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 .
Response to Arguments
Applicant’s arguments, see pg. 6, filed 5-20-2026, with respect to double patenting have been fully considered and are persuasive. The rejection has been held in abeyance.
Applicant’s arguments, see pgs 11-15, filed 5-20-2026, with respect to the 102 and 103 rejection(s) have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Maier et al (US 5408587)
Applicant's arguments with respect to 101 rejection have been fully considered but they are not persuasive.
In re pgs. 7-8, applicant argues
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In response, the Examiner respectfully disagrees.
recognizing objects in high level is an observation, evaluation, judgment, opinion mental process which can reasonably be performed in one’s mind with the aid of pencil and paper. The attribute is not defined in any shape or form. Such attribute can be anything compose in plain text.
hypothesis generation/establishing in high level is an observation, evaluation, judgment, opinion mental process which can reasonably be performed in one’s mind with the aid of pencil and paper. Again, the attribute is not defined in any shape or form. Such attribute can be anything compose in plain text. As such, the hypothesis generation/establishing can be reasonably be performed in one’s mind with the aid of pencil and paper.
In re pgs. 8-9, applicant argues
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In response, the Examiner respectfully disagrees.
Those are additional elements, not the mental process.
In re pgs. 9-10, applicant argues
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In response, the Examiner respectfully disagrees.
A data interface and a computing device to render an output are mere generic components.
There is no specific architecture in the claim that is similar to Enfish; thus are non-analogous.
In re pg. 10, applicant argues
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In response, the Examiner respectfully disagrees.
As set forth in the rejection, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The judicial exceptions are not integrated into a practical application, even considered as an order combination.
In re pg. 11, applicant argues
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Applicant’s arguments have been fully considered and are persuasive. The rejection has been withdrawn.
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.
Claim 53-74 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim 53: Step 1: the claim is directed to statuary category.
Step 2A Prong 1: The claim recites the following limitations:
recognize target data objects from the aspects of the environment, each target data object having object attributes (recognizing objects in high level is an observation, evaluation, judgment, opinion mental process which can reasonably be performed in one’s mind with the aid of pencil and paper);
generate at least one hypothesis according to at least one reasoning rule set operating based on the environment data or object attributes of the target data objects, the at least one hypothesis representing a suspected correlation among the recognized target data objects (hypothesis generation/establishing in high level is an observation, evaluation, judgment, opinion mental process which can reasonably be performed in one’s mind with the aid of pencil and paper);
The claim recites an abstract idea.
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application. The claim recites the following additional elements:
53. A computer-based reasoning system comprising:
a data interface configured to acquire environment data representing aspects of an environment (amounts to mere data gathering, an insignificant extra-solution activity as discussed in MPEP 2106.05(g)), and at least one inference engine having at least one processor and at least one computer readable non-transitory memory storing software instructions, the at least one inference engine coupled with the data interface and that performs the following operations upon execution of the software instructions (applying it as discussed in MPEP 2106.05(f)):
receive an inquiry relating to the aspects of the environment (amounts to mere data gathering, an insignificant extra-solution activity as discussed in MPEP 2106.05(g));
cause a computing device to render an output related to the at least one hypothesis and reasoning steps taken according to the at least one reasoning rule set that generated the output related to the at least one hypothesis (amounts to mere insignificant application, an insignificant extra-solution activity as discussed in MPEP 2106.05(g)).
Accordingly, the additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The judicial exceptions are not integrated into a practical application.
53. A computer-based reasoning system comprising:
a data interface configured to acquire environment data representing aspects of an environment (amounts to mere data gathering, an insignificant extra-solution activity as discussed in MPEP 2106.05(g)), which is well understood, routine and convention activity of receiving or gathering data as identified by the court in MPEP 2106.05(d)); and at least one inference engine having at least one processor and at least one computer readable non-transitory memory storing software instructions, the at least one inference engine coupled with the data interface and that performs the following operations upon execution of the software instructions (applying it as discussed in MPEP 2106.05(f)):
receive an inquiry relating to the aspects of the environment (amounts to mere data gathering, an insignificant extra-solution activity as discussed in MPEP 2106.05(g), which is well understood, routine and convention activity of receiving or gathering data as identified by the court in MPEP 2106.05(d);
cause a computing device to render an output comprising the at least one hypothesis and a presentation of reasoning steps taken according to the at least one reasoning rule set that generated the at least one hypothesis, the presentation of reasoning steps identifying at least one of: a type of reasoning applied, a correlation made among the recognized target data objects, or an inference drawn among the recognized target data objects (amounts to mere insignificant application, an insignificant extra-solution activity as discussed in MPEP 2106.05(g), which is extra-solution activity of well, understood routine and conventional operation of presentation of offer or statistics under MPEP 2106.05(d)).
The claim is not patent eligible.
Claim 54: Step 1: the claim is directed to statuary category.
Step 2A Prong 1: The claim recites the abstract idea of parent claim.
54. (New) The system of claim 53, wherein the reasoning steps include mapping to a known concept (concept mapping in high level is an observation, evaluation, judgment, opinion mental process which can reasonably be performed in one’s mind with the aid of pencil and paper);
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application.
The claim recites no additional element:
Step 2B: As shown above, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The judicial exceptions are not integrated into a practical application.
The claim is not patent eligible.
Claim 55: Step 1: the claim is directed to statuary category.
Step 2A Prong 1: The claim recites the abstract idea of parent claim.
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application.
The claim recites additional element(s):
55. (New) The system of claim 53, wherein the reasoning steps include a type of reasoning (amounts to generally linking the abstract ideas to the technological environment or field of use as discussed in in MPEP 2106.05(h)).
Step 2B: As shown above, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The judicial exceptions are not integrated into a practical application.
The claim is not patent eligible.
Claim 56: Step 1: the claim is directed to statuary category.
Step 2A Prong 1: The claim recites the abstract idea of parent claim.
56. (New) The system of claim 53, wherein the reasoning steps include a correlation made among target data objects (correlation making in high level is an observation, evaluation, judgment, opinion mental process which can reasonably be performed in one’s mind with the aid of pencil and paper);
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application.
The claim recites no additional element:
Step 2B: As shown above, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The judicial exceptions are not integrated into a practical application.
The claim is not patent eligible.
Claim 57: Step 1: the claim is directed to statuary category.
Step 2A Prong 1: The claim recites the abstract idea of parent claim.
57. (New) The system of claim 53, wherein the reasoning steps include inferences among target data objects (inference in high level is an observation, evaluation, judgment, opinion mental process which can reasonably be performed in one’s mind with the aid of pencil and paper);
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application.
The claim recites no additional element:
Step 2B: As shown above, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The judicial exceptions are not integrated into a practical application.
The claim is not patent eligible.
Claim 58: Step 1: the claim is directed to statuary category.
Step 2A Prong 1: The claim recites the abstract idea of parent claim.
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application.
The claim recites additional element(s):
58. (New) The system of claim 53, wherein the output comprises ranked hypotheses (amounts to mere insignificant application, an insignificant extra-solution activity as discussed in MPEP 2106.05(g), which is extra-solution activity of well, understood routine and conventional operation of presentation of offer or statistics under MPEP 2106.05(d)).
Step 2B: As shown above, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The judicial exceptions are not integrated into a practical application.
The claim is not patent eligible.
Claim 59: Step 1: the claim is directed to statuary category.
Step 2A Prong 1: The claim recites the abstract idea of parent claim.
