DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Election/Restrictions
Claims 11-20 are withdrawn from further consideration pursuant to 37 CFR 1.142(b) as being drawn to nonelected inventions, there being no allowable generic or linking claim. Election was made without traverse in the reply filed on 03/09/2026.
Claim Objections
Claims 3-5 are objected to because of the following informalities:
Regarding claim 3, the limitation “the processor is further configured to: … obtain the visual attribute information in response to the attribute information request from the large language model” is grammatically unclear. Based solely on the claim limitation, it is unclear whether the statement intends to means that the processor is configured to obtain information visual attribute information from the large language model, or whether the process is configured to obtain visual attribute information as a result of the large language model issuing an attribute information request. Paragraph 119 of the applicant’s filed specifications filed 04/23/204 recites that the processor requests a request for attribute information and obtains information from the large language module in response to said request for attribute information. Thus, for purposes of examination, it will be assumed that the recited claim limitation intends that the processor is configured to obtain information visual attribute information from the large language model.
Claims 4-5 are objected upon their dependence on claim 3.
Appropriate correction is required.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1, 6-7, and 9-10 is/are rejected under 35 U.S.C. 103 as being unpatentable over CHAE (US 20210137311 A1) in view of Bhogal (US 20200278117 A1).
Regarding claim 1, CHAE (US 20210137311 A1) teaches a cooking device (Paragraph 141, AI device 100 includes a cooking unit), comprising:
a cooking chamber (Figure 9 Paragraph 233, oven which comprises a chamber);
a heater configured to heat the cooking chamber (Paragraph 146, AI device generates heat through the coil according to a command to start cooking of the food and causes a fan to cause convection phenomenon of generated heat);
a camera configured to photograph food (Paragraph 147-148, camera 121 of the AI device 100 captures an image of the food);
a user input interface configured to receive a user input (Paragraph 157, processor identifies the food based on a voice command uttered by the user; Paragraph 186, user may store the level 713 of the doneness class included in the cooking completion notification as a preference level through the smartphone 700); and
a processor (Paragraphs 155-158, processor 180) configured to:
identify a type of the food based on a food image photographed by the camera (Paragraphs 149-150, processor 180 can capture the food wherein the processor can recognize the food from the captured food image using an image recognition model),
obtain visual attribute information indicating a cooking state of the food based on the user input (Paragraph 186, smartphone transmits the level of the user preference class to the AI device wherein the level of the user preference class is stored in the memory; Paragraph 197, processor may determine whether the level of the determined doneness class of the corresponding food is equal to the level of the user preference class), and
control the heater to heat the cooking chamber to cook the food based on cooking information corresponding to the identified type of food and the visual attribute information (Paragraphs 199-200, either end the cooking the food or control the cooking unit to finish the cooking of the food based on whether the determined doneness class is equal to the user preference class).
CHAE fails to explicitly teach:
a camera configured to photograph food located inside the cooking chamber
Bhogal (US 20200278117 A1) teaches a tailored food preparation with an oven, wherein:
a camera configured to photograph food located inside the cooking chamber (Paragraph 42, sensor includes a camera configured to record images 11 of the cooking cavity)
It would have thus been obvious to someone of ordinary skill in the art before the filing date of the claimed invention to have modified CHAE with Bhogal and have the camera configured to photograph food inside the cooking cavity. This would have been done to allow the camera to capture images of the food inside the cavity (Bhogal Paragraph 42) without having to open the door of the oven, which is inconvenient for the user (CHAE Paragraph 6).
Regarding claim 6, CHAE as modified teaches the cooking device of claim 1, wherein
the processor (processor 180) is further configured to:
compare the visual attribute information with a cooking image photographed by the camera during cooking of the food (Paragraph 108, processor captures the food and determines a level of doneness from the captured food image using the doneness class classification model; Paragraph 196, processor 180 of the AI device may determine whether the determined doneness class is equal to the user preference class S509), and
determine whether or not to continue cooking the food based on a result of the comparison (Paragraph 288, processor 180 extends the cooking time of the food until the level of determined doneness class is equal to the level of the user preference class).
Regarding claim 7, CHAE as modified teaches the cooking device of claim 1, wherein
the determination on whether to continue cooking the food is based on whether a similarity between the cooking image and the visual attribute information is greater than or equal to a preset similarity threshold (Paragraph 288, processor 180 extends the cooking time of the food until the level of determined doneness class is equal to the level of the user preference class), and
wherein the processor is further configured to output a cooking completion notification based on the similarity being greater than or equal to the preset similarity threshold (Paragraphs 232-236, a notification is output to the user when the cooking of the food is equal to a level of a user preference class and the cooking is completed).
