DETAILED ACTION
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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 5/6/2026 has been entered.
Status
This action is in response to the amendment filed on 5/6/2026. Claims 1-5, 7-19, 21-22 are pending. Claims 1, 21-22 are amended. No claims have been added. Claim 20 has been cancelled.
Response to Arguments
Applicant’s arguments with respect to the previous 103 rejections claim(s) have been considered but are moot because the applicant has amended the claims. The examiner has done an updated search and an updated prior art rejection is below.
Applicant's arguments filed 5/6/2026 have been fully considered but they are not persuasive. The applicant has argued that “applying actions based in part on a sensor integrates into a practical application.” The examiner respectfully disagrees. In the claim there is no particular sensor claimed, no manner of reading it, or any technical detail of the sensor or sensor interface. The sensor as claimed appears to be a temperature sensor (a thermometer?). Taking a temperature setting from a thermometer would not integrate the invention into a practical application. At most it would be using a known tool in its known capacity to help perform a step of the invention. The applicant has not made it clear as to why the use of a temperature sensor would cause the invention to be directed to a practical application. Applicant’s arguments are not found persuasive. The previous rejection is updated below.
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 1-5, 7-19, 21-22 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e. abstract idea) without anything significantly more.
Step 1: Claims 1-5, 7-19, are directed to a method, claim 21 is directed to a system, and claim 22 is directed to a computer program product. Therefore, claims 1-5, 7-19, 21-22 are directed to patent eligible categories of invention.
Step 2A, Prong 1: Claims 1, 21, 22, recite an incremental rule condition evaluation including receiving, performing, selecting, and determining data, constituting an abstract idea based on “Certain Methods of Organizing Human Activity” related to personal behavior or interactions between individuals including social activities (see ¶ 17 medical decisions, ¶ 122 financial decisions). The claim(s) also recite a mental processes, as drafted, the claim recites the limitation of determining a ruleset which is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind/with pen and paper but for the recitation of generic computer components. That is, other than reciting claim 21 reciting “a processor” and claim 22 reciting “a computer program” nothing in the claim precludes the determining step from practically being performed in the human mind. For example, but for the “a processor” language, the claim encompasses the user manually making rule determinations. The mere nominal recitation of a generic network appliance does not take the claim limitation out of the mental processes grouping. This limitation is a mental process. The claims “select a first input… based at least in part on a cost-information gain score… wherein a cost… is associated with a first input cost of testing the monitored system, and wherein an information gain…is associated with a reduction in information entropy” this is a mathematical calculation. Specifically an entropy based scoring formula. As a whole the claims are directed to selecting which question to ask next and eliminating possibilities based on the answer. With the exception of the “processor” language, the claim steps in the context of the claim encompass an abstract idea directed to a “Mental Process”, “Mathematical Concept,” and “Certain Methods of Organizing Human Activity.”
Dependent claims 2-5, 7-19 further narrow the abstract idea identified in the independent claims and do not introduce further additional elements for consideration.
Dependent claim 6 will be evaluated under Step 2A, Prong 2 below.
Step 2A, Prong 2: Independent claims 1, 21, 22, do not integrate the judicial exception into a practical application. Claim 1 is a method, comprising “a monitored system; memory; reading a sensor… applying actions of rules….” Claim 21 is a system, comprising “a communication interface; a processor coupled to the communication interface and configured to: receive a first ruleset associated with a rule-based system from the communication interface… a monitored system; a memory; reading a sensor… applying actions of rules…” Claim 22 is a computer program product “embodied in a non-transitory computer readable medium and comprising computer instructions for… a monitored system; memory; reading a sensor… applying actions of rules…” These additional elements are mere instructions to implement an abstract idea using a computer in its ordinary capacity, or merely uses the computer as a tool to perform the identified abstract idea. Use of a computer or other machinery in its ordinary capacity for performing the steps of the abstract idea or other tasks (e.g., to receive, select, determine data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., certain methods of organizing human activity) does not integrate a judicial exception into a practical application. See MPEP 2106.05(f). The claim also employs generic computer functions to execute an abstract idea, even when limiting the use of the idea to one particular environment. This type of generally linking is not sufficient to prove integration into a practical application. See MPEP 2106.05(h).
Therefore, the additional elements of the independent claims, when considered both individually and in combination, are not sufficient to prove integration into a practical application.
Dependent claims 2-5, 7-19 further narrow the abstract idea identified in the independent claims and do not introduce further additional elements for consideration, which does not integrate the judicial exception into a practical application.
Therefore, the additional elements of the dependent claims, when considered both individually and in the context of the independent claims, are not sufficient to prove integration into a practical application.
Step 2B: Independent claims 1, 21, and 22 do not comprise anything significantly more than the judicial exception. As can be seen above with respect to Step 2A, Prong 2, Claim 1 is a method, comprising “a monitored system; memory; reading a sensor… applying actions of rules….” Claim 21 is a system, comprising “a communication interface; a processor coupled to the communication interface and configured to: receive a first ruleset associated with a rule-based system from the communication interface… a monitored system; a memory; reading a sensor… applying actions of rules…” Claim 22 is a computer program product “embodied in a non-transitory computer readable medium and comprising computer instructions for… a monitored system; memory; reading a sensor… applying actions of rules…” These additional elements are mere instructions to implement an abstract idea using a computer in its ordinary capacity, or merely uses the computer as a tool to perform the identified abstract idea. Use of a computer or other machinery in its ordinary capacity for performing the steps of the abstract idea or other tasks (e.g., to receive, select, determine data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., certain methods of organizing human activity) is not anything significantly more than the judicial exception. See MPEP 2106.05(f). The claim employs generic computer functions to execute an abstract idea, even when limiting the use of the idea to one particular environment. This type of generally linking is not anything significantly more than the judicial exception. See MPEP 2106.05(h).
