Image Processing Method, Electronic Device, Image Processing System, and Chip System

§101 §103 §112

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 . Response to Remark(s) Applicant's amendment filed June 23rd, 2026 have been fully entered and considered.. Regarding the arguments to the 101 rejections the examiner respectfully finds the Applicants’ argument to be non-persuasive, and regarding the previous prior art rejections, the examiner respectfully finds the arguments to be persuasive. Since the amendment has introduced some new features into the claims, all new grounds of rejection set forth in the present action were necessitated by Applicants’ claim amendments. Accordingly, this action is made final. Status of Claims Claims 1, 4, 12-13, 16-17, 20-23 and 26-35 are pending, claims 1, 4, 12, 16-17, 20-21, 23, 26-27 and 33 have been amended, claims 2-3, 5-11, 14-15, 18-19 and 24-25 have been canceled, claims 34-35 have been added . Claims 1, 4, 12-13, 16-17, 20-23 and 26-35 remains rejected. Response to Argument(s) 101 rejection: In pages 12-16 of the Applicants’ remarks, the Applicants argue that the present disclosure addresses the specific technical problem that different feature analysis network models require different types of image features, which a single shared feature extraction model cannot simultaneously satisfy for all tasks; and the current independent claims 1, 17 and 23 expressly recite a technical solution to this problem by requiring a plurality of feature extraction network models stored in the first device comprises an image classification network model, a target detection network model, and a semantic segmentation network model, wherein each in a deep neural network model configured to meet the feature information requirements of a respective different feature analysis network model, as described in the instant disclosure’s Pars. [0004], [0049], [0058] and [0145] by which the first device selects an appropriately matched feature extraction model for the given task and generates identification information that enables the second device to route the received feature information to the correct corresponding feature analysis network model. Therefore, the claims are directed to a particular improvement in the capabilities of a computing device. Moreover, the Applicants find the claims’ limitations to not be directed to mathematical concepts, certain method of organizing human activities, or mental processes. Examiner’s reply: The examiner respectfully disagrees with the Applicants’ arguments, moreover, the Applicants are reminded that the claims are construed based on BRI (broadest reasonable interpretation) in light of the specification, therefore, the use of direct importing of teachings from the disclosure to then be the instant scope of the claims while claims’ language do not reflect these teachings, is not a persuasive. Particularly, the examiner respectfully finds no recitation in the claims reflect this discussed technical problem and solution. Moreover, there is no different types of features appear in the claims, moreover, there is no appropriate matched feature extraction model, which requires specific conditioning to why only a specific type of model would work with a specific type of feature which further requires matching step. Nevertheless, the claims recite the different models being deep neural network, however, doesn’t give distinct differences or definition to what these models are and why they are different in processing different types of features, even so, there is no support from the disclosure of specific neural network algorithms are for these models, the models are disclosed nominally as mere mentioning of general names. Furthermore, the examiner finds the limitations of storing, to a memory of a first device… to be of an insignificant data gathering pre-solution activity additional element, under Step 2A Prong 2 and Step 2B, of storing data and information here being data of a plurality feature extraction network models to be stored being deep neural network to meet some feature information requirements, which is still mere data/information even by giving specifics. Same for receiving, from a sensor…to-be-processed image; receiving, by the first device…models to be used to extract…which are all specific steps of receiving data/gathering data insignificant extra-solution activities, even by giving specifics they are still mere data to be stored and received. The step of processing, by one or more processors…the to-be-processed image…, is a step of insignificant extra-solution activity of processing instructions, data, generic function of a processor to perform processing program instructions and data. The step of “identifying…the feature information to obtain identification information of the feature information…by the feature extraction instruction” is a mental process by giving instructions that a human mind can also observe and identify. The step of sending….feature information is an insignificant post-solution activity of sending data/information. Furthermore, the additional elements or a memory of a first device, and a second device, a sensor, processors, the different models and neural network models, are recited at high level of generality to be generic computer and computer components and generic neural network models. See 101 analysis below for more details. Prior Art Rejection: In view of the Amendments to independent claims 1, 17 and 23 the previously applied prior art rejections are withdrawn since the amendment has introduced a narrower scope with new featured added into the claims. Applicants’ arguments are rendered moot in view of the new grounds of rejection set forth below. However, regarding some arguments regarding the teaching of the previously proposed prior arts, the examiner respectfully finds them to be non-persuasive. Particularly, in pages 17-24 of the Applicants’ remarks, the Applicants argue