DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
The instant application having Application No. 19/042,579 has a total of 20 claims pending in the application, there are 3 independent claims and 17 dependent claims, all of which are ready for examination by the examiner.
Oath/Declaration
The applicant’s oath/declaration has been reviewed by the examiner and is found to conform to the requirements prescribed in 37 C.F.R. 1.63.
Drawings
The applicant’s drawings submitted are acceptable for examination purposes.
Specification
The applicant’s specification submitted is acceptable for examination purposes.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory obviousness-type double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); and In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on a nonstatutory double patenting ground provided the conflicting application or patent either is shown to be commonly owned with this application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement.
Effective January 1, 1994, a registered attorney or agent of record may sign a terminal disclaimer. A terminal disclaimer signed by the assignee must fully comply with 37 CFR 3.73(b).
Claims 1, 4-7, 9-11, 13-15 and 17-19 are rejected on the ground of nonstatutory obviousness-type double patenting as being unpatentable over claims 1, 4-7, 9-11, 13-15 and 17-19 of U.S. Application No. 18/438,128. Although the conflicting claims are not identical, they are not patentably distinct from each other.
Claims
Instant Application
App#: 18/438,128
1, 4-7, 9-11, 13-15 and 17-19
1. (Currently Amended) A system for rearranging tags associated with real estate listing information displayed at a graphical user interface (GUI) of a computer system using known and unknown levels of webpage traffic, the system comprising: at least one processor; at least one memory coupled to the at least one processor and storing instructions that, when executed by the at least one processor, cause the system to perform operations comprising: obtaining a first set of real estate listing tags, wherein individual tags of the first set of real estate listing tags indicate home attributes and are associated with (i) a geographic region and (ii) a known level of webpage traffic; obtaining a second set of real estate listing tags, wherein individual tags of the second set of real estate listing tags indicate home attributes and are associated with (i) the geographic region and (ii) an unknown level of webpage traffic; generating a set of popular real estate listing tags by selecting known real estate listing tags from the first set of real estate listing tags having a respective known level of webpage traffic meeting or exceeding a threshold level of webpage traffic; performing clustering using the set of popular real estate listing tags and the second set of real estate listing tags, to determine a set of clusters, wherein individual clusters of the set of clusters indicate similar real estate listing tags, and wherein individual clusters of the set of clusters include at least one of the popular real estate listing tags of the set of popular real estate listing tags and one or more real estate listing tags of the second set of real estate listing tags; for individual clusters of the set of clusters: determining, for individual real estate listing tags of the second set of real estate listing tags, an estimated level of webpage traffic based on (i) a similarity distance value between two or more listing tags in the respective cluster and (ii) the level of known webpage traffic of the popular real estate listing tag included in the respective cluster; ranking a combined set of real estate listing tags, based on the respective level of at least one of known webpage traffic or estimated webpage traffic associated with the respective real estate listing tag, wherein the combined set of real estate tags comprises the second set of real estate listing tags and the set of popular real estate listing tags; selecting, from the combined set of ranked real estate listing tags, a real estate listing tag based on the level of the known webpage traffic or the estimated webpage traffic as compared to allone or more of the other ranked real estate listing tags of the combined set of ranked real estate listing tags;and generating, for display at the GUI, the selected real estate listing tag with a real estate listing in the geographic region.
4. (Original) The system of claim 1, wherein the known level of webpage traffic indicates an amount of (i) clicks, (ii) saves, (iii) time spent viewing, or (iii) scrolling on a webpage associated with a respective real estate listing tag of the first set of real estate listing tags.
5. (Currently Amended) A method for rearranging real estate phrases associated with a real estate listing displayed on a graphical user interface (GUI) of a computer system using known and unknown user interaction information comprising: obtaining a first set of real estate listing phrases, wherein individual real estate listing phrases of the first set of real estate listing phrases are associated with a predetermined user interaction value; obtaining a second set of real estate listing phrases, wherein individual real estate listing phrases of the second set of real estate listings are associated with an unknown user interaction value; determining a first subset of real estate listing phrases from the first set of real estate listing phrases having a respective user interaction value satisfying a user interaction threshold value; performing clustering on the first subset of real estate listing phrases and the second set of real estate listing phrases to generate a set of clusters, wherein individual clusters of the set of clusters represent a cluster of similar real estate phrases; generating a predicted user interaction value for individual real estate listing phrases of the second set of real estate listing phrases based on a similarity between a respective real estate listing phrase of the second set of real estate phrases and a respective real estate listing phrase of the first subset of real estate listing phrases with respect to a given cluster of the set of clusters; and selecting, for display on a GUI, in association with a real estate listing, a real estate listing phrase of the first subset of real estate listing phrases or the second set of real estate listing phrases, wherein the real estate listing phrase is selected based on the predicted or predetermined user interaction value as compared to one or more of the other real estate listing phrases of the first subset of real estate listing phrases or the second set of real estate listing phrases.
