DETAILED ACTION
Status of Claims
• The following is an office action in response to the communication filed 11/14/2024.
• Claims 1-20 are currently pending and have been examined.
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). The certified copy of Application No. JP 2024-035801, filed on 03/08/2024 has been received.
Information Disclosure Statement
Information Disclosure Statement received 11/14/2024 has been reviewed and considered.
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 .
Claim Interpretation
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitations use a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitations are: “first acquisition component…”; “second acquisition component…”; “search component…”; “evalutation component…”; “presentation component…”; “display controller…” (claims 1-5, 8-9, 11-15, and 18-19) with the functional language “configured to,” which are not preceded by a structural modifier.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof. The examiner notes that in light of Specification paragraphs [0027], [0030-0032], [0073], [0084], [0129] and Figs. 2 and 8, the components are interpreted to be hardware operating software.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
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 a judicial exception without significantly more. The claims recite an abstract idea. The judicial exception is not integrated into a practical application. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
First, it is determined whether the claims are directed to a statutory category of invention. See MPEP 2106.03(II). In the instant case, claims 1-10 are directed to a system, and claims 11-20 are directed to a process. Therefore, claims 1-20 are directed to statutory subject matter under Step 1 of the Alice/Mayo test (Step 1: YES).
The claims are then analyzed to determine if the claims are directed to a judicial exception. See MPEP 2106.04. In determining whether the claims are directed to a judicial exception, the claims are analyzed to evaluate whether the claims recite a judicial exception (Prong 1 of Step 2A), as well as analyzed to evaluate whether the claims recite additional elements that integrate the judicial exception into a practical application of the judicial exception (Prong 2 of Step 2A). See MPEP 2106.04.
Taking claim 1 as representative, claim 1 recites at least the following limitations that are believed to recite an abstract idea:
acquire customer information including information related to an interest tendency of a customer;
acquire commodity information related to a target commodity in a purchase consideration of the customer;
search for, based on the commodity information, a related content including an image related to the target commodity from a plurality of contents including images stored;
evaluate a priority of the related content from a search based on the customer information; and
present the related content to the customer according to the priority.
The above limitations recite the concept of presenting a product for purchase consideration based on user interest. These limitations, under their broadest reasonable interpretation, fall within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas, enumerated in the MPEP, in that they recite commercial or legal interactions such as advertising, marketing, or sales activities or behaviors. Specifically, providing product recommendations is a marketing and sales activity. This is further illustrated in paragraph [0004] of the Specification, describing the invention relating to items for purchase. Further, these limitations, under their broadest reasonable interpretation, fall within the “Mental Processes” grouping of abstract ideas, enumerated in the MPEP, in that they recite concepts performed in the human mind, including observations, evaluations, judgments, and opinions. Specifically, the determinations are observations, evaluations, and judgements. These limitations are similar to the mental process of collecting information, analyzing it, and displaying certain results of the collection and analysis. Independent claim 11 recites similar limitations as claim 1 and as such, claim 11 falls within the same identified grouping of abstract ideas. Accordingly, under Prong One of Step 2A of the Alice/Mayo test, claims 1 and 11 recite an abstract idea (Step 2A, Prong One: YES).
Under Prong Two of Step 2A of the MPEP, claims 1 and 11 recite additional elements, such as an information processing system, a first acquisition component, a second acquisition component, a search component, an external device, an evaluation component, and a presentation component. These additional elements are described at a high level in Applicant’s specification without any meaningful detail about their structure or configuration. As such, these computer-related limitations are not found to be sufficient to integrate the abstract idea into a practical application. Although these additional computer-related elements are recited, claims 1 and 11 merely invoke such additional elements as a tool to perform the abstract idea. Implementing an abstract idea on a generic computer is not indicative of integration into a practical application. Similar to the limitations of Alice, claims 1 and 11 merely recite a commonplace business method (i.e., presenting a product for purchase consideration based on user interest) being applied on a general purpose computer. See MPEP 2106.05(f). Furthermore, claims 1 and 11 generally link the use of the abstract idea to a particular technological environment or field of use. The courts have identified various examples of limitations as merely indicating a field of use/technological environment in which to apply the abstract idea, such as specifying that the abstract idea of monitoring audit log data relates to transactions or activities that are executed in a computer environment, because this requirement merely limits the claims to the computer field, i.e., to execution on a generic computer (see FairWarning v. Iatric Sys.). Likewise, claims 1 and 11 specifying that the abstract idea of presenting a product for purchase consideration based on user interest is executed in a computer environment merely indicates a field of use in which to apply the abstract idea because this requirement merely limits the claims to the computer field, i.e., to execution on a generic computer. As such, under Prong Two of Step 2A of the MPEP, when considered both individually and as a whole, the limitations of claims 1 and 11 are not indicative of integration into a practical application (Step 2A, Prong Two: NO).
