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 amendment
2. This is a Final Office action in response to applicant's remarks and arguments filed on 10/22/2025.
3. Status of the claims:
• No claims have been amended.
• Claims 1-3, 5-6, 8-12, 14-15, 17-20 are currently pending and have been examined.
Response to remarks/arguments
4. Applicant’s remarks and arguments filed on 10/22/2025 with respect to the rejection of claims 1-3, 5-6, 8-12, 14-15, 17-20 have been fully considered but they are not persuasive.
5. On page 1 of Applicant’s remarks and arguments dated 10/22/2025, Applicant argues that Raymond teaches away from Applicant's claims and disclosure, because Raymond desires to reduce user input which is in contrast to the user input wanted in Applicant's claim combinations. In fact, in the Office Action, the motivation to combine Gross and Raymond is because “... doing so would have been to provide tools and techniques for reducing the amount of user input required to produce an effective user preference profile”.
6. In response to Applicant’s above arguments, the examiner respectfully disagrees. That is, a reference does not teach away merely because it discloses an embodiment that optimizes or reduces user input. Raymond explicitly discloses that user-provided information is one of several sources used to generate a user profile (see at least Raymond, para. 25, 27, 50). The fact that Raymond also permits automated data collection does not criticize, discredit, or discourage systems involving explicit user participation, nor does it render such systems incompatible. Applicant’s claims do not exclude automated profiling, nor do they require a specific quantity of user input beyond defining and inviting additional users. Raymond therefore does not teach away from the claimed subject matter. Rather, Raymond remains compatible with systems in which a primary user initiates or controls aspects of profile creation, including defining social relationships. Further, the stated motivation in the Office Action improving profile generation efficiency remains a valid rationale to combine even if Applicant’s embodiment emphasizes user involvement. Optimization of user input does not negate user control.
7. On page 2 of Applicant’s remarks and arguments dated 10/22/2025, Applicant argues that Raymond does not disclose “additional users comprising one or more collaborators defined and invited by the primary user”, as recited in independent claims 1 and 10.
8. In response to Applicant’s above arguments, the examiner respectfully disagrees. That is, Raymond explicitly discloses the use of social network connections and associated users as sources of profile data (see at least para. 25, 27, 50). Social network relationships necessarily involve user-defined connections, including accepting, rejecting, or inviting other users. One of ordinary skill in the art would reasonably understand that collaborators within a social network context are defined and invited by users, even if the reference does not use Applicant’s exact terminology.
Moreover, the claims do not require a particular invitation protocol or user interface mechanism; therefore, Raymond’s disclosure reasonably meets this limitation when read in light of the knowledge of one skilled in the art.
9. On page 2 of Applicant’s remarks and arguments dated 10/22/2025, Applicant argues that Vallery does not disclose “wherein the primary user determines an influence priority of each of the additional users and the primary user selects a weighting level for each of the additional users”, as recited in independent claims 1 and 10.
10. In response to Applicant’s above arguments, the examiner respectfully disagrees. That is, Vallery discloses that users are associated in a “posse,” where affinity values and link distance are used to weight the influence of different associated users (see at least Vallery, para. 54, 57, 79; Fig. 1A, Table 6). These values directly affect how much influence each associated user has on attribute determination and recommendation scoring. While Applicant argues that link distance or affinity is not expressly labeled as an “influence priority,” the claims do not require a specific label, only that users have relative influence. Vallery clearly establishes relative influence among users based on affinity and correlation, which meets the claimed concept of influence priority.
11. On page 3 of Applicant’s remarks and arguments dated 10/22/2025, Applicant argues that there is no selection by the primary user of a weighting level for any of the additional users in Vallery, as recited in independent claims 1 and 10.
12. In response to Applicant’s above arguments, the examiner respectfully disagrees. That is, the claims merely recite that the primary user “selects” a weighting level, which reasonably encompasses selection via system configuration, acceptance of defaults, or participation in establishing social links that determine weighting. Moreover, in Vallery, the weighting of associated users is determined based on: affinity, similarity of interests, behavioral data, and community relationships. Thus, the primary user’s actions, such as linking to associates, forming a posse, and interacting with content, directly determine these weighting outcomes. This constitutes selection of weighting levels within the broadest reasonable interpretation of the claims.
