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 .
This office action is in response to application filed on February 15, 2026, in which claims 1-17 are presented for further examination.
Response to Arguments
Applicant's arguments filed February 15, 2026 have been fully considered but they are not persuasive. (See Remarks)
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 1-17 are rejected under 35 USC 101 because the claims are directed to a judicial exception (abstract ideas) and do not recite additional elements that amount to significantly more than the abstract ideas.
Claim 1 recites “A method comprising: providing a plurality of first messages; providing a data driven process model; allocating data relating to data fields within the plurality of first messages into a data driven process modeled by the data driven process model; determining some data of the plurality of first messages that is misaligned with a ground truth for the data driven process; determining a likelihood that the some data is part of one or more first messages that though misaligned are a source of information for said ground truth; and when the likelihood is above a first threshold but less than 100%, selecting the one or more first messages as the source of the information for said ground truth.” The claim recites mathematical concepts and mental processes, including probability/likelihood determinations, threshold comparisons, correlation of data fields, and selection/association of information sources. These limitations constitute abstract ideas under the USPTO Subject Matter Eligibility Guidance. (Step 2A, Prong One: Abstract Idea)
Step 2A, Prong Two: Not Integrated into a Practical Application
The claim does not integrate the abstract idea into a practical application because it merely recites functional data processing performed by generic computing components. The claim does not recite any specific improvement to computer technology, specialized hardware implementation, or particular technological application beyond generalized information analysis.
Step 2B: No Inventive Concept
The additional elements recited, including the use of a data driven process model, ledger alignment, and likelihood thresholding, represent well-understood, routine, and conventional data analysis techniques. The claim merely instructs practitioners to apply the abstract idea on generic computing devices, which does not provide an inventive concept sufficient to transform the judicial exception into patent-eligible subject matter.
Accordingly, Claim 1 is rejected under 35 U.S.C. §101 as being directed to patent-ineligible subject matter.
Claims 2-9 depend on claim 1 and include all the limitations of this claim. Therefore, these claims are directed to the same abstract idea and the analysis must proceed to (Step 2A, Prong 2).
With respect to claim 2, the limitations are directed to when the likelihood is above a second threshold but less than the first threshold, selecting the one or more first messages as a potential source of the information for said ground truth. The additional limitation does not integrate the abstract idea into a practical application and merely represents an insignificant extra-solution activity to the judicial exception. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
With respect to claim 3, the limitations are directed to presenting the one or more first messages for disambiguation by a user as one of a source of the information for said ground truth and other than a source of the information. The additional limitation does not integrate the abstract idea into a practical application and merely represents an insignificant extra-solution activity to the judicial exception. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
With respect to claim 4, the limitations are directed to presenting a plurality of messages of the first messages and that are misaligned as a potential source of the information for said ground truth and allowing a user to select one or more of the first messages presented as the source of the information for said ground truth. The additional limitation does not integrate the abstract idea into a practical application and merely represents an insignificant extra-solution activity to the judicial exception. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
With respect to claim 5, the limitations are directed to for the some data, determining second messages from the first messages that are associated with a same data driven process instance and, in dependence upon the second messages, the data driven process instance and data within the ground truth, determining a likelihood that the some data is a relevant source of information for said ground truth. The additional limitation does not integrate the abstract idea into a practical application and merely represents an insignificant extra-solution activity to the judicial exception. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
With respect to claim 6, the limitations are directed to for the some data, determining second messages from the first messages that are associated with a same data driven process instance and, in dependence upon the second messages, the data driven process instance and data within the ground truth, determining a likelihood that the second messages are a relevant source of information for said ground truth. The additional limitation does not integrate the abstract idea into a practical application and merely represents an insignificant extra-solution activity to the judicial exception. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
With respect to claim 7, the limitations are directed to for the some data, determining second messages from the first messages that are associated with a same data driven process instance and, in dependence upon the second messages, the data driven process instance and data within the ground truth, determining a likelihood that one or more of the second messages are a relevant source of information for said ground truth. The additional limitation does not integrate the abstract idea into a practical application and merely represents an insignificant extra-solution activity to the judicial exception. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
With respect to claim 8, the limitations are directed to for the some data, determining second messages from the first messages that are associated with a same data driven process instance and, in dependence upon the second messages, the data driven process instance and data within the ground truth, determining a first likelihood for each of the some data that is a relevant source of information for said ground truth and determining a second likelihood for at least one of the second messages that the second messages are a relevant source of information for said ground truth. The additional limitation does not integrate the abstract idea into a practical application and merely represents an insignificant extra-solution activity to the judicial exception. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