The additional elements of the independent claims, when considered both individually and in combination, do not comprise anything significantly more than the judicial exception.
Dependent claims 2-5, 7-19 further narrow the abstract idea identified in the independent claims and do not introduce further additional elements for consideration, which is not anything significantly more than the judicial exception.
The additional elements of the dependent claims, when considered both individually and in the context of the independent claims, are not anything significantly more than the judicial exception.
Accordingly, claims 1-5, 7-19, 21-22 are rejected under 35 USC 101.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-5, 7-19, 21-22 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention. The applicant has amended the independent claims to include the language of determining the first new input value dynamically at evaluation time based at least in part on testing the monitored system based at least in part on reading a sensor associated with the first input. Although the applicant has support in the originally filed disclosure for a temperature sensor there is not support in the originally filed disclosure for the determining the first new input value dynamically at evaluation time based at least in part on testing the monitored system based at least in part on reading a sensor associated with the first input. Appropriate action is required.
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 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, 21, 22, recites the limitation “the first input value.” There is insufficient antecedent basis for this limitation in the claim. The claim previously claims “a first new input value,” but not “a first input value.”
Claim 2 recites the limitation “the second input value.” There is insufficient antecedent basis for this limitation in the claim. The claim previously claims “a second new input value,” but not “a second input value.”
Regarding claim 5, the phrase "an additional input" renders the claim indefinite because it is unclear whether the claims “an additional input” refers back to the same input already received in claim 1, or if it is a second, different additional input.
Claim 7, 8, recites the limitation “the first input value.” There is insufficient antecedent basis for this limitation in the claim.
Regarding claim 15, the phrase "is represented a conjunction of subconditions" renders the claim indefinite because the language of the claim is unclear. It appears as though claim is lacking the word “by.” As in “is represented by a conjunction.” Clarification is requested.
The following is a quotation of 35 U.S.C. 112(d):
(d) REFERENCE IN DEPENDENT FORMS.—Subject to subsection (e), a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
The following is a quotation of pre-AIA 35 U.S.C. 112, fourth paragraph:
Subject to the following paragraph [i.e., the fifth paragraph of pre-AIA 35 U.S.C. 112], a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
Claim 5 is rejected under 35 U.S.C. 112(d) or pre-AIA 35 U.S.C. 112, 4th paragraph, as being of improper dependent form for failing to further limit the subject matter of the claim upon which it depends, or for failing to include all the limitations of the claim upon which it depends. Claim 5 contains all the same limitations as newly amended claim 1, therefore claim 5 is not further limiting the previous claim. Applicant may cancel the claim(s), amend the claim(s) to place the claim(s) in proper dependent form, rewrite the claim(s) in independent form, or present a sufficient showing that the dependent claim(s) complies with the statutory requirements.
The dependent claims inherit the rejections of the claims from which they depend.
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.
Claim(s) 1-5, 8, 12-14, 21-22, is/are rejected under 35 U.S.C. 103 as being unpatentable over Voit et al. (US 20160292581 A1) in view of Cheriton (US 20200081882 A1) in view of Wang et al. (US 20140351185 A1) in view of Zheng et al. “Efficient Test Selection in Active Diagnosis via Entropy Approximation”, published 2005 (cited as reference 1-U; referred to hereinafter as ‘Zheng’).
Regarding claim 1, Voit teaches receiving a first ruleset associated with a rule-based system (¶ 11-12, discloses receiving a first group of data objects (rules). ¶ 20-21, disclose identifying a first set of rules. ¶ 46-50, 61-62, 37, 28, 90);
performing incremental rule evaluation dynamically at evaluation time wherein not all input values are known at the start of rule evaluation (¶ 38, 81-83, discloses the use of an optimized rule evaluation program. ¶ 21, discloses evaluating rule dependencies. ¶ 43-45, discloses evaluating and identifying the rules of a dependency chain. ¶ 59-61, 81, 21);
and, at least in part by: selecting a first input for which to determine a first new input value based at least in part on a cost-information gain score associated with the monitored system (¶ 21, 39, 40, discloses rules having an input and determining an input. ¶ 81-82, discloses the use of value checks. ¶ 16, 40, 43, 66, 68, discloses the cost of acquiring a data object. ¶ 65, 91-96.);
determining the first new value dynamically at evaluation time (¶ 21, discloses identifying rules that are not identified. ¶ 81, discloses the use of value checks. ¶ 43, 65, 95-96);
and determining a second ruleset by removing from the first ruleset rules whose rule condition can no longer be true based on the first input value (¶ 104-106, there are one or more rules within the second set of rules that no longer have an input that depends on an output of at least one rule of the first set of rules. Upon positive determination, such rules are removed from the second set of rules, thereby reducing the second rule set by eliminating rules which are no longer affected, e.g. the reasoning engine preprocessor determines, in step (b), that rule R12 no longer has an input that depends on the output of RS and removes R12 from the second rule set. ¶ 21, 81);
receiving an additional input (¶ 21, discloses receiving additional input, ¶ 28, discloses performing actions. ¶ 104-105 discloses the use of rulesets, ¶ 94-98).
and applying actions of rules associated with the second ruleset whose condition cannot be determined to evaluate as false based at least in part on the additional input (¶ 21, discloses receiving additional input for each rule that is not identified, ¶ 28, discloses performing actions. ¶ 104-105 discloses the use of rulesets, ¶ 94-98, 53).
