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 .
DETAILED ACTION
This communication is a Final Office Action in response to communications received on 3/2/26.
Claims 1, 6, 9-11 have been amended.
Claims 2, 12-14 have been previously cancelled. Claims 5, 8 has been cancelled
Therefore, Claims 1, 3-4, 6-7, 9-11 are now pending and have been addressed below.
Response to Amendment
Applicant has amended Claims 6-7, 9-11 to overcome the 35 U.S.C 112b rejections. Examiner withdraws the 35U.S.C 112b rejections with respect to these and all depending claims unless otherwise indicated.
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, 3-4, 6-7, 9-11 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to a judicial exception (an abstract idea) without significantly more.
Step 1: Identifying Statutory Categories
In the instant case, claims 1, 3-4, 6-7 are directed to a system and claims 9-11 are directed to a method. Thus, this claim falls within one of the four statutory categories. Nevertheless, the claim falls within the judicial exception of an abstract idea.
Step 2A: Prong 1 Identifying a Judicial Exception
Under Step 2A, prong 1, Claims 1, 3-4, 6-7, 9-11 are rejected under 35 U.S.C. 101 because the claimed invention recites an abstract idea without significantly more. Independent claims 1, 6 and 9 recite methods for a surface image of the fried food being displayed, and data indicative of a condition of the fried food being displayed, including at least one of a color tone detected by a color difference meter, size, water content , amount of volatile components and volatile component composition, or acid value, anisidine value, carbonyl value, peroxide value, iodine value, and amount of polar compounds; stores a determination reference that is a reference for determining the time to dispose the fried food, the determination reference being set for each of the color tone, size, water content, amount of volatile components, volatile component composition, acid value, anisidine value, carbonyl value, peroxide value, iodine value, and amount of polar compounds, and a sample image serving as a reference for identifying a type of the fried food being displayed; determines that the time to dispose the fried food being displayed is reached when the data acquired by the data acquisition unit meets a corresponding determination reference stored; and outputs, a notification for notifying a determination result, which is indicative of a result that the time to dispose the fried food being displayed is reached, made by the determination unit by at least either visualizing or sounding; identifies the type of the fried food being displayed in the display cabinet by comparing the surface image acquired by the data acquisition unit with the sample image stored; and update the determination reference stored to the determination reference estimated based on the determination result, which is indicative of the result that the time to dispose the fried food being displayed is reached; and update the sample image stored to the sample image estimated based on an identification result, which is indicative of the type of the fried food.
These limitations as drafted, are a process that, under its broadest reasonable interpretation, covers methods of organizing human activity (including commercial interactions such as business relations, managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions)), but for the recitation of generic computer components. That is, other than reciting the structural elements (such as (Claim 1) a fired food disposal time management device, a camera, a data acquisition unit, a condition sensor, a smell sensor, a near-infrared sensor display cabinet, a storage unit, a determination unit, a notification unit, notification signal, notification device, a storage device, a machine learning unit that generates a learning model for use in machine learning (Claims 6, 9) a fired food disposal time management device, a condition sensor, a notification device, a storage device, a machine learning unit, display cabinet, a notification signal ), the claims are directed to managing a time to dispose a fried food and provide notification for fried food disposal. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation of organizing human activity but for the recitation of generic computer components, the claim recites an abstract idea.