With respect to claim 9, the limitations are directed to based on all determined likelihoods, filtering data that has a likelihood below a second threshold, lower than the first threshold and filtering data that is unlikely to be a source of information relating to a ground truth in view of all the determined likelihoods and their associated data. The additional limitation does not integrate the abstract idea into a practical application and merely represents an insignificant extra-solution activity to the judicial exception. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Claim 10 recites “A method comprising: providing first data from a variety of data sources; providing ledger data; providing a data driven process model; classifying the first data in accordance with the data driven process model to connect fields within the first data with entries in the ledger data; when the first data aligns with the ledger data, associating the first data with the ledger data; when the first data does not align with the ledger data, determining a likelihood that the first data aligns with the ledger data, the likelihood a value between 0 and 100 percent; when the likelihood is above a predetermined threshold, associating the first data with the ledger data and flagging the association; and when the likelihood is above a second predetermined threshold less than the first predetermined threshold and below the first predetermined threshold, one of providing the first data for verification and associating the first data with the ledger data and flagging the first data for disambiguation.” The claim recites mathematical concepts and mental processes, including probability/likelihood determinations, threshold comparisons, correlation of data fields, and selection/association of information sources. These limitations constitute abstract ideas under the USPTO Subject Matter Eligibility Guidance. (Step 2A, Prong One: Abstract Idea)
Step 2A, Prong One: Abstract Idea
The claim recites mathematical concepts and mental processes, including probability/likelihood determinations, threshold comparisons, correlation of data fields, and selection/association of information sources. These limitations constitute abstract ideas under the USPTO Subject Matter Eligibility Guidance.
Step 2A, Prong Two: Not Integrated Into a Practical Application
The claim does not integrate the abstract idea into a practical application because it merely recites functional data processing performed by generic computing components. The claim does not recite any specific improvement to computer technology, specialized hardware implementation, or particular technological application beyond generalized information analysis.
Step 2B: No Inventive Concept
The additional elements recited, including the use of a data driven process model, ledger alignment, and likelihood thresholding, represent well-understood, routine, and conventional data analysis techniques. The claim merely instructs practitioners to apply the abstract idea on generic computing devices, which does not provide an inventive concept sufficient to transform the judicial exception into patent-eligible subject matter.
Accordingly, Claim 10 is rejected under 35 U.S.C. §101 as being directed to patent-ineligible subject matter.
Claims 11-15 depend on claim 10 and include all the limitations of this claim. Therefore, these claims are directed to the same abstract idea and the analysis must proceed to (Step 2A, Prong 2).
With respect to claim 11, the limitations are directed to providing the first data to a user for verification. The additional limitation does not integrate the abstract idea into a practical application and merely represents an insignificant extra-solution activity to the judicial exception. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
With respect to claim 12, the limitations are directed to associating the first data with the ledger data and flagging the first data for disambiguation. The additional limitation does not integrate the abstract idea into a practical application and merely represents an insignificant extra-solution activity to the judicial exception. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
With respect to claim 13, the limitations are directed to wherein classifying the first data in accordance with the data driven model to connect fields within the first data with entries in the ledger data comprises classifying the first data based on content of the first data and content of data associated with the first data. The additional limitation does not integrate the abstract idea into a practical application and merely represents an insignificant extra-solution activity to the judicial exception. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
With respect to claim 14, the limitations are directed to wherein determining a likelihood comprises determining a likelihood based on content of the first data, ledger data, and content of other of the first data associated with the first data. The additional limitation does not integrate the abstract idea into a practical application and merely represents an insignificant extra-solution activity to the judicial exception. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
With respect to claim 15, the limitations are directed to wherein providing a data driven process model comprises: extracting from the first data a plurality of data elements that are associated with a same data driven process instance; determining data within each of the plurality of data elements that correlates with fields of a data driven process model; forming a model of a data driven process including data for the data driven process model, forms for the data driven process model, and a flow of the data driven process model; and providing the model so formed as the data driven process model. The additional limitation does not integrate the abstract idea into a practical application and merely represents an insignificant extra-solution activity to the judicial exception. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Claim 16 recites “A method comprising: providing first data from a variety of data sources; providing ledger data; extracting from the first data a plurality of data elements that are associated with an instance of a same data driven process to provide extracted data; determining data within the extracted data that correlates with fields within a data driven process model; forming a model of a data driven process including data fields for the data driven process model, forms for the data driven process model, and a flow of the data driven process model; and providing the data driven process model so formed for use in analysing data to extract therefrom related data, the related data related by the data driven process model.” The claim recites mathematical concepts and mental processes, including probability/likelihood determinations, threshold comparisons, correlation of data fields, and selection/association of information sources. These limitations constitute abstract ideas under the USPTO Subject Matter Eligibility Guidance. (Step 2A, Prong One: Abstract Idea)
Step 2A, Prong One: Abstract Idea
The claim recites mathematical concepts and mental processes, including probability/likelihood determinations, threshold comparisons, correlation of data fields, and selection/association of information sources. These limitations constitute abstract ideas under the USPTO Subject Matter Eligibility Guidance.
