DETAILED ACTION
Notice of Pre-AIA or AIA Status
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
2. This Office Action is in response to the application filed on 10/03/2024.
3. The IDSs filed on 10/03/24, 07/16/25, 11/26/25, and 07/20/26 are reviewed and entered into the application file.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefore, subject to the conditions and requirements of this title.
4. Claim 20 is rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim does not fall within at least one of the four categories of patent eligible subject matter because "A machine-readable storage device” as described in at least paragraph 130 of the specification did not exclude carrier wave or transmission medium. Since the claimed machine-readable storage device covers signals and carrier waves, which are not a manufacture within the meaning of 101, on which a program is still unavailable for the data processing system (processor). In such embodiment, the instructions (programs) is still unable to act as a computer component and have its function realized. Thus, the claimed machine-readable storage device should recite and limited to non-statutory storage type medium in order to satisfy the 101 statutory requirement.
Claim Rejections - 35 USC § 103
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.
5. Claims 1, 13, 19 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Abhyanker (US 20150161719 A1) in view of MINAMINO et al (US 20120162251 A1).
Abhyanker (US 20150161719 A1) is directed to private residence and residential room rental system and method between a host and a renter.
MINAMINO et al (US 20120162251 A1) is directed to ELECTRONIC APPARATUS, DISPLAY CONTROL METHOD AND PROGRAM
As per claim 1, Abhyanker discloses method (e.g., flowchart of Fig. 9) comprising: receiving, by a network site of a listing network platform, input comprising a plurality of images associated with an individual listing ([0026] FIG. 5 illustrates a remote association view 550 in which a mobile device 505 (e.g., the recipient device) of a recipient 114 receives the place-to-stay listing data 102 of FIG. 3. [0009] Furthermore, the method may process a listing criteria includes a real estate type, a lot size, a square footage, a photograph, a video, a bedroom count, a room size, a description, a cost per month, a rental price, a leasing rate, a number of stories, and/or a location)
presenting, in a graphical user interface (GUI), an option to arrange the plurality of images according to classifications associated with the plurality of images ([0206] FIG. 13 is a map view 1350 illustrating a short-term residential map 1300, a set of profiles 1301, a price slider 1302, an entire place selector 1304, a private room 1306, a shared room selector 1308, a type indicator 1310, a booking selector 1312, and a host listing 1314, according to one embodiment.) ;
in response to receiving input that selects the option, ([206] FIG. 13 is a map view 1350 illustrating a short-term residential map 1300, a set of profiles 1301, a price slider 1302, an entire place selector 1304, a private room 1306, a shared room selector 1308, a type indicator 1310, a booking selector 1312, and a host listing 1314, according to one embodiment).
after the subset of the plurality of images are 0207] According to one or more embodiments, the type indicator 1310 may comprise the entire place selector 1304, which may allow users to indicate they wish to view and/or book an entire residence (e.g., house, apartment), the private room selector 1306, which may allow users to indicate they wish to view and/or book a private (e.g., unshared) room, and/or the shared room selector 1308, which may allow users to indicate that they wish to view and/or book a shared room. ; and
populating a first region of the plurality of regions associated with a first classification with a first image that corresponds to the first classification and a second region of the plurality of regions associated with a second classification with a second image that corresponds to the second classification.(see Fig. 13, [0206] FIG. 13 is a map view 1350 illustrating a short-term residential map 1300, a set of profiles 1301, a price slider 1302, an entire place selector 1304, a private room 1306, a shared room selector 1308, a type indicator 1310, a booking selector 1312, and a host listing 1314, according to one embodiment).
Although Abhyanker discloses several images of properties (e.g. Fig. 13) displayed for the user, Abhyanker falls short to mention animating images, shuffling image and template of regions.
