DETAILED ACTION
This action is in reply to the original application filed on 03/26/2025.
Claims 1-20 are rejected.
Claims 1-20 are currently pending and have been examined.
Priority
This patent Application claims priority to U.S. Provisional Patent Application No. 63/570,141 filed 03/26/2024. This benefit has been received and acknowledged and therefore, the instant claims receive the effective filing date of 03/26/2024.
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 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 (i.e., law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Under Step 1 of the Subject Matter Eligibility Test for Products and Processes, the claims must be directed to one of the four statutory categories (see MPEP 2106.03). Some of the claims are directed to one of the four statutory categories (YES/NO).
Claims 17-20 are directed to signals per se as they don’t preclude transitory forms of signal transmission, such as a propagating electrical or electromagnetic signal or carrier wave (see MPEP 2106.03(I)). For the purpose of compact prosecution, claims 17-20 will be further analyzed under Step 2.
Under Step 2A of the Subject Matter Eligibility Test, it is determined whether the claims are directed to a judicially recognized exception (see MPEP 2106.04). Step 2A is a two-prong inquiry.
Under Prong 1, it is determined whether the claim recites a judicial exception (YES). Taking Claim 11 as representative, the claim recites limitations that fall within the certain methods of organizing human activity groupings of abstract ideas, including:
-one or more processors; and
-one or more memories storing instructions that, upon execution by the one or more processors, configure the system to perform operations comprising:
-receiving commerce-related data;
-receiving user data, the user data having been obtained by at least one sensor of a user device;
-inputting the user data into an AI model;
-generating, via the AI model, at least one consumer attribute based on the user data; and
-generating a commerce recommendation comprising one or more products, the commerce recommendation being generated based on at least one feature of each of the one or more products relating to the at least one consumer attribute
The above limitations recite the concept of generating a commerce recommendation comprising one or more products based on features of the products relating to consumer attributes. The above limitations fall within the “Certain Methods of Organizing Human Activity” groupings of abstract ideas, enumerated in MPEP 2106.04(a).
Certain methods of organizing human activity include:
fundamental economic principles or practices (including hedging, insurance, and mitigating risk)
commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; and business relations)
managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions)
The limitations of receiving commerce-related data; and generating a commerce recommendation comprising one or more products, the commerce recommendation being generated based on at least one feature of each of the one or more products relating to the at least one consumer attribute are processes that, under their broadest reasonable interpretation, cover a commercial interaction. For example, “receiving” and “generating” in the context of this claim encompass advertising, and marketing or sales activities.
Similarly, the limitations of one or more memories storing instructions that, upon execution by the one or more processors, configure the system to perform operations comprising: receiving user data, the user data having been obtained by at least one sensor of a user device; inputting the user data into an AI model; and generating, via the AI model, at least one consumer attribute based on the user data are processes that, under their broadest reasonable interpretation, cover a commercial interaction. That is, other than reciting that the operations are performed upon execution of instructions stored on one or more memories by the one or more processors, that the user data has been obtained by at least one sensor of a user device, and that the model is an AI model, nothing in the claim element precludes the step from practically being performed by people. For example, but for the “one or more memories,” “one or more processors,” “at least one sensor,” “a user device,” and “an AI model” language, “perform,” “receiving,” “inputting,” and “generating” in the context of this claim encompasses advertising, and marketing or sales activities.
Under Prong 2, it is determined whether the claim recites additional elements that integrate the exception into a practical application of the exception. This judicial exception is not integrated into a practical application (NO).
-one or more processors; and
-one or more memories storing instructions that, upon execution by the one or more processors, configure the system to perform operations comprising:
-receiving commerce-related data;
-receiving user data, the user data having been obtained by at least one sensor of a user device;
-inputting the user data into an AI model;
-generating, via the AI model, at least one consumer attribute based on the user data; and
-generating a commerce recommendation comprising one or more products, the commerce recommendation being generated based on at least one feature of each of the one or more products relating to the at least one consumer attribute
These limitations are not indicative of integration into a practical application because:
The additional elements of claim 11 are recited at a high level of generality (i.e. as generic computing hardware) such that they amount to nothing more than mere instructions to implement or apply the abstract idea on a generic computing hardware (or, merely use a computer as a tool to perform an abstract idea) as supported by paragraph [0141] of Applicant’s specification – “well-known circuits, processes, algorithms, structures, and techniques have been shown without unnecessary detail in order to avoid obscuring the examples.” Specifically, the additional elements of one or more processors, one or more memories, at least one sensor, a user device, and an AI model are recited at a high-level of generality (i.e. as a generic processor performing the generic computer functions of receiving data, inputting data, and generating data) such that they amount do no more than mere instructions to apply the exception using generic computer components. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea. Further, the additional elements do no more than generally link the use of the judicial exception to a particular technological environment or field of use (such as computers or computing networks). Employing well-known computer functions to execute an abstract idea, even when limiting the use of the idea to one particular environment, does not integrate the exception into a practical application.
