DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claims 1-20 have been examined.
Claim Rejections - 35 USC § 103
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.
Claims 1-8 and 10-20 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Application Publication 20130055278 by Zaitsev et al. ("Zaitsev") in view of U.S. Patent 5487130 to Ichimori et al. ("Ichimori").
In regard to claim 1, Zaitsev discloses:
1. A computer-implemented method comprising:
determining one or more relevancy factors for at least one category of resources based at least in part on user input pertaining to the at least one category of resources; Zaitsev ¶ 0069 “For each selected category of objects, the estimated degree of its impact on key resources of the computer system is obtained, …” Also ¶ 0070, “Alternatively, the threshold can be manually set by the user or an administrator.”
defining one or more relevancy-based membership functions associated with the at least one category of resources based at least in part on at least a portion of the one or more relevancy factors; Zaitsev ¶ 0069, “In a particular embodiment, the degree of impact of each category of objects on key system resources is predefined in terms of fuzzy logic: an assigned linguistic variable, a term set for that variable, and the membership function.”
configuring one or more artificial intelligence techniques based at least in part on at least a portion of the one or more relevancy-based … functions and one or more inference rules; Zaitsev ¶ 0031, “Fuzzification is the introduction of fuzziness into the analysis. To perform this operation linguistic variables are defined for all input variables, a term set is formed for each linguistic variable, and a membership function is defined for each term.” Also ¶ 0032, “The fuzzy knowledge base includes production rules …” Also ¶ 0059, “the measure of removing a given category of objects is determined using a trained artificial neural network (ANN). … At the output of the ANN the value of the measure of exigency of removing unused objects is obtained. Once the ANN is trained, it will be able to automatically make decisions as to the exigency of garbage removal.”
Also ¶ 0069, “In a particular embodiment, the degree of impact of each category of objects on key system resources is predefined in terms of fuzzy logic: an assigned linguistic variable, a term set for that variable, and the membership function.”
Zaitsev does not expressly disclose artificial intelligence techniques based on membership functions. However, this is taught by Ichimori. See Ichimori Fig. 10 depicting configuration of a fuzzy inference model. Also col. 1, lines 19-22, “Fuzzy inference is a method of estimating an output relative to an inference input, by using fuzzy rules and membership functions derived from human experience and knowledge.” It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use Ichimori’s fuzzy inference in order to provide automatic rule acquisition which reflects experience, knowledge, request, and the like of a user as suggested by Ichimori (e.g. see col. 5, lines 55-64).
Zaitsev also discloses:
quantifying relevancy of at least a first resource associated with the at least one category of resources relative to at least a second resource associated with the at least one category by processing data pertaining to the at least a first resource and data pertaining to the at least a second resource using the one or more artificial intelligence techniques; and Zaitsev ¶ 0070, “In one embodiment, a membership function is used to determine the degree of impact on the key categories of objects, taking into account an appropriate weighting factor, which is stored in database 140.”
performing one or more automated actions based at least in part on the quantified relevancy; Zaitsev, ¶ 0073, “Based on the generated list of categories and objects, instruction module 160 generates instructions, such as scripts, for example, for removing unused objects.”
wherein the method is performed by at least one processing device comprising a processor coupled to a memory. Zaitsev, Fig. 2, element 120, depicting a processing device, along with Fig. 6 depicting processor 21 coupled to memory 22.
In regard to claim 2, Zaitsev also discloses:
2. The computer-implemented method of claim 1, wherein performing one or more automated actions comprises automatically excluding data pertaining to at least one of the at least a first resource and the at least a second resource from one or more data processing tasks. Zaitsev, ¶ 0073 as cited above. Also ¶ 0082, “Next, at block 260, instructions, such as scripts, are formed by instruction module 160 to be executed on computer system 120 to remove the identified unused objects (or categories thereof). These instructions are executed at block 270 by execution module 170.”
In regard to claim 3, Zaitsev and Ichimori also teach:
3. The computer-implemented method of claim 1, wherein performing one or more automated actions comprises automatically training at least a portion of the one or more artificial intelligence techniques based at least in part on feedback related to the quantified relevancy. Zaitsev, ¶ 0035, “the threshold can be dynamically adjusted.” Also ¶ 0061, “… the coefficients can be adjusted or refined by using a feedback arrangement, …” Also see Ichimori, Fig. 10, depicting automatic training based on feedback.
In regard to claim 4, Zaitsev and Ichimori also teach:
4. The computer-implemented method of claim 1, wherein configuring one or more artificial intelligence techniques comprises configuring at least one fuzzy model based at least in part on at least a portion of the one or more relevancy-based membership functions and one or more inference rules. Zaitsev ¶ 0031, “Fuzzification is the introduction of fuzziness into the analysis. To perform this operation linguistic variables are defined for all input variables, a term set is formed for each linguistic variable, and a membership function is defined for each term.” Also ¶ 0032, “The fuzzy knowledge base includes production rules …” Also ¶ 0069, “In a particular embodiment, the degree of impact of each category of objects on key system resources is predefined in terms of fuzzy logic: an assigned linguistic variable, a term set for that variable, and the membership function.” Also see Ichimori, Fig. 10, depicting configuration of fuzzy inference as cited above.
In regard to claim 5, Zaitsev also discloses:
5. The computer-implemented method of claim 4, wherein defining one or more relevancy-based membership functions comprises fuzzifying data associated with the one or more relevancy factors. Zaitsev, ¶ 0031, e.g. “Fuzzification is the introduction of fuzziness into the analysis. To perform this operation linguistic variables are defined for all input variables, a term set is formed for each linguistic variable, and a membership function is defined for each term.”
