DETAILED ACTION
Status
This Final Office Action is in response to the communication filed on 1 June 2026. Claims 2-4 and 10-11 have been cancelled currently or previously, claims 1, 5-6, 8-9, and 13-15 have been amended, and new claim 16 has been added. Therefore, claims 1, 5-9, and 12-16 are pending and presented for examination.
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 .
Response to Amendment
A summary of the Examiner’s Response to Applicant’s amendment:
The claim objection(s) were based entirely on the previous amendment; therefore, the Examiner withdraws the objection(s).
Applicant’s amendment does not overcome the rejection(s) under 35 USC § 101; therefore, the Examiner maintains the rejection(s) while updating phrasing in keeping with current examination guidelines.
Applicant’s amendment overcomes the rejection(s) under 35 USC §§ 102 and/or 103; therefore, since the claims appear impossible to perform, the Examiner indicates allowability over the prior art.
Applicant’s arguments are found to be not persuasive; please see the Response to Arguments below.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 8 November 2024 was filed after the mailing date of the application on 14 November 2023. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Examiner Note
Claims 6 and 13 each indicate “the pre-stored classification table is generated by inputting at least one image to an artificial intelligence-based machine learning model as training data and training the machine learning model to learn the respective classes of a plurality of foods and includes at least one class for identifying each of the plurality of foods, wherein the controller updates the pre-stored classification table by further training the machine learning model using the labeled image as the training data” (at claim 6, claim 13 having slightly different phrasing, but the same concept). However, the classification table is “pre-stored” – whether it has been updated or not, when the system or method operates to classify a food item, the table is whatever the table is at that time. Whether that table was generated by artificial intelligence, machine learning, manual labor, arbitrary guessing, picking random phrases, etc. does not appear to matter with regard to the table being used. As such, little if any patentable weight may be granted to the manner in which that table was formed, generated, or updated.
Claim Interpretation
The Examiner notes that claims 8 and 14 recite a “common menu name”, where this apparently means a “base class” or “largest concept for the corresponding food”, i.e. a type or category of food per the light of the specification (see, e.g., Applicant ¶¶ 0062, 0070).
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1, 5-9, and 12-16 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Independent claim 1 recites “add the subdivided individual foods to the extracted food list, thereby generating a subdivided food list … as food information prior to classifying the at least one food”, “generate and store meal food information including at least the identity and quantity of food information”, and also “the subdivided food list defines a bounded set of candidate food classes”. Independent claim 9 recites the same limitations, but uses gerund phrasing.
Regarding the “prior to classifying the at least one food” phrase – this is being done when or if “a food is determined to correspond to the subclass”. The Examiner has searched for this concept and does not find any indication regarding the generating of a subdivided food list and/or food information would be prior to, before, previous to, after, or any other timing in relation to classifying. As noted below, it is indefinite what classifying is being referred to that the generating of a subdivided food list and/or food information must be prior to, or before; however, aside from that issue, the Examiner has not found information regarding the timing of the activity. Therefore, there is found to be a lack of written description regarding something being done “prior to classifying”.
Regarding a “quantity of food”, the Examiner has searched for the concept and does not find it – there does not appear to be any indication the Examiner has found related to a quantity of food, identifying a quantity, or other related concepts. The term “quantity” can refer to a quantity by volume or by weight (generally, other measures could possibly also apply) or a volume of information, and the Examiner has checked for these as well. There does not appear to be any indication of measuring a quantity be weight, volume, mass, etc., or any derivations of those terms, or a quantity of information (e.g., a sentence, a paragraph, etc.). Therefore, there is found to be a lack of written description regarding storing food information including a quantity of some form.
Regarding “the subdivided food list defin[ing] a bounded set of candidate food classes”, the Examiner has also searched for this concept and also does not find it. Per the claim, the “subdivided food list” is generated by (“thereby generating a subdivided food list”) adding to the extracted food list the subdivided individual foods from the subclass – the extracted food list also having, from the mixed class, any “individual foods that can be combined with the food to form combined foods” added from the mixed class. Since, apparently, ANY food CAN be added to, or combined, so as to “form combined foods”, the generated subdivided food list would apparently contain ANY food that is known – and this is ‘including foods not explicitly listed in the pre-stored food menu” (per the second-to-last element of claims 1 and 9). Therefore, it does not appear reasonable or possible to somehow limit the subdivided food list to in some manner “defin[ing] a bounded set of candidate food classes” – it appears that if the claim activities are actually performed as recited, the subdivided food list would contain any and all foods and NOT be any form of “a bounded set of candidate food classes”. The Examiner’s searching for this concept included all references to the subdivided food list, any indications of sets or groups (or derivations thereof), and what the classes may be limited to. There does not appear to be any mention of “candidate” food classes, nor of any “food classes” or “food class” of any type. The only apparent indication of what food classes would be considered it that they are apparently listed in the classification table; however, there is no apparent indication that the classification table is limited or defined in any manner. As such, based on the above, there is found to be a lack of written description regarding “the subdivided food list defin[ing] a bounded set of candidate food classes”.