Additionally, the additional elements are insufficient to integrate the abstract idea into a practical application because the claim fails to i) reflect an improvement in the functioning of a computer or an improvement to another technology or technical field, ii) apply the judicial exception with, or use the judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim, iii) effect a transformation or reduction of a particular article to a different state or thing, or iv) apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment.
Accordingly, the judicial exception is not integrated into a practical application.
Under Step 2B, it is determined whether the claims recite additional elements that amount to significantly more than the judicial exception. The claims of the present application do not include additional elements that are sufficient to amount to significantly more than the judicial exception (NO).
In the case of claim 11, taken individually or as a whole, the additional elements of claim 9 do not provide an inventive concept. As discussed above under step 2A (prong 2) with respect to the integration of the abstract idea into a practical application, the additional elements used to perform the claimed functions amount to no more than a general link to a technological environment.
Even considered as an ordered combination (as a whole), the additional elements do not add anything significantly more than when considered individually.
Claim 1 is a method reciting similar functions as claim 11. Examiner notes that claim 1 recites the additional elements of a computer-implemented method, at least one sensor, a user device, and an AI model, however, claim 1 does not qualify as eligible subject matter for similar reasons as claim 11 indicated above.
Claim 17 is one or more computer-readable storage media reciting similar functions as claim 11. Examiner notes that claim 17 recites the additional elements of one or more computer-readable storage media, one or more processors, at least one sensor, a user device, and an AI model, however, claim 17 does not qualify as eligible subject matter for similar reasons as claim 11 indicated above.
Therefore, claims 1, 11, and 17 do not provide an inventive concept and do not qualify as eligible subject matter.
Dependent claims 2-10, 12-16, and 18-20, when analyzed as a whole, are held to be patent ineligible under 35 U.S.C. § 101 because they do not add “significantly more” to the abstract idea. More specifically, dependent claims 2-10, 12-16, and 18-20 further fall within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas in that they recite commercial interactions. Dependent claim 10 does not recite any farther additional elements, and as such are not indicative of integration into a practical application for at least similar reasons discussed above. Dependent claims 2-9, 12-16, and 18-20 recite the additional elements of an IoT device, the AI model, a software application, the user device, and a digital token, but similar to the analysis under prong two of Step 2A these additional elements are used as a tool to perform the abstract idea. As such, under prong two of Step 2A, claims 2-10, 12-16, and 18-20 are not indicative of integration into a practical application for at least similar reasons as discussed above. Thus, dependent claims 2-10, 12-16, and 18-20 are “directed to” an abstract idea. Next, under Step 2B, similar to the analysis of claims 1, 11, and 17, dependent claims 2-10, 12-16, and 18-20 when analyzed individually and as an ordered combination, merely further define the commonplace business method (i.e. generating a commerce recommendation comprising one or more products based on features of the products relating to consumer attributes) being applied on a general-purpose computer and, therefore, do not amount to significantly more than the abstract idea itself. Accordingly, the Examiner concludes that there are no meaningful limitations in the claims that transform the judicial exception into a patent eligible application such that the claims amount to significantly more than the judicial exception itself. The analysis above applies to all statutory categories of invention.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(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.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1, 10-11, and 17 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Kornilov et al. (US 2019/0164210 A1), hereinafter Kornilov.
Regarding claim 1, Kornilov discloses a computer-implemented method comprising:
-receiving commerce-related data (Kornilov, see at least: “Product information storage 312 is configured to store information about inventory [i.e. receiving commerce-related data]. The inventor may include a set of products available to a user such as glasses frames available for purchase” [0045]);
-receiving user data, the user data having been obtained by at least one sensor of a user device (Kornilov, see at least: “the client device includes an input component such as a camera, depth sensor, other sensor, or a combination of multiple sensors [i.e. the user data having been obtained by at least one sensor of a user device]. A camera may be configured to observe and/or capture images of the user [i.e. receiving user data] from which physical characteristics may be determined. The user may be instructed to operate the camera or pose for the camera as further described herein. The information collected by the input components may be used and/or stored for making a recommendation” [0041]);
-inputting the user data into an AI model (Kornilov, see at least: “the facial features are extracted from the images directly … The detected facial contours may be input into a machine learning model [i.e. inputting the user data into an AI model] associated with detecting face shape” [0093] and “deep neural networks may be used to find correlations between a user's face and purchases. Data about user faces [i.e. inputting the user data into an AI model], purchase history, and product information (such as product characteristics) is stored. The deep neural networks may be trained to determine combinations (relationships, correlations) of faces and product information and purchase history. In other words, a data model (deep neural network) may be constructed to describe which faces will purchase which products. Using the data model, recommendations may be made for users without a purchase history or with a short purchase history” [0067]);
