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 .
1. Claims 1-20 filed 03/25/2025 are pending for examination.
2. Continuity: This application filed 03/25/2025 is a Continuation of 17830641 , filed 06/02/2022 ,now U.S. Patent # 12288238.
Claim Objections
3. Claims 1, 7, 8, 14 and 15 objected to because of the following informalities:
In these claims the term “complimentary “ needs to be corrected to ----complementary-. Appropriate correction is required.
Double Patenting
4. The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of U.S. Patent No. 12288238, hereinafter Patent’ 238. Although the claims at issue are not identical, they are not patentably distinct from each other because the claims in both the application are directed to recommending complementary items listings based on input images containing multiple items/objects using trained machine learning models to predict complementary item listings as is evident from the comparison provided below between the limitations of claim 1 , considered as exemplary, with the limitations of claims 1, 2, 4, and 5 od US Patent# 12288238, hereinafter Patent’ 238. All the highlighted limitations of claim 1 of the instant application are taught by the underlined limitations of the claims 1, 2, 4, and 5 of the Patent’238.
Claim 1 of the instant Application:
.(i) A computer-implemented method comprising:
tracking user interactions with search results provided on search results pages in response to multi-target searches on input images having a plurality of objects;[Claims 1 , 2 and 4 of the Patent’ 238 teach using a user’s interactions from muti-search results of accessing plurality of images]
generating training data based on the user interactions, wherein the training data comprises complementary item listing data identifying complementary item listings based on the user interactions with the search results from the multi-target searches; and training a machine learning model using the training data to provide a trained machine learning model that predicts complementary item listings for input item listings [See claims 4 and 5 depending from claim 1 of the Patent’238 teach using complementary item listing data based on stored user interactions and behavior resulting from multi-search results for training a machine learning model to predict complementary item listings for input item listings
Claims 1,2, 4-5 of the Patent’238:
1. A computer-implemented method comprising:
accessing a plurality of images at a search system of a listing platform, the search system comprising one or more servers and one or more storage devices;
generating a complementary object datastore on at least one storage device of the one or more storage devices of the search system, the complementary object datastore generated by: for each image from the plurality of images,
performing, by at least one server of the one or more servers, object detection on the image to identify each of a plurality of objects in the image, and for each image from the plurality of images, storing, in the complementary objects datastore, complementary object data identifying a first object from the image and a second object from the image as complementary objects;
generating a complementary item listing datastore on at least one storage device of the one or more storage devices of the search system, the complimentary item listing datastore generated by: for each image from the plurality of images,
performing a multi-target search by at least one server from the one or more servers using each object in the image as a search query to query an item listings datastore on at least one storage device of the one or more storage devices using each object to identify a plurality of one or more item listings for each object in the image, and for each image from the plurality of images,
storing, in the complementary item listings datastore, complementary item listing data identifying the one or more item listings identified for a first object in the image and the one or more item listings identified for a second object in the image as complimentary item listings; and providing a webpage presenting at least one complimentary item listing recommendation for an input item listing using the complementary object datastore and the complimentary item listings datastore by:
receiving, by at least one server of the one or more servers, an indication of the input item listing for which data is stored in the item listings datastore, querying the complementary object data in the complementary objects datastore using an object from the input item listing as a search query to identify one or more complementary objects,
querying the item listings datastore using each of the one or more complementary objects as a search query to identify a first set of complementary item listings, querying the complementary item listings data in the complementary item listings datastore using the input item listing as a search query to identify a second set of complementary item listings, and
providing the webpage presenting the at least one complimentary item listing recommendation based on the first set of complementary item listings and the second set of complementary item listings.
2. The computer-implemented method of claim 1, wherein the input item listing is received in response to a user interacting with the input item listing.
4. The computer-implemented method of claim 1, wherein the complementary item listing data is stored based on user behavior information regarding user interaction with one or more item listings from the plurality of item listings.
5. The computer-implemented method of claim 1, wherein the method further comprises: training, using the complementary item listing data from the complementary item listings datastore, a machine learning model to predict complementary item listings for a given item listing; and identifying, using the machine learning model, a third set of complementary item listings based on the input item listing, wherein the at least one complimentary item listing recommendation is further based on the third set of complementary item listings.