13. On page 4 of Applicant’s remarks and arguments dated 10/22/2025, Applicant further argues that Vallery appears to teach away from Applicant's claim combinations and disclosure, because it wants to provide recommendations to buyers that are not what the buyer is specifically searching for. This is in contrast to the specific entering of information and search parameters as determined by the primary user in Applicant's claim combinations.
14. In response to Applicant’s above arguments, the examiner respectfully disagrees. That is, teaching away requires a reference to criticize, discredit, or discourage the claimed approach. Vallery does not discourage user-driven searches or user-specified preferences; instead, it supplements them with additional contextual information. This enhancement does not negate the use of user-entered data and does not render Vallery incompatible with Applicant’s claimed invention.
Moreover, combining user-entered preferences (as taught by Gross and Raymond) with socially weighted influence (as taught by Vallery) would have been predictable and obvious to one of ordinary skill seeking to improve recommendation relevance.
In view of the above Examiner’s responses, Applicant’s arguments regarding independent claims 1 and 10 are not persuasive, then Applicant’s arguments regarding dependent claims 2-3, 5-6, 8-9, 11-12, 14-15 and 17-20 are also not persuasive.
Please the rejections below.
Claim Rejections - 35 USC § 103
15. 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.
16. 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.
17. 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.
18. 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.
19. Claim(s) 1-3, 5-6, 8-12, 14-15, 17-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Gross et al. (US 20160027051 A1) in view of Raymond et al. (US 20140358943 A1) and further in view of Valley et al. (US 20120173324 A1).
Regarding claim 1, Gross discloses a radiocommunication system comprising: a first wireless communication device including a first interface for entering information associated with a plurality of preference criteria associated with the good or service (Gross, para. 138: FIG. 3A depicts a process 300 by which users can search for real estate/building stock that meets particular criteria of interest, including certain visual aesthetics, architectural features, predicted occupancy criteria, etc. This is done preferably through providing search parameters to Lead Generator Engine 160 (FIG. 1) which then identifies matching properties and outputs reports through Report Logic 170 to a client device 112), wherein each preference criteria indicates at least one degree of importance that a user assigns to a corresponding attribute of the good or service (Gross, para. 158: To collect information first hand on building stock inventory, mobile handset users can be solicited to directly rate the quality or aesthetic appeal of a building structure that they are viewing on location within interface 380 as well using any convenient scale); and a central server configured to store and process the plurality of preference criteria and to search for electronic records for relevant items associated with the good or service using the stored plurality of preference criteria (Gross, para. 271, 298: This allows a vendor to adjust up or down the constraints in a campaign, to determine how many leads are desired for focused advertising. The leads are then stored in a database, table or index 2594 for later matching and comparison in response to a user query relating to the property in question); wherein the central server also determines a score for each corresponding attribute for each of the relevant items using the electronic records (Gross, Fig. 2, para. 124: step 230 the classifier preferably outputs a score for each entry in a Building Stock images 142 (or other particular unknown target image presented in a list 232) along with a confidence score for each of N possible attributes, M possible conditions for each, and additional information such as an estimated location in the target image), and generates a prioritized list of the relevant items using at least one ranking algorithm (Gross, para. 428, 464, 479: The filling of the clusters may be based on a time based priority (first opened, first closed) or other factors such as a percentage of cluster completion).
Gross does not appear to explicitly disclose wherein the central server sends the prioritized list to the first wireless communication device for display thereon, and wherein the entering information associated with the plurality of preference criteria further comprises entering, by a primary user, the plurality of preference criteria associated with the good or service and entering, by additional users comprising one or more collaborators defined and invited by the primary user, the plurality of preference criteria associated with a same good or service, wherein the primary user determines an influence priority of each of the additional users and the primary user selects a weighting level for each of the additional users.
