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 .
Remarks
This action is in response to the application received on 11/14/25. Claims 1-20 are pending in the application.
Claims 1-20 are rejected under 35 U.S.C. 101.
Claims 1, 6-10, 17, 18, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Guy et al. (US 2023/0011114), and further in view of Dagan et al. (US 2023/0009267).
Claims 2-5 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Guy in view of Dagan, and further in view of Wilensky (US 2006/0074861).
Claims 11-13 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Guy et al. (US 2023/0011114), and further in view of Chalkley (US 2025/0200115).
Claims 14 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Guy in view of Chalkley, and further in view of Wilensky (US 2006/0074861).
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 therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 2A, Prong One asks: Is the claim directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea? See MPEP 2106.04 Part I. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. See MPEP 2106.04(a).
With respect to claims 1, 11, and 17, the limitations of “process a first image and an instruction received from a user device,” “determine a first confidence score,” and “determine that the first confidence score is above a confidence threshold”, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, nothing in the claim element precludes the step from practically being performed in the mind. For example, “process” in the context of this claim encompasses the user mentally thinking about data.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
At step 2a, prong two, this judicial exception is not integrated into a practical application. Claims 1 and 17 recite a processor to execute the operations, however, this is recited as a high-level of generality (i.e., as a generic processor performing a generic computer function) such that it amounts to no more than mere instructions to apply the exception using a generic computer component. Additionally, the claim recites “provide the first image and the instruction as an input to a matching model” and “cause the first property listing to be displayed.” These elements do not integrate the abstract idea into a practical application because they do not impose a meaningful limit on the judicial exception and provide only insignificant extra solution activity that is mere data gathering in conjunction with the abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements amount to no more than mere instructions to apply an exception using generic computer components. Mere instructions to apply an exception using generic computer components cannot provide an inventive concept.
With respect to “provide the first image and the instruction as an input to a matching model” and “accessing a first property listing on a user device”, the courts have found limitations directed towards data gathering to be well-understood, routine, and conventional. See MPEP 2106.05(d)(II). Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information).
With respect to “cause the first property listing to be displayed”, the courts have found limitations directed towards storing to be well-understood, routine, and conventional. See MPEP 2106.05(d)(II). Presenting offers and gathering statistics, OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-93.
Considering the additional elements individually and in combination and the claim as a whole, the additional elements do not provide significantly more than the abstract idea. The claim is not patent eligible.
With respect to claims 2-4, 7-9, 13, 14, 16, and 18-20, the limitations are directed towards further abstract ideas that are processes that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, nothing in the claim element precludes the step from practically being performed in the mind. For example, “process,” “determine,” “rank,” “output,” and “filter” in the context of this claim encompasses the user mentally thinking about data.
Additionally, the claim recites “provide… to the matching model.” These elements do not integrate the abstract idea into a practical application because they do not impose a meaningful limit on the judicial exception and provide only insignificant extra solution activity that is mere data gathering in conjunction with the abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements amount to no more than mere instructions to apply an exception using generic computer components. Mere instructions to apply an exception using generic computer components cannot provide an inventive concept.
With respect to “provide … to the matching model”, the courts have found limitations directed towards data gathering to be well-understood, routine, and conventional. See MPEP 2106.05(d)(II). Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information).
With respect to claims 5, 6, 10, 12, and 15, the limitations further define elements discussed above and do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
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.
Claims 1, 6-10, 17, 18, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Guy et al. (US 2023/0011114), and further in view of Dagan et al. (US 2023/0009267).
