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 .
Claim Objections
Claim 1 is objected to because of the following informalities: The claim recites at least one node of the first version BN. This appears to be a typographical error, and will be interpreted as if it had read at least one node of the first version of the BN.
Appropriate correction is required.
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.
Claim 20 is 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 20 recites the acronyms CD and BN, without providing definitions or explanations, thus rendering the scope of these claim limitations indefinite. For the purpose of examination, any term which shares these initials will be interpreted as being within the scope of the limitation.
Claim 20 further recites, on the final line, the limitation the BN which lacks proper antecedent basis in the claims. For the purpose of examination, the claim will be interpreted as if it had read the BN model.
Claim 20 further recites the term locally, which a relative term which renders the claim indefinite. The term locally 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. For the purpose of examination, any training on a computer, versus on a remote device in a client-server configuration, will be interpreted to fall with in the scope of locally training.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim 1 recites a method, thus a process, one of the four statutory categories of patentable subject matter. However, Claim 1 further recites steps of computing at least one CPT (conditional probability table representing statistic characteristics of a corresponding domain variable) of one node of the first version BN (which is both a mental process capable of performance in the human mind and a mathematical process, see, for example, Fig. 2 of the specification), calculating a CPT distance value between the at least one first CPT of the first version and at least one corresponding CPT of the second version (both a mental process and a mathematical process); and identifying at least one suspected BN as undergoing CD based on the at least one calculated CPT distance value (a mental process of observation and decision). Thus, the claim recites the abstract idea of computing a table based on statistics of data and comparing it to a different table.
The claim does not include any additional elements which could integrate the abstract idea into a practical application, because the additional elements of the claim consist of using at least one processor (because, by MPEP 2106.05(f)(2), merely implementing an abstract idea on a computer cannot integrate the abstract idea into a practical application) and receiving a first version of the DN model, comprising a plurality of interconnected nodes, each node comprising a CPT which is insignificant extra-solution activity of data gathering required for all uses of the abstract idea (by MPEP 2106.05(g)). Thus, the claim is directed towards the abstract idea of computing a table based on statistics of data and comparing it to a different table.
Finally, the additional elements, taken alone and in combination, cannot provide significantly more than the abstract idea itself, because implementation on generic computer components cannot do so (MPEP 2106.05(f)(2)) and because the extra-solution activity of receiving is well-understood, routine, and conventional (MPEP 2106.05(d), transmitting and receiving data over network). Thus, the claim is ineligible.
Claims 2-6, 8, 9, and 16 each only recite additional mathematical process steps or descriptions of previously recited mathematical steps, and thus comprise no additional elements which could integrate the abstract idea into a practical application nor provide significantly more than the abstract idea itself.
Claims 7 and 11-15 recite only additional abstract idea steps comprising mental processes capable of performance in the human mind, or only using a pencil and paper as a memory aid, and some steps characterizable as both mental and mathematical (Claim 7: monitoring at least one CPT distance value of the at least one node is a mental process of observation, or of determining the distance value over time; calculating a distance limit value is a mental or mathematical process of determining a limit; analyzing the monitored distance value is a mental process of determining whether the value exceeds the limit; identifying the at least one node is a mental process of determination; Claim 11: producing a plurality of directed acyclic graphs is determining an interconnection of nodes and edges, capable of performance by a human using a pencil and paper as a memory aid; calculating a statistical score is a mathematical process; selecting a DAG based on the score is a mental process; and producing an optimized version of the BN based on the selected DAG is a mental process of filling the CPTs for the selected DAG; Claim 12: producing a DAG, selecting a DAG, calculating an improve of a score of the DAG, and repeating are all mental process steps; Claim 13: recites only repeating the mental process until a condition occurs; Claim 14 only limits how to select the DAG based a selection probability¸ a mental process; Claim 15 recites only limitation on how to calculate the selection probability).
Claim 10 recites the additional mental process step of producing an optimized version of the BN based on the at least one identified node (a mental process of filling the CPTs for the selected DAG). Claim 10 further recites the additional elements of receiving a set of domain variables, which is insignificant extra-solution activity of data gathering (see MPPE 2106.05(g)) and of predicting at least one value of at least one domain variable based on the optimized version of the BN, which is merely using a computer or other machinery as a tool to perform the mental process of predicting¸ which by MPEP 210.05(f)(2) cannot integrate the abstract idea into a practical application. Further, both of these elements are also well-understood, routine, and conventional, as evidenced by MPEP 2106.05(d), “transmitting and receiving data over a network” and by the specification, [00157], “System 100 may thus predict … the value of the at least one target variable, based on the optimized version 40 of the BN, as known in the art”).