Regarding claim 9, CHAE as modified teaches the cooking device of claim 7, wherein
the cooking completion notification includes a cooking completion image of a state in which the cooking of the food is completed (Paragraph 234, oven transmits information indicating that cooking of the chicken is completed and an image of the chicken is captured in a cooked state)
Bhogal further teaches:
the cooking completion notification includes feedback information for receiving a user feedback (Paragraph 32, user feedback can be collected after a cooking session has been completed).
It would have thus been obvious to someone of ordinary skill in the art before the filing date of the claimed invention to have modified CHAE with Bhogal and have the cooking completion notification allow the user to provide feedback. This would have been done to allow the system to make more accurate adjustments to operation instructions than conventional methods (Bhogal Paragraph 32).
Regarding claim 10, CHAE as modified teaches the cooking device of claim 1, wherein
the user input is any one of a voice command uttered by a user (voice; Paragraph 157, user input includes voice command to indicate food type), text input by the user through the user input interface, or text input by the user received through a mobile terminal.
Claim(s) 2 is/are rejected under 35 U.S.C. 103 as being unpatentable over CHAE (US 20210137311 A1) in view of Bhogal (US 20200278117 A1) as applied to claim 1 above, and further in view of PASHUT (US 20230405834 A1) and HAN (US 20210401223 A1).
Regarding claim 2, CHAE as modified teaches the cooking device of claim 1, wherein the processor (processor 180) is further configured to:
obtain the visual attribute information based on an analysis result of the user input (Paragraph 186, smartphone transmits the level of the user preference class to the AI device wherein the level of the user preference class is stored in the memory; Paragraph 197, processor may determine whether the level of the determined doneness class of the corresponding food is equal to the level of the user preference class; Paragraphs 60-61, acquiring information from a user’s voice input as intention information and analyzing the result) from a natural language processing (Paragraph 61, processor acquires information using speech input into a text string or a natural language process engine for acquiring intention information of a natural language)
CHAE as modified fails to explicitly teach:
obtain the visual attribute information based on an analysis result of the user input using a large language model (LLM) learned with a deep learning algorithm.
PASHUT (US 20230405834 A1) teaches a robotic food preparation system, wherein:
a large language model (LLM) learned with a deep learning algorithm is used for natural language process (Paragraph 146, ML system is a generative AI system such as a large language model which is capable of natural language processing)
It would have thus been obvious to someone of ordinary skill in the art before the filing date of the claimed invention to have modified CHAE with PASHUT and have the natural language processing engine for acquiring intention information of a natural language (CHAE Paragraph 61) be a large language model. This would have been done as large language models are known in the art to be capable of performing natural language processing, and thus would have been the result of obvious engineering choice (PASHUT Paragraph 146).
While the Office does not concede the point, the applicant may argue that CHAE does not explicitly teach that the voice input is analyzed and used to set the preference class of the cooking system. However, HAN (US 20210401223 A1) teaches a cooking device wherein a microphone is used to recognize a voice command of a user (HAN Paragraph 95), and which is used to set the desired cooking state of food (HAN Paragraph 32). Thus, it would have thus been obvious to someone of ordinary skill in the art before the filing date of the claimed invention to have modified CHAE with HAN and have the preference class of the cooking system be set using a voice input by the user as well as have a system to analyze the user’s voice command to control the parameters of the cooking system. This would have been done to allow the user to have more convenient input manipulation and setting of cooking parameters (HAN Paragraphs 12-14).
Claim(s) 3-5 is/are rejected under 35 U.S.C. 103 as being unpatentable over CHAE (US 20210137311 A1) in view of Bhogal (US 20200278117 A1), PASHUT (US 20230405834 A1) and HAN (US 20210401223 A1) as applied to claim 3 above, and further in view of Gonzalez (US 11355122 B1).