Voit does not specifically teach a root cause analysis.
However, Cheriton teaches receiving a first ruleset associated with a rule-based system for root cause analysis of a monitored system wherein a rule condition table is a root cause table (¶ 40-42, 53, 114-115, discloses rule conditions. ¶ 65, 66, 70, 107, 112, discloses a root cause analysis of a monitored system. ¶ 101-103, discloses the use of a root cause table); a minimum set of symptoms needed to diagnose a root case (¶ 102).
It would have been obvious to one of ordinary skill in the art at the time of filing to modify Voit to include/perform root cause analysis of a monitored system wherein a rule condition table is a root cause table, as taught/suggested by Cheriton. This known technique is applicable to the system of Voit as they both share characteristics and capabilities, namely, they are directed to automatic rule processing and rule engine implementation. One of ordinary skill in the art would have recognized that applying the known technique of Cheriton would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Cheriton to the teachings of Voit would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such root cause analysis features into similar systems. Further, applying a root cause analysis of a monitored system wherein a rule condition table is a root cause table would have been recognized by those of ordinary skill in the art as resulting in an improved system that would allow for identification of failure sources, moving beyond symptoms to resolve issues.
Voit does not specifically teach determining the first new input value based at least in part on testing the monitored system.
However, Cheriton teaches determining the first new input value based at least in part on testing the monitored system based at least in part on reading a sensor associated with the first input (¶ 97, 102, 104, 211, discloses obtaining symptom values from physical instruments, power and temperature sensors, ¶ 122, 132, 199, 209, 217-218, 253-254, 276, discloses evaluating and determining a new input. ¶ 90, 104-106, 177, 178, disclose the use of unknown values.). Cheriton also teaches applying actions to rules associated with a ruleset whose condition cannot be determined to evaluate as false based on additional input (¶ 105, 173-181, 202-206).
It would have been obvious to one of ordinary skill in the art at the time of filing to modify Voit to include/perform determining the first new input value based at least in part on testing the monitored system, as taught/suggested by Cheriton. This known technique is applicable to the system of Voit as they both share characteristics and capabilities, namely, they are directed to automatic rule processing and rule engine implementation. One of ordinary skill in the art would have recognized that applying the known technique of Cheriton would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Cheriton to the teachings of Voit would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such testing analysis features into similar systems. Further, applying determining the first new input value based at least in part on testing the monitored system would have been recognized by those of ordinary skill in the art as resulting in an improved system that would allow for testing to identify potential issues.
Voit does not specifically teach reducing memory usage for the rule-based system at least in part by freeing memory associated with the first ruleset.
However, Wang teaches reducing memory usage for the rule-based system at least in part by freeing memory associated with the first ruleset (¶ 12-13, 19-22, 48, 62, disclose using a reduced evaluated data set which results in reduced memory requirements.).
It would have been obvious to one of ordinary skill in the art at the time of filing to modify Voit to include/perform reducing memory usage for the rule-based system at least in part by freeing memory associated with the first ruleset, as taught/suggested by Wang. This known technique is applicable to the system of Voit as they both share characteristics and capabilities, namely, they are directed to automatic rule processing and memory management. One of ordinary skill in the art would have recognized that applying the known technique of Wang would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Wang to the teachings of Voit would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such memory reduction features into similar systems. Further, applying reducing memory usage for the rule-based system at least in part by freeing memory associated with the first ruleset would have been recognized by those of ordinary skill in the art as resulting in a more stable system.
The combination of Voit, Cheriton, and Wang does not specifically teach cost-information gain score with a reduction in information entropy.
However, Zheng teaches wherein a cost associated with the cost-information gain score is associated with a first input cost of testing the monitored system, and wherein an information gain associated with the cost- information gain score is associated with a reduction in information entropy associated with a first input state (section 1, ¶ 2 discloses a tradeoff between cost of testing and diagnostic accuracy, and entropy is used as a cost function to select tests providing maximum information, or minimum conditional entropy. Section 3, discloses minimizing both the conditional entropy and the cost of testing. Section 5, discloses setting up a monitored system as a network of computers with binary fault states, current state where entropy is reduced. Section 6, discloses diagnostic quality directly with conditional entropy. Appendix, equivalence section used the term information gain and explicitly shows that it is maximized by the same text that minimizes conditional entropy.)
It would have been obvious to one of ordinary skill in the art at the time of filing to modify Voit to include/perform cost-information gain score with a reduction in information entropy, as taught/suggested by Zheng. This known technique is applicable to the system of Voit as they both share characteristics and capabilities, namely, they are directed to fault analysis and data management. One of ordinary skill in the art would have recognized that applying the known technique of Zheng would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Zheng to the teachings of Voit would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such cost-information gain score with a reduction in information entropy features into similar systems. Further, applying cost-information gain score with a reduction in information entropy would have been recognized by those of ordinary skill in the art as resulting in a system that would bring forward data with relevance and value in decision-making and information delivery.
Regarding claim 2, Voit teaches wherein performing incremental rule evaluation further comprises, in the event the first ruleset is not sufficiently reduced: selecting a second input for which to determine a second new value (¶ 104-106, discloses assigning and using reducing a second rule set); determining the second new value (¶ 104-106, discloses assigning and using reducing a second rule set leading to a new value); and determining a third ruleset by removing from the second ruleset rules whose rule condition can no longer be true based on the second input value (¶ 104-108, there are one or more rules within the second set of rules that no longer have an input that depends on an output of at least one rule of the first set of rules. Upon positive determination, such rules are removed from the second set of rules, thereby reducing the second rule set by eliminating rules which are no longer affected, e.g. the reasoning engine preprocessor determines, in step (b), that rule R12 no longer has an input that depends on the output of RS and removes R12 from the second rule set.).