With respect to claim 1, Guy teaches a system, comprising: a computer-readable storage medium storing computer-executable instructions; and one or more processors, wherein the computer-executable instructions, when executed by the one or more processors, cause the one or more processors to:
process a first image and an instruction received from a user device (Guy, pa 0086, block diagram 300 begins with receiving a search image as a search query at a search engine at step 302. By way of example, the search query is for an inventory listing in a product listing inventory);
provide the first image and the instruction as an input to a matching model, wherein providing the first image and the instruction as input to the matching model causes the matching model to output a second image associated with a first property listing (Guy, pa 0090, Turning to step 308, an image similarity for the plurality of images or a subset of the plurality of images is determined based on comparing the plurality of images or the subset from the image corpus and the search image. In aspects, the image similarity is determined by an image similarity determination model.);
determine a first confidence score relating to a first similarity between the first image and the second image (Guy, pa 0061, the image similarity determiner 124 may identify a minimum and a maximum similarity value of the similarity values for each of the set of images. & pa 0090, the image similarity determination model may provide the image similarity ( e.g., a score) once trained from image groups satisfying a relevance criteria and feature vectors and a similarity function. Further, the image quality indication and the image similarity indicate a search query performance for the search image.);
determine that the first confidence score is above a confidence threshold (Guy, pa 0061, Continuing the example, the minimum and the maximum may be used for determining which image from the set of images will be provided as the recommended image for the search. & pa 0091, Turning to step 310, a first image that exceeds a search query performance indicated by the image similarity and the image quality indication is identified.); and
Guy doesn’t expressly discuss cause the first property listing to be displayed in a user interface on the user device, wherein the first property listing includes the second image.
Dagan teaches provide the first image and the instruction as an input to a matching model, wherein providing the first image and the instruction as input to the matching model causes the matching model to output a second image associated with a first property listing (Dagan, pa 0073, The search query may be received (e.g., from the search query input 204 from a computing device, such as computing device 102). At block 404, search results having item listings having item listing images for the search query are identified. The item listing images may be associated with item listings.);
cause the first property listing to be displayed in a user interface on the user device, wherein the first property listing includes the second image (Dagan, pa 0064, search results comprising item listings 208 are provided for display on the GUI 200.).
It would have been obvious at the effective filing date of the invention to a person having ordinary skill in the art to which said subject matter pertains to have modified Guy with the teachings of Dagan because it provides information about products available in a network-based marketplace (Dagan, pa 0030).
With respect to claim 6, Guy in view of Dagan teaches the system of claim 1, wherein the instruction is a request for property listings similar to the first image (Guy, Fig. 3 step 302 & 308 DETERMINE AN IMAGE SlMILARITY INDICATION BASED ON A COMPARISON BETWEEN THE PLURALITY OF IMAGES AND·THE SEARCH IMAGE).
With respect to claim 7, Guy in view of Dagan teaches the system of claim 1, wherein the matching model is to output a third image associated with a second property listing (Guy, pa 0098, Based on this selection, a search results page comprising search result images corresponding to the selected recommended image is received at step 508. & Dagan, pa 0073, At block 404, search results having item listings having item listing images for the search query are identified. The item listing images may be associated with item listings.).
With respect to claim 8, Guy in view of Dagan teaches the system of claim 7, wherein the computer-executable instructions, when executed, further cause the one or more processors to: determine a second confidence score relating to a similarity between the first image and the third image (Guy, pa 0098, the set of the search result images may be received based on the set exceeding the image quality indication and the image similarity. In aspects, the set of the search result images are presented as selectable options.); and rank the first property listing and the second property listing based on a comparison between the first confidence score and the second confidence score (Guy, pa 0068, the image similarity determiner 124 may determine a set of search results based on the search image. Further, the image similarity determiner 124 then ranks the search results using a ranker).
With respect to claim 10, Guy in view of Dagan teaches the system of claim 1, wherein the matching model is a machine learning model (Guy, pa 0043, the machine learning model(s) 114 is an image similarity determination model.).
With respect to claims 17, 18, and 20, the limitations are essentially the same as claims 1, 7, and 9, and are rejected for the same reasons
Claims 2-5 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Guy in view of Dagan, and further in view of Wilensky (US 2006/0074861).
With respect to claim 2, Guy in view of Dagan teaches the system of claim 1, as discussed above.