Step 2A Prong 2 - This judicial exception is not integrated into a practical application because the claim merely describes how to generally “apply” the concept of receiving data, analyzing it, and providing notification of result. In particular, the claims only recites the additional element – (Claim 1) a fired food disposal time management device, a camera, a data acquisition unit, a condition sensor, a smell sensor, a near-infrared sensor display cabinet, a storage unit, a determination unit, a notification unit, notification signal, notification device, a storage device, a machine learning unit that generates a learning model for use in machine learning (Claims 6, 9) a fired food disposal time management device, a condition sensor, a notification device, a storage device, a data acquisition unit, an identification unit, a machine learning unit, display cabinet, a notification signal. The additional elements are recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component. Simply implementing the abstract idea on generic components is not a practical application of the abstract idea. Accordingly, these additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. a) The additional elements merely add the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea, as discussed in MPEP 2106.05(f). The limitation of “machine learning unit” merely add the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea, as discussed in MPEP 2106.05(f). Further, the limitation of “generating a learning model” is simply application of a computer model, itself an abstract idea. Furthermore, such training and applying of a model is no more than putting data into a black box machine learning operation, devoid of technological implementation and application details. Each step requires a generic computer to perform generic computer functions. The claims are directed to an abstract idea. When considered in combination, the claims do not amount to improvements to the functioning of a computer, or to any other technology or technical field, as discussed in MPEP 2106.05(a), applying the judicial exception with, or by use of, a particular machine, as discussed in MPEP 2106.05(b), effecting a transformation or reduction of a particular article to a different state or thing, as discussed in MPEP 2106.05(c), or applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception, as discussed in MPEP 2106.05(e). Accordingly, the additional elements do not integrate the abstract idea into a practical application because they does not impose any meaningful limits on practicing the abstract idea. Therefore, the claims are directed to an abstract idea.
Step 2B: Considering Additional Elements
The claimed invention is directed to an abstract idea without significantly more. The claim does not include additional elements that are sufficient to amount significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the claims describe how to generally “apply” to; provide notification for fried food disposal. The claim(s) do not include additional elements that are sufficient to amount to significantly more than the judicial exception because mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The independent claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. Even when viewed as a whole, nothing in the claim adds significantly more (i.e., an inventive concept) to the abstract idea. The claims are not patent eligible. The dependent claim(s) when analyzed as a whole are held to be patent ineligible under 35 U.S.C. 101 because the additional recited limitation(s) fail to establish that the claim(s) is/are not directed to an abstract idea. The dependent claims are not significantly more because they are part of the identified judicial exception. See MPEP 2106.05(g). The claims are not patent eligible. With respect to the (Claim 1) a fired food disposal time management device, a data acquisition unit, a condition sensor, display cabinet, a determination unit, a notification unit, notification signal, notification device, a storage device, a machine learning unit (Claims 6, 9) a fired food disposal time management device, a condition sensor, a notification device, a storage device, a machine learning unit, display cabinet, a notification signal, these limitations are described in Applicant’s own specification as generic and conventional elements. See Applicants specification, Paragraph [0061] details “camera 5 capable of capturing still images and moving images are used as condition sensors for detecting the conditions of the fried food X being displayed in the hot showcase, Fig 11 # 308 monitor/notification device, a fried food disposal time management device (spec [0080], Fig 11 #4, Fig 4 hardware configuration)”. These are basic computer elements applied merely to carry out data processing such as, discussed above, receiving, analyzing, transmitting and displaying data, which fall under well-understood, routine and conventional functions of generic computers. Furthermore, the use of such generic computers to receive or transmit data over a network has been identified as a well understood, routine and conventional activity by the courts. See Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AVAuto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); but see DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1258, 113 USPQ2d 1097, 1106 (Fed. Cir. 2014) ("Unlike the claims in Ultramercial, the claims at issue here specify how interactions with the Internet are manipulated to yield a desired result-a result that overrides the routine and conventional sequence of events ordinarily triggered by the click of a hyperlink." (emphasis added)); Also see MPEP 2106.05(d) discussing elements that the courts have recognized as well-understood, routine and conventional activities in particular fields. Lastly, the additional elements provides only a result-oriented solution which lacks details as to how the computer performs the claimed abstract idea. Therefore, the additional elements amount to mere instructions to apply the exception. See MPEP 2106.05(f).