Regarding claim 3, CHAE as modified teaches the cooking device of claim 2, wherein the processor (Paragraphs 155-158, processor 180) is further configured to:
input an attribute information request including the identified type of food and the analysis result to the large language model (Figure 7B Paragraphs 192, level of the user preference class according to each food is updated according to the user’s feedback; Paragraph 157, processor 180 converts the voice command into text and the analysis is performed on the text; HAN Paragraph 32, voice recognition module recognizes a voice command and sets a cooking parameter based on the detected voice; user using voice command to speak their preference level would cause the speech to text module to convert the applicant’s speech into a text string which would include the user’s preference class and type of food; said text string, which would include the type of food and analysis result, would be input into a LLM to at least perform the step of making corrections, modifying the parameters in response, and responding in voice based on the user’s input; said steps are known in the art to be beneficial as evidenced by Figure 2 of Gonzalez (US 11355122 B1) and thus it would have thus been obvious to someone of ordinary skill in the art before the filing date of the claimed invention to have modified CHAE with Gonzalez and have the speech conversion include the steps of Figure 2 of Gonzalez such as to correct and process the converted text [Gonzalez Column 8 Lines 17-30]),
and obtain the visual attribute information in response to the attribute information request from the large language model (Paragraph Paragraphs 175-178, storing a user preference class within the memory 170).
See 112b rejection above for “obtain the visual attribute information in response to the attribute information request from the large language model”.
Regarding claim 4, CHAE as modified teaches the cooking device of claim 3, wherein
the visual attribute information includes at least one of color information of the food, texture information of the food, or state information of the food (state information; Paragraph 169, degrees of doneness are classified into multiple levels of the degree of how cooking the food is).
Regarding claim 5, CHAE as modified teaches the cooking device of claim 3.
CHAE as modified fails to explicitly teach:
the visual attribute information is expressed in one sentence.
Gonzalez (US 11355122 B1) teaches using machine learning to correct the output of an automatic speech recognition system, wherein:
the visual attribute information is expressed in one sentence (Column 11 Lines 60-64, utterances 115 which are inserted into the NLP pipeline is a sentence; Column 8 Lines 17-30, speech to text converts the audio into text which is processed by an NLP post processor which makes corrections to created corrected utterances; LLM would perform the corrections and would output a sentence when utterances are inserted as a sentence)
It would have thus been obvious to someone of ordinary skill in the art before the filing date of the claimed invention to have modified CHAE with Gonzalez and have the visual attribute information be expressed in one sentence. This would have been done as the output is dependent upon the input and it is well known in the art that users can input utterances which are expressed in one sentence (Gonzalez Column 11 Lines 60-64)
Claim(s) 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over CHAE (US 20210137311 A1) in view of Bhogal (US 20200278117 A1) as applied to claim 7 above, and further in view of Scheau (US 20190188285 A1).
Regarding claim 8, CHAE as modified teaches the cooking device of claim 7, wherein
CHAE as modified fails to teach:
the similarity is calculated by:
converting the cooking image to a first vector, converting text corresponding to the visual attribute information to a second vector, locating the converted first and second vectors in an embedded vector space, measuring a distance between the first vector and the second vector, and calculating the similarity using the measured distance.
Scheau (US 20190188285 A1) teaches an image search with embedding-based models, wherein:
the similarity (Paragraph 78, a relevance-score) is calculated by:
converting the cooking image to a first vector (Paragraph 74, image embedding is generated representing the image object based on one or more features of the image object),
converting text corresponding to the visual attribute information to a second vector (Paragraph 65, query embedding corresponding to a point in the embedding space is generated based on the query; Paragraph 38, query involves the user submitting text into the query field),
locating the converted first and second vectors in an embedded vector space (Figure 4 Paragraph 58, objects are located in the vector space),
measuring a distance between the first vector and the second vector (Euclidian distance), and
calculating the similarity using the measured distance (Paragraph 78, a relevance-score based on a similarity metric between the transformed query embedding and transformed image embedding representing the identified image object wherein the similarity metric is a Euclidian distance; Paragraph 59, similarity metric of vectors in the vector space is calculated include distances).
It would have thus been obvious to someone of ordinary skill in the art before the filing date of the claimed invention to have modified CHAE with Scheau and have the similarity be calculated by converting the image and text into vectors and comparing said vectors. This would have been done to provide a score of how similar the image is to the user’s desired result (Scheau Paragraph 81).
The Office further notes that converting images into their feature vectors and comparing said vectors with text embeddings converted from text inputs, such as to perform a similarity calculation to determine how close of a match they are is known in the art as evidenced by Yu (US 20240378230 A1).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to FRANKLIN JEFFERSON WANG whose telephone number is (571)272-7782. The examiner can normally be reached M-F 10AM-6PM (E.S.T).
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Ibrahime Abraham can be reached at (571) 270-5569. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/F.J.W./Examiner, Art Unit 3761
/WOODY A LEE JR/Primary Examiner, Art Unit 3761
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