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 .
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 06/16/2026 has been entered.
The following action is in response to the amendment/remarks of 06/16/2026.
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 06/16/2026), regarding the 35 USC 103 rejection of claims 1-20 over WANG in view of KEGEL, that the combination of WANG and KEGEL do not teach or suggest at least the amended subject matter present in the amended claims, including the modification of numerical importance factors of real estate preferences in response to feedback yielding a changed users’ real-estate preference dataset leading to a second training dataset comprising the changed real-estate preference dataset that is used to retrain the model. The Examiner partially agrees.
On closer review of WANG, WANG does discloses the majority of the claimed limitations.
In particular, WANG discloses a collected dataset comprising users’ real estate listing preferences:
“[0058] As described above, the recommendation process may be a function of both a user and a property. Data associated with users and properties may be referred to as “features”. The user may have features such as profile information, browsing history, location, age, etc. “
“[0059] In some cases, each user may be associated with standard features, such as age, location, family information, income, ownership of properties, and others. Users may also be associated with dynamic features that impact the recommendation system model, such as browsing history, time spent on each property, number of times properties have been visited, and others.”
WANG does disclose collecting feedback from users’ which is used to modify by increasing or decreasing importance factors of real-estate preference of a user:
“[0049] The recommendation system may monitor user activity and use the data to improve itself over time. For example, the recommendation system may determine whether a property A is preferable to a property B. if the user chooses to view property B”
WANG discloses that the modified preference factors are collected as data points and added to the training data to generate an augmented preference dataset:
“[0049] if the user chooses to view property B, their interest may he collected as a data point and added to a set of training data”
WANG discloses that the augmented preference training dataset is used to update/retrain the model:
“[0049] their interest may he collected as a data point and added to a set of training data. The previous model may be loaded from a saved state and updated according to the augmented training data”
Accordingly, WANG does anticipate the majority of the invention as claimed: modifying numerical importance factors associated with individual real-estate preference attributes themselves wherein the transformation occurs at the preference-feature level, user feedback used to alter assigned preference attributes, thereby generating transformed preference data that is then incorporated into a newly created training set that is used for a subsequent training/retraining stage.
The sole deficiency of WANG is that the importance factors are numerical. The Examiner agrees that KEGEL fails to meet this particular deficiency. Accordingly, the 35 USC 103 rejection of claims 1-20 over WANG in view KEGEL is withdrawn.
On further search and consideration, the newly found prior art of HANOWELL (US 2018/0341668 A1) was found to meet this deficiency.
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 HANOWELL (US 2018/0341668 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: set of training data, ¶76-77: input vector 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);
applying one or more transformations to the second data set by modifying an importance factor associated with an individual real-estate listing preference attribute of the second data set in response to collected feedback from one or more remote computer users (WANG ¶49: modified interest indication/importance factor is added to user preferences data used in a set of training data, ¶55: user preferences, ¶59: user data includes preferences) used in generating to generate an augmented set of training data), wherein modifying comprises 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 remote computer users (WANG ¶49: monitor user activity to determine user preference/importance factor and adjusting underlying data associated with real-estate listing preferences, ¶55, ¶59);
generating a transformed preference data set comprising the second data set after modification of the importance factors (¶49: generate augmented training data);
creating a second training set for a second stage of training by altering the first training set with the transformed preference data set (¶49: generate augmented training data); and
retraining the neural network in a second stage using the second training set (¶49: update model using augmented training data).
WANG fails to explicitly disclose wherein the importance factors are numerical importance factors.
HANOWELL discloses methods for utilizing and optimizing real-estate listing management using machine learning (HANOWELL ¶15, ¶18-22), analogous art. In particular, HANOWELL discloses that real-estate preference importance factors of a user may be adjusted through feedback (HANOWELL ¶41: factors are variable importance factors that are adjusted by user) particularly that the importance factors comprise adjustable weightings (HANOWELL ¶46: adjusting weightings of variable factors based on counting). Therefore it would have been obvious to one having ordinary skill in the art and the teachings of WANG and HANOWELL before them before the effective filing of the claimed invention to combine the use of adjustable weighted numerical importance factors of real-estate preferences of users, as taught by HANOWELL, with the adjustable importance factors of real-estate preferences of users of WANG. One would have been motivated to make this combination in order to provide more customizable insight for a particular user while providing higher accuracy of predictive recommendations/results, as suggested by HANOWELL (HANOWELL ¶4, ¶20, ¶41).
Regarding claim 2, WANG and HANOWELL 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 HANOWELL 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 HANOWELL 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 HANOWELL 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 HANOWELL 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 HANOWELL 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 HANOWELL 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 HANOWELL 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 HANOWELL 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 HANOWELL 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 HANOWELL 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 HANOWELL disclose the computer-implemented method of claim 1, and HANOWELL 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 (HANOWELL ¶46: iterative process repeated by user);
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 (HANOWELL ¶46); and
training the neural network in a third stage using the third training set (HANOWELL ¶46, ¶38: reiterative training cycles).
Regarding claim 14, WANG and HANOWELL 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 HANOWELL 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 HANOWELL 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 HANOWELL 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 HANOWELL 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 HANOWELL 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
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
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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