In the same field of endeavor, Raymond discloses wherein the central server sends the prioritized list to the first wireless communication device for display thereon (Raymond, para. 177: displaying the ordered list to the user might include displaying to one or more user devices associated with the user), and wherein the entering information associated with the plurality of preference criteria further comprises entering, by a primary user, the plurality of preference criteria associated with the good or service and entering, by additional users comprising one or more collaborators defined and invited by the primary user, the plurality of preference criteria associated with a same good or service (Raymond, para. 25, 27, 50: generate a user profile based at least in part on the plurality of factors, and infer a prioritized list of real estate listing characteristics to satisfy the user based on a prioritized list of homeseeker criteria that is part of the user profile. To generate the user profile, the computer might collect data about the user from a variety of sources, including, without limitation, the user himself or herself, social network servers and/or databases, data collection servers and/or databases that collect data about the user (knowingly or unbeknownst to the user), and/or the like).
Therefore, it would have been obvious to one with ordinary skill in the art before the effective filing date of the claimed invention to combine the teaching of Gross with the teaching of Raymond by using the above features such as the central server sends the prioritized list to the first wireless communication device for display thereon, and wherein the entering information associated with the plurality of preference criteria further comprises entering, by a primary user, the plurality of preference criteria associated with the good or service and entering, by additional users comprising one or more collaborators defined and invited by the primary user, the plurality of preference criteria associated with a same good or service as taught by Raymond. The motivation for doing so would have been to provide tools and techniques for reducing the amount of user input required to produce an effective user preference profile.
The combination of Gross and Raymond does not appear to explicitly disclose wherein the primary user determines an influence priority of each of the additional users and the primary user selects a weighting level for each of the additional users.
In the same field of endeavor, Valley discloses wherein the primary user determines an influence priority of each of the additional users and the primary user selects a weighting level for each of the additional users (Valley, para. 8, 46, 52, 57: allowing a primary user to enter information related to products or services; at least one storage device that stores a user profile comprising the information entered by the primary user, and community user information of other users, wherein the other users have an affinity with the primary user based on comparisons of distance or attribute preferences between the primary user and the other users. Data entered by the primary user as well as information from other users that are related to or have common interests with the primary user, wherein the user profile dynamically changes based on, at least in part, new actions by the primary user as well as new actions by the other users; weigh the information in the user profile to determine targeted recommendations for the primary user; and provide the targeted recommendations to the primary user based on the weighted information of the user profile).
Therefore, it would have been obvious to one with ordinary skill in the art before the effective filing date of the claimed invention to combine the teaching of Gross as modified by Raymond with the teaching of Valley by using the above features such as the primary user determines an influence priority of each of the additional users and the primary user selects a weighting level for each of the additional users as taught by Valley. The motivation for doing so would have been to provide the targeted recommendations to the primary user based on the weighted information of the user profile.
Regarding claim 2, Gross as modified by Raymond and Valley discloses the radiocommunication system of claim 1, however, Raymond further discloses wherein the good or service is real estate properties for sale, the relevant items are real estate properties in a certain location, the corresponding attributes include number of bedrooms, and the at least one degree of importance includes a must have preference criteria and a want preference criteria (Raymond, para. 21, 59, 143, 158: the list of real estate listings might be a list of properties for sale and the attributes include number of bedrooms and a degree of importance).
Therefore, it would have been obvious to one with ordinary skill in the art before the effective filing date of the claimed invention to combine the teaching of Gross with the teaching of Raymond by using the above features such as the list of real estate listings might be a list of properties for sale and the attributes include number of bedrooms as taught by Raymond. The motivation for doing so would have been to provide tools and techniques for reducing the amount of user input required to produce an effective user preference profile.