Since claims 1 and 11 recite an abstract idea and fail to integrate the abstract idea into a practical application, claims 1 and 11 are “directed to” an abstract idea (Step 2A: YES).
Next, under Step 2B, the claims are analyzed to determine if there are additional claim limitations that individually, or as an ordered combination, ensure that the claim amounts to significantly more than the abstract idea. See MPEP 2106.05. The instant claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception for at least the following reasons.
Returning to independent claims 1 and 11, these claims recite additional elements, such as an information processing system, a first acquisition component, a second acquisition component, a search component, an external device, an evaluation component, and a presentation component. As discussed above with respect to Prong Two of Step 2A, although additional computer-related elements are recited, the claims merely invoke such additional elements as a tool to perform the abstract idea. See MPEP 2106.05(f). Moreover, the limitations of claims 1 and 11 are manual processes, e.g., receiving information, analyzing information, etc. The courts have indicated that mere automation of manual processes is not sufficient to show an improvement in computer-functionality (see MPEP 2106.05(a)(I)). Furthermore, as discussed above with respect to Prong Two of Step 2A, claims 1 and 11 merely recite the additional elements in order to further define the field of use of the abstract idea, therein attempting to generally link the use of the abstract idea to a particular technological environment, such as the Internet or computing networks (see Ultramercial, Inc. v. Hulu, LLC. (Fed. Cir. 2014); Bilski v. Kappos (2010); MPEP 2106.05(h)). Similar to FairWarning v. Iatric Sys., claims specifying that the abstract idea of s presenting a product for purchase consideration based on user interest is executed in a computer environment merely indicates a field of use in which to apply the abstract idea because this requirement merely limits the claim to the computer field, i.e., to execution on a generic computer.
Even when considered as an ordered combination, the additional elements do not add anything that is not already present when they are considered individually. In Alice Corp., the Court considered the additional elements “as an ordered combination,” and determined that “the computer components…‘[a]dd nothing…that is not already present when the steps are considered separately’ and simply recite intermediated settlement as performed by a generic computer.” Id. (citing Mayo, 566 U.S. at 79, 101 USPQ2d at 1972). Similarly, viewed as a whole, claims 1 and 11 simply convey the abstract idea itself facilitated by generic computing components. Therefore, under Step 2B of the Alice/Mayo test, there are no meaningful limitations in claims 1 and 11 that transform the judicial exception into a patent eligible application such that the claims amount to significantly more than the judicial exception itself (Step 2B: NO).
Dependent claims 2-10 and 12-20, when analyzed as a whole, are held to be patent ineligible under 35 U.S.C. 101 because they recite an abstract idea, are not integrated into a practical application, and do not add “significantly more” to the abstract idea. More specifically, dependent claims 2-10 and 12-20 further fall within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas, enumerated in the MPEP, in that they further recite commercial or legal interactions such as advertising, marketing, or sales activities or behaviors and managing personal behavior or relationships or interactions between people. These claims, under their broadest reasonable interpretation, further fall within the “Mental Processes” grouping of abstract ideas, enumerated in the MPEP, in that they recite concepts performed in the human mind, including observations, evaluations, judgments, and opinions. Dependent claims 2-4, 10, 12-14, and 20 fail to identify additional elements and as such, are not indicative of integration into a practical application. Dependent claims 5-9 and 15-19 further identify additional elements, such as a display controller, a display device, SNS, a trained search support model, and machine learning. Similar to discussion above the with respect to Prong Two of Step 2A, although additional computer-related elements are recited, the claims merely invoke such additional elements as a tool to perform the abstract idea. See MPEP 2106.05(f). As such, under Step 2A, dependent claims 2-10 and 12-20 are “directed to” an abstract idea. Similar to the discussion above with respect to claims 1 and 11, dependent claims 2-10 and 12-20 analyzed individually and as an ordered combination, invoke such additional elements as a tool to perform the abstract idea and merely indicate a field of use in which to apply the abstract idea because this requirement merely limits the claims to the computer field, i.e., to execution on a generic computer, and therefore, do not amount to significantly more than the abstract idea itself. See MPEP 2106.05(f)(2). Accordingly, under the Alice/Mayo test, claims 1-20 are ineligible.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis 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. 102 that forms the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless —
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-3, 5-7, 10-13, 15-17, and 20 are rejected under 35 U.S.C. 102(a)(1) as being unpatentable over Wiesel et al. (US 20190050427 A1), hereafter Wiesel.