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application.
The claim recites additional element(s):
59. (New) The system of claim 58, wherein the ranked hypotheses are based on merit scores assigned to individual hypotheses (amounts to generally linking the abstract ideas to the technological environment or field of use as discussed in in MPEP 2106.05(h)).
Step 2B: As shown above, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The judicial exceptions are not integrated into a practical application.
The claim is not patent eligible.
Claim 60: Step 1: the claim is directed to statuary category.
Step 2A Prong 1: The claim recites the abstract idea of parent claim.
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application.
The claim recites additional element(s):
60. (New) The system of claim 59, wherein the merit scores are multi- dimensional, representing different aspects of relevance (amounts to generally linking the abstract ideas to the technological environment or field of use as discussed in in MPEP 2106.05(h)).
Step 2B: As shown above, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The judicial exceptions are not integrated into a practical application.
The claim is not patent eligible.
Claim 61: Step 1: the claim is directed to statuary category.
Step 2A Prong 1: The claim recites the abstract idea of parent claim.
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application.
The claim recites additional element(s):
61. (New) The system of claim 53, wherein the output includes the reasoning steps presented in a browser on the computing device (amounts to mere insignificant application, an insignificant extra-solution activity as discussed in MPEP 2106.05(g), which is extra-solution activity of well, understood routine and conventional operation of presentation of offer or statistics under MPEP 2106.05(d)).
Step 2B: As shown above, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The judicial exceptions are not integrated into a practical application.
The claim is not patent eligible.
Claim 62: Step 1: the claim is directed to statuary category.
Step 2A Prong 1: The claim recites the abstract idea of parent claim.
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application.
The claim recites additional element(s):
62. (New) The system of claim 53, wherein the reasoning steps comprise a mapping to a namespace (amounts to generally linking the abstract ideas to the technological environment or field of use as discussed in in MPEP 2106.05(h)).
Step 2B: As shown above, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The judicial exceptions are not integrated into a practical application.
The claim is not patent eligible.
Claim 63: Step 1: the claim is directed to statuary category.
Step 2A Prong 1: The claim recites the abstract idea of parent claim.
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application.
The claim recites additional element(s):
63. (New) The system of claim 53, wherein the least one inference engine interacts with the environment via the data interface (amounts to generally linking the abstract ideas to the technological environment or field of use as discussed in in MPEP 2106.05(h)).
Step 2B: As shown above, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The judicial exceptions are not integrated into a practical application.
The claim is not patent eligible.
Claim 64: Step 1: the claim is directed to statuary category.
Step 2A Prong 1: The claim recites the abstract idea of parent claim.
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application.
The claim recites additional element(s):
64. (New) The system of claim 63, wherein the data interface includes at least one of the following: a mobile device, a sensor, a router, a switch, an appliance, anon-mobile device, or a garment (amounts to generally linking the abstract ideas to the technological environment or field of use as discussed in in MPEP 2106.05(h)).
Step 2B: As shown above, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The judicial exceptions are not integrated into a practical application.
The claim is not patent eligible.
Claim 65: Step 1: the claim is directed to statuary category.
Step 2A Prong 1: The claim recites the abstract idea of parent claim.
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application.
The claim recites additional element(s):
65. (New) The system of claim 63, wherein the data interface comprises an application program interface (API) (amounts to generally linking the abstract ideas to the technological environment or field of use as discussed in in MPEP 2106.05(h)).
Step 2B: As shown above, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The judicial exceptions are not integrated into a practical application.
The claim is not patent eligible.
Claim 66: Step 1: the claim is directed to statuary category.
Step 2A Prong 1: The claim recites the abstract idea of parent claim.
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application.
The claim recites additional element(s):
66. (New) The system of claim 53, wherein the at least one reasoning rule set includes deductive reasoning (amounts to generally linking the abstract ideas to the technological environment or field of use as discussed in in MPEP 2106.05(h)).
Step 2B: As shown above, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The judicial exceptions are not integrated into a practical application.
The claim is not patent eligible.
Claim 67: Step 1: the claim is directed to statuary category.
Step 2A Prong 1: The claim recites the abstract idea of parent claim.
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application.
The claim recites additional element(s):
67. (New) The system of claim 53, wherein the at least one reasoning rule set includes abductive reasoning (amounts to generally linking the abstract ideas to the technological environment or field of use as discussed in in MPEP 2106.05(h)).
Step 2B: As shown above, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The judicial exceptions are not integrated into a practical application.
The claim is not patent eligible.
Claim 68: Step 1: the claim is directed to statuary category.
Step 2A Prong 1: The claim recites the abstract idea of parent claim.
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application.
The claim recites additional element(s):
68. (New) The system of claim 53, wherein the operations further include conducting reflexive reasoning and the reasoning steps include the reflexive reasoning (amounts to generally linking the abstract ideas to the technological environment or field of use as discussed in in MPEP 2106.05(h)).
Step 2B: As shown above, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The judicial exceptions are not integrated into a practical application.
The claim is not patent eligible.
Claim 69: Step 1: the claim is directed to statuary category.
Step 2A Prong 1: The claim recites the abstract idea of parent claim.
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application.
The claim recites additional element(s):
69. (New) The system of claim 53, wherein at least one reasoning rule set comprises inductive reasoning (amounts to generally linking the abstract ideas to the technological environment or field of use as discussed in in MPEP 2106.05(h)).
Step 2B: As shown above, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The judicial exceptions are not integrated into a practical application.
The claim is not patent eligible.
Claim 70: Step 1: the claim is directed to statuary category.
Step 2A Prong 1: The claim recites the abstract idea of parent claim.
construct a validation plan for the at least one hypothesis (validation plan construction in high level is an observation, evaluation, judgment, opinion mental process which can reasonably be performed in one’s mind with the aid of pencil and paper); and
derive a merit score for the at least one hypothesis based on the validation data (merit score derivation/calculation in high level is an observation, evaluation, judgment, opinion mental process which can reasonably be performed in one’s mind with the aid of pencil and paper);
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application.
The claim recites additional element(s):
70. (New) The system of claim 53, further comprising a validation module coupled with the data interface and that performs the following validation operations upon execution of the software instructions (amounts to mere data gathering, an insignificant extra-solution activity as discussed in MPEP 2106.05(g), which is well understood, routine and convention activity of receiving or gathering data as identified by the court in MPEP 2106.05(d):
acquire validation data according to the validation plan (amounts to mere data gathering, an insignificant extra-solution activity as discussed in MPEP 2106.05(g), which is well understood, routine and convention activity of receiving or gathering data as identified by the court in MPEP 2106.05(d)).
Step 2B: As shown above, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The judicial exceptions are not integrated into a practical application.
The claim is not patent eligible.
Claim 71: Step 1: the claim is directed to statuary category.
Step 2A Prong 1: The claim recites the abstract idea of parent claim.
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application.
The claim recites additional element(s):
71. (New) The system of claim 70, wherein the output further includes the validation plan and the merit score (amounts to mere insignificant application, an insignificant extra-solution activity as discussed in MPEP 2106.05(g), which is extra-solution activity of well, understood routine and conventional operation of presentation of offer or statistics under MPEP 2106.05(d)).
Step 2B: As shown above, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The judicial exceptions are not integrated into a practical application.
The claim is not patent eligible.
Claim 72: Step 1: the claim is directed to statuary category.