Regarding claim 3, Gross as modified by Raymond and Valley discloses the radiocommunication system of claim 2, however, Raymond further discloses wherein the plurality of preference criteria entered by the user indicates that the user must have three bedrooms, and that the user wants four bedrooms and further wherein the at least one ranking algorithm prioritizes a first relevant item in the prioritized list that has four bedrooms higher than a second relevant item that has three bedrooms if the first and second relevant items have a same score for others of the plurality of preference criteria (Raymond, para. 65, 66, 109: Every residential real estate property has a unique set of endogenous and exogenous qualities that determine the property's utility in satisfying a plurality of homebuyer or renter needs. By quantifying these needs and the tradeoffs potential customers are willing to make, and by quantifying a residential property's ability to deliver relevant benefits on a plurality of dimensions, the degree to which a given potential home is likely to satisfy a particular homeseeker can be determined. When this match is calculated between a user and multiple residential properties, these results may be presented in a rank-ordered display, in which the items displayed most prominently are more likely to suit the user's preferences than items displayed less prominently).
Therefore, it would have been obvious to one with ordinary skill in the art before the effective filing date of the claimed invention to combine the teaching of Gross with the teaching of Raymond by using the above features such as prioritizing a first relevant item in the prioritized list that has four bedrooms higher than a second relevant item that has three bedrooms as taught by Raymond. The motivation for doing so would have been to provide tools and techniques for reducing the amount of user input required to produce an effective user preference profile.
Regarding claim 5, Gross as modified by Raymond and Valley discloses the radiocommunication system of claim 1, however, Raymond further discloses wherein the ranking algorithm uses average scores associated with the preference criteria entered by each of the additional users for corresponding attributes having a same degree of importance assigned thereto, the average scores being weighted by a factor that is different for different degrees of importance to determine where each relevant item is ranked in the prioritized list (Raymond, para. 22, 50, 52, 59: generate and prioritize a list of potential homes from the list of real estate listings, based on comparisons, and at least in part on matches, between the prioritized list of real estate listing characteristics and attributes of at least some potential homes in the listings. A computer may receive a list of real estate listings, each pertaining to, and including a plurality of attributes about, an available home for purchase or rent/lease. The computer might collect information about a plurality of factors relating to a prospective homeseeker, generate a user profile based at least in part on the plurality of factors, and infer a prioritized list of real estate listing characteristics to satisfy the user based on a prioritized list of homeseeker criteria that is part of the user profile).
Therefore, it would have been obvious to one with ordinary skill in the art before the effective filing date of the claimed invention to combine the teaching of Gross with the teaching of Raymond by using the above features such as the average scores being weighted by a factor that is different for different degrees of importance to determine where each relevant item is ranked in the prioritized list as taught by Raymond. The motivation for doing so would have been to provide tools and techniques for reducing the amount of user input required to produce an effective user preference profile.
Regarding claim 6, Gross as modified by Raymond and Valley discloses the radiocommunication system of claim 5, wherein the weights used by the ranking algorithm for the average scores generated using the corresponding attributes associated with the primary user's plurality of preference criteria are different from the weights used by the ranking algorithm for the average scores generated using the corresponding attributes associated with each of the additional user's plurality of preference criteria (Gross, para. 124, 158: the classifier preferably outputs a score for each entry in a Building Stock images 142 (or other particular unknown target image presented in a list 232) along with a confidence score for each of N possible attributes, M possible conditions for each, and additional information such as an estimated location in the target image. Tentative structure classifications (architecture type, attributes, conditions, etc.) are identified at step 240 and then stored at step 250 along with unique structure id in database 140. For better accuracy it may be useful to employ multiple classifiers trained with different algorithms to give a combined or averaged score to identify the attributes and classify the structure).
Regarding claim 8, Gross as modified by Raymond and Valley discloses the radiocommunication system of claim 1, wherein the prioritized list indicates information about each relevant item including how many of the preference criteria are satisfied by each relevant item (Gross, para. 448: other relevant property records 400 are queried, to retrieve a set of relevant tags that match the user query. In a preferred embodiment, all forms of tags from all stakeholders (experts, crowd, merchants) are retrieved as candidates. In the case of a particular property of interest to the user, step 3158 filters the tags accordingly to match the user's specific intent for property-specific tags).