In regards to claim 1, Wiesel discloses an information processing system, comprising (Wiesel: [0006-0007]):
a first acquisition component configured to acquire customer information including information related to an interest tendency of a customer (Wiesel: [0589] – “analyze data in order to infer, deduce, estimate and/or determine characteristics or preferences of a user, in an indirect manner, based on operations that the user had performed with regard to questions or polls that were posed by other users of the system”; [0592] – “Shirt 1 is a red shirt, Shirt 2 is a green shirt; Dress 3 is a yellow dress, Dress 4 is a red dress; Skirt 5 is a white skirt, Skirt 6 is a red skirt, Skirt 7 is a blue skirt. Wendy had indicated on different polls, that she prefers Shirt 1 (red shirt), that she prefers Dress 4 (red dress), and that she prefers Skirt 6 (red skirt). The system may thus deduce or estimate that generally, Wendy prefers clothes having red color”; [0598] – “determining that the end-user has previously browsed a particular jacket that has a descriptor of a ‘jeans jacket’, and later browsed a particular pants that have a description of ‘jeans pants’, and thereby deducing that the common characteristic of ‘jeans’ may be estimated to describe a preference of that end-user”);
a second acquisition component configured to acquire commodity information related to a target commodity in a purchase consideration of the customer (Wiesel: [0486] and Fig. 30 – “the system asks the user ‘what would you like…to purchase today?’; the user enters a search query (e.g., ‘summer dress’, or ‘red vase’)”; [0491] – “receive a user-query for a clothing article (e.g., the user enters a search query of ‘little black dress’)”; see also [0455]);
a search component configured to search for, based on the commodity information, a related content including an image related to the target commodity from a plurality of contents including images stored in an external device (Wiesel: [0486] – “the user enters a search query (e.g., ‘summer dress’, or ‘red vase’); the system performs a search of one or more sources (e.g., online retailer(s) that are relevant to the search query), obtains data and meta-data and generic images of each product”; [0554] – “the generated code may be short and may include only a location-reference or a shortcut or URL or URI to a remotely-stored data-set that comprises the whole set of product images, product hyperlinks, textual components, and/or other components. In other embodiments, the generated code may optionally comprise within itself, one or more data-items that are utilized by the code in order to locally generate the carousel GUI element; for example, the generated code may inherently comprise the URLs to the product pages, and/or hard-coded hyperlinks to a remote repository in which the product images are statically stored”; [0658] – “operations and/or determinations may be performed…partially remotely (e.g., at a remote server) by optionally utilizing a communication channel to exchange raw data and/or processed data and/or processing results”; see also [0136]; [0536]);
an evaluation component configured to evaluate a priority of the related content from a search based on the customer information (Wiesel: [0483] – “system may further order search results in a user-specific manner, taking into account user preferences, user settings, user profile, user questionnaire, user body type or ratio or dimensions…e.g., ranking higher the products”; [0566] – “The end-user Sarah enters a search query of ‘red clothes’ in that website or catalog or search engine. Based on pre-defined tags or categories, the server identifies three relevant (or most-relevant) search results: the red shirt, the red skirt, and the red scarf.”); and
a presentation component configured to present the related content to the customer according to the priority (Wiesel: [0483] – “system may further order search results in a user-specific manner, taking into account user preferences, user settings, user profile, user questionnaire, user body type or ratio or dimensions…e.g., ranking higher the products”; [0457] – “system may thus generate, compose, serve and/or display a set of graphical search results or query results, one next to the other or one after the other (e.g., allowing a user to scroll or swipe among them)”; [0566] – “The end-user Sarah enters a search query of ‘red clothes’ in that website or catalog or search engine. Based on pre-defined tags or categories, the server identifies three relevant (or most-relevant) search results: the red shirt, the red skirt, and the red scarf.”).
