DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
In response to communications filed on 13 May 2025, claims 1-20 are presently pending in the application, of which, claims 1, 9 and 15 are presented in independent form. The Examiner acknowledges that no claims were amended, cancelled, or newly.
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 18 December 2025 and 13 May 2025, respectively, are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Drawings
The drawings, filed 13 May 20253, have been reviewed and accepted by the Examiner.
Specification
The title of the invention is not descriptive. A new title is required that is clearly indicative of the invention to which the claims are directed.
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 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 1-20 are rejected under 35 U.S.C. 102(a)(1)/(a)(2) as being unpatentable over Hadad, Yaron, et al (U.S. 20190290172 and known hereinafter as Hadad).
As per claim 1, Hadad teaches a method comprising:
obtaining a food product model trained using a plurality of data items (e.g. Hadad, see paragraphs [0088-0091], which discloses a food analysis system that can create, update, and/or utilize food ontology, where the system can continuously receive, analyze, and organize nutrition information of all food types into the food ontology.), the plurality of data items each labeled with values corresponding to a set of attributes across matching food supplied by different sources (e.g. Hadad, see paragraphs [0234-0242], which discloses insights and recommendation engine that can analyze a collection of data aggregated and then suggest a meal plan recommendation that includes specific foods to consume, where to find the specific foods, basic ingredients for the specific food, how to prepare the specific food.), wherein the set of attributes include one or more of:
a visual attribute, a categorical attribute, or a quantitative non-nutritional attribute of the matching food supplied by the different sources (e.g. Hadad, see paragraphs [0118-0125], which discloses the food analysis system can perform optical character recognition to extract textual information from the images, where the food system can also implement a convolutional neural network (CNN) to extract information from various icons that often appear on packaged foods. Additionally, see paragraphs [0132-0134], which discloses the food analysis stem may include food image recognition engine to classify foods from images, and analyze the content, volume and nutritional values of the food (e.g. sets of attributes.).);
providing, to the food product model, an input associated with a user, wherein the input includes a food product supplied by a first food product source (e.g. Hadad, see paragraphs [0214-0221], which discloses the metric for comparing the food based on similarity or metrics for comparing and assessing similarities among the nodes in the food ontology to develop a score representative of the meal.);
determining, using the food product model, a confidence score that the input has a first value corresponding to a first food product attribute of the set of attributes across the matching food (e.g. Hadad, see paragraphs [0214-0221], which discloses the metric for comparing the food based on similarity or metrics for comparing and assessing similarities among the nodes in the food ontology to develop a score representative of the meal.); and
returning, using the food product model, a set of results including a second food product supplied by a second food product source, wherein the second food product source represents a substitute for the first food product source (e.g. Hadad, see paragraphs [0167-0172], which discloses a food classification model that includes a confidence score, where the first model may include a food classification model that associates relative food categories, such as packaged foods, certain restaurant menus, recipes, etc. The second model may include a food parsing model that includes couscous as a type of rice category, thereby illustrating a second food product.).
As per claim 2, Hadad teaches the method of claim 1, wherein the input associated with the user includes one or more of: a set of images or a set of text (e.g. Hadad, see paragraphs [0132-0134], which discloses the food analysis stem may include food image recognition engine to classify foods from images, and analyze the content, volume and nutritional values of the food (e.g. sets of images or text.).).
As per claim 3, Hadad teaches the method of claim 1, wherein the set of attributes includes at least one of:
variety, origin, size, storage method, organic status, or physical appearance (e.g. Hadad, see paragraphs [0132-0134], which discloses the food analysis stem may include food image recognition engine to classify foods from images, and analyze the content, volume and nutritional values of the food (e.g. sets of attributes.).).
As per claim 4, Hadad teaches the method of claim 1, wherein the second food product is included in the set of results based on one or more of:
a food usage history associated with the user or one or more attributes of the set of attributes associated with the user (e.g. Hadad, see paragraphs [0088-0091], which discloses a food analysis system that can create, update, and/or utilize food ontology, where the system can continuously receive, analyze, and organize nutrition information of all food types into the food ontology. See further paragraphs [0132-0134], which discloses the food analysis stem may include food image recognition engine to classify foods from images, and analyze the content, volume and nutritional values of the food (e.g. sets of attributes.).).
As per claim 5, Hadad teaches the method of claim 4, wherein the food usage history associated with the user includes one or more previous food product orders made by the user (e.g. Hadad, see paragraphs [0088-0091], which discloses a food analysis system that can create, update, and/or utilize food ontology, where the system can continuously receive, analyze, and organize nutrition information of all food types into the food ontology.).
