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 the Claims
The office action is in response to the claims filed on April 1, 2026 for the application filed on November 27, 2024. Claims 3 – 4 have been cancelled, and new claims 12 – 16 have been added. Claims 1 – 2 and 5 – 16 are currently pending and have been examined as discussed below.
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 – 2 and 5 – 16 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Examiners should determine whether a claim satisfies the criteria for subject matter eligibility by evaluating the claim in accordance with the flowchart in MPEP 2016(III).
Eligibility Step 1:
Under Step 1 of the 2019 Revised Patent Subject Matter Eligibility Guidance, it must be determined whether each claim as a whole falls within one of the statutory categories of invention (i.e., a process, machine, manufacture, or composition of matter). See MPEP 2106.03. In the instant application, claims 1 – 2, 5 – 8, and 12 – 16 are directed to an electronic device (i.e., an apparatus); claims 9 is directed to a system (i.e., a machine); claims 10 is directed to a control method (i.e., a process); and claim 11 is directed to a computer readable medium (i.e., an article of manufacture).
While each one of claims 1 – 2 and 5 – 16 appears to fall within one or more statutory categories of invention, the Office has determined that the full eligibility analysis is required because there is doubt as to whether the applicant is effectively seeking coverage for a judicial exception itself. The eligibility of each claim is not self-evident at least because each claim as a whole did not appear to clearly improve a technology or computer functionality. To the contrary, each claim as a whole appeared to merely apply one or more judicial exceptions on a computer.
Accordingly, it has been determined that each one of claims 1 – 2 and 5 – 16 as a whole falls within one or more statutory categories under Step 1, and the Office proceeds with the full eligibility analysis (the Alice/Mayo test described in MPEP 2106(III)) as discussed below.
Eligibility Step 2A, Prong One:
Under Step 2A, Prong One of the 2019 Revised Patent Subject Matter Eligibility Guidance, it must be determined whether each claim is directed to one or more of the judicial exceptions (i.e., an abstract idea, law of nature, or natural phenomenon). See MPEP 2106.04(II)(A)(1). After evaluation, it has been determined that claims 1 – 2 and 5 – 16 are directed to judicial exceptions because claims 1 – 2 and 5 – 16 recite abstract ideas. (The Office will not determine that a claim is not directed to a judicial exception under Step 2A, Prong One for the mere reason that claim further recites one or more additional elements beyond the judicial exception.)
Independent claims 1 and 9 – 11 are determined to be directed to a judicial exception including abstract ideas (i.e., mental process). Claim 1 recites the mental process identified in bold as:
An electronic device comprising:
a memory that stores an Al model for identifying food intake information of a user from food images before and after a meal; and
a processor that inputs the food images before and after the meal into the Al model to identify the food intake information of the user, and that monitors health conditions of the user, based on the food intake information,
wherein the processor is further configured to identify a mass of food consumed by the user by calculating a volume of reduced food according to a difference between a first food image captured before the meal and a second food image captured after the meal; and
determining the mass of the food consumed by the user by applying a density value, selected based on a type of the food identified by the Al model, and reflecting a change in the density according to a cooking method of the food.
Regarding claims 9 – 11, representative claim 9 recites the mental process identified in bold as:
A system comprising:
a user terminal that generates food images before and after a meal through image capturing; and
an electronic device that receives the generated food images from the user terminal to analyze the generated food images,
wherein the electronic device includes a memory that stores an AI model for identifying food intake information of a user from the food images before and after the meal, and a processor that inputs the food images before and after the meal into the AI model to identify the food intake information of the user, and that monitors health conditions of the user, based on the food intake information,
wherein the processor is further configured to identify a mass of food consumed by the user by:
calculating a volume of reduced food according to a difference between the food images before and after the meal; and
determining the mass of the food consumed by the user by applying a density value, selected based on a type of the food identified by the Al model, and reflecting a change in the density according to a cooking method of the food.
Claim 1 recites the combination of limitations identified as “identifying food intake information of a user from food images before and after a meal,” “monitors health conditions of the user, based on the food intake information,” “identify a mass of food consumed by the user by calculating a volume of reduced food according to a difference between a first food image captured before the meal and a second food image captured after the meal,” “determining the mass of the food consumed by the user by applying a density value, selected based on a type of the food,” and “reflecting a change in the density according to a cooking method of the food.” Claim 9 recites the combination of limitations identified as “analyze the generated food images,” “identifying food intake information of a user from the food images before and after the meal,” “monitors health conditions of the user, based on the food intake information,” and “identify a mass of food consumed by the user by: calculating a volume of reduced food according to a difference between the food images before and after the meal; and determining the mass of the food consumed by the user by applying a density value, selected based on a type of the food … and reflecting a change in the density according to a cooking method of the food.” A broadest reasonable interpretation of each combination of limitations amounts to the activity of determining the mass of the food consumed by the user and monitoring the user’s health conditions based on food intake, which may be practically performed in the human mind using observation, evaluation, judgment, and opinion, and thus represents an abstract idea falling in the “mental process” grouping.. With the exception of generic computer-implemented steps, there is nothing in each of claims 1 and 9 – 11 themselves that forecloses them from being performed by a human, mentally or with tools such as pen and paper. Thus, this activity is an abstract idea in the "mental process" grouping.
