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 .
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 31-50 rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-30 of U.S. Patent No. 9262719. Although the claims at issue are not identical, they are not patentably distinct from each other because of anticipation.
Claims 31-50 rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-30 of U.S. Patent No. 9530100. Although the claims at issue are not identical, they are not patentably distinct from each other because of anticipation.
Claims 31-50 rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-33 of U.S. Patent No. 10255552. Although the claims at issue are not identical, they are not patentably distinct from each other because of anticipation.
Claims 31-50 rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of U.S. Patent No. 10354194. Although the claims at issue are not identical, they are not patentably distinct from each other because of anticipation.
Claims 31-50 rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-30 of U.S. Patent No. 10762433. Although the claims at issue are not identical, they are not patentably distinct from each other because of anticipation.
Claims 31-50 rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-24 of U.S. Patent No. 11900276. Although the claims at issue are not identical, they are not patentably distinct from each other because of anticipation.
Response to Arguments
Applicant’s arguments, see pg.7, filed 3-31-2026, with respect to double patenting have been fully considered. The rejection has been held in abeyance.
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 31-50 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim 31: Step 1: the claim is directed to statuary category.
Step 2A Prong 1: The claim recites the following limitations:
recognizing aspects in the environment data as target objects, the target objects 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);
selecting at least one reasoning rule set from the available reasoning rule sets as a function of the environment data and object attributes of the target objects (rule selection 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);
establishing at least one hypothesis according to the selected at least one reasoning rule set, the hypothesis representing a suspected correlation among the target 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);
deriving at least one merit score associated with the at least one hypothesis based at least in part on the environment data (score calculation/deriving 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
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:
31. (New) A computer-based reasoning system, comprising:
at least one computer-readable non-transitory memory storing software instructions and available reasoning rule sets (amounts to mere insignificant application, an insignificant extra-solution activity as discussed in MPEP 2106.05(g));
a data interface configured to acquire environment data from at least a news outlet; and at least one computer-based inference engine coupled with the data interface and the at least one memory, and that performs the following operations upon execution of the software instructions (amounts to mere insignificant application, an insignificant extra-solution activity as discussed in MPEP 2106.05(g)):
a validation module operable to control acquisition of the environment data according to a validation plan by influencing operation of the data interface to cause acquisition of environment data for validating at least one hypothesis to be established (amounts to mere insignificant application, an insignificant extra-solution activity as discussed in MPEP 2106.05(g));
rendering, via a presentation module, the at least one hypothesis according to the at least one merit score (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.
31. (New) A computer-based reasoning system, comprising:
at least one computer-readable non-transitory memory storing software instructions and available reasoning rule sets (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 applying it under MPEP 2106.05(d));
a data interface configured to acquire environment data from at least a news outlet; and at least one computer-based inference engine coupled with the data interface and the at least one memory, and that performs the following operations upon execution of the software instructions (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 applying it under MPEP 2106.05(d)):
a validation module operable to control acquisition of the environment data according to a validation plan by influencing operation of the data interface to cause acquisition of environment data for validating at least one hypothesis to be established (amounts to mere insignificant application, 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));
rendering, via a presentation module, the at least one hypothesis according to the at least one 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)).
The claim is not patent eligible.