Regarding claim 3, Voit teaches further comprising reporting the second ruleset (¶ 28, discloses reporting. ¶ 104-105 discloses the use of rulesets).
Regarding claim 4, Voit teaches outputting the second ruleset, at least in part by performing actions associated with the second ruleset (¶ 28, discloses performing actions. ¶ 104-105 discloses the use of rulesets. ¶ 94-98).
Regarding claim 5, Voit teaches outputting the second ruleset, at least in part by: receiving an additional input (¶ 21, discloses receiving additional input, ¶ 28, discloses performing actions. ¶ 104-105 discloses the use of rulesets, ¶ 94-98).
and applying actions of rules associated with the second ruleset whose condition cannot be determined to evaluate as false based at least in part on the additional input (¶ 21, discloses receiving additional input for each rule that is not identified, ¶ 28, discloses performing actions. ¶ 104-105 discloses the use of rulesets, ¶ 94-98, 53).
Regarding claim 8, Voit teaches wherein selecting the first input is based at least in part on the cost of determining the first input value (¶ 37-39, discloses the cost of data acquisition, ¶ 43-45, 66, 68, 81).
Regarding claim 12, Voit teaches wherein performing incremental rule evaluation further comprises reducing the first ruleset at least in part by filtering out rules based on their associated actions (¶ 66, discloses grouping. ¶ 101-107, there are one or more rules within the second set of rules that no longer have an input that depends on an output of at least one rule of the first set of rules. Upon positive determination, such rules are removed from the second set of rules, thereby reducing the second rule set by eliminating rules which are no longer affected, e.g. the reasoning engine preprocessor determines, in step (b), that rule R12 no longer has an input that depends on the output of RS and removes R12 from the second rule set. ¶ 112-113, discloses grouping and removing objects that are no longer required.)
Regarding claim 13, Voit teaches reducing the first ruleset prior to performing incremental rule evaluation, at least in part by receiving an already known input value and removing from the first ruleset rules whose rule condition can no longer be true based on the already known input value (¶ 66, discloses grouping. ¶ 101-107, there are one or more rules within the second set of rules that no longer have an input that depends on an output of at least one rule of the first set of rules. Upon positive determination, such rules are removed from the second set of rules, thereby reducing the second rule set by eliminating rules which are no longer affected, e.g. the reasoning engine preprocessor determines, in step (b), that rule R12 no longer has an input that depends on the output of RS and removes R12 from the second rule set. ¶ 112-113, discloses grouping and removing objects that are no longer required.)
Regarding claim 14, Voit teaches further comprising reducing the first ruleset prior to performing incremental rule evaluation, at least in part by receiving a hypothesis and removing from the first ruleset rules whose actions do not correspond to the hypothesis (¶ 38, discloses a hypothesis, ¶ 66, ¶ 101-108, there are one or more rules within the second set of rules that no longer have an input that depends on an output of at least one rule of the first set of rules. Upon positive determination, such rules are removed from the second set of rules, thereby reducing the second rule set by eliminating rules which are no longer affected, e.g. the reasoning engine preprocessor determines, in step (b), that rule R12 no longer has an input that depends on the output of RS and removes R12 from the second rule set. ¶ 112-113, discloses grouping and removing objects that are no longer required.)
Regarding claim 21, Voit teaches
a communication interface; a processor coupled to the communication interface (¶ 26, 28, 37-40 discloses a processor, ¶ 37-40, 48-49, 53, 115-118, discloses an interface);
receive a first ruleset associated with a rule-based system from the communication interface (¶ 11-12, discloses receiving a first group of data objects (rules). ¶ 20-21, disclose identifying a first set of rules. ¶ 37-40, 48-49, 53, 115-118, discloses an interface, ¶ 61-62, 37, 28, 90);
perform incremental rule evaluation dynamically wherein not all input values are known at the start of rule evaluation (¶ 38, 81-83, discloses the use of an optimized rule evaluation program. ¶ 21, discloses evaluating rule dependencies. ¶ 43-45, discloses evaluating and identifying the rules of a dependency chain. ¶ 59-61, 81, 21);
and, at least in part by: selecting a first input for which to determine a first new input value based at least in part on a cost-information gain score associated with the monitored system (¶ 21, 39, 40, discloses rules having an input and determining an input. ¶ 81-82, discloses the use of value checks. ¶ 16, 40, 43, 66, 68, discloses the cost of acquiring a data object. ¶ 65, 91-96.);
determine the first new value dynamically at evaluation time (¶ 21, discloses identifying rules that are not identified. ¶ 81, discloses the use of value checks. ¶ 65, 95-96);
and determine a second ruleset by removing from the first ruleset rules whose rule condition can no longer be true based on the first input value (¶ 104-106, there are one or more rules within the second set of rules that no longer have an input that depends on an output of at least one rule of the first set of rules. Upon positive determination, such rules are removed from the second set of rules, thereby reducing the second rule set by eliminating rules which are no longer affected, e.g. the reasoning engine preprocessor determines, in step (b), that rule R12 no longer has an input that depends on the output of RS and removes R12 from the second rule set. ¶ 21, 81).
receiving an additional input (¶ 21, discloses receiving additional input, ¶ 28, discloses performing actions. ¶ 104-105 discloses the use of rulesets, ¶ 94-98).
and applying actions of rules associated with the second ruleset whose condition cannot be determined to evaluate as false based at least in part on the additional input (¶ 21, discloses receiving additional input for each rule that is not identified, ¶ 28, discloses performing actions. ¶ 104-105 discloses the use of rulesets, ¶ 94-98, 53).