Claims 17-19 recite a system comprising … at least one processor configured to perform precisely the methods of Claims 1, 11, and 12, respectively, and are thus rejected for reasons set forth in those claims.
Claim 20 recites a method, thus a process, one of the four statutory categories of patentable subject matter. However, Claim 20 further recites the steps of identifying at least one node of the BN as suspected of underdoing CD between the first version of the BN model and the second version of the BN model (a mental process of determination) and locally relearning the BN based on the identified suspected node (a mental process of observation and decision, as learning a BN consists of determining a graph of nodes and edges, and of assigning statistical values to takes for the nodes, as shown in Fig. 3, which can reasonably be performed by a human – i.e. calculating how many times A follows B in a string of data). Thus the claim recites the abstract idea of comparing models, identifying a potential difference, and learning a new model in response.
The claim does not recite any additional elements which could integrate the abstract idea into a practical application nor provide significantly more than the abstract idea itself, because the additional elements consist of implementing the abstract idea by at least one processor (which can do neither by MPEP 2106.05(f)(2)) and in receiving the data to be analyzed, which is insignificant extra-solution activity of data gathering (MPEP 2106.05(g)) as well as well-understood, routine, and conventional (by MPEP 2106.05(d), “transmitting and receiving data over a network”), and there is no nexus between the additional elements to provide significantly more in combination.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The 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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim 20 is rejected under 35 U.S.C. 103 as being unpatentable over Alsuwat et al., “Modelling Concept Drift in the Context of Discrete Bayesian Networks,” in view of Borchani et al., “Mining multi-dimensional concept-drifting data streams using Bayesian network classifiers”.
Regarding Claim 20, Alsuwat teaches a method of mitigating … CD in a BN model (Alsuwat, Abstract, “There is, therefore, a need to detect concept drift in order to ensure the validity of learned models … we study the issue of concept drift in the context of discrete Bayesian networks”) … by at least one processor (Alsuwat implements their algorithm in software, in which a processor is inherent, see pg. 221, 1st column, 2nd paragraph, using “Hugin Research 8.4” software package) comprising: receiving a first version of the BN model, corresponding to a first timing (Alsuwat, pg. 216, 1st column, 2nd paragraph, “at each time point t=i we use the incoming batch, Batch i, to update the current Bayesian network”), said BN model comprising a plurality of interconnected nodes, wherein each node represents a domain variable (inherent in Bayesian Networks, but see Alsuwat, pg. 217, Figure 1); receiving a second version of the BN model, corresponding to a second timing (Alsuwat, pg. 216, 1st column, 2nd paragraph, “at each time point t=i we use the incoming batch, Batch i, to update the current Bayesian network”), identifying at least one node of the BN as suspected of undergoing CD between the first version of the BN model and the second version of the BN model (Alsuwat, pg. 216, 1st column, 3rd paragraph, “for each edge A→ B in a Bayesian network model BN1, we detect the existence of concept drift by monitoring the posterior distribution drift and uncertainty drifty of A→ B over time”).
Alsuwat is primarily concerned about detecting concept drift at a particular node so that it can be dealt with, but does not explicitly teach what to do once concept drift has been detected at that node, and as such does not teach locally relearning the BN based on the identified suspected node. However, Borchani, also regarding mitigating concept drift with Bayesian networks, teaches this limitation (Borchani, Abstract, “if a concept drift is detected, [our method] adapts the current MBC network locally around each changed node”). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Alsuwat’s network monitoring with Borchani’s local updating of the network. The motivation to do so is that “so if a concept drift occurs, only the changed parts of the current MBC are re-learned from the new combing batch stream and not the whole network” i.e. efficiency, and the entire structure of the network (nodes and edges) does not need to be relearned (Borchani, pg. 15, 2nd paragraph).
Conclusion
Claims 1-19 have been searched, but no combination of prior art which teaches the combinations of limitation of the independent claims has been uncovered.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
CN 109343952 A detects concept drift with respect to a data stream for a Bayesian network, but does not appear to construct conditional probability tables.
Castillo et al., “Adaptative Bayesian Network Classifiers,” mitigates concept drift with respect to Bayesian networks, but does not appear to identify a particular local node and while mentioning a conditional probability table, does not appear to compare conditional probability tables in their analysis.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BRIAN M SMITH whose telephone number is (469)295-9104. The examiner can normally be reached Monday - Friday, 8:00am - 4pm Pacific.
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/BRIAN M SMITH/Primary Examiner, Art Unit 2122