DETAILED ACTION
Claims 1-15 are pending in the present application and are under examination on the merits. This communication is the first action on the merits (FAOM).
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 .
Information Disclosure Statement
Applicant filed an Information Disclosure Statement (IDS) on 4/29/2025. This filing is in compliance with 37 C.F.R. 1.97.
As required by M.P.E.P. 609(C), the applicant's submission of the Information Disclosure Statement is acknowledged by the examiner, and the cited references have been considered in the examination of the claims now pending. As required by M.P.E.P. 609(C), a copy of the PTOL -1449 form, initialed and dated by the examiner, is attached to the instant office action.
Drawings
The drawings filed on 4/29/2025 are acceptable as filed.
Claim Rejections - 35 USC§ 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-15 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. Here, under considerations of the broadest reasonable interpretation of the claimed invention, Examiner finds that the Applicant invented a method and system for determining an optimized process graph representing process data. Examiner formulates an abstract idea analysis, following the framework described in the MPEP, as follows:
Step 1: The claims are directed to a statutory category, namely a "method" (claims 1-15).
Step 2A - Prong 1: The claims are found to recite limitations that set forth the abstract idea(s), namely, regarding claim 1:
… determining an optimized process graph representing process data, wherein the process data is recorded during execution of processes, wherein each process is executed by executing a number of process steps, wherein a number of entity instances participates in executing the number of process steps, wherein each entity instance is assigned to exactly one entity type, each process step is assigned to exactly one event type, the method comprising:
determining at least one event type related to entity instances of the subset of entity types,
determining at least two subsets of entity instances and their respective frequency from the subset of entity types on the basis of corresponding event types
…
determining a specific union of the stored unions of subsets of entity instances, wherein the specific union maximizes a predefined utility function
generating the optimized process graph by generating a process graph from the specific union of subsets of entity instances
Dependent claims 2-15 recite the same or similar abstract idea(s) as independent claims 1, and 18 with merely a further narrowing of the abstract idea(s) to particular data characterization and/or additional data analyses performed as part of the abstract idea.
The limitations in claims 1-15 above falling well-within the groupings of subject matter identified by the courts as being abstract concepts, specifically the claims are found to correspond to the category of:
"Certain methods of organizing human activity- fundamental economic principles or practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations); managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions)" as the limitations identified above are directed to determining an optimized process graph representing process data and thus is a method of organizing human activity including at least commercial or business interactions or relations and/or a management of user personal behavior; and/or
"Mental processes - concepts performed in the human mind (including an observation, evaluation, judgement, opinion)" as the limitations identified above include mere data observations, evaluations, judgements, and/or opinions, e.g. including user observation and evaluation by determining an optimized process graph representing process data, which is capable of being performed mentally and/or using pen and paper.
Step 2A - Prong 2: Claims 1-15 are found to clearly be directed to the abstract idea identified above because the claims, as a whole, fail to integrate the claimed judicial exception into a practical application, specifically the claims recite the additional elements of:
" A computer-implemented method… storing unions of the determined subsets of entity instances in a data structure in a storage device in a predefined order," (claim 1) wherein the data structure is a linear data structure, in particular an array, in which the subsets of entity instances are stored according to the predefined order” (claim 12), however the aforementioned elements merely amount to generic components of a general purpose computer used to "apply" the abstract idea (MPEP 2106.0S(f)) and thus fails to integrate the recited abstract idea into a practical application, furthermore the high-level recitation of receiving data from a generic "building system" is at most an attempt to limit the abstract to a particular field of use (MPEP 2106.0S(h), e.g.: "For instance, a data gathering step that is limited to a particular data source (such as the Internet) or a particular type of data (such as power grid data or XML tags) could be considered to be both insignificant extra-solution activity and a field of use limitation. See, e.g., Ultramercial, 772 F.3d at 716, 112 USPQ2d at 1755 (limiting use of abstract idea to the Internet); Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data); Intellectual Ventures I LLC v. Erie lndem. Co., 850 F.3d 1315, 1328-29, 121 USPQ2d 1928, 1939 (Fed. Cir. 2017) (limiting use of abstract idea to use with XML tags).") and/or merely insignificant extra-solution activity (MPE 2106.05(g)) and thus further fails to integrate the abstract idea into a practical application;
Step 2B: Claims 1-15 do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements as described above with respect to Step 2A Prong 2 merely amount to a general purpose computer that attempts to apply the abstract idea in a technological environment (MPEP 2106.0S(f)), including merely limiting the abstract idea to a particular field of use of determining an optimized process graph representing process data via a "computer", as explained above, and/or performs insignificant extra-solution activity, e.g. data gathering or output, (MPEP 2106.0S(g)), as identified above, which is further found under step 2B to be merely well-understood, routine, and conventional activities as evidenced by MPEP 2106.0S(d)(II) (describing conventional activities that include transmitting and receiving data over a network, electronic recordkeeping, storing and retrieving information from memory, electronically scanning or extracting data from a physical document, and a web browser's back and forward button functionality). Therefore, similarly the combination and arrangement of the above identified additional elements when analyzed under Step 2B also fails to necessitate a conclusion that the claims amount to significantly more than the abstract idea directed to determining an optimized process graph representing process data.