Step 2A Prong 1: The claim recites the abstract idea of parent claim.
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application.
The claim recites additional element(s):
72. (New) The system of claim 53, wherein the inference engine is a member of a multi-layered set of inference engines (amounts to generally linking the abstract ideas to the technological environment or field of use as discussed in in MPEP 2106.05(h)).
Step 2B: As shown above, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The judicial exceptions are not integrated into a practical application.
The claim is not patent eligible.
Claim 73 is method claim having similar limitation as claim 53 and is rejected under the same rationale.
Claim 74 is non-transitory computer readable medium claims having similar limitation as claim 53 and is rejected under the same rationale. The additional elements in claim 74 is A non-transitory computer-readable medium comprising computer- readable instructions thereon, which, when executed by a processor, configure the processor as a computer-based reasoning system operable to (amounts to performing generic function of execution of stored instructions (MPEP 2106.05(f)). Accordingly, the additional elements do not integrate the abstract into practical application and are not sufficient to amount to significant more than the abstract idea. Therefore, the claims are an abstract idea.
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.
Claim(s) 53-61, 63-65, 67, 69-74 is/are rejected under 35 U.S.C. 103 as being unpatentable over Talbot et al (US 2006/0112048 A1) in view of Maier et al (US 5408587)
53. Talbot disclose A computer-based reasoning system comprising:
a data interface configured to acquire environment data representing aspects of an environment (See Fig. 4-118 on information extraction. [0024] In the illustrated example, evidence and stories can be input into the knowledge base 114 in a number of ways. For example, an information extraction component 118 can be used to reduce an evidence source, such as a text document or a transcripted conversation, into a desired evidence format. This evidence can be linked with existing stories in the knowledge base or new stories can be assembled in response to the evidence. The information extraction component 118 breaks down a input text segment into individual words or phrases, interprets the context and meaning of the various words or phrases, and uses the extracted information to generate a template representing the text segment. For example, the information extraction component 118 can look for details relating to an event described in the document, such as the nature of the event, the cause or motivation for the event, the mechanism of the event, the identity of an actor, the location of the event, the time or date of the event, and the magnitude of the event. Each of these details can be added to a template related to the text segment. In accordance with one aspect of the invention, the information extraction component 118 can look for hedge words (e.g., maybe, probably, certainly, never) within the text segment. The information extraction component 118 can use a co-referencing routine to determine what nouns relate to a given hedge word, and use this information to determine the weight of the evidence associated with the template, in the form of belief values and disbelief values. Examiner Note: event information (e.g. evidence and stories) that are inputted/acquired/extracted are environment data representing aspects of an environment. The details of an event reads on environment data representing aspects of an environment); and
at least one inference engine having at least one processor and at least one computer readable non-transitory memory storing software instructions, the at least one inference engine coupled with the data interface and that performs the following operations upon execution of the software instructions (See Fig. 4-102-107. [0027] The plurality of inferencing algorithms 102-107 can include any of a variety of appropriate algorithms for evaluating stored data to determine significant patterns and trends. Specifically, the inferencing algorithms 102-107 search the knowledge base for unanticipated story fragments, comprising at least a single new hypothesis, and more typically of story fragments comprising linked hypotheses and any associated evidence. In the illustrated example, the plurality of inferencing algorithms include an inductive reasoner 102 that computes changes in rules based on new evidence within the knowledge base. The change is based on rule induction, using the old evidence supporting the old structure along with exceptions to the old rules in the existing evidence to induce new rules that better account for the entire body of old and the new evidence. In essence, new rules defining a revised network structure are computed from an aggregate of old and new evidence. In one implementation, the Weka algorithm can be utilized within the inductive reasoner 102. [0046] The computer system 300 can include a hard disk drive 314, a magnetic disk drive 316, e.g., to read from or write to a removable disk 318, and an optical disk drive 320, e.g., for reading a CD-ROM or DVD disk 322 or to read from or write to other optical media. The hard disk drive 314, magnetic disk drive 316, and optical disk drive 320 are connected to the system bus 306 by a hard disk drive interface 324, a magnetic disk drive interface 326, and an optical drive interface 334, respectively. The drives and their associated computer-readable media provide nonvolatile storage of data, data structures, and computer-executable instructions for the computer system 300. Although the description of computer-readable media above refers to a hard disk, a removable magnetic disk and a CD, other types of media which are readable by a computer, may also be used. For example, computer executable instructions for implementing systems and methods described herein may also be stored in magnetic cassettes, flash memory cards, digital video disks and the like. Examiner Note: See Fig. 4 on module couplings):
receive an inquiry relating to the aspects of the environment ([0017] FIG. 2 illustrates a functional block diagram of an artificial intelligence system 20 comprising an assisted decision making system 22 utilizing automated discovery of unknown unknowns. The assisted decision making system 22 includes at least one associated story of interest 24. In accordance with an aspect of the invention, a story of interest comprises an executable belief network augmented by one or more characteristics of the hypotheses comprising the belief network, the evidence, and the content from which the evidence was extracted. For example, the characteristics of a given hypothesis can include the answers to the so-called "reporter's questions" for items of evidence supporting the hypothesis (e.g., the source of the evidence, an associated location, an associated time of occurrence, an associated actor, etc.). It will be appreciated that each story of interest will relate to a question of interest to a decision maker utilizing the assisted decision making system 22. [0018] The assisted decision making system 22 further comprises an arbitrator 26 that controls the flow of new data to the assisted decision making system. Specifically, the arbitrator 26 reviews story fragments provided by a plurality of inferencing algorithms 28 and 30 to determine if any of the fragments are sufficiently relevant to the at least one story of interest 22 as to warrant its consideration at the decision making system 22. For example, a given story fragment can be compared to a story of interest to determine to what degree the hypotheses within the story fragment and their associated characteristics resemble those of the hypotheses comprising the story of interest. In an exemplary embodiment, the arbitrator 26 can also evaluate the relatedness of multiple story fragments and combine related fragments prior to applying them to a story of interest. Examiner Note: each story fragment is an inquiry related to story of interest and the inquiry is the question);
recognize target data objects from the aspects of the environment, each target data object having object attributes ([0018] The assisted decision making system 22 further comprises an arbitrator 26 that controls the flow of new data to the assisted decision making system. Specifically, the arbitrator 26 reviews story fragments provided by a plurality of inferencing algorithms 28 and 30 to determine if any of the fragments are sufficiently relevant to the at least one story of interest 22 as to warrant its consideration at the decision making system 22. For example, a given story fragment can be compared to a story of interest to determine to what degree the hypotheses within the story fragment and their associated characteristics resemble those of the hypotheses comprising the story of interest. In an exemplary embodiment, the arbitrator 26 can also evaluate the relatedness of multiple story fragments and combine related fragments prior to applying them to a story of interest. For example, items of evidence and hypotheses provided from a first inferencing algorithm (e.g., 28) that support a hypothesis provided from a second inferencing algorithm (e.g., 30) can be linked with that hypothesis to provide a larger, more complete story fragment; [0023] The plurality of inferencing systems 102-107 utilize data from an associated knowledge base 114 and, optionally, external sources 115. It will be appreciated that the stored data within the knowledge base can include current instantiations of stories of interest 116 and 117 associated with the assisted decision making systems 110 and 112. The external sources can include general knowledge bases such as Cyc or WordNET. In accordance with an aspect of the present invention, the knowledge base 114 comprises a plurality of stories, where each story comprises an executable belief network comprising at least one hypothesis, evidence supporting the at least one hypothesis, and a reference (e.g., a pointer) to the context from which the evidence was gathered. Each story is executable, such that it can produce mathematically consistent results in response to any change in its associated evidence, belief values, or weights. Accordingly, the stories can be updated and propagated to multiple decision algorithms in real time, allowing for a flexible exchange between a large number of decision algorithms or analysts. Examiner Note: each story is a target object; the evidence and reference are attributes);