As per claim 6, Hadad teaches the method of claim 1, wherein the confidence score is based on identifying a threshold number of attributes for the food product (e.g. Hadad, see paragraphs [0214-0221], which discloses the metric for comparing the food based on similarity or metrics for comparing and assessing similarities among the nodes in the food ontology to develop a score representative of the meal.).
As per claim 7, Hadad teaches the method of claim 1, wherein said determining the confidence score further includes a scoring weight applied to each attribute of the set of attributes (e.g. Hadad, see paragraphs [0214-0221], which discloses the metric for comparing the food based on similarity or metrics for comparing and assessing similarities among the nodes in the food ontology to develop a score representative of the meal.).
As per claim 8, Hadad teaches the method of claim 1, further comprising:
displaying the set of results on a hosted marketplace application (e.g. Hadad, see paragraphs [0088-0091], which discloses a food analysis system that can create, update, and/or utilize food ontology, where the system can continuously receive, analyze, and organize nutrition information of all food types into the food ontology.).
As per claim 9, Hadad teaches a method comprising:
providing, to a food product model via a user, an input that refers to a first food product supplied by a first food product source (e.g. Hadad, see paragraphs [0210-0222], which discloses a textual query ‘BLT’ is received to which the food analysis system performs a query upon.);
comparing, using the food product model, values associated with a set of attributes across matching food of the first food product with values associated with the set of attributes across the matching food supplied by other food product sources (e.g. Hadad, see paragraphs [0234-0242], which discloses insights and recommendation engine that can analyze a collection of data aggregated and then suggest a meal plan recommendation that includes specific foods to consume, where to find the specific foods, basic ingredients for the specific food, how to prepare the specific food.), wherein the set of attributes include one or more of:
a visual attribute, a categorical attribute, or a quantitative non-nutritional attribute of the matching food supplied by the other food product sources (e.g. Hadad, see paragraphs [0118-0125], which discloses the food analysis system can perform optical character recognition to extract textual information from the images, where the food system can also implement a convolutional neural network (CNN) to extract information from various icons that often appear on packaged foods. Additionally, see paragraphs [0132-0134], which discloses the food analysis stem may include food image recognition engine to classify foods from images, and analyze the content, volume and nutritional values of the food (e.g. sets of attributes.).);
generating, using the food product model, a set of scores between the first food product and other food products that correspond to the first food product and are supplied by different sources (e.g. Hadad, see paragraphs [0214-0221], which discloses the metric for comparing the food based on similarity or metrics for comparing and assessing similarities among the nodes in the food ontology to develop a score representative of the meal.); and
returning a set of results including a second food product supplied by a second food product source based on the set of scores, wherein the second food product source represents a substitute for the first food product source (e.g. Hadad, see paragraphs [0167-0172], which discloses a food classification model that includes a confidence score, where the first model may include a food classification model that associates relative food categories, such as packaged foods, certain restaurant menus, recipes, etc. The second model may include a food parsing model that includes couscous as a type of rice category, thereby illustrating a second food product.).
As per claim 10, Hadad teaches the method of claim 9, wherein the set of attributes includes a set of patterns associated with at least one of:
color, fat marbling, or shape (e.g. Hadad, see paragraph [0186-0187], which discloses a food analysis system that includes estimate range of nutrients, such as total fat.).
As per claim 11, Hadad teaches the method of claim 9, wherein the set of results includes one or more food products from vendors within a particular proximity to a location of the user (e.g. Hadad, see paragraph [0271], which discloses insight and recommendation engine, where parameters are used to generate a prediction model for passive food tracking, where the parameter includes the user’s geolocation using GPS to generate a list of available foods in the vicinity of the user.).
As per claim 12, Hadad teaches the method of claim 9, wherein the second food product is included in the set of results based on a food usage history associated with the user and a particular subset of attributes of the set of attributes associated with the user (e.g. Hadad, see paragraph [0328], which discloses providing recommendation, where based on the history of the user or complied histories of two or more users, the food analysis module can suggest a change to the user’s consumption of foods or individual foods.).
As per claim 13, Hadad teaches the method of claim 9, wherein the food product model is trained to identify attributes of the first food product from packaging information visible in an image of the first food product (e.g. Hadad, see paragraphs [0214-0221], which discloses the metric for comparing the food based on similarity or metrics for comparing and assessing similarities among the nodes in the food ontology to develop a score representative of the meal.).