6. (Currently Amended) The method of claim 5, further comprising: accessing a remote data store to obtain a set of real estate listing descriptions associated with a geographic region; generating a set of raw real estate listing phrases, based on the set of real estate listing descriptions associated with the geographic region, using a second machine learning model; generating, a vector embedding for individual raw real estate listing phrases of the set of raw real estate listing phrases, using a third machine learning model, wherein individual vector embeddings are associated with contextual information indicating home attribute information of the respective raw real estate listing phrase; and using the vector embedding for individual raw real estate listing phrases of the set of raw real estate listing phrases in the second set of real estate listing phrases.
7. (Currently Amended) The method of claim 6, further comprising: training the second machine learning model on a second set of real estate listing descriptions, wherein individual real estate listing descriptions of the second set of real estate listing descriptions are associated with different geographic regions.
9. (Original) The method of claim 5, wherein the predetermined user interaction value indicates an amount of (i) clicks, (ii) saves, (iii) time spent viewing, or (iv) scrolling on a webpage associated with a respective real estate listing phrase of the first set of real estate listing phrases.
10. (Currently Amended) The method of claim 5, wherein the generating, for display on the GUI, in association with a real estate listing, a real estate listing phrase of the first subset of real estate listing phrases or the second set of real estate listing phrases, further comprises: accessing a remote data store to obtain a set of real estate listings, wherein individual real estate listings of the set of real estate listings are associated with a real estate listing description; identifying, based on the real estate listing descriptions of set of real estate listings, at least one real estate listing having the real estate listing phrase included in the respective real estate listing description; and generating, for display, the real estate listing phrase on a primary image associated with the real estate listing.
11. (Original) The method of claim 5, wherein the clustering is performed using a K-Nearest- Neighbor machine learning model.
13. (Currently Amended) One or more non-transitory computer-readable media comprising instructions that, when executed by one or more processors, cause operations comprising: determining a first subset of real estate listing phrases from the a first set of real estate listing phrases, wherein individual real estate listing phrases of the first set of real estate listing phrases are associated with a predetermined user interaction value, and wherein individual real estate listing phrases of the first subset of real estate listing phrases have a respective user interaction value satisfying a user interaction threshold value; performing clustering on the first subset of real estate listing phrases and a second set of real estate listing phrases to generate a set of clusters, wherein individual real estate listing phrases of the second set of real estate listing phrases are associated with an unknown user interaction value, and wherein individual clusters of the set of clusters represent a cluster of similar real estate phrases; generating a predicted user interaction value for individual real estate listing phrases of the second set of real estate listing phrases based on a distance between a respective real estate listing phrase of the second set of real estate phrases and a respective real estate listing phrase of the first subset of real estate listing phrases with respect to a given cluster of the set of clusters; and selecting, for display at a GUI, in association with a real estate listing, a real estate listing phrase of the first subset of real estate listing phrases or the second set of real estate listing phrases, wherein the real estate listing phrase is selected based on the predicted or predetermined user interaction value as compared to a threshold number of other real estate listing phrases of the first subset of real estate listing phrases or the second set of real estate listing phrases.
14. (Currently Amended) The media of claim 13, the operations further comprising: accessing a remote data store to obtain a set of real estate listing descriptions associated with a geographic region; generating a set of raw real estate listing phrases, based on the set of real estate listing descriptions associated with the geographic region, using a second machine learning model; generating, a vector embedding for individual raw real estate listing phrases of the set of raw real estate listing phrases, using a third machine learning model, wherein individual vector embeddings are associated with contextual information indicating home attribute information of the respective raw real estate listing phrase; and using the vector embedding for individual raw real estate listing phrases of the set of raw real estate listing phrases in the second set of real estate listing phrases.
15. (Currently Amended) The media of claim 14, the operations further comprising: training the second machine learning model on a second set of real estate listing descriptions, wherein individual real estate listing descriptions of the second set of real estate listing descriptions are associated with different geographic regions.
17. (Original) The media of claim 13, wherein the predetermined user interaction value indicates an amount of (i) clicks, (ii) saves, (iii) time spent viewing, or (iv) scrolling on a webpage associated with a respective real estate listing phrase of the first set of real estate listing phrases.
18. (Currently Amended) The media of claim 13, wherein the generating, for display on the GUI, in association with a real estate listing, a real estate listing phrase of the first subset of real estate listing phrases and the second set of real estate listing phrases, the operations further comprising: accessing a remote data store to obtain a set of real estate listings, wherein individual real estate listings of the set of real estate listings are associated with a real estate listing description; identifying, based on the real estate listing descriptions of set of real estate listings, at least one real estate listing having the real estate listing phrase included in the respective real estate listing description; and generating, for display, the real estate listing phrase on a primary image associated with the real estate listing.