generate at least one hypothesis according to at least one reasoning rule set operating based on the environment data or object attributes of the target data objects, the at least one hypothesis representing a suspected correlation among the recognized target data objects ([0017] FIG. 2 illustrates a functional block diagram of an artificial intelligence system 20 comprising an assisted decision making system 22 utilizing automated discovery of unknown unknowns. The assisted decision making system 22 includes at least one associated story of interest 24. In accordance with an aspect of the invention, a story of interest comprises an executable belief network augmented by one or more characteristics of the hypotheses comprising the belief network, the evidence, and the content from which the evidence was extracted. For example, the characteristics of a given hypothesis can include the answers to the so-called "reporter's questions" for items of evidence supporting the hypothesis (e.g., the source of the evidence, an associated location, an associated time of occurrence, an associated actor, etc.). It will be appreciated that each story of interest will relate to a question of interest to a decision maker utilizing the assisted decision making system 22. [0018] The assisted decision making system 22 further comprises an arbitrator 26 that controls the flow of new data to the assisted decision making system. Specifically, the arbitrator 26 reviews story fragments provided by a plurality of inferencing algorithms 28 and 30 to determine if any of the fragments are sufficiently relevant to the at least one story of interest 22 as to warrant its consideration at the decision making system 22. For example, a given story fragment can be compared to a story of interest to determine to what degree the hypotheses within the story fragment and their associated characteristics resemble those of the hypotheses comprising the story of interest. In an exemplary embodiment, the arbitrator 26 can also evaluate the relatedness of multiple story fragments and combine related fragments prior to applying them to a story of interest. For example, items of evidence and hypotheses provided from a first inferencing algorithm (e.g., 28) that support a hypothesis provided from a second inferencing algorithm (e.g., 30) can be linked with that hypothesis to provide a larger, more complete story fragment. Examiner Note: degree of resemblance represents a suspected correlation among the recognized target data objects); and
cause a computing device to render an output comprising the at least one hypothesis and a presentation of reasoning steps taken according to the at least one reasoning rule set that generated the at least one hypothesis, (Fig. 4-136, 138. [0032] The arbitrators 126 and 128 evaluate the story fragments provided by the plurality of decision algorithms 102-107 to determine if a given story fragment is relevant to respective sets of one or more stories of interest 116 and 117. Specifically, an arbitrator (e.g., 126) examines each story fragment and determines if it is sufficiently related to any of its associated stories of interest (e.g., 116) to warrant inclusion of the story fragment in the related story of interest. For example, the arbitrators 126 and 128 can compare the hypotheses, evidence, and links within a story fragment and its associated characteristics with the hypotheses and associated characteristics of a given story of interest. It will be appreciated that the arbitrators 126 and 128 can evaluate the story fragments individually or combine related story fragments from multiple inferencing algorithms to provide a more complete story fragment for evaluation. [0033] Where a threshold level of similarity is found, the story fragment can be provided to human analysts through associated user interfaces 136 and 138. A user interface (e.g., 136) can include a graphical user interface that allows the analyst to quickly review the pertinent portions of the story fragment and determine its relationship to the story of interest. If the human analyst agrees that the story fragment is relevant to the story of interest, he or she can add information to the story fragment and incorporate the story fragment into the story of interest. A fusion engine 140 can mathematically reconcile the story of interest in light of the added story fragments to allow the analyst to see the impact of the story fragment. [0034] FIG. 5 illustrates a functional block diagram of an exemplary graphic user interface (GUI) 150 that can be utilized in an assisted decision making system in accordance with the present invention. The GUI 150 includes an editor 152 that allows a user to view and edit a structure argument. The editor 152 has four associated high-level functions, an editing function 154, a visualization function 156, a belief propagation function 158, and an administrative function 160. [0027] The plurality of inferencing algorithms 102-107 can include any of a variety of appropriate algorithms for evaluating stored data to determine significant patterns and trends. Specifically, the inferencing algorithms 102-107 search the knowledge base for unanticipated story fragments, comprising at least a single new hypothesis, and more typically of story fragments comprising linked hypotheses and any associated evidence. In the illustrated example, the plurality of inferencing algorithms include an inductive reasoner 102 that computes changes in rules based on new evidence within the knowledge base. The change is based on rule induction, using the old evidence supporting the old structure along with exceptions to the old rules in the existing evidence to induce new rules that better account for the entire body of old and the new evidence. In essence, new rules defining a revised network structure are computed from an aggregate of old and new evidence. In one implementation, the Weka algorithm can be utilized within the inductive reasoner 102. [0019] Each of the plurality of inferencing algorithms 28 and 30 utilize data, including formatted evidence, executable stories, and story fragments from an associated knowledge base 32 to produce story fragments. It will be appreciated that the inferencing algorithms 28 and 30 are not limited to retrieving data from the knowledge base, but can also access data from one or more external sources (e.g., Cyc, WordNET, and similar knowledge bases). In one implementation, the plurality of inferencing algorithms 28 and 30 can include an abductive reasoning algorithm that compiles the best explanation for a body of data in the form of a rule tree. Similarly, the plurality of decision algorithms 24 and 26 can comprise an unsupervised clustering algorithm that attempts to form clusters from the data to determine new hypotheses for the decision making system 22. Examiner Note: reasoning rule sets is not further defined, reads on any inferencing algorithms, such as inductive reasoner 102 that uses induction rule; abductive reasoner 105 that form a rule tree).
Talbot fails to disclose the presentation of reasoning steps identifying at least one of: a type of reasoning applied, a correlation made among the recognized target data objects, or an inference drawn among the recognized target data objects.
However, Maier disclose inference engine and process (thereby in the same field of endeavor) and explicitly disclose the presentation of reasoning steps identifying at least one of: a type of reasoning applied, a correlation made among the recognized target data objects, or an inference drawn among the recognized target data objects (C3L50-65) (2) The structure of the apparatus used in this invention is depicted in FIG. 1. It comprises an input 10 in which the known facts are input, an inference engine 20 in which the facts are analyzed and inferences drawn from the facts, a rule base 25 containing rules used to analyze the facts and an explanation system 30 which provides explanations about the reasoning process. The inference engine 20 can use any type of inference mechanism; in the preferred embodiment a forward-chaining mechanism is used. The rule base 25 and the explanation system 30 are both connected to an output 40. The output 40 can be any type of output device capable of displaying text such as a printer or a display screen.)
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the inference display/output Talbot to incorporate reasoning explanation of Maier ((6) Reason explanations give the background knowledge which forms the basis for the reasoning process. They give explanations about why something happens. An example of a reason explanation is "Block A contains important information about the problem, therefore it makes sense to look there for background information". Inference explanations on the other hand are not taken from the explanation table 60 but are derived from the rule base itself as outlined below. This type of explanation may vary during every reasoning process as they comprise concrete statements which are derived during the reasoning process from the input facts. Inference explanations inform the user about how the function of the rule class had been fulfilled and what results have been achieved. An example of an inference explanation is: "As the patient has a temperature, runny nose and a headache, it is concluded that the patient has influenza").