Regarding claim 9, Gross as modified by Raymond and Valley discloses the radiocommunication system of claim 1, however, Raymond further discloses wherein the radiocommunication system determines a current location of the first wireless device and sends information associated with a map display to the first wireless device and wherein the first wireless device displays the map on the first wireless device including icons associated with its current location and the locations of items on the prioritized list (Raymond, para. 14, 16, 22-25, 52, 144, 150, 172, 177: determining a location of a likely frequent destination of the prospective homeseeker, and information about locations of a plurality of associates of the homeseeker. Moreover, Raymond further discloses providing tools and techniques for using the present and/or historical locations of associates as a means of determining the desirability of a prospective residence for a user. Additionally, Raymond discloses that the system may automatically display a map highlighting nearby places to buy gourmet ingredients. Service of content can take place over a plurality of electronically mediated communications systems including, without limitation, web pages, electronic mail, social media, mobile application, or custom- generated physical collateral. Paragraph 16 discloses generating a user profile based at least in part on the plurality of factors, and infer a prioritized list of real estate listing characteristics to satisfy the user based on a prioritized list of homeseeker criteria that is part of the user profile).
Therefore, it would have been obvious to one with ordinary skill in the art before the effective filing date of the claimed invention to combine the teaching of Gross with the teaching of Raymond by using the above features such as determining a current location of the first wireless device and sends information associated with a map display to the first wireless device as taught by Raymond. The motivation for doing so would have been to provide tools and techniques for reducing the amount of user input required to produce an effective user preference profile.
Regarding claim 10, Gross discloses a method for generating a prioritized list of relevant items associated with a good or a service, the method comprising: onboarding a plurality of preference criteria associated with the good or service, wherein each preference criteria indicates at least one degree of importance that a user assigns to a corresponding attribute of the good or service (Gross, para. 138: FIG. 3A depicts a process 300 by which users can search for real estate/building stock that meets particular criteria of interest, including certain visual aesthetics, architectural features, predicted occupancy criteria, etc. This is done preferably through providing search parameters to Lead Generator Engine 160 (FIG. 1) which then identifies matching properties and outputs reports through Report Logic 170 to a client device 112); storing the plurality of preference criteria (Gross, para. 271, 298: This allows a vendor to adjust up or down the constraints in a campaign, to determine how many leads are desired for focused advertising. The leads are then stored in a database, table or index 2594 for later matching and comparison in response to a user query relating to the property in question); searching for electronic records for the relevant items associated with the good or service using the stored plurality of preference criteria (Gross, Fig. 2, para. 124: step 230 the classifier preferably outputs a score for each entry in a Building Stock images 142 (or other particular unknown target image presented in a list 232) along with a confidence score for each of N possible attributes, M possible conditions for each, and additional information such as an estimated location in the target image); determining a score for each corresponding attribute for each of the relevant items using the electronic records (Gross, Fig. 2, para. 124: step 230 the classifier preferably outputs a score for each entry in a Building Stock images 142 (or other particular unknown target image presented in a list 232) along with a confidence score for each of N possible attributes, M possible conditions for each, and additional information such as an estimated location in the target image); and generating the prioritized list of the relevant items using at least one ranking algorithm (Gross, para. 428, 464, 479: The filling of the clusters may be based on a time based priority (first opened, first closed) or other factors such as a percentage of cluster completion).
Gross does not appear to explicitly disclose wherein the step of onboarding further comprises onboarding, by a primary user, the plurality of preference criteria associated with the good or service and onboarding, by additional users comprising one or more collaborators defined and invited by the primary user, the plurality of preference criteria associated with a same good or service, wherein the primary user determines an influence priority of each of the additional users and the primary user selects a weighting level for each of the additional users.