The Examiner notes that there are, as above, at least three separate issues related to written support.
Claims 5-8, and 12-16 depend from claims 1 and 9, but do not resolve the above issues and inherit the deficiencies of the parent claim(s); therefore claims 5-8 and 12-16 are also lacking written description support.
Claims 1, 5-9, and 12-16 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Independent claims 1 and 9 each recite “add the subdivided individual foods to the extracted food list, thereby generating a subdivided food list … as food information prior to classifying the at least one food”, “performing labeling on the at least one food based on at least one class included in the pre-stored classification table, wherein the at least one class is constrained to classes corresponding to foods in the subdivided food list”; however, these claims earlier recite to “check the pre-stored classification table to determine whether the food corresponds to a mix class or a subclass” (quoting claim 1, parallel gerund phrasing used at claim 9).
Regarding the “prior to classifying the at least one food” phrase – this is being done when or if “a food is determined to correspond to the subclass”. Therefore, the food has already been classified as “subclass”. It appears indefinite whether there is more or different classifying being referred to beyond the classifying as “mixed class” or “subclass”, or if the classifying is only the determination whether a food is “mixed class” or “subclass”.
Regarding the labeling, the Examiner is uncertain if this labeling is restricted to just the “mix class or a subclass” as has already been determined at the “check the pre-stored classification table”, or if this is another, further classification labeling using other classes that may be in the classification table.
It appears with respect to both of the phrases above, that the “classify” or “classifying” may be a reference to either the determination whether a food is “mixed class” or “subclass”, or that this may be referring to the classifying by inputting into and artificial intelligence-based machine learning (“AI-ML”) model. If this is assumed to be in reference to the AI-ML model, which at first glance appears somewhat more likely, then the full phrasing is to “classify … based on at least one class included in the pre-stored classification table, wherein the at least one class is constrained to classes corresponding to foods in the subdivided food list”. However, the labeling is recited as being “based on at least one class included in the pre-stored classification table”, but that table appears to only actually have, or be required to have, “a mix class or a subclass” as alternatives (per the “check the pre-store classification table” element). The phrasing then goes on to say “wherein the at least one class is constrained to classes corresponding to foods in the subdivided food list” – the “subdivided food list” being (apparently) the “subdivided individual foods”, i.e., ingredients that can be combined or assembled so as to form “subclass” foods.
Based on the above, the claims appear indefinite as to whether there is one classification at the determination according to the classification table, and a second classification according to the AI-ML model, or if the classification table determination is being performed by the AI-ML model, or if only one of the two classifications is required, and if only one is required, which is actually being used to generate and store meal information.
Claims 5-8, and 12-16 depend from claims 1 and 9, but do not resolve the above issues and inherit the deficiencies of the parent claim(s); therefore claims 5-8 and 12-16 are also indefinite.
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, 5-9, and 12-16 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Please see the following Subject Matter Eligibility (“SME”) analysis:
For analysis under SME Step 1, the claims herein are directed to an apparatus (claims 1 and 5-8) and method (claims 9 and 12-16), which would be classified under one of the listed statutory classifications (SME Step 1=Yes).
