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 .
Drawings
The drawings are objected to as failing to comply with 37 CFR 1.84(p)(5) because they do not include the following reference sign(s) mentioned in the description: “task based model 313” disclosed by specification paragraph [0024], line 1.
Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Claim Objections
Claims 1-2 and 8-9 are objected to because of the following informalities:
In claim 1, line 10, the phrase “network for configured to model” contains a typographical error. Examiner suggests deleting “for”.
In claim 1 on page 27, line 6 and claim 8 on page 29, line 5, “participants” should recite “survey participants”.
In claim 2, line 1, “cluster identifier function” should recite “the cluster identifier function” which has antecedent basis from claim 1, line 17. Claim 9 is objected for the same reasons as claim 2.
In claim 2, line 4 and claim 9, line 5, “participant” should recite “survey participant”. Appropriate correction is required.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are:
“a cluster identifier” in claim 1, line 17,
“a key feature extractor” in claim 1, line 19,
“a correlation module” in claim 1, line 21; claim 7, line 1
“a causality module” in claim 1, line 25; claim 5, line 1; claim 6, line 1
“a perturbation module” in claim 1 on page 27, line 4; claim 4, line 3, and
“a topic module” in claim 4, line 1.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-14 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1 recites the limitation "the probability distribution" in line 14. It is unclear if this limitation refers to “a prediction probability distribution p” from line 11. Examiner treats this limitation as “the prediction probability distribution p”.
The term “best” in claim 1, line 15 is a relative term which renders the claim indefinite. The term “best” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. The term “best” is a subjective term under MPEP 2173.05(b) subsection (IV) because the specification does not supply an objective standard for ascertaining a best-fitting bias distribution. Examiner treats “a bias distribution that best fits” as “a bias distribution that fits”.
Claim 1 recites the limitation "the space of essential graphs" on page 27, line 1. There is insufficient antecedent basis for this limitation in the claim. It is unclear what “essential graphs” are, and it is unclear if they are related to any other graphs recited in the claim. Examiner treats this limitation as “a space of graphs”.
In claim 1 on page 27, line 7, the limitation “the most likely cause for identified group bias clusters” renders the claim indefinite for the following reasons. There is insufficient antecedent basis for the limitation “the most likely cause” in the claim. The earlier limitations of claim 1 do not explicitly recite identifying group bias clusters. At best, claim 1, line 17 recites defining sets of group bias clusters. Examiner treats the limitation “the most likely cause for identified group bias clusters” as “a most likely cause for defined sets of group bias clusters”.
Claims 2-7 are rejected for failing to cure the deficiencies of claim 1.
The term “best” in claim 2, line 2 is a relative term which renders the claim indefinite. The term “best” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. The term “best” is a subjective term under MPEP 2173.05(b) subsection (IV) because the specification does not supply an objective standard for ascertaining a best-fitting bias distribution. Examiner treats “a bias distribution that best fits” as “a bias distribution that fits”.
Claim 2 recites the limitation "the prediction distribution f" in line 2. It is unclear if this limitation means “the model prediction distribution f” which would have proper antecedent basis from claim 1, line 15. Examiner treats this limitation as “the model prediction distribution f”.
Claim 2 recites the limitation "the actual prediction distribution p" in line 3. There is insufficient antecedent basis for this limitation in the claim. It is unclear if this limitation means “the prediction probability distribution p” which would have antecedent basis from claim 1, lines 10-11. Examiner treats the limitation "the actual prediction distribution p" as “the prediction probability distribution p”.
Claim 2 recites the limitation “the curve fitting function” in line 4. There is insufficient antecedent basis for this limitation in the claim.
The term “similar” in claim 2, final line is a relative term which renders the claim indefinite. The term “similar” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. The term “similar” is a subjective term under MPEP 2173.05(b) subsection (IV) because the specification does not provide a standard for ascertaining similar values from dissimilar values. Examiner treats the term “similar values” as values whose difference is below a threshold.
