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 .
Claim Status
Claims 1-15 are pending for examination in the application filed 02/12/2025.
Priority
Acknowledgement is made of Applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). The certified copies have been received for parent applications KR10-2022-0106448, filing date: 08/24/2022; KR10-2022-0113827, filing date: 09/07/2022; KR10-2023-0029527, filing date: 03/06/2023. Acknowledgement is additionally made of the present application as a continuation of PCT/KR2023/009188, filing date: 06/30/2023.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 02/12/2025 has been considered by the examiner.
Drawings
The drawings are objected to because of the following: “HIRDT LEVEL” in Fig 8, “SRART” in Fig. 9, and “IDENTIFY OBJECT EL TO BE COOKED” in Fig. 17. Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
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 1-2, 4-8, 10-11, and 13-15 are rejected under 35 U.S.C. 103 as being unpatentable over Hur (US20200018551A1) in view of Kim (US20220365962A1).
Regarding claim 1, Hur teaches a cooking apparatus ([Abstract] an artificial intelligence cooking device) comprising:
a display ([0161] display unit 16 and a door display unit 40);
a camera configured to photograph an inside of a chamber ([0152] a camera 32 capable of photographing the inside of the cooking compartment 11);
a memory configured to store an artificial intelligence (AI) model used to estimate an object to be cooked and configured to a numerical conversion table including numerical information corresponding to the object to be cooked ([Abstract] The artificial intelligence cooking device…configured to acquire a weight of the at least one food material by providing an image obtained by photographing the at least one food material and the detected distance to an artificial intelligence model. [0065] The memory 170 may store data that supports various functions of the AI device 100. For example, the memory 170 may store input data acquired by the input unit 120, learning data, a learning model, a learning history. [0294] In a memory, a table corresponding to cooking courses and various weight of various food materials may be stored);
and a controller operatively connected to the display, the camera, and the memory (Fig. 1. [0373] The computer may include a control unit 180), the controller being configured to:
estimate at least one of a type, a number, and a size of the object to be cooked, by inputting an image obtained by the camera to the Al model ([0287] providing the image 1010 obtained by photographing the food material to the artificial intelligence model. [0288] the artificial intelligence model may output a result value, specifically the kind and weight of the food material),
determine a number and a numerical unit corresponding to the at least one of the type, the number, and the size of the object to be cooked, by referring to the numerical conversion table ([0357] Particularly, the processor may calculate a calorie of the photographed first food material on the basis of the kind and weight of the first food material and the calorie per unit weight of the first food material stored in the memory),
and control the display to display a name of the object to be cooked, the number, and the numerical unit ([0321] the processor may control the display unit to display a notice).
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Hur does not explicitly teach determine a numerical range and a numerical unit corresponding to the at least one of the type, the number, and the size of the object; control the display to display the numerical range, and the numerical unit.
Kim, in the same field of endeavor of food image analysis, teaches determine a numerical range and a numerical unit corresponding to the at least one of the type, the number, and the size of the object; control the display to display the numerical range, and the numerical unit ([0086] As shown in FIG. 3, in step 310, the food-information input apparatus 100 is operated in the photographing mode where the input guide 302 and the input button 301 moved along the input guide 302 are displayed on the photographing screen. Here, an input range related to the food information may be displayed on the input guide 302. For instance, the input guide 302 may display the input range from 0 servings to 50 servings, which is a unit related to spaghetti).
Therefore, it would have been obvious to a person of ordinary skill in the art before the time of filing to modify the apparatus of Hur with the teachings of Kim to determine a numerical range and numerical unit corresponding to the type of food and displaying it because "a unit of the food displayed on the input button may be changed according to a type of the recognized food" [0020] "and to analyze an individual's eating habits according to information about the identified food" [0003].
Regarding claim 2, Hur and Kim teach the apparatus of claim 1. Hur does not explicitly teach wherein the controller is further configured to: determine the numerical range as a weight range, a number of pieces range, or a serving size range, and match the numerical unit to the numerical range.