As per claim 14, Hadad teaches the method of claim 9, further comprising:
storing respective sets of attributes across matching food in an indexed database configured to link one or more food products with a corresponding set of attributes defining the one or more food products (e.g. Hadad, see paragraphs [0219-0220], which discloses storing food intake via APIs and fed into one or more databases, where the data stored in the database is mapped to a food ontology.).
As per claim 15, Hadad teaches a system comprising:
a food product machine learning model (e.g. Hadad, see paragraphs [0088-0091], which discloses a food analysis system that can create, update, and/or utilize food ontology, where the system can continuously receive, analyze, and organize nutrition information of all food types into the food ontology.) configured using a plurality of data items each labeled with values corresponding to a set of attributes across matching food supplied by different food product sources (e.g. Hadad, see paragraphs [0234-0242], which discloses insights and recommendation engine that can analyze a collection of data aggregated and then suggest a meal plan recommendation that includes specific foods to consume, where to find the specific foods, basic ingredients for the specific food, how to prepare the specific food.), wherein the set of attributes include one or more of:
a visual attribute, a categorical attribute, or a quantitative non-nutritional attribute of the matching food supplied by the different food product sources (e.g. Hadad, see paragraphs [0118-0125], which discloses the food analysis system can perform optical character recognition to extract textual information from the images, where the food system can also implement a convolutional neural network (CNN) to extract information from various icons that often appear on packaged foods. Additionally, see paragraphs [0132-0134], which discloses the food analysis stem may include food image recognition engine to classify foods from images, and analyze the content, volume and nutritional values of the food (e.g. sets of attributes.).); and
a search engine configured to receive an input including a food product from a user (e.g. Hadad, see paragraphs [0232-0235], which discloses an insights and recommendation engine that can access the food ontology in the food analysis system, access a plethora of personal biomarkers data from the device, analyze how food affects a user’s biomarkers, and generating personal nutrition recommendations.), the search engine configured to
(i) use the food product machine learning model to link the input to a first food product supplied by a first food product source based on the values corresponding to the set of attributes across the matching food (e.g. Hadad, see paragraphs [0214-0221], which discloses the metric for comparing the food based on similarity or metrics for comparing and assessing similarities among the nodes in the food ontology to develop a score representative of the meal.), and
(ii) return a set of results including a second food product supplied by a second food product source, wherein the second food product source represents a substitute for the first food product source (e.g. Hadad, see paragraphs [0167-0172], which discloses a food classification model that includes a confidence score, where the first model may include a food classification model that associates relative food categories, such as packaged foods, certain restaurant menus, recipes, etc. The second model may include a food parsing model that includes couscous as a type of rice category, thereby illustrating a second food product.).
As per claim 16, Hadad teaches the system of claim 15, wherein the search engine is further configured to provide a score for each food product in the set of results to indicate a degree of match to the input.
As per claim 17, Hadad teaches the system of claim 15, further comprising: a user interface configured to display the set of results (e.g. Hadad, see paragraphs [0088-0091], which discloses a food analysis system that can create, update, and/or utilize food ontology, where the system can continuously receive, analyze, and organize nutrition information of all food types into the food ontology.).
As per claim 18, Hadad teaches the system of claim 15, wherein the search engine is further configured to include in the set of results a third food product previously paired with the first food product.
As per claim 19, Hadad teaches the system of claim 15, wherein the second food product is determined based on a previously ordered food product associated with the user (e.g. Hadad, see paragraph [0271], which discloses insight and recommendation engine, where parameters are used to generate a prediction model for passive food tracking, where the parameter includes the user’s geolocation using GPS to generate a list of available foods in the vicinity of the user.).
As per claim 20, Hadad teaches the system of claim 15, wherein the second food product is determined based on a previously ordered food product associated with different users associated with the first food product (e.g. Hadad, see paragraphs [0088-0091], which discloses a food analysis system that can create, update, and/or utilize food ontology, where the system can continuously receive, analyze, and organize nutrition information of all food types into the food ontology. See further paragraphs [0132-0134], which discloses the food analysis stem may include food image recognition engine to classify foods from images, and analyze the content, volume and nutritional values of the food (e.g. sets of attributes.).).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure. See attached PTO-892 that includes additional prior art of record describing the general state of the art in which the invention is directed to.
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to FARHAN M SYED whose telephone number is (571)272-7191. The examiner can normally be reached M-F 8:30AM-5:30PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Apu Mofiz can be reached at 571-272-4080. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/FARHAN M SYED/Primary Examiner, Art Unit 2161 April 22, 2026
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