Voit does not specifically teach a root cause analysis.
However, Cheriton teaches receive a first ruleset associated with a rule-based system for root cause analysis of a monitored system wherein a rule condition table is a root cause table (¶ 40-42, 53, 114-115, discloses rule conditions. ¶ 65, 66, 70, 107, 112, discloses a root cause analysis of a monitored system. ¶ 101-103, discloses the use of a root cause table); a minimum set of symptoms needed to diagnose a root case (¶ 102).
It would have been obvious to one of ordinary skill in the art at the time of filing to modify Voit to include/perform root cause analysis of a monitored system wherein a rule condition table is a root cause table, as taught/suggested by Cheriton. This known technique is applicable to the system of Voit as they both share characteristics and capabilities, namely, they are directed to automatic rule processing and rule engine implementation. One of ordinary skill in the art would have recognized that applying the known technique of Cheriton would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Cheriton to the teachings of Voit would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such root cause analysis features into similar systems. Further, applying a root cause analysis of a monitored system wherein a rule condition table is a root cause table would have been recognized by those of ordinary skill in the art as resulting in an improved system that would allow for identification of failure sources, moving beyond symptoms to resolve issues.
Voit does not specifically teach determining the first new input value based at least in part on testing the monitored system.
However, Cheriton teaches determine the first new input value based at least in part on testing the monitored system based at least in part on reading a sensor associated with the first input (¶ 97, 102, 104, 211, discloses obtaining symptom values from physical instruments, power and temperature sensors, ¶ 122, 132, 199, 209, 217-218, 253-254, 276, discloses evaluating and determining a new input. ¶ 90, 104-106, 177, 178, disclose the use of unknown values.). Cheriton also teaches applying actions to rules associated with a ruleset whose condition cannot be determined to evaluate as false based on additional input (¶ 105, 173-181, 202-206).
It would have been obvious to one of ordinary skill in the art at the time of filing to modify Voit to include/perform determining the first new input value based at least in part on testing the monitored system, as taught/suggested by Cheriton. This known technique is applicable to the system of Voit as they both share characteristics and capabilities, namely, they are directed to automatic rule processing and rule engine implementation. One of ordinary skill in the art would have recognized that applying the known technique of Cheriton would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Cheriton to the teachings of Voit would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such testing analysis features into similar systems. Further, applying determining the first new input value based at least in part on testing the monitored system would have been recognized by those of ordinary skill in the art as resulting in an improved system that would allow for testing to identify potential issues.
Voit does not specifically teach reducing memory usage for the rule-based system at least in part by freeing memory associated with the first ruleset.
However, Wang teaches reducing memory usage for the rule-based system at least in part by freeing memory associated with the first ruleset (¶ 12-13, 19-22, 48, 62, disclose using a reduced evaluated data set which results in reduced memory requirements.).
It would have been obvious to one of ordinary skill in the art at the time of filing to modify Voit to include/perform reducing memory usage for the rule-based system at least in part by freeing memory associated with the first ruleset, as taught/suggested by Wang. This known technique is applicable to the system of Voit as they both share characteristics and capabilities, namely, they are directed to automatic rule processing and memory management. One of ordinary skill in the art would have recognized that applying the known technique of Wang would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Wang to the teachings of Voit would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such memory reduction features into similar systems. Further, applying reducing memory usage for the rule-based system at least in part by freeing memory associated with the first ruleset would have been recognized by those of ordinary skill in the art as resulting in a more stable system.
The combination of Voit, Cheriton, and Wang does not specifically teach cost-information gain score with a reduction in information entropy.
However, Zheng teaches wherein a cost associated with the cost-information gain score is associated with a first input cost of testing the monitored system, and wherein an information gain associated with the cost- information gain score is associated with a reduction in information entropy associated with a first input state (section 1, ¶ 2 discloses a tradeoff between cost of testing and diagnostic accuracy, and entropy is used as a cost function to select tests providing maximum information, or minimum conditional entropy. Section 3, discloses minimizing both the conditional entropy and the cost of testing. Section 5, discloses setting up a monitored system as a network of computers with binary fault states, current state where entropy is reduced. Section 6, discloses diagnostic quality directly with conditional entropy. Appendix, equivalence section used the term information gain and explicitly shows that it is maximized by the same text that minimizes conditional entropy.)
It would have been obvious to one of ordinary skill in the art at the time of filing to modify Voit to include/perform cost-information gain score with a reduction in information entropy, as taught/suggested by Zheng. This known technique is applicable to the system of Voit as they both share characteristics and capabilities, namely, they are directed to fault analysis and data management. One of ordinary skill in the art would have recognized that applying the known technique of Zheng would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Zheng to the teachings of Voit would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such cost-information gain score with a reduction in information entropy features into similar systems. Further, applying cost-information gain score with a reduction in information entropy would have been recognized by those of ordinary skill in the art as resulting in a system that would bring forward data with relevance and value in decision-making and information delivery.