Accordingly, claims 1 and 9 – 11 are directed to judicial exceptions under Step 2A, Prong One.
Dependent claims 2, 5 – 8, and 12 – 16 are directed to one or more judicial exceptions (i.e., abstract idea exceptions) under Step 2A, Prong One of the full eligibility analysis as follows:
Regarding claims 2, 5 – 8, and 12 – 16, each combination of limitations identified in bold as “determines whether the food images before and after the meal satisfy requirements for identifying the food intake information, based on whether containers and food menus which are included in each of the food images before and after the meal are identical, and requests the user to change the food images before and after the meal, when the requirements are not satisfied” in claim 2, “sets a target diet in response to a weight control need of the user, and generates the target diet in which a numerical value of at least one intake item is increased or decreased from a usual diet of the user, in response to the need” in claim 5, “determines information on the usual diet of the user, based on the food intake information of the user which is accumulated over a reference period” in claim 6, “calculates a type and a volume of nutritional ingredients consumed by the user, based on the food intake information, and determines whether the type and the volume of the nutritional ingredients consumed by the user are appropriate” in claim 7, “acquires user basic information including at least one of body measurement information, a currently held disease, a gender, and an age of the user, predicts a potential disease with a risk of developing disease is equal to or greater than a reference value from the user basic information, and generates the target diet to prevent the potential disease” in claim 8, “the processor requests the user to capture the food images before and after the meal at an angle at which a depth and a width of the container are confirmed, corresponding to a visual angle at which three axes of the container are each tilted at the same angle or an isometric perspective view at an angle of approximately 120 degrees” in claim 12, “the Al model comprises: a first model that identifies a volume of the container from the food image; a second model that identifies a volume of the food based on a degree of the food filling the container; a third model that identifies the cooking method from color, texture, and shape information in the food image; and a fourth model that derives a change in the density according to the cooking method” in claim 13, “the processor determines whether to apply a cooking-method density correction by checking a correction condition, and performs a basic mode that uses only the type-based density when the condition is not satisfied, or performs a correction mode that applies the cooking-method density change when the condition is satisfied, wherein the correction condition is satisfied when at least one of (i) the number of types of food identified in the image before the meal is equal to or greater than a reference value or (ii) the volume information of the food is equal to or greater than a reference value” in claim 14, “the processor calculates a mass correction value by multiplying a basic mass value by a coefficient according to the cooking method, and determines a final correction mass value by adding the mass correction value to the basic mass value” in claim 15, and “the processor, after receiving the food image before a meal, identifies whether a restricted food intake item already exceeds a daily recommended amount based on accumulated intake information, and generates a warning message to the user before the next meal to limit intake of that item” in claim 16 represents a mental process of determining the mass of the food consumed by the user and monitoring the user’s health conditions based on food intake. This mental process may be practically performed in the human mind using observation, evaluation, judgment, and/or opinions. See MPEP 2106.04(a)(2)(III). Thus, claims 2, 5 – 8, and 12 – 16 recite an abstract idea in the "mental process" grouping, and claims 2, 5 – 8, and 12 – 16 recite a judicial exception under Step 2A, Prong One.
Eligibility Step 2A, Prong Two:
Claims 1 and 9 – 11 recite additional limitations beyond the judicial exceptions. Claim 1 recites the additional limitations identified in bold as:
An electronic device comprising:
a memory that stores an Al model for identifying food intake information of a user from food images before and after a meal; and
a processor that inputs the food images before and after the meal into the Al model to identify the food intake information of the user, and that monitors health conditions of the user, based on the food intake information,
wherein the processor is further configured to identify a mass of food consumed by the user by calculating a volume of reduced food according to a difference between a first food image captured before the meal and a second food image captured after the meal; and
determining the mass of the food consumed by the user by applying a density value, selected based on a type of the food identified by the Al model, and reflecting a change in the density according to a cooking method of the food.
Regarding claims 9 – 11, representative claim 9 recites the additional limitations identified in bold as:
A system comprising:
a user terminal that generates food images before and after a meal through image capturing; and
an electronic device that receives the generated food images from the user terminal to analyze the generated food images,
wherein the electronic device includes a memory that stores an AI model for identifying food intake information of a user from the food images before and after the meal, and a processor that inputs the food images before and after the meal into the AI model to identify the food intake information of the user, and that monitors health conditions of the user, based on the food intake information,
wherein the processor is further configured to identify a mass of food consumed by the user by:
calculating a volume of reduced food according to a difference between the food images before and after the meal; and
determining the mass of the food consumed by the user by applying a density value, selected based on a type of the food identified by the Al model, and reflecting a change in the density according to a cooking method of the food.
Claim 1 recites the additional limitations identified in bold as “an electronic device,” “a memory that stores an Al model,” and “a processor that inputs the food images before and after the meal into the Al mode.” Claim 9 recites the additional limitations identified in bold as “a system,” “a user terminal that generates food images before and after a meal through image capturing,” “an electronic device that receives the generated food images from the user terminal,” “the electronic device includes a memory that stores an AI model,” and “a processor that inputs the food images before and after the meal into the AI model,” At best, each claim as a whole amounts to determining the mass of the food consumed by the user and monitoring the user’s health conditions based on food intake by using generic computer technology (i.e., the system, the user terminal, the electronic device, the memory, the AI model, the processor, etc.).