Claims 1-15 are accordingly rejected under 35 USC§ 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea(s)) without significantly more.
Note: The analysis above applies to all statutory categories of invention. As such, the presentment of any claim otherwise styled as a machine or manufacture, for example, would be subject to the same analysis.
For further authority and guidance, see:
MPEP § 2106
https://www.uspto.gov/patents/laws/examination-policy/subject-matter-eligibility
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102 of this title, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-7 and 10-15 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Application Publication Number 2017/0111245 to Ishakian et al. (hereafter referred to as Ishakian) in view of U.S. Patent Application Publication Number 2019/0213484 to Winn et al. (hereafter referred to as Winn).
As per claim 1, Ishakian teaches:
A computer-implemented method for determining an optimized process graph representing process data, wherein the process data is recorded during execution of processes, wherein each process is executed by executing a number of process steps, (Paragraph Number [0009] teaches generating process models from process event logs, including receiving an identification of node types and edge types of an application event log to generate a heterogeneous information network graph of the application event log, where node types include events and traces, where each trace is a finite sequence of event type nodes, reducing a number of event types of the set of input traces to generate clusters of new event types).
wherein a number of entity instances participates in executing the number of process steps, (Paragraph Number [0009] teaches generating process models from process event logs, including receiving an identification of node types and edge types of an application event log to generate a heterogeneous information network graph of the application event log, where node types include events and traces, where each trace is a finite sequence of event type nodes, reducing a number of event types of the set of input traces to generate clusters of new event types).
each process step is assigned to exactly one event type, Paragraph Number [0009] teaches generating process models from process event logs, including receiving an identification of node types and edge types of an application event log to generate a heterogeneous information network graph of the application event log, where node types include events and traces, where each trace is a finite sequence of event type nodes, reducing a number of event types of the set of input traces to generate clusters of new event types).
the method comprising: providing a subset of entity types from the recorded process data (Paragraph Number [0082] and FIG. 5 teaches a block diagram that provides a high level illustration of the functioning of a system according to an embodiment of the disclosure. Block 500 represents an initial set of traces that have been filtered, an initial set of meta-paths provided to a user who may select, modify, delete and add, and a set of parameters entered by the user, such as number of clusters, budget, etc. The system then proceeds to run process steps 430 to 455 illustrated in FIG. 4, represented by block 510, which generates a set of clusters which can be visualized at block 520. If the users decides at block 530 that all or a subset of visualized clusters are deemed to be unacceptable or require further analysis, then that subset can be selected at block 540 along with additional changes to the meta-paths or other parameters at block 550. Given the new set of information, a system according to an embodiment processes the new results and provides the users with new set of clusters. The iteration is completed once the user decides that the results are satisfactory. (See also Paragraph Number [0009])).
determining at least one event type related to entity instances of the subset of entity types, (Paragraph Number [0082] and FIG. 5 teaches a block diagram that provides a high level illustration of the functioning of a system according to an embodiment of the disclosure. Block 500 represents an initial set of traces that have been filtered, an initial set of meta-paths provided to a user who may select, modify, delete and add, and a set of parameters entered by the user, such as number of clusters, budget, etc. The system then proceeds to run process steps 430 to 455 illustrated in FIG. 4, represented by block 510, which generates a set of clusters which can be visualized at block 520. If the users decides at block 530 that all or a subset of visualized clusters are deemed to be unacceptable or require further analysis, then that subset can be selected at block 540 along with additional changes to the meta-paths or other parameters at block 550. Given the new set of information, a system according to an embodiment processes the new results and provides the users with new set of clusters. The iteration is completed once the user decides that the results are satisfactory. (See also Paragraph Number [0009])).