For analysis under revised SME Step 2A, Prong 1, independent claim 1 recites a meal monitoring apparatus, the apparatus comprising: at least one camera configured to capture an image including a food tray; a storage storing a pre-stored food menu, a pre-stored classification table, and at least one application program; and a controller configured to execute the at least one application program to: extract a food list from the pre-stored food menu; for each food in the extracted food list, check the pre-stored classification table to determine whether the food corresponds to a mix class or a subclass; when a food is determined to correspond to the mix class, add individual foods that can be combined with the food to form combined foods to the extracted food list; when a food is determined to correspond to the subclass, subdivide the food into at least two individual foods and add the subdivided individual foods to the extracted food list, thereby generating a subdivided food list comprising both the individual foods added in the mix class processing and the subdivided individual foods added in the subclass processing as food information prior to classifying the at least one food; when an image of a meal is captured by the at least one camera, classify at least one food included in the captured image by inputting at least a portion of the captured image to an artificial intelligence-based machine learning model and performing labeling on the at least one food based on at least one class included in the pre-stored classification table, wherein the at least one class is constrained to classes corresponding to foods in the subdivided food list; and generate and store meal information including at least the identity and quantity of food information based on the at least one food as classified from the captured image, wherein the pre-stored classification table defines a relationship for each of a plurality of foods as at least one of a mix class and the subclass, wherein the mix class is a class for classifying a food resulting from a combination of at least two foods, wherein the subclass is a class for classifying a food which is subdivided into at least two foods, wherein the subdivided food list defines a bounded set of candidate food classes for classifying the at least one food included in the captured image, including foods not explicitly listed in the pre-stored food menu, wherein, when the food information is generated, the controller defines a relationship between the extracted food list and the subdivided food list based on correlation.
Independent claim 9 is analyzed similarly since directed to “a meal monitoring method, performed by a controller of a meal monitoring apparatus, the method comprising” the same or similar operations or activities as at claim 1 above, except that an image is actually obtained “based on at least one camera” at claim 9 and the classifying is of food included in the captured image.
The Examiner notes that claim 15 depends from claim 9 (i.e., “a … method of any one of claims 9, 12 to 14, and 16”, where claims 12-14 and 16 depend directly or indirectly from claim 9) but recites “A non-transitory computer-readable recording medium being integrated with a computer and storing a computer program for operating the computer to perform a meal monitoring method of any one of claims 9, 12 to 14, and 16” and is therefore analyzed similarly to claims 1 and 9.
The other remaining dependent claims (claims 5-8, 12-14 and 16) appear to be encompassed by the abstract idea of the independent claims since they merely indicate that a correlation and/or relationship is determined by identifying food and a proportion of the food on a list (claims 5, 12, and 16), that the table was generated by machine learning and re-learning or re-training (claims 6-7 and 13), and/or converting the food menu into a common menu name using a word2vec word embedding model (claims 8 and 14).
The underlined portions of the claims are an indication of elements additional to the abstract idea (to be considered below).
The claim elements may be summarized as the idea of generating and storing meal information. However, the Examiner notes that although this summary of the claims is provided, the analysis regarding subject matter eligibility considers the entirety of the claim elements, both individually and as a whole (or ordered combination). This idea is within the Mental processes (e.g., concepts performed in the human mind such as observation, evaluation, judgment, and/or opinion) grouping(s) of subject matter as based on the observation and evaluation of the food being able to be performed by a person mentally – a person can remember and recognize food items as well as classify them, including as based on a stored menu. The Examiner notes that the Mathematical concepts grouping is also implicated at least via the use of an artificial intelligence-based machine learning model since such a model is necessarily merely performing mathematical calculations (as a sub-grouping within mathematical concepts – see MPEP § 2106.04(a)(2)(I)(C); however, since this modeling appears to be using mathematics to inherently emulate human observations, evaluations, judgments, and/or opinions, the more applicable grouping appears to be the Mental processes grouping.
Therefore, the claims are found to be directed to an abstract idea.
For analysis under revised SME Step 2A, Prong 2, the above judicial exception is not integrated into a practical application because the additional elements do not impose a meaningful limit on the judicial exception when evaluated individually and as a combination. The additional elements are the indications of an apparatus, the apparatus comprising: at least one camera configured to capture an image, a storage storing a menu and classification table, and at least one application program; and a controller configured to execute the at least one application program to: perform the activities, including capturing an image by the at least one camera, (at claim 1 and 9) and A non-transitory computer-readable recording medium being integrated with a computer and storing a computer program for operating the computer to perform (at claim 15) and converting to a common name by using a word2vec word embedding model (at dependent claims 8 and 14). These additional elements do not reflect an improvement in the functioning of a computer or an improvement to other technology or technical field, effect a particular treatment or prophylaxis for a disease or medical condition (there is no medical disease or condition, much less a treatment or prophylaxis for one), implement the judicial exception with, or by using in conjunction with, a particular machine or manufacture that is integral to the claim, effect a transformation or reduction of a particular article to a different state or thing (there is no transformation/reduction of a physical article), and/or apply or use the judicial exception in some other meaningful way beyond generically linking use of the judicial exception to a particular technological environment.