MPEP 2106.05(a) states: “In determining patent eligibility, examiners should consider whether the claim ‘purport(s) to improve the functioning of the computer itself’ or ‘any other technology or technical field.’… [A]n improvement in the abstract idea itself is not an improvement in technology.” Furthermore, MPEP 2106.05(a)(II) states: “Merely adding generic computer components to perform the method is not sufficient.” In the instant application, none of claims 1 and 9 – 11 as a whole improves the functioning of the electronic device, the memory, the processor, or the user terminal; nor does any of the claims as a whole improve any other technology or technical field. Each claim as a whole merely adds the electronic device, the memory, the processor, or the user terminal as generic computer components to perform the abstract idea (i.e., determining the mass of the food consumed by the user and monitoring the user’s health conditions based on food intake) and provides an improvement exclusively in the abstract idea itself (i.e., not an improvement in the technology). In the Specification as filed (Page 1, 19 – 21), Applicant admitted that a problem in the prior art is that users were required to manually input information on their own meals (i.e., the user’s food intake information). The claim as a whole improves the abstract idea itself (i.e., determining the mass of the food consumed by the user and monitoring the user’s health conditions based on food intake) by using conventional and generic computer technology in the nascent but well known environment of artificial intelligence to automate the manual process of determining the mass of the food consumed by the user. See MPEP 2106.05(a). The claim as a whole represents mere instructions to apply the abstract idea to conventional and generic computer technology recited at a high level of generality. See MPEP 2106.05(f). None of the claims as a whole Regarding the consideration under MPEP 2106.05(g), the limitations of the user terminal (in claim 9) generating food images before and after a meal through image capturing, the electronic device (in claim 1, 9, and 11) receiving the generated food images, and the step (in claim 10) of acquiring food images before and after a meal are determined to not add more than insignificant extra-solution activities to the judicial exception. These limitations represent the well-known pre-solution activities of necessary input data gathering because they are incidental to the primary process of determining the mass of the food consumed by the user and monitoring the user’s health conditions based on food intake and thus are merely nominal or tangential additions to the associated claims. Regarding the consideration under MPEP 2106.05(h), the additional limitations, individually or in combination, also amount to merely indicating a field of use or technological environment in which to apply the judicial exception. In the instant application, the additional limitations of the electronic device, the memory, the processor, the AI model, and the user terminal do no more than link the abstract idea to the particular technological environment of artificial intelligence. Thus, each claim as a whole fails to have an inventive concept.
Accordingly, in view of these considerations, the Office has determined that each one of claims 1 and 9 – 11 as a whole does not integrate the abstract idea exception into a practical application under Step 2A, Prong Two, and thus each claim as a whole is directed to a judicial exception under Step 2A.
Dependent claims 2, 5 – 8, and 12 – 16 present additional information in tandem with further details regarding elements and the abstract idea from an associated one of independent claims 1 and 10 and are therefore directed to an abstract idea for similar reasons as given Under Step 2A, Prong One above. Claims 2, 5 – 8, and 12 – 16 do not recite any additional limitations beyond the abstract idea of determining the mass of the food consumed by the user and monitoring the user’s health conditions based on food intake. Accordingly, in view of these considerations, the Office has determined that each one of claims 2, 5 – 8, and 12 – 16 as a whole does not integrate the abstract idea exception into a practical application under Step 2A, Prong Two, and thus each claim as a whole is directed to a judicial exception under Step 2A.
Eligibility Step 2B:
Regarding independent claims 1 and 9 – 11, the Office carries over its identification of the additional elements (and combinations thereof) from Step 2A, Prong Two so as to apply the same additional elements in Step 2B. See MPEP 2106.05(II). The Office further carries over its conclusions from the considerations discussed in MPEP 2106.05(a) through (c), (e) through (h) in Step 2A, Prong Two so as to apply the same considerations in Step 2B.
Under Step 2B of the 2019 Revised Patent Subject Matter Eligibility Guidance, it must be determined whether provide an inventive concept by determining if the claims include additional elements or a combination of elements that are sufficient to amount to significantly more than the judicial exception. After evaluation, there is no indication that an additional element or combination of elements are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, each claim as a whole does not provide an improvement to technology or technical field under MPEP 2106.05(a). Each claim as a whole only recites the idea of a desired outcome (i.e., determining the mass of the food consumed by the user and monitoring the user’s health conditions based on food intake). None of the claims as a whole provides detail on the particular electronic device, the particular memory, the particular processor, the particular AI model, or the particular user terminal. None of the claims as a whole provides a particular solution, including at least details on: how the Al model identifies food intake information of a user from food images before and after a meal; how the processor monitors health conditions of the user based on the food intake information; how the volume of reduced food is calculated according to a difference between a first food image captured before the meal and a second food image captured after the meal; how the volume of food in a first food image captured before the meal is calculated; how the volume of food in a second food image captured after the meal is calculated; how the type of the food is identified by the Al model; how the density value of the type of food is selected by the AI model; and how the density value reflects a change in the density according to a cooking method of the food. Each claim as a whole invokes computers or other machinery merely as a tool to perform the existing process of determining the mass of the food consumed by the user and monitoring the user’s health conditions based on food intake. The additional limitations amount to mere instructions to apply an abstract idea under MPEP 2106.05(f) and/or necessary data gathering under MPEP 2106.05(g). Each claim recites inputting food images before and after the meal into the AI model to determine the mass of the food consumed by the user. This activity is merely generic manner (e.g., at a high level of generality) and thus represent well‐understood, routine, and conventional functions. MPEP 2106.05(d), subsection II. Evidence that using an AI model to determine the mass of the food consumed by the user is provided by Panetta (U.S. Pub. No. 20240055101 A1). Evidence that scanning or extracting data (i.e., food intake information) from a physical document (i.e., food images before and after meals) is well-understood, routine, and conventional is provided by MPEP 2106.05(d), subsection II.