that the proposed prior arts of Feurer, Vanschoren, and Lukac fails to teach or suggest the features of the claim: “receive, by a first device and from a second device that is a distinct device communicatively coupled to the first device via a network, a feature extraction instruction indicating a target image processing task to be performed on a to-be-processed image and identifying at least one of a plurality of feature extraction network models to be used to extract feature information, and process, by one or more processors of the first device and using the at least one of the plurality of feature extraction network models identified by the feature extraction instruction, the to-be-processed image to extract the feature information of the to-be-processed image; and send, by the first device and to the second device, the feature information and the identification information to indicate to the second device to select a feature analysis network model corresponding to the at least one of the plurality of feature extraction network models used to extract the feature information” regarding the arguments for the step of “receive, by a first device…to be used to extract feature information”, the examiner finds the argument so be persuasive and since these are newly added features into the claims, new grounds of rejections are set forth below. Regarding the limitation of “process, by one or more processors…by feature extraction instruction…the to-be-processed image”, the examiner finds the Applicants’ argument to centrally argue that proposed Feurer and the other mentioned prior arts does not teach or suggest that the processing is also based on the models identified by the feature extraction instruction, which is newly added features into the claims, the arguments found to be persuasive, new grounds of rejections set forth below. Regarding the limitation of “send, by the first device….used to extract the feature information”, the Applicants centrally argue that the proposed prior arts does not teach or suggest the sending step include feature analysis network model corresponding to the at least one of the plurality of feature extraction network models used to extract the feature information, the examiner recognize these are newly added features hence, narrow down the scope of the claim, the Applicants’ arguments founds to be persuasive, new grounds of rejections are set forth below. Claim Interpretation The following is a quotation of 35 U.S.C. 112(f): (f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof. The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph: An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof. The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked. As explained in MPEP § 2181, subsection I, claim limitation(s) that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph: (A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function; (B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and (C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function. Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function. Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function. Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. This application includes one or more claim limitations that use the word “means” or “step” but are nonetheless not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph because the claim limitation(s) recite(s) sufficient structure, materials, or acts to entirely perform the recited function. Claims 1, 17 and 23, 30-31, 33 and 35 recite(s) limitation(s) that use words like “means” (or “step”) or similar terms with functional language and do invoke 35 U.S.C. 112(f): Claim 1; recites the limitation, “storing, to a memory of a first device, a plurality of…” [Line 2] and antecedent basis instances [lines 8-9, 14, 17 and 22 of claim 1] and mentioned in the dependent claims 30 [line 1], 31 [line 3], 33 [line 2] and 35 [lines 2, 5 and 7]. Claim 1; recites the limitation, “the plurality of feature extraction network models comprises an image classification network model, a target detection network model, and a semantic segmentation network model, and wherein each of the plurality of feature extraction network models is a deep neural network model configured to meet…a respective different feature analysis network model” [Lines 3-7]. Claim 1; recites the limitation, “the plurality of feature extraction network models comprises an image classification network model, a target detection network model, and a semantic segmentation network model, and wherein each of the plurality of feature extraction network models is a deep neural network model configured to meet…a respective different feature analysis network model” [Lines 3-7]. Claim 1; recites the limitation, “the plurality of feature extraction network models comprises an image classification network model, a target detection network model, and a semantic segmentation network model, and wherein each of the plurality of feature extraction network models is a deep neural network model configured to meet…a respective different feature analysis network model” [Lines 3-7]. Claim 1; recites the limitation, “the plurality of feature extraction network models comprises an image classification network model, a target detection network model, and a semantic segmentation network model, and wherein each of the plurality of feature extraction network models is a deep neural network model configured to meet…a respective different feature analysis network model” [Lines 3-7]. Claim 1; recites the limitation, “receiving, by the first device and from a second device that is a distinct device…a feature extraction instruction…” [Lines 9-10 and 15] and mentioned in the dependent claims 31 [line 3] and 35 [lines 2 and 8]. Claim 1; recites the limitation, “a feature analysis network model corresponding to the at least one of the plurality of feature extract network models used to extract