Regarding claim 22, Voit teaches
a computer program product embodied in a non-transitory computer readable medium and comprising computer instructions for (¶ 26, 28, 37-40 discloses a processor, ¶ 26, 117-118, discloses a medium);
receiving a first ruleset associated with a rule-based system (¶ 11-12, discloses receiving a first group of data objects (rules). ¶ 20-21, disclose identifying a first set of rules. ¶ 61-62, 37, 28, 90);
performing incremental rule evaluation dynamically at evaluation time wherein not all input values are known at the start of rule evaluation (¶ 38, 81-83, discloses the use of an optimized rule evaluation program. ¶ 21, discloses evaluating rule dependencies. ¶ 43-45, discloses evaluating and identifying the rules of a dependency chain. ¶ 81, 21);
at least in part by: selecting a first input for which to determine a first new input value based at least in part on a cost-information gain score associated with the monitored system (¶ 21, 39, 40, discloses rules having an input and determining an input. ¶ 81-82, discloses the use of value checks. ¶ 16, 40, 43, 66, 68, discloses the cost of acquiring a data object. ¶ 65, 91-96.);
determining the first new value evaluation dynamically at evaluation time (¶ 21, discloses identifying rules that are not identified. ¶ 81, discloses the use of value checks. ¶ 65, 95-96);
and determining a second ruleset by removing from the first ruleset rules whose rule condition can no longer be true based on the first input value (¶ 104-106, there are one or more rules within the second set of rules that no longer have an input that depends on an output of at least one rule of the first set of rules. Upon positive determination, such rules are removed from the second set of rules, thereby reducing the second rule set by eliminating rules which are no longer affected, e.g. the reasoning engine preprocessor determines, in step (b), that rule R12 no longer has an input that depends on the output of RS and removes R12 from the second rule set. ¶ 21, 81).
receiving an additional input (¶ 21, discloses receiving additional input, ¶ 28, discloses performing actions. ¶ 104-105 discloses the use of rulesets, ¶ 94-98).
and applying actions of rules associated with the second ruleset whose condition cannot be determined to evaluate as false based at least in part on the additional input (¶ 21, discloses receiving additional input for each rule that is not identified, ¶ 28, discloses performing actions. ¶ 104-105 discloses the use of rulesets, ¶ 94-98, 53).
Voit does not specifically teach a root cause analysis.
However, Cheriton teaches receiving a first ruleset associated with a rule-based system for root cause analysis of a monitored system wherein a rule condition table is a root cause table (¶ 40-42, 53, 114-115, discloses rule conditions. ¶ 65, 66, 70, 107, 112, discloses a root cause analysis of a monitored system. ¶ 101-103, discloses the use of a root cause table); a minimum set of symptoms needed to diagnose a root case (¶ 102).
It would have been obvious to one of ordinary skill in the art at the time of filing to modify Voit to include/perform root cause analysis of a monitored system wherein a rule condition table is a root cause table, as taught/suggested by Cheriton. This known technique is applicable to the system of Voit as they both share characteristics and capabilities, namely, they are directed to automatic rule processing and rule engine implementation. One of ordinary skill in the art would have recognized that applying the known technique of Cheriton would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Cheriton to the teachings of Voit would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such root cause analysis features into similar systems. Further, applying a root cause analysis of a monitored system wherein a rule condition table is a root cause table would have been recognized by those of ordinary skill in the art as resulting in an improved system that would allow for identification of failure sources, moving beyond symptoms to resolve issues.
Voit does not specifically teach determining the first new input value based at least in part on testing the monitored system.
However, Cheriton teaches determining the first new input value based at least in part on testing the monitored system based at least in part on reading a sensor associated with the first input (¶ 97, 102, 104, 211, discloses obtaining symptom values from physical instruments, power and temperature sensors, ¶ 122, 132, 199, 209, 217-218, 253-254, 276, discloses evaluating and determining a new input. ¶ 90, 104-106, 177, 178, disclose the use of unknown values.). Cheriton also teaches applying actions to rules associated with a ruleset whose condition cannot be determined to evaluate as false based on additional input (¶ 105, 173-181, 202-206).
It would have been obvious to one of ordinary skill in the art at the time of filing to modify Voit to include/perform determining the first new input value based at least in part on testing the monitored system, as taught/suggested by Cheriton. This known technique is applicable to the system of Voit as they both share characteristics and capabilities, namely, they are directed to automatic rule processing and rule engine implementation. One of ordinary skill in the art would have recognized that applying the known technique of Cheriton would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Cheriton to the teachings of Voit would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such testing analysis features into similar systems. Further, applying determining the first new input value based at least in part on testing the monitored system would have been recognized by those of ordinary skill in the art as resulting in an improved system that would allow for testing to identify potential issues.
Voit does not specifically teach reducing memory usage for the rule-based system at least in part by freeing memory associated with the first ruleset.
However, Wang teaches reducing memory usage for the rule-based system at least in part by freeing memory associated with the first ruleset (¶ 12-13, 19-22, 48, 62, disclose using a reduced evaluated data set which results in reduced memory requirements.).
It would have been obvious to one of ordinary skill in the art at the time of filing to modify Voit to include/perform reducing memory usage for the rule-based system at least in part by freeing memory associated with the first ruleset, as taught/suggested by Wang. This known technique is applicable to the system of Voit as they both share characteristics and capabilities, namely, they are directed to automatic rule processing and memory management. One of ordinary skill in the art would have recognized that applying the known technique of Wang would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Wang to the teachings of Voit would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such memory reduction features into similar systems. Further, applying reducing memory usage for the rule-based system at least in part by freeing memory associated with the first ruleset would have been recognized by those of ordinary skill in the art as resulting in a more stable system.
The combination of Voit, Cheriton, and Wang does not specifically teach cost-information gain score with a reduction in information entropy.
