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 .
The following action is in response to the amendment/remarks of 09/23/2025.
By the amendment, claims 1 and 15 have been amended.
Claims 1-20 are pending and have been considered below.
Response to Arguments/Amendment
Applicant argues (Remarks 09/23/2025 page 9) that the amendment to claims 1 and 15 overcomes the claim objection (Non-Final Rejection 02/27/2025 page 2). The Examiner agrees and the corresponding objection of claims 1 and 15 is withdrawn.
Applicant argues (Remarks 09/23/2025 pages 14-17), regarding the 35 USC 103 rejection of claims 1-20 over WANG in view of KEGEL (Non-Final Rejection 02/27/2025 pages 3-9) that the combination of WANG and KEGEL does not teach or suggest all the elements recited in at least independent claim 1. Particularly that (1) KEGEL does not remedy the cited deficiency of WANG (Remarks 09/23/2025 pages 15-16), (2) KEGEL teaches away from the claimed recitations (Remarks 09/23/2025 page 16) and (3) KEGEL is directed to non-analogous art (Remarks 09/23/2025 pages 16-17). Similar arguments are presented regarding dependent claims 2-14 (Remarks 09/23/2025 page 17), independent claim 15 (Remarks 09/23/2025 page 17) and dependent claims 16-20 (Remarks 09/23/2025 page 18). The Examiner respectfully disagrees as discussed below.
Regarding the argument that (1) KEGEL does not remedy the cited deficiency of WANG, the Examiner respectfully disagrees. Particularly, Applicant argues that KEGEL’s recommendation model is trained on only one set of training data and thus cannot be construed to teach applying one or more transformation to the second data set as claimed (Remarks 09/23/2025 page 15). KEGEL, relied on to teach the deficiency of WANG, teaches adjustments to the amount of training data supplied to achieve a desired level of performance (KEGEL ¶15: controlling amount of training data supplied to achieve desired level of performance). In particular, KEGEL discloses adjusting a ranking of training data based on user usage (KEGEL ¶32, ¶34), and transforming the training data into a modified set of training data (KEGEL ¶94: data supplied to client u(t), ¶100: “When the training data processor 16a receives the feedback signal (y(t) or e(t)), it calculates how much it needs to modify the amount of u(t) provided in that cycle, and based on this and its knowledge of the amount of data which has been assigned to each ranking level, filters the amount of data forwarded to the recommender system 18.”). That is, KEGEL does disclose creating a second training set based on user feedback because an altered or modified first training set is broadly a second training set. Claim 1 expressly supports this interpretation: “creating a second training set for a second stage of training by altering the first training set with collected remote-computer user feedback”. The argument that KEGEL fails to disclose the cited limitations is not persuasive.
Regarding the argument (2) that KEGEL teaches away from the claimed recitations, the Examiner respectfully disagrees. Particularly, Applicant argues that KEGEL, by relying on one and only one training data set, teaches away by unduly limiting from the flexibility of having additional factors influence a second training set. As discussed above, KEGEL teaches relying on a training set that is modified based on user feedback collected as usage data and not “one and only one training data set” in the way argued by Applicant. The examiner recognizes that obviousness may be established by combining or modifying the teachings of the prior art to produce the claimed invention where there is some teaching, suggestion, or motivation to do so found either in the references themselves or in the knowledge generally available to one of ordinary skill in the art. See In re Fine, 837 F.2d 1071, 5 USPQ2d 1596 (Fed. Cir. 1988), In re Jones, 958 F.2d 347, 21 USPQ2d 1941 (Fed. Cir. 1992), and KSR International Co. v. Teleflex, Inc., 550 U.S. 398, 82 USPQ2d 1385 (2007). As stated in the 35 USC 103 rejection, motivation is provided for combining the features of KEGEL with the features of WANG by KEGEL’s motivation for providing more efficient retraining in large scale recommender systems (KEGEL ¶2-4, ¶6, ¶75). The argument that KEGEL teaches away from the claimed recitations is not persuasive.