MINAMINO, on the other hand, discloses animation display of thumbnail images using display forms (i.e. template). For example [0194] In the first embodiment of MINAMINO’s disclosure, the example has been described in which when a thumbnail image (index image) is moved, the movement transition is displayed by animation. However, when the movement transition is displayed by animation, the transition may be displayed by animation using other display forms. [0117] FIG. 5B shows the transition button 334 to a reproduction mode setting screen when MIX is selected in the setting screen. [0198] As shown in FIG. 29B, the event images 701 to which the event images 321 are enlarged are displayed by animation immediately after the selection operation for selecting the event images 321 is performed, and thereby it is possible to easily recognize the event images having undergone the selection operation. [0085] The display control unit 250 displays by animation a thumbnail image which is a movement target during the movement. Examples of such display will be described in detail with reference to FIGS. 5A to 25B).
Before effective filling date of the invention, it would have been obvious to a person of ordinary skill in the art to animate and shuffle/mix the images of Abhyanker as shown in MINAMINO. Thereby, it is possible to provide an animation display which gives such an impression that a user lifts up and scatters the thumbnail images related to the event images having undergone the selection operation with the hand. Furthermore the user would be able to arrange and mix the image with a chosen or desired template or arrangement.
Therefore, it would have been obvious to combine MINAMINO with Abhyanker to obtain the invention as specified in claim 1.
As per claim 13, Abhyanker in view of MINAMINO, further discloses that the method of claim 1, further comprising: presenting an animation comprising celebration graphics on top of the template that includes the first region with the first image and the second region with the second image (MINAMINO, [0017] the display control unit may perform animation display while changing an angle of the corresponding index image during the movement between a movement source and a movement destination such that the index image which is the rotation target has an angle at the movement destination. This produces an operation where animation display is performed while changing an angle of the corresponding index image during the movement between a movement source and a movement destination such that the index image which is the rotation target has an angle at the movement destination. [0127] FIGS. 7A to 9A show transition screens 351 to 355 displayed by the display control unit 250. In this example, for convenience of description, only the transition screens 351 to 355 are representatively displayed as animation display of the thumbnail images).
As per claim 19, Abhyanker in view of MINAMINO further discloses a system (Fig. 24). The limitations of system claim 19 correspond to method claim 1, thus claim 19 is also rejected under similar citations given to the method claim 1.
As per claim 20, Abhyanker, in view of MINAMINO further discloses a machine-readable storage device (see Fig. 18, 19, or 24). The limitations claim 20 correspond to method claim 1, thus claim 20 is also rejected under similar citations given to the method claim 1.
6. Claims 2-12, and 15-18 are rejected under 35 U.S.C. 103 as being unpatentable over Abhyanker (US 20150161719 A1) in view of MINAMINO et al (US 20120162251 A1) and Wang et al (US 20220309597 A1)
Wang et al (“Wang”) is directed to COMPUTER VISION FRAMEWORK FOR REAL ESTATE
As per claim 2, although Abhyanker in view of MINAMINO further discloses rendering a plurality of images but Abhyanker in view of MINAMINO fails to teach analyzing the plurality of images by a machine learning model to determine respective classifications associated with each of the plurality of images (Abhyanker, [0086} Radial distribution module 140 (e.g., that applies the radial algorithm 240 of FIG. 2 using a series of modules working in concert as described in FIG. 2) may solve a technical challenge by defining ranges based on a type of real estate listing, a type of neighborhood, and/or boundary condition of a neighborhood by analyzing whether the place-to-stay listing data 102 may be associated with a particular kind of job, a particular neighborhood, a temporal limitation, and/or through another criteria). But still Abhyanker in view of MINAMINO falls short to teach a machine learning model as required in the claim
Wang on the other hand discloses machine learning model, wherein [0039] Image labeling applications 210 may include usage of machine learning-based predictive models to label property images correctly based on room types, architecture types, objects, features or other characteristics in rooms. Further, image labeling applications 210 and image labeling techniques described herein may serve to level condition information and analysis of rooms/property, recommendation of improvement of room/property based upon it, and non-compliance of listing images per real estate industry regulation and standards (e.g., as image labeling techniques described herein may serve as a consistent standard by which differing real estate images are analyzed and compared).
