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 .
DETAILED ACTION
Claims 1-20 are currently pending.
Priority
This application claims priority to U.S. Provisional Patent Application No. 63/400,964, filed August 25, 2022.
Information Disclosure Statement
The information disclosure statements (IDS) is submitted on 1/30/2024 was filed in compliance with the provisions of 37 CFR 1.97. According, the information disclosure statement has been considered by the examiner.
Drawings
The drawings are objected to because it appears that the drawings are in greyscale and making the drawings less legible. Examiner suggests provide corrected drawing in black and white format. Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Specification
The abstract of the disclosure is objected to because the abstract does not have a punctuation period “.” at the end of the paragraph. A corrected abstract of the disclosure is required and must be presented on a separate sheet, apart from any other text. See MPEP § 608.01(b).
Claim Objections
Claim 13 is objected to because of the following informalities:
Claim 13 recites “the location” on line 1. Firstly, the word “location” is in a different color scale from rest of the claims. Examiner suggest changing the color format for the word “location” to black and white format. Further, Examiner suggest changing the limitation from “the location” to “a location” or “current location”, or other appropriate phrase.
. Appropriate correction is required.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim 1-20 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Tan et al. (US 20250173351 A1 and Tan hereinafter).
Regarding claim 1, Tan teaches a computer implemented method for determining locations of interest, the method comprising:
receiving, from a user device associated with a user, location data for a point of interest (Paragraph 0010; a user enters a query specifying a geographic area and a responsive set of area travel experiences are retrieved. Figure 1 and Paragraph 0016; web server 15 may have a communication interface 13 via which the web server may receive query data and/or user data from one or more user devices 16. The user device 16 presents information to a user via a display on or connected to the device 16 and takes input from the user via a touchscreen, mouse, keyboard, or other appropriate input devices. Paragraphs 0064 and 0065; the query input by the user via user interface 25 and used by the retrieval logic 222 includes a value that specifies a geographic location in which (or near which) the retrieval logic 222 will obtain travel experiences. The location information obtained through the query may be broad or high level, for example a city, state, or region, or may be more granular, for example a specific address, a building, an intersection, a neighborhood, or any other appropriate area. The location specified through the query will be used as a center point from which the retrieval of experiences within a retrieval range is performed);
processing the location data (Paragraphs 0012 and 0014; provider of a service display travel experiences near a target travel destination to provide listings of properties offered for reservation based on factors that may be of interest to guests in making the reservation, such as in the same geographic region/position as the travel destination) and a user profile associated with the user (Paragraph 0026; the data stored in database 240 may include information from the user's profile 242, such as, e.g., user ID, user account information (e.g., name, contact information (e.g., email address, mailing address, telephone number, date of birth, payment card information), demographic data about the user, such as age, gender, location, language, and device information, GPS or location data, operating system, browser, or other device data, user preferences, connected third party accounts (e.g., social networks) if applicable, and the like) through a locations-of-interest model (Paragraphs 0056 and 0057; retrieval logic 222 may tune the signal weights in accordance with another type of supervised regression or classification algorithm. In one embodiment, it may be trained on a set of data comprising all available experiences. In other embodiments, the algorithm(s) may be trained on a set of data limited to a geographic area or region, though other boundaries of the training set, such as travel distance, travel time, or city/state/country borders may be used in different embodiments) to determine a plurality of locations of interest from a curated set of locations (Paragraph 0011; ranking of travel experiences may be performed using a supervised learning to create a curated list of travel experiences can be presented to the user in association with their planned travel in real-time, the curation being automatic), wherein each of the locations in the set of locations includes a respective location profile (Paragraph 0028; memory 210 also stores experience database 250 containing information identifying and/or related or unique to a number of travel experience listings stored with the service provider. Each experience may correspond to a listing that can be presented to the user, via user interface 25, so as to provide information about the experience. This information is collected from experience profile data 252 and may include such data as the location of the experience, a description of the experience, contact information for the experience (e.g., phone number or website), user reviews or ratings, date/hours or other availability information, booking information (e.g., allowing the user to making a reservation or booking), directions, and/or other appropriate information), wherein the each of the locations of interest is determined based on a mapping between the respective location profile and the user profile (Paragraph 0011; to determine a ranked set of suitable day trip experiences. In some embodiments, this ranking may be performed using a supervised learning algorithm. By these means, a curated list of travel experiences can be presented to the user in association with their planned travel in real-time, the curation being automatic); and
providing the locations of interest and each of the respective location profiles to the user device (Paragraph 0010; in response to user query, a responsive set of area travel experiences are retrieved for display to a user).