Claim 3 recites the limitation "the curve fitting analysis" in line 1. There is insufficient antecedent basis for these limitations in the claim. It is unclear how the curve fitting analysis is related to any features of claim 1. Examiner treats claim 3 as if it was dependent on claim 2, which would provide proper antecedent basis.
Claim 5 recites the limitation "the effect" in line 2. There is insufficient antecedent basis for these limitations in the claim. Examiner treats “the effect” as “an effect”.
Claim 8 recites the limitation "the probability distribution" in line 8. It is unclear if this limitation refers to “a prediction probability distribution p” from lines 3-4. Examiner treats this limitation as “the prediction probability distribution p”.
The term “best” in claim 8, line 8 is a relative term which renders the claim indefinite. The term “best” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. The term “best” is a subjective term under MPEP 2173.05(b) subsection (IV) because the specification does not supply an objective standard for ascertaining a best-fitting bias distribution. Examiner treats “a bias distribution that best fits” as “a bias distribution that fits”.
Claim 8 recites the limitation "the space of essential graphs" in line 18. There is insufficient antecedent basis for this limitation in the claim. It is unclear what “essential graphs” are, and it is unclear if they are related to any other graphs recited in the claim. Examiner treats this limitation as “a space of graphs”.
In claim 8 on page 29, line 6, the limitation “the most likely cause for identified group bias clusters” renders the claim indefinite for the following reasons. There is insufficient antecedent basis for the limitation “the most likely cause” in the claim. The earlier limitations of claim 8 do not explicitly recite identifying group bias clusters. At best, claim 8, line 10 recites defining sets of group bias clusters. Examiner treats the limitation “the most likely cause for identified group bias clusters” as “a most likely cause for defined sets of group bias clusters”.
Claims 9-14 are rejected for failing to cure the deficiencies of claim 8.
Claims 9-10 and 12 each recite a method which implements the same features as the system of claims 2-3 and 5, respectively.
Claim 14 recites the limitation "the causality module" in the final line. There is insufficient antecedent basis for these limitations in the claim. Examiner treats “the causality module” as “a causality module”.
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 1-14 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claims 1-7 recite a system comprising a processor, and claims 8-14 recite a method. A system comprising a processor and a method each fall under one of the four statutory categories of patent eligible subject matter.
Claim 1
Step 2A Prong 1: Predictions of future events by survey participants and event outcomes, the predictions having latent bias are judgement and evaluation mental processes which can reasonably be performed in the human mind with the aid of pencil and paper.
Model the time series event data as a prediction probability distribution p, and a Bayesian [model]
Define sets of group bias clusters from the bias distribution is a judgement and evaluation mental process which can reasonably be performed in the human mind with the aid of pencil and paper.
Identify key features according to common personal characteristics within the group bias clusters is a judgement and evaluation mental process which can reasonably be performed in the human mind with the aid of pencil and paper.
Receive information related to each of the group bias clusters and to estimate correlation between key identified features using a dependency analysis network to construct for each of the group bias clusters a dependency graph based on singular value decomposition is a judgement and evaluation mental process which can reasonably be performed in the human mind with the aid of pencil and paper.
Perform a causality analysis to derive for each of the group bias clusters a causal graph from the dependency graph using a greedy equivalence search algorithm to move through the space of essential graphs to construct the causal graph, the causal graph providing causal relationship between personal characteristics in each group bias cluster and for all group bias clusters combined is a judgement and evaluation mental process which can reasonably be performed in the human mind with the aid of pencil and paper.
Infer bias explainability by perturbing features derived from the causal graph to determine which of the causal relationships are most sensitive to alter group membership of participants, wherein the bias explainability includes an indication of which personal characteristics are the most likely cause for identified group bias clusters based on highest sensitivity values is a judgement and evaluation mental process which can reasonably be performed in the human mind with the aid of pencil and paper. The claim recites an abstract idea.