In the same field of endeavor, Raymond discloses wherein the step of onboarding further comprises onboarding, by a primary user, the plurality of preference criteria associated with the good or service and onboarding, by additional users comprising one or more collaborators defined and invited by the primary user, the plurality of preference criteria associated with a same good or service (Raymond, para. 25, 27, 50: generate a user profile based at least in part on the plurality of factors, and infer a prioritized list of real estate listing characteristics to satisfy the user based on a prioritized list of homeseeker criteria that is part of the user profile. To generate the user profile, the computer might collect data about the user from a variety of sources, including, without limitation, the user himself or herself, social network servers and/or databases, data collection servers and/or databases that collect data about the user (knowingly or unbeknownst to the user), and/or the like).
Therefore, it would have been obvious to one with ordinary skill in the art before the effective filing date of the claimed invention to combine the teaching of Gross with the teaching of Raymond by using the above features such as the composite score of each relevant item is used to determine where each relevant item is ranked in the prioritized list and displaying the prioritized list to the first wireless communication device as taught by Raymond. The motivation for doing so would have been to provide tools and techniques for reducing the amount of user input required to produce an effective user preference profile.
The combination of Gross and Raymond does not appear to explicitly disclose wherein the primary user determines an influence priority of each of the additional users and the primary user selects a weighting level for each of the additional users.
In the same field of endeavor, Valley discloses wherein the primary user determines an influence priority of each of the additional users and the primary user selects a weighting level for each of the additional users (Valley, para. 8, 46, 52, 57: allowing a primary user to enter information related to products or services; at least one storage device that stores a user profile comprising the information entered by the primary user, and community user information of other users, wherein the other users have an affinity with the primary user based on comparisons of distance or attribute preferences between the primary user and the other users. Data entered by the primary user as well as information from other users that are related to or have common interests with the primary user, wherein the user profile dynamically changes based on, at least in part, new actions by the primary user as well as new actions by the other users; weigh the information in the user profile to determine targeted recommendations for the primary user; and provide the targeted recommendations to the primary user based on the weighted information of the user profile).
Therefore, it would have been obvious to one with ordinary skill in the art before the effective filing date of the claimed invention to combine the teaching of Gross as modified by Raymond with the teaching of Valley by using the above features such as the primary user determines an influence priority of each of the additional users and the primary user selects a weighting level for each of the additional users as taught by Valley. The motivation for doing so would have been to provide the targeted recommendations to the primary user based on the weighted information of the user profile.
Regarding claim 11, Gross as modified by Raymond and Valley discloses the method of claim 10, however, Raymond further discloses wherein the good or service is real estate properties for sale, the relevant items are real estate properties in a certain location, the corresponding attributes include number of bedrooms, and at least one degree of importance includes a must have preference criteria and a want preference criteria (Raymond, para. 21, 59, 143, 158: the list of real estate listings might be a list of properties for sale and the attributes include number of bedrooms and a degree of importance).
Therefore, it would have been obvious to one with ordinary skill in the art before the effective filing date of the claimed invention to combine the teaching of Gross with the teaching of Raymond by using the above features such as the list of real estate listings might be a list of properties for sale and the attributes include number of bedrooms as taught by Raymond. The motivation for doing so would have been to provide tools and techniques for reducing the amount of user input required to produce an effective user preference profile.
Regarding claim 12, Gross as modified by Raymond and Valley discloses the method of claim 11, however, Raymond further discloses wherein the plurality of preference criteria onboarded by the user indicates that the user must have three bedrooms, and that the user wants four bedrooms and further wherein at least one ranking algorithm prioritizes a first relevant item in the prioritized list that has four bedrooms higher than a second relevant item that has three bedrooms if the first and second relevant items have a same score for others of the plurality of preference criteria (Raymond, para. 65, 66, 109: Every residential real estate property has a unique set of endogenous and exogenous qualities that determine the property's utility in satisfying a plurality of homebuyer or renter needs. By quantifying these needs and the tradeoffs potential customers are willing to make, and by quantifying a residential property's ability to deliver relevant benefits on a plurality of dimensions, the degree to which a given potential home is likely to satisfy a particular homeseeker can be determined. When this match is calculated between a user and multiple residential properties, these results may be presented in a rank-ordered display, in which the items displayed most prominently are more likely to suit the user's preferences than items displayed less prominently).