-generating, via the AI model, at least one consumer attribute based on the user data (Kornilov, see at least: “deep neural networks [i.e. generating, via the AI model] may be used to find correlations between a user's face and purchases. Data about user faces [i.e. inputting the user data into an AI model], purchase history, and product information (such as product characteristics) is stored. The deep neural networks may be trained to determine combinations (relationships, correlations) of faces and product information and purchase history. In other words, a data model (deep neural network) may be constructed to describe which faces will purchase which products [i.e. at least one consumer attribute based on the user data]. Using the data model, recommendations may be made for users without a purchase history or with a short purchase history” [0067] Examiner notes that the consumer attribute determined is the shopping preference of the consumer); and
-generating a commerce recommendation comprising one or more products, the commerce recommendation being generated based on at least one feature of each of the one or more products relating to the at least one consumer attribute (Kornilov, see at least: “a combined model output is generated from outputs of the classifiers 1102-1106. Outputs corresponding to respective ones of a plurality of trained machine learning models associated with a plurality of facial feature types are combined. The combined output may be correlated with a plurality of products to selects a subset of the plurality of products for further analysis [i.e. the commerce recommendation being generated based on at least one feature of each of the one or more products relating to the at least one consumer attribute]. In one aspect, potential products to be recommended [i.e. generating a commerce recommendation comprising one or more products] are narrowed down by the correlation process. In some embodiments, additional types of recommendations, collaborative filtering, and the like may be used to determine recommendations” [0107] and “Correlator 410 is configured to determine correlations between features of a user (such as facial features, user history) and products. The correlator may determine correlations based on product properties such as price, color, etc. The correlator may determine correlations based on one or more of the following factors: similarity of faces (content-based filtering), interaction with a site and/or purchase history in brick-and-mortar stores (collaborative filtering), etc. … a user may wish to emphasize style, face-flattering, fit, etc. [i.e. the commerce recommendation being generated based on at least one feature of each of the one or more products relating to the at least one consumer attribute]” [0062] and “deep neural networks may be used to find correlations between a user's face and purchases. Data about user faces, purchase history, and product information (such as product characteristics) is stored. The deep neural networks may be trained to determine combinations (relationships, correlations) of faces and product information and purchase history. In other words, a data model (deep neural network) may be constructed to describe which faces will purchase which products [i.e. the commerce recommendation being generated based on at least one feature of each of the one or more products relating to the at least one consumer attribute]. Using the data model, recommendations may be made for users without a purchase history or with a short purchase history” [0067]).
Regarding claim 10, Kornilov discloses the method of claim 1. Kornilov further discloses:
-generating a user profile for the user based on the at least one consumer attribute (Kornilov, see at least: “User information storage 318 is configured to store information associated with particular users. For example, a user profile describing a user such as the subject's physical characteristics [i.e. generating a user profile for the user based on the at least one consumer attribute], past purchases, preferences, and history of interactions with a user interface (e.g., Web browsing) may be stored” [0047]).
Claim 11 recites limitations directed towards system comprising: one or more processors; and one or more memories storing instructions that, upon execution by the one or more processors, configure the system to perform operations (Kornilov, see at least: “The invention can be implemented in numerous ways, including as a process; an apparatus; a system; a composition of matter; a computer program product embodied on a computer readable storage medium; and/or a processor, such as a processor configured to execute instructions stored on and/or provided by a memory coupled to the processor” [0024]). The limitations recited in claim 11 is parallel in nature to those addressed above for claim 1, and are therefore rejected for those same reasons set forth above in claim 1.
Claim 17 recites limitations directed towards one or more computer-readable storage media storing instructions that, upon execution by one or more processors, cause operations (Kornilov, see at least: “The invention can be implemented in numerous ways, including as a process; an apparatus; a system; a composition of matter; a computer program product embodied on a computer readable storage medium; and/or a processor, such as a processor configured to execute instructions stored on and/or provided by a memory coupled to the processor” [0024]). The limitations recited in claim 17 is parallel in nature to those addressed above for claim 1, and are therefore rejected for those same reasons set forth above in claim 1.
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.
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 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 2-4, 12-14, and 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over Kornilov in view of Shinde et al. (US 2024/0265433 A1), hereinafter Shinde.
Regarding claim 2, Kornilov discloses the method of claim 1. Kornilov further discloses:
-wherein the user data is first user data (Kornilov, see at least: “the client device includes an input component such as a camera, depth sensor, other sensor, or a combination of multiple sensors. A camera may be configured to observe and/or capture images of the user [i.e. wherein the user data is first user data] from which physical characteristics may be determined” [0041]).
Kornilov does not explicitly disclose receiving second user data indicative of an environment of the user, the second user data being obtained by an IoT device in the environment of the user; inputting the first user data and the second user data into the AI model; and generating, via the AI model, the at least one consumer attribute based on the first user data and the second user data.
