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 .
Response to Amendment
The amendment filed on 08/04/2026 has been entered and accepted. The amendment with regard to the objection of claims 3-5 has been accepted and the objection has been withdrawn.
Response to Arguments
Applicant’s arguments with respect to claim(s) 1 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument. A new rejection has been made over MAENG (US 20210182667 A1) in view of CHAE (US 20210137311 A1), PASHUT (US 20230405834 A1), and HAN (US 20210401223 A1).
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, 7, and 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over MAENG (US 20210182667 A1) in view of CHAE (US 20210137311 A1), PASHUT (US 20230405834 A1), and HAN (US 20210401223 A1).
Regarding claim 1, MAENG (US 20210182667 A1) teaches a cooking device, comprising:
a cooking chamber (Paragraph 143, interior of the cooking apparatus 100);
a heater configured to heat the cooking chamber (Paragraph 135, cooking material is disposed within a space inside a main body 101; Paragraph 160, heater 140 for supplying heat for cooking a cooking material);
a camera configured to photograph food located inside the cooking chamber (Paragraph 142, camera 120 photographs a cooking material that is being cooked in the main body 101);
a user input interface configured to receive a user input (Paragraph 158, input interface 130 is configured to receive information inputted by a user); and
a processor (Figures 3-5 Paragraph 179, one or more controller used to drive the cooking apparatus 100; Paragraph 134, learning processor 160) configured to:
identify a type of the food based on a food image photographed by the camera (Claim 4 Paragraph 172, learning processor 160 determines the type of cooking material extracted from the camera 120),
obtain visual attribute information corresponding to a desired cooking state of the food (Paragraphs 148-149, cooked-state determination neural network reprocesses the image of the cooking material after cooking such as to generate an image of the cooking material after cooking wherein the images and changes in images are learned; Paragraph 150, image of the cooked instant rice is compared with the learned information to determine whether or not the food is sufficiently cooked or not; Paragraph 162, memory stores image information of each kind of cooking material), the visual attribute information including color information of the food and texture information of the food (Paragraph 146, RGB camera 121 photographs the exterior and color of the cooking material; Paragraph 148, RGB camera captures an image of the completely cooked cooking material; Paragraphs 148-149, cooked-state determination neural network reprocesses the image of the cooking material after cooking such as to generate an image of the cooking material after cooking wherein the images and changes in images are learned; this indicates that the image of cooking material used in the cooked-state determination neural network includes both color and exterior texture information), 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 41 and 144, heater is controlled based on the state of the surface of the cooking material, preset recipe, and cooked state of the cooking material so that the cooking material is appropriately cooked),
compare the visual attribute information with a cooking image photographed by the camera during cooking of the food (Paragraph 150, cooked state extraction model 122 determines whether the image of the cooked instant rice matches with the learned information and concludes whether the rice has been sufficiently or insufficiently cooked), and
determine whether or not to continue cooking the food based on a result of the comparison (Paragraph 150, cooked state extraction model 122 continues to cook the rice until the capture image of the instant rice matches with the learned information).
MAENG fails to teach:
obtain visual attribute information indicating corresponding to a desired cooking state of the food based on an analysis result of the user input using a large language model (LLM) learned with a deep learning algorithm
CHAE (US 20210137311 A1) teaches an artificial intelligence device and operating method, comprising:
obtain visual attribute information indicating corresponding to a desired cooking state of the food 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)
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 MAENG with CHAE and have the cooked state extraction model learn finished conditions based on user preference classes for the cooking material and use the corresponding learned information of cooked food to control the heater. This would have been done such that the apparatus can receive feedback regarding a degree of doneness preferred by the user and automatically cook the corresponding food based on the feedback (CHAE Paragraph 8).
MAENG modified with CHAE fails to teach:
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 MAENG with PASHUT and have the natural language processing for acquiring intention information of a natural language (MAENG Paragraphs 184-186; 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 MAENG 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 MAENG 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).
Regarding claim 7, MAENG 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 155, cooked state extraction model 122 determines whether the internal temperature and external temperature of the cooked instant rice fall within the temperature range that is preset based on the learned information; use of threshold/range values to determine whether food is cooked or not is known and would have been obvious to apply to also apply to visual attribute information), 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 (Paragraph 264, cooking apparatus 100 indicates the completion of cooking to the user)
CHAE further teaches:
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).
It would have been obvious for the same motivation as claim 1.
Regarding claim 10, MAENG as modified teaches the cooking device of claim 1.
CHAE further teaches:
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.
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 MAENG with CHAE and have the user input be a voice command. This would have been done to allow the processor to convert voice information from the user into text and utilize said information (CHAE Paragraph 157).
Claim(s) 5 is/are rejected under 35 U.S.C. 103 as being unpatentable over MAENG (US 20210182667 A1) in view of CHAE (US 20210137311 A1), PASHUT (US 20230405834 A1), and HAN (US 20210401223 A1) as applied to claim 1 above, and further in view of Gonzalez (US 11355122 B1).
Regarding claim 5, MAENG as modified teaches the cooking device of claim 1.
MAENG 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 MAENG 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 MAENG (US 20210182667 A1) in view of CHAE (US 20210137311 A1), PASHUT (US 20230405834 A1), and HAN (US 20210401223 A1) as applied to claim 7 above, and further in view of Scheau (US 20190188285 A1).
Regarding claim 8, MAENG as modified teaches the cooking device of claim 7, wherein
MAENG 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 MAENG 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).
Claim(s) 9 is/are rejected under 35 U.S.C. 103 as being unpatentable over MAENG (US 20210182667 A1) in view of CHAE (US 20210137311 A1), PASHUT (US 20230405834 A1), and HAN (US 20210401223 A1) as applied to claim 7 above, and further in view of Bhogal (US 20200278117 A1)
Regarding claim 9, MAENG as modified teaches the cooking device of claim 7.
CHAE further teaches:
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)
MAENG modified with CHAE fails to explicitly teach:
the cooking completion notification includes feedback information for receiving a user feedback
Bhogal (US 20200278117 A1) teaches a tailored food preparation with an oven, wherein:
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 MAENG 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).
Conclusion
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.
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).
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
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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