Claim 32: 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):
32. (New) The system of claim 31, wherein the environment data includes at least one of: visual data, audible data, or news data (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 33: 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):
33. (New) The system of claim 31, wherein the operation of rendering the at least one hypothesis includes causing a mobile device to render at least one hypothesis (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 applying it 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 34: 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:
34. (New) The system of claim 31, wherein the operation of recognizing the target objects includes executing at least one implementation of a recognition algorithm (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 applying it 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 35: 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):
35. (New) The system of claim 31, wherein the object attributes adhere to a normalized 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 36: 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):
36. (New) The system of claim 31, wherein the available reasoning rule sets include at least one of: a deductive reasoning rule set, an abductive reasoning rule set, or an inductive reasoning rule set (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 37: 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):
37. (New) The system of claim 31, wherein controlling acquisition of the environment data according to a validation plan comprises issuing, by the validation module via the data interface, one or more acquisition control commands specifying at least one acquisition parameter comprising at least one of: a timing condition, a sampling parameter, a triggering condition, a data modality, or a data source (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 38: 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):
38. (New) The system of claim 37, wherein the validation module is operable to inject environment data via the data interface according to the validation plan (injecting data amounts to mere data manipulation/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 39: 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):
39. (New) The system of claim 31, wherein the merit score represents a measure of validity of the hypothesis (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 40: 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):
40. (New) The system of claim 31, wherein the merit score is multi-valued, each value representing a different dimension 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 41: 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):
The claim recites no additional element:
41. (New) The system of claim 31, wherein the presentation module is further configured to present reasoning steps taken to generate the hypothesis (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 42: 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):
42. (New) The system of claim 31, wherein the at least one hypothesis comprises multiple 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 43: 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):
43. (New) The system of claim 42, wherein the operation of presenting the at least one hypothesis includes presenting the multiple hypotheses ranked according to their respective merit scores (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 44: 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:
44. (New) The system of claim 31, wherein the at least one inference engine is distributed across multiple computing devices (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 applying it 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 45: 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):
45. (New) The system of claim 31, wherein the data interface is further configured to acquire environment data from multiple news outlets (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 46: 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):
46. (New) The system of claim 31, wherein the hypothesis represents at least one of: an intuition, an imaginative construct, a creative work, or an emotion (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 47: Step 1: the claim is directed to statuary category.
Step 2A Prong 1: The claim recites the abstract idea of parent claim.
47. (New) The system of claim 31, wherein the operations further include updating the at least one hypothesis based on newly acquired environment data (updating hypothesis 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(s):
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 48: 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):
48. (New) The system of claim 31, wherein the hypothesis comprises a temporal nature where its suspected correlation has time-based values (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 49: Step 1: the claim is directed to statuary category.
Step 2A Prong 1: The claim recites the abstract idea of parent claim.
49. (New) The system of claim 31, wherein the operations further include updating the merit score based on newly acquired validation data (updating score 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(s):
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 50: Step 1: the claim is directed to statuary category.
Step 2A Prong 1: The claim recites the abstract idea of parent claim.
50. (New) The system of claim 31, wherein the operations further include selecting the at least one reasoning rule set based on a user preference (rule selection based on preference 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(s):
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 51: Step 1: the claim is directed to statuary category.
Step 2A Prong 1: The claim recites the following limitations:
51. A method for computer-based reasoning, the method comprising:
recognizing aspects in environment data as target objects, the target objects 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);
selecting at least one reasoning rule set from available reasoning rule sets as a function of the environment data and object attributes of the target objects (rule selection 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);
establishing at least one hypothesis according to the selected at least one reasoning rule set, the hypothesis representing a suspected correlation among the target 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);
deriving at least one merit score associated with the at least one hypothesis based at least in part on the
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environment data (score calculation/deriving 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
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:
acquiring environment data from at least a news outlet via a data interface (amounts to mere insignificant application, an insignificant extra-solution activity as discussed in MPEP 2106.05(g)):
control acquisition of the environment data according to a validation via a validation module by influencing operation of a data interface to cause acquisition of environment data for validating at least one hypothesis to be established (amounts to mere insignificant application, an insignificant extra-solution activity as discussed in MPEP 2106.05(g));
rendering the at least one hypothesis according to the at least one merit score (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.
acquiring environment data from at least a news outlet via a data interface (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 applying it under MPEP 2106.05(d)):
control acquisition of the environment data according to a validation via a validation module by influencing operation of a data interface to cause acquisition of environment data for validating at least one hypothesis to be established (amounts to mere insignificant application, 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));
rendering the at least one hypothesis according to the at least one 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)).
The claim is not patent eligible.