However, Zheng teaches wherein a cost associated with the cost-information gain score is associated with a first input cost of testing the monitored system, and wherein an information gain associated with the cost- information gain score is associated with a reduction in information entropy associated with a first input state (section 1, ¶ 2 discloses a tradeoff between cost of testing and diagnostic accuracy, and entropy is used as a cost function to select tests providing maximum information, or minimum conditional entropy. Section 3, discloses minimizing both the conditional entropy and the cost of testing. Section 5, discloses setting up a monitored system as a network of computers with binary fault states, current state where entropy is reduced. Section 6, discloses diagnostic quality directly with conditional entropy. Appendix, equivalence section used the term information gain and explicitly shows that it is maximized by the same text that minimizes conditional entropy.)
It would have been obvious to one of ordinary skill in the art at the time of filing to modify Voit to include/perform cost-information gain score with a reduction in information entropy, as taught/suggested by Zheng. This known technique is applicable to the system of Voit as they both share characteristics and capabilities, namely, they are directed to fault analysis and data management. One of ordinary skill in the art would have recognized that applying the known technique of Zheng would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Zheng to the teachings of Voit would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such cost-information gain score with a reduction in information entropy features into similar systems. Further, applying cost-information gain score with a reduction in information entropy would have been recognized by those of ordinary skill in the art as resulting in a system that would bring forward data with relevance and value in decision-making and information delivery.
Claim(s) 7, 9-11, is/are rejected under 35 U.S.C. 103 as being unpatentable over Voit et al. (US 20160292581 A1) in view of Cheriton (US 20200081882 A1) in view of Wang et al. (US 20140351185 A1) in view of Zheng (2005) in further view of Konstantinou et al. (US 20050097146 A1).
Regarding claim 7, Voit teaches the limitations of claim 1, but does not specifically teach wherein selecting the first input is based at least in part on expected information gained from determining the first input value.
However, Konstantinou teaches wherein selecting the first input is based at least in part on expected information gained from determining the first input value (¶ 224-228, discloses evaluating an expression over an element input value, ¶ 267-268).
It would have been obvious to one of ordinary skill in the art at the time of filing to modify Voit to include/perform wherein selecting the first input is based at least in part on expected information gained from determining the first input value, as taught/suggested by Konstantinou. This known technique is applicable to the system of Voit as they both share characteristics and capabilities, namely, they are directed to managing data sets. One of ordinary skill in the art would have recognized that applying the known technique of Konstantinou would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Konstantinou to the teachings of Voit would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such selecting features into similar systems. Further, applying wherein selecting the first input is based at least in part on expected information gained from determining the first input value would have been recognized by those of ordinary skill in the art as resulting in an improved system that would allow for a more specific/accurate selection of input.
Regarding claim 9, Voit teaches the limitations of claim 1, but does not specifically teach wherein selecting the first input is based at least in part on an attribute selection process.
However, Konstantinou teaches wherein selecting the first input is based at least in part on an attribute selection process (¶ 224-228, discloses evaluating an expression over an element input value, ¶ 123, a class attribute relationship, ¶ 148-149, 111-118, 224).
It would have been obvious to one of ordinary skill in the art at the time of filing to modify Voit to include/perform wherein selecting the first input is based at least in part on an attribute selection process, as taught/suggested by Konstantinou. This known technique is applicable to the system of Voit as they both share characteristics and capabilities, namely, they are directed to managing data sets. One of ordinary skill in the art would have recognized that applying the known technique of Konstantinou would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Konstantinou to the teachings of Voit would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such selecting features into similar systems. Further, applying wherein selecting the first input is based at least in part on an attribute selection process would have been recognized by those of ordinary skill in the art as resulting in an improved system that would allow for the defined selection of input.
Regarding claim 10, Voit teaches the limitations of claim 1, but does not specifically teach wherein selecting the first input is based at least in part on an RBDT-1 process.
However, Konstantinou teaches wherein selecting the first input is based at least in part on an RBDT-1 process (¶ 4-7, discloses behavior programs, ¶ 25-26, discloses behavior events, ¶ 36, 48, 226).
It would have been obvious to one of ordinary skill in the art at the time of filing to modify Voit to include/perform wherein selecting the first input is based at least in part on an RBDT-1 process, as taught/suggested by Konstantinou. This known technique is applicable to the system of Voit as they both share characteristics and capabilities, namely, they are directed to managing data sets. One of ordinary skill in the art would have recognized that applying the known technique of Konstantinou would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Konstantinou to the teachings of Voit would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such selecting features into similar systems. Further, applying wherein selecting the first input is based at least in part on an RBDT-1 process would have been recognized by those of ordinary skill in the art as resulting in an improved system that would allow for the leveraging of consistent knowledge.
Regarding claim 11, Voit teaches the limitations of claim 1, but does not specifically teach wherein selecting the first input is sensitive to a scenario in which rules are being evaluated.
However, Konstantinou teaches wherein selecting the first input is sensitive to a scenario in which rules are being evaluated (¶ 352, choosing for evaluation, ¶ 25-26, discloses behavior events, ¶ 303-310, 343).
It would have been obvious to one of ordinary skill in the art at the time of filing to modify Voit to include/perform wherein selecting the first input is sensitive to a scenario in which rules are being evaluated, as taught/suggested by Konstantinou. This known technique is applicable to the system of Voit as they both share characteristics and capabilities, namely, they are directed to managing data sets. One of ordinary skill in the art would have recognized that applying the known technique of Konstantinou would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Konstantinou to the teachings of Voit would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such selecting features into similar systems. Further, applying wherein selecting the first input is sensitive to a scenario in which rules are being evaluated would have been recognized by those of ordinary skill in the art as resulting in an improved system that would allow for analysis of sensitive information.
Claim(s) 15-19, is/are rejected under 35 U.S.C. 103 as being unpatentable over Voit et al. (US 20160292581 A1) in view of Cheriton (US 20200081882 A1) in view of Wang et al. (US 20140351185 A1) in view of Zheng (2005) in further view of Cheriton2 (US 20200264900 A1).