determining at least two subsets of entity instances and their respective frequency from the subset of entity types on the basis of corresponding event types (Paragraph Number [0077] and Fig. 5 teach deriving a “hard” assignment solely based on the result of the existing dimension reduction techniques, however, ignores the information about the relationships between events in a process model. According to embodiments, a process model can be obtained by projecting the process traces' heterogeneous graph G=(V, E) onto the set of event nodes E, denoted as G.sub.E=(V.sub.E, E.sub.E). Because a process model according to an embodiment can capture the follow-up relationships between events, since edge weights in a process model represent the number of times an event follows another event in a trace, it can provide strong indication in assigning events to clusters. For example, events that are frequently following each other are likely to be in the same cluster. Therefore, according to embodiments, another component, denoted as Δ, is added to the objective function in EQ. (4) to account for the regularization based on the process model. According to an embodiment, Δ is used to maximize the collective similarities between pairs of events that follow one or the other in a process execution model. (Examiner asserts that if clusters are formed they inherently contain at least two items, see also Paragraph Number [0008] for the processing of event logs)).
storing unions of the determined subsets of entity instances in a data structure in a storage device in a predefined order (Paragraph Number [0080] teaches each event is treated as a singleton cluster and pairs of events that are closest to each other are successively merge, or agglomerated until all clusters have been merged into a single cluster that contains all events. . While there are a number of criteria that can be used to decide the cutting point on the hierarchy, embodiments of the disclosure use a simple approach that is based on finding a minimum similarity threshold so that the distance between any two events in the same cluster is no more than that threshold, and no more than κ clusters are formed. (Examiner notes that this organization of the hierarchy and clustering constitutes storing information in a predefined order) Paragraph Number [0082] and FIG. 5 teaches a block diagram that provides a high level illustration of the functioning of a system according to an embodiment of the disclosure. Block 500 represents an initial set of traces that have been filtered, an initial set of meta-paths provided to a user who may select, modify, delete and add, and a set of parameters entered by the user, such as number of clusters, budget, etc. The system then proceeds to run process steps 430 to 455 illustrated in FIG. 4, represented by block 510, which generates a set of clusters which can be visualized at block 520. If the users decides at block 530 that all or a subset of visualized clusters are deemed to be unacceptable or require further analysis, then that subset can be selected at block 540 along with additional changes to the meta-paths or other parameters at block 550. Given the new set of information, a system according to an embodiment processes the new results and provides the users with new set of clusters. The iteration is completed once the user decides that the results are satisfactory. (See also Paragraph Number [0009])).
determining a specific union of the stored unions of subsets of entity instances, wherein the specific union maximizes a predefined utility function (Paragraph Number [0077] teaches deriving a “hard” assignment solely based on the result of the existing dimension reduction techniques, however, ignores the information about the relationships between events in a process model. According to embodiments, a process model can be obtained by projecting the process traces' heterogeneous graph G=(V, E) onto the set of event nodes E, denoted as G.sub.E=(V.sub.E, E.sub.E). Because a process model according to an embodiment can capture the follow-up relationships between events, since edge weights in a process model represent the number of times an event follows another event in a trace, it can provide strong indication in assigning events to clusters. For example, events that are frequently following each other are likely to be in the same cluster. Therefore, according to embodiments, another component, denoted as Δ, is added to the objective function in EQ. (4) to account for the regularization based on the process model. According to an embodiment, Δ is used to maximize the collective similarities between pairs of events that follow one or the other in a process execution model. (See also Paragraph Number [0076] for the objective function)).
generating the optimized process graph by generating a process graph from the specific union of subsets of entity instances (Paragraph Number [0080] teaches a greedy approximation algorithm, whose pseudocode is shown in FIG. 3, uses a bottom-up strategy, similar to that of an agglomerative clustering algorithm, to assign original events into clusters, i.e., new dimensions. First, at lines 3-4 and 6-7, a PathSim-based similarity σ.sub.P.sub.EE and a similarity matrix S are calculated between all events in E using EQ. (6). Next, at line 9, using EQ. (7), each event is treated as a singleton cluster and pairs of events that are closest to each other are successively merge, or agglomerated until all clusters have been merged into a single cluster that contains all events. This step, which can use an off-the-shelf hierarchical clustering algorithm, creates a hierarchy H, where each leaf node is an event and the root is the single cluster of the last merge. At line 11, a final step is to cut the hierarchy at some point to obtain the desirable number of clusters K. While there are a number of criteria that can be used to decide the cutting point on the hierarchy, embodiments of the disclosure use a simple approach that is based on finding a minimum similarity threshold so that the distance between any two events in the same cluster is no more than that threshold, and no more than κ clusters are formed. (Examiner asserts that finding the minimum similarity threshold is equivalent to finding an optimization and thus teaches generating an optimized process graph)).