Step 2A, Prong Two: Not Integrated into a Practical Application
The claim does not integrate the abstract idea into a practical application because it merely recites functional data processing performed by generic computing components. The claim does not recite any specific improvement to computer technology, specialized hardware implementation, or particular technological application beyond generalized information analysis.
Step 2B: No Inventive Concept
The additional elements recited, including the use of a data driven process model, ledger alignment, and likelihood thresholding, represent well-understood, routine, and conventional data analysis techniques. The claim merely instructs practitioners to apply the abstract idea on generic computing devices, which does not provide an inventive concept sufficient to transform the judicial exception into patent-eligible subject matter.
Accordingly, Claim 16 is rejected under 35 U.S.C. §101 as being directed to patent-ineligible subject matter.
Claim 17 depend on claim 16 and include all the limitations of this claim. Therefore, this claim is directed to the same abstract idea and the analysis must proceed to (Step 2A, Prong 2).
With respect to claim 17, the limitations are directed to extracting from the first data a plurality of data elements that are associated with a second instance of the same data driven process to provide second extracted data; determining data within the second extracted data that correlates with fields within the data driven process model; refining the model of the data driven process based on the second extracted data to provide a refined data driven process model; and providing the refined data driven process model so formed for use in analysing data to extract therefrom related data, the related data related by at least one of the data driven process model and the refined data driven process model.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-17 are rejected under 35 USC 103 (a) as being unpatentable over Kushmerick et al. (US 7412483 B2) (hereinafter Kushmerick) in view of Dong et al. (US 20100293129 A1) (hereinafter Dong).
As per claim 1, Kushmerick discloses providing a plurality of first messages to at least a computer [a corpus of email messages, col. 3, line 42]; providing a data driven process model to at least a computer [A process model for the temporal sequential process is inferred, col. 4, line 14]; allocating by the at least a computer data relating to data fields within the plurality of first messages into a data driven process modeled by the data driven process model [a categorizer operable to accept an incoming message and map the aforesaid incoming message to a transition in the aforesaid temporal sequential process., col. 2, line 6]. However Kushmerick does not disclose determining by the at least a computer first data within the plurality of first messages that is misaligned with a ground truth for the data driven process; determining by the at least a computer a likelihood that the first data is part of one or more first messages that though misaligned are a source of information for said ground truth; and when the likelihood is above a first threshold but less than 100%, selecting by the at least a computer the one or more first messages as the source of the information for said ground truth. On the other hand, Dong discloses determining some data of the plurality of first messages that is misaligned with a ground truth for the data driven process [If two data sources provide the same true fact, it may not be evident if they are dependent or not. However, if the two sources provide the same false fact, a much greater likelihood that one source copies from the other may be assumed, paragraph 20, also see 21-30]; determining a likelihood that the some data is part of one or more first messages that though misaligned are a source of information for said ground truth [The shared factor and the false shared factor may both contribute to the dependency probability. The function may include a linear term for the false shared factor, or exhibit another sensitivity to the false shared factor. In some embodiments, either the shared factor or the false shared factor may be omitted. The shared factor and/or the false shared factor may be weighted, for example, according to some number of data objects. The dependency probability may then be output, paragraph 65-70]; and when the likelihood is above a first threshold but less than 100%, selecting the one or more first messages as the source of the information for said ground truth [truth discovery process 700 may be performed for all sources providing the same data object value (not shown), to determine a measure of truthfulness, such as a truth count or a truth probability, for the data object value. Truth discovery process 700 may accordingly be repeated for other data object values to determine corresponding measures of truthfulness for different data object values provided by sources of the data object. In some embodiments, truth discovery process 700 is repeated until truth counts are obtained for all data object values provided for a data object, paragraphs 72-75]. Both reference Kushmerick and Dong are in the field of endeavor of incoming message to map the incoming message to a transition in the temporal sequential process. Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine the message/process allocation framework of Reference Kushmerick with the likelihood-based truth discovery and threshold confidence techniques of Reference Dong to improve reliability when associating uncertain or misaligned data with structured records or ground truth.