Furthermore, these steps/components are not explicitly recited and therefore must be construed at the highest level of generality and are mere instructions to implement the abstract idea on a computer. Therefore, the claimed invention does not demonstrate a technologically rooted solution to a computer-centric problem or recite an improvement to another technology or technical field, an improvement to the function of any computer itself, applying the exception with, or by use of, a particular machine, effect a transformation or reduction of a particular article to a different state or thing, add a specific limitation other than what is well-understood, routine and conventional in the field, add unconventional steps that confine the claim to a particular useful application, or provide meaningful limitations beyond generally linking an abstract idea to a particular technological environment such as computing. Viewing the limitations as an ordered combination does not add anything further than looking at the limitations individually. Taking the additional claimed elements individually and in combination, the computer components at each step of the process perform purely generic computer functions. Viewed as a whole, the claims do not purport to improve the functioning of the computer itself, or to improve any other technology or technical field. Use of an unspecified, generic computer does not transform an abstract idea into a patent-eligible invention. Thus, the claim does not amount to significantly more than the abstract idea itself. Further, claims to a system and computer-readable storage medium are held ineligible for the same reason, e.g., the generically-recited computers add nothing of substance to the underlying abstract idea.
Dependent claims 3-4, 7, and 10-11 add additional limitations, for example but these only serve to further limit the abstract idea, and hence are nonetheless directed towards fundamentally the same abstract idea as representative claims 1,6 and 9.
Claims 3-4,7 and 10 recites the data acquired by the data acquisition unit is data related to at least one of indicators including a color tone, size, water content, amount of volatile components, volatile component composition, acid value, anisidine value, carbonyl value, peroxide value, iodine value, and amount of polar compounds of the fried food being displayed in the display cabinet; analyzes, when the data acquisition unit acquires a surface image of the fried food being displayed in the display cabinet as data related to the color tone, color components of the fried food from the surface image is simply data gathering and using a computer as a tool to analyze data. Claim 11 recites an identification unit that identifies a type of the fried food being displayed in the display cabinet, wherein the determination unit determines whether the time to dispose the fried food being displayed in the display cabinet is reached based on the condition of the fried food acquired as the data by the data acquisition unit, the type of the fried food identified by the identification unit, and the determination reference set for each type of the fried food is simply data gathering. These limitations merely adds the words apply it (or an equivalent) with the judicial exception , or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea as discussed in MPEP 2106.05(f). The identifying/data acquisition function is similar to a data gathering function. The determining function is a mental process or organizing human activity (and is also a generic computer function). The displaying/transmitting function is a generic computer function and is also considered as an insignificant post solution activity. The dependent claims do not integrate into a practical application. As such, the additional elements individually or in combination do not integrate the exception into a practical application, but rather, the recitation of any additional element amounts to merely reciting the words “apply it” (or equivalent) with the judicial exception, or merely includes instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (See MPEP 2106.05(f)). The dependent claims also do not include additional elements that 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, the additional elements of a computing system is merely being used to apply the abstract idea to a technological environment. These limitations do not include an improvement to another technology or technical field, an improvement to the functioning of the computer itself, or meaningful limitations beyond generally linking the use of the abstract idea to a particular technological environment. See MPEP 2106.05d. Thus, the claims do not add significantly more to an abstract idea. The claims are ineligible. Therefore, since there are no limitations in the claim that transform the exception into a patent eligible application such that the claim amounts to significantly more than the exception itself, the claims are rejected under 35 USC 101 as being directed to non-statutory subject matter. See (Alice Corporation Pty. Ltd. v. CLS Bank International, et al.).