Ishakian teaches determining an optimized process graph representing process data but does not explicitly teach wherein each entity instance is assigned to exactly one entity type as described by the following citations from Winn:
wherein each entity instance is assigned to exactly one entity type (Paragraph Number [0044] teaches templates are text or other formatted data with placeholders to insert formatted values of properties of an entity (an entity is an instance of an entity type, and is also referred to herein as an entity value). There are typically many templates per entity type, and these may be represented as a distribution over string values, or may be enumerated into a list. Templates combine the formatted property value into text or other formatted data. Paragraph Number [0126] teaches the knowledge base construction and/or maintenance system described above wherein the probabilistic generative model comprises a process linking, for a given entity type of the knowledge base, a schema of the entity type with instances of the entity type, templates and text or formatted data referring to the instances of the entity type; and where the process has one or more probability distributions representing uncertainty of any one or more of the instances of the entity type, schemas, templates and text or formatted data referring to the instances of the entity type).
Both Ishakian and Winn are directed to process graphing. Ishakian discloses determining an optimized process graph representing process data. Winn improves upon Ishakian by disclosing wherein each entity instance is assigned to exactly one entity type. One of ordinary skill in the art would be motivated to further include utilizing wherein each entity instance is assigned to exactly one entity type, to efficiently link instances to entity types and have multiple pairs to analyze without creating too many connections or creating difficult visualizations to understand.
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system and method of determining an optimized process graph representing process data in Ishakian to further utilize wherein each entity instance is assigned to exactly one entity type as disclosed in Winn, since the claimed invention is merely a combination of old elements, and in combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
As per claim 2, the combination of Ishakian and Winn teaches each of the limitations of claim 1.
In addition, Ishakian teaches:
wherein determining the specific union comprises determining a process graph for at least a subset of the stored unions of subsets of entity instances by way of a process discovery algorithm (Paragraph Number [0050] teaches motivated by the ability of using HIN to capture the peer similarity between nodes in other domain, embodiments of the present disclosure model process traces as a heterogeneous graph G=(V, E), as shown in FIG. 1(a), with the set of nodes V=T∪E∪R that includes three node types: trace, event, and resource. The set of edges E outline different types of interactions between different node types. Paragraph Number [0077] teaches deriving a “hard” assignment solely based on the result of the existing dimension reduction techniques, however, ignores the information about the relationships between events in a process model. According to embodiments, a process model can be obtained by projecting the process traces' heterogeneous graph G=(V, E) onto the set of event nodes E, denoted as G.sub.E=(V.sub.E, E.sub.E). Because a process model according to an embodiment can capture the follow-up relationships between events, since edge weights in a process model represent the number of times an event follows another event in a trace, it can provide strong indication in assigning events to clusters).
wherein the utility function assigns a cost value to each process graph based on a tradeoff between at least two parameters out of a group of parameters, the group at least consisting of: a complexity of the process graph, a coverage of the process graph, a precision of the process graph, a generalization of the process graph, and combinations thereof (Paragraph Number [0047] teaches unlike classic data clustering tasks, where the objective is either maximizing the precision and recall, in case ground-truth labels are available, or minimizing the intra-cluster and maximizing the inter-cluster distances, in case the ground-truth labels are not available, the effectiveness of clustering results in process mining is measured by how traces in resulting clusters can generate process models that have (1) a high degree of fitness, which quantifies how the discovered model can accurately reproduce the process instances from the event logs, and (2) low degree of structural complexity. Paragraph Number [0089] teaches instead of using conventional metrics for generic clustering results, such as average cluster density, average inter- or intra-cluster distance, embodiments of the disclosure evaluate process traces clustering using process-specific metrics. In particular, embodiments of the disclosure may use two metrics that have been extensively used in other process traces clustering work: weighted average conformance fitness, denoted herein as AvgFitness, and weighted average structure complexity, denoted herein as AvgComplexity. (Examiner asserts that these sections teach the alternatives of precision of the process graph and complexity of the process graph respectively)).
As per claim 3, the combination of Ishakian and Winn teaches each of the limitations of claim 1.