Claims 52 is non-transitory computer readable medium claims having similar limitation as claim 51 and is rejected under the same rationale. The additional elements in claim 52 is a non-transitory computer-readable medium having computer instructions stored thereon for computer-based reasoning, which, when executed by a processor, cause the processor to perform one or more steps comprising (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.
Response to Arguments
Applicant's arguments have been fully considered but they are not persuasive.
In re pgs. 8-9, applicant argues
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In response, the Examiner respectfully disagrees.
The claim only recites “a data interface configured to acquire environment data from at least a news outlet”. There is nothing in the claim that limits data to be machine-recognizable only. Such data can be in plain text where human can read and process mentally.
In re pg. 9, applicant argues
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In response, the Examiner respectfully disagrees.
Again, there is nothing in the claim that limits the data, rules, hypothesis, validation to be machine-recognizable only. Such data, rules, hypothesis, validation can be in plain text where human can read and process mentally.
In re pg. 11, applicant argues
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In response, the Examiner respectfully disagrees.
a validation module operable to control acquisition of the environment data according to a validation plan by influencing operation of the data interface to cause acquisition of environment data for validating at least one hypothesis to be established (amounts to mere insignificant application, 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)).
The validation module is just a generic module.
The acquisition of data is mere data gathering.
The environment data for validating at least one hypothesis to be established is just intended use.
As such, there is no technical improvement in the claimed invention.
In re pg. 12, applicant argues
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In response, the Examiner respectfully disagrees.
The data inface, validation module, inference engine, presentation modules are just generic modules. Each module and its functions/arrangement do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Even the ordered combinations of additional elements do not amount to “significantly more”.
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) 31-32, 34-52 is/are rejected under 35 U.S.C. 103 as being unpatentable over Talbot et al (US 2006/0112048 A1) in view of OMOIGUI (US 20100070448 A1)
31. (New) Talbot disclose a computer-based reasoning system ([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.), comprising:
at least one computer-readable non-transitory memory storing software instructions and available reasoning rule sets ([0046]… The drives and their associated computer-readable media provide nonvolatile storage of data, data structures, and computer-executable instructions for the computer system 300…. 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. [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);
a data interface configured to acquire environment data from at least 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.); and
at least one computer-based inference engine coupled with the data interface and the at least one memory, 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.)
recognizing aspects in the environment data as target objects, the target objects 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)
selecting at least one reasoning rule set from the available reasoning rule sets as a function of the environment data and object attributes of the target objects ([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. [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. Examiner Note: The inferencing algorithms used by the arbitrators to provide a more complete story are the selected algorithms/reasoning rule sets);
establishing at least one hypothesis according to the selected at least one reasoning rule set, the hypothesis representing a suspected correlation among the target 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.)
deriving at least one merit score associated with the at least one hypothesis based at least in part on the environment data ([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); and
rendering, via a presentation module, the at least one hypothesis according to the at least one merit score (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.).
While Talbot disclose acquiring data from external sources (See Fig. 4-115. [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), Talbot fails to explicitly call for news outlet.
However, OMOIGUI disclose knowledge representation and inference (thereby in the same field of endeavor) and explicitly disclose gathering data from news source/outlet ([0010] Information has been long accessible in a variety of forms, such as in newspapers, books, radio and television media, and in electronic form, with varying degrees of proliferation. Information management and access changed dramatically with the use of computers and computer networks. Networked computer systems provide access throughout the system to information maintained at any point along the system. Users need only establish the requisite connection to the network, provide proper authorization and identify the desired information to obtain access. [0166] FIG. 84 illustrates a method of using semantic sounds to notify a user regarding the arrival of news in accordance with an embodiment of the invention; [0182] FIG. 100 is a block diagram for a method of generating information on experts, interest groups, or newsmakers, in accordance with an embodiment of the invention;).
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 external source to incorporate news outlet of OMOIGUI.
Given the fact that information has been long accessible from news (OMOIGUI [0010]), one having ordinary skill in the art would have been motivated to make this obvious modification.