Given the fact that providing explanation is useful, one having ordinary skill in the art would have been motivated to make this obvious modification.
54. Talbot disclose The system of claim 53, wherein the reasoning steps include mapping to a known concept ([0015] FIG. 1 illustrates a number of categories of knowledge considered by decision makers in the form of a knowledge pyramid 10. At the peak 12 of the knowledge pyramid 10 is the knowledge that is both known to the decision maker and effectively incorporated into a story of interest, referred to as "known knowns." Put simply, known knowns are information that an organization is aware of and is utilizing effectively. On the next level 14 of the pyramid are "unknown knowns". An unknown known is an available item of information that has not been incorporated into a story of interest because its relationship to the story has not yet been realized or because the information has not been communicated to the decision maker from associated decision makers. Unknown knowns are information that an organization knows but has not yet effectively utilized. [0016] On a third level 16 of the pyramid is the information that a decision maker understands would be pertinent to a story of interest, but the information is not available. Such an item of information is referred to as a "known unknown." Known unknowns represent the information that an organization is aware that it lacks. Accordingly, the decision making process can be adapted to account for the missing data, for example, by assuming the worst possible instantiation of the missing data. The final level 18 of the pyramid represents information that a decision maker does not realize is missing from a story of interest. This information is referred to as "unknown unknowns," and represents the facts that an organization does not know it doesn't know. It will be appreciated that unknown unknowns can have a significant impact on the effectiveness of a decision maker, as it is impossible to account for their effect on the decision making process with any degree of precision).
55 Talbot disclose The system of claim 53, wherein the reasoning steps include a type of reasoning ([0027] The plurality of inferencing algorithms 102-107 can include any of a variety of appropriate algorithms for evaluating stored data to determine significant patterns and trends. Specifically, the inferencing algorithms 102-107 search the knowledge base for unanticipated story fragments, comprising at least a single new hypothesis, and more typically of story fragments comprising linked hypotheses and any associated evidence. In the illustrated example, the plurality of inferencing algorithms include an inductive reasoner 102 that computes changes in rules based on new evidence within the knowledge base. The change is based on rule induction, using the old evidence supporting the old structure along with exceptions to the old rules in the existing evidence to induce new rules that better account for the entire body of old and the new evidence. In essence, new rules defining a revised network structure are computed from an aggregate of old and new evidence. In one implementation, the Weka algorithm can be utilized within the inductive reasoner 102. [0019] Each of the plurality of inferencing algorithms 28 and 30 utilize data, including formatted evidence, executable stories, and story fragments from an associated knowledge base 32 to produce story fragments. It will be appreciated that the inferencing algorithms 28 and 30 are not limited to retrieving data from the knowledge base, but can also access data from one or more external sources (e.g., Cyc, WordNET, and similar knowledge bases). In one implementation, the plurality of inferencing algorithms 28 and 30 can include an abductive reasoning algorithm that compiles the best explanation for a body of data in the form of a rule tree. Similarly, the plurality of decision algorithms 24 and 26 can comprise an unsupervised clustering algorithm that attempts to form clusters from the data to determine new hypotheses for the decision making system 22. Examiner Note: the plurality of inference algorithms are of various types).
56. Talbot disclose The system of claim 53, wherein the reasoning steps include a correlation made among target data objects ([0018] The assisted decision making system 22 further comprises an arbitrator 26 that controls the flow of new data to the assisted decision making system. Specifically, the arbitrator 26 reviews story fragments provided by a plurality of inferencing algorithms 28 and 30 to determine if any of the fragments are sufficiently relevant to the at least one story of interest 22 as to warrant its consideration at the decision making system 22. For example, a given story fragment can be compared to a story of interest to determine to what degree the hypotheses within the story fragment and their associated characteristics resemble those of the hypotheses comprising the story of interest. In an exemplary embodiment, the arbitrator 26 can also evaluate the relatedness of multiple story fragments and combine related fragments prior to applying them to a story of interest. For example, items of evidence and hypotheses provided from a first inferencing algorithm (e.g., 28) that support a hypothesis provided from a second inferencing algorithm (e.g., 30) can be linked with that hypothesis to provide a larger, more complete story fragment. Examiner Note: degree of resemblance represents a correlation among the recognized target data objects).
57. Talbot disclose the system of claim 53, wherein the reasoning steps include inferences among target data objects ([0018] The assisted decision making system 22 further comprises an arbitrator 26 that controls the flow of new data to the assisted decision making system. Specifically, the arbitrator 26 reviews story fragments provided by a plurality of inferencing algorithms 28 and 30 to determine if any of the fragments are sufficiently relevant to the at least one story of interest 22 as to warrant its consideration at the decision making system 22. For example, a given story fragment can be compared to a story of interest to determine to what degree the hypotheses within the story fragment and their associated characteristics resemble those of the hypotheses comprising the story of interest. In an exemplary embodiment, the arbitrator 26 can also evaluate the relatedness of multiple story fragments and combine related fragments prior to applying them to a story of interest. For example, items of evidence and hypotheses provided from a first inferencing algorithm (e.g., 28) that support a hypothesis provided from a second inferencing algorithm (e.g., 30) can be linked with that hypothesis to provide a larger, more complete story fragment. Examiner Note: story fragments and story of interest are among target data objects).
58. Talbot disclose The system of claim 53, wherein the output comprises ranked hypotheses ([0030] An analogical reasoner 106 can examine the similarity between the present state of a story and past successful decision networks. The analogical reasoner 106 finds successful cases in the knowledge base that are most similar to the present state of a story and suggests differences in hypotheses based on the successful cases. A link analysis component 107 can be used to compute link values between hypotheses based on the characteristics of the hypotheses. When new evidence creates a drastic change in the strength of a link or provides the basis for a new link, the new link data can be provided as an unknown unknown. Examiner Note: most similar indicated ranked result).