19. (Original) The media of claim 13, wherein the clustering is performed using a K-Nearest- Neighbor machine learning model.
1. A system for rearranging tags associated with real estate listing information displayed at a graphical user interface (GUI) of a computer system using known and unknown levels of webpage traffic, the system comprising: at least one processor; at least one memory coupled to the at least one processor and storing instructions that, when executed by the at least one processor, perform operations comprising: accessing a data store to obtain (i) a first set of real estate listing tags and (ii) a second set of real estate listing tags, wherein each tag of the first set of real estate listing tags indicates a home attribute and is associated with (i) a geographic region and (ii) an known level of webpage traffic; and wherein each tag of the second set of real estate listing tags indicates a home attribute and is associated with (i) the geographic region and (ii) an unknown level of webpage traffic; generating a set of popular real estate listing tags by selecting known real estate listing tags from the first set of known real estate listing tags having a respective known level of webpage traffic meeting or exceeding a threshold level of webpage traffic; performing clustering using the set of popular real estate listing tags and the second set of real estate listing tags, to determine a set of clusters, wherein each cluster of the set of clusters indicates similar real estate listing tags, and wherein each cluster of the set of clusters includes at least one of the popular real estate listing tags of the set of popular real estate listing tags and one or more real estate listing tags of the second set of real estate listing tags; for each cluster of the set of clusters: determining, for each of real estate listing tag of the second set of real estate listing tags, an estimated level of webpage traffic based on (i) a similarity distance value between two or more listing tags in the respective cluster and (ii) the level of known webpage traffic of the popular real estate listing tag included in the respective cluster; ranking each real estate listing tag of a combined set of real estate listing tags, in descending order, based on the respective level of known webpage traffic and/or estimated webpage traffic associated with the respective real estate listing tag, wherein the combined set of real estate tags comprises the second set of real estate listing tags and the set of popular real estate listing tags; selecting, from the combined set of ranked real estate listing tags, a real estate listing tag having a highest level of the known webpage traffic or the estimated webpage traffic as compared to all other ranked real estate listing tags of the combined set of ranked real estate listing tags; and generating, for display at the GUI, the selected real estate listing tag with a real estate listing in the geographic region.
4. The system of claim 1, wherein the known level of webpage traffic indicates an amount of (i) clicks, (ii) saves, (iii) time spent viewing, or (iii) scrolling on a webpage associated with a respective real estate listing tag of the first set of real estate listing tags.
5. A method for rearranging real estate phrases associated with a real estate listing displayed on a graphical user interface (GUI) of a computer system using known and unknown user interaction information comprising: accessing a remote data store to obtain (i) a first set of real estate listing phrases each associated with a predetermined user interaction value, and (ii) a second set of real estate listing phrases each associated with an unknown user interaction value; determining a first subset of real estate listing phrases from the first set of real estate listing phrases having a respective user interaction value satisfying a user interaction threshold value; performing clustering on the first subset of real estate listing phrases and the second set of real estate listing phrases to generate a set of clusters, wherein each cluster of the set of clusters represent a cluster of similar real estate phrases; generating a predicted user interaction value for each real estate listing phrase of the second set of real estate listing phrases based on a similarity between a respective real estate listing phrase of the second set of real estate phrases and a respective real estate listing phrase of the first subset of real estate listing phrases with respect to a given cluster of the set of clusters; and selecting, for display on a GUI, in association with a real estate listing, a real estate listing phrase of the first subset of real estate listing phrases or the second set of real estate listing phrases, the selected real estate listing phrase having a highest predicted or predetermined user interaction value than all other real estate listing phrases of the first subset of real estate listing phrases or the second set of real estate listing phrases.
6. The method of claim 5, further comprising: accessing the remote data store to obtain a set of real estate listing descriptions associated with a geographic region; generate a set of raw real estate listing phrases, based on the set of real estate listing descriptions associated with the geographic region, using a second machine learning model; generate, a vector embedding for each raw real estate listing phrase of the set of raw real estate listing phrases, using a third machine learning model, wherein each vector embedding is associated with contextual information indicating home attribute information of the respective raw real estate listing phrase; and using the vector embedding for each raw real estate listing phrase of the set of raw real estate listing phrases as the second set of real estate listing phrases.
7. The method of claim 6, further comprising: training the second machine learning model on a second set of real estate listing descriptions, wherein each real estate listing description of the second set of real estate listing descriptions are associated with different geographic regions.
9. The method of claim 5, wherein the predetermined user interaction value indicates an amount of (i) clicks, (ii) saves, (iii) time spent viewing, or (iv) scrolling on a webpage associated with a respective real estate listing phrase of the first set of real estate listing phrases.