Furthermore, while Talbot disclose data validation plan ([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), Talbot fails to disclose a validation module operable to control acquisition of the environment data according to a validation plan by influencing operation of the data interface to cause acquisition of environment data for validating at least one hypothesis to be established.
However, OMOIGUI explicitly disclose a validation module operable to control acquisition of the environment data according to a validation plan by influencing operation of the data interface to cause acquisition of environment data for validating at least one hypothesis to be established ([2957] FIG. 92 is a block diagram of a method for developing and maintaining ontologies, in accordance with an embodiment of the invention. In this embodiment, a cross-ontology validation application 3008 is in communication with ontology one 3002, ontology two 3004, ontology three 3006, or ontology four 3008. The validation application 3008 is in communication with more or less than four ontologies. In an alternative embodiment, the cross-ontology validation application 3008 assists in developing and maintaining ontologies. For example, the cross ontology validation application 3008 may determine whether there are discrepancies in naming schemes between multiple ontologies and notify an ontology administrator (e.g., artificial intelligence sub-categories may be different in the IT and Products and Services ontologies. In another example, the cross-ontology validation application 3008 suggests the hooks in one domain to be exclusions for another domain and vice versa (e.g., virus in a health database should have exclusions that are themselves hooks for virus in an IT database). In an alternative embodiment, the cross-ontology validation application considers that multiple-word forms include the same exclusions or hooks.;).
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 validation plan of Talbot to incorporate validation application/module of OMOIGUI.
Given the fact that validation step is well known to be performed by an application/module, one having ordinary skill in the art would have been motivated to make this obvious modification.
32. (New) OMOIGUI disclose The system of claim 31, wherein the environment data includes at least one of: visual data, audible data, or news data ([0010] Information has been long accessible in a variety of forms, such as in newspapers, books, radio and television media, and in electronic form, with varying degrees of proliferation. Information management and access changed dramatically with the use of computers and computer networks. Networked computer systems provide access throughout the system to information maintained at any point along the system. Users need only establish the requisite connection to the network, provide proper authorization and identify the desired information to obtain access. [0166] FIG. 84 illustrates a method of using semantic sounds to notify a user regarding the arrival of news in accordance with an embodiment of the invention; [0182] FIG. 100 is a block diagram for a method of generating information on experts, interest groups, or newsmakers, in accordance with an embodiment of the invention). Examiner Note: ¶ 1 applies. The kind of data is Nonfunctional descriptive material.
34. (New) Talbot disclose The system of claim 31, wherein the operation of recognizing the target objects includes executing at least one implementation of a recognition algorithm ([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: also inherent since any object recognition requires a recognition algorithm of some sort.
35. (New) OMOIGUI disclose The system of claim 31, wherein the object attributes adhere to a normalized namespace ([1782] Note that such a compound entity that includes other entities gets checked by the client-side semantic consistency checker for referential integrity. In other words, if Entity A refers to Entity B and the user attempts to delete Entity B, the semantic browser will detect this and flag the user that Entity B has an outstanding reference. If the user deletes Entity B anyway, the reference in Entity A (and any other references to Entity B) will get removed. Alternately, in some embodiments, the user could be prohibited (whether informed or not) from deleting Entity B in the same situation, based on permissions of others within an organization associated with the entity. For example, employers could monitor activities of employees for risk management purposes, like as is done with email in some companies, only much potentially much more powerfully (Of course, appropriate policies and privacy considerations would have to be addressed). The same process applies to Request Collections (Blenders), Portfolios (Entity Collections--see below), and other compound items in the semantic namespace/environment (items that could refer to other items in the namespace/environment).
36. (New) Talbot disclose The system of claim 31, wherein the available reasoning rule sets include at least one of: a deductive reasoning rule set, an abductive reasoning rule set, or an inductive reasoning rule set ([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). Examiner Note: ¶ 1 applies. The kind of reasoning rule sets is Nonfunctional descriptive material.
