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 .
Continuity/reexam data
Parent data
18650082 filed 04/30/2024
claims foreign priority to 2023-076104, filed 05/02/2023
Child data
None
Foreign data
Priority number
Date
Country code
Country
Last modified user
Last modified timestamp
2023-076104
05/02/2023
JP
JAPAN
tlo
05/10/2024 06:34:37
Last updated by tlo on 05/10/2024 06:34:37
(*) - Request to retrieve electronic copy of foreign priority from participating receiving offices.
1. Claims presented for examination: 1-5
Priority
2. Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d).
Information Disclosure Statement
3. The information disclosure statement (IDS) submitted on 04/30/2024 and 08/21/2025. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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.
4. Claims 1-5 are rejected under 35 U.S.C. 101 because the claimed invention is directed to abstract idea without significantly more.
Step 1 (See MPEP 2106)
Claims 1-5 are directed to a method which belongs to a statutory class.
Step 2A, Prong One:
Claim recite “training a user model, with first data about the providers and second data about the plurality of evaluators who have made evaluations of products or services provided by the providers in past as input data, and third data indicating degrees of usefulness of the evaluations made by the plurality of evaluators in the past as output data” which is a process that, under its broadest reasonable interpretation, covers performance of the limitation by Mental Process, but for the recitation of generic computer components. Nothing in the claim element precludes the steps from practically being performed in the human mind. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation by mental process, but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A, Prong Two:
Claim recite the method which performs by the computer. The computer is a generic computer which perform the abstract idea.
The additional element:
“Selecting the evaluator suitable for the one of the providers from among the plurality of evaluators by using the user model” is the process for making the selection which is the computer process.
The limitation is thus insignificant extra-solution activity. Limitations that the courts have found not to be enough to qualify as "significantly more” when recited in a claim with a judicial exception include: i. Adding the words "apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, e.g., a limitation indicating that a particular function such as creating and maintaining electronic records is performed by a computer, as discussed in Alice Corp., 134 S. Ct. at 2360, 110 USPQ2d at 1984 (see MPEP § 2106.05(f)). 2106.05(g)--Insignificant Extra-Solution Activity.
Step 2B:
The conclusions for the mere implementation using a computer are carried over and does not provide significantly more.
Looking at the claim as a whole does not change this conclusion and the claim is ineligible.
As to claim 2, the limitation:
“The degrees of usefulness are values calculated based on results of the providers attempting to improve the products or services provided by the providers in the past, based on the evaluations made by the plurality of evaluators in the past” is a mathematical algorithm.
As to claim 3, the limitation:
“The degrees of usefulness are scores given by the providers to the evaluations made by the plurality of evaluators in the past” is a mathematical algorithm.
As to claim 4, the limitation:
“The second data is sets of a plurality of attributes of the each of the plurality of evaluators” is only further defined what second data is and insignificantly to amount significantly more.
As to claim 5, the limitation:
“The user model is a model configured to, when the first data and the second data are inputted, output predicted values of degrees of usefulness of evaluations made by the plurality of evaluators to the one of the providers” is only further defined what first data and second data are and insignificantly to amount 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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
6. Claim(s) 1 and 4 is/are rejected under 35 U.S.C. 103 as being unpatentable over Herz et al. (Patent No. 10,475,100 B1) in view of OIds (Pub. No. US 2022/0327582 A1).
As to claim 1, Herz discloses an information processing apparatus for determining an evaluator to evaluate a product or service provided by one of providers from among a plurality of evaluators, the information processing apparatus comprising a controller configured to execute:
training a user model (intelligent learning search engine) (col. 5, line15-16), with first data about the providers (company scores) (col. 6, lines 51-53) and second data about the plurality of evaluators who have made evaluations of products or services provided by the providers in past as input data (rating to determine the best match…) (col. 5, lines 17-19), and third data indicating degrees of usefulness of the evaluations made by the plurality of evaluators in the past as output data (the incentivization to drive search users to utilize (or obtain) their expertise in the form of quality or value based recommendations is economically driven by the fact that the review…) (col. 3, lines 16-45); and
Herz discloses the search engine system which learned from the user feedback after buying the product or the rating has been provided to the buyer which was helpful to make decision for buying the product (col. 1, lines 66 to col. 2, lines 1-3).