Furthermore, looking at the limitations individually or as any ordered combination adds nothing that is not already present when looking at each claim as a whole. There is no indication that the individual elements or combinations of elements amount to an inventive concept.
Therefore, claims 1 and 9 – 11 are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Regarding dependent claims 2, 5 – 8, and 12 – 16, the Office carries over its determination that none of claims 2, 5 – 8, and 12 – 16 recites additional elements from Step 2A, Prong Two so as to apply the same determination in Step 2B. See MPEP 2106.05(II). The Office further carries over its conclusions from the considerations discussed in MPEP 2106.05(a) through (c), (e) through (h) in Step 2A, Prong Two so as to apply the same considerations in Step 2B. The dependent claims merely present additional abstract information in tandem with further details regarding the elements from the independent claims and are, therefore, directed to an abstract idea for similar reasons as given above. Claims 2, 5 – 8, and 12 – 16 are all encompassed by the abstract idea grouping of mental processes.
Therefore, claims 2, 5 – 8, and 12 – 16 are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
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:
Determining the scope and contents of the prior art.
Ascertaining the differences between the prior art and the claims at issue.
Resolving the level of ordinary skill in the pertinent art.
Considering objective evidence present in the application indicating obviousness or nonobviousness.
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.
Claims 1, 5 – 7, and 9 – 11 are rejected under 35 U.S.C. 103(a) as being unpatentable over Panetta (U.S. Pub. No. 2024/0055101 A1) in view of Baldwin (U.S. Pub. No. 2022/0007689 A1) and Wersborg (U.S. Pub. No. 2024/0029020 A1).
Regarding independent claim 1, Panetta teaches the limitations of identified in bold as:
An electronic device (Abstract of Panetta. In the instant application, the broadest reasonable interpretation of “an electronic device” reads on the systems, artificial intelligence (AI) automatic methods, and computer program product in Panetta (Abstract) for dietary planning, food waste estimation, analyzing three-dimensional food image construction, measurement, nutrient estimation, nutritional assessment, evaluation, prediction and management.) comprising:
a memory that stores an Al model for identifying food intake information of a user from food images before and after a meal (Paragraphs [0015] and [0179] of Panetta. In the instant application, the broadest reasonable interpretation of “a memory that stores an Al model for identifying food intake information of a user from food images before and after a meal” reads on the non-volatile memory (e.g., hard disk) in Panetta (Paragraph [0179]) storing computer instructions and an article comprising non-transitory computer-readable instructions. The system in Panetta (Paragraphs [0015] and [0022]) includes computer vision techniques in combination with artificial intelligence methods. to reconstruct 3-D images from 2-D images and compute volume using the 3-D image.); and
a processor that inputs the food images before and after the meal into the Al model to identify the food intake information of the user, and that monitors health conditions of the user, based on the food intake information (Paragraphs [0015], [0078] – [0079], [0166], [0173], and [0179] of Panetta. In the instant application, the broadest reasonable interpretation of “a processor that inputs the food images before and after the meal into the Al model to identify the food intake information of the user, and that monitors health conditions of the user, based on the food intake information” reads on the processor in Panetta (Paragraphs [0015], [0166], and [0179]) that inputs the images before and after consumption to execute the computer instructions (i.e., computer vision techniques and AI methods) to reconstruct 3-D images and estimate volume (i.e., before and after consumption) based on the associated 3-D image, compute the difference in volume between the images before and after consumption, and determine nutrition-related data (e.g., calories, nutrients consumed) by mapping the estimated difference in volume to estimated food weight and linking to nutrient databases. The processor in Panetta (Paragraphs [0078] – [0079]) further receives, from different wearable health monitoring sensors, integrated information to track the user’s heart rate, blood pressure, etc.),
wherein the processor is further configured to identify a mass of food consumed by the user by calculating a volume of reduced food according to a difference between a first food image captured before the meal and a second food image captured after the meal (Paragraphs [0166] and [0173] of Panetta. In the instant application, the broadest reasonable interpretation of “the processor is further configured to identify a mass of food consumed by the user by calculating a volume of reduced food according to a difference between a first food image captured before the meal and a second food image captured after the meal” reads on the procedure in Panetta (Paragraphs [0166] and [0173]) executed on multimedia content before (MCB) (i.e., first food image captured before the meal) and after (MCA) food consumption (i.e., second food image captured after the meal), such that for each food item, the difference in volume between these MCB and MCA food consumption will be computed, with the estimated volume of the consumed food (i.e., volume of reduced food) is mapped to food weight estimation (i.e., mass of food consumed) and linked to nutrient databases to determine nutrition-related data (e.g., calories, nutrients).); and
determining the mass of the food consumed by the user by applying a density value, selected based on a type of the food identified by the Al model, and reflecting a change in the density according to a cooking method of the food (Paragraphs [0015], [0078] – [0079], [0166], [0173], and [0179] of Panetta. In the instant application, the broadest reasonable interpretation of “the Al model” reads on the computer instructions in Panetta (Paragraphs [0015], [0166], and [0179]), i.e., computer vision techniques and AI methods.).