Wilensky teaches wherein the computer-executable instructions, when executed, further cause the one or more processors to process a third image received from the user device (Wilensky, pa 0100, the user can choose whether to add a reference object to the set of reference objects (step 722)).
It would have been obvious at the effective filing date of the invention to a person having ordinary skill in the art to which said subject matter pertains to have modified Guy in view of Dagan with the teachings of Wilensky because the user may, for example, want to adjust the search to incorporate features or aspects of additional objects, such as objects identified in a previous search (Wilensky, pa 0100).
With respect to claim 3, Guy in view of Dagan and Wilensky teaches the system of claim 2, wherein the computer-executable instructions, when executed, further cause the one or more processors to: provide the first image, the third image, and the instruction as input to the matching model to cause the matching model to output the second image associated with the first property listing (Wilensky, Fig. 9, step 718-720, & pa 0104, If a new search is conducted (the YES branch of step 718) the newly defined composite information, the new weighting vector, if any, and the newly defined comparison function, if any, are used. & pa 0099, If the search is conducted (the YES branch of step 718), the composite reference information is compared to the information for each of the media objects in the previously selected collection of media objects using the previously defined comparison function (step 720).); determine a second confidence score relating to a second similarity between the third image and the second image (Wilensky, pa 0099, For each comparison, a similarity value can be determined. The similarity values can then be used to identify media objects that are more or less similar to the composite reference information.); and determine that the first confidence score and the second confidence score are above the confidence threshold (Guy, pa 0061, Continuing the example, the minimum and the maximum may be used for determining which image from the set of images will be provided as the recommended image for the search.).
With respect to claim 4, Guy in view of Dagan and Wilensky teaches the system of claim 2, wherein the computer-executable instructions, when executed, further cause the one or more processors to: provide the first image, the third image, and the instruction as input to the matching model to cause the matching model to output the second image associated with the first property listing and a fourth image associated with a second property listing (Wilensky, Fig. 9, step 718-720, & pa 0104, If a new search is conducted (the YES branch of step 718) the newly defined composite information, the new weighting vector, if any, and the newly defined comparison function, if any, are used. & pa 0099, If the search is conducted (the YES branch of step 718), the composite reference information is compared to the information for each of the media objects in the previously selected collection of media objects using the previously defined comparison function (step 720).); determine a second confidence score relating to a second similarity between the third image and the fourth image (Wilensky, pa 0099, For each comparison, a similarity value can be determined. The similarity values can then be used to identify media objects that are more or less similar to the composite reference information.); and rank the second image and the fourth image based on a comparison between the first confidence score and the second confidence score (Wilensky, pa 0043, The similarity values 141-144 can be used to rank the associated media objects).
With respect to claim 5, Guy in view of Dagan and Wilensky teaches the system of claim 2, wherein ranking of the first image and the third image is based on a weighted average (Wilensky, pa 0042, Object information can be combined by a weighted sum of reference object information. If the reference images are ranked in importance, then the rank may serve as the weights.).
With respect to claim 19, the limitations are essentially the same as claim 4 and are rejected for the same reasons.
Claims 11-13 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Guy et al. (US 2023/0011114), and further in view of Chalkley (US 2025/0200115).
With respect to claim 11, Guy teaches a method, comprising:
providing the first image as input into a matching model, wherein providing the first image as input to the matching model causes the matching model to output a second image included with a second property listing (Guy, pa 0090, Turning to step 308, an image similarity for the plurality of images or a subset of the plurality of images is determined based on comparing the plurality of images or the subset from the image corpus and the search image. In aspects, the image similarity is determined by an image similarity determination model.);
determining a first confidence score relating to a first similarity between the first image and the second image (Guy, pa 0061, the image similarity determiner 124 may identify a minimum and a maximum similarity value of the similarity values for each of the set of images. & pa 0090, the image similarity determination model may provide the image similarity ( e.g., a score) once trained from image groups satisfying a relevance criteria and feature vectors and a similarity function. Further, the image quality indication and the image similarity indicate a search query performance for the search image.);
determining that the first confidence score is above a confidence threshold (Guy, pa 0061, Continuing the example, the minimum and the maximum may be used for determining which image from the set of images will be provided as the recommended image for the search. & pa 0091, Turning to step 310, a first image that exceeds a search query performance indicated by the image similarity and the image quality indication is identified.).