Step 2A Prong 2: A system for latent bias detection by artificial intelligence modeling of human decision making, the system comprising: a processor, and a non-transitory memory having stored thereon modules executed by the processor amount to generic computer components for applying the abstract ideas on a generic computer under MPEP 2106.05(f).
A data repository of time series event data amounts to insignificant extra-solution activity under MPEP 2106.05(g).
A data repository of personal characteristics data of each survey participant amounts to insignificant extra-solution activity under MPEP 2106.05(g).
A deep Bayesian model module comprising: a recurrent neural network amounts to mere instructions to apply the abstract ideas on a generic computer under MPEP 2106.05(f).
A Bayesian network with at least a hidden node and a personal data node amounts to mere instructions to apply the abstract ideas on a generic computer under MPEP 2106.05(f).
A cluster identifier, a key feature extractor, a correlation module, a causality module, and a perturbation module amount to generic computer components for applying the abstract ideas on a generic computer under MPEP 2106.05(f).
The additional elements as disclosed above, alone or in combination, do not integrate the abstract ideas into a practical application as they are mere insignificant extra solution activities as disclosed in combination with generic computer functions that are implemented to perform the abstract ideas disclosed above. The claim is directed to an abstract idea.
Step 2B: A system for latent bias detection by artificial intelligence modeling of human decision making, the system comprising: a processor, and a non-transitory memory having stored thereon modules executed by the processor amount to generic computer components for applying the abstract ideas on a generic computer under MPEP 2106.05(f).
A data repository of time series event data amounts to information stored in a memory, which the courts have recognized as a well-understood, routine, conventional activity under MPEP 2106.05(d)(II).
A data repository of personal characteristics data of each survey participant amounts to information stored in a memory, which the courts have recognized as a well-understood, routine, conventional activity under MPEP 2106.05(d)(II).
A deep Bayesian model module comprising: a recurrent neural network amounts to mere instructions to apply the abstract ideas on a generic computer under MPEP 2106.05(f).
A Bayesian network with at least a hidden node and a personal data node amounts to mere instructions to apply the abstract ideas on a generic computer under MPEP 2106.05(f).
A cluster identifier, a key feature extractor, a correlation module, a causality module, and a perturbation module amount to generic computer components for applying the abstract ideas on a generic computer under MPEP 2106.05(f).
The additional elements as disclosed above, in combination with the abstract ideas, are not sufficient to amount to significantly more than the abstract ideas as they are well-understood, routine and conventional activities as disclosed in combination with generic computer functions that are implemented to perform the abstract ideas disclosed above. The claim is not patent eligible.
Claim 2 incorporates the rejection of claim 1.
Step 2A Prong 1: The abstract ideas of claim 1 are incorporated. Apply a curve fitting analysis to solve for the bias distribution that best fits the prediction distribution f to the actual prediction distribution p, and upon convergence of the curve fitting, current parameter values of the curve fitting function associated with each participant are examined collectively for presence of clusters of similar values, which is used to define the sets of group bias cluster is a judgement and evaluation mental process which can reasonably be performed with the aid of pencil and paper.
Step 2A Prong 2 and Step 2B: The cluster identifier function amount to mere instructions and a generic computer component for applying the abstract ideas on a generic computer under MPEP 2106.05(f). The claim is not patent eligible.
Claim 3 incorporates the rejection of claim 1.
Step 2A Prong 1: The abstract ideas of claim 1 are incorporated. Applying the curve fitting analysis which is a latent Dirichlet analysis is a judgment and evaluation mental process which can reasonably be performed in the human mind with the aid of pencil and paper.
Step 2A Prong 2 and Step 2B: The claim does not recite additional elements which, alone or in combination, would integrate the abstract ideas into a practical application or which, in combination with the abstract ideas, would be sufficient to amount to significantly more than the abstract ideas. The claim is not patent eligible.