Regarding claim 14, claim 14 recites similar features as claim 1, therefore is rejected for at least the same reason as discussed above regarding claim 1. Further, Tan teaches a system for providing locations of interest (Figures 1 and 2) comprising:
a user device associated with a user (Figure 1 and Paragraph 0018; user devices 16 with user interface 25);
an electronic processor configured to perform functions (Figure 2 and Paragraph 0024; processor 260).
Regarding claim 20, claim 20 recites similar features as claim 1, therefore is rejected for at least the same reason as discussed above regarding claim 1. Further, Tan teaches a non-transitory computer readable medium having stored thereon executable instructions (Figure 2 and Paragraph 0024; instructions of software stored in memory 210) that, when executed by an electronic processor, cause the electronic processor to perform operations (Figure 2 and Paragraph 0024; the processor 260 may execute instructions of software stored in memory 210).
Regarding claims 2 and 15, Tan teaches all of the limitations of claims 1 and 14, as described above. Further, Tan teaches receiving, for each of the locations in the curated set of locations, location data associated with the respective location (Paragraph 0028; memory 210 also stores experience database 250 containing information identifying and/or related or unique to a number of travel experience listings stored with the service provider. Each experience may correspond to a listing that can be presented to the user, via user interface 25, so as to provide information about the experience. This information is collected from experience profile data 252 and may include such data as the location of the experience, a description of the experience, contact information for the experience (e.g., phone number or website), user reviews or ratings, date/hours or other availability information, booking information (e.g., allowing the user to making a reservation or booking), directions, and/or other appropriate information); receiving, from a verification application, a verification of the respective location data for each of the locations (Paragraph 0031; external databases 270 may also include local event information 278, which may identify, for example, local events that the user may wish to visit/travel to that are not otherwise listed as experiences, such as temporary events, installations, pop-ups, and other transient occurrences that may overlap with the user's intended period of travel. Local event information 278 may also include information on street or transportation closures or changes, or other data that may impact or relate to travel between the user's intended travel destination and a travel experience location proximate thereto) in the curated set of locations (Paragraph 0011; ranking of travel experiences may be performed using a supervised learning to create a curated list of travel experiences can be presented to the user in association with their planned travel in real-time, the curation being automatic); and amending each location profile with the respective, verified location data (Paragraphs 0028 and 0031; external databases 270 may also include local event information 278, which may identify, for example, local events that the user may wish to visit/travel to that are not otherwise listed as experiences, such as temporary events, installations, pop-ups, and other transient occurrences that may overlap with the user's intended period of travel. Local event information 278 may also include information on street or transportation closures or changes, or other data that may impact or relate to travel between the user's intended travel destination and a travel experience location proximate thereto. Further, for example, when a business change phone number, they can do so to update the location profile after verification/confirmation. In addition, user reviews may be verified and added to the location profile for each business).
Regarding claims 3 and 16, Tan teaches all of the limitations of claims 1 and 14, as described above. Further, Tan teaches wherein the user profile includes a plurality of user attributes (Paragraph 0026; the data stored in database 240 may include information from the user's profile 242, such as, e.g., user ID, user account information (e.g., name, contact information (e.g., email address, mailing address, telephone number, date of birth, payment card information), demographic data about the user, such as age, gender, location, language, and device information, GPS or location data, operating system, browser, or other device data, user preferences, connected third party accounts (e.g., social networks) if applicable, and the like), and wherein each location profile includes a plurality of location attributes (Paragraph 0028; memory 210 also stores experience database 250 containing information identifying and/or related or unique to a number of travel experience listings stored with the service provider. Each experience may correspond to a listing that can be presented to the user, via user interface 25, so as to provide information about the experience. This information is collected from experience profile data 252 and may include such data as the location of the experience, a description of the experience, contact information for the experience (e.g., phone number or website), user reviews or ratings, date/hours or other availability information, booking information (e.g., allowing the user to making a reservation or booking), directions, and/or other appropriate information).
Regarding claim 4, Tan teaches all of the limitations of claim 3, as described above. Further, Tan teaches wherein the user attributes include at least one of an itinerary, flagged locations, default search settings, and demographic information (Paragraph 0026; data stored in database 240 may include information from the user's profile 242, such as, e.g., user ID, user account information (e.g., name, contact information (e.g., email address, mailing address, telephone number, date of birth, payment card information), demographic data about the user, such as age, gender, location, language, and device information, GPS or location data, operating system, browser, or other device data, user preferences, connected third party accounts (e.g., social networks) if applicable, and the like. In some embodiments, where the advertiser provides a platform for property rental, user database 240 may also include information about a user's current bookings (or purchases) 244 and/or information about past bookings (or purchases) of properties or of experiences 246, and/or any other appropriate type of information specific to or related to the user).