As per claim 2, Dong discloses when the likelihood is above a second threshold but less than the first threshold, selecting the one or more first messages as a potential source of the information for said ground truth [if the two sources provide the same false fact, a much greater likelihood that one source copies from the other may be assumed. Using this intuitive assumption, an objectivist Bayesian analysis using probability calculus may be applied to determine dependency between sources, paragraph 20].
As per claim 3, Dong discloses presenting the one or more first messages for disambiguation by a user as one of a source of the information for said ground truth and other than a source of the information [the copying of data between sources may lead to an even greater bias in truth discovery using certain voting algorithms, paragraph 21].
As per claim 4, Dong discloses presenting a plurality of messages of the first messages and that are misaligned as a potential source of the information for said ground truth and allowing a user to select one or more of the first messages presented as the source of the information for said ground truth [If two data sources provide the same true fact, it may not be evident if they are dependent or not. However, if the two sources provide the same false fact, a much greater likelihood that one source copies from the other may be assumed, paragraph 20, also see 21-30].
As per claim 5, Dong discloses for the some data, determining second messages from the first messages that are associated with a same data driven process instance and, in dependence upon the second messages, the data driven process instance and data within the ground truth, determining a likelihood that the some data is a relevant source of information for said ground truth [estimating a dependency probability indicative of a likelihood that the first source and the second source are dependent, wherein the dependency probability is a function of the shared factor and the false shared factor, and outputting the dependency probability. The dependency probability may increase with the shared factor, and may further increase with the false shared factor, paragraph 22].
As per claim 6, Dong discloses for the some data, determining second messages from the first messages that are associated with a same data driven process instance and, in dependence upon the second messages, the data driven process instance and data within the ground truth, determining a likelihood that the second messages are a relevant source of information for said ground truth [The shared factor and the false shared factor may both contribute to the dependency probability. The function may include a linear term for the false shared factor, or exhibit another sensitivity to the false shared factor. In some embodiments, either the shared factor or the false shared factor may be omitted. The shared factor and/or the false shared factor may be weighted, for example, according to some number of data objects. The dependency probability may then be output, paragraph 65-70].
As per claim 7, Dong discloses for the some data, determining second messages from the first messages that are associated with a same data driven process instance and, in dependence upon the second messages, the data driven process instance and data within the ground truth, determining a likelihood that one or more of the second messages are a relevant source of information for said ground truth [The shared factor and the false shared factor may both contribute to the dependency probability. The function may include a linear term for the false shared factor, or exhibit another sensitivity to the false shared factor. In some embodiments, either the shared factor or the false shared factor may be omitted. The shared factor and/or the false shared factor may be weighted, for example, according to some number of data objects. The dependency probability may then be output, paragraph 65-70].