The limitations of the dependent claims 2, 3, 4, 5, 6 , and 7 of the instant application are taught in the limitations of the claims 1 &6, 1 and 2, 3, 1, 1, and 1 respectively and are subject to rejection on the ground of nonstatutory double patenting as being unpatentable over claims 1, 2, 3, and 6 of U.S. Patent No. 12288238, hereinafter Patent’ 238 .
Since the limitations of claims 8-14, and 15-20 of the instant application are similar to the limitations of claims 1-7 of the instant application, they are analyzed on the same basis to be rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-6 of the Patent’ 238.
Claim Rejections - 35 USC § 101
5. 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 8-14 are ejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim(s) does/do not fall within at least one of the four categories of patent eligible subject matter because they are directed to a computer storage media , which the claims 8-14, as recited, do not explicitly state that the storage media is a non-transitory. Therefore, claims 8-14 can be construed claiming transitory storage media which is a non-statutory subject matter.
Note: Examiner suggests amending the claims 8-14 by replacing the terms “storage media” by ---non-transitory storage media.
5.2. Claims 1--20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more, when analyzed as per MPEP 2106.
Step 1 analysis:
Claims 1-7 are to a process comprising a series of steps, and claims 15-20 to a system /apparatus, which are statutory (Step 1: Yes).
Claims 8-14 have been determined, see paragraph 5.1 above as non-statutory.
Step 2A Analysis:
Claim 1 recites:
(i) A computer-implemented method comprising:
(i) tracking user interactions with search results provided on search results pages in response to multi-target searches on input images having a plurality of objects;
(ii) generating training data based on the user interactions, wherein the training data comprises complementary item listing data identifying complimentary item listings based on the user interactions with the search results from the multi-target searches; and
(iii) training a machine learning model using the training data to provide a trained machine learning model that predicts complementary item listings for input item listings.
Step 2A Prong 1 analysis: This part of the eligibility analysis evaluates whether the claim recites a judicial exception. As explained in MPEP 2106.04, subsection II, a claim “recites” a judicial exception when the judicial exception is “set forth” or “described” in the claim.
Claims 1-20 recite abstract idea.
The highlighted limitations comprising, “ A method comprising: tracking user interactions with search results provided on search results pages in response to multi-target searches on input images having a plurality of objects; identifying complimentary item listings based on the user interactions with the search results from the multi-target searches; and model that predicts complementary item listings for input item listings” , under their broadest reasonable interpretation relates to certain methods of organizing an human activity of processing a user’s interaction with items contained in images , which show his interest, and based on this data identifying complimentary items, for example if a user is concentrating on items related to breakfast items, such as eggs and bead, identify complimentary items coffee or tea , hash browns, etc. and use this data for training a model to make predictions on complimentary item listings. Thus, claim 1 and its dependent claims 2-7 recite an abstract idea grouping of “Certain Methods of Organizing Human Activity”.
The limitations in claim 1 comprising, “ tracking user interactions with search results provided on search results pages in response to multi-target searches on input images having a plurality of objects; generating training data based on the user interactions, wherein the training data comprises complementary item listing data identifying complimentary item listings based on the user interactions with the search results from the multi-target searches; and training a model using the training data to provide a trained model that predicts complementary item listings for input item listings.”, under their broadest reasonable interpretation, cover steps fall within the mental process groupings of abstract ideas because they cover concepts performed in the human mind, including observation, evaluation, judgment, and opinion. See MPEP 2106.04(a)(2), subsection III. For example, a human operator can collect and analyze a user’s interaction data with images and items in the images to identify complementary item listings and can then use the list of complementary item listings for different items to train a model [such as a statistical model] for predicting complementary item listings. That is, other than reciting “by a computer and using a machine learning algorithm” nothing in the claim elements precludes the steps from practically being performed in the mind. The mere nominal recitation of by a computer and using a machine learning model does not take the claim limitations out of the mental process grouping. Thus, the claim 1 and its dependent claims 2-7 recite a mental process.