10. The method of claim 5, wherein the generating, for display on the GUI, in association with a real estate listing, a real estate listing phrase of the first subset of real estate listing phrases or the second set of real estate listing phrases, further comprises: accessing the remote data store to obtain a set of real estate listings, wherein each real estate listing of the set of real estate listings are associated with a real estate listing description; identifying, based on the real estate listing descriptions of set of real estate listings, at least one real estate listing having the real estate listing phrase included in the respective real estate listing description; and generating, for display, the real estate listing phrase on a primary image associated with the real estate listing.
11. The method of claim 5, wherein the clustering is performed using a K-Nearest-Neighbor machine learning model.
13. One or more non-transitory computer-readable media comprising instructions that, when executed by one or more processors, cause operations comprising: accessing a remote data store to obtain (i) a first set of real estate listing phrases each associated with a predetermined user interaction value and (ii) a second set of real estate listing phrases each associated with an unknown user interaction value; determining a first subset of real estate listing phrases from the first set of real estate listing phrases having a respective user interaction value satisfying a user interaction threshold value; performing clustering on the first subset of real estate listing phrases and the second set of real estate listing phrases to generate a set of clusters, wherein each cluster of the set of clusters represent a cluster of similar real estate phrases; generating a predicted user interaction value for each real estate listing phrase of the second set of real estate listing phrases based on a distance between a respective real estate listing phrase of the second set of real estate phrases and a respective real estate listing phrase of the first subset of real estate listing phrases with respect to a given cluster of the set of clusters; and selecting, for display at a GUI, in association with a real estate listing, a real estate listing phrase of the first subset of real estate listing phrases or the second set of real estate listing phrases, the selected real estate listing phrase having a highest predicted or predetermined user interaction value than a threshold number of other real estate listing phrases of the first subset of real estate listing phrases or the second set of real estate listing phrases.
14. The media of claim 13, the operations further comprising: accessing the remote data store to obtain a set of real estate listing descriptions associated with a geographic region; generate a set of raw real estate listing phrases, based on the set of real estate listing descriptions associated with the geographic region, using a second machine learning model; generate, a vector embedding for each raw real estate listing phrase of the set of raw real estate listing phrases, using a third machine learning model, wherein each vector embedding is associated with contextual information indicating home attribute information of the respective raw real estate listing phrase; and using the vector embedding for each raw real estate listing phrase of the set of raw real estate listing phrases as the second set of real estate listing phrases.
15. The media of claim 14, the operations further comprising: training the second machine learning model on a second set of real estate listing descriptions, wherein each real estate listing description of the second set of real estate listing descriptions are associated with different geographic regions.
17. The media of claim 13, wherein the predetermined user interaction value indicates an amount of (i) clicks, (ii) saves, (iii) time spent viewing, or (iv) scrolling on a webpage associated with a respective real estate listing phrase of the first set of real estate listing phrases.
18. The media of claim 13, wherein the generating, for display on the GUI, in association with a real estate listing, a real estate listing phrase of the first subset of real estate listing phrases and the second set of real estate listing phrases, the operations further comprising: accessing the remote data store to obtain a set of real estate listings, wherein each real estate listing of the set of real estate listings are associated with a real estate listing description; identifying, based on the real estate listing descriptions of set of real estate listings, at least one real estate listing having the real estate listing phrase included in the respective real estate listing description; and generating, for display, the real estate listing phrase on a primary image associated with the real estate listing.
19. The media of claim 13, wherein the clustering is performed using a K-Nearest-Neighbor machine learning model.
Allowable Subject Matter
Claims 1, 4-7, 9-11, 13-15, 17-19 and 21-26 are in condition for allowance.
For independent claims 1, 5 and 13, prior art, in current interpretation of the perceived language, teaches clustering of listing data using K-nearest-neighbor algorithm like cited reference Gross et al. (US 2016/0027051 A1) but does not teach using levels of distance and network traffic based popularity and user interaction, and website traffic based tagging clustering.
Conclusion
The Examiner requests, in response to this Office action, that support be shown for language added to any original claims on amendment and any new claims. That is, indicate support for newly added claim language by specifically pointing to page(s) and line no(s) in the specification and/or drawing figure(s). This will assist the Examiner in prosecuting the application.
When responding to this Office action, Applicant is advised to clearly point out the patentable novelty which he or she thinks the claims present, in view of the state of the art disclosed by the references cited or the objections made. He or she must also show how the amendments avoid such references or objections See 37 CFR 1.111(c).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to AJITH M JACOB whose telephone number is (571)270-1763. The examiner can normally be reached on Monday-Friday: Flexible Hours.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Apu Mofiz can be reached on 571-272-4080. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/AJITH JACOB/Primary Examiner, Art Unit 2161
12/12/2025