Kim, in the same field of endeavor of food image analysis, teaches wherein the controller is further configured to: determine the numerical range as a weight range, a number of pieces range, or a serving size range, and match the numerical unit to the numerical range ([0086] As shown in FIG. 3, in step 310, the food-information input apparatus 100 is operated in the photographing mode where the input guide 302 and the input button 301 moved along the input guide 302 are displayed on the photographing screen. Here, an input range related to the food information may be displayed on the input guide 302. For instance, the input guide 302 may display the input range from 0 servings to 50 servings, which is a unit related to spaghetti).
Therefore, it would have been obvious to a person of ordinary skill in the art before the time of filing to modify the apparatus of Hur with the teachings of Kim for the numerical range to be a serving size range "to analyze an individual's eating habits according to information about the identified food" [0003].
Regarding claim 4, Hur and Kim teach the apparatus of claim 1. Hur further teaches wherein the controller is further configured to: extract a partial image corresponding to the object to be cooked from the image ([0309] Also, the processor may separate the image 1110 obtained by photographing the plurality of food materials and individually provide a plurality of images to the artificial intelligence model. [0310] Particularly, the processor may separate the image 1110 obtained by photographing the plurality of food materials into an image including the first food material 1111 of the plurality of food materials, an image including the second-first food material 1112 of the plurality of food materials, an image including the second-second food material 1113 of the plurality of food materials, and an image including the second-third food material 1114 of the plurality of food materials);
estimate at least one of the type and the size of the object to be cooked from the partial image ([0311] Also, the processor may acquire the weight of the first food material (or, the kind and weight of the first food material) by providing the image including the first food material 1111 to the artificial intelligence model, acquire the weight of the second-first food material (or, the kind and weight of the second-first food material) by providing the image including the second-first food material 1112 to the artificial intelligence model, acquire the weight of the second-second food material (or, the kind and weight of the second-second food material) by providing the image including the second-second food material 1113 to the artificial intelligence model, and acquire the weight of the second-third food material (or, the kind and weight of the second-third food material) by providing the image including the second-third food material 1114 to the artificial intelligence model);
and determine the number of objects to be cooked, by counting a number of partial images ([0306] Particularly, the processor 180 may recognize a plurality of food materials of the image 1110 and determine the kind of the plurality of food materials according to a recognition result. For example, the processor may determine the first food material 1111 as a chicken and the second-first, second-second, and second-third food materials 1112, 1113, and 1114 as potatoes).
Regarding claim 5, Hur and Kim teach the apparatus of claim 4. Hur further teaches wherein the controller is further configured to: based on a plurality of partial images being extracted from the image, estimate the at least one of the type and the size of the object to be cooked included in each of the plurality of partial images ([0311] Also, the processor may acquire the weight of the first food material (or, the kind and weight of the first food material) by providing the image including the first food material 1111 to the artificial intelligence model, acquire the weight of the second-first food material (or, the kind and weight of the second-first food material) by providing the image including the second-first food material 1112 to the artificial intelligence model, acquire the weight of the second-second food material (or, the kind and weight of the second-second food material) by providing the image including the second-second food material 1113 to the artificial intelligence model, and acquire the weight of the second-third food material (or, the kind and weight of the second-third food material) by providing the image including the second-third food material 1114 to the artificial intelligence model);
and determine the number and the numerical unit based on the type of the object to be cooked having a largest number of pieces or the type of the object to be cooked having a largest size ([0357] Particularly, the processor may calculate a calorie of the photographed first food material on the basis of the kind and weight of the first food material and the calorie per unit weight of the first food material stored in the memory. [0358] Also, the processor may calculate a calorie of the photographed second food material on the basis of the kind and weight of the second food material and the calorie per unit weight of the second food material stored in the memory. [0359] Also, the processor may calculate a total calorie by adding the calorie of the first food material and the calorie of the second food material and output the calculated total calorie).
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Hur does not explicitly teach determine the numerical range and numerical unit.
Kim, in the same field of endeavor of food image analysis, teaches determine the numerical range and numerical unit ([0086] As shown in FIG. 3, in step 310, the food-information input apparatus 100 is operated in the photographing mode where the input guide 302 and the input button 301 moved along the input guide 302 are displayed on the photographing screen. Here, an input range related to the food information may be displayed on the input guide 302. For instance, the input guide 302 may display the input range from 0 servings to 50 servings, which is a unit related to spaghetti).