Since the other independent claims 8 and 15 recite similar limitations as discussed for claim 1, they are analyzed with their dependent claims 9-14, and 16-20 on the basis of same rationale as established for claim 1 and its dependent claims 2-7 reciting limitations falling within “Mental Process” and “Certain Methods of Organizing Human Activity” groupings of abstract ideas.
If a claim that includes two or more abstract ideas groupings per Step 2A, Prong One , under such circumstances as per MPEP 2106.04, subsection IIB, the Supreme Court has treated such claims in the same manner as claims reciting a single judicial exception. Id. (discussing Bilski v. Kappos, 561 U.S. 593 (2010)). Here, the limitations of the claims 1-20, as analyzed above, fall within the mental process grouping and certain methods of organizing human activity groupings of abstract ideas, and steps (b) and (c) fall within the mathematical concepts grouping of abstract ideas, which are considered reciting a single abstract idea.
Thus, claims 1-20 recite an abstract idea [Step 2A, prong, One=Yes].
Step 2A Prong 2 analysis: This part of the eligibility analysis evaluates whether the claim as a whole integrates the recited judicial exception into a practical application of the exception or whether the claim is “directed to” the judicial exception. This evaluation is performed by (1) identifying whether there are any additional elements recited in the claim beyond the judicial exception, and (2) evaluating those additional elements individually and in combination to determine whether the claim as a whole integrates the exception into a practical application. See MPEP 2106.04(d).
Claims 1-20: The judicial exception is not integrated into a practical application.
Claim 1 recites the additional limitations of using generic computer components executing the steps of:
(i) tracking user interactions with search results provided on search results pages in response to multi-target searches on input images having a plurality of objects;
(ii) generating training data based on the user interactions, wherein the training data comprises complementary item listing data identifying complimentary item listings based on the user interactions with the search results from the multi-target searches; and
(iii) training a machine learning model using the training data to provide a trained machine learning model that predicts complementary item listings for input item listings.
The limitations in step (iii) reciting “training a machine learning model” provide nothing more than mere instructions to implement an abstract idea on a generic computer. See MPEP 2106.05(f). MPEP 2106.05(f) provides the following considerations for determining whether a claim simply recites a judicial exception with the words “apply it” (or an equivalent), such as mere instructions to implement an abstract idea on a computer: (1) whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished; (2) whether the claim invokes computers or other machinery merely as a tool to perform an existing process; and (3) the particularity or generality of the application of the judicial exception. The abstract idea of “predicting a complementary item listings for input item listings”, by training a machine learning model is used to generally apply the abstract idea without placing any limits on how the trained machine learning model functions. Rather, these limitations only recite implementing an abstract idea to use a model to predict complementary items listing and do not include any details on the about how the steps of predicting are carried out reflecting a technical improvement in the machine learning algorithm or technical improvements to computer functioning, or hardware or network or database. See MPEP 2106.05(f). The recitation of “using a trained machine learning model” in these limitations also merely indicates a field of use or technological environment in which the judicial exception is performed. Although the additional element “using a trained machine leaning model” limits the identified judicial exceptions “model predicting complementary items listings ” and “analyzing the one or more detected anomalies using the trained ANN to generate this type of limitation merely confines the use of the abstract idea to a particular technological environment (training a machine learning model) and thus fails to add an inventive concept to the claims. See MPEP 2106.05(h).
Even when viewed individually and in combination, the additional elements of claim 1 do not integrate the recited judicial exception into a practical application because they do not add any meaningful limits on practicing the abstract idea (Step 2A, Prong Two: NO), and the claim is directed to the judicial exception. (Step 2A: YES).
Dependent claims 2-4 and 7 recite collecting data, and providing a recommendation which are mere data gathering and outputting recited at a high level of generality and storing data , which are insignificant extra-solution activity. See MPEP 2106.05(g). The use of computer for these steps is recited at a high ;level of generality and it is used as a tool to perform the generic computer function of receiving data. See MPEP 2106.05(f). None of these limitations recite or reflect a technical improvement in the machine learning algorithm or technical improvements to computer functioning, or hardware or network or database. See MPEP 2106.05(f). The element of identifying complementary listings using the trained machine learning model, as already analyzed is merely implementing an abstract idea without details as how the identification process is carried out reflecting a technical improvement in the machine learning algorithm or technical improvements to computer functioning, or hardware or network or database. See MPEP 2106.05(f).
Limitations in dependent claims 5-6, relate to non-functional descriptive subject describing the recommendations, which do not add any meaningful limits on practicing the abstract idea.
Accordingly, when viewed individually and in combination, the additional elements in claims 1-7 do not integrate the recited judicial exception into a practical application because they do not add any meaningful limits on practicing the abstract idea (Step 2A, Prong Two: NO), and the claims are directed to the judicial exception. (Step 2A: YES).
Since the other independent claims 8 and 15 and their dependent claims 9-14, and 16-20 recite similar limitations as discussed for claims 1-7, they are analyzed on the basis of same rationale as established for claims 1-7 not integrating the recited judicial exception into a practical application because they do not add any meaningful limits on practicing the abstract idea (Step 2A, Prong Two: NO), and the claims are directed to the judicial exception. (Step 2A: YES).
Thus, claims 1-20 are directed to an abstract idea.
Step 2B analysis: This part of the eligibility analysis evaluates whether the claim as a whole amounts to significantly more than the recited exception i.e., whether any additional element, or combination of additional elements, adds an inventive concept to the claim. See MPEP 2106.05.
The claims 1-20 do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Since claims are as per Step 2A are directed to an abstract idea, they have to be analyzed per Step 2B, if they recite an inventive step, i.e., the claims recite additional elements or a combination of elements that amount to “Significantly More” than the judicial exception in the claim.
As discussed above with respect to Step 2A Prong Two, the additional elements in the claims 1-20 amount to no more than mere instructions to apply the exception using a generic computer components, and generally linking the judicial exception to a particular technological environment or field of use. The same analysis applies here in 2B, i.e., mere instructions to apply the exception using a generic computer components, and generally linking the judicial exception to a particular technological environment or field of use using a generic computer components cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Additional elements comprising receiving, outputting and storing data were found to be insignificant extra-solution activity in Step 2A, Prong Two, because they were determined to be insignificant limitations as necessary data gathering/transmitting/outputting/ displaying/ presenting/storing data . However, a conclusion that an additional element is insignificant extra-solution activity in Step 2A, Prong Two should be re-evaluated in Step 2B. See MPEP 2106.05, subsection I.A. At Step 2B, the evaluation of the insignificant extra-solution activity consideration takes into account whether or not the extra-solution activity is well understood, routine, and conventional in the field. See MPEP 2106.05(g). ). The background of the example does not provide any indication that the computer components are anything other than a generic, off the shelf computer component and the Symantec, TLI, OIP Techs, Versata court decisions cited in MPEP 2106.05(d) (ii) indicate that mere data gathering/ transmitting/ outputting/ displaying/presenting/ data steps using a generic computer are well-understood, routine, conventional function when they are claimed in a merely generic manner (as it is here).
Accordingly, a conclusion that the receiving, acquiring, transmitting, and displaying steps are well-understood, routine conventional activities are supported under Berkheimer Option 2. See MPEP 2106.05 (f) 2: Whether the claim invokes computers or other machinery merely as a tool to perform an existing process. Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit).
Even when considered individually and in combination, the additional elements represent mere instructions to implement an abstract idea or other exception on a computer and insignificant extra-solution activity, which do not provide an inventive concept. (Step 2B: NO).
Thus, claims 1-20 , as recited are patent ineligible.
Claim Rejections - 35 USC § 103
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 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.
6.1. Claims 1--20 are rejected under 35 U.S.C. 103 as being unpatentable over Karpas et al. [US 20190102601 A1], hereinafter Karpas in view of Acott et al. [US 2017/0255985 A1], hereinafter Acott in view of Bhattacharjee [US 2019/0370879].
Regarding claim 1, Karpas teaches a computer-implemented method comprising:
tracking user interactions with search results provided on search results pages in response to multi-target searches on input images having a plurality of objects [[See paras 0008-0011, 0043, 0046-0047“ [0008] A customer captures, with the smartphone's camera, a video sequence of frames (hereinafter “images”), of a space that includes one or more items of furniture. ……[0009] For each image, the smartphone detects the items of furniture, generates a respective bounding box for each detected item of furniture, and sends the respective set of pixels that represents at least a portion of each detected item of furniture to the cloud. [0010] In response to each set of pixels, the one or more cloud servers determine a respective category, and a respective one or more descriptors, for a corresponding one of the detected items of furniture, and return, to the smartphone, the determined categories and descriptors. [0011] The smartphone displays each image, and, for each detected item of furniture, displays the respective bounding box, category, and descriptor(s) overlaying the image and “anchored” to the item of furniture. Herein, “anchored” means that the bounding box is displayed around at least a portion of the furniture item, and that the category and descriptor(s) are displayed adjacent to the furniture item in a manner that conveys, to a viewer, that the category and descriptor(s) correspond to the furniture item. For example, the image may include a tag that points to the furniture item to which a list of the category and the other descriptor(s) is anchored.[0043] The list 58 is anchored to the chair 16 with an anchor icon 64, and each of the descriptors 60 describes a style or another attribute of the chair. For example, the descriptors 60 indicate that the chair 16 is of a contemporary style, has a black finish, a sleek design, nail-head trim, is paired, in the space 12, with a Persian rug and an end table 64 that can support a table lamp (not shown in FIG. 3), and is suitable for use in an entertainment space and in a breakfast nook. The system (not shown in FIG. 3) is able to indicate that the chair 16 is paired with a Persian rug and an end table 64 because the system also detected, categorized, and described (with descriptors) these items….……. [“ 0046]…., the system can be configured to perform, automatically or at the request of the person, an online search for chairs that have attributes the same as, or similar to those, indicated by the descriptors 60. ……[0047] In another alternative, the person or the system can upload the list 58 to a furniture provider's webserver, which then returns a list of all of the provider's furniture items having attributes that match the attributes indicated by the descriptors 60. The person, or the furniture provider's webserver, can adjust, in a conventional manner, the level of similarity between the attributes of a furniture item and the attributes indicated by the descriptors 60 needed to yield a match. …..“[0048] …. referring to FIGS. 2-3, …... Moreover, the system can capture and display a video sequence of images of the space 12, even as the system camera moves (e.g., pans across the space), and the system can display one or more of bounding boxes, category names, and lists in each of the images such that the one or more bound boxes, category names, and lists each “follows” its respective object as the camera moves. The system can do this by determining one or more of a bounding box, category name, and a list for each item of furniture in each image.”. These excerpts describe performing detection plurality of objects from each image and those plurality of objects can relate to furniture such as sofa, chair, and table [see paras 0033 and 0035 which describe the image of a space including an object sofa paired with complementary objects chairs, tables, para 0043 describes that the images could include an object chair which is paired with complementary objects comprising a suitable Persian rug and an end table and para, and para 0046 describes a large banquet table paired with other associated furniture objects] and the identified objects their listings can be saved in any of the various computer systems including a smart phone or one of the cloud servers where the objects are analyzed to determine their descriptors, categories and then a search can be made based on a user’s selection. An item listings comprising a main object with their paired objects is automatically searched [para 0046] for querying/checking /identifying the objects, the main item could be a sofa and along with that complementary items such as chairs and tables can be identified and are stored in a cloud based on a request and the user can receive the searched results and paras 0008 and 0009 describe receiving an input representing an indication of an put item in the form of captured image for an object which has been captured from a listing of furniture ]. Karpas , here,, fails to teach tracking user interactions with multi-target search results. Acott discloses a recommendation engine recommending gifts to a shopper in response to user interactions wherein the items are selected based on the user’s behavior and past interactions [See Acott para 0020, “A recommendation engine selects item listings describing items for sale for presentation to a user. The selection is based on interactions by the user with item listings (e.g., past purchases by the user), attributes of the selected item listings, and behavior of other users.’]. Therefore, in view of the teachings of Acott it would be obvious to POSITA at the time of the effective filing date of the Application to have modified the teachings of Karpas to incorporate the concept of tracking the users’ search results selecting and storing complimentary objects considering the user behavior information such as past purchases and behavior of the user, because the user’s interactions can be used in making predictions for the complementary items.
Karpas in view of Acott fails to teach conducting muri-target searches on input images having a plurality of objects. Bhattacharjee, in the same field of endeavor of generating a complementary item listing and making recommendation for a complementary item from the displayed complementary items listing in response to an input from the user, teaches performing a multi-target search by querying an item listings datastore on at least one storage device of the one or more storage devices using each object to identify a plurality of item listings and storing, in a complementary item listings datastore on at least one storage device of the one or more storage devices, complementary item listing data associating item listings from the plurality of item listings [See Fig.2 and paras 0036—0038 which describe making a multi-target search from an item collection list, which is a large list of items, and then performing identification of complementary items list which is stored as a “subset item list 202”. Bhattacharjee teaches receiving, by at least one server of the one or more servers, an indication of an input item listing for which data is stored in the item listings datastore [see para 0039 and Fig. 2 “207’ describing receiving a selection of one item from subset item collection 202 ] and ; querying the complementary object data in the complementary objects datastore using an object from the input item listing to identify one or more complementary objects; querying the item listings datastore using the one or more complementary objects to identify a first set of complementary item listings; querying the complementary item listings data in the complementary item listings datastore using the input item listing to identify a second set of complementary item listings [See Fig.2 and paras 0036-0039. These limitations, under their broadest reasonable interpretation, cover checking the complimentary data listing 202 using the user’s selection and then select a narrowed down complementary object from complementary item listing 208 in Fig.2 [second set of complementary item listing 208] ; and providing a webpage presenting a recommendation based on the first set of complementary item listings and the second set of complementary item listings [see para 0039, “ In response to receipt of the item identifier, at 208, a predetermined number of complementary items can be returned to a retail website server 12, for example, for display to the user.” Therefore, in view of the teachings of Bhattacharjee in the same field of endeavor as Karpas of being able to create complementary item listings, at the time of the effective filing date of the Application one POSITA would have modified Karpas to incorporate teachings of Bhattacharjee to incorporate the missing steps of because it would, as shown in Bhattacharjee, [See para 0002—0003] help to overcome the difficulty in the prior art systems by conducting muti-target item searches in presenting optional complementary items for purchase alongside the primary item selected by a user.
Karpas further teaches generating training data based on the user interactions, wherein the training data comprises complementary item listing data identifying complimentary item listings based on the user interactions with the search results from the multi-target searches; training a machine learning model using the training data to provide a trained machine learning model that predicts complementary item listings for input item listings [See Karpas para 0058, “the image-analysis subsystem 70 generates training images from electronic representations of objects, implements one or more neural networks, trains the neural networks with the training images, and stores the electronic representations of objects in a database for comparing to objects that the image-analysis subsystem categorizes and otherwise describes as described above in conjunction with FIGS. 2-3 and below in conjunction with FIGS. 6-8.. “. Here, Karpas teaches training, using the complementary item listing data from the complementary item listings datastore, a machine learning model by using neural networks, which is a machine learning process, to analyze the images and also training the neural networks for carrying out image analysis to identify objects to predict complementary item listings for a given item listing; and identifying, using the machine learning model, a third set of complementary item listings based on the input item listing, wherein the recommendation is further based on the third set of complementary item listings
Regarding claim 2,, the limitations, “ receiving an input item listing; identifying, using the trained machine learning model, a set of complementary item listings based on the input item listing; and providing a recommendation based on the set of complementary item listings”, are already covered in the analysis of claim 1, as described in Karpas para 0058.
Regarding claim 3, the limitations, “The computer-implemented method of claim 2, wherein the input item listing is received in response to a user interacting with the input item listing”, are already covered in the analysis of claim 1 in view of the combined teachings of Karpas and Acott.
Regarding claim 4, the combined teachings of Karpas, Acott and Bhatatcharjee as applied to claims 1-2 teach that computer-implemented method of claim 2, wherein the input item listing is received from a seller providing the input item listing [See Bhattacharjee para 0032, “The item collection data 130 represents item description information and item identification information for each of the items that may be provided by the retailer.”.
.
Regarding claim 5, the limitations, “ The computer-implemented method of claim 2, wherein the recommendation comprises one or more recommended complementary item listings from the set of complementary item listings”, are already covered in the analysis of claim 1 in view of the teachings of Karpas and Bhattacharjee. .
Regrading claim 6, its limitations, “The computer-implemented method of claim 2, wherein the recommendation comprises a query suggestion generated based on the set of complementary item listings. “ in view of the , in view of the teachings of Karpas and Bhattacharjee [See Bhattacharjee Fig 2 and paras 0036—0039].
7. The computer-implemented method of claim 1, wherein the method further comprises:
generating a complementary object datastore storing complementary object data associating objects from the plurality of objects in the input images; and generating a complementary item listing datastore storing the complementary item listing data identifying the complimentary item listings based on the user interactions with the search results from the multi-target searches [These limitations are already covered in the analysis of claim1 in view of the combined teachings of Karpas and Bhattacharjee [See Bhattacharjee Fig.2 and paras 0036—0039 which describe making a multi-target search from an item collection list, which is a large list of items, and then performing identification of complementary items list which is stored as a “subset item list 202”. Bhattacharjee teaches receiving, by at least one server of the one or more servers, an indication of an input item listing for which data is stored in the item listings datastore [see para 0039 and Fig. 2 “207’ describing receiving a selection of one item from subset item collection 202 ] and ; querying the complementary object data in the complementary objects datastore using an object from the input item listing to identify one or more complementary objects; querying the item listings datastore using the one or more complementary objects to identify a first set of complementary item listings; querying the complementary item listings data in the complementary item listings datastore using the input item listing to identify a second set of complementary item listings [See Fig.2 and paras 0036-0039. [see para 0039, “ In response to receipt of the item identifier, at 208, a predetermined number of complementary items can be returned to a retail website server 12, for example, for display to the user.”].
Regrading claims 8-14, and 15-20, since their limitations are similar to the limitations of claims 1-7, they are rejected as being unpatentable over the combined teachings of Karpas in view of Acott in view of Bhattacharjee on the same basis.
Conclusion
8. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
(i) Ma et al. (US 2022/0245709 A1; see para 0046 and Fig 5 illustrates a process flowchart for automatically generating complimentary item recommendations with user-topic awareness for each user and query item using a machine-learning model.
(ii) Sinha et al. [US Patent# 11468494 B2 ; see claim 6 ] describes a system to generate a set of personalized complementary recommendations to: generate a set of ranked complimentary-weighted items by ranking the set of complimentary-weighted items according to at least one metric.
Foreign reference:
(iii) EP 3321855 A1; see Figs 9A, 10A and 10B and associated text ] describing a process performing a complementary item search, as described in FIG. 9A, and see Fig.10 A describes in response to selecting a complementary search option from the parameter menu 820, and a region of interest 825A, the complementary search engine 260 determines which items have similar classification indices to the region of interest 825A. The a listing engine 230 generates a listing 1020 that displays complementary items 1000A-D, as illustrated in FIG. 10B..
NPL references:
(iv) Kvernadze, Giorgi • Sudyanti, Putu Ayu G. • Subedi, Nishan • Hajiaghayi, Mohammad; “Two Is Better Than One: Dual Embeddings for Complementary Product Recommendations “ ARXIV ID: 2211.14982 Publication Date: 2022-11-27; retrieved from IP.COM on 06/13/2026 describes a process for finding complementary items by leveraging dual embedding representations for products by training item representations using co-purchase data.
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/YOGESH C GARG/ Primary Examiner, Art Unit 3688
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