Panetta does not appear to explicitly disclose, but Baldwin teaches the limitations in bold identified as “determining the mass of the food consumed by the user by applying a density value, selected based on a type of the food identified by the Al model, and reflecting a change in the density according to a cooking method of the food” (Paragraph [0093] of Baldwin. In the instant application, the broadest reasonable interpretation of “determining the mass of the food consumed by the user by applying a density value, selected based on a type of the food” reads on the activities in Baldwin (Paragraph [0093]) of estimating the type of the food item and estimating the weight of the food item based on the average density for the type of food item.).
Panetta does not appear to explicitly disclose, but Wersborg teaches the limitations in bold identified as “determining the mass of the food consumed by the user by applying a density value, selected based on a type of the food identified by the Al model, and reflecting a change in the density according to a cooking method of the food” (Paragraph [0049] of Wersborg. In the instant application, the broadest reasonable interpretation of “reflecting a change in the density according to a cooking method of the food” reads on the activity in Wersborg (Paragraph [0049]) of correlating the volumetric assessment with food item ingredient data and preparation specific food density.).
Therefore, it would have been obvious to one of ordinary skill in the art of medical data mining at the time of filing to modify the system of Panetta to include the activity of determining the mass of the food consumed by the user by applying a density value, selected based on a type of the food, as taught by Baldwin (Paragraph [0093]), in order to accurately estimate certain characteristics of a food item (Paragraph [0004] of Baldwin); and include the activity of reflecting a change in the density according to a cooking method of the food, as taught by Wersborg (Paragraph [0049]), in order to provide a system or food production system or food processing system or scheduling system or tracking and scheduling system that enables efficient planning and control of food preparation or food supply (Paragraph [0008] of Wersborg).
Regarding independent claims 9 – 11, Panetta teaches the limitations of representative claim 9 identified in bold as:
A system (Abstract of Panetta. In the instant application, the broadest reasonable interpretation of “system” reads on the systems, artificial intelligence (AI) automatic methods, and computer program product in Panetta (Abstract) for dietary planning, food waste estimation, analyzing three-dimensional food image construction, measurement, nutrient estimation, nutritional assessment, evaluation, prediction and management.) comprising:
a user terminal that generates food images before and after a meal through image capturing (Paragraphs [0116] and [0119] of Panetta. In the instant application, the broadest reasonable interpretation of “a user terminal that generates food images before and after a meal through image capturing” reads on the device in Panetta (Paragraphs [0116] and [0119]), (e.g., smartphones, laptops, digital cameras, desktop computers or servers) that contains one or more sensors along with a computing platform for capturing multimedia content including images of the food plate and/or food items before consumption and after consumption.); and
an electronic device that receives the generated food images from the user terminal to analyze the generated food images (Paragraphs [0116] and [0179] of Panetta. In the instant application, the broadest reasonable interpretation of “an electronic device that receives the generated food images from the user terminal to analyze the generated food images” reads on the computer in Panetta (Paragraphs [0116] and [0179]) that receives, through the cloud system, the images to perform at least a portion of the processing of those images.), and
wherein the electronic device includes a memory that stores an AI model for identifying food intake information of a user from the food images before and after the meal (Paragraphs [0015] and [0179] of Panetta. In the instant application, the broadest reasonable interpretation of “the electronic device includes a memory that stores an AI model for identifying food intake information of a user from the food images before and after the meal” reads on the computer in Panetta (Paragraph [0179]) that includes a non-volatile memory (e.g., hard disk) storing computer instructions and an article comprising non-transitory computer-readable instructions. The system in Panetta (Paragraphs [0015] and [0022]) includes computer vision techniques in combination with artificial intelligence methods to reconstruct 3-D images from 2-D images and compute volume using the 3-D image.), and a processor that inputs the food images before and after the meal into the AI model to identify the food intake information of the user, and that monitors health conditions of the user, based on the food intake information (Paragraphs [0015], [0078] – [0079], [0166], [0173], and [0179] of Panetta. In the instant application, the broadest reasonable interpretation of “a processor that inputs the food images before and after the meal into the AI model to identify the food intake information of the user, and that monitors health conditions of the user, based on the food intake information” reads on the processor in Panetta (Paragraphs [0015], [0166], and [0179]) that inputs the images before and after consumption to execute the computer instructions (i.e., computer vision techniques and AI methods) to reconstruct 3-D images and estimate volume (i.e., before and after consumption) based on the associated 3-D image, compute the difference in volume between the images before and after consumption, and determine nutrition-related data (e.g., calories, nutrients consumed) by mapping the estimated difference in volume to estimated food weight and linking to nutrient databases. The processor in Panetta (Paragraphs [0078] – [0079]) further receives, from different wearable health monitoring sensors, integrated information to track the user’s heart rate, blood pressure, etc.);
wherein the processor is further configured to identify a mass of food consumed by the user by: calculating a volume of reduced food according to a difference between the food images before and after the meal (Paragraphs [0166] and [0173] of Panetta. In the instant application, the broadest reasonable interpretation of “the processor is further configured to identify a mass of food consumed by the user by: calculating a volume of reduced food according to a difference between the food images before and after the meal” reads on the procedure in Panetta (Paragraphs [0166] and [0173]) executed on multimedia content before (MCB) (i.e., food image before the meal) and after (MCA) food consumption (i.e., food image after the meal), such that for each food item, the difference in volume between these MCB and MCA food consumption will be computed, with the estimated volume of the consumed food (i.e., mass of food consumed) is mapped to food weight estimation and linked to nutrient databases to determine nutrition-related data (e.g., calories, nutrients).); and
determining the mass of the food consumed by the user by applying a density value, selected based on a type of the food identified by the Al model, and reflecting a change in the density according to a cooking method of the food (Paragraphs [0015], [0078] – [0079], [0166], [0173], and [0179] of Panetta. In the instant application, the broadest reasonable interpretation of “the Al model” reads on the computer instructions in Panetta (Paragraphs [0015], [0166], and [0179]), i.e., computer vision techniques and AI methods.).