Claim 4 incorporates the rejection of claim 1.
Step 2A Prong 1: The abstract ideas of claim 1 are incorporated. Determine event topic groups from the time series event data using a latent Dirichlet allocation analysis is a judgment and evaluation mental process which can reasonably be performed in the human mind with the aid of pencil and paper.
Include event topic groups for the inferring of bias explainability is a judgment and evaluation mental process which can reasonably be performed in the human mind with the aid of pencil and paper.
Step 2A Prong 2 and Step 2B: A topic module and the perturbation module amount to generic computer components for applying the abstract ideas on a generic computer under MPEP 2106.05(f). The claim is not patent eligible.
Claim 5 incorporates the rejection of claim 1.
Step 2A Prong 1: The abstract ideas of claim 1 are incorporated. Perform counterfactual analysis to determine the effect of enforcing a particular edge on the causal graph is a judgment and evaluation mental process which can reasonably be performed in the human mind with the aid of pencil and paper.
Step 2A Prong 2 and Step 2B: The causality module amounts to a generic computer component for applying the abstract ideas on a generic computer under MPEP 2106.05(f). The claim is not patent eligible.
Claim 6 incorporates the rejection of claim 1.
Step 2A Prong 1: The abstract ideas of claim 1 are incorporated. Derive the causal graph by pruning non-causal relationships of the dependency graph is a judgment and evaluation mental process which can reasonably be performed in the human mind with the aid of pencil and paper.
Step 2A Prong 2 and Step 2B: The causality module amounts to a generic computer component for applying the abstract ideas on a generic computer under MPEP 2106.05(f). The claim is not patent eligible.
Claim 7 incorporates the rejection of claim 1.
Step 2A Prong 1: The abstract ideas of claim 1 are incorporated. Determine a number of top features from the dependency graph is a judgment and evaluation mental process which can reasonably be performed in the human mind with the aid of pencil and paper.
Step 2A Prong 2: The correlation module amounts to a generic computer component for applying the abstract ideas on a generic computer under MPEP 2106.05(f).
The dependency graph comprising a network of nodes representing the features, the top features being ones with highest node activities defined by influence of a node with respect to other nodes amount to mere instructions for applying the abstract ideas on a generic computer under MPEP 2106.05(f).
The top features being sent to the causality module for the causality analysis amounts to mere data-gathering, an insignificant extra-solution activity under MPEP 2106.05(g).
Step 2B: The correlation module amounts to a generic computer component for applying the abstract ideas on a generic computer under MPEP 2106.05(f).
The dependency graph comprising a network of nodes representing the features, the top features being ones with highest node activities defined by influence of a node with respect to other nodes amount to mere instructions for applying the abstract ideas on a generic computer under MPEP 2106.05(f).
The top features being sent to the causality module for the causality analysis is analogous to transmitting data over a network, which the courts have recognized as a well-understood, routine, conventional activity under MPEP 2106.05(d)(II). The claim is not patent eligible.
Claim 8
Step 2A Prong 1: Latent bias detection by
Modelling,
…
Solving for a bias distribution that best fits a model prediction distribution f to the prediction probability distribution p is a judgement and evaluation mental process which can reasonably be performed in the human mind with the aid of pencil and paper.
Defining sets of group bias clusters from the bias distribution is a judgement and evaluation mental process which can reasonably be performed in the human mind with the aid of pencil and paper.
Identifying key features according to common personal characteristics within the group bias clusters is a judgement and evaluation mental process which can reasonably be performed in the human mind with the aid of pencil and paper.
Estimating correlation between the key identified features using a dependency analysis network to construct for each of the group bias clusters a dependency graph based on singular value decomposition is a judgement and evaluation mental process which can reasonably be performed in the human mind with the aid of pencil and paper.