the feature information” [Lines 24-26]. Claim 17; recites the limitation, “a plurality of feature extraction network models comprising an image classification network model, a target detection network model, and a semantic segmentation network model, wherein each of the plurality of feature extraction network models is a deep neural network model configured to meet…different feature analysis network model,” [Lines 3-7]. Claim 17; recites the limitation, “a plurality of feature extraction network models comprising an image classification network model, a target detection network model, and a semantic segmentation network model, wherein each of the plurality of feature extraction network models is a deep neural network model configured to meet…different feature analysis network model,” [Lines 3-7]. Claim 17; recites the limitation, “a plurality of feature extraction network models comprising an image classification network model, a target detection network model, and a semantic segmentation network model, wherein each of the plurality of feature extraction network models is a deep neural network model configured to meet…different feature analysis network model,” [Lines 3-7]. Claim 17; recites the limitation, “a plurality of feature extraction network models comprising an image classification network model, a target detection network model, and a semantic segmentation network model, wherein each of the plurality of feature extraction network models is a deep neural network model configured to meet…different feature analysis network model,” [Lines 3-7]. Claim 17; recites the limitation, “receive, from a second device, a feature extraction instruction…” [Line 15] and antecedent basis instances in lines 16, 30-31 of claim 17. Claim 17; recites the limitation, “a feature analysis network model corresponding to the at least one of the plurality of feature extract network models used to extract the feature information” [Lines 31-34]. Claim 23; recites the limitation, “a plurality of feature extraction network models comprising an image classification network model, a target detection network model, and a semantic segmentation network model, wherein each of the plurality of feature extraction network models is a deep neural network model configured to meet…different feature analysis network model,” [Lines 4-9]. Claim 23; recites the limitation, “a plurality of feature extraction network models comprising an image classification network model, a target detection network model, and a semantic segmentation network model, wherein each of the plurality of feature extraction network models is a deep neural network model configured to meet…different feature analysis network model,” [Lines 4-9]. Claim 23; recites the limitation, “a plurality of feature extraction network models comprising an image classification network model, a target detection network model, and a semantic segmentation network model, wherein each of the plurality of feature extraction network models is a deep neural network model configured to meet…different feature analysis network model,” [Lines 4-9]. Claim 23; recites the limitation, “a plurality of feature extraction network models comprising an image classification network model, a target detection network model, and a semantic segmentation network model, wherein each of the plurality of feature extraction network models is a deep neural network model configured to meet…different feature analysis network model,” [Lines 4-9]. Claim 23; recites the limitation, “receive, from a second device, a feature extraction instruction…” [Line 11] and antecedent instances in lines 14 and 25-26 of claim 23. Claim 23; recites the limitation, “a feature analysis network model corresponding to the at least one of the plurality of feature extract network models used to extract the feature information” [Lines 26-28]. Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof. After a careful analysis, as disclosed above, and a careful review of the specification the following limitations in claims 1, 17 and 23; (i) “a first device” the instant disclosure, filed on January 27th, 2023, discloses in paragraphs [0064-0065] wherein, the first device can be an electronic device with a camera, such as photographing apparatus, a mobile phone, or a tablet computer, thus have sufficient structure or material wherein is electronic device with a camera). (ii) “an image classification network model” there is no sufficient support of a written disclosure for this recited feature of an image classification network model in the instant specification, for this feature to perform the recited function, hence, is lack of a written support of a structure, material and/or act, thus have insufficient structure, material and/or act. (iii) “a target detection network model” there is no sufficient support of a written disclosure for this recited feature of a target detection network model in the instant specification, for this feature to perform the recited function, hence, is lack of a written support of a structure, material and/or act, thus have insufficient structure, material and/or act. (iv) “a semantic segmentation network model” there is no sufficient support of a written disclosure for this recited feature of a semantic segmentation network model in the instant specification, for this feature to perform the recited function, hence, is lack of a written support of a structure, material and/or act, thus have insufficient structure, material and/or act. (v) “a deep neural network model” there is no sufficient support of a written disclosure for this recited feature of a deep neural network model in the instant specification, for this feature to perform the recited function, hence, is lack of a written support of a structure, material and/or act, thus have insufficient structure, material and/or act. (vi) “a second device” the instant disclosure, filed on January 27th, 2023, discloses in paragraphs [0064-0065] wherein, the second device can be an electronic device having an image feature analysis capability, such as a cloud platform, a server, a computer, a notebook computer, or a mobile phone, thus have sufficient structure or material wherein is electronic device with an image feature analysis capability, such as a cloud platform, a server, a computer, a notebook computer, or a mobile phone). (vii) “feature analysis network model” there is no sufficient written support of a structure, material and/or act for the recites feature analysis network model to perform the recited function, the closest support can be found in the instant disclosure, filed on January 27th, 2023, discloses in paragraph [0005] wherein, a feature analysis network model is in a multi-task deep neural network model, however, is nominally mentioned and not provided sufficient structure, material and/or act for the element to perform its recited function, thus have insufficient structure, material and/or act. If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. Claim Rejections - 35 USC § 112 The following is a quotation of 35 U.S.C. 112(b): (b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention. The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph: The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention. Claims 1, 17 and 23 along with their dependent claims are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, or for pre-AIA the applicant regards as the invention. Claims 1, 17 and 23 with limitations: Claim 1; recites the limitation, “the plurality of feature extraction network models comprises an image classification network model, a target detection network model, and a semantic segmentation network model, and wherein each of the plurality of feature extraction network models is a deep neural network model configured to meet…a respective different feature analysis network model” [Lines 3-7]. Claim 1; recites the limitation, “the plurality of feature extraction network models comprises an image classification network model, a target detection network model, and a semantic segmentation network model, and wherein each of the plurality of feature extraction network models is a deep neural network model configured to meet…a respective different feature analysis network model” [Lines 3-7]. Claim 1; recites the limitation, “the plurality of feature extraction network models comprises an image classification network model, a target detection network model, and a semantic segmentation network model, and wherein each of the plurality of feature extraction network models is a deep neural network model configured to meet…a respective different feature analysis network model” [Lines 3-7]. Claim 1; recites the limitation, “the plurality of feature extraction network models comprises an image classification network model, a target detection network model, and a semantic segmentation network model, and wherein each of the plurality of feature extraction network models is a deep neural network model configured to meet…a respective different feature analysis network model” [Lines 3-7]. Claim 1; recites the limitation, “a feature analysis network model corresponding to the at least one of the plurality of feature extract network models used to extract the feature information” [Lines 24-26]. Claim 17; recites the limitation, “a plurality of feature extraction network models comprising an image classification network model, a target detection network model, and a semantic segmentation network model, wherein each of the plurality of feature extraction network models is a deep neural network model configured to meet…different feature analysis network model,” [Lines 3-7]. Claim 17; recites the limitation, “a plurality of feature extraction network models comprising an image classification network model, a target detection network model, and a semantic segmentation network model, wherein each of the plurality of feature extraction network models is a deep neural network model configured to meet…different feature analysis network model,” [Lines 3-7]. Claim 17; recites the limitation, “a plurality of feature extraction network models comprising an image classification network model, a target detection network model, and a semantic segmentation network model, wherein each of the plurality of feature extraction network models is a deep neural network model configured to meet…different feature analysis network model,” [Lines 3-7]. Claim 17; recites the limitation, “a plurality of feature extraction network models comprising an image classification network model, a target detection network model, and a semantic segmentation network model, wherein each of the plurality of feature extraction network models is a deep neural network model configured to meet…different feature analysis network model,” [Lines 3-7]. Claim 17; recites the limitation, “a feature analysis network model corresponding to the at least one of the plurality of feature extract network models used to extract the feature information” [Lines 31-34]. Claim 23; recites the limitation, “a plurality of feature extraction network models comprising an image classification network model, a target detection network model, and a semantic segmentation network model, wherein each of the plurality of feature extraction network models is a deep neural network model configured to meet…different feature analysis network model,” [Lines 4-9]. Claim 23; recites the limitation, “a plurality of feature extraction network models comprising an image classification network model, a target detection network model, and a semantic segmentation network model, wherein each of the plurality of feature extraction network models is a deep neural network model configured to meet…different feature analysis network model,” [Lines 4-9]. Claim 23; recites the limitation, “a plurality of feature extraction network models comprising an image classification network model, a