The claims appear to merely apply the judicial exception, include instructions to implement an abstract idea on a computer, or merely use a computer as a tool to perform the abstract idea. The additional elements appear to merely add insignificant extra-solution activity to the judicial exception and/or generally link the use of the judicial exception to a particular technological environment or field of use.
For analysis under SME Step 2B, the claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements, as indicated above, are merely “[a]dding the words ‘apply it’ (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, e.g., a limitation indicating that a particular function such as creating and maintaining electronic records is performed by a computer, as discussed in Alice Corp.” that MPEP § 2106.05(I)(A) indicates to be insignificant activity.
There is no indication the Examiner can find in the record regarding any specialized computer hardware or other “inventive” components, but rather, the claims merely indicate computer components which appear to be generic components and therefore do not satisfy an inventive concept that would constitute “significantly more” with respect to eligibility. The server providing information to the user terminal is only described as a generic “server” (see, e.g., Applicant ¶¶ 0039, 0044, 0045, 0048, 0078), the storage and controller are only indicated generically (see, e.g., Applicant ¶¶ 0056-0059), and the user terminal is also merely indicated as generic (Applicant ¶ 0047, including that the user terminal may just be “a black box”).
The individual elements therefore do not appear to offer any significance beyond the application of the abstract idea itself, and there does not appear to be any additional benefit or significance indicated by the ordered combination, i.e., there does not appear to be any synergy or special import to the claim as a whole other than the application of the idea itself.
The dependent claims, as indicated above, appear encompassed by the abstract idea since they merely limit the idea itself; therefore the dependent claims do not add significantly more than the idea.
Therefore, SME Step 2B=No, any additional elements, whether taken individually or as an ordered whole in combination, do not amount to significantly more than the abstract idea, including analysis of the dependent claims.
Please see the Subject Matter Eligibility (SME) guidance and instruction materials at https://www.uspto.gov/patent/laws-and-regulations/examination-policy/subject-matter-eligibility, which includes the latest guidance, memoranda, and update(s) for further information.
Allowable Subject Matter
Claims 1, 5-9, and 12-16 are indicated as allowable over the prior art.
The following is a statement of reasons for the indication of allowable subject matter: Since it appears impossible to “generat[e] a subdivided food list … prior to classifying the at least one food”, when the subdivided food list necessarily requires the classifying of the at least one food in order to generate the subdivided food list, the claims appear to impossible to interpret in such a manner as to reasonably combine art or interpret art as applicable to the claims.
Response to Arguments
Applicant's arguments filed 1 June 2026 have been fully considered but they are not persuasive.
Applicant first argues the 101 rejection (Remarks at 7-9), first alleging that “t amended claims now recite a specific technical implementation that cannot be practically performed in the human mind. Specifically, Claim 1 requires inputting at least a portion of the captured image to an artificial intelligence-based machine learning model and performing labeling on the at least one food where the candidate classes are constrained to classes corresponding to foods in the subdivided food list. Operation of a trained machine-learning inference engine on image data, with the inference space algorithmically narrowed by a menu-derived bounded set, is not an act a person performs mentally.” (Id. at 8). However, first, this is NOT a specific technical implementation” – the claims reflect using generic computers and devices (i.e., a camera) to do what persons can do: recognize and identify food items on a tray. Second, the claims do not appear to recite any “inference space algorithmically narrowed by a menu-derived bounded set” – the claims explicitly recite “including foods not explicitly listed in the pre-stored food menu”. This apparently means that the claims encompass recognizing any and all food items that may be known to humankind.
Applicant then argues that “the claims integrate any alleged abstract idea into a practical application by addressing a concrete technical problem in cafeteria operation” (Id. at 8). However, this is part of the abstract idea – people can observe the food on a tray, evaluate whether or when there are ingredients mixed together (or that they are not mixed), and make a judgment or form an opinion regarding what ingredients may be present and/or what the proportion of ingredients may be.
Applicant then argues “the amended claims recite significantly more than the alleged abstract idea by providing a concrete improvement to the functioning of the computer itself. By dynamically narrowing the candidate classes from a full trained class pool (potentially numbering in the thousands) to a menu-derived bounded set (typically on the order of a few to a few dozen), the system reduces computational cost of the inference step and structurally prevents misclassification among visually similar foods (e.g., visually similar noodle dishes)” (Id. at 8). However, as above – the argued limitations are part of the abstract idea, and therefore not “significantly more”, but also this argument is not commensurate with the scope of the claims since the claims expressly indicate NOT being limited to just the menu, and “including foods not explicitly listed in the pre-stored food menu”.