Therefore, it would have been obvious to a person of ordinary skill in the art before the time of filing to modify the apparatus of Hur with the teachings of Kim to determine the numerical range and the numerical unit because "a unit of the food displayed on the input button may be changed according to a type of the recognized food" [0020] "and to analyze an individual's eating habits according to information about the identified food" [0003].
Regarding claim 6, Hur and Kim teach the apparatus of claim 1. Hur further teaches a heater (heating unit 17) configured to supply heat to the inside of the chamber, wherein the controller is further configured to determine a cooking temperature, a cooking time, and an output of the heater based on the at least one of the type, the number, and the size of the object to be cooked ([0147] The heating unit 17 heats the inside of the cooking compartment 11. [0295] Also, the processor may perform cooking according to a set cooking course. For example, when a cooking course having a cooking temperature of 200° C. and a cooking time of 50 minutes is set, the processor may drive a heating unit to apply heat to the food material for 50 minutes at a cooking temperature of 200° C).
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Regarding claim 7, Hur and Kim teach the apparatus of claim 1. Hur further teaches a speaker, wherein the controller is further configured to control the speaker to output a voice message comprising the name of the object to be cooked, the number, and the numerical unit ([0064] the output unit 150 may include…a speaker for outputting auditory information).
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Hur does not explicitly teach the numerical range and numerical unit.
Kim, in the same field of endeavor of food image analysis, teaches the numerical range and numerical unit ([0086] As shown in FIG. 3, in step 310, the food-information input apparatus 100 is operated in the photographing mode where the input guide 302 and the input button 301 moved along the input guide 302 are displayed on the photographing screen. Here, an input range related to the food information may be displayed on the input guide 302. For instance, the input guide 302 may display the input range from 0 servings to 50 servings, which is a unit related to spaghetti).
Therefore, it would have been obvious to a person of ordinary skill in the art before the time of filing to modify the apparatus of Hur with the teachings of Kim to determine the numerical range and the numerical unit because "a unit of the food displayed on the input button may be changed according to a type of the recognized food" [0020] "and to analyze an individual's eating habits according to information about the identified food" [0003].
Regarding claim 8, Hur and Kim teach the apparatus of claim 1. Hur further teaches communication circuitry configured to communicate with a server, wherein the controller is further configured to control the communication circuitry to transmit display information for displaying the name of the object to be cooked, the number, and the numerical unit on a user device to the server (See Fig. 13-14 above. [0053] The communication unit 110 may transmit and receive data to and from external devices such as other AI devices 100a to 100e and the AI server 200 by using wire/wireless communication technology. For example, the communication unit 110 may transmit and receive sensor information, a user input, a learning model, and a control signal to and from external devices. [0269] a feature of acquiring a cooking course may represent that a cooking course corresponding to the kind and weight of the food material is determined by using information stored in the memory of the artificial intelligence cooking device, or represent that related information is transmitted to the server, the server determines a cooking course corresponding to the kind and weight of the food material by using information stored in the memory of the server, and the determined cooking course is received from the server. [0321] Particularly, when first cooking course and the second cooking course are different, the processor may control the display unit to display a notice that a plurality of food materials are inappropriate to be cooked together or output a voice that a plurality of food materials are inappropriate to be cooked together through the speaker).
Hur does not explicitly teach the numerical range and numerical unit.
Kim, in the same field of endeavor of food image analysis, teaches the numerical range and numerical unit ([0086] As shown in FIG. 3, in step 310, the food-information input apparatus 100 is operated in the photographing mode where the input guide 302 and the input button 301 moved along the input guide 302 are displayed on the photographing screen. Here, an input range related to the food information may be displayed on the input guide 302. For instance, the input guide 302 may display the input range from 0 servings to 50 servings, which is a unit related to spaghetti).
Therefore, it would have been obvious to a person of ordinary skill in the art before the time of filing to modify the apparatus of Hur with the teachings of Kim to determine the numerical range and the numerical unit because "a unit of the food displayed on the input button may be changed according to a type of the recognized food" [0020] "and to analyze an individual's eating habits according to information about the identified food" [0003].