Subject matter free of prior art
Regarding Claims 1, 6 and 9. (currently amended) Sandvick (US 20150002299A1) discloses food disposal time management device/system/method for managing a time to dispose a food ([0008] a food safety device that not only identifies whether products are safe for consumption, but that also tracks the source, location, and destination of those products so they can be efficiently and effectively identified and recalled when they are not safe for consumption, [0029] food safety device 100 to indicate product freshness and/or safety), the food disposal time management device comprising:
Sandvick discloses a data acquisition unit that acquires data output from a condition sensor configured to detect a condition of the food ([0006] The food safety device comprises one or more sensors that are configured to measure at least one condition of the product and/or its environment, one or more visual indicators that are configured to display a visual indication of freshness and/or safety of the product, an antenna that is configured to transmit and receive data regarding the at least one measured condition of the product and the freshness and/or safety of the product, [0022] sensors 110, [0023] The sensors 110 can operate based on chemical and/or electrical reactions to the condition being measured. Moreover, by measuring multiple conditions, the food safety device 100 can more accurately determine the remaining shelf life of a product and/or identify contaminants. [0029] food safety device 100 to indicate product freshness and/or safety);
Sandvick discloses a determination unit that determines whether the time to dispose the food is reached based on the condition of the food acquired as the data by the data acquisition unit and a determination reference that is a reference for determining the time to dispose the food ([0016] identifying whether products are safe for consumption. Determine whether a perishable product has exceeded its shelf-life based on a chemical reaction with the product that measures the spoilage/decay of the product. The present invention can determine whether a perishable product has exceeded its shell-life based on a time- and/or temperature-dependent chemical reaction that is initiated by a predefined condition, such as packaging the product or opening the product. [0017] determine whether a perishable product has exceeded its shelf-life by chemically and/or electronically measuring exposure to moisture, sunlight, radiation, or any other environmental factor that can contribute to spoilage and reduce a perishable product's shelf-life, [0023] the sensors 110 can operate based on chemical and/or electrical reactions to the condition being measured. Moreover, by measuring multiple conditions, the food safety device 100 can more accurately determine the remaining shelf life of a product and/or identify contaminants. [0024] by measuring the temperature of a product as well as the time that the product has been in its container, the logic module 102 of the food safety device 100 can actively determine the remaining shelf-life of the product. [0026] The logic module 102 can store data identifying the time, date, and amount in which each of those conditions occurred to create an ongoing log of the product's environment throughout its life cycle. (reference set for product), [0029] the first visual indicator 116 will display a green color when the product is fresh and/or not contaminated. The second visual indicator 118 will display a yellow color when the product has experienced some condition that may contribute to spoilage or contamination (e.g., passage of time, temperature change, etc.). And the third visual indicator 120 will display a red color when the product has spoiled (time to dispose) and/or has been contaminated. [0030] the measurements taken by the sensors 110-114 can be used by the logic module 102 in conjunction with a clocking circuit to determine the remaining shelf-life of a product and/or to identify if and when that product was contaminated. The reactions that occur in the chemical strips may also be detected and used by the logic module 102 to determine the remaining shelf-life of a product and/or to identify if and when that product was contaminated.); and
Sandvick teaches the data indicative of a condition of the food including at least one of a color tone, size, water content, amount of volatile components, volatile component composition, acid value, anisidine value, carbonyl value, peroxide value, iodine value, and amount of polar compounds of the food being displayed in the display cabinet. ([0017] the present invention can chemically or electronically measure the amount of hazardous chemicals, toxins, food borne pathogens, and other contaminants that may be present in a product and make it unsafe for use and/or consumption., [0023] A sensor 110 may protrude into the product to measure conditions that indicate spoilage (e.g., core temperature. PH level, etc.) and/or conditions that indicate contamination (e.g., the presence of certain chemicals or bacteria, a pressure change in the container, etc.).Claim 5)
Sandvick teaches wherein the determination unit determines whether the time to dispose is reached based on the condition of the food, and the determination reference set for each type of the food([0016] identifying whether products are safe for consumption. Determine whether a perishable product has exceeded its shelf-life based on a chemical reaction with the product that measures the spoilage/decay of the product. The present invention can determine whether a perishable product has exceeded its shell-life based on a time- and/or temperature-dependent chemical reaction that is initiated by a predefined condition, such as packaging the product or opening the product. [0017] determine whether a perishable product has exceeded its shelf-life by chemically and/or electronically measuring exposure to moisture, sunlight, radiation, or any other environmental factor that can contribute to spoilage and reduce a perishable product's shelf-life, [0023] the sensors 110 can operate based on chemical and/or electrical reactions to the condition being measured. Moreover, by measuring multiple conditions, the food safety device 100 can more accurately determine the remaining shelf life of a product and/or identify contaminants. [0024] by measuring the temperature of a product as well as the time that the product has been in its container, the logic module 102 of the food safety device 100 can actively determine the remaining shelf-life of the product. [0026] The logic module 102 can store data identifying the time, date, and amount in which each of those conditions occurred to create an ongoing log of the product's environment throughout its life cycle. (reference set for product), [0030] the measurements taken by the sensors 110-114 can be used by the logic module 102 in conjunction with a clocking circuit to determine the remaining shelf-life of a product and/or to identify if and when that product was contaminated. The reactions that occur in the chemical strips may also be detected and used by the logic module 102 to determine the remaining shelf-life of a product and/or to identify if and when that product was contaminated.);.