59. Talbot disclose The system of claim 58, wherein the ranked hypotheses are based on merit scores assigned to individual hypotheses (([0018] The assisted decision making system 22 further comprises an arbitrator 26 that controls the flow of new data to the assisted decision making system. Specifically, the arbitrator 26 reviews story fragments provided by a plurality of inferencing algorithms 28 and 30 to determine if any of the fragments are sufficiently relevant to the at least one story of interest 22 as to warrant its consideration at the decision making system 22. For example, a given story fragment can be compared to a story of interest to determine to what degree the hypotheses within the story fragment and their associated characteristics resemble those of the hypotheses comprising the story of interest. In an exemplary embodiment, the arbitrator 26 can also evaluate the relatedness of multiple story fragments and combine related fragments prior to applying them to a story of interest. For example, items of evidence and hypotheses provided from a first inferencing algorithm (e.g., 28) that support a hypothesis provided from a second inferencing algorithm (e.g., 30) can be linked with that hypothesis to provide a larger, more complete story fragment. [0042] If the story fragment meets the internal threshold of its associated inferencing algorithm (Y), the story fragment is provided to an arbitrator associated with a decision making system at 260. At 262, it is determined if the story fragment is sufficiently related to one or more stories of interest associated with the arbitrator. For example, the hypotheses, links, and evidence in a story fragment and its associated characteristics can be compared with the hypotheses and associated characteristics of a given story of interest to determine the relatedness of the story fragment and the story. If the story fragment is not sufficiently related to the story of interest (N), the story fragment is rejected at 258. Examiner Note: the degree/relatedness reads on merit score. [0020] FIG. 3 illustrates a representation of a belief network 50 in accordance with an aspect of the present invention. The belief network 50 of FIG. 2 is illustrated as a Dempster-Shafer belief network, but it will be appreciated that other belief networks, such as Bayesian belief networks, can be utilized as stories of interest in accordance with an aspect of the present invention. The decision network 50 includes a top layer 52, a first intermediate layer 54, a second intermediate layer 56, and a bottom layer 58. The top layer 52 includes nodes N1-N6 linked to the first intermediate or hypothesis layer 54 by links or multipliers L1-L10. The first intermediate layer 54 includes nodes N7-N11 linked to the second intermediate layer 54 by links or multipliers L11-L17. The second intermediate layer 56 includes nodes N11-N13 linked to the bottom layer 58 by links or multipliers L18-L21. Each node represents a given variable and hypothesis associated with that variable that can affect the variable and hypothesis of other nodes in lower layers mathematically. Associated with each of the nodes N1-N15 are three parameters, which are a belief parameter B, a disbelief parameter D, and an unknown parameter U. The parameters B, D, and U conform to the Dempster-Shafer evidential interval such that the parameter B, D and U add up to one for each node N1-N15. [0021] The links represent multipliers or weights of a given parameter on a lower node. Link values can be constant, or computed by an algorithm. For example, the belief of node N7 of the first intermediate layer 54 depends on the belief of nodes N1, N2, and N3, each multiplied by its respective link value L1, L2, and L3. Additionally, the disbelief of node N7 of the first intermediate layer 54 depends on the disbelief of nodes N1, N2, and N3, each multiplied by its respective link value L1, L2, and L3. The unknown is computed based on the Dempster-Shafer combination rule. The belief and disbelief of node N7 then propagate to N11 through link L11, which is combined with the belief and disbelief of N18 multiplied by link L12 and the belief and disbelief of node N9 multiplied by link L14. The belief and disbelief of node N11 then propagate to node N14 through link L18 which is combined with the belief and disbelief of N13 multiplied by link L20. The ignorance, or unknowns, of each row can be evaluated using the Dempster-Shafer combination rule. Similar propagation occurs to provide the beliefs, the disbeliefs, and unknowns of the node N15. Examiner Note: the link value also reads on hypothesis score).
60. Talbot disclose The system of claim 59, wherein the merit scores are multi- dimensional, representing different aspects of relevance ([0018] The assisted decision making system 22 further comprises an arbitrator 26 that controls the flow of new data to the assisted decision making system. Specifically, the arbitrator 26 reviews story fragments provided by a plurality of inferencing algorithms 28 and 30 to determine if any of the fragments are sufficiently relevant to the at least one story of interest 22 as to warrant its consideration at the decision making system 22. For example, a given story fragment can be compared to a story of interest to determine to what degree the hypotheses within the story fragment and their associated characteristics resemble those of the hypotheses comprising the story of interest. In an exemplary embodiment, the arbitrator 26 can also evaluate the relatedness of multiple story fragments and combine related fragments prior to applying them to a story of interest. For example, items of evidence and hypotheses provided from a first inferencing algorithm (e.g., 28) that support a hypothesis provided from a second inferencing algorithm (e.g., 30) can be linked with that hypothesis to provide a larger, more complete story fragment. [0042] If the story fragment meets the internal threshold of its associated inferencing algorithm (Y), the story fragment is provided to an arbitrator associated with a decision making system at 260. At 262, it is determined if the story fragment is sufficiently related to one or more stories of interest associated with the arbitrator. For example, the hypotheses, links, and evidence in a story fragment and its associated characteristics can be compared with the hypotheses and associated characteristics of a given story of interest to determine the relatedness of the story fragment and the story. If the story fragment is not sufficiently related to the story of interest (N), the story fragment is rejected at 258. Examiner Note: the degree/relatedness reads on validity. [0020] FIG. 3 illustrates a representation of a belief network 50 in accordance with an aspect of the present invention. The belief network 50 of FIG. 2 is illustrated as a Dempster-Shafer belief network, but it will be appreciated that other belief networks, such as Bayesian belief networks, can be utilized as stories of interest in accordance with an aspect of the present invention. The decision network 50 includes a top layer 52, a first intermediate layer 54, a second intermediate layer 56, and a bottom layer 58. The top layer 52 includes nodes N1-N6 linked to the first intermediate or hypothesis layer 54 by links or multipliers L1-L10. The first intermediate layer 54 includes nodes N7-N11 linked to the second intermediate layer 54 by links or multipliers L11-L17. The second intermediate layer 56 includes nodes N11-N13 linked to the bottom layer 58 by links or multipliers L18-L21. Each node represents a given variable and hypothesis associated with that variable that can affect the variable and hypothesis of other nodes in lower layers mathematically. Associated with each of the nodes N1-N15 are three parameters, which are a belief parameter B, a disbelief parameter D, and an unknown parameter U. The parameters B, D, and U conform to the Dempster-Shafer evidential interval such that the parameter B, D and U add up to one for each node N1-N15. [0021] The links represent multipliers or weights of a given parameter on a lower node. Link values can be constant, or computed by an algorithm. For example, the belief of node N7 of the first intermediate layer 54 depends on the belief of nodes N1, N2, and N3, each multiplied by its respective link value L1, L2, and L3. Additionally, the disbelief of node N7 of the first intermediate layer 54 depends on the disbelief of nodes N1, N2, and N3, each multiplied by its respective link value L1, L2, and L3. The unknown is computed based on the Dempster-Shafer combination rule. The belief and disbelief of node N7 then propagate to N11 through link L11, which is combined with the belief and disbelief of N18 multiplied by link L12 and the belief and disbelief of node N9 multiplied by link L14. The belief and disbelief of node N11 then propagate to node N14 through link L18 which is combined with the belief and disbelief of N13 multiplied by link L20. The ignorance, or unknowns, of each row can be evaluated using the Dempster-Shafer combination rule. Similar propagation occurs to provide the beliefs, the disbeliefs, and unknowns of the node N15. Examiner Note: each layer of link value representing a different dimension of relevance).
61. Talbot disclose The system of claim 53, wherein the output includes the reasoning steps presented in a browser on the computing device ([0037] The visualization function 156 controls the display of the information within the argument model to the user. A graphic configuration sub-function 180 allows the user to specify display preferences for the editor interface 152. For example, the user can specify the colors associated with the nodes and connectors within the argument model. A change view sub-function 182 allows the user to select a zoom level or a portion of the argument model for viewing. A collapse node sub-function 184 allows the user to hide one or more of nodes and connectors downstream of a particular node to simplify the display of the argument model. The sub-function 184 also allows the argument to be reexpanded upon a command from the user. Finally, a confidence slider sub-function 186 allows a user to display a confidence value or an influence value as a line graph scale, ranging from a minimum value to a maximum value. This allows the user to more easily visualize the range of available values when editing these parameters. [0044] FIG. 7 illustrates a computer system 300 that can be employed to implement systems and methods described herein, such as based on computer executable instructions running on the computer system. The computer system 300 can be implemented on one or more general purpose networked computer systems, embedded computer systems, routers, switches, server devices, client devices, various intermediate devices/nodes and/or stand alone computer systems. Additionally, the computer system 300 can be implemented as part of the computer-aided engineering (CAE) tool running computer executable instructions to perform a method as described herein. Examiner Note: display of output on a networked computer indicates the use of a browser).