Before effective filling date of the invention, it would have been obvious to a person of ordinary skill in the art to incorporate the machine learning model of Wang with Abhyanker in view of MINAMINO so that the plurality of image of Abhyanker in view of MINAMINO will be analyzing using the machine learning model and determine respective classifications associated with each of the plurality of images as required in the claim
Therefore, it would have been obvious to combine Wang with MINAMINO and Abhyanker to obtain the invention as specified in claim 2.
As per claim 3, Abhyanker in view of MINAMINO and Wang further discloses that the method of claim 2, wherein the machine learning model analyzes the plurality of images in response to receiving the input that selects the option (Wang, [0039] Image labeling applications 210 may include usage of machine learning-based predictive models to label property images correctly based on room types, architecture types, objects, features or other characteristics in rooms. Further, image labeling applications 210 and image labeling techniques described herein may serve to level condition information and analysis of rooms/property, recommendation of improvement of room/property based upon it, and non-compliance of listing images per real estate industry regulation and standards (e.g., as image labeling techniques described herein may serve as a consistent standard by which differing real estate images are analyzed and compared)).
As per claim 4, Abhyanker in view of MINAMINO and Wang further discloses that method of claim 2, wherein the machine learning model analyzes the plurality of images as each respective image of the plurality of images is received by the input and prior to presenting the option to arrange the plurality of images in the GUI (Wang, [0053] As described herein, in some cases, techniques described herein may implement one or more aspects of neural networks. Neural networks are a class of machine learning models that use many different ways to describe the input feature and the connection between the weights to model the data. Neural Network models a concept of neuron which receives the input. In our neural network models, neurons receive different types of features such as the property price history, property images, tax history etc. Typically in neural networks, a propagation function is used, which computes the output from predecessor neurons and their connection as a weighted sum).
As per claim 5, Abhyanker in view of MINAMINO and Wang further discloses that the method of claim 4, further comprising: receiving additional input comprising an additional image associated with the listing; and in response to receiving the additional input, re-analyzing the plurality of images that have previously been classified by the machine learning model together with the additional image to generate or update classifications for each of the plurality of images including the additional image (Wang, [0047] When a node receives a signal, it processes the signal and then transmits the processed signal to other connected nodes. In some cases, the signals between nodes comprise real numbers, and the output of each node is computed by a function of the sum of its inputs. In some examples, nodes may determine their output using other mathematical algorithms (e.g., selecting the max from the inputs as the output) or any other suitable algorithm for activating the node. Each node and edge is associated with one or more node weights that determine how the signal is processed and transmitted).
As per claim 6, Abhyanker in view of MINAMINO and Wang further discloses that the method of claim 1, wherein the first classification comprises a first room type; and wherein the second classification comprises a second room type (Wang, [0039] Image labeling applications 210 may include usage of machine learning-based predictive models to label property images correctly based on room types, architecture types, objects, features or other characteristics in rooms).
As per claim 7, Abhyanker in view of MINAMINO and Wang further discloses that the method of claim 1, further comprising: enabling a user to modify the classifications associated with the plurality of images by interacting with the template presented in the GUI (Wang, [0049] In some cases, ML model 315 may include (e.g., or implement) one or more aspects of a convolutional neural network (CNN). A CNN is a class of neural network that is commonly used in computer vision or image classification systems. In some cases, a CNN may enable processing of digital images with minimal pre-processing. A CNN may be characterized by the use of convolutional (or cross-correlational) hidden layers. These layers apply a convolution operation to the input before signaling the result to the next layer. Each convolutional node may process data for a limited field of input (i.e., the receptive field). During a forward pass of the CNN, filters at each layer may be convolved across the input volume, computing the dot product between the filter and the input. During the training process, the filters may be modified so that they activate when they detect a particular feature within the input).