In addition, Ishakian teaches:
wherein the utility function is maximized by determining a specific process graph to which the utility function assigns a cost value below a predefined cost value, wherein the specific process graph is the optimized process graph (Paragraph Number [0015] teaches clustering the set of input traces includes creating a hierarchy of the plurality of disjoint partitions by successively merging pairs of events that are closest to each other until all clusters have been merged into a single hierarchical cluster that contains all events, where each leaf node of the single hierarchical cluster is an event and a root of the single hierarchical cluster is the single hierarchical cluster formed by the last merge. Paragraph Number [0080] teaches a greedy approximation algorithm, whose pseudocode is shown in FIG. 3, uses a bottom-up strategy, similar to that of an agglomerative clustering algorithm, to assign original events into clusters, i.e., new dimensions. First, at lines 3-4 and 6-7, a PathSim-based similarity σ.sub.P.sub.EE and a similarity matrix S are calculated between all events in E using EQ. (6). Next, at line 9, using EQ. (7), each event is treated as a singleton cluster and pairs of events that are closest to each other are successively merge, or agglomerated until all clusters have been merged into a single cluster that contains all events. This step, which can use an off-the-shelf hierarchical clustering algorithm, creates a hierarchy H, where each leaf node is an event and the root is the single cluster of the last merge. At line 11, a final step is to cut the hierarchy at some point to obtain the desirable number of clusters K. While there are a number of criteria that can be used to decide the cutting point on the hierarchy, embodiments of the disclosure use a simple approach that is based on finding a minimum similarity threshold so that the distance between any two events in the same cluster is no more than that threshold, and no more than κ clusters are formed).
As per claim 4, the combination of Ishakian and Winn teaches each of the limitations of claim 1.
In addition, Ishakian teaches:
wherein the predefined utility function is parametrized, wherein the method further comprises adapting, by way of an input of a user interface, at least one parameter of the predefined utility function on the basis of which an adapted specific process graph is determined (Paragraph Number [0082] teaches a block diagram that provides a high level illustration of the functioning of a system according to an embodiment of the disclosure. Block 500 represents an initial set of traces that have been filtered, an initial set of meta-paths provided to a user who may select, modify, delete and add, and a set of parameters entered by the user, such as number of clusters, budget, etc. The system then proceeds to run process steps 430 to 455 illustrated in FIG. 4, represented by block 510, which generates a set of clusters which can be visualized at block 520. If the users decides at block 530 that all or a subset of visualized clusters are deemed to be unacceptable or require further analysis, then that subset can be selected at block 540 along with additional changes to the meta-paths or other parameters at block 550. Given the new set of information, a system according to an embodiment processes the new results and provides the users with new set of clusters. The iteration is completed once the user decides that the results are satisfactory).
and wherein the adapted specific process graph updates the optimized process graph (Paragraph Number [0082] teaches a block diagram that provides a high level illustration of the functioning of a system according to an embodiment of the disclosure. Block 500 represents an initial set of traces that have been filtered, an initial set of meta-paths provided to a user who may select, modify, delete and add, and a set of parameters entered by the user, such as number of clusters, budget, etc. The system then proceeds to run process steps 430 to 455 illustrated in FIG. 4, represented by block 510, which generates a set of clusters which can be visualized at block 520. If the users decides at block 530 that all or a subset of visualized clusters are deemed to be unacceptable or require further analysis, then that subset can be selected at block 540 along with additional changes to the meta-paths or other parameters at block 550. Given the new set of information, a system according to an embodiment processes the new results and provides the users with new set of clusters. The iteration is completed once the user decides that the results are satisfactory).
As per claim 5, the combination of Ishakian and Winn teaches each of the limitations of claim 1.
In addition, Ishakian teaches:
further comprising: selecting a subset of event types from the recorded process data (Paragraph Number [0009] teaches generating process models from process event logs, including receiving an identification of node types and edge types of an application event log to generate a heterogeneous information network graph of the application event log, where node types include events and traces, where each trace is a finite sequence of event type nodes, reducing a number of event types of the set of input traces to generate clusters of new event types. Paragraph Number [0010] teaches the method includes filtering the events in the application event log to select those entries that contain attributes needed for generating process models.).
determining the at least one event type on the basis of the selected subset of event types (Paragraph Number [0009] teaches generating process models from process event logs, including receiving an identification of node types and edge types of an application event log to generate a heterogeneous information network graph of the application event log, where node types include events and traces, where each trace is a finite sequence of event type nodes, reducing a number of event types of the set of input traces to generate clusters of new event types. Paragraph Number [0010] teaches the method includes filtering the events in the application event log to select those entries that contain attributes needed for generating process models).