63. Talbot disclose The system of claim 53, wherein the least one inference engine interacts with the environment via the data interface (See Fig. 4. [0033] Where a threshold level of similarity is found, the story fragment can be provided to human analysts through associated user interfaces 136 and 138. A user interface (e.g., 136) can include a graphical user interface that allows the analyst to quickly review the pertinent portions of the story fragment and determine its relationship to the story of interest. If the human analyst agrees that the story fragment is relevant to the story of interest, he or she can add information to the story fragment and incorporate the story fragment into the story of interest. A fusion engine 140 can mathematically reconcile the story of interest in light of the added story fragments to allow the analyst to see the impact of the story fragment. [0034] FIG. 5 illustrates a functional block diagram of an exemplary graphic user interface (GUI) 150 that can be utilized in an assisted decision making system in accordance with the present invention. The GUI 150 includes an editor 152 that allows a user to view and edit a structure argument. The editor 152 has four associated high-level functions, an editing function 154, a visualization function 156, a belief propagation function 158, and an administrative function 160).
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64. Talbot disclose The system of claim 63, wherein the data interface includes at least one of the following: a mobile device, a sensor, a router, a switch, an appliance, anon-mobile device, or a garment ([0044] FIG. 7 illustrates a computer system 300 that can be employed to implement systems and methods described herein, such as based on computer executable instructions running on the computer system. The computer system 300 can be implemented on one or more general purpose networked computer systems, embedded computer systems, routers, switches, server devices, client devices, various intermediate devices/nodes and/or stand alone computer systems. Additionally, the computer system 300 can be implemented as part of the computer-aided engineering (CAE) tool running computer executable instructions to perform a method as described herein.).
65. Talbot disclose The system of claim 63, wherein the data interface comprises an application program interface (API) (See Fig. 4-136 and 138. [0047] A number of program modules may also be stored in one or more of the drives as well as in the RAM 310, including an operating system 330, one or more application programs 332, other program modules 334, and program data 336. [0048] A user may enter commands and information into the computer system 300 through user input device 340, such as a keyboard, a pointing device (e.g., a mouse). Other input devices may include a microphone, a joystick, a game pad, a scanner, a touch screen, or the like. These and other input devices are often connected to the processor 302 through a corresponding interface or bus 342 that is coupled to the system bus 306. Such input devices can alternatively be connected to the system bus 306 by other interfaces, such as a parallel port, a serial port or a universal serial bus (USB). One or more output device(s) 344, such as a visual display device or printer, can also be connected to the system bus 306 via an interface or adapter 346).
67. Talbot disclose The system of claim 53, wherein the at least one reasoning rule set includes abductive reasoning ([0027] The plurality of inferencing algorithms 102-107 can include any of a variety of appropriate algorithms for evaluating stored data to determine significant patterns and trends. Specifically, the inferencing algorithms 102-107 search the knowledge base for unanticipated story fragments, comprising at least a single new hypothesis, and more typically of story fragments comprising linked hypotheses and any associated evidence. In the illustrated example, the plurality of inferencing algorithms include an inductive reasoner 102 that computes changes in rules based on new evidence within the knowledge base. The change is based on rule induction, using the old evidence supporting the old structure along with exceptions to the old rules in the existing evidence to induce new rules that better account for the entire body of old and the new evidence. In essence, new rules defining a revised network structure are computed from an aggregate of old and new evidence. In one implementation, the Weka algorithm can be utilized within the inductive reasoner 102. [0019] Each of the plurality of inferencing algorithms 28 and 30 utilize data, including formatted evidence, executable stories, and story fragments from an associated knowledge base 32 to produce story fragments. It will be appreciated that the inferencing algorithms 28 and 30 are not limited to retrieving data from the knowledge base, but can also access data from one or more external sources (e.g., Cyc, WordNET, and similar knowledge bases). In one implementation, the plurality of inferencing algorithms 28 and 30 can include an abductive reasoning algorithm that compiles the best explanation for a body of data in the form of a rule tree. Similarly, the plurality of decision algorithms 24 and 26 can comprise an unsupervised clustering algorithm that attempts to form clusters from the data to determine new hypotheses for the decision making system 22).
69. Talbot disclose The system of claim 53, wherein at least one reasoning rule set comprises inductive reasoning ([0027] The plurality of inferencing algorithms 102-107 can include any of a variety of appropriate algorithms for evaluating stored data to determine significant patterns and trends. Specifically, the inferencing algorithms 102-107 search the knowledge base for unanticipated story fragments, comprising at least a single new hypothesis, and more typically of story fragments comprising linked hypotheses and any associated evidence. In the illustrated example, the plurality of inferencing algorithms include an inductive reasoner 102 that computes changes in rules based on new evidence within the knowledge base. The change is based on rule induction, using the old evidence supporting the old structure along with exceptions to the old rules in the existing evidence to induce new rules that better account for the entire body of old and the new evidence. In essence, new rules defining a revised network structure are computed from an aggregate of old and new evidence. In one implementation, the Weka algorithm can be utilized within the inductive reasoner 102. [0019] Each of the plurality of inferencing algorithms 28 and 30 utilize data, including formatted evidence, executable stories, and story fragments from an associated knowledge base 32 to produce story fragments. It will be appreciated that the inferencing algorithms 28 and 30 are not limited to retrieving data from the knowledge base, but can also access data from one or more external sources (e.g., Cyc, WordNET, and similar knowledge bases). In one implementation, the plurality of inferencing algorithms 28 and 30 can include an abductive reasoning algorithm that compiles the best explanation for a body of data in the form of a rule tree. Similarly, the plurality of decision algorithms 24 and 26 can comprise an unsupervised clustering algorithm that attempts to form clusters from the data to determine new hypotheses for the decision making system 22).
70. Talbot disclose The system of claim 53, further comprising a validation module coupled with the data interface and that performs the following validation operations upon execution of the software instructions: construct a validation plan for the at least one hypothesis; acquire validation data according to the validation plan; and derive a merit score for the at least one hypothesis based on the validation data ([0026] During operation, the extracted evidence templates can be provided to one or more evidence classifiers 120. The evidence classifiers 120 can assign the evidence to associated hypotheses according to the evidence content. It will be appreciated that the evidence classifiers 120 can assign the templates to one or more existing hypotheses in the knowledge base 114 or generate a new suggested hypothesis. In an exemplary embodiment, the evidence classifiers 120 can include a rule-based classifier that classifies the templates according to a set of user defined rules. For example, rules can be defined relating to the fields within the template or the source of the data. Other classifiers can include, for example, supervised and unsupervised neural network classifiers, semantic network classifiers, statistical classifiers, and other classifier models. These classifiers can be orchestrated to increase the efficiency of the classification. For example, the rule-based classifier can be applied first, and if a rule is not actuated, a statistical classifier can be used. If a pre-specified probability threshold is not reached at the statistical classifier, a semantic distance classifier can be applied and the results shown to the user for validation. [0018] The assisted decision making system 22 further comprises an arbitrator 26 that controls the flow of new data to the assisted decision making system. Specifically, the arbitrator 26 reviews story fragments provided by a plurality of inferencing algorithms 28 and 30 to determine if any of the fragments are sufficiently relevant to the at least one story of interest 22 as to warrant its consideration at the decision making system 22. For example, a given story fragment can be compared to a story of interest to determine to what degree the hypotheses within the story fragment and their associated characteristics resemble those of the hypotheses comprising the story of interest. In an exemplary embodiment, the arbitrator 26 can also evaluate the relatedness of multiple story fragments and combine related fragments prior to applying them to a story of interest. For example, items of evidence and hypotheses provided from a first inferencing algorithm (e.g., 28) that support a hypothesis provided from a second inferencing algorithm (e.g., 30) can be linked with that hypothesis to provide a larger, more complete story fragment. [0042] If the story fragment meets the internal threshold of its associated inferencing algorithm (Y), the story fragment is provided to an arbitrator associated with a decision making system at 260. At 262, it is determined if the story fragment is sufficiently related to one or more stories of interest associated with the arbitrator. For example, the hypotheses, links, and evidence in a story fragment and its associated characteristics can be compared with the hypotheses and associated characteristics of a given story of interest to determine the relatedness of the story fragment and the story. If the story fragment is not sufficiently related to the story of interest (N), the story fragment is rejected at 258. Examiner Note: the degree/relatedness reads on validity/merit score).