As per claim 8, Abhyanker in view of MINAMINO and Wang further discloses that the method of claim 1, wherein an ordering in which the plurality of regions are presented in the GUI is predefined, the ordering comprising a living area, full kitchen, kitchenette, dining area, bedroom, full bathroom, half bathroom, office, dedicated workspace, backyard, patio, balcony, front yard, deck, porch, courtyard, garden, terrace, rooftop, laundry area, garage, gym, exterior, pool, hot tub, theme room, children's playroom, bowling alley, movie theater, art studio, music studio, workshop, photography studio, darkroom, wood shop, event space, library, game room, sunroom, and wine cellar (Wang, [0075] For example, techniques described herein (e.g., image classification/image labeling techniques described, for example, with reference to FIG. 6) may be implemented to label a set of images. In the example of FIG. 7, such techniques may be implemented to label or classify images into a first set of labeled images 705 (e.g., bathroom images), a second set of labeled images 710 (e.g., bedroom images), and third set of labeled images 715 (e.g., kitchen images)).
As per claim 9, Abhyanker in view of MINAMINO and Wang further discloses that the method of claim 1, further comprising: selecting one or more images from the plurality of images based on one or more criteria; and presenting the one or more images that have been selected in a graphical element comprising the option to arrange the plurality of images according to classifications (Wang, [0105] At operation 1020, the system obtains (e.g., or receive) a query image. For instance, as described herein, a user may submit or select a search image to be used to search for similar property listings, property or room designs, etc. In some cases, the operations of this step refer to, or may be performed by, an image processing apparatus as described with reference to FIGS. 1-3, 6, and 8)).
As per claim 10, Abhyanker in view of MINAMINO and Wang further discloses that the method of claim 9, wherein the one or more criteria comprise at least one of an order in which the images are presented in the listing or respective classifications of the images (Wang, [0031] In some examples, one or more aspects of the techniques described herein may be implemented to generate listings, generate offers (e.g., generate property purchase offers based on property conditions), etc.). [0040] For instance, as described in more detail herein, image search applications 210 may enable users to input images (e.g., images of desired property types, property conditions, etc.) for searching related property listings more effectively.
As per claim 11, Abhyanker in view of MINAMINO and Wang further discloses that method of claim 9, wherein the subset of the plurality of images comprises the one or more images that have been selected (Wang, [0114] FIG. 5B shows the list display screen 330 displayed by the display control unit 250. The list display screen 330 is a screen where the thumbnail images (index images) for selecting the content items (content items to be reproduced) belonging to one or a plurality of groups are disposed at predetermined positions on the display surface of the display unit 181 with content item units), further comprising:
presenting a graphical element that presents the animating of the subset of the plurality of images on top of the template, wherein each of the plurality of regions of the template comprises a placeholder for respective images (Wang, [0051] FIG. 27 is a flowchart illustrating an animation process to a list display screen among the process procedures of the display control process performed by the image capturing device according to the first embodiment of the present disclosure. Also see animation display of thumbnail images in at least Figs. 18A-29B).
As per claim 12, Abhyanker in view of MINAMINO and Wang further discloses that the method of claim 11, wherein the placeholder comprises a gray box (MINAMINO, [0119] The thumbnail image display region 340 is a region displaying the thumbnail images stored in the content management information storage unit 280, and, for example, displays the thumbnail images in a list view in a 4.times.4 matrix. See at least Figs. 5B, 9B, 10A ).
As per claim 15, Abhyanker in view of MINAMINO and Wang further discloses that the method of claim 1, further comprising:
determining that an individual classification of the plurality of classifications is unassigned to any of the plurality of images; and identifying an individual region of the plurality of regions associated with the individual classification; and populating the individual region in the GUI with a three-dimensional graphical element that represents the individual classification in response to determining that the individual classification is unassigned to any of the plurality of images (Wang, [0075] For example, techniques described herein (e.g., image classification/image labeling techniques described, for example, with reference to FIG. 6) may be implemented to label a set of images. In the example of FIG. 7, such techniques may be implemented to label or classify images into a first set of labeled images 705 (e.g., bathroom images), a second set of labeled images 710 (e.g., bedroom images), and third set of labeled images 715 (e.g., kitchen images). For instance, images may be processed, using an image processing apparatus described herein, to classify and label images based on objects detected in the images, etc. Such labeled images may be used for various applications described herein. For example, labeled images may be used to compare conditions of similar properties or room types, to appraise the value of property based on comparison with other similar labeled images, to search property listings for similar properties or room types based on an embedded search query and labeled candidate vectors (e.g., as described in more detail herein, for example, with reference to FIG. 10), etc.).