Regarding claim 10, Hur teaches method for controlling a cooking apparatus, (Fig. 7) the method comprising:
obtaining, by a camera, an image of an inside of a chamber in which an object to be cooked is contained ([0152] a camera 32 capable of photographing the inside of the cooking compartment 11);
estimating, by a controller, at least one of a type, a number, and a size of the object to be cooked, by inputting the image to an artificial intelligence (AI) model stored in a memory ([0287] providing the image 1010 obtained by photographing the food material to the artificial intelligence model. [0288] the artificial intelligence model may output a result value, specifically the kind and weight of the food material. [0065] The memory 170 may store data that supports various functions of the AI device 100. For example, the memory 170 may store input data acquired by the input unit 120, learning data, a learning model, a learning history);
determining, by the controller, a number and a numerical unit corresponding to the at least one of the type, the number, and the size of the object to be cooked, by referring to a numerical conversion table stored in the memory ([0357] Particularly, the processor may calculate a calorie of the photographed first food material on the basis of the kind and weight of the first food material and the calorie per unit weight of the first food material stored in the memory. [0294] In a memory, a table corresponding to cooking courses and various weight of various food materials may be stored);
and controlling, by the controller, a display to display a name of the object to be cooked, the number, and the numerical unit ([0321] the processor may control the display unit to display a notice).
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Hur does not explicitly teach determining, by the controller, a numerical range and a numerical unit corresponding to the at least one of the type, the number, and the size of the object; and controlling, by the controller, the display to display the numerical range, and the numerical unit.
Kim, in the same field of endeavor of food image analysis, teaches determining, by the controller, a numerical range and a numerical unit corresponding to the at least one of the type, the number, and the size of the object; and controlling, by the controller, the display to display the numerical range, and the numerical unit ([0086] As shown in FIG. 3, in step 310, the food-information input apparatus 100 is operated in the photographing mode where the input guide 302 and the input button 301 moved along the input guide 302 are displayed on the photographing screen. Here, an input range related to the food information may be displayed on the input guide 302. For instance, the input guide 302 may display the input range from 0 servings to 50 servings, which is a unit related to spaghetti).
Therefore, it would have been obvious to a person of ordinary skill in the art before the time of filing to modify the method of Hur with the teachings of Kim to determine a numerical range and numerical unit corresponding to the type of food and displaying it because "a unit of the food displayed on the input button may be changed according to a type of the recognized food" [0020] "and to analyze an individual's eating habits according to information about the identified food" [0003].
Regarding claim 11, Hur and Kim teach the method of claim 10. Hur does not explicitly teach wherein the determining of the numerical range and the numerical unit comprises: determining the numerical range as a weight range, a number of pieces range, or a serving size range; and matching the numerical unit to the numerical range.
Kim, in the same field of endeavor of food image analysis, teaches wherein the determining of the numerical range and the numerical unit comprises: determining the numerical range as a weight range, a number of pieces range, or a serving size range; and matching the numerical unit to the numerical range ([0086] As shown in FIG. 3, in step 310, the food-information input apparatus 100 is operated in the photographing mode where the input guide 302 and the input button 301 moved along the input guide 302 are displayed on the photographing screen. Here, an input range related to the food information may be displayed on the input guide 302. For instance, the input guide 302 may display the input range from 0 servings to 50 servings, which is a unit related to spaghetti).
Therefore, it would have been obvious to a person of ordinary skill in the art before the time of filing to modify the method of Hur with the teachings of Kim for the numerical range to be a serving size range "to analyze an individual's eating habits according to information about the identified food" [0003].