As per claim 6, the combination of Ishakian and Winn teaches each of the limitations of claim 1.
In addition, Ishakian teaches:
wherein the predefined order is an order of decreasing frequency of the determined subsets (Paragraph Number [0015] teaches clustering the set of input traces includes creating a hierarchy of the plurality of disjoint partitions by successively merging pairs of events that are closest to each other until all clusters have been merged into a single hierarchical cluster that contains all events, where each leaf node of the single hierarchical cluster is an event and a root of the single hierarchical cluster is the single hierarchical cluster formed by the last merge. Paragraph Number [0080] teaches each event is treated as a singleton cluster and pairs of events that are closest to each other are successively merge, or agglomerated until all clusters have been merged into a single cluster that contains all events. . While there are a number of criteria that can be used to decide the cutting point on the hierarchy, embodiments of the disclosure use a simple approach that is based on finding a minimum similarity threshold so that the distance between any two events in the same cluster is no more than that threshold, and no more than κ clusters are formed. (Examiner notes that this organization of the hierarchy and clustering constitutes storing information in a predefined order)).
As per claim 7, the combination of Ishakian and Winn teaches each of the limitations of claim 1.
In addition, Ishakian teaches:
wherein the at least two subsets comprising entity instances and respective event types are determined using a clustering algorithm (Paragraph Number [0077] and Fig. 5 teach deriving a “hard” assignment solely based on the result of the existing dimension reduction techniques, however, ignores the information about the relationships between events in a process model. According to embodiments, a process model can be obtained by projecting the process traces' heterogeneous graph G=(V, E) onto the set of event nodes E, denoted as G.sub.E=(V.sub.E, E.sub.E). Because a process model according to an embodiment can capture the follow-up relationships between events, since edge weights in a process model represent the number of times an event follows another event in a trace, it can provide strong indication in assigning events to clusters. For example, events that are frequently following each other are likely to be in the same cluster. Therefore, according to embodiments, another component, denoted as Δ, is added to the objective function in EQ. (4) to account for the regularization based on the process model. According to an embodiment, Δ is used to maximize the collective similarities between pairs of events that follow one or the other in a process execution model. (Examiner asserts that if clusters are formed they inherently contain at least two items, see also Paragraph Number [0008] for the processing of event logs)).
As per claim 10, the combination of Ishakian and Winn teaches each of the limitations of claim 1.
In addition, Ishakian teaches:
wherein the specific union of subsets of entity instances is filtered on process steps thereby eliminating process steps without respective entity instance (Paragraph Number [0081] teaches a flowchart of a general process according to an embodiment of the disclosure that generates business process clusters. A process according to an embodiment is generally an iterative process which relies on user feedback and allows the user to go back and forth between steps. Referring now to the figure, given a set of application level event logs that contain different metadata that may or may not be useful for clustering, a process begins at step 415 with an initial filtering of the events by a user to decide if the logs contain the necessary attributes. Depending on the input from the user, control may return to step 430 to generate new meta-paths, or to step 415, to re-filter the events.).
As per claim 11, the combination of Ishakian and Winn teaches each of the limitations of claim 1.
In addition, Ishakian teaches:
wherein the specific union of subsets of entity instances is extended by related entity instances. (Paragraph Number [0077] teaches deriving a “hard” assignment solely based on the result of the existing dimension reduction techniques, however, ignores the information about the relationships between events in a process model. According to embodiments, a process model can be obtained by projecting the process traces' heterogeneous graph G=(V, E) onto the set of event nodes E, denoted as G.sub.E=(V.sub.E, E.sub.E). Because a process model according to an embodiment can capture the follow-up relationships between events, since edge weights in a process model represent the number of times an event follows another event in a trace, it can provide strong indication in assigning events to clusters.).
As per claim 12, the combination of Ishakian and Winn teaches each of the limitations of claim 1.
In addition, Ishakian teaches:
wherein the data structure is a linear data structure, in particular an array, in which the subsets of entity instances are stored according to the predefined order (Paragraph Number [0123] teaches computer system/server 1212 may be described in the general context of computer system executable instructions, such as program modules, being executed by a computer system. Generally, program modules may include routines, programs, objects, components, logic, data structures, and so on that perform particular tasks or implement particular abstract data types. Paragraph Number [0129] teaches examples, include, but are not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data archival storage systems, etc. (See Paragraph Numbers [0015] and [0080] in regard to predefined ordering of data sets)).
