some set of rules. In some embodiments, a filtering analysis is performed on the data-zone vectors to determine the dominant clusters in the data, and a distance matrix is constructed and a distance threshold determined,”)
However, Zhao and Burch and the additional teaching of the prior art of record including Reuhman-Huisken et al. (US Patent Publication No. 2007/0002295 A1); Koyama et al. (US Patent Publication No. 2019/0164101 A1); US Patent Publication No. 2021/0263505 A1 to Zheng et al., do not expressly teach or suggest “wherein the analyzing comprises, for one or more of the at least one cluster, identifying a plurality of sub-groups of KPI data relating to a failure of a product unit, each of the plurality of subgroups of KPI data associated with a failure mode of the plurality of failure modes; and determining, by a hardware computer, a classification model comprising KPI thresholds for classifying product units by assigning, for each identified failure mode, a threshold to each KPI associated with the failure mode”, as recited in independent claim 1.
Claims 2-17 are dependent claims of independent claim 1. Independent claim 1 is allowable over prior art, and therefore, provided that the non-statutory subject matter rejection to claims 1-17 is overcome, claims 2-17 would be allowable.
Claim 18
Independent claim 18 includes similar limitations and reasons for prior art allowance as independent claim 1. Zhao and Burch and the additional teaching of the prior art of record including Reuhman-Huisken et al. (US Patent Publication No. 2007/0002295 A1); Koyama et al. (US Patent Publication No. 2019/0164101 A1); US Patent Publication No. 2021/0263505 A1 to Zheng et al., do not expressly teach or suggest “wherein the analyzing comprises, for one or more of the at least one cluster, identifying a plurality of sub-groups of KPI data relating to a failure of a product unit, each of the plurality of sub- groups of KPI data associated with a failure mode of the plurality of failure modes; for each identified failure mode assigning a threshold to each KPI associated with the failure mode; and for each of the plurality of product units: determining the likelihood of each of the plurality of failure modes based on KPI data of the product unit and the thresholds assigned to each KPI associated with one of the plurality of failure mode”, as recited in independent claim 18.
Claims 19 and 20 are dependent claims of independent claim 18. Independent claim 18 is allowable over prior art, and therefore, claims 19 and 20 are allowable, provided that the non-statutory subject matter rejection of claims 18-20 is overcome.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Reuhman-Huisken et al. (US Patent Publication No. 2007/0002295 A1) describes in paragraph [0021] “…the key performance indicators may include a range of acceptable values (i.e., norms) or other metrics that are determined for data received from the sensing devices. The key performance indicators typically are determined for data that corresponds to upper levels of the hierarchy. In contrast, the diagnostic data sets typically are maintained for data that corresponds to lower levels of the hierarchy.”
Koyama et al. (US Patent Publication No. 2019/0164101 A1) describes a data analysis apparatus generates M (M is an integer of 3 or greater) groups each including data regarding a plurality of units from data where first KPIs and K (K is an integer of 2 or greater) explanatory variables are given by 1:1, generates a second KPI indicating the state of the group based on the values of a plurality of first KPIs included in the group, and selects a feature for the first KPIs based on a correlation analysis between the second KPI of each group and the feature of each group calculated based on the explanatory variables.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ALICIA M. CHOI whose telephone number is (571)272-1473. The examiner can normally be reached on Monday - Friday 7:30 am to 5:00 pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Robert Fennema can be reached on 571-272-2748. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see http://pair-direct.uspto.gov. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/ALICIA M. CHOI/Primary Patent Examiner, Art Unit 2117
Prosecution Insights