As per claim 8, Dong discloses for the some data, determining second messages from the first messages that are associated with a same data driven process instance and, in dependence upon the second messages, the data driven process instance and data within the ground truth, determining a first likelihood for each of the some data that is a relevant source of information for said ground truth and determining a second likelihood for at least one of the second messages that the second messages are a relevant source of information for said ground truth [The method may still further include estimating a first probability of dependency polarity indicating a likelihood that the first source is dependent on the second source, wherein the first probability of dependency polarity increases as a first absolute difference between the shared accuracy and the first unshared accuracy increases, and outputting the first probability of dependency polarity. The method may yet further include determining a second unshared accuracy indicative of the fraction of the unshared values for the second source that are true, estimating a second probability of dependency polarity indicating a likelihood that the second source is dependent on the first source, wherein the second probability of dependency polarity increases as a second absolute difference between the shared accuracy and the second unshared accuracy increases. The second probability of dependency polarity may also be outputted., paragraph 23].
As per claim 9, Kushmerick discloses based on all determined likelihoods, filtering data that has a likelihood below a second threshold, lower than the first threshold and filtering data that is unlikely to be a source of information relating to a ground truth in view of all the determined likelihoods and their associated data [Transitions correspond to different messages within the same transaction and can be identified by defining the distance between every pair of messages and employing a clustering algorithm to partition the messages, creating a mapping from messages to transition labels as in FIG. 3. In a preferred embodiment, this distance is defined as the length of the longest common subsequence of characters between pairs of messages, col. 4, line 6].
As per claim 10, Kushmerick discloses providing first data from a variety of data sources [a corpus of email messages, col. 3, line 42]; providing ledger data; providing a data driven process model [A process model for the temporal sequential process is inferred, col. 4, line 14]; classifying the first data in accordance with the data driven process model to connect fields within the first data with entries in the ledger data [cluster messages with high similarity, Fig. 3]; when the first data aligns with the ledger data, associating the first data with the ledger data [mapping from messages to transition labels, Fig. 3]. However Kushmerick does not disclose when the first data does not align with the ledger data, determining a likelihood that the first data aligns with the ledger data, the likelihood a value between 0 and 100 percent; when the likelihood is above a predetermined threshold, associating the first data with the ledger data and flagging the association; and when the likelihood is above a second predetermined threshold less than the first predetermined threshold and below the first predetermined threshold, one of providing the first data for verification and associating the first data with the ledger data and flagging the first data for disambiguation. On the other hand, Dong discloses disclose when the first data does not align with the ledger data, determining a likelihood that the first data aligns with the ledger data, the likelihood a value between 0 and 100 percent [truth discovery process 700 may be performed for all sources providing the same data object value (not shown), to determine a measure of truthfulness, such as a truth count or a truth probability, for the data object value. Truth discovery process 700 may accordingly be repeated for other data object values to determine corresponding measures of truthfulness for different data object values provided by sources of the data object. In some embodiments, truth discovery process 700 is repeated until truth counts are obtained for all data object values provided for a data object, paragraphs 72-75]; when the likelihood is above a predetermined threshold, associating the first data with the ledger data and flagging the association [comparing a threshold probability for dependency with the first and second probabilities of dependency polarity, asserting dependency for probabilities of dependency polarity above the threshold probability, paragraph 24]; and when the likelihood is above a second predetermined threshold less than the first predetermined threshold and below the first predetermined threshold, one of providing the first data for verification and associating the first data with the ledger data and flagging the first data for disambiguation [determining whether the data object value is true or false may be accomplished by comparing the truth count for the data object value with a threshold truth count. A truth probability for the data object value for the plurality of sources may be determined based on the truth count. The truth probability may be indicative of a fraction given by the truth count over a number of the plurality of sources, paragraph 28]. Both reference Kushmerick and Dong are in the field of endeavor of incoming message to map the incoming message to a transition in the temporal sequential process. Before the17ncometive filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine the message/process allocation framework of Reference Kushmerick with the likelihood-based truth discovery and threshold confidence techniques of Reference Dong to improve reliability when associating uncertain or misaligned data with structured records or ground truth.
As per claim 11, Dong discloses providing the first data to a user for verification [the copying of data between sources may lead to an even greater bias in truth discovery using certain voting algorithms, paragraph 21].
As per claim 12, Dong discloses associating the first data with the ledger data and flagging the first data for disambiguation [comparing a threshold probability for dependency with the first and second probabilities of dependency polarity, asserting dependency for probabilities of dependency polarity above the threshold probability, paragraph 24].