As per claim 13, the combination of Ishakian and Winn teaches each of the limitations of claim 1.
In addition, Ishakian teaches:
further comprising determining, for each subset of entity instances, a weight, and wherein the predefined order is determined by finding a union of subsets of entity instances having minimal weight and covering a universe of corresponding event types (Paragraph Number [0015] teaches clustering the set of input traces includes creating a hierarchy of the plurality of disjoint partitions by successively merging pairs of events that are closest to each other until all clusters have been merged into a single hierarchical cluster that contains all events, where each leaf node of the single hierarchical cluster is an event and a root of the single hierarchical cluster is the single hierarchical cluster formed by the last merge. Paragraph Number [0080] teaches each event is treated as a singleton cluster and pairs of events that are closest to each other are successively merge, or agglomerated until all clusters have been merged into a single cluster that contains all events. . While there are a number of criteria that can be used to decide the cutting point on the hierarchy, embodiments of the disclosure use a simple approach that is based on finding a minimum similarity threshold so that the distance between any two events in the same cluster is no more than that threshold, and no more than κ clusters are formed. (Examiner notes that this organization of the hierarchy and clustering constitutes storing information in a predefined order) Paragraph Number [0089] teaches instead of using conventional metrics for generic clustering results, such as average cluster density, average inter- or intra-cluster distance, embodiments of the disclosure evaluate process traces clustering using process-specific metrics. In particular, embodiments of the disclosure may use two metrics that have been extensively used in other process traces clustering work: weighted average conformance fitness, denoted herein as AvgFitness, and weighted average structure complexity, denoted herein as AvgComplexity).
wherein the subsets of entity instances of the union are each assigned a highest priority and each other subset of entity instances is assigned a decreasing priority value with increasing distance to the union and decreasing frequency in the recorded process data. (Paragraph Number [0015] teaches clustering the set of input traces includes creating a hierarchy of the plurality of disjoint partitions by successively merging pairs of events that are closest to each other until all clusters have been merged into a single hierarchical cluster that contains all events, where each leaf node of the single hierarchical cluster is an event and a root of the single hierarchical cluster is the single hierarchical cluster formed by the last merge. Paragraph Number [0080] teaches each event is treated as a singleton cluster and pairs of events that are closest to each other are successively merge, or agglomerated until all clusters have been merged into a single cluster that contains all events. . While there are a number of criteria that can be used to decide the cutting point on the hierarchy, embodiments of the disclosure use a simple approach that is based on finding a minimum similarity threshold so that the distance between any two events in the same cluster is no more than that threshold, and no more than κ clusters are formed. (Examiner notes that this organization of the hierarchy and clustering constitutes storing information in a predefined order) (Examiner asserts that ordering into a hierarchy is functionally equivalent to determining a priority as used in the claim language)).
As per claim 14, the combination of Ishakian and Winn teaches each of the limitations of claims 1 and 13.
In addition, Ishakian teaches:
wherein the weight of each subset of entity instances is inversely proportional to the frequency of the respective subset of entity instances in the recorded process data (Paragraph Number [0089] teaches instead of using conventional metrics for generic clustering results, such as average cluster density, average inter- or intra-cluster distance, embodiments of the disclosure evaluate process traces clustering using process-specific metrics. In particular, embodiments of the disclosure may use two metrics that have been extensively used in other process traces clustering work: weighted average conformance fitness, denoted herein as AvgFitness, and weighted average structure complexity, denoted herein as AvgComplexity. Paragraph Number [0091] teaches an evaluation according to an embodiment uses two publicly available plugins from the ProM framework, disclosed in van Dongen, et al., “The ProM framework: A new era in process mining tool support”, Applications and Theory of Petri Nets, pages 444-454, Springer, 2005, the contents of which are herein incorporated by reference in their entirety, for fitness and complexity analysis: (1) the conformance checker plugin to measure the fitness of a generated process model; and (2) the Petri-Net Complexity Analysis plugin to analyze the structural complexity of a process model. After fitness and complexity scores are calculated for each cluster, the final scores are calculated as the average score over all clusters, weighted by clusters' sizes).
As per claim 15, the combination of Ishakian and Winn teaches each of the limitations of claim 1.