Regarding claim 13, Hur and Kim teach the method of claim 10. Hur further teaches wherein the estimating comprises: extracting a partial image corresponding to the object to be cooked from the image ([0309] Also, the processor may separate the image 1110 obtained by photographing the plurality of food materials and individually provide a plurality of images to the artificial intelligence model. [0310] Particularly, the processor may separate the image 1110 obtained by photographing the plurality of food materials into an image including the first food material 1111 of the plurality of food materials, an image including the second-first food material 1112 of the plurality of food materials, an image including the second-second food material 1113 of the plurality of food materials, and an image including the second-third food material 1114 of the plurality of food materials);
estimating at least one of the type and the size of the object to be cooked from the partial image ([0311] Also, the processor may acquire the weight of the first food material (or, the kind and weight of the first food material) by providing the image including the first food material 1111 to the artificial intelligence model, acquire the weight of the second-first food material (or, the kind and weight of the second-first food material) by providing the image including the second-first food material 1112 to the artificial intelligence model, acquire the weight of the second-second food material (or, the kind and weight of the second-second food material) by providing the image including the second-second food material 1113 to the artificial intelligence model, and acquire the weight of the second-third food material (or, the kind and weight of the second-third food material) by providing the image including the second-third food material 1114 to the artificial intelligence model);
and determining the number of objects to be cooked, by counting a number of partial images ([0306] Particularly, the processor 180 may recognize a plurality of food materials of the image 1110 and determine the kind of the plurality of food materials according to a recognition result. For example, the processor may determine the first food material 1111 as a chicken and the second-first, second-second, and second-third food materials 1112, 1113, and 1114 as potatoes).
Regarding claim 14, Hur and Kim teach the method of claim 13. Hur further teaches wherein the estimating comprises: based on a plurality of partial images being extracted from the image, estimating the at least one of the type and the size of the object to be cooked included in each of the plurality of partial images ([0311] Also, the processor may acquire the weight of the first food material (or, the kind and weight of the first food material) by providing the image including the first food material 1111 to the artificial intelligence model, acquire the weight of the second-first food material (or, the kind and weight of the second-first food material) by providing the image including the second-first food material 1112 to the artificial intelligence model, acquire the weight of the second-second food material (or, the kind and weight of the second-second food material) by providing the image including the second-second food material 1113 to the artificial intelligence model, and acquire the weight of the second-third food material (or, the kind and weight of the second-third food material) by providing the image including the second-third food material 1114 to the artificial intelligence model);
and determining the number and the numerical unit based on the type of the object to be cooked having a largest number of pieces or the type of the object to be cooked having a largest size ([0357] Particularly, the processor may calculate a calorie of the photographed first food material on the basis of the kind and weight of the first food material and the calorie per unit weight of the first food material stored in the memory. [0358] Also, the processor may calculate a calorie of the photographed second food material on the basis of the kind and weight of the second food material and the calorie per unit weight of the second food material stored in the memory. [0359] Also, the processor may calculate a total calorie by adding the calorie of the first food material and the calorie of the second food material and output the calculated total calorie).
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Hur does not explicitly teach determining the numerical range and numerical unit.
Kim, in the same field of endeavor of food image analysis, teaches determining the numerical range and numerical unit ([0086] As shown in FIG. 3, in step 310, the food-information input apparatus 100 is operated in the photographing mode where the input guide 302 and the input button 301 moved along the input guide 302 are displayed on the photographing screen. Here, an input range related to the food information may be displayed on the input guide 302. For instance, the input guide 302 may display the input range from 0 servings to 50 servings, which is a unit related to spaghetti).
Therefore, it would have been obvious to a person of ordinary skill in the art before the time of filing to modify the method of Hur with the teachings of Kim to determine the numerical range and the numerical unit because "a unit of the food displayed on the input button may be changed according to a type of the recognized food" [0020] "and to analyze an individual's eating habits according to information about the identified food" [0003].
Regarding claim 15, Hur and Kim teach the method of claim 10. Hur further teaches determining a cooking temperature, a cooking time, and an output of the heater based on the at least one of the type, the number, and the size of the object to be cooked ([0147] The heating unit 17 heats the inside of the cooking compartment 11. [0295] Also, the processor may perform cooking according to a set cooking course. For example, when a cooking course having a cooking temperature of 200° C. and a cooking time of 50 minutes is set, the processor may drive a heating unit to apply heat to the food material for 50 minutes at a cooking temperature of 200° C).
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Claims 3 and 12 are rejected under 35 U.S.C. 103 as being unpatentable over Hur in view of Kim and Sung (US20210161329A1).