Regarding the argument (3) that KEGEL is directed to non-analogous art, the Examiner respectfully disagrees. Particularly, Applicant argues that because the telecom industry and real-estate industry are non-analogous to each other, KEGEL is non-analogous are as being directed to a recommendation system for users of a telecom system and not users of a real-estate system. The Examiner notes that it has been held that a prior art reference must either be in the field of the inventor’s endeavor or, if not, then be reasonably pertinent to the particular problem with which the inventor was concerned, in order to be relied upon as a basis for rejection of the claimed invention. See In re Oetiker, 977 F.2d 1443, 24 USPQ2d 1443 (Fed. Cir. 1992). In this case, KEGEL is directed to recommendation systems in large scale data systems that adjust to user feedback. Particularly, KEGEL discloses that the recommendation systems are not limited to consumable items by also extend to any item for sale and/or services (KEGEL ¶14-15, ¶107). As real-estate services are for buying and selling a real-estate item and the instant invention is for solving a problem of providing a configurable process for matching buyers with real-estate listings (Specification ¶3), KEGEL does provide an analogous art. The argument that KEGEL is non-analogous art is not persuasive.
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.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over WANG (US 2018/0253780 A1), in view of KEGEL (US 2015/0020126 A1).
Regarding claim 1, WANG discloses a computer-implemented method for utilizing and optimizing a neural network for real-estate listing management (WANG ¶5, ¶63-64)comprising:
collecting a first data set of real-estate listings that are stored in a real-estate listing service database (WANG ¶56, ¶60, ¶69: property database of property listing features, ¶82);
collecting a second data set from one or more remote computer users about real-estate listing preference of the one or more remote computer users (WANG ¶59, ¶69: user database of user features, ¶82);
creating a first training set comprising the collected first data set of real-estate listings and the second data set of real-estate listing preferences (WANG ¶49: training data used to train model, ¶76-77: input data sets of property characteristics and user characteristics);
training the neural network in a first stage using the first training set (WANG ¶78-80: training the model on the training sets of data).
While WANG discloses that identified user interactions with the listings lead to updates in the neural network (WANG ¶79), WANG fails to explicitly disclose wherein these updates occur by applying one or more transformations to the second data set including increasing or decreasing an importance factor associated with each of the real-estate listing preferences in response to collected feedback from the one or more remoter computer users, creating a second training set for a second stage of training by altering the first training set with the collected remote-computer user feedback and finally training the neural network in a second stage using the second training set.
KEGEL discloses methods for providing recommendations based on characteristics of products and attributes of users (KEGEL ¶14-15, ¶107), an analogous art. In particularly, KEGEL discloses updating a trained learning algorithm used in making recommendations (KEGEL ¶91: learning algorithm of recommendation system is continually updated by training data updates) by applying one or more transformations to the user characteristic data by increasing or decreasing an importance factor associated with each of a listing in response to collected feedback from the one or more remote computer users (KEGEL ¶32, ¶34: usage processor adjusts ranking of training data based on user usage, ¶94:-100: transforming the training data based on collected user feedback factor adjustments), creating a second training set for a second stage of training by altering a first training set with the collected remote-computer user feedback (KEGEL ¶100) and finally training the learning algorithm in a second stage using the second training set (KEGEL ¶105, Fig. 10). Therefore it would have been obvious to one having ordinary skill in the art and the teachings of WANG and KEGEL before them before the effective filing of the claimed invention to train a learning model in a second stage using an updated second training set through applying transformations to a second data set by modifying a factor associated with a listing preference based on collected user interactions and creating the updated second training data by altering the first training set using the user interactions, as suggested by KEGEL, when updating the neural network learning algorithm of the real-estate listing management method of WANG. One would have been motivated to make this combination to provide more efficient retraining in large scale recommender systems, as suggested by KEGEL (KEGEL ¶2-4, ¶6, ¶75).
Regarding claim 2, WANG and KEGEL disclose the computer-implemented method of claim 1, and WANG further discloses wherein collecting the first data set of real-estate listings comprises collecting walkability data about real estate listings (WANG ¶17).
Regarding claim 3, WANG and KEGEL disclose the computer-implemented method of claim 1, and WANG further discloses wherein collecting the first data set of real-estate listings comprises collecting school district data about real estate listings (WANG ¶17).
Regarding claim 4, WANG and KEGEL disclose the computer-implemented method of claim 1, and WANG further discloses wherein collecting the first data set of real-estate listings comprises collecting crime statistic data corresponding to a neighborhood about real estate listings (WANG ¶17).
Regarding claim 5, WANG and KEGEL disclose the computer-implemented method of claim 1, and WANG further discloses wherein collecting the first data set of real-estate listings comprises collecting individual room data about real estate listings (WANG ¶17).
Regarding claim 6, WANG and KEGEL disclose the computer-implemented method of claim 1, and WANG further discloses wherein collecting the first data set of real-estate listings comprises collecting style data about real estate listings (WANG ¶17).
Regarding claim 7, WANG and KEGEL disclose the computer-implemented method of claim 1, and WANG further discloses wherein collecting the second data set of real-estate listings comprises increasing or decreasing an importance factor associated with style data about real estate listings (WANG ¶77, ¶120).