In regards to claim 2, Wiesel discloses the system of claim 1. Wiesel further discloses
wherein the evaluation component evaluates the priority of the related content to be higher as the related content includes an image related to the interest tendency (Wiesel: [0483] – “system may further order search results in a user-specific manner, taking into account user preferences, user settings, user profile, user questionnaire, user body type or ratio or dimensions…e.g., ranking higher the products”; [0566] – “The end-user Sarah enters a search query of ‘red clothes’ in that website or catalog or search engine. Based on pre-defined tags or categories, the server identifies three relevant (or most-relevant) search results: the red shirt, the red skirt, and the red scarf”; [0486] – “the user enters a search query (e.g., ‘summer dress’, or ‘red vase’); the system performs a search of one or more sources (e.g., online retailer(s) that are relevant to the search query), obtains data and meta-data and generic images of each product”).
In regards to claim 3, Wiesel discloses the system of claim 2. Wiesel further discloses wherein the customer information includes information representing an attribute of the customer, and the evaluation component evaluates the priority of the related content to be higher as the related content includes an image of a person having an attribute similar to the attribute of the customer (Wiesel: [0483] – “system may further order search results in a user-specific manner, taking into account user preferences, user settings, user profile, user questionnaire, user body type or ratio or dimensions…e.g., ranking higher the products”; [0499] – “a clothing-article recommendation unit 5013, to receive an image of said user (as detailed above), to determine real-life dimensions of multiple body parts of said user as depicted in said image (as detailed above), to determine from said dimensions a size of a clothing-article that would match said user (as detailed above), to search a digital catalog of clothes for clothing articles that other users, who also have said dimensions, had purchased, and to present to said user the results of said search; for example, by determining that the user's body size is ‘XS’, and… searching historical purchase transactions of other user's whose body size was estimated to be ‘XS’ based on their own images that had been similarly processed by the system”).
In regards to claim 5, Wiesel discloses the system of claim 1. Wiesel further discloses a display controller configured to display the presented related content on a display device (Wiesel: [0470] and Fig. 21 – “Augmented Reality (AR) search results, may be displayed side-by-side, and/or may be displayed in a mobile-friendly or touchscreen-friendly presentation format, in which one or more images are shown at a time, and other images are displayed only upon a swipe or a scroll command is performed manually by the user, or are shown at pre-defined time intervals (e.g., autonomously scrolling the search results every two seconds, in an automatic slide-show mode)”; [0503] – “may be utilized in, or with, or in conjunction with, a variety of devices or systems; for example, a smartphone, a tablet, a smart-watch, a ‘magic mirror’ device intended for utilization at real-life stores, a laptop computer”).
In regards to claim 6, Wiesel discloses the system of claim 5. Wiesel further discloses wherein the display device is provided in a store that handles the target commodity (Wiesel: [0070] – “allows for an instant virtual dressing experience of the specific in-store item onto the virtual user image, bringing the consumer from offline medium (physical shop/physical clothes) to online medium (application, web, electronic device, or any other platform that shows the simulation on the taken user image). This may further allow a user, that is physically located within a store, to virtually try on a product or an article of clothing, without actually finding and walking to a changing room, waiting in line, taking off clothes, putting on new clothes to try them on, taking off the new clothes, and putting on the user's own clothes”; [0470] and Fig. 21 – “Augmented Reality (AR) search results, may be displayed side-by-side, and/or may be displayed in a mobile-friendly or touchscreen-friendly presentation format, in which one or more images are shown at a time, and other images are displayed only upon a swipe or a scroll command is performed manually by the user, or are shown at pre-defined time intervals (e.g., autonomously scrolling the search results every two seconds, in an automatic slide-show mode)”; [0503] – “may be utilized in, or with, or in conjunction with, a variety of devices or systems; for example, a smartphone, a tablet, a smart-watch, a ‘magic mirror’ device intended for utilization at real-life stores, a laptop computer”).