In regard to claim 6, Zaitsev and Ichimori also teach:
6. The computer-implemented method of claim 4, wherein configuring one or more artificial intelligence techniques comprises defining the one or more inference rules by defining one or more fuzzy rules based at least in part on at least a portion of the one or more relevancy-based membership functions. Zaitsev ¶ 0032, “The fuzzy knowledge base includes production rules of the form "IF (premise of the rule), THEN (conclusion of the rule)".” Also ¶ 0055, “… the measure of exigency of removing garbage of one or another category is defined using logical rules …” Also see Ichimori, col. 1, lines 65-67, “… a rule is defined for an area without a rule, by using already prepared rules and membership functions.”
In regard to claim 7, Zaitsev also discloses:
7. The computer-implemented method of claim 1, wherein defining one or more relevancy-based membership functions comprises defining one or more relevancy-based fuzzy membership functions, and Zaitsev ¶ 0069, “In a particular embodiment, the degree of impact of each category of objects on key system resources is predefined in terms of fuzzy logic: an assigned linguistic variable, a term set for that variable, and the membership function.”
Zaitsev does not expressly disclose: wherein defining one or more relevancy-based fuzzy membership functions comprises determining one or more shapes to be associated with the one or more relevancy-based fuzzy membership functions. This is taught by Ichimori. See Ichimori, col. 3, lines 41-42, “… a neural network learning method is applied to this network to automatically tune the shape of a membership function.” Also col. 4 lines 3-4, “the shape parameter of a membership function is modified so as to reduce the output error.” It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use Ishimori’s function shape with Zaitsev’s membership function in order to reduce output error as suggested by Ishimori.
In regard to claim 8, Zaitsev also discloses:
8. The computer-implemented method of claim 1, wherein defining one or more relevancy-based membership functions comprises determining one or more value-based parameters to be implemented in connection with each of the one or more relevancy-based membership functions. Zaitsev, ¶ 0031, “For example, for the linguistic variable "degree of congestion of the hard disk" the term-set can have the form {"very low", "low", "medium", "high", "very high"}, which simplifies the analysis from having to reconcile various numerical values and incompatible units for different types of resources.”
In regard to claim 10, Zaitsev also discloses:
10. The computer-implemented method of claim 1, wherein determining one or more relevancy factors comprises identifying one or more value discrepancies across resources associated with the at least one category of resources. Zaitsev, ¶ 0035, “the system resource manager examines the process list and compares a knowledge base of popular programs (or their processes) against the most resource-intensive programs or processes currently running, and adjusts the threshold dynamically based on a pre-associated level of user sensitivity for each of the primary currently-running programs or processes.”
In regard to claim 11, Zaitsev discloses:
11. A non-transitory processor-readable storage medium having stored therein program code of one or more software programs, wherein the program code when executed by at least one processing device causes the at least one processing device: Zaitsev, p. 9, claim 22, “A computer-readable medium comprising instructions adapted to cause a computer system to …”
All further limitations of claim 11 have been addressed in the above rejection of claim 1.
In regard to claims 12-15, parent claim 11 is addressed above.
All further limitations of claims 12-15 have been addressed in the above rejections of claims 2, 4-6, respectively.
In regard to claim 16, Zaitsev discloses:
16. An apparatus comprising: at least one processing device comprising a processor coupled to a memory; the at least one processing device being configured: See Figs. 2 and 6, depicting an apparatus.
All further limitations of claim 16 have been addressed in the above rejection of claim 1.
In regard to claims 17-20, parent claim 16 is addressed above.
All further limitations of claims 17-20 have been addressed in the above rejections of claims 2, 4-6, respectively.
Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over Zaitsev in view of Ichimori as applied above, and further in view of U.S. Patent Application Publication 20190384329 by Cybulsky et al. ("Cybulsky").
In regard to claim 9, Zaitsev also discloses:
9. The computer-implemented method of claim 1, wherein the at least one category of resources comprises at least one category of hardware devices, and Zaitsev, ¶ 0035, “In one example case, additional parameters, such as the extent of use of user input devices, the extent of use of graphics processing and sound devices, and the like, may be monitored, and the threshold can be dynamically adjusted up or down based on a heavier weighting assigned to such parameters.”
Zaitsev does not expressly disclose:
wherein determining one or more relevancy factors comprises identifying one or more specification discrepancies across hardware devices associated with the at least one category. This is taught by Cybulsky ¶ 0046, “Computing device 16 may be configured to compare the control parameter deviations, measured process parameters, or process output deviations to the nominal or designed specifications or parameter ranges, for example, in real-time, or in near real-time during operation of thermal spray system 10. In some examples, process deviations may include at least one of … a process deviation resulting from incompatibility (for e.g., wrong type or configuration of a component, for instance, using a nozzle that is unsuitable for a high viscosity flow) or wear of the at least one component.” It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use Cybulsky’s specification analysis with Zaitsev’s impact estimate in order to control cost increases and delays as suggested by Cybulsky (see ¶ 0030).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
An intelligent approach to ERP software selection through fuzzy ANP by Ayağ et al. See p. 2173, section 3, “Triangular fuzzy numbers show the participants’ judgements or preferences among the options such as equally important, weakly more important, strongly more important, very strongly more important, and extremely more important preferred (table 2).” Also p. 2176, Table 1, “… we considered three determinants, seven different dimensions, and 22 attribute-enablers, as shown in table 1.” Also p. 2179, section 3.6.2.1, “The ultimate objective of hierarchy is to identify alternatives that are significant to determine the best ERP software.”
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/James D. Rutten/Primary Examiner, Art Unit 2121
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