Sandvich teaches the determination reference is a reference value set for each of the indicators ([0026] The logic module 102 can store data identifying the time, date, and amount in which each of those conditions occurred to create an ongoing log of the product's environment throughout its life cycle. (reference set for product), [0029] the first visual indicator 116 will display a green color when the product is fresh and/or not contaminated. The second visual indicator 118 will display a yellow color when the product has experienced some condition that may contribute to spoilage or contamination (e.g., passage of time, temperature change, etc.). And the third visual indicator 120 will display a red color when the product has spoiled (time to dispose) and/or has been contaminated); is used to determine whether the time to dispose the food is reached ([0029] the third visual indicator 120 will display a red color when the product has spoiled (time to dispose) and/or has been contaminated)
Sandvick discloses a notification unit that outputs, when the determination unit makes a determination result that the time to dispose the food is reached, a notification signal for notifying the determination result to a notification device ([0034] wirelessly transmit a signal that indicates the different levels of product freshness and/or safety. Those signals are received by computer (notification device) that tracks the product on which the food safety device 100 is placed such that, when problems are detected, that product can be easily identified and removed from its life cycle before it is consumed. For example, if a product exceeds its shelf-life and/or becomes contaminated while sitting on a warehouse or retail store shelf, a signal will automatically be sent to an inventory system to identify that product for immediate removal from the shelf., [0061] instruct the end user to dispose of it if the product 200 is discovered at any point to be unsafe for consumption. the retail store 320 can also send a signal to the food safety device 100 to trigger any one of its indicators (e.g., visual indicators 116-124, tactile indicators, and/or audible indicators) at any point in its life cycle to alert the end user that it is potentially unsafe. ); a storage unit that stores the determination reference therein ([0020] The logic module 102 includes a data storage device that is configured to store data about a product and/or a product container as well as the product's environment throughout the product's life cycle (reference data), programmable logic that is configured to monitor the shelf-life and/or contamination of the product as well as the product's environment throughout the product's life cycle
Sandvick does not specifically teach a surface image of the fried food being displayed in the display cabinet, and data indicative of a condition of the fried food being displayed in the display cabinet, including at least one of a color tone detected by a camera or a color difference meter, size detected by the camera, water content detected by a moisture content measurement sensor, amount of volatile components and volatile component composition detected by a smell sensor, or acid value, anisidine value, carbonyl value, peroxide value, iodine value, and amount of polar compounds detected by a near-infrared sensor; a storage unit that stores: a determination reference that is a reference for determining the time to dispose the fried food, the determination reference being set for each of the color tone, size, water content, amount of volatile components, volatile component composition, acid value, anisidine value, carbonyl value, peroxide value, iodine value, and amount of polar compounds, and a sample image serving as a reference for identifying a type of the fried food being displayed in the display cabinet; a determination unit that determines that the time to dispose the fried food being displayed in the display cabinet is reached when the data acquired by the data acquisition unit meets a corresponding determination reference stored in the storage unit; and a notification unit that outputs, a notification signal for notifying a determination result, which is indicative of a result that the time to dispose the fried food being displayed in the display cabinet is reached, made by the determination unit by at least either visualizing or sounding; an identification unit that identifies the type of the fried food being displayed in the display cabinet by comparing the surface image acquired by the data acquisition unit with the sample image stored in the storage unit; a machine learning unit that generates a learning model for use in machine learning, and the machine learning unit generates the learning model used to: update the determination reference stored in the storage unit to the determination reference estimated based on the determination result, which is indicative of the result that the time to dispose the fried food being displayed in the display cabinet is reached, made by the determination unit; and update the sample image stored in the storage unit to the sample image estimated based on an identification result, which is indicative of the type of the fried food, made by the identification unit