In addition, Ishakian teaches:
further comprising determining the optimized process graph for each provided entity type and related event type separately and merging the determined optimized process graphs into a single optimized multi-entity process graph (Paragraph Number [0015] teaches clustering the set of input traces includes creating a hierarchy of the plurality of disjoint partitions by successively merging pairs of events that are closest to each other until all clusters have been merged into a single hierarchical cluster that contains all events, where each leaf node of the single hierarchical cluster is an event and a root of the single hierarchical cluster is the single hierarchical cluster formed by the last merge. Paragraph Number [0080] teaches a greedy approximation algorithm, whose pseudocode is shown in FIG. 3, uses a bottom-up strategy, similar to that of an agglomerative clustering algorithm, to assign original events into clusters, i.e., new dimensions. First, at lines 3-4 and 6-7, a PathSim-based similarity σ.sub.P.sub.EE and a similarity matrix S are calculated between all events in E using EQ. (6). Next, at line 9, using EQ. (7), each event is treated as a singleton cluster and pairs of events that are closest to each other are successively merge, or agglomerated until all clusters have been merged into a single cluster that contains all events. This step, which can use an off-the-shelf hierarchical clustering algorithm, creates a hierarchy H, where each leaf node is an event and the root is the single cluster of the last merge. At line 11, a final step is to cut the hierarchy at some point to obtain the desirable number of clusters K. While there are a number of criteria that can be used to decide the cutting point on the hierarchy, embodiments of the disclosure use a simple approach that is based on finding a minimum similarity threshold so that the distance between any two events in the same cluster is no more than that threshold, and no more than κ clusters are formed).
Claims 8 and 9 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Application Publication Number 2017/0111245 to Ishakian et al. (hereafter referred to as Ishakian) in view of U.S. Patent Application Publication Number 2019/0213484 to Winn et al. (hereafter referred to as Winn) and in further view of U.S. Patent Application Publication Number 2023/0177825 to Gaidon et al. (hereafter referred to as Gaidon).
As per claim 8, the combination of Ishakian and Winn teaches each of the limitations of claim 1:
Ishakian teaches determining an optimized process graph representing process data but does not explicitly teach using a direct line search algorithm like a binary search algorithm as described by the following citations from Gaidon:
wherein the specific union of subsets of entity instances is determined using a direct line search algorithm (Paragraph Number [0035] teaches within network 602 and network 604, respectively, the nodes and edges may form a neural architecture searching path and may, for example, include different possible convolution modules. When networks 602 and 604 are fused, dashed lines 610, 612, 614, and 616 can represent the formation of a binary search path in which layers or nodes are skipped, indicating the merging of the respective feature maps of networks 602 and 604. In this manner, at least a partial union between networks 602 and 604 can be formed according to one or more embodiments).
Both the combination of Ishakian and Winn and Gaidon are directed to process graphing. The combination of Ishakian and Winn discloses determining an optimized process graph representing process data. Gaidon improves upon the combination of Ishakian and Winn by disclosing using a direct line search algorithm like a binary search algorithm. One of ordinary skill in the art would be motivated to further include using a direct line search algorithm like a binary search algorithm, to efficiently connect and make at least partial unions between networks and network nodes.
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system and method of determining an optimized process graph representing process data in the combination of Ishakian and Winn to further utilize a direct line search algorithm like a binary search algorithm as disclosed in Gaidon, since the claimed invention is merely a combination of old elements, and in combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
As per claim 9, the combination of Ishakian, Winn, and Gaidon teaches each of the limitations of claims 1 and 8.
Ishakian teaches determining an optimized process graph representing process data but does not explicitly teach using a direct line search algorithm like a binary search algorithm as described by the following citations from Gaidon:
wherein the direct line search algorithm is a binary search algorithm or a golden-section search algorithm (Paragraph Number [0035] teaches within network 602 and network 604, respectively, the nodes and edges may form a neural architecture searching path and may, for example, include different possible convolution modules. When networks 602 and 604 are fused, dashed lines 610, 612, 614, and 616 can represent the formation of a binary search path in which layers or nodes are skipped, indicating the merging of the respective feature maps of networks 602 and 604. In this manner, at least a partial union between networks 602 and 604 can be formed according to one or more embodiments).
One of ordinary skill in the art would be motivated to combine these references as described in regard to claim 8.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MATTHEW H DIVELBISS whose telephone number is (571)270-0166. The examiner can normally be reached on 7:30 am - 6:00 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, Jerry O'Connor can be reached on (571) 272-6787. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MATTHEW H DIVELBISS/Examiner, Art Unit 3624
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