Shinde, however, teaches recommending products based on facial attributes (i.e. abstract), including the known technique of receiving second user data indicative of an environment of the user, the second user data being obtained by an IoT device in the environment of the user (Shinde, see at least: “Examples of the cloud server 140 may include, but are not limited to, an application server, a storage server, an Internet-of-Things (IoT) server, or a combination thereof” [0055] and “the pre-trained neural network module 132 is trained using a dataset in combination with one or more of: an image segmentation operation, a color analysis operation, a pattern recognition operation, a skin analysis operation, and a facial recognition operation. The pre-trained neural network module 132 is trained on a large dataset comprising analysis a number of images of different users in different environmental conditions as well with different facial features. Various machine learning and computer vision techniques, such as image segmentation, the color analysis, the pattern recognition, the skin analysis, and the facial recognition are used to extract the plurality of facial features of the user 124 under different environmental conditions” [0048] and “the analysis of the image of the facial portion of the user 302 captured at different wavelengths further comprises calibrating and correcting any distortion present in the captured image in real time and further analyze the one or more visible and invisible skin attributes of the user 302. The skincare recommendation system 122 may be configured to analyze the skin attributes that are directly visible from naked eye, such as spurts of acne, dark spots, and the like, as well as the invisible skin attributes, which are not the most obvious to the naked eye, such as oxygenation, hydration, firmness, and the like. Thus, the skincare recommendation system 122 performs a deeper analysis of the skin surface keeping in account the ambient environmental parameters like lighting, camera contrast, noise, etc. These factors lead to normalize the texture driven assessment of the skin and hence, enables the skincare recommendation system 122 to perform a more holistic and correct analysis” [0073]);
the known technique of inputting the first user data and the second user data into the AI model (Shinde, see at least: “the interactive system 102 may also be stated as an Artificial Intelligence (AI) based lifestyle products recommendation system including beauty and personal care products to the user 124 by analyzing one or more health parameters of the user 124 and external environmental parameters in nearby surroundings of the user 124 [i.e. inputting the first user data and the second user data into the AI model]. The health parameters may include facial structure, facial attributes, eye color, hair color, hair style, and derma parameters of the user 124, such as skin attributes, dullness of skin, skin texture, acne, dark spots, and the like. The external environmental parameters may include weather of a particular region, pollution, Ultra-Violet (UV) index, age and gender of the user 124, and the like” [0021]); and
the known technique of generating, via the AI model, the at least one consumer attribute based on the first user data and the second user data (Shinde, see at least: “the plurality of facial attributes comprises two or more of: a face shape, one or more skin attributes, a hairstyle, a unique user-specific eye shape, a user-specific eye size, a user-specific nose shape, a nose type, and a facial jawline. The plurality of facial attributes collectively contributes to the overall appearance and aesthetics of the user's face (i.e., the face of the user 124) [i.e. generating, via the AI model, the at least one consumer attribute based on the first user data and the second user data]. By considering the interrelation between the plurality of facial attributes, a comprehensive classification of nose shapes and styles, eye sizes and shapes, the facial jawline, face shapes and sizes, and the like can be obtained, which may be further used as a valuable tool for cosmetic professionals and individuals who are seeking of personalized facial enhancements” [0027] and “Various machine learning and computer vision techniques, such as image segmentation, the color analysis, the pattern recognition, the skin analysis, and the facial recognition are used to extract the plurality of facial features of the user 124 under different environmental conditions [i.e. generating, via the AI model, the at least one consumer attribute based on the first user data and the second user data]” [0048]). These known techniques are applicable to the method of Kornilov as they both share characteristics and capabilities, namely, they are directed to recommending products based on facial attributes.
It would have been recognized that applying the known techniques of receiving second user data indicative of an environment of the user, the second user data being obtained by an IoT device in the environment of the user; inputting the first user data and the second user data into the AI model; and generating, via the AI model, the at least one consumer attribute based on the first user data and the second user data, as taught by Shinde, to the teachings of Kornilov would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such references into similar methods. Further, adding the modifications of receiving second user data indicative of an environment of the user, the second user data being obtained by an IoT device in the environment of the user; inputting the first user data and the second user data into the AI model; and generating, via the AI model, the at least one consumer attribute based on the first user data and the second user data, as taught by Shinde, into the method of Kornilov would have been recognized by those of ordinary skill in the art as resulting in an improved method that would perform a more holistic and correct analysis (Shinde, [0073]).