Regarding claim 15, Voit teaches the limitations of claim 1, but does not specifically teach a CRCT.
However, Cheriton2 teaches wherein the first ruleset is represented by a conjunctive rule condition table (CRCT), (¶ 155-161, 132-133, 137, 193-195, all disclose RCT): wherein each rule condition in the first ruleset is represented a conjunction of subconditions (¶ 155-161, 132-133, 137, 193-195, all disclose RCT); each rule is represented by a row in the CRCT (¶ 125-0128, 155]-161, 193-195); each subcondition occurring in a rule condition in the first ruleset is represented by a column in the CRCT (¶ 155-161. 193-195, 116).
It would have been obvious to one of ordinary skill in the art at the time of filing to modify Voit to include/perform the use of a CRCT, as taught/suggested by Cheriton2. This known technique is applicable to the system of Voit as they both share characteristics and capabilities, namely, they are directed to rule based control. One of ordinary skill in the art would have recognized that applying the known technique of Cheriton2 would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Cheriton2 to the teachings of Voit would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such ruleset features into similar systems. Further, applying the use of CRCT would have been recognized by those of ordinary skill in the art as resulting in an improved system that would allow for defined conditions.
Regarding claim 16, Voit teaches the limitations of claim 15, but does not specifically teach a CRCT.
However, Cheriton2 teaches wherein an entry in the CRCT may be a "don't care" value indicating that the entry matches for any input (¶ 115-117, teaches “don’t care” value, ¶ 132, 133, 143, 329).
It would have been obvious to one of ordinary skill in the art at the time of filing to modify Voit to include/perform the use of a CRCT, as taught/suggested by Cheriton2. This known technique is applicable to the system of Voit as they both share characteristics and capabilities, namely, they are directed to rule based control. One of ordinary skill in the art would have recognized that applying the known technique of Cheriton2 would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Cheriton2 to the teachings of Voit would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such ruleset features into similar systems. Further, applying the use of CRCT would have been recognized by those of ordinary skill in the art as resulting in an improved system that would allow for defined conditions.
Regarding claim 17, Voit teaches the limitations of claim 1, but does not specifically teach wherein the first ruleset is applied to root cause identification.
However, Cheriton2 teaches wherein the first ruleset is applied to root cause identification (¶ 132, teaches root cause, ¶ 114-117, 140-142, 189).
It would have been obvious to one of ordinary skill in the art at the time of filing to modify Voit to include/perform wherein the first ruleset is applied to root cause identification, as taught/suggested by Cheriton2. This known technique is applicable to the system of Voit as they both share characteristics and capabilities, namely, they are directed to rule based control. One of ordinary skill in the art would have recognized that applying the known technique of Cheriton2 would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Cheriton2 to the teachings of Voit would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such ruleset features into similar systems. Further, applying wherein the first ruleset is applied to root cause identification would have been recognized by those of ordinary skill in the art as resulting in an improved system that would improve consistently.
Regarding claim 18, Voit teaches the limitations of claim 1, but does not specifically teach wherein the first ruleset is applied to medical diagnosis.
However, Cheriton2 teaches wherein the first ruleset is applied to medical diagnosis (¶ 2, 29, discloses medical application, ¶ 44, 47, 70, 381).
It would have been obvious to one of ordinary skill in the art at the time of filing to modify Voit to include/perform wherein the first ruleset is applied to medical diagnosis, as taught/suggested by Cheriton2. This known technique is applicable to the system of Voit as they both share characteristics and capabilities, namely, they are directed to rule based control. One of ordinary skill in the art would have recognized that applying the known technique of Cheriton2 would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Cheriton2 to the teachings of Voit would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such ruleset features into similar systems. Further, applying wherein the first ruleset is applied to medical diagnosis would have been recognized by those of ordinary skill in the art as resulting in an improved system that would identify a specific application of the invention.
Regarding claim 19, Voit teaches the limitations of claim 1, but does not specifically teach wherein the first ruleset is applied to medical diagnosis.
However, Cheriton2 teaches wherein the first ruleset is applied to investment decision making (¶ 29, discloses a financial application, ¶ 39, 43, 45, 51 66-67).
It would have been obvious to one of ordinary skill in the art at the time of filing to modify Voit to include/perform wherein the first ruleset is applied to investment decision making, as taught/suggested by Cheriton2. This known technique is applicable to the system of Voit as they both share characteristics and capabilities, namely, they are directed to rule based control. One of ordinary skill in the art would have recognized that applying the known technique of Cheriton2 would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Cheriton2 to the teachings of Voit would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such ruleset features into similar systems. Further, applying wherein the first ruleset is applied to investment decision making would have been recognized by those of ordinary skill in the art as resulting in an improved system that would identify a specific application of the invention.
Other pertinent prior art includes Hodjat et al. (US 20170293849 A1) which discloses the use of genetic algorithms to extract useful rules or relationships from a data set for use in controlling systems. Wagstaff et al. (US 20180212830 A1) which discloses the use of uploaded rulesets. Ghasemzadeh et al. (US 9754081 B2) discloses information gain as an expected reduction in entropy.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JAMIE H AUSTIN whose telephone number is (571)272-7363. The examiner can normally be reached Monday, Tuesday, Thursday, Friday 7am-2pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Brian Epstein can be reached at (571) 270 5389. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
JAMIE H. AUSTIN
Examiner
Art Unit 3625
/JAMIE H AUSTIN/Primary Examiner, Art Unit 3625
Prosecution Insights