Kim et al. (US 2020/0097776 A1) teaches time to dispose a food being displayed ([0091] the information related to the current state of the at least one object may include, but is not limited to, the current state of the at least one object, a consumable period (or an expected disposal time point) based on the current state, [0141] the refrigerator 1000 may provide a graphical user interface (GUI) 900 displaying notification information about consumable periods of objects stored in the refrigerator 1000, via a display 1411 arranged on a door of the refrigerator 1000. According to an embodiment of the disclosure, the refrigerator 1000 may analyze camera images of the objects stored in the refrigerator 1000, identify the objects, and display an expected disposal date of each of the objects on each of the camera images. For example, the refrigerator 1000 may display “sixteen (16) days left for mango” 901 around an image of a mango, display “four (4) days left for banana” 902 around an image of a banana, display “spinach rotten” 903 around an image of spinach); a machine learning unit that updates the determination reference stored in the storage unit based on a learning model generated by performing machine learning using the determination reference estimated based on the determination result by the determination unit (Fig 5 # 504 consumable period, [0014] obtaining temperature information of a temperature around the at least one object by using a temperature sensor, and the predicting of the information related to the current state of the at least one object may include predicting the information related to the current state of the at least one object by applying the first camera image and the temperature information to the AI model., Fig 3 # 300 deep learning AI model for food items; [0092] the AI model may divide the state of the object into first through tenth stages via training, and determine the state of the object in the first through fourth stages as the fresh state (reference), the state of the object in the fifth through seventh stages as the ripe state, and the state of the object after the eighth stage as the spoiled state. [0093]when 90% of the skin of the banana included in the camera image is black and the temperature around the banana is 2° C., the AI model may determine the current state of the banana as the spoiled state (updates the reference stored), Fig 5 # 310 learning data (historical disposal event), [0109] the AI processor may obtain a set of learning data 310 including an input value 311 and a result value 312. Here, the input value 311 may include at least one of a camera image (an RGB image), a spectrometric image, or environmental information (for example, temperature information, humidity information, and odor (gas) information) and the result value 312 may include at least one of a time until actual disposal [0292] the model learner 1310-4 may train the data recognition model, for example, through unsupervised learning based on which the type of the data required for determining the state of the object is self-trained without a particular instruction to discover a reference for determining the state of the object. Also, the model learner 1310-4 may train the data recognition model, for example, through reinforcement learning based on which feedback (updates) about whether a result of determining the state of the object based on learning is correct or not is used).
Hirofumi (WO2019202846A1) teaches fried food displayed in a display cabinet (Page 6 The display shelf 60 displays food. The display shelf 60 may display cooked foods (fried food) and display products other than the cooked foods. The display shelf 60 includes a sensor 61 and a heat retaining unit 62. The sensor 61 may be, for example, a camera or other sensor. For example, the sensor 61 as a camera may image a predetermined area of the display shelf and image-recognize the food identification information reflected in the captured image)
Mathew et al. (US11,393,082 B2) teaches analysis unit that analyzes, when the data acquisition unit acquires a surface image of the food being displayed as data related to the color tone, color components of the food from the surface image; a surface image of the food (Fig 4 # 402 receive an image of an item, Col 4 lines 65-67,Col 5 lines 1-5After exploratory data analysis, fifteen different defects were found in strawberries. The top five defects (decay, bruise, discoloration, overripe soft berries and color) accounted for 96% of the defective strawberries); an identification unit that identifies a type of the food being displayed(Col 5 lines 39-42, 47-58 using deep learning/computer vision for object detection and classification to aid in quality inspection. A camera 104 takes a picture of a produce product 102, resulting in an image 106. The image 106 is compared to other images stored in an image database 108, and unrelated images are removed 110. The system identifies defects 112 within the image 106 based on the related images, and generates a feature map 114 of the features within the image 106.)