In regards to claim 7, Wiesel discloses the system of claim 1. Wiesel further discloses wherein the customer information comprises at least one of purchase history of the customer, SNS information of the customer, age of the customer, and gender of the customer (Wiesel: [0101-0102] – “extract or determine or estimate, for example, user gender… user image is obtained from an application or an electronic device or from other source (e.g., from a profile page of the user on a social network)”; [0466] – “may receive different search results, due to…other parameters, such as, user-specific purchase history or browsing history, user-specific data obtained from social media account or from user-defined settings or preferences, or the like)”).
In regards to claim 10, Wiesel discloses the system of claim 1. Wiesel further discloses wherein the commodity information comprises at least one of a brand name, a color of the commodity, and a price of the commodity (Wiesel: [0136] – “may import, download and/or extract images of products, as well as data, meta-data, descriptors, and/or other product information (e.g., price, vendor, maker, model name, model number, or the like).”; [0448] – “when asked to show images of ‘black leather jacket’…generates and displays one or more image(s) of a black leather jacket (or multiple, different, such jackets)”).
In regards to claim 11, claim 11 is directed to a method. Claim 11 recites limitations that are substantially parallel in nature to those addressed above for claim 1 which is directed towards a system. The system of Wiesel teaches the limitations of claim 1 as noted above. Wiesel further discloses an information processing method (Wiesel: [abstract]). Claim 11 is therefore rejected for the reasons set forth above in claim 1 and in this paragraph.
In regards to claims 12-13, 15-17, and 20, all the limitations in method claims 12-13, 15-17, and 20 are closely parallel to the limitations of system claims 2-3, 5-7, and 10 analyzed above and rejected on the same bases.
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 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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention
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.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 4 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Wiesel, in view of Kimmerling (US 20150142787 A1), hereinafter Kimmerling.
In regards to claim 4, Weisel discloses the method of claim 3. Wiesel further discloses wherein the evaluation component performs weighting according to the attribute of the customer to evaluate the priority (Wiesel: [0483] – “system may further order search results in a user-specific manner, taking into account user preferences, user settings, user profile, user questionnaire, user body type or ratio or dimensions…e.g., ranking higher the products”). Yet Wiesel does not explicitly disclose weighting according to a degree of importance of each of the interest tendency.
However, Kimmerling teaches a similar recommendation method (Kimmerling: [0027]), including
weighting according to a degree of importance of each of the interest tendency (Kimmerling: [0028] – “attribute categories and subcategories are weighed based on preference data. For example, the preference data may indicate that one attribute category is more important than another. To scale an attribute category, the attribute values for the category are multiplied by a preference weight. For example, the color of a dress may be more important than the sleeve type, so the color attribute category may be multiplied by a preference weight of X to represent importance. X may equal 2, for example, to represent higher importance, or 0.5, for example, representing lower importance”).
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to have included the weighting of Kimmerling in the system of Wiesel because Wiesel already discloses weighting and Kimmerling is merely demonstrating how the weighting may occur. Additionally, it would have been obvious to have included weighting according to a degree of importance of each of the interest tendency as taught by Kimmerling because weighting is well-known and the use of it in a recommendation system would have refined search results (Kimmerling: [0030]).
In regards to claim 14, all the limitations in method claim 14 are closely parallel to the limitations of system claim 4 analyzed above and rejected on the same bases.
Claims 8-9 and 18-19 are rejected under 35 U.S.C. 103 as being unpatentable over Wiesel, in view of Biswas et al. (US 20190220694 A1), hereinafter Biswas.
In regards to claim 8, Weisel discloses the method of claim 1. Wiesel further discloses wherein the search component is a trained search support model using machine learning techniques to search for the image related to the target commodity (Wiesel: [0486] – “the user enters a search query (e.g., ‘summer dress’, or ‘red vase’); the system performs a search of one or more sources (e.g., online retailer(s) that are relevant to the search query), obtains data and meta-data and generic images of each product”; [0099] – “using image processing, computer vision and/or machine learning. In some embodiments, the user may take several pictures or videos. In some embodiments, additional parameters and preferences, from the user and or external resources, are taken into account. In some embodiments, the clothes or products are from different brands, vendors and e-commerce sites. In some embodiments, the user scans a code attached to physical clothes (or other items, such as an accessory) in a shop. In some embodiments, the method may enable for shopping for clothes from different brands, vendors and e-commerce sites immediately, using a one-click universal wallet. In some embodiments, the method may comprise crawling clothes data from the web using machine learning”). Yet Wiesel does not explicitly disclose the image on a plurality of SNSs.