Panetta does not appear to explicitly disclose, but Baldwin teaches the limitations in bold identified as “determining the mass of the food consumed by the user by applying a density value, selected based on a type of the food identified by the Al model, and reflecting a change in the density according to a cooking method of the food” (Paragraph [0093] of Baldwin. In the instant application, the broadest reasonable interpretation of “determining the mass of the food consumed by the user by applying a density value, selected based on a type of the food” reads on the activities in Baldwin (Paragraph [0093]) of estimating the type of the food item and estimating the weight of the food item based on the average density for the type of food item.).
Panetta does not appear to explicitly disclose, but Wersborg teaches the limitations in bold identified as “determining the mass of the food consumed by the user by applying a density value, selected based on a type of the food identified by the Al model, and reflecting a change in the density according to a cooking method of the food” (Paragraph [0049] of Wersborg. In the instant application, the broadest reasonable interpretation of “reflecting a change in the density according to a cooking method of the food” reads on the activity in Wersborg (Paragraph [0049]) of correlating the volumetric assessment with food item ingredient data and preparation specific food density.).
Therefore, it would have been obvious to one of ordinary skill in the art of medical data mining at the time of filing to modify the system of Panetta to include the activity of determining the mass of the food consumed by the user by applying a density value, selected based on a type of the food, as taught by Baldwin (Paragraph [0093]), in order to accurately estimate certain characteristics of a food item (Paragraph [0004] of Baldwin); and include the activity of reflecting a change in the density according to a cooking method of the food, as taught by Wersborg (Paragraph [0049]), in order to provide a system or food production system or food processing system or scheduling system or tracking and scheduling system that enables efficient planning and control of food preparation or food supply (Paragraph [0008] of Wersborg).
Regarding claim 5, Panetta as modified by Baldwin and Wersborg teaches the limitation identified in bold as “the processor sets a target diet in response to a weight control need of the user, and generates the target diet in which a numerical value of at least one intake item is increased or decreased from a usual diet of the user, in response to the need” (Paragraphs [0016], [0081], [0082], and [0179]of Panetta. In the instant application, the broadest reasonable interpretation of “the processor sets a target diet in response to a weight control need of the user” reads on the processor in Panetta (Paragraphs [0016] and [0179]) performing an artificial intelligence-based method to generate and/or recommend meal plans based on the user’s body composition, weight fluctuation trends, and individual goals. The broadest reasonable interpretation of “the processor … generates the target diet in which a numerical value of at least one intake item is increased or decreased from a usual diet of the user, in response to the need” reads on the processing module in Panetta (Paragraphs [0081], [0082], and [0179]) including a machine learning algorithm (MLA) that aids a meal planning engine (MPE) and a recipe suggestion/recommendation engine (RRE) 226 to select an optimal meal/recipe, such that the next meal(/s)/recipe includes nutrients that were out of range in the previous meal.).
Regarding claim 6, Panetta as modified by Baldwin and Wersborg teaches the limitation identified in bold as “the processor determines information on the usual diet of the user, based on the food intake information of the user which is accumulated over a reference period” (Paragraphs [0080] – [0082] and [0179] of Panetta. In the instant application, the broadest reasonable interpretation of “the food intake information of the user which is accumulated over a reference period” reads on the data records in Panetta (Paragraph [0080]) indicative of previously generated meal plans/recipes and other data saved in connection, number, and type of meals per day, calories tracked over time. The broadest reasonable interpretation of “the processor determines information on the usual diet of the user, based on the food intake information” reads on the processor/processing module in Panetta (Paragraphs [0082] and [0179]) using one or more algorithms to find optimum results according to the user's conditions, e.g., to recommend the next optimal meal(s)/recipe including nutrients that were out of range in the previous meal.).