Hayward (US2021/0333185) teaches a machine learning unit ([0018] generating a trained model to determine a quality characteristic of a food item) that generates a learning model capable of determining the time to dispose the food by machine learning ([0018]The method can also include generating, by the computing system, a machine learning model based on mapping the volatiles marker profile to quality characteristics identified by (iii) and (iv). The machine learning model can correlate presence and concentrations of the volatiles with one or more quality characteristics of food items of the same food type., [0065] The volatiles can be analyzed and mapped to different quality features using machine learning techniques. A disappearance, appearance, or accumulation of volatiles can provide valuable information about quality of such agricultural products), wherein the machine learning unit estimates the determination reference based on the determination result by the determination unit, performs the machine learning using the determination reference as estimated to generate the learning model, and updates the determination reference based on the learning model as generated. ([0062] Machine learning or other similar methods and techniques can be used to update and refine volatile marker profiles for particular traits and/or characteristics of the agricultural product. [0072] the analysis computer system 102 can also apply one or more machine learning trained models to the volatiles data (step E). The models can be trained to classify and correlate volatiles with different quality characteristics associated with the produce 108A-N. Models can be generated to determine and predict different quality characteristics associated with the produce 108A-N., [0094])
Mattingly et al. (US 2017/0344935 A1) teaches notifying step includes removal of expired food from the display cabinet, cleaning of the cabinet, and preparation for displaying new fried food ([0033] The central computing system may then flag the issue and send out an alert to an employee of the shopping facility and/or create a task that needs to be addressed immediately. These issues may then be corrected, such as by stocking the shelf with more inventory, cleaning up the liquid spill (cleaning), adjusting or repairing the temperature settings at the shelf, [0044] the determination of freshness level may include a task to be performed by a sales associate such as: place the perishable product on a sales floor, relocate the perishable product, remove the perishable product from sales (disposal), move the perishable product into climate controlled storage)
CN108846376 discusses a food safety intelligent monitoring method. It mainly comprises the following items: 1, cooling temperature is not higher than 25 degrees; 2, UV lamp in daily operation process needs to open for air sterilizing, 3, into the cold room must be specially appointed people, namely person enters, with other people enter to alarm
NPL- Ivar, ”Food security, safety and sustainability”, 2020 discusses tradeoffs between sustainability, food security and food safety (as noted in Non-final action)
However, the prior art fails to teach or suggest at least “a storage unit that stores: a determination reference that is a reference for determining the time to dispose the fried food, the determination reference being set for each of the color tone, size, water content, amount of volatile components, volatile component composition, acid value, anisidine value, carbonyl value, peroxide value, iodine value, and amount of polar compounds, and a sample image serving as a reference for identifying a type of the fried food being displayed in the display cabinet; a determination unit that determines that the time to dispose the fried food being displayed in the display cabinet is reached when the data acquired by the data acquisition unit meets a corresponding determination reference stored in the storage unit; and a notification unit that outputs, a notification signal for notifying a determination result, which is indicative of a result that the time to dispose the fried food being displayed in the display cabinet is reached, made by the determination unit by at least either visualizing or sounding; an identification unit that identifies the type of the fried food being displayed in the display cabinet by comparing the surface image acquired by the data acquisition unit with the sample image stored in the storage unit; a machine learning unit that generates a learning model for use in machine learning, and the machine learning unit generates the learning model used to: update the determination reference stored in the storage unit to the determination reference estimated based on the determination result, which is indicative of the result that the time to dispose the fried food being displayed in the display cabinet is reached, made by the determination unit; and update the sample image stored in the storage unit to the sample image estimated based on an identification result, which is indicative of the type of the fried food, made by the identification unit”. The prior art teachings as recited above fail to set forth any sufficient rationale for combining or otherwise modifying any of the relevant prior art to arrive at the claimed invention, as a whole. To arrive at the claimed invention with the precise combination of claimed features would not have been obvious to one of ordinary skill in the art without relying on improper hindsight to substantially reconstruct Applicant's claimed invention. Thus, the aforementioned combination of features claimed, as a whole, are not anticipated nor rendered obvious for any sufficient rationale by any of the prior art teachings. Furthermore, the prior art of record does not anticipate nor render obvious the combination of limitations for the dependent claims due to their respective dependencies to the independent claims 1, 6 and 9.