Regarding claim 3, Hur and Kim teach the apparatus of claim 1. Hur does not explicitly teach wherein the controller is further configured to: acquire attribute information of a reference image that matches the image obtained by the camera through the Al model; and determine the at least one of the type, the number, and the size of the object to be cooked, from the attribute information of the reference image.
Sung, in the same field of endeavor of food image analysis, teaches wherein the controller is further configured to: acquire attribute information of a reference image that matches the image obtained by the camera through the Al model; and determine the at least one of the type, the number, and the size of the object to be cooked, from the attribute information of the reference image ([0043] a cooking target may be photographed using a camera, and the captured image may be recognized. [0062] AI technology capable of recognizing a cooking target and cooking the cooking target. [0198] Various algorithms may be applied in detecting the cooking target. Examples of the algorithms may include various recognition methods of comparing an input image with reference images stored in a database based on the outward characteristics of the cooking target, and determining whether the input image matches a reference image).
Therefore, it would have been obvious to a person of ordinary skill in the art before the time of filing to modify the apparatus of Hur with the teachings of Sung to acquire attribute information of a reference image and determine the type of object to be cooked based on matching the attribute information because "When the cooking instruction corresponding to the recognized cooking target is stored in the memory 170 of the cooking apparatus 100, the stored cooking instruction may be used" [0207].
Regarding claim 12, Hur and Kim teach the method of claim 10. Hur does not explicitly teach wherein the estimating comprises: acquiring attribute information of a reference image that matches the image obtained by the camera through the Al model; and determining the at least one of the type, the number, and the size of the object to be cooked, from the attribute information of the reference image
Sung, in the same field of endeavor of food image analysis, teaches wherein the estimating comprises: acquiring attribute information of a reference image that matches the image obtained by the camera through the Al model; and determining the at least one of the type, the number, and the size of the object to be cooked, from the attribute information of the reference image ([0043] a cooking target may be photographed using a camera, and the captured image may be recognized. [0062] AI technology capable of recognizing a cooking target and cooking the cooking target. [0198] Various algorithms may be applied in detecting the cooking target. Examples of the algorithms may include various recognition methods of comparing an input image with reference images stored in a database based on the outward characteristics of the cooking target, and determining whether the input image matches a reference image).
Therefore, it would have been obvious to a person of ordinary skill in the art before the time of filing to modify the method of Hur with the teachings of Sung to acquire attribute information of a reference image and determine the type of object to be cooked based on matching the attribute information because "When the cooking instruction corresponding to the recognized cooking target is stored in the memory 170 of the cooking apparatus 100, the stored cooking instruction may be used" [0207].
Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over Hur in view of Kim and Pichara (US20210103796A1).
Regarding claim 9, Hur and Kim teach the apparatus of claim 1. Hur further teaches communication circuitry configured to communicate with a server, wherein the controller is further configured to acquire an updated AI model from the server ([0072] The processor 180 may collect history information including the operation contents of the AI apparatus 100 or the user's feedback on the operation and may store the collected history information in the memory 170 or the learning processor 130 or transmit the collected history information to the external device such as the AI server 200. The collected history information may be used to update the learning model).
Hur does not explicitly teach acquire an updated AI model from the server at preset intervals.
Pichara, in the same field of endeavor of food image analysis, teaches acquire an updated AI model from the server at preset intervals ([0076] Models described herein can be run or updated…at a predetermined frequency. [0081] embodiments of the system can entirely or partially be executed by, hosted on, communicate with, and/or otherwise include one or more: remote computing systems (e.g., one or more servers…). [0041] prediction models 216…can include, apply, employ, perform, use, be based on, and/or otherwise be associated with artificial intelligence).
Therefore, it would have been obvious to a person of ordinary skill in the art before the time of filing to modify the apparatus of Hur with the teachings of Pichara to acquire an updated AI model from the server at preset intervals so that "Each model can be validated, verified, reinforced, calibrated, or otherwise updated based on newly received, up-to-date data; historical data" [0076].
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Stork (US20180184668A1) teaches an oven camera monitoring system.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Jacqueline R Zak whose telephone number is (571)272-4077. The examiner can normally be reached M-F 9-5.
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/JACQUELINE R ZAK/Examiner, Art Unit 2666
/SJ Park/Primary Examiner, Art Unit 2675