Regarding claim 7, Panetta as modified by Baldwin and Wersborg teaches the limitation identified in bold as “the processor calculates a type and a volume of nutritional ingredients consumed by the user, based on the food intake information, and determines whether the type and the volume of the nutritional ingredients consumed by the user are appropriate” (Paragraphs [0173] and [0179] of Panetta. In the instant application, the broadest reasonable interpretation of “the processor calculates a type and a volume of nutritional ingredients consumed by the user, based on the food intake information” reads on the processor in Panetta (Paragraphs [0173] and [0179]) detecting, identifying, and classifying a select set of images from a quick-service restaurant (QSR), use those images to create a 3-D model reconstruction of those foods, estimate volume and weight from the 3-D reconstruction, and report accurate nutrient intake. The broadest reasonable interpretation of “the processor … determines whether the type and the volume of the nutritional ingredients consumed by the user are appropriate” reads on the processor/processing module in Panetta (Paragraphs [0082] and [0179]) comparing the consumed nutrients after every meal with the database with the RDA, Ai, UL, and EAR tables to check if the daily nutritional value is within the allowable range.).
Claim 2 is rejected under 35 U.S.C. 103(a) as being unpatentable over Panetta as modified by Baldwin and Wersborg and applied to claim 1 in view of Seo (U.S. Pub. No. 2012/0096405 A1).
Regarding claim 2, Panetta as modified by Baldwin and Wersborg and applied to claim 1 teaches the limitation identified in bold as “the processor determines whether the food images before and after the meal satisfy requirements for identifying the food intake information, based on whether containers and food menus which are included in each of the food images before and after the meal are identical, and requests the user to change the food images before and after the meal, when the requirements are not satisfied” (Paragraphs [0081] and [0179] of Seo. In the instant application, the broadest reasonable interpretation of “the processor” reads on the processor/processing module in Panetta (Paragraphs [0081] and [0179]).).
Panetta as modified by Baldwin and Wersborg and applied to claim 1 does not appear to explicitly disclose, but Seo teaches the limitation identified in bold as “the processor determines whether the food images before and after the meal satisfy requirements for identifying the food intake information, based on whether containers and food menus which are included in each of the food images before and after the meal are identical, and requests the user to change the food images before and after the meal, when the requirements are not satisfied” (Paragraphs [0206] – [0208] of Seo. In the instant application, the broadest reasonable interpretation of “the processor determines whether the food images before and after the meal satisfy requirements for identifying the food intake information, based on whether containers and food menus which are included in each of the food images before and after the meal are identical, and requests the user to change the food images before and after the meal, when the requirements are not satisfied” reads on the activities in Seo (Paragraphs [0206] – [0208]) of comparing a first image of a food item included in the menu image before mealtime and a second image of a food item included in the menu image after mealtime, determining that the first and second images are related to the same food based on the form, size, and color of the plates appearing on the first and second images, and determining the intake of the food item based on it (i.e., determining the requirements are satisfied when the first and second images show the same plate and are not satisfied when the first and second images show the two different plates.).
Therefore, it would have been obvious to one of ordinary skill in the art of healthcare informatics relating to nutrition control at the time of filing to modify the system, method, and non-transitory computer readable medium of Panetta as modified by Baldwin and Wersborg and applied to claim 1 to implement the processor determining whether the food images before and after the meal satisfy requirements for identifying the food intake information, based on whether containers and food menus which are included in each of the food images before and after the meal are identical, and requests the user to change the food images before and after the meal, when the requirements are not satisfied, as taught by Seo (Paragraphs [0206] – [0208]), in order to provide an apparatus and a method for a diet management capable of improving user convenience in selecting a food category of food items included in a menu (or diet or meals) (Paragraph [0008] of Seo).
Claim 8 is rejected under 35 U.S.C. 103(a) as being unpatentable over Panetta as modified by Baldwin and Wersborg and applied to claim 1 in view of Kishi (U.S. Pub. No. 2023/0352184 A1).
Regarding claim 8, Panetta as modified by Baldwin and Wersborg and applied to claim 1 teaches the limitation identified in bold as “the processor acquires user basic information including at least one of body measurement information, a currently held disease, a gender, and an age of the user, predicts a potential disease with a risk of developing disease is equal to or greater than a reference value from the user basic information, and generates the target diet to prevent the potential disease” (Paragraphs [0073], [0078], [0080], [0081], and [0179] of Panetta. In the instant application, the broadest reasonable interpretation of “the processor acquires user basic information including at least one of body measurement information, a currently held disease, a gender, and an age of the user” reads on the processor/processing module in Panetta (Paragraphs [0073], [0078], [0080], [0081], and [0179]) acquiring personal data (including height, weight, size measurements of various body parts, metabolic heart rate, current body fat percentage, gender, birth date, etc.), acquiring medical information (including medical conditions, such as current illnesses, diseases, and the history of medical conditions for the user and the user’s family).).
Panetta as modified by Baldwin and Wersborg and applied to claim 1 does not appear to explicitly disclose, but Kishi teaches the limitation identified in bold as “the processor acquires user basic information including at least one of body measurement information, a currently held disease, a gender, and an age of the user, predicts a potential disease with a risk of developing disease is equal to or greater than a reference value from the user basic information, and generates the target diet to prevent the potential disease” (Paragraphs [0127] and [0128] of Kishi. In the instant application, the broadest reasonable interpretation of “the processor … predicts a potential disease with a risk of developing disease is equal to or greater than a reference value from the user basic information, and generates the target diet to prevent the potential disease” reads on the activities in Kishi (Paragraphs [0127] and [0128]) of predicting a user’s elevated risk for developing a metabolic syndrome when the user’s disease prediction score is more than or equal to a predetermined threshold value, and generating a menu such as “Try not to drink sweetened drinking water or a menu such as “Try not to drink alcohol.”).