Regarding claim 3, Kornilov in view of Shinde teaches the method of claim 2. Kornilov further discloses:
-deploying a software application on the user device (Kornilov, see at least: “system 200 includes client device 204, network 206, and server 208. The client device 204 is coupled to the server 208 via network 206 [i.e. deploying a software application on the user device]. Network 206 may include high speed data networks and/or telecommunications networks. A user 202 may interact with the client device to provide information useful for making recommendations and receive recommendations according to the methods further described herein” [0039] and “FIG. 3 is a block diagram illustrating an example of a system for making recommendations based on a user's physical features. System 300 may be included in server 208 [i.e. deploying a software application] of FIG. 2. In this example, system 300 includes sensor data storage 316, product information storage 312, user information storage 318, recommendation engine 314, and output engine 312” [0043]);
-receiving user profile data from the software application (Kornilov, see at least: “Server 108 is configured to determine physical characteristics from input images, determine a correlation between the physical characteristics and a product, and recommend a product. The server [i.e. from the software application] may make the correlation and recommendation based on a variety of collected data including images of a user, images of product, user browsing habits, user profile [i.e. receiving user profile data from the software application], and the like as more fully described herein. Making a recommendation based on a user's physical characteristics has many applications. Example applications of making a recommendation based on user's physical characteristics include virtual try-on of facial accessories such as eyewear, makeup, jewelry, etc.” [0042] and “FIG. 3 is a block diagram illustrating an example of a system for making recommendations based on a user's physical features. System 300 may be included in server 208 of FIG. 2. In this example, system 300 includes sensor data storage 316, product information storage 312, user information storage 318, [i.e. receiving user profile data from the software application] recommendation engine 314, and output engine 312” [0043]);
-inputting the first user data and the user profile data into the AI model (Kornilov, see at least: “Correlator 410 is configured to determine correlations between features of a user (such as facial features, user history) and products [i.e. inputting the first user data and the user profile data]. The correlator may determine correlations based on product properties such as price, color, etc. The correlator may determine correlations based on one or more of the following factors: similarity of faces (content-based filtering), interaction with a site and/or purchase history in brick-and-mortar stores (collaborative filtering), etc. The correlator may combine the scores output by each of the scoring systems 402-406. In some embodiments, the scores are weighted when they are combined. The weighting of the system may be adjusted dynamically or predefined. For example, a user may wish to emphasize style, face-flattering, fit, etc.” [0062] and “deep neural networks may be used to find correlations between a user's face and purchases [i.e. inputting the first user data and the user profile data into the AI model]. Data about user faces, purchase history, and product information (such as product characteristics) is stored. The deep neural networks may be trained to determine combinations (relationships, correlations) of faces and product information and purchase history” [0067]); and
-generating, via the AI model, the at least one consumer attribute based on the first user data and the user profile data (Kornilov, see at least: “Correlator 410 is configured to determine correlations between features of a user (such as facial features, user history) and products [i.e. inputting the first user data and the user profile data]. The correlator may determine correlations based on product properties such as price, color, etc. The correlator may determine correlations based on one or more of the following factors: similarity of faces (content-based filtering), interaction with a site and/or purchase history in brick-and-mortar stores (collaborative filtering), etc. The correlator may combine the scores output by each of the scoring systems 402-406. In some embodiments, the scores are weighted when they are combined. The weighting of the system may be adjusted dynamically or predefined. For example, a user may wish to emphasize style, face-flattering, fit, etc. [i.e. the at least one consumer attribute]” [0062] and “deep neural networks may be used to find correlations between a user's face and purchases [i.e. generating, via the AI model, the at least one consumer attribute based on the first user data and the user profile data]. Data about user faces, purchase history, and product information (such as product characteristics) is stored. The deep neural networks may be trained to determine combinations (relationships, correlations) of faces and product information and purchase history” [0067]).
Kornilov does not explicitly disclose inputting the second user data into the AI model; and generating, via the AI model, the at least one consumer attribute based on the second user data.
Shinde, however, teaches recommending products based on facial attributes (i.e. abstract), including the known technique of inputting the second user data into the AI model (Shinde, see at least: “the interactive system 102 may also be stated as an Artificial Intelligence (AI) based lifestyle products recommendation system including beauty and personal care products to the user 124 by analyzing one or more health parameters of the user 124 and external environmental parameters in nearby surroundings of the user 124 [i.e. i inputting the second user data into the AI model]. The health parameters may include facial structure, facial attributes, eye color, hair color, hair style, and derma parameters of the user 124, such as skin attributes, dullness of skin, skin texture, acne, dark spots, and the like. The external environmental parameters may include weather of a particular region, pollution, Ultra-Violet (UV) index, age and gender of the user 124, and the like” [0021]);
the known technique of generating, via the AI model, the at least one consumer attribute based on the second user data (Shinde, see at least: “the plurality of facial attributes comprises two or more of: a face shape, one or more skin attributes, a hairstyle, a unique user-specific eye shape, a user-specific eye size, a user-specific nose shape, a nose type, and a facial jawline. The plurality of facial attributes collectively contributes to the overall appearance and aesthetics of the user's face (i.e., the face of the user 124) [i.e. generating, via the AI model, the at least one consumer attribute based on the second user data]. By considering the interrelation between the plurality of facial attributes, a comprehensive classification of nose shapes and styles, eye sizes and shapes, the facial jawline, face shapes and sizes, and the like can be obtained, which may be further used as a valuable tool for cosmetic professionals and individuals who are seeking of personalized facial enhancements” [0027] and “Various machine learning and computer vision techniques, such as image segmentation, the color analysis, the pattern recognition, the skin analysis, and the facial recognition are used to extract the plurality of facial features of the user 124 under different environmental conditions [i.e. generating, via the AI model, the at least one consumer attribute based on the second user data]” [0048]). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Kornilov with Shinde for the reasons identified above with respect to claim 2.
Regarding claim 4, Kornilov in view of Shinde teaches the method of claim 3. Kornilov further discloses:
-outputting the commerce recommendation on the user device via the software application (Kornilov, see at least: “Client device 204 is configured to provide a user interface for user 202. For example, client device 204 may receive input or observe user interaction by user 202 with the client device. Based on at least some of the information collected by the client device, a recommendation is output to the user [i.e. outputting the commerce recommendation on the user device via the software application]” [0040] and “system 200 includes client device 204, network 206, and server 208. The client device 204 is coupled to the server 208 via network 206 [i.e. via the software application]. Network 206 may include high speed data networks and/or telecommunications networks. A user 202 may interact with the client device to provide information useful for making recommendations and receive recommendations according to the methods further described herein” [0039] and “FIG. 3 is a block diagram illustrating an example of a system for making recommendations based on a user's physical features. System 300 may be included in server 208 of FIG. 2. In this example, system 300 includes sensor data storage 316, product information storage 312, user information storage 318, recommendation engine 314, and output engine 312 [i.e. outputting the user device via the software application]” [0043]).