As per claim 16, Abhyanker in view of MINAMINO and Wang further discloses that the method of claim 15, further comprising: storing a database that associates a plurality of classifications with respective three-dimensional graphical elements representing the classifications; and retrieving, from the database, the three-dimensional graphical element by searching the database based on the individual classification (Wang, [0040] Image search applications 210 may enable users to conduct image search on properties, based on similarity of images, certain specific features of property/rooms from an image, or other characteristics from an image, and find their ideal choices of property or rooms, from the essence of the images. For instance, as described in more detail herein, image search applications 210 may enable users to input images (e.g., images of desired property types, property conditions, etc.) for searching related property listings more effectively. [0044] Some examples of the image processing apparatus 300 further include a search component 335 configured to retrieve the image based on a search query. Some examples of the image processing apparatus 300 further include a property classification head 340 configured to classify the image according to a set of real estate property types based on the embedded representation of the image. Some examples of the image processing apparatus 300 further include an object detection head 345 configured to identify an object in the image based on the embedded representation of the image. Also see three-dimensional graphical elements representing the classifications; Figs. 6 and 7).
7. Claim 14 is rejected under 35 U.S.C. 103 as being unpatentable over Abhyanker in view of MINAMINO and Silvernail (US 20150310523 A1).
As per claim 14, although Abhyanker in view of MINAMINO further discloses several animation graphics but Abhyanker in view of MINAMINO does not seem to teach celebration graphics comprise at least one of confetti, balloons, or a logo associated with the network site is not shown. Silvernail is directed to system and method for uploading video files to a real estate listing and for reporting interest level in a real estate listing. Silvernail further discloses [0104] The system allows agents to upload company, team and MLS logos and the server 10 is coded to enable the recognition of a referring site, and determine if the referred viewer is viewing the videos from an MLS site, and then play the videos through a non-branded video player. If the referring site was a non-MLS site, then server 10 will play the videos back in the branded version of the video player.
Before effective filling date of the invention, it would have been obvious to a person of ordinary skill in the art to incorporate any identifiable features of a property listing websites such as a logo to website to Abhyanker in view of MINAMINO so that users searching for a real estate’s property will be able to identify the websites easily.
Therefore, it would have been obvious to combine Silvernail with Abhyanker in view of MINAMINO to obtain the invention as specified in claim 14.
8. Claims 17 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Abhyanker (US 20150161719 A1) in view of MINAMINO et al (US 20120162251 A1) and Shutsa et al (US 20240362545 A1).
Shutsa et al (“Shutsa”) is directed to searching listings for reservations.
As per claim 17, Abhyanker in view of MINAMINO does not seem to disclose amenities associated with the individual listing as recite in the claim. That is Abhyanker in view of MINAMINO does not teach obtaining a list of amenities associated with the individual listing; detecting a physical space in an individual image of the plurality of images; and selecting an individual classification of the plurality of classifications based on the list of amenities associated with the individual listing and the detected physical space; and associating the individual classification with the individual image (Shutsa, on the other hand, discloses [0017] a techniques that provide a network site that allows a user to interact with the listing network site and view search results in an efficient manner. Namely, the network site can receive input that includes search criteria and identifies a plurality of listings matching the search criteria. The network site can generate a graphical user interface that includes a plurality of graphical objects (e.g., tiles) each associated with a respective one of the identified plurality of listings. The network site can determine that the search criteria satisfy an amenity criterion. In such cases, the network site can, in response, cause one or more amenities associated with an individual listing of the identified plurality of listings to be presented (visually distinguished or highlighted) conditionally in an individual graphical object of the plurality of graphical objects associated with the individual listing. Namely, the presentation of one or more amenities in the tiles for matching listings can be conditioned on whether the search criteria or attributes associated with the search criteria satisfy an amenity criterion. Also see [0073]).