Guy doesn’t expressly discuss accessing a first property listing on a user device, wherein the first property listing includes a first image and displaying the second property listing, wherein the second property listing includes the second image.
Chalkley teaches accessing a first property listing on a user device, wherein the first property listing includes a first image (Chalkley, pa 0069, The search vector generator 202 can analyze user interaction histories using one or more generative AI model(s) 202D. … apply a second generative AI model or a second set of generative AI models to those particular item listings for generation of the search vector… one or more generative AI image models are applied to the images of those identified item listings);
displaying the second property listing, wherein the second property listing includes the second image (Chalkley, pa 0064, the search results can be provided based on the search vector ( e.g., generated using a combination of search query terms and the user interaction histories 128) having a closest distance … to the item listing vector compared to other item listing vectors.).
It would have been obvious at the effective filing date of the invention to a person having ordinary skill in the art to which said subject matter pertains to have modified Guy with the teachings of Chalkley because it accurately identifies particular item listings associated with a user's true intent and provides an enhanced conceptualized search based on enriched categorizations of item listings that are instrumental in assisting the user (Chalkley, pa 0025).
With respect to claim 12, Guy in view of Chalkley teaches the method of claim 11, wherein the first property listing includes a plurality of images (Chalkley, pa 0073, one or more images from the item listing).
With respect to claim 13, Guy in view of Chalkley teaches the method of claim 11, wherein the matching model is to output a third image associated with a second property listing (Guy, pa 0098, Based on this selection, a search results page comprising search result images corresponding to the selected recommended image is received at step 508.).
With respect to claim 16, Guy in view of Chalkley teaches the method of claim 11, further comprising filtering the first property listing based on a filter parameter (Chalkley, 0064, the search vector can be reduced or expanded based on the user feedback upon receiving an initial set of search results provided by the search results generator 116.).
Claims 14 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Guy in view of Chalkley, and further in view of Wilensky (US 2006/0074861).
With respect to claim 14, Guy in view of Chalkley teaches the system of claim 13, as discussed above.
Wilensky teaches determine a second confidence score relating to a similarity between the first image and the third image (Wilensky, pa 0099, For each comparison, a similarity value can be determined. The similarity values can then be used to identify media objects that are more or less similar to the composite reference information.); and rank the second image and the fourth image based on a comparison between the first confidence score and the second confidence score (Wilensky, pa 0043, The similarity values 141-144 can be used to rank the associated media objects).
It would have been obvious at the effective filing date of the invention to a person having ordinary skill in the art to which said subject matter pertains to have modified Guy in view of Chalkley with the teachings of Wilensky because the user may, for example, want to adjust the search to incorporate features or aspects of additional objects, such as objects identified in a previous search (Wilensky, pa 0100).
With respect to claim 15, Guy in view of Chalkley and Wilensky teaches the system of claim 14, wherein ranking of the first image and the third image is based on a weighted average (Wilensky, pa 0042, Object information can be combined by a weighted sum of reference object information. If the reference images are ranked in importance, then the rank may serve as the weights.).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
C. Kottmyer, K. Zhao, Z. Kostic and A. Jevremovic, "Roomsemble: Progressive web application for intuitive property search," 2021 Fifth International Conference On Intelligent Computing in Data Sciences (ICDS), Fez, Morocco, 2021, pp. 1-7 teaches a search process that determines recommendations of properties based on a user upload.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BRITTANY N ALLEN whose telephone number is (571)270-3566. The examiner can normally be reached M-F 9 am - 5:00 pm EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Sherief Badawi can be reached at 571-272-9782. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/BRITTANY N ALLEN/ Primary Examiner, Art Unit 2169
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