As per claim 13, Kushmerick discloses wherein classifying the first data in accordance with the data driven model to connect fields within the first data with entries in the ledger data comprises classifying the first data based on content of the first data and content of data associated with the first data [cluster messages with high similarity, Fig. 3].
As per claim 14, Dong discloses wherein determining a likelihood comprises determining a likelihood based on content of the first data, ledger data, and content of other of the first data associated with the first data [if the two sources provide the same false fact, a much greater likelihood that one source copies from the other may be assumed, paragraph 20].
As per claim 15, Dong discloses wherein providing a data driven process model comprises: extracting from the first data a plurality of data elements that are associated with a same data driven process instance; determining data within each of the plurality of data elements that correlates with fields of a data driven process model; forming a model of a data driven process including data for the data driven process model, forms for the data driven process model, and a flow of the data driven process model; and providing the model so formed as the data driven process model [Data object values from a first source and a second source may be obtained, along with a true or false indication for each data value object from each source, wherein each source provides up to one value for a data object (operation 602). Values and true/false indications from a plurality of data object values from the first and the second source may be obtained. Some sources may not provide a data object value for a given data object. In some cases, the true/false indication may be obtained from a truth discovery process, for example true/false determination 104 (see FIG. 1). Data object values, which are shared between the first source and the second source, may be determined (operation 604). The data objects provided by both the first and second source having matching data object values may be determined in operation, Fig. 6, paragraph 68].
As per claim 16, Kushmerick discloses providing first data from a variety of data sources [a corpus of email messages, col. 3, line 42]; providing ledger data [A process model for the temporal sequential process is inferred, col. 4, line 14]. However Kushmerick does not disclose extracting from the first data a plurality of data elements that are associated with an instance of a same data driven process to provide extracted data; determining data within the extracted data that correlates with fields within a data driven process model; forming a model of a data driven process including data fields for the data driven process model, forms for the data driven process model, and a flow of the data driven process model; and providing the data driven process model so formed for use in analysing data to extract therefrom related data, the related data related by the data driven process model. On the other hand Dong discloses extracting from the first data a plurality of data elements that are associated with an instance of a same data driven process to provide extracted data; determining data within the extracted data that correlates with fields within a data driven process model; forming a model of a data driven process including data fields for the data driven process model, forms for the data driven process model, and a flow of the data driven process model; and providing the data driven process model so formed for use in analysing data to extract therefrom related data, the related data related by the data driven process model [Data object values from a first source and a second source may be obtained, along with a true or false indication for each data value object from each source, wherein each source provides up to one value for a data object (operation 602). Values and true/false indications from a plurality of data object values from the first and the second source may be obtained. Some sources may not provide a data object value for a given data object. In some cases, the true/false indication may be obtained from a truth discovery process, for example true/false determination 104 (see FIG. 1). Data object values, which are shared between the first source and the second source, may be determined (operation 604). The data objects provided by both the first and second source having matching data object values may be determined in operation, Fig. 6, paragraph 68]. Both reference Kushmerick and Dong are in the field of endeavor of incoming message to map the20ncomeng message to a transition in the temporal sequential process. Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine the message/process allocation framework of Reference Kushmerick with the likelihood-based truth discovery and threshold confidence techniques of Reference Dong to improve reliability when associating uncertain or misaligned data with structured records or ground truth.
As per claim 17, Dong discloses extracting from the first data a plurality of data elements that are associated with a second instance of the same data driven process to provide second extracted data; determining data within the second extracted data that correlates with fields within the data driven process model; refining the model of the data driven process based on the second extracted data to provide a refined data driven process model; and providing the refined data driven process model so formed for use in analysing data to extract therefrom related data, the related data related by at least one of the data driven process model and the refined data driven process model [Data object values from a first source and a second source may be obtained, along with a true or false indication for each data value object from each source, wherein each source provides up to one value for a data object (operation 602). Values and true/false indications from a plurality of data object values from the first and the second source may be obtained. Some sources may not provide a data object value for a given data object. In some cases, the true/false indication may be obtained from a truth discovery process, for example true/false determination 104 (see FIG. 1). Data object values, which are shared between the first source and the second source, may be determined (operation 604). The data objects provided by both the first and second source having matching data object values may be determined in operation, Fig. 6, paragraph 68].
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Remarks
Applicant asserted, page 1, that the amended claim 1 falls properly within 35 USC 101.