37. (New) OMOIGUI disclose The system of claim 31, wherein controlling acquisition of the environment data according to a validation plan comprises issuing, by the validation module via the data interface, one or more acquisition control commands specifying at least one acquisition parameter comprising at least one of: a timing condition, a sampling parameter, a triggering condition, a data modality, or a data source ([0022] Today's Web lacks context-sensitivity. The implication of a lack of context is that Today's Web is not personal. For example, documents in accessible storage are independently static and therefore stupid. Information relevant to the subject matter of the document has already been published, is being newly published, or will soon be published. Because the document in storage is static, however, there is no way to dynamically associate its subject matter with this relevant information in real-time. Stated differently, users have no way to dynamically connect their private context with external information in real-time. Information sources (such as the document) that form context sit in their own islands, totally isolated from other relevant information sources. This results in information and productivity losses. [2957] FIG. 92 is a block diagram of a method for developing and maintaining ontologies, in accordance with an embodiment of the invention. In this embodiment, a cross-ontology validation application 3008 is in communication with ontology one 3002, ontology two 3004, ontology three 3006, or ontology four 3008. The validation application 3008 is in communication with more or less than four ontologies. In an alternative embodiment, the cross-ontology validation application 3008 assists in developing and maintaining ontologies. For example, the cross ontology validation application 3008 may determine whether there are discrepancies in naming schemes between multiple ontologies and notify an ontology administrator (e.g., artificial intelligence sub-categories may be different in the IT and Products and Services ontologies. In another example, the cross-ontology validation application 3008 suggests the hooks in one domain to be exclusions for another domain and vice versa (e.g., virus in a health database should have exclusions that are themselves hooks for virus in an IT database). In an alternative embodiment, the cross-ontology validation application considers that multiple-word forms include the same exclusions or hooks. Examiner Note: the ontologies are data source or information source).
38. (New) Talbot disclose The system of claim 37, wherein the validation module is operable to inject environment data via the data interface according to the validation plan ([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.).
39. (New) Talbot disclose The system of claim 31, wherein the merit score represents a measure of validity of the hypothesis ([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: the link value also reads on validity; the higher the beliefs; the lower the disbeliefs).
40. (New) Talbot disclose The system of claim 31, wherein the merit score is multi-valued, each value representing a different dimension 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).
41. (New) Talbot disclose The system of claim 31, wherein the presentation module is further configured to present reasoning steps taken to generate the hypothesis ([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. [0028] The plurality of inferencing algorithms can further include an unsupervised clustering algorithm 103. In the unsupervised clustering algorithm 103, evidence templates are grouped according to their associated characteristics. These clusters can provide an indication of previously unknown hypotheses. Further, the unsupervised clustering shows the changes in the density of evidence in support of various existing hypotheses. This may be an indicator of unknown unknowns associated with the changed hypotheses. Another inferencing algorithm can utilize an evidential reasoner 104 that reviews new stories in the knowledge base to determine story fragments and evidence that are related to but not represented in a story of interest. The unaccounted for fragments can represent unknown unknowns. [0029] An abductive reasoner 105 can be used to find the best explanation for new data using positive and negative examples of evidence drawn from past stories that relate to a story of interest. The output of the abductive reasoner 105 is provided in a hierarchically structured way with different degrees of granularity computed that compresses the information represented in a decision network. New hypotheses or linked groups of hypotheses can be extracted from this network as unknown unknowns. One example of an abductive reasoning algorithm is the SUBDUE algorithm developed at the University of Texas at Arlington. [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: each reasoner has unique way of reasoning steps to generate the hypothesis).