Therefore, it would have been obvious to one with ordinary skill in the art before the effective filing date of the claimed invention to combine the teaching of Gross with the teaching of Raymond by using the above features such as prioritizing a first relevant item in the prioritized list that has four bedrooms higher than a second relevant item that has three bedrooms as taught by Raymond. The motivation for doing so would have been to provide tools and techniques for reducing the amount of user input required to produce an effective user preference profile.
Regarding claim 14, Gross as modified by Raymond and Valley discloses the method of claim 10, however, Raymond further discloses wherein the ranking algorithm also uses average scores associated with the preference criteria onboarded by each of the additional users for corresponding attributes having a same degree of importance assigned thereto, the average scores being weighted by a factor that is different for different degrees of importance to determine where each relevant item is ranked in the prioritized list (Raymond, para. 22, 50, 52, 59: generate and prioritize a list of potential homes from the list of real estate listings, based on comparisons, and at least in part on matches, between the prioritized list of real estate listing characteristics and attributes of at least some potential homes in the listings. A computer may receive a list of real estate listings, each pertaining to, and including a plurality of attributes about, an available home for purchase or rent/lease. The computer might collect information about a plurality of factors relating to a prospective homeseeker, generate a user profile based at least in part on the plurality of factors, and infer a prioritized list of real estate listing characteristics to satisfy the user based on a prioritized list of homeseeker criteria that is part of the user profile).
Therefore, it would have been obvious to one with ordinary skill in the art before the effective filing date of the claimed invention to combine the teaching of Gross with the teaching of Raymond by using the above features such as the average scores being weighted by a factor that is different for different degrees of importance to determine where each relevant item is ranked in the prioritized list as taught by Raymond. The motivation for doing so would have been to provide tools and techniques for reducing the amount of user input required to produce an effective user preference profile.
Regarding claim 15, Gross as modified by Raymond and Valley discloses the method of claim 10, wherein the weights used by the ranking algorithm for the average scores generated using the corresponding attributes associated with the primary user's plurality of preference criteria are different from the weights used by the ranking algorithm for the average scores generated using the corresponding attributes associated with each of the additional user’s plurality of preference criteria (Gross, para. 124, 158: the classifier preferably outputs a score for each entry in a Building Stock images 142 (or other particular unknown target image presented in a list 232) along with a confidence score for each of N possible attributes, M possible conditions for each, and additional information such as an estimated location in the target image. Tentative structure classifications (architecture type, attributes, conditions, etc.) are identified at step 240 and then stored at step 250 along with unique structure id in database 140. For better accuracy it may be useful to employ multiple classifiers trained with different algorithms to give a combined or averaged score to identify the attributes and classify the structure).
Regarding claim 17, Gross as modified by Raymond and Valley discloses the method of claim 10, wherein the prioritized list indicates information about each relevant item including how many of the preference criteria are satisfied by each relevant item (Gross, para. 448: other relevant property records 400 are queried, to retrieve a set of relevant tags that match the user query. In a preferred embodiment, all forms of tags from all stakeholders (experts, crowd, merchants) are retrieved as candidates. In the case of a particular property of interest to the user, step 3158 filters the tags accordingly to match the user's specific intent for property-specific tags).
Regarding claim 18, Gross as modified by Raymond and Valley discloses the method of claim 10, however, Raymond discloses further comprising: determining a current location; sending information associated with a map display; and displaying the map including icons associated with its current location and the locations of items on the prioritized list (Raymond, para. 14, 16, 22-25, 52, 144, 150, 172, 177: determining a location of a likely frequent destination of the prospective homeseeker, and information about locations of a plurality of associates of the homeseeker. Moreover, Raymond further discloses providing tools and techniques for using the present and/or historical locations of associates as a means of determining the desirability of a prospective residence for a user. Additionally, Raymond discloses that the system may automatically display a map highlighting nearby places to buy gourmet ingredients. Service of content can take place over a plurality of electronically mediated communications systems including, without limitation, web pages, electronic mail, social media, mobile application, or custom- generated physical collateral. Paragraph 16 discloses generating a user profile based at least in part on the plurality of factors, and infer a prioritized list of real estate listing characteristics to satisfy the user based on a prioritized list of homeseeker criteria that is part of the user profile).
