DETAILED ACTION
This action is in response to claims filed 07 June 2023 for application 18330603 filed 07 June 2023. Currently claims 1-12 are pending.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-12 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
The term “at least some” in claims 1, 5 and 9 is a relative term which renders the claim indefinite. The term “at least some” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. Correction or clarification is required.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-12 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
In step 1, claims 1-12 are directed to the statutory category of an article of manufacture, a method, and a system respectively.
In step 2a prong 1, claims 1, 5 and 9 recite, in part, receiving appropriateness determination for rules through machine learning, performing a second appropriateness determination based on the first determination and a similarity, and updating the determination rules based on the first and second determination. The limitations of receiving, performing and updating are processes that, under its broadest reasonable interpretation, covers performance of the limitations in the mind but for the recitation of generic computer components. That is, other than reciting “computer-readable recording medium”, “machine learning”, “computer”, “memory”, and “processor” in the context of the claims, the limitations encompass a person evaluating rules, finding similar rules and updating the rules based on the evaluation and similarity in the mind or with aid. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
In step 2a prong 2, this judicial exception is not integrated into a practical application. In particular, the claims recite the additional elements of “computer-readable recording medium”, “machine learning”, “computer”, “memory”, and “processor”. The computer components in the claim are recited at a high-level of generality (i.e., as a generic processor performing a generic computer function) such that it amounts to no more than mere instructions to apply the exception using a generic computer component (MPEP 2106.05(f)). Accordingly, these 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. Please see MPEP §2106.04.(a)(2).III.C.
In step 2b, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception, either alone or in combination. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of “computer-readable recording medium”, “machine learning”, “computer”, “memory”, and “processor” to perform the steps of the claims amount to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claim is not patent eligible.
Claims 2-4, 6-8, and 10-12 recite further limitations of displaying the rules in order of importance, executing a second determination based on a similarity of a specified rule, and determining correctness and deleting incorrect rules. These limitations amount to the same abstract idea in step 2a prong 1. An additional element of displaying information is presented. Displaying information is merely insignificant extra-solution activity and does not amount to a practical application in step 2a prong 2 nor significantly more in step 2b.
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) 1-12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Adomavicius et al. (User Profiling in Personalization Applications through Rule Discovery and Validation) in view of Sumanth et al. (US 20210365806 A1).
Regarding claims 1, 5 and 9, Adomavicius discloses: A non-transitory computer-readable recording medium storing a rule update program for causing a computer to execute processing comprising:
receiving first appropriateness determination for at least some determination rules among a plurality of determination rules (“In this paper we present an approach to the profiling problem where user profiles are learned from the transactional histories using data mining methods.” P377 §1 ¶4, Figure 1 All Rules go to a validation operator (appropriateness determination));
performing second appropriateness determination for a determination rule other than the some determination rules, based on a result of the first appropriateness determination and similarity between the some determination rules and the determination rule other than the some determination rules (Figure 1 All Rules go to a validation operator (appropriateness determination while a subset are undecided and need further appropriateness determination, “Similarity-based rule grouping. Puts “similar” rules into groups according to the expert-specified similarity criterion. As a result, the expert can inspect groups of rules instead of inspecting individual rules one-by-one. Validation (acceptance or rejection) of a group of rules means that all rules contained in the group are validated together as a group. To accomplish this, we have developed a method providing the expert with abilities to specify different levels of similarity of the rules. We also developed an efficient (linear running time) rule grouping algorithm, presented in [AT], which takes a set of rules and a similarity condition specified by the expert and produces groups of similar rules.” P379 §4 ¶2); and
updating the plurality of determination rules, based on the result of the first appropriateness determination and a result of the second appropriateness determination (Fig 1 accepted rules).
However, while Adomavicius discloses data mining, it does not explicitly disclose: derived through machine learning by using training data.
Sumanth teaches: derived through machine learning by using training data (“In some embodiments, the machine learning model may output a ranked list of rules according to the importance score of each rule. Aggregated importance scores may be determined as described above, and an app, recipient, or combination may be extracted from a specified number of rules having highest importance scores to provide suggestions. In other embodiments, the aggregation of importance scores and extraction of app, recipient, or combination may be may be performed by the machine learning model and output directly as suggestions without outputting a ranked list of rules. In such cases, the importance scores may be determined for the app, recipient, or combination rather than for the rules.” [0165]).
Adomavicius and Sumanth are in the same field of endeavor of evaluating rules and are analogous. Adomavicius discloses a method of analyzing a subset of rules and then applying similarity to remaining rules to accept or reject rules. Sumanth discloses a method fo using machine learning to create a ranked list of rules. It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the data-mining based method for rule analysis and appropriateness determination as disclosed by Adomavicius with the known machine learning based approach for rule analysis as taught by Sumanth to yield predictable results.
Regarding claims 2, 6 and 10, Adomavicius does not explicitly disclose, however, Sumanth teaches: The non-transitory computer-readable recording medium according to claim 1, further for causing to execute processing comprising: displaying the plurality of determination rules in order of an importance, wherein the importance is set to the plurality of determination rules (“In some embodiments, the machine learning model may output a ranked list of rules according to the importance score of each rule. Aggregated importance scores may be determined as described above, and an app, recipient, or combination may be extracted from a specified number of rules having highest importance scores to provide suggestions. In other embodiments, the aggregation of importance scores and extraction of app, recipient, or combination may be may be performed by the machine learning model and output directly as suggestions without outputting a ranked list of rules. In such cases, the importance scores may be determined for the app, recipient, or combination rather than for the rules.” [0165]).
Regarding claims 3, 7 and 11, Adomavicius discloses: The non-transitory computer-readable recording medium according to claim 1, wherein a first determination rule included in the some determination rules and a second determination rule included in the determination rules other than the some determination rules are similar determination rules, and the processing of performing the second appropriateness determination specifies a rule with which a probable result is derived from a result of the first appropriateness determination for the first determination rule, and executes the second appropriateness determination for the second determination rule, based on the specified rule (Figure 1 All Rules go to a validation operator (appropriateness determination while a subset are undecided and need further appropriateness determination, “Similarity-based rule grouping. Puts “similar” rules into groups according to the expert-specified similarity criterion. As a result, the expert can inspect groups of rules instead of inspecting individual rules one-by-one. Validation (acceptance or rejection) of a group of rules means that all rules contained in the group are validated together as a group. To accomplish this, we have developed a method providing the expert with abilities to specify different levels of similarity of the rules. We also developed an efficient (linear running time) rule grouping algorithm, presented in [AT], which takes a set of rules and a similarity condition specified by the expert and produces groups of similar rules.” P379 §4 ¶2).
Regarding claims 4, 8 and 12, Adomavicius discloses: The non-transitory computer-readable recording medium according to claim 1, wherein the result of the first appropriateness determination and the result of the second appropriateness determination are information in which a first label that indicates that the determination rule is correct or a second label that indicates that the determination rule is not correct is associated with the determination rule, and the updating executes processing of deleting a determination rule to which the second label is added from the plurality of determination rules (Fig 2 discarded/rejected rules).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ERIC NILSSON whose telephone number is (571)272-5246. The examiner can normally be reached M-F: 7-3.
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/ERIC NILSSON/Primary Examiner, Art Unit 2151