42. (New) Talbot disclose The system of claim 31, wherein the at least one hypothesis comprises multiple hypotheses ([0013] The present invention relates to systems and methods for assisted decision making utilizing automated discovery of unknown unknowns. In accordance with an aspect of the present invention, unknown unknowns can be determined for one or more stories of interest by a plurality of inferencing algorithms mining an associated knowledge base. In this context, a story is 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. The information mined from the knowledge base is filtered at an arbitrator to ensure that only relevant information is considered for inclusion in the stories of interest. In one implementation, a human analyst provides a final review of information from the plurality of inferencing algorithms, with the arbitrator ensuring that the analyst is not overwhelmed by irrelevant information from the plurality of decision making algorithms.).
43. (New) Talbot disclose The system of claim 42, wherein the operation of presenting the at least one hypothesis includes presenting the multiple hypotheses ranked according to their respective merit scores ([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)
44. (New) Talbot disclose The system of claim 31, wherein the at least one inference engine is distributed across multiple computing devices [0031] The output of the plurality of decision algorithms 102-107 is provided to the plurality of assisted decision making systems 110 and 112 at respective arbitrators 126 and 128. It will be appreciated that while the arbitrators 126 and 128 operate to screen the input to their respective assisted making system, the function of the arbitrators 126 and 128 can be distributed between the assisted decision making systems 110 and 112 and the plurality of decision algorithms 102-107.).
45. (New) OMOIGUI disclose The system of claim 31, wherein the data interface is further configured to acquire environment data from multiple news outlets ([0010] Information has been long accessible in a variety of forms, such as in newspapers, books, radio and television media, and in electronic form, with varying degrees of proliferation. Information management and access changed dramatically with the use of computers and computer networks. Networked computer systems provide access throughout the system to information maintained at any point along the system. Users need only establish the requisite connection to the network, provide proper authorization and identify the desired information to obtain access. [0166] FIG. 84 illustrates a method of using semantic sounds to notify a user regarding the arrival of news in accordance with an embodiment of the invention; [0182] FIG. 100 is a block diagram for a method of generating information on experts, interest groups, or newsmakers, in accordance with an embodiment of the invention). [0560] Agency Agent Views. An alternative embodiment of the present invention includes Agency Agent Views. An Agency Agent View is a query that filters Agents based on predefined criteria. For example, the Agent view "Documents" returns only Agents that manage objects of the document semantic class. The Agent view "Reuters News" returns a list of Agents that manage news objects with "Reuters" as the publisher. Agency Agent Views are important in order to give users an easy way to navigate through Agents. The Agency administrator is able to create and delete Agent views. [0992] "Headlines" Context Template. The Headlines Context Template (and its resulting Special Agent) can be analogized to a personal, digital version of CNN's "Headline News" program in how it conveys semantic information). Examiner Note: ¶ 1 applies. Single outlet or multiple outlets is Nonfunctional descriptive material.
46. (New) OMOIGUI disclose The system of claim 31, wherein the hypothesis represents at least one of: an intuition ([0060] Furthermore, knowledge has multiple axes, and/or search is only one of those axes. Knowledge-workers also wish to discover information they might not know they need ahead of time, share information with others (especially those that have similar interests), annotate information in order to provide commentary, and/or have information presented to them in a way that is contextual, intuitive, and/or dynamic--allowing for further (and/or potentially endless) exploration and/or navigation based on their context. Even within the search axis, there are multiple sub-axes, for instance, based on time-sensitivity, semantic-sensitivity, popularity, quality, brand, trust, etc. The axis of choice depends on the scenario at hand.), an imaginative construct, a creative work, or an emotion. Examiner Note: ¶ 1 applies. What the hypothesis represent is Nonfunctional descriptive material.
47. (New) Talbot disclose The system of claim 31, wherein the operations further include updating the at least one hypothesis based on newly acquired environment data ([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.).
48. (New) Talbot disclose The system of claim 31, wherein the hypothesis comprises a temporal nature where its suspected correlation has time-based values ([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. [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.).
49. (New) Talbot disclose The system of claim 31, wherein the operations further include updating the merit score based on newly acquired validation data ([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.).
50. (New) Talbot disclose The system of claim 31, wherein the operations further include selecting the at least one reasoning rule set based on a user preference ([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. Examiner Note: user defined rules indicated user preference).