71. Talbot disclose The system of claim 70, wherein the output further includes the validation plan and the merit score (See Fig. 4. [0033] Where a threshold level of similarity is found, the story fragment can be provided to human analysts through associated user interfaces 136 and 138. A user interface (e.g., 136) can include a graphical user interface that allows the analyst to quickly review the pertinent portions of the story fragment and determine its relationship to the story of interest. If the human analyst agrees that the story fragment is relevant to the story of interest, he or she can add information to the story fragment and incorporate the story fragment into the story of interest. A fusion engine 140 can mathematically reconcile the story of interest in light of the added story fragments to allow the analyst to see the impact of the story fragment. [0034] FIG. 5 illustrates a functional block diagram of an exemplary graphic user interface (GUI) 150 that can be utilized in an assisted decision making system in accordance with the present invention. The GUI 150 includes an editor 152 that allows a user to view and edit a structure argument. The editor 152 has four associated high-level functions, an editing function 154, a visualization function 156, a belief propagation function 158, and an administrative function 160).
72. Talbot disclose The system of claim 53, wherein the inference engine is a member of a multi-layered set of inference engines (See Fig. 2 on lower layer 28 and 30 and higher layer 22).
Claim 73 is method claim having similar limitation as claim 53 and is rejected under the same rationale.
Claim 74 is non-transitory computer readable medium claims having similar limitation as claim 53 and is rejected under the same rationale. See [0046] and claim 22 for computer readable medium.
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.
Claim(s) 62, 66, 68 is/are rejected under 35 U.S.C. 103 as being unpatentable over Talbot et al (US 2006/0112048 A1) view of Maier et al (US 5408587) and further in view of OMOIGUI (US 20100070448 A1)
62. Talbot fails to disclose the system of claim 53, wherein the reasoning steps comprise a mapping to a namespace.
However, OMOIGUI disclose knowledge representation and inference (thereby in the same field of endeavor) and explicitly disclose reasoning steps comprise a mapping to a namespace [(0313] Dynamic Linking.TM.. Trademarked name for the ability of the Information Nervous System of the present invention to allow users to link information dynamically, semantically, and at the speed of thought, even if those information items do not contain links themselves. By virtue of employing smart objects that have intrinsic behavior and using recursive intelligence embedded in the Information Agency's XML Web Service, each node in the Semantic Network is much smarter than a regular link or node on Today's Web or the conceptual Semantic Web. In other words, each node in the Smart Virtual Network or Web of the present invention can link to other nodes, independent of authoring. Each node has behavior that can dynamically link to Agencies and Smart Agents via drag and drop and smart copy and paste, create links to Agencies in the Semantic Environment, respond to lens requests from Smart Agents to create new links, include intrinsic alerts that will dynamically create links to context and time-sensitive information on its Agency, include presentation hints for breaking news (wherein the node can automatically link to breaking news Agents in the namespace), form the basis for deep info that can allow the user to find new links, etc. A user of the present invention is therefore not at the mercy of the author of the metadata. Once the user reaches a node in the network, the user has many semantic means of navigating dynamically and automatically--using context, time, relatedness to Smart Agencies and Agents, etc.).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the knowledge acquisition of Talbot to incorporate namespace mapping of OMOIGUI.
Given the fact that namespace mapping/linking form the basis for deep info, one having ordinary skill in the art would have been motivated to make this obvious modification.
66. OMOIGUI disclose The system of claim 53, wherein the at least one reasoning rule set includes deductive reasoning ([1789] To appreciate some of the potential power of this feature, it is useful to note that while the system or Entities "know" who is posing the query, the Entities do not depend for that knowledge on the user informing them and keeping them constantly updated and informed (although user information can be supplied and considered at any time). If that were the case, the system could be too labor intensive to be efficient and useful in many situations; it would just be too much work. Instead, the Entities "know" who the requester is by inference and from semantics from characteristics sometimes supplied by others, sometimes derived or deduced, sometimes collected from other requests and the like, as explained throughout this application and its parent application. Examiner Note: ¶ 1 applies. The kind of reasoning rule sets is Nonfunctional descriptive material).
68. OMOIGUI disclose The system of claim 53, wherein the operations further include conducting reflexive reasoning and the reasoning steps include the reflexive reasoning ([3025] 6. Semantic Cross-Referencing: A user interface allows the user to cross-reference context across ontologies. For instance, it is possible to use one perspective to view results that were generated via another perspective. Such "cross-fertilization of perspectives" accurately reflects how knowledge is acquired and/or how research evolves in the real-world. Furthermore, a user interface allows the user to cross-reference context in order to dynamically create new semantic views. Examiner Note: ¶ 1 applies. The kind of reasoning rule sets is Nonfunctional descriptive material).
Examiner Note (EN)
¶ 1: In re Curry, the Board held that in a computer-implemented method of providing "wellness-related services," "the 'wellness-related data in the databases.., does not functionally change either the data storage system or communication system used in the method of claim 81. Nonfunctional descriptive material cannot render nonobvious an invention that would have otherwise been obvious." See Ex parte Curry, 84 USPQ2d 1272 (BPAI 2005), aff'd (Fed. Cir. Appeal No. 2006-1003, aff'd Rule 36 June 12, 2006).
In re John, the Board held that the descriptive material (i.e., "control information" and "request" comprising a description of a development environment) recited in claim 1 is non-functional descriptive material because each of the "control information" and "request" does not functionally affect the process of managing a development environment. Rather, the control information is merely information that is used for "managing said first request" by a computer program and the request is data that is received ("receiving a first request") and processed ("processing said first request") by the system. In each case, the data (i.e., "control information" and "request") do not affect how the method of the prior art is performed on a computer system. In other words, the method of receiving and processing the request and reviewing the request "in accordance with control information" is carried out in the same way regardless of the nature of the request or control information. See Ex parte John F. Bisceglia, Appeal 2007-3447.
Pertinent Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Omoigui (US 20070260580 A1) disclose knowledge retrieval, management, delivery and presentation. See abstract. See also [0145], [0312] on news provider/outlets; [0580]-[0583] on reasoning and hypothesis validation.
Horvitz et al (US 20080004926 A1) disclose automated reasoning based on user preference. See [0083].
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/LUT WONG/Primary Examiner, Art Unit 2127
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