Herz does not explicitly disclose selecting the evaluator suitable for the one of the providers from among the plurality of evaluators by using the user model.
However, OIds discloses selecting the evaluator suitable for the one of the providers from among the plurality of evaluators by using the user model (the deep learning algorithm may access all input data form service providers, service recipient and crowdsourced information to discover previous unknown connections, strength, weakness, and conditional actions for individuals referenced within the collected data. This information may be used by the deep learning algorithms to predict future advantageous combination of service provider and service recipients, and provide recommendations and feedback regarding the success of the match and the outcome of the match to a supervisor or manager of the service provider…) (paragraph 0021). The matching between the service provider (provider as claimed) and recipient (as the evaluator) which provide the outcome by using the deep learning algorithm (user model). Therefore, it would have been obvious to one ordinary skill in the art before the effective filing date of the instant application to include selecting the evaluator suitable for the one of the providers from among the plurality of evaluators by using the user model as disclosed by the OIds in order allow the system to provide corrected evaluator for feedbacks on product or service.
As to claim 4, Herz discloses the information processing apparatus according to claim 1, wherein the second data is sets of a plurality of attributes of the each of the plurality of evaluators (profiles) (col. 6, line 33).
7. Claim(s) 2-3 and 5 is/are rejected under 35 U.S.C. 103 as being unpatentable over Herz et al. (Patent No. 10,475,100 B1) in view of OIds (Pub. No. US 2022/0327582 A1) and further in view of Onishi et al. (Pub. No. US 2015/0220632 A1).
As to claim 2, Herz discloses the information processing apparatus according to claim 1 excepting for wherein the degrees of usefulness are values calculated based on results of the providers attempting to improve the products or services provided by the providers in the past, based on the evaluations made by the plurality of evaluators in the past. However, Onishi discloses the degrees of usefulness are values calculated based on results of the providers attempting to improve the products or services provided by the providers in the past, based on the evaluations made by the plurality of evaluators in the past (the usefulness degree calculation unit 21 calculates a usefulness degree by using the length of the phrase, the frequency at which the phase appears within the positive example group…) (paragraph 0034). Therefore, it would have been obvious to one ordinary skill in the art before the effective filing date of the instant application to include wherein the degrees of usefulness are values calculated based on results of the providers attempting to improve the products or services provided by the providers in the past, based on the evaluations made by the plurality of evaluators in the past as disclosed by Onishi in order to use the calculation to determine what best match between provider and evaluator.
As to claim 3, Herz discloses the information processing apparatus according to claim 1 excepting for wherein the degrees of usefulness are scores given by the providers to the evaluations made by the plurality of evaluators in the past. However, Onishi discloses the degrees of usefulness are scores given by the providers to the evaluations made by the plurality of evaluators in the past (the detection condition determination unit 22 calculates a score based on usefulness degree and the feature degree, and carries out the determination based on the score) (paragraph 0044). The degree calculations produce scores, and the calculation can be used to implement the process. Therefore, it would have been obvious to one ordinary skill in the art before the effective filing date of the instant application to include the degrees of usefulness are scores given by the providers to the evaluations made by the plurality of evaluators in the past as disclosed by Onishi to provide the grading for the product or service.
As to claim 5, Herz discloses the information processing apparatus according to claim 1, wherein the user model is a model (intelligent learning search engine) (col. 5, line15-16) configured to, when the first data (company scores) (col. 6, lines 51-53) and the second data (rating to determine the best match…) (col. 5, lines 17-19) are inputted excepting for output predicted values of degrees of usefulness of evaluations made by the plurality of evaluators to the one of the providers. However, Onishi discloses output predicted values of degrees of usefulness of evaluations made by the plurality of evaluators to the one of the providers (the output unit 4 output as a phrase to be registered in a dictionary, a phrase judged to be appropriate… The output unit 4 not only outputs …. Usefulness degree… score…) (paragraph 0039). Therefore, it would have been obvious to one ordinary skill in the art before the effective filing date of the instant application to include output predicted values of degrees of usefulness of evaluations made by the plurality of evaluators to the one of the providers as disclosed by Onishi to provide the grading for the product or service.
Conclusion
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BAOQUOC N. TO
Examiner
Art Unit 2154
/BAOQUOC N TO/Primary Examiner, Art Unit 2154
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