Examiner respectfully disagrees with this assertion. The claim is directed to the abstract idea of collecting information, analyzing information, and making a determination based on the analysis. Specifically, the claim recites receiving messages, allocating data into a model, determining whether certain data is misaligned with a ground truth, determining a likelihood regarding the informational value of the data, and selecting messages based on that likelihood. These limitations describe mental processes and methods of organizing and evaluating information, which fall within the abstract idea grouping identified in the 2019 PEG. Applicant amended the claim to recite actions being performed 'by at least a computer' and providing information 'to at least a computer.' However, merely adding a generic computer implementation does not render an abstract idea patent eligible. The additional computer-related language amounts only to instructions to apply the abstract idea using generic computer components performing generic computer functions, such as receiving, processing, analyzing, and selecting data.The claim does not recite any improvement to computer functionality, any particular machine integral to the claim, any transformation of an article, or any other meaningful limitation that would integrate the abstract idea into a practical application. The computer is invoked merely as a tool to perform the abstract data analysis process faster or more efficiently.Further, under Step 2B of the Alice/Mayo framework, the claim does not include an inventive concept sufficient to transform the abstract idea into patent-eligible subject matter. The recited computer elements are described at a high level of generality and perform only well-understood, routine, and conventional computer functions. As such, the additional elements, individually and in combination, do not amount to significantly more than the judicial exception itself.
Applicant asserted, page 3, that neither Dong nor Kushmerick discloses, "determining by the at least a computer a likelihood that the first data is part of one or more first messages that though misaligned are a source of information for said ground truth" as recited in claim 1.
Examiner respectfully disagrees with this assertion. the rejection does not require the exact phrase “ground truth” to appear verbatim in the cited disclosure. Rather, the rejection relies on what is reasonably conveyed to a person of ordinary skill in the art by the cited passages.Paragraph [20] states:“If two data sources provide the same true fact, it may not be evident if they are dependent or not. However, if the two sources provide the same false fact, a much greater likelihood that one source copies from the other may be assumed.”This passage expressly distinguishes between “true” facts and “false” facts. Determining whether information is “true” or “false” necessarily requires comparison against some known or accepted correctness standard. That correctness standard corresponds to the claimed “ground truth.”Further, paragraphs [21]-[30] discuss evaluating information consistency, correctness, reliability, and dependency among sources. Such evaluation inherently requires an underlying reference point against which the data is assessed. Therefore, the cited disclosure reasonably teaches or suggests determining whether data is aligned or misaligned with an accepted truth condition or informational baseline.
Applicant asserted, page 3, that neither Dong nor Kushmerick discloses, "when the first data does not align with the ledger data associating by the at least a computer the first data with the ledger data" as recited in claim 10.
Examiner respectfully disagrees with this assertion. The claims recite, in relevant part, “when the first data does not align with the ledger data, determining a likelihood that the first data aligns with the ledger data, the likelihood a value between 0 and 100 percent.” Dong teaches this limitation through its disclosed “truth discovery process 700,” which determines a “measure of truthfulness,” including a “truth probability,” for a data object value. See Dong, paragraphs 72–75. Dong further explains that the truth discovery process evaluates whether provided data values correspond to trustworthy or truthful data and assigns a corresponding probability value.
A “truth probability” is reasonably interpreted as a likelihood that the evaluated data corresponds or aligns with the reference/true data. Because probability values inherently represent values on a percentage scale between 0 and 100 percent, Dong’s disclosure of determining a “truth probability” reads on the claimed “determining a likelihood that the first data aligns with the ledger data, the likelihood a value between 0 and 100 percent.”
Further, the claim does not require any particular mathematical technique, threshold, or specialized form of likelihood determination beyond determining a likelihood value. Dong expressly teaches generating such probabilistic truth/alignment determinations for data values that may not match other data values, thereby satisfying the claimed limitation under the broadest reasonable interpretation.
Applicant asserted, page 4, that neither Dong nor Kushmerick discloses, "extracting by the at least a computer from the first data a plurality of data elements that are associated with an instance of the data driven process to provide extracted data."
Examiner respectfully disagrees with this assertion.
Conclusion
THIS ACTION IS MADE FINAL. 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 extension fee 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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