Regarding claim 5, Tan teaches all of the limitations of claim 3, as described above. Further, Tan teaches wherein the location attributes include at least one of images, videos, address, map and direction information, geographical information, a location type, contact information, location category, age related information, pet related information, ideal for, event data, facility information, additional services, information related to food service, things to do, outdoor types, and tips and tricks (Paragraph 0028; memory 210 also stores experience database 250 containing information identifying and/or related or unique to a number of travel experience listings stored with the service provider. Each experience may correspond to a listing that can be presented to the user, via user interface 25, so as to provide information about the experience. This information is collected from experience profile data 252 and may include such data as the location of the experience, a description of the experience, contact information for the experience (e.g., phone number or website), user reviews or ratings, date/hours or other availability information, booking information (e.g., allowing the user to making a reservation or booking), directions, and/or other appropriate information).
Regarding claim 6, Tan teaches all of the limitations of claim 3, as described above. Further, Tan teaches wherein the user attributes include a threshold distance, and wherein each of the locations of interest is within the threshold distance from the point of interest (Paragraph 0050; retrieval logic 222 may perform a weighted analysis of the structured information, the unstructured information, and/or travel time and distance calculation to obtain a scored value for the travel experience. Paragraphs 0076 and 0082; all local experiences within a city boundary [interpreted as a threshold distance] (or another geographic area) are collected for inclusion in a set of experiences within a retrieval range).
Regarding claim 7, Tan teaches all of the limitations of claim 3, as described above. Further, Tan teaches wherein the threshold distance is set to a default distance or a threshold attribute (Paragraph 0050; retrieval logic 222 may perform a weighted analysis of the structured information, the unstructured information, and/or travel time and distance calculation to obtain a scored value for the travel experience. Paragraphs 0076 and 0082; all local experiences within a city boundary [interpreted as a threshold distance] (or another geographic area) are collected for inclusion in a set of experiences within a retrieval range. Circles 412, 424 and 426 represent the boundaries of three virtual areas that are located at threshold distances from the area, which threshold distances are determined based on the type of user) in the user profile (Paragraph 0026; data stored in database 240 may include information from the user's profile 242, such as, e.g., user ID, user account information (e.g., name, contact information (e.g., email address, mailing address, telephone number, date of birth, payment card information), demographic data about the user, such as age, gender, location, language, and device information, GPS or location data, operating system, browser, or other device data, user preferences, connected third party accounts (e.g., social networks) if applicable, and the like. In some embodiments, where the advertiser provides a platform for property rental, user database 240 may also include information about a user's current bookings (or purchases) 244 and/or information about past bookings (or purchases) of properties or of experiences 246, and/or any other appropriate type of information specific to or related to the user).
Regarding claim 8, Tan teaches all of the limitations of claim 3, as described above. Further, Tan teaches wherein the locations-of-interest model assigns a weight to a relationship between each of the user attributes and each of the location attributes based on the mapping between the location profiles and the user profile.
Regarding claim 9, Tan teaches all of the limitations of claim 1, as described above. Further, Tan teaches wherein the locations-of-interest model comprises a trained artificial intelligence (AI) model (Paragraphs 0056 and 0057; retrieval logic 222 may tune the signal weights in accordance with another type of supervised regression or classification algorithm. In one embodiment, it may be trained on a set of data comprising all available experiences. In other embodiments, the algorithm(s) may be trained on a set of data limited to a geographic area or region, though other boundaries of the training set, such as travel distance, travel time, or city/state/country borders may be used in different embodiments).
Regarding claim 10, Tan teaches all of the limitations of claim 9, as described above. Further, Tan teaches wherein the locations-of-interest model is trained with a plurality of user profiles associated with other users (Paragraph 0055; a predictive set of one or more weights indicating which of the signals are more or less likely to impact the user's ability and/or likelihood of booking or visiting the experience within the duration of his stay in the area. In some embodiments, a linear regression model trains on historical relationships between users' bookings of an experience in relation to user rating score, user review sentiment, number of reviews and/or level of detail in reviews, comparison of temporal conditions of the reviews of the experience (such as the time/season/date of review) to temporal conditions at the time of the analysis of retrieval logic 222, characteristics of the user (such as their locality and set preferences) in comparison to characteristics of the users who provided reviews of the experience, and/or other relevant historical data) and the curated set of locations (Paragraph 0011; ranking of travel experiences may be performed using a supervised learning to create a curated list of travel experiences can be presented to the user in association with their planned travel in real-time, the curation being automatic).