Applicant then argues the prior art rejections (Remarks at 9-12); however, as noted above, since the claims appear impossible to actually perform, it does not appear reasonable to attempt to apply prior art. The Examiner further notes that it appears the each of the headings at Points 1-4 (at Remarks, pp. 9-11) and in relation to “Claims 8 and 14” (at Remarks, pp. 11-12) recites phrasing that is not related to the claim scope; therefore, it is unclear why or how any of the arguments would apply to the claims if they were considered in relation to the prior art.
Therefore, the Examiner is not persuaded by Applicant’s arguments.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Big Data definition search results page, from Google, downloaded from https://www.google.com/search?q=big+data+definition&safe=active&sca_esv=50afecde28fc5d24&ei=YdQUaIq9LqjawN4PkLCS4AM&ved=0ahUKEwjK8cKt_YSNAxUoLdAFHRCYBDwQ4dUDCBA&uact=5&oq=big+data+definition&gs_lp=Egxnd3Mtd2l6LXNlcnAiE2JpZyBkYXRhIGRlZmluaXRpb24yDhAAGIAEGJECGLEDGIoFMgsQABiABBiRAhiKBTIFEAAYgAQyBRAAGIAEMgsQABiABBiRAhiKBTILEAAYgAQYkQIYigUyBRAAGIAEMgUQABiABDIFEAAYgAQyBRAAGIAESIk8UJcWWPE4cAR4AZABApgB6QGgAekPqgEGMi4xMy4xuAEDyAEA-AEBmAISoALODcICChAAGLADGNYEGEfCAg0QABiABBiwAxhDGIoFwgINEAAYgAQYsQMYQxiKBcICChAAGIAEGEMYigXCAhAQABiABBixAxhDGIMBGIoFwgIIEAAYgAQYsQPCAgUQLhiABMICBhAAGBYYHsICCxAAGIAEGIYDGIoFwgIIEAAYgAQYogTCAgUQABjvBcICChAAGIAEGLEDGA3CAgcQABiABBgNmAMAiAYBkAYKkgcENS4xM6AH7mOyBwQxLjEzuAezDQ&sclient=gws-wiz-serp on 3 May 2025, indicating various descriptions of what the term “big data” may mean.
Word embedding search results page, from Google, downloaded from https://www.google.com/search?q=word+embedding&safe=active&sca_esv=50afecde28fc5d24&ei=GDgVaK-NJP2-p84PgMC-uQc&oq=word+embe&gs_lp=Egxnd3Mtd2l6LXNlcnAiCXdvcmQgZW1iZSoCCAAyBRAAGIAEMgUQABiABDIFEAAYgAQyBRAAGIAEMgUQABiABDIFEAAYgAQyBRAAGIAEMgUQABiABDIFEAAYgAQyBRAAGIAESJszUABYzBBwAHgBkAEAmAGAAaAB4waqAQM2LjO4AQHIAQD4AQGYAgmgAqgHwgIKEAAYgAQYQxiKBcICEBAAGIAEGLEDGEMYgwEYigXCAhEQLhiABBixAxjRAxiDARjHAcICCxAuGIAEGNEDGMcBwgILEAAYgAQYsQMYgwHCAgsQLhiABBixAxiDAcICCxAAGIAEGJECGIoFwgINEAAYgAQYsQMYQxiKBcICDhAuGIAEGLEDGNEDGMcBwgIIEAAYgAQYsQPCAgcQABiABBgKmAMAkgcDNi4zoAfFObIHAzYuM7gHqAc&sclient=gws-wiz-serp on 3 May 2025, indicating what the term “word embedding” may mean.
Rayner (U.S. Patent Application Publication No. 2014/0172313) also appears to be a anticipation reference, indicating at least “the health-modulating device of the disclosure is capable of determining the type and/or amount of food being consumed. By "determining the type of food" is meant any manner by which one item of food can be distinguished from another type of food, such as by physical appearance, e.g., color, physical feel, weight; biological type, e.g., carbohydrate, protein, fat; caloric content or nutritional content; food group classification, e.g., meat, vegetable, diary, etc., as well as specific identification of the particular food item, such as milk, water, beef, chicken, etc., and the like” (Rayner at 0032).