Claims 51-52 are method and non-transitory computer readable storage medium claims having similar limitation as of claim 31 and are rejected under the same rationale. See [0046] and claim 22 for computer readable medium.
Claim(s) 33 is/are rejected under 35 U.S.C. 103 as being unpatentable over Talbot et al (US 2006/0112048 A1) in view of OMOIGUI (US 20100070448 A1), and further in view of Mons et al (US 20080301174 A1)
33. While Talbot disclose outputting result ([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.), Talbot fails to disclose output to a mobile device. However, Mons disclose (New) The system of claim 31, wherein the operation of rendering the at least one hypothesis includes causing a mobile device to render at least one hypothesis.
However, Mons disclose knowledge discover and reasoning (thereby in the same field of endeavor) and explicitly disclose output to a mobile device ([0054] FIG. 1 presents an exemplary system diagram 100 of various hardware components and other features in accordance with an aspect of the present invention. As shown in FIG. 1, in an aspect of the present invention, data and other information hand services for use in the system is, for example, input by a user 101 via a terminal 102, such as a personal computer (PC), minicomputer, laptop, palmtop, mainframe computer, microcomputer, telephone device, mobile device, personal digital assistant (PDA), or other device having a processor and input and display capability. The terminal 102 is coupled to a server 106, such as a PC, minicomputer, mainframe computer, microcomputer, or other device having a processor and a repository for data or connection to a repository for maintaining data, via a network 104, such as the Internet, via communication couplings 103 and 105. [0057] As will be appreciated by those skilled in the relevant art(s), in an aspect, graphical user interface (GUI) screens may be generated by server 106 in response to input from user 101 over the Internet 104. That is, in such an aspect, server 106 is a typical Web server running a server application at a Web site which sends out Web pages in response to Hypertext Transfer Protocol (HTTP) or Hypertext Transfer Protocol Secured (HTTPS) requests from remote browsers being used by users 101. Thus, server 106 (while performing any of the steps of process 300 described below) is able to provide a GUI to users 101 of system 100 in the form of Web pages. These Web pages sent to the user's PC, laptop, mobile device, PDA or the like device 102, and would result in GUI screens being displayed).
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 output device of Talbot to incorporate mobile device of Mons.
Given the fact that mobile device is one of the many well known input/output device, one having ordinary skill in the art would have been motivated to make this obvious modification.
Furthermore, Examiner Note: ¶ 1 applies. The kind of output/rendering device is immaterial to the reasoning system; thus a nonfunctional descriptive material.
Response to Arguments
Applicant's arguments have been fully considered but they are not persuasive.
In re pg. 14, applicant argues
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In response, the Examiner respectfully disagrees.
Omoigui does disclose a validation module.
In re pg. 14, applicant argues
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In response, the Examiner respectfully disagrees.
The claim only recites “selecting at least one reasoning rule set from the available reasoning rule sets as a function of the environment data and object attributes of the target objects”. The claim does not recite any specific function. As such any selection that ties to data is “as a function” of the data ([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.);
In re pg. 15, applicant argues
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In response, the Examiner respectfully disagrees.
The claim only recites “deriving at least one merit score associated with the at least one hypothesis based at least in part on the environment data”. The claim does not further define merit score. As such, it could reads on multiple teachings, such as degree/relatedness, link value of a hypothesis, etc.
In re pg. 15, applicant argues
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In response, the Examiner respectfully disagrees.
The claim only recites “recognizing aspects in the environment data as target objects, the target objects having object attributes”. The claim does not further define recognizing step. As such, as long as target objects is being identified/processed, it is being recognized.
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.
Java et al (“Using a Natural Language Understanding System to Generate Semantic Web Content” 2007) disclose collecting data from news source and reasoning from semantic web. See abstract.
Perkins et al (“Generation of efficient parsers through direct compilation of XML Schema grammars” 2006) disclose XML namespace and normalization. See introduction.
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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