Performing a causality analysis to derive for each of the group bias clusters a causal graph from the dependency graph using a greedy equivalence search algorithm to move through the space of essential graphs to construct the causal graph, the causal graph providing causal relationship between personal characteristics in each group bias cluster and for all group bias clusters combined is a judgement and evaluation mental process which can reasonably be performed in the human mind with the aid of pencil and paper.
Inferring bias explainability by perturbing features derived from the causal graph to determine which of the causal relationships are most sensitive to alter group membership of participants, wherein the bias explainability includes an indication of which personal characteristics are the most likely cause for identified group bias clusters based on highest sensitivity values is a judgement and evaluation mental process which can reasonably be performed in the human mind with the aid of pencil and paper. The claim recites an abstract idea.
Step 2A Prong 2 and Step 2B: Artificial intelligence modeling amounts to mere instructions to apply the abstract ideas on a generic computer under MPEP 2106.05(f).
Using a recurrent neural network amounts to mere instructions to apply the abstract ideas on a generic computer under MPEP 2106.05(f).
Receiving, by a Bayesian network with at least a hidden node representing estimated bias distribution and a personal data node representing personal characteristics data of each survey participant, the probability distribution amounts to mere instructions to apply the abstract ideas on a generic computer under MPEP 2106.05(f).
The additional elements as disclosed above, alone or in combination, do not integrate the abstract ideas into a practical application as they are generic computer functions as disclosed that are implemented to perform the abstract ideas disclosed above. The claim is directed to an abstract idea.
The additional elements as disclosed above, in combination with the abstract ideas, are not sufficient to amount to significantly more than the abstract ideas as they are generic computer functions as disclosed that are implemented to perform the abstract ideas disclosed above. The claim is not patent eligible.
Claims 9-10 and 12-14 each recites a method which implements the same features as the system of claims 2-3 and 5-7, respectively, and are therefore rejected for at least the same reasons.
Claim 11 incorporates the rejection of claim 8.
Step 2A Prong 1: The abstract ideas of claim 8 are incorporated. Determining event topic groups from the time series event data using a latent Dirichlet allocation analysis, and including event topic groups for the inferring of bias explainability are judgment and evaluation mental processes which can reasonably be performed in the human mind with the aid of pencil and paper.
Step 2A Prong 2 and Step 2B: The claim does not recite additional elements which, alone or in combination, would integrate the abstract ideas into a practical application or which, in combination with the abstract ideas, would be sufficient to amount to significantly more than the abstract ideas. The claim is not patent eligible.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Furbish et al. (US 20200380597 A1) at [0028], final 10 lines and all [0032] teaches users ranking likelihoods of a making a given financial decision; [0033], lines 1-16 teaches collecting financial data from users; and [0037]-[0038] and [0044], lines 1-6 teaches clustering users into clusters based on their bias.
Ikemoto et al. (US 20170303079 A1) at [0065]-[0067], [0069], [0080], [0094]-[0096], and Figs. 4 and 7 teaches generating clusters of users based on their position data, and identifying dominant attributes within each cluster. [0185]-[0188] teaches matching dominant attributes of cluster k=1 from Fig. 7 to the attribute item column 29B from Fig. 8.
Jacob et al. (“Dependency Network Analysis (DEPNA) Reveals Context Related Influence of Brain Network Nodes”) at page 3, subsection “Dependency network analysis (DEPNA)”, lines 1-21 teaches creation of context related graph visualizations using dependency network analysis.
Hauser et al. (“Characterization and Greedy Learning of Interventional Markov Equivalence Classes of Directed Acyclic Graphs”) at page 2420, subsection 4, lines 1-3 and first two bullet points teaches a Greedy Equivalence Search algorithm.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Asher H. Jablon whose telephone number is (571)270-7648. The examiner can normally be reached Monday - Friday, 9:00 am - 6:00 pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Abdullah Al Kawsar can be reached at (571)270-3169. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/A.H.J./Examiner, Art Unit 2127
/ABDULLAH AL KAWSAR/Supervisory Patent Examiner, Art Unit 2127