Therefore, it would have been obvious to one of ordinary skill in the art of healthcare informatics relating to nutrition control at the time of filing to modify the system, method, and non-transitory computer readable medium of Panetta as modified by Baldwin and Wersborg and applied to claim 1 to implement the processor acquiring user basic information including at least one of body measurement information, a currently held disease, a gender, and an age of the user, predicting a potential disease with a risk of developing disease is equal to or greater than a reference value from the user basic information, and generating the target diet to prevent the potential disease, as taught by Kishi (Paragraphs [0127] and [0128]), in order to more efficiently and effectively treat a patient having contracted a metabolic syndrome or the like (Paragraph [0008] of Kishi).
Allowable Subject Matter
Claims 12 – 16 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims. The following is a statement of reasons for the indication of allowable subject matter: none of the cited art appears to explicitly disclose the limitations of “the processor requests the user to capture the food images before and after the meal at an angle at which a depth and a width of the container are confirmed, corresponding to a visual angle at which three axes of the container are each tilted at the same angle or an isometric perspective view at an angle of approximately 120 degrees” in claim 12, “the Al model comprises: a first model that identifies a volume of the container from the food image; a second model that identifies a volume of the food based on a degree of the food filling the container; a third model that identifies the cooking method from color, texture, and shape information in the food image; and a fourth model that derives a change in the density according to the cooking method” in claim 13, “the processor determines whether to apply a cooking-method density correction by checking a correction condition, and performs a basic mode that uses only the type-based density when the condition is not satisfied, or performs a correction mode that applies the cooking-method density change when the condition is satisfied, wherein the correction condition is satisfied when at least one of (i) the number of types of food identified in the image before the meal is equal to or greater than a reference value or (ii) the volume information of the food is equal to or greater than a reference value” in claim 14, “the processor calculates a mass correction value by multiplying a basic mass value by a coefficient according to the cooking method, and determines a final correction mass value by adding the mass correction value to the basic mass value” in claim 15, and “the processor, after receiving the food image before a meal, identifies whether a restricted food intake item already exceeds a daily recommended amount based on accumulated intake information, and generates a warning message to the user before the next meal to limit intake of that item” in claim 16.
Response to Arguments
Applicant's arguments (Third Paragraph on Page 9 of the Amendment filed April 1, 2026) regarding the Specification Objections have been fully considered and are persuasive to overcome the objections. Thus, the Office has withdrawn the objections to the specification.
Applicant's arguments (Fourth Paragraph on Page to First Paragraph on Page 10 of the Amendment filed April 1, 2026) regarding the rejection of claims 1 – 11 under 35 U.S.C. § 101 have been fully considered and are moot in view of the new grounds of rejection necessitated by the amendment.
In the Amendment (Fifth Paragraph on Page 9 to First Paragraph on Page 10), Applicant argued the claims recite “a specific technical improvement in computer-vision nutritional monitoring: a multi-step Al pipeline that (1) calculates the exact volume of reduced food from the direct difference between before/after image pairs, (2) dynamically selects density based on the Al-identified food type, and (3) further adjusts that density to reflect the cooking method of the food (Specification, pages 28 – 32). This combination improves the accuracy of mass estimation beyond generic image analysis and cannot practically be performed in the human mind. Dependent claim 2 adds an additional technical safeguard (container/menu validation with user re-request) that ensures clean input data. These elements improve the functioning of the image-analysis technology itself.”. The Office respectfully disagrees. The Applicant cites pages 28 – 32 of the specification; however the specification does not have pages 28 - 32. MPEP 2016.05(a) states: “If the specification explicitly sets forth an improvement but in a conclusory manner (i.e., a bare assertion of an improvement without the detail necessary to be apparent to a person of ordinary skill in the art), the examiner should not determine the claim improves technology. An indication that the claimed invention provides an improvement can include a discussion in the specification that identifies a technical problem and explains the details of an unconventional technical solution expressed in the claim, or identifies technical improvements realized by the claim over the prior art.” If it is determined that the invention disclosed in the specification improves technology, the claim must be evaluated to ensure that it reflects the disclosed improvement in technology. See MPEP 2016.05(a). The Office respectfully submits that the claims do specify a particular way in which a multi-step Al pipeline (1) calculates the exact volume of reduced food from the direct difference between before/after image pairs, (2) dynamically selects density based on the Al-identified food type, and (3) further adjusts that density to reflect the cooking method of the food. Thus, the rejections are maintained.
Applicant's arguments (Second Paragraph on Page 10 to Last Paragraph on Page 13 of the Amendment filed March 19, 2026) regarding the rejections of claims 1, 3, 5 – 7, and 9 – 11 under 35 U.S.C. § 102 have been fully considered and are moot, in view of claim 3 being cancelled and the new grounds of rejection necessitated by the amendment.
Applicant's arguments (Second Paragraph on Page 10 to Last Paragraph on Page 13 of the Amendment filed March 19, 2026) regarding the rejections of claims 4 and 8 under 35 U.S.C. § 103 have been fully considered and are moot, in view of claim being cancelled and the new grounds of rejection necessitated by the amendment.
Conclusion
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/V.C.I./Examiner, Art Unit 3686
/DEVIN C HEIN/Examiner, Art Unit 3686
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