Claims 12-14 recite limitations directed towards a system. The limitations recited in claims 12-14 are parallel in nature to those addressed above for claims 2-4, respectively, and are therefore rejected for those same reasons set forth above in claims 2-4, respectively.
Claims 18-20 recite limitations directed towards one or more computer-readable storage media. The limitations recited in claims 18-20 are parallel in nature to those addressed above for claims 2-4, respectively, and are therefore rejected for those same reasons set forth above in claims 2-4, respectively.
Claims 5-7 and 15-16 are rejected under 35 U.S.C. 103 as being unpatentable over Kornilov in view of Kumar et al. (US 2018/0218441 A1), hereinafter Kumar.
Regarding claim 5, Kornilov discloses the method of claim 1. Kornilov further discloses:
-receiving an input from the user device indicating an intent of the user to purchase a product of the one or more products (Kornilov, see at least: “FIG. 13A is an example GUI of a product catalog for browsing an inventory. GUI 1300 includes an optional navigational area 1302, which can be populated with menus, filters, and the like to assist navigation of an inventory displayed in the GUI [i.e. receiving an input from the user device indicating an intent of the user to purchase a product]. In this example, GUI 1300 displays a product catalog including a number of glasses frames” [0113] and “The group 1350 may include one or more recommended products [i.e. of the one or more products] selected based at least in part on the techniques described herein such as products selected by recommendation engine 314 of FIG. 3” [0116]).
Kornilov does not explicitly disclose, in response to receiving the input, controlling the user device to facilitate a user purchase of the product.
Kumar, however, teaches recommending items (i.e. [0056]), including the known technique of, in response to receiving the input, controlling the user device to facilitate a user purchase of the product (Kumar, see at least: “The method 160 includes the steps of receiving a shopping list from the customer 112 in block 164. For example, receiving, by the server computing device 104 or the system 100, a shopping list of items from the customer 112 [i.e. in response to receiving the input] to be picked in the retail store 106” [0059] and “The item recommendation module 158 determines at least one or more suggested or recommended items preferred by the customer based on the items in the shopping list, each suggested/recommended item being indicative of an item or product preferred by the customer and sold at the location of the retail store 106 [i.e. controlling the user device to facilitate a user purchase of the product]” [0056] and “the path generation module 156 receives a list of suggested/recommended items relating to the items in the pick path. The suggested/recommended items are provided by the item recommendation module 158. The path generation module 156 decides which of the suggested/recommended items are in proximity to the initial pick path to create an adjusted pick path for the store map. The path generation module 156 suggests at least one or more items based on the items in the shopping list, each item being indicative of an item or product sold at the location of the retail location 12 [i.e. controlling the user device to facilitate a user purchase of the product]” [0057]). This known technique is applicable to the method of Kornilov as they both share characteristics and capabilities, namely, they are directed to recommending items.
It would have been recognized that applying the known technique of, in response to receiving the input, controlling the user device to facilitate a user purchase of the product, as taught by Kumar, to the teachings of Kornilov would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such references into similar methods. Further, adding the modification of, in response to receiving the input, controlling the user device to facilitate a user purchase of the product, as taught by Kumar, into the method of Kornilov would have been recognized by those of ordinary skill in the art as resulting in an improved method that would minimize the amount of distance traveled and/or the amount of time spent traveling through the retail store to pick items (Kumar, [0055]).
Regarding claim 6, Kornilov in view of Kumar teaches the method of claim 5.
Kornilov does not explicitly disclose wherein controlling the user device to facilitate the user purchase of the product comprises: controlling a software application executing on the user device to output, via the user device, a position of the product within a store location.
Kumar, however, teaches recommending items (i.e. [0056]), including the known technique of controlling the user device to facilitate the user purchase of the product comprising controlling a software application executing on the user device to output, via the user device, a position of the product within a store location (Kumar, see at least: “The item recommendation module 158 determines at least one or more suggested or recommended items preferred by the customer based on the items in the shopping list, each suggested/recommended item being indicative of an item or product preferred by the customer and sold at the location of the retail store 106 [i.e. wherein controlling the user device to facilitate the user purchase of the product comprises:]” [0056] and “the path generation module 156 receives a list of suggested/recommended items relating to the items in the pick path. The suggested/recommended items are provided by the item recommendation module 158. The path generation module 156 decides which of the suggested/recommended items are in proximity to the initial pick path to create an adjusted pick path for the store map. The path generation module 156 suggests at least one or more items based on the items in the shopping list, each item being indicative of an item or product sold at the location of the retail location 12 … The path generation module 66 generates the adjusted pick path based on the suggested items and presents the initial pick path and the adjusted pick path positioned overlaid on the store map to the user device 118 for display [i.e. controlling a software application executing on the user device to output, via the user device, a position of the product within a store location]” [0057]). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Kornilov with Kumar for the reasons identified above with respect to claim 5.