Mossier et al. (U.S. Patent Application Publication No. 2022/0020471, hereinafter Mossier) also appears to be a anticipation reference, indicating at least “Inspection may be done when the tray is delivered to an identified patient, the tray having a serving of food including, for example a glass (114) and a plate (112) with the serving of food, or the meal, comprising one or more food components. The tray (100) may further have cutlery (116) and may still further have other non-foodstuffs, like a napkin, for instance. According to the embodiment, the inspection and analysis unit comprises at least one image capture device (124), such as a camera, for capturing one or more images of the tray and its contents including the meal” (Mossier at 0035).
Goto (U.S. Patent Application Publication No. 2021/0158502) discusses that “An analyzing apparatus extracts, from a food image, food information relating to types and served states of foods included in the food image and an edible portion of each food, calculates an area ratio between the edible portion of the same kind of food extracted from a plurality of food images acquired at different timings, stores conversion information for converting the area ratio into a volume ratio corresponding to each type and served state of food, and converts the area ratio of each food into a volume ratio using the stored conversion information corresponding to the food whose area ratio is to be converted from among the stored conversion information based on the food information.” (Goto at Abstract).
Pouladzadeh, et al., "Measuring Calorie and Nutrition From Food Image," in IEEE Transactions on Instrumentation and Measurement, vol. 63, no. 8, pp. 1947-1956, Aug. 2014, doi: 10.1109/TIM.2014.2303533. Downloaded 2 September 2025 from https://ieeexplore.ieee.org/abstract/document/6748066, indicating that “As people across the globe are becoming more interested in watching their weight, eating more healthy, and avoiding obesity, a system that can measure calories and nutrition in every day meals can be very useful. In this paper, we propose a food calorie and nutrition measurement system that can help patients and dietitians to measure and manage daily food intake. Our system is built on food image processing and uses nutritional fact tables. Recently, there has been an increase in the usage of personal mobile technology such as smartphones or tablets, which users carry with them practically all the time. Via a special calibration technique, our system uses the built-in camera of such mobile devices and records a photo of the food before and after eating it to measure the consumption of calorie and nutrient components. Our results show that the accuracy of our system is acceptable and it will greatly improve and facilitate current manual calorie measurement techniques.” (at Abstract).
Li et al., Deep Cooking: Predicting Relative Food Ingredient Amounts from Images, downloaded 27 February 2026 from https://arxiv.org/pdf/1910.00100, dated 26 September 2019, indicating “In this paper, we study the novel problem of not only predicting ingredients from a food image, but also predicting the relative amounts of the detected ingredients. We propose two prediction based models using deep learning that output sparse and dense predictions, coupled with important semi-automatic multi-database integrative data pre-processing, to solve the problem. Experiments on a dataset of recipes collected from the Internet show the models generate encouraging experimental results” (at Abstract), and including using Word2vec (at § 4.1).
Stojanov et al., A Fine-Tuned Bidirectional Encoder Representations From Transformers Model for Food Named-Entity Recognition: Algorithm Development and Validation. J Med Internet Res. 2021 Aug 9;23(8):e28229. doi: 10.2196/28229. PMID: 34383671; PMCID: PMC8415558. Downloaded from https://pmc.ncbi.nlm.nih.gov/articles/PMC8415558/ on 27 February 2026, indicating “We introduce FoodNER, which is a collection of corpus-based food named-entity recognition methods. It consists of 15 different models obtained by fine-tuning 3 pretrained BERT models on 5 groups of semantic resources: food versus nonfood entity, 2 subsets of Hansard food semantic tags, FoodOn semantic tags, and Systematized Nomenclature of Medicine Clinical Terms food semantic tags.
Van Wymelbeke-Delannoy et al., Cross-Sectional Reproducibility Study of a Standard Camera Sensor Using Artificial Intelligence to Assess Food Items: The FoodIntech Project. Nutrients 2022, 14, 221. https://doi.org/10.3390/nu14010221, downloaded 3 August 2026 from https://www.mdpi.com/2072-6643/14/1/221, indicating “we tested a smartphone-based food consumption assessment system named FoodIntech. FoodIntech, which is based on AI using deep neural networks (DNN), automatically recognizes food items and dishes and calculates food leftovers using an image-based approach, i.e., it does not require human intervention to assess food consumption. This method uses one-input and one-output images by means of the detection and synchronization of a QRcode located on the meal tray. The DNN are then used to process the images and implement food detection, segmentation and recognition. Overall, 22,544 situations analyzed from 149 dishes were used to test the reliability of this method” (at Abstract).
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/SCOTT D GARTLAND/
Primary Examiner, Art Unit 3685
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