Regarding claim 8, WANG and KEGEL disclose the computer-implemented method of claim 1, and WANG further discloses wherein collecting the second data set of real-estate listings comprises increasing or decreasing an importance factor associated with walkability data about real estate listings (WANG ¶77, ¶120).
Regarding claim 9, WANG and KEGEL disclose the computer-implemented method of claim 1, and WANG further discloses wherein collecting the second data set of real-estate listings comprises increasing or decreasing an importance factor associated with school district data about real estate listings (WANG ¶77, ¶120).
Regarding claim 10, WANG and KEGEL disclose the computer-implemented method of claim 1, and WANG further discloses wherein collecting the second data set of real-estate listings comprises increasing or decreasing an importance factor associated with crime data about real estate listings (WANG ¶77, ¶120).
Regarding claim 11, WANG and KEGEL disclose the computer-implemented method of claim 1, and WANG further discloses wherein collecting the second data set of real-estate listings comprises increasing or decreasing an importance factor associated with room data about real estate listings (WANG ¶77, ¶120).
Regarding claim 12, WANG and KEGEL disclose the computer-implemented method of claim 1, and WANG further discloses:
electronically sending a first matched set of real-estate listings to the one or more remote computer users based on the one or more remote computer user's preferences before applying the transformations (WANG ¶88: user interface presented to user is continually updated based on user interactions); and
electronically sending a second matched set of real-estate listings to the one or more remote computer users based on the one or more remote computer user's preferences after applying the transformations (WANG ¶88: user interface presented to user is continually updated based on user interactions).
Regarding claim 13, WANG and KEGEL disclose the computer-implemented method of claim 1, and KEGEL further discloses:
applying one or more second transformations to the second data set including increasing or decreasing an importance factor associated with each of the about real-estate listing preferences in response to second collected feedback from the one or more remote computer users (KEGEL ¶32, ¶34, ¶94-100: transformation is iterative as more feedback is collected);
creating a third training set for a third stage of training by altering the second training set with the second collected remote-computer user feedback (KEGEL ¶100); and
training the neural network in a third stage using the third training set (KEGEL ¶105, Fig. 8: continual retraining based on input user feedback).
Regarding claim 14, WANG and KEGEL disclose the computer-implemented method of claim 13, and WANG further discloses:
electronically sending a first matched set of real-estate listings to the one or more remote computer users based on the one or more remote computer user's preferences before applying the second transformations (WANG ¶88: user interface presented to user is continually updated based on user interactions); and
electronically sending a second matched set of real-estate listings to the one or more remote computer users based on the one or more remote computer user's preferences after applying the second transformations (WANG ¶88: user interface presented to user is continually updated based on user interactions).
Regarding claim 15, claim 15 recites limitations similar to claim 1.
Further WANG discloses the management system comprising: a real-estate listing management computing device and real-estate database each coupled to a network (WANG ¶40-42, Fig. 1) and configured to perform the method of claim 1.
Regarding claim 16, WANG and KEGEL disclose the computer system of claim 15, and WANG further discloses a remote-user computing device coupled to the network and configured to facility collecting the remote-user feedback (WANG ¶40-42, Fig. 1).
Regarding claim 17, WANG and KEGEL disclose the computer system of claim 15, and WANG further discloses a remote-user computing device coupled to the network and configured to provide crime statistic data used for transforming the second data set (WANG ¶40-42, Fig. 1, ¶77, ¶120).
Regarding claim 18, WANG and KEGEL disclose the computer system of claim 15, and WANG further discloses a remote-user computing device coupled to the network and configured to provide walkability data used for transforming the second data set (WANG ¶40-42, Fig. 1, ¶77, ¶120).
Regarding claim 19, WANG and KEGEL disclose the computer system of claim 15, and WANG further discloses a remote-user computing device coupled to the network and configured to provide school district data used for transforming the second data set (WANG ¶40-42, Fig. 1, ¶77, ¶120).
Regarding claim 20, WANG and KEGEL disclose the computer system of claim 15, and WANG further discloses a remote-user computing device coupled to the network and configured to provide weather data used for transforming the second data set (WANG ¶40-42, Fig. 1, ¶77, ¶120).
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 ANDREW L TANK whose telephone number is (571)270-1692. The examiner can normally be reached Monday-Thursday 9a-6p.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Matthew Ell can be reached at 571-270-3264. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ANDREW L TANK/Primary Examiner, Art Unit 2141