Therefore, it would have been obvious to one with ordinary skill in the art before the effective filing date of the claimed invention to combine the teaching of Gross with the teaching of Raymond by using the above features such as determining a current location of the first wireless device and sends information associated with a map display to the first wireless device as taught by Raymond. The motivation for doing so would have been to provide tools and techniques for reducing the amount of user input required to produce an effective user preference profile.
Regarding claim 19, Gross as modified by Raymond and Valley discloses the radiocommunication system of claim 1, wherein the ranking algorithm uses average scores for corresponding attributes having a same degree of importance assigned thereto, the average scores being weighted by a factor that is different for different degrees of importance and summed to generate a composite score and the composite score of each relevant item is used to determine where each relevant item is ranked in the prioritized list (Raymond, para. 22, 50, 52, 59: generate and prioritize a list of potential homes from the list of real estate listings, based on comparisons, and at least in part on matches, between the prioritized list of real estate listing characteristics and attributes of at least some potential homes in the listings. A computer may receive a list of real estate listings, each pertaining to, and including a plurality of attributes about, an available home for purchase or rent/lease. The computer might collect information about a plurality of factors relating to a prospective homeseeker, generate a user profile based at least in part on the plurality of factors, and infer a prioritized list of real estate listing characteristics to satisfy the user based on a prioritized list of homeseeker criteria that is part of the user profile).
Therefore, it would have been obvious to one with ordinary skill in the art before the effective filing date of the claimed invention to combine the teaching of Gross with the teaching of Raymond by using the above features such as the average scores being weighted by a factor that is different for different degrees of importance to determine where each relevant item is ranked in the prioritized list as taught by Raymond. The motivation for doing so would have been to provide tools and techniques for reducing the amount of user input required to produce an effective user preference profile.
Regarding claim 20, Gross as modified by Raymond and Valley discloses the method of claim 10, wherein the ranking algorithm uses average scores for corresponding attributes having a same degree of importance assigned thereto, the average scores being weighted by a factor that is different for different degrees of importance and summed to generate a composite score and the composite score of each relevant item is used to determine where each relevant item is ranked in the prioritized list (Raymond, para. 22, 50, 52, 59: generate and prioritize a list of potential homes from the list of real estate listings, based on comparisons, and at least in part on matches, between the prioritized list of real estate listing characteristics and attributes of at least some potential homes in the listings. A computer may receive a list of real estate listings, each pertaining to, and including a plurality of attributes about, an available home for purchase or rent/lease. The computer might collect information about a plurality of factors relating to a prospective homeseeker, generate a user profile based at least in part on the plurality of factors, and infer a prioritized list of real estate listing characteristics to satisfy the user based on a prioritized list of homeseeker criteria that is part of the user profile).
Therefore, it would have been obvious to one with ordinary skill in the art before the effective filing date of the claimed invention to combine the teaching of Gross with the teaching of Raymond by using the above features such as the average scores being weighted by a factor that is different for different degrees of importance to determine where each relevant item is ranked in the prioritized list as taught by Raymond. The motivation for doing so would have been to provide tools and techniques for reducing the amount of user input required to produce an effective user preference profile.
Conclusion
20. 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.
21. Any inquiry concerning this communication or earlier communications from the examiner should be directed to JEAN F VOLTAIRE whose telephone number is (571)272-3953. The examiner can normally be reached M-F 9:00-6:45 PM.
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/JEAN F VOLTAIRE/Examiner, Art Unit 2466
/CHRISTOPHER M CRUTCHFIELD/Primary Examiner, Art Unit 2466