Response to Arguments
Applicant's arguments filed 3/2/26 have been fully considered but they are not persuasive.
Regarding 101 rejection, examine has considered all arguments. 101 rejection has been updated per new limitations. The additional elements are recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component. Simply implementing the abstract idea on generic components is not a practical application of the abstract idea. Accordingly, these additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. a) The additional elements merely add the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea, as discussed in MPEP 2106.05(f). The limitation of “machine learning unit” merely add the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea, as discussed in MPEP 2106.05(f). Further, the limitation of “generating a learning model” is simply application of a computer model, itself an abstract idea. Furthermore, such training and applying of a model is no more than putting data into a black box machine learning operation, devoid of technological implementation and application details. Each step requires a generic computer to perform generic computer functions. The claims are directed to an abstract idea.
Applicant further states that claims directed to technological improvements but they are not persuasive. The claims, as amended, recited certain technological elements. Those technological elements amount to performing ordinary computer analysis functions in addition to data gathering and display. Relying on a computer to perform routine tasks more quickly or more accurately is insufficient to render a claim patent eligible. OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 1363 (Fed. Cir. 2015). It is well-settled that mere recitation of concrete, tangible components is insufficient to confer patent eligibility to an otherwise abstract idea" where those components simply perform their "well-understood, routine, conventional" functions. In re TLI Commc'ns LLC Patent Litig., 823 F.3d 607, 613 (Fed. Cir. 2016). “The inclusion of a generic computer to automate activity or behavior that has long existed does not suffice to meaningfully restrict the … patent from preempting the abstract idea ….” Voxathon LLC v. Alpine Elecs. Of America, Inc., No. 2:15-cv00562, Op. at 8-9 (E.D. Tex. Jan. 21, 2016). ("But relying on a computer to perform routine tasks more quickly or more accurately is insufficient to render a claim patent eligible.").
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Alfarra (US 10682018) discloses at determine spoilage operation 1225, the food system determines that a location of the spoilage of the food item or determines the entire food item is soiled based on the measured reflection, refraction, or absorption of the EM waves. In some implementations, the food system determines that a food item is spoiled partially or completely based on approximately dielectric properties of the food item. The food system can also determine a location of spoilage based on water content or density of the food item location based on the measured EM waves.
WO2016019417A1 discusses monitoring hot environments, such as warming ovens and roller grills, is also an application of the invention. For the food industry, temperatures in the range of +5?C and +60?C fall within the Temperature Danger Zone. This is because bacteria can grow to unsafe levels between these temperatures. All cooked foods must be maintained at a temperature of greater than +60?C and this should be monitored in same way as chilled or frozen foods. Smart Tags can accommodate any analogue or RTD sensor including, but not limited to sensors which measure temperature, humidity, shock, discrete events, gas concentrations, fluid levels, conductivity, dissolved oxygen or any other measurable environmental condition. These sensors may be connected to a Smart Tag via Ports 3 or 4
Espinoza (US 11,104502B2) discloses technology that communicates, interacts, tracks, monitors, benefits or enhances the use, access, understanding, storage or appearance of a perishable food, user interface consumer or electronic device, good or appliance which can also be in a container
Johnsen (US 11,138,554B2) discloses the system includes an inspection component implemented on at least one computing device with at least one sensor configured to capture data about a good; and a communication component communicatively coupled to the computing device and configured to receive information regarding regulations and quality control standards associated with the good, and transmit the information regarding the acceptability of a good to an inventory system.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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.
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/SANGEETA BAHL/Primary Examiner, Art Unit 3626
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