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 .
Response to Arguments
Applicant’s arguments, filed 8/24/2026, with respect to the rejection(s) of claim(s) 15 and 17-20 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of US 2017/0243120 A1 Doan et al in view of JP 2020177498 A Iwasebari et al.
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.
1: Claim(s) 15 and 17-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 2017/0243120 A1 Doan et al in view of JP 2020177498 A Iwasebari et al.
2: As for Claim 15, Doan et al teaches in Paragraphs [0003, 0017, 0020, 0021, 0038, and 0044-0070] and depicts in Figures 1-5 An information processing system (machine learning system discussed in Paragraph [0020]), comprising: a generation unit configured to generate data (displayed content to the user) using a generation model (Paragraph [0003] teaches the social media site generates content to display to a user); an evaluation unit configured to, by using an evaluation model, generate evaluation information (sentiments of a user) for the generated data by the generation unit (sentiments of a user based on the displayed content); an acquisition unit configured to acquire user evaluation information (determination if the sentiment is positive, negative or other characteristic) for the evaluation information (Paragraphs [0019-0021] ); an evaluation model update unit configured to update the evaluation model (sentiment analytics) based on the user evaluation information (the sentiment data is updated based on the determination if the sentiment of the user is positive or negative). However, does not teach a generation model update unit configured to update the generation model based on the user evaluation information.
Iwasebari et al teaches a system in which in the exemplary embodiment of FIG. 1, the prices displayed (generated content to the user) within the sales content are dynamically determined according to "user sentiment" (the user sentiment values are used to change and update the generated content) as described above. In other words, in emotional dynamic pricing, the price of a product is determined based on whether the customer is happy with the purchase of the product. This means that emotional dynamic pricing optimizes the customer experience. As described above, the decision device 100 can dynamically determine prices displayed within sales content based on user sentiment as well as user demand. This allows the decision making device 100 to optimize the seller's profits and increase sales opportunities. Therefore, the determining device 100 can achieve overall optimization of product sales on EC sites and EC applications. Therefore, Iwasebari et al teaches updating what content is displayed based on the determined positive sentiment in order to improve the system by displaying the most appropriate content based on the detected sentiment.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to allow the generation model of Doan et al to use a generation model update unit configured to update the generation model based on the user evaluation information as taught by Iwasebari et al in order to update what content is displayed based on the determined positive sentiment in order to improve the system by displaying the most appropriate content based on the detected sentiment.
3: As for Claim 17, Doan et al further teaches wherein the evaluation unit is further configured to output the evaluation information to an information terminal (client device), and the acquisition unit is further configured to acquire, from the information terminal, the user evaluation information (user sentiment scores) input through an interface (display) presented on the information terminal (Paragraph [0023]).
4: As for Claim 18, Doan et al further teaches in Paragraphs [0003, 0017, 0020, 0021, 0038 and 0044-0070] and depicts in Figures 1-5 wherein the evaluation unit (machine learning system) divides a feature of the generated data into a plurality of elements to perform evaluation for each of the elements (see Figure 5 different values are assigned to different statements), and the acquisition unit acquires user evaluation information (user sentiment scores) including information for adjusting the evaluation for each element (sentiment values are updated based on the users interactions) of the plurality of elements.
5: As for Claim 19, Doan et al teaches in Paragraphs [0003, 0017, 0020, 0021, 0038 and 0044-0070] and depicts in Figures 1-5 wherein the evaluation unit (machine learning system) is further configured to generate related generation data related to the generated data, and the acquisition unit is further configured to acquire the user evaluation information (user sentiment values) including a user intention (Paragraph [0019]) for the related generation data.
6: As for Claim 20, Claim 20 is rejected for reasons discussed related to Claim 15.
Allowable Subject Matter
Claims 1-5 and 7-14 are allowed.
The following is an examiner’s statement of reasons for allowance: The prior art does not teach outputting the user evaluation acquired by the user feedback acquisition unit to a device that updates each of a generation model that generates the generation data and the evaluation model that evaluates the generation data when taken in combination with all the limitations of the independent Claims.
Any comments considered necessary by applicant must be submitted no later than the payment of the issue fee and, to avoid processing delays, should preferably accompany the issue fee. Such submissions should be clearly labeled “Comments on Statement of Reasons for Allowance.”
Conclusion
THIS ACTION IS MADE FINAL. 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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JAMES M HANNETT whose telephone number is (571)272-7309. The examiner can normally be reached 8:00 AM-5:00 PM Monday thru Thursday.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Twyler Haskins can be reached at 571-272-7406 The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/JAMES M HANNETT/Primary Examiner, Art Unit 2639
JMH
October 5, 2026
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