Before effective filling date of the invention, it would have been obvious to a person of ordinary skill in the art to incorporate amenities with property listings so that the user/buyer will be able to know what comes or include with the property he/she is going to purchase, wherein having one or more listing amenity associated with the listing will rank the listing higher and more attractable.
Therefore, it would have been obvious to combine listing amenity of Grag with Abhyanker in view of MINAMINO and Shutsa to obtain the invention as specified in claim 17.
As per claim 18, Abhyanker in view of MINAMINO and Shutsa disclose determining that the list of amenities includes a first amenity and excludes a second amenity; in response to determining that the list of amenities includes the first amenity and excludes the second amenity, selecting a first classification as the individual classification; and in response to determining that the list of amenities excludes the first amenity and excludes the second amenity, selecting a second classification as the individual classification. (Shutsa, [0039] For example, the search criteria component 220 can retrieve an amenity criterion from the amenity component 240. The search criteria component 220 can compare one or more of the search attributes from the search criteria to the amenity criterion. If the search attributes satisfy the amenity criterion, the search criteria component 220 can activate display of a particular amenity for presentation in a given graphical object for which a corresponding listing includes the particular amenity or is associated with the particular amenity. For a second graphical object for which the corresponding listing excludes the particular amenity or is not associated with the particular amenity, the search criteria component 220 can prevent displaying the particular amenity in the second graphical object. In some examples, the search criteria component 220 can visually highlight (e.g., present a border or present the corresponding graphical object in a different color from other graphical objects) a particular graphical object that is associated with a listing that includes the particular amenity in response to determining that the search attributes satisfy the amenity criterion. This can be performed in addition to, or alternative to, presenting the amenity (amenity indicator and/or identifier) in the graphical object).
Conclusion
9. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 20250086914 A1 COMPUTER-GENERATED INTERACTIVE HYBRID VIRTUAL ENVIRONMENT FOR ITEM PLACEMENT BASED ON FAMILIARITY [0067] An AR item placement system 432 receives an image or video from a user system 102 that depicts a real-world environment (e.g., a room in a home), as well as data from the position components 1234 and the motion components 1230. The AR item placement system 432 detects one or more real-world objects or features depicted in the image or video and uses the detected one or more real-world objects to characterize the real-world environment, for example by generating a 3D mesh model of the environment. The AR item placement system 432 may also compute a classification for the real-world environment. For example, the AR item placement system 432 can classify the real-world environment as a kitchen, a bedroom, a nursery, a toddler room, a teenager room, an office, a living room, a den, a formal living room, a patio, a deck, a balcony, a bathroom, or any other suitable home-based room classification. Once classified, the AR item placement system AR item placement system 432 identifies one or more items (such as physical products or electronically consumable content items) related to the real-world environment classification.
US 20160092959 A1 is directed to Tag Based Property Platform & Method. A property tagging system permits stakeholders (owners, neighbors, gamers, merchants, realtors) to comment, rate and tag features of properties in accordance with customizable filters. The annotations, labels, etc., can be presented and targeted within mobile interfaces for user ease and engagement.
10. Any inquiry concerning this communication or earlier communications from the examiner should be directed to TADESSE HAILU whose telephone number is (571)272-4051; and the email address is Tadesse.hailu@USPTO.GOV. The examiner can normally be reached Monday- Friday 9:30-5:30 (Eastern time).
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Bashore, William L. can be reached (571) 272-4088. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/TADESSE HAILU/Primary Examiner, Art Unit 2174
Prosecution Insights