Regarding claim 11, Tan teaches all of the limitations of claim 9, as described above. Further, Tan teaches wherein the locations-of-interest model is trained using supervised learning (Paragraphs 0056 and 0057; retrieval logic 222 may tune the signal weights in accordance with another type of supervised regression or classification algorithm. In one embodiment, it may be trained on a set of data comprising all available experiences. In other embodiments, the algorithm(s) may be trained on a set of data limited to a geographic area or region, though other boundaries of the training set, such as travel distance, travel time, or city/state/country borders may be used in different embodiments).
Regarding claim 12, Tan teaches all of the limitations of claim 9, as described above. Further, Tan teaches wherein the locations-of-interest model comprises a trained neural network (Paragraph 0057; a weighted analysis may be performed as a non-linear supervised regression or classification task using a neural network).
Regarding claims 13 and 19, Tan teaches all of the limitations of claims 1 and 14, as described above. Further, Tan teaches wherein the point of interest is at a location other than the location of the user device (Figure 1 and Paragraphs 0010 and 0064; a user enters a query specifying a geographic area and a responsive set of area travel experiences are retrieved. Paragraphs 0030 and 0035; a user may enter a search query specifying a geographic location to which the user wishes to travel. Thus the point of interest is at a location other than the current location of the user device).
Regarding claim 17, Tan teaches all of the limitations of claim 14, as described above. Further, Tan teaches wherein the locations-of-interest model comprises a trained artificial intelligence (AI) model (Paragraphs 0056 and 0057; retrieval logic 222 may tune the signal weights in accordance with another type of supervised regression or classification algorithm. In one embodiment, it may be trained on a set of data comprising all available experiences. In other embodiments, the algorithm(s) may be trained on a set of data limited to a geographic area or region, though other boundaries of the training set, such as travel distance, travel time, or city/state/country borders may be used in different embodiments), and wherein the locations-of-interest model is trained with a plurality of user profiles associated with other users (Paragraph 0055; a predictive set of one or more weights indicating which of the signals are more or less likely to impact the user's ability and/or likelihood of booking or visiting the experience within the duration of his stay in the area. In some embodiments, a linear regression model trains on historical relationships between users' bookings of an experience in relation to user rating score, user review sentiment, number of reviews and/or level of detail in reviews, comparison of temporal conditions of the reviews of the experience (such as the time/season/date of review) to temporal conditions at the time of the analysis of retrieval logic 222, characteristics of the user (such as their locality and set preferences) in comparison to characteristics of the users who provided reviews of the experience, and/or other relevant historical data) and the curated set of locations (Paragraph 0011; ranking of travel experiences may be performed using a supervised learning to create a curated list of travel experiences can be presented to the user in association with their planned travel in real-time, the curation being automatic).
Regarding claim 18, Tan teaches all of the limitations of claim 17, as described above. Further, Tan teaches wherein the locations-of-interest model is trained using supervised learning (Paragraphs 0056 and 0057; retrieval logic 222 may tune the signal weights in accordance with another type of supervised regression or classification algorithm. In one embodiment, it may be trained on a set of data comprising all available experiences. In other embodiments, the algorithm(s) may be trained on a set of data limited to a geographic area or region, though other boundaries of the training set, such as travel distance, travel time, or city/state/country borders may be used in different embodiments); and wherein the locations-of-interest model comprises a trained neural network (Paragraph 0057; a weighted analysis may be performed as a non-linear supervised regression or classification task using a neural network).
Regarding claim 20, claim 20 recites similar features as claim 1, therefore is rejected for at least the same reason as discussed above regarding claim 1. Further, Tan teaches a non-transitory computer readable medium having stored thereon executable instructions (Figure 2 and Paragraph 0024; instructions of software stored in memory 210) that, when executed by an electronic processor, cause the electronic processor to perform operations (Figure 2 and Paragraph 0024; the processor 260 may execute instructions of software stored in memory 210).
Pertinent Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Sheha et al. (US 20060229807 A1) discloses searching and retrieving location information associated with one or more points of interests.
Mimassi (US 20250037041 A1) disclose real-time geophysical social grouping comprising customer profiles and venue profiles to generate recommendations for social group pairing, venues and activities.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Jing Gao whose telephone number is (571)270-7226. The examiner can normally be reached on 9am - 6pm M-F.
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/Jing Gao/
Examiner
Art Unit 2647
Prosecution Insights