Regarding claim 7, Kornilov in view of Kumar teaches the method of claim 6.
Kornilov does not explicitly disclose controlling the software application to output, via the user device, a location of the user device; generating a set of directions indicative of a route from the location of the user device to the location of the product within the store location; and controlling the software application to output the set of directions via the user device.
Kumar, however, teaches recommending items (i.e. [0056]), including the known technique of controlling the software application to output, via the user device, a location of the user device (Kumar, see at least: “Referring to FIG. 7, an exemplary pick path for the retail store 106 is shown. As illustrated, an initial pick path may be generated for the customer 112 showing the path in dotted lines from the user device 118 of the customer 112 [i.e. controlling the software application to output, via the user device, a location of the user device] to the item waypoints for the items on the shopping list. The path generation module 156 can display the store map to the customer 112 via the display 138 (FIG. 3). In the illustrated example, the item waypoints are displayed as graphical icons, e.g., black circles” [0068] and Fig. 7);
the known technique of generating a set of directions indicative of a route from the location of the user device to the location of the product within the store location (Kumar, see at least: “Referring to FIG. 7, an exemplary pick path for the retail store 106 is shown. As illustrated, an initial pick path may be generated for the customer 112 showing the path in dotted lines from the user device 118 of the customer 112 to the item waypoints for the items on the shopping list [i.e. generating a set of directions indicative of a route from the location of the user device to the location of the product within the store location]. The path generation module 156 can display the store map to the customer 112 via the display 138 (FIG. 3). In the illustrated example, the item waypoints are displayed as graphical icons, e.g., black circles” [0068] and Fig. 7 displays directions such as ‘turn right’ in 168 feet); and
the known technique of controlling the software application to output the set of directions via the user device (Kumar, see at least: “Referring to FIG. 7, an exemplary pick path for the retail store 106 is shown. As illustrated, an initial pick path may be generated for the customer 112 showing the path in dotted lines from the user device 118 of the customer 112 to the item waypoints for the items on the shopping list. The path generation module 156 can display the store map to the customer 112 via the display 138 (FIG. 3) [i.e. controlling the software application to output the set of directions via the user device]. In the illustrated example, the item waypoints are displayed as graphical icons, e.g., black circles” [0068] and Fig. 7 displays directions such as ‘turn right’ in 168 feet). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Kornilov with Kumar for the reasons identified above with respect to claim 5.
Claims 15-16 recite limitations directed towards a system. The limitations recited in claims 15-16 are parallel in nature to those addressed above for claims 5-6, respectively, and are therefore rejected for those same reasons set forth above in claims 5-6, respectively.
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Kornilov in view of Kumar, in further view of Bruno et al. (US 2022/0027979 A1), hereinafter Bruno.
Regarding claim 8, Kornilov in view of Kumar teaches the method of claim 5.
Kornilov in view of Kumar does not explicitly teach controlling the user device to facilitate the user purchase of the product comprising controlling a software application executing on the user device to output, via the user device, a link to an online store location associated with the product.
Bruno, however, teaches recommending products (i.e. [0025]), including the known technique of controlling the user device to facilitate the user purchase of the product comprising controlling a software application executing on the user device to output, via the user device, a link to an online store location associated with the product (Bruno, see at least: “an exemplary smart user interface 90 prompts the customer with recommended products 92, or smart need products 68 that they may desire based on smart needs as they input information into the user input 74 … The customer may select one of the recommended products by clicking on the icon for that product and the smart interface may recommend more specific product details or a specific product as shown in FIG. 11. After the customer has selected a particular product or products, a smart interface display map 94, [i.e. controlling a software application executing on the user device to output, via the user device,] with merchants supplying the product(s) may be provided to the customer on the mobile device, as shown in FIG. 12. For example, the customer may select shoes in FIG. 10, and then, as shown on FIG. 11, recommended shoes may be displayed as icons for selection by the customer. As shown in FIG. 12, after selecting the recommended AIRMAX shoe, a map showing merchants carrying the product is provided. A customer may click on one of the merchants [i.e. output, via the user device, a link to an online store location associated with the product] to find out more about the product, receive a contextual offer from the merchant for the product and purchase the product [i.e. wherein controlling the user device to facilitate the user purchase of the product comprises:]” [0083]). This known technique is applicable to the method of Kornilov in view of Kumar as they both share characteristics and capabilities, namely, they are directed to recommending products.
It would have been recognized that applying the known technique of controlling the user device to facilitate the user purchase of the product comprising controlling a software application executing on the user device to output, via the user device, a link to an online store location associated with the product, as taught by Bruno, to the teachings of Kornilov in view of Kumar would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such references into similar methods. Further, adding the modification of controlling the user device to facilitate the user purchase of the product comprising controlling a software application executing on the user device to output, via the user device, a link to an online store location associated with the product, as taught by Bruno, into the method of Kornilov in view of Kumar would have been recognized by those of ordinary skill in the art as resulting in an improved method that would allow for shopping in the most time effective manner (Bruno, [0017]).
Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over Kornilov in view of Ho et al. (US 11,880,788 B1), hereinafter Ho.
Regarding claim 9, Kornilov discloses the method of claim 1. Kornilov further discloses:
Kornilov does not explicitly disclose tracking each of the one or more products; generating a digital token for each of the one or more products, the digital token comprising data indicative of the at least one feature of each of the one or more products; and generating commerce-related data associated with the one or more products based on the tracking of the one or more products and based on the data indicative of the at least one feature of each of the one or more products, wherein the commerce-related data receiving comprises the commerce-related data associated with the one or more products.
Ho, however, teaches assisting a user during a shopping process (i.e. abstract), including the known technique of tracking each of the one or more products (Ho, see at least: “the products or items have location sensors, such as radio frequency identification (RFID) tags. Additionally or operationally, the items may have Bluetooth Low Energy (BLE) tags, that operate on BLE Beacon technology. A manufacturer, a distributor or a retailer attaches a Radio Frequency Identification (RFID) tag on each product to identify and track their merchandise [i.e. tracking each of the one or more products]” Col 4 Ln. 3-10);
the known technique of generating a digital token for each of the one or more products, the digital token comprising data indicative of the at least one feature of each of the one or more products (Ho, see at least: “the products or items have location sensors, such as radio frequency identification (RFID) tags. Additionally or operationally, the items may have Bluetooth Low Energy (BLE) tags, that operate on BLE Beacon technology. A manufacturer, a distributor or a retailer attaches a Radio Frequency Identification (RFID) tag on each product to identify and track their merchandise. By the transmission and reception of radio signals to and from the RFID tag on the product, the product can be tracked from the time of manufacture to the time of sale without any direct visual or physical contact with the product being monitored. In one implementation, the RFID tag information [i.e. generating a digital token for each of the one or more products] includes an RFID tag of a product includes (1) a retail SKU number (e.g., a universal product code) identifying the name, manufacturer and/or suggested price of the product, (2) a unique serial number identifying the product, (3) the SKU number and the unique serial number or (4) the aisle where the item is ideally placed, for example: apparel, snacks, perishable, etc. [i.e. the digital token comprising data indicative of the at least one feature of each of the one or more products]” Col 4 Ln. 3-21); and
the known technique of generating commerce-related data associated with the one or more products based on the tracking of the one or more products and based on the data indicative of the at least one feature of each of the one or more products, wherein the commerce-related data receiving comprises the commerce-related data associated with the one or more products (Ho, see at least: “the products or items have location sensors, such as radio frequency identification (RFID) tags. Additionally or operationally, the items may have Bluetooth Low Energy (BLE) tags, that operate on BLE Beacon technology. A manufacturer, a distributor or a retailer attaches a Radio Frequency Identification (RFID) tag on each product to identify and track their merchandise. By the transmission and reception of radio signals to and from the RFID tag on the product, the product can be tracked from the time of manufacture to the time of sale [i.e. generating commerce-related data associated with the one or more products based on the tracking of the one or more products and based on the data indicative of the at least one feature of each of the one or more products, wherein the commerce-related data receiving comprises the commerce-related data associated with the one or more products] without any direct visual or physical contact with the product being monitored. In one implementation, the RFID tag information includes an RFID tag of a product includes (1) a retail SKU number (e.g., a universal product code) identifying the name, manufacturer and/or suggested price of the product, (2) a unique serial number identifying the product, (3) the SKU number and the unique serial number or (4) the aisle where the item is ideally placed, for example: apparel, snacks, perishable, etc.” Col 4 Ln. 3-21). These known techniques are applicable to the method of Kornilov as they both share characteristics and capabilities, namely, they are directed to assisting a user during a shopping process.
It would have been recognized that applying the known techniques of tracking each of the one or more products; generating a digital token for each of the one or more products, the digital token comprising data indicative of the at least one feature of each of the one or more products; and generating commerce-related data associated with the one or more products based on the tracking of the one or more products and based on the data indicative of the at least one feature of each of the one or more products, wherein the commerce-related data receiving comprises the commerce-related data associated with the one or more products, as taught by Ho, to the teachings of Kornilov would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such references into similar methods. Further, adding the modifications of tracking each of the one or more products; generating a digital token for each of the one or more products, the digital token comprising data indicative of the at least one feature of each of the one or more products; and generating commerce-related data associated with the one or more products based on the tracking of the one or more products and based on the data indicative of the at least one feature of each of the one or more products, wherein the commerce-related data receiving comprises the commerce-related data associated with the one or more products, as taught by Ho, into the method of Kornilov would have been recognized by those of ordinary skill in the art as resulting in an improved method that would allow for products to be tracked from the time of manufacture to the time of sale (Ho, Col. 4 Ln. 10-13).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
-Dissanayake et al. (US 2025/0166040 A1) teaches using AI to provide personalized skin product recommendations.
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/ARIELLE E WEINER/ Primary Examiner, Art Unit 3689
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