However, Biswas teaches a similar recommendation method (Biswas: [abstract]), including
the image on a plurality of SNSs (Biswas: [0028] – “model developer 102 includes a data preprocessor 122 for generating the training data for the ML model 108. In an example, the training data can include processed images 164 of various products. The images 164 are altered and sized evenly for improving feature identification by the ML model 108. The recommender system 100 may receive digital images 162 via the communications network 180. Images 162 can be collected from various sources available online such as search engines, social media”).
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to have included the SNS of Biswas in the system of Wiesel because Wiesel already discloses images and Biswas is merely demonstrating where the images may be. Additionally, it would have been obvious to have included the image on a plurality of SNSs as taught by Biswas because weighting is well-known and the use of it in a recommendation system would have improved recommendations (Biswas: [0021]).
In regards to claim 9, Weisel discloses the method of claim 1. Wiesel further discloses wherein the search component is a search support model that compares characteristics of a commodity image with characteristics, calculates indices of similarity and relevance, calculates indices with weighting according to the commodity information (Wiesel: [0059] – “system may further receive an indication of a product, such as a clothes item, that the user would like to virtually dress on his image. The indication may be provided by the user, who…may capture an image of the product”; [0481-0483] – “system may further sort the search results based on one or more criteria…based on user-specific browsing history and/or shopping history (e.g., detecting that user Adam typically purchased items in the price range of 10 to 30 dollars, and therefore placing search results in this price-range prior to search results that are more expensive)…utilize a tagging module able to generate tags based on…image analysis and computer vision…based on similarity of the product image to other products and/or to products that were previously indexed or analyzed, or the like…may further order search results in a user-specific manner, taking into account user preferences, user settings, user profile, user questionnaire, user body type or ratio or dimensions, current trends or shopping trends or fashion trends (e.g., ranking higher the products that match a current shopping trend), current weather or season (e.g., ranking higher Summer Dresses, if the user searches for ‘dress’ in the United States in July), geographic location”). Yet Wiesel does not explicitly disclose the characteristics of each image on the SNS, and then extracts the image that is similar to the commodity image or an image of a related commodity.
However, Biswas teaches a similar recommendation method (Biswas: [abstract]), including
the characteristics of each image on the SNS, and then extracts the image that is similar to the commodity image or an image of a related commodity (Biswas: [0028] – “recognize objects in images and extract product attributes…model developer 102 includes a data preprocessor 122 for generating the training data for the ML model 108. In an example, the training data can include processed images 164 of various products. The images 164 are altered and sized evenly for improving feature identification by the ML model 108. The recommender system 100 may receive digital images 162 via the communications network 180. Images 162 can be collected from various sources available online such as search engines, social media”; [0018] – “recommender system then executes a product matching process wherein a set of stored images corresponding to the various products are scored for similarity with the various attributes of the product in the input image. Similarity scoring techniques that are currently known can be employed for obtaining the similarity scores in accordance with the disclosed examples. The similarity scores are compared and the products having higher similarity scores can be provided as recommendations in response to the received input image”).
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to have included the image analysis of Biswas in the system of Wiesel because Wiesel already discloses images and Biswas is merely demonstrating an image analysis. Additionally, it would have been obvious to have included the characteristics of each image on the SNS, and then extracts the image that is similar to the commodity image or an image of a related commodity as taught by Biswas because image analysis is well-known and the use of it in a recommendation system would have improved recommendations (Biswas: [0021]).
In regards to claims 18-19, all the limitations in method claims 18-19 are closely parallel to the limitations of system claims 8-9 analyzed above and rejected on the same bases.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
NPL Reference U teaches a virtual clothing try-on method. Machine learning models may be used and an item of apparel may be shown on a user’s frame. Customers may virtually try on items.
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/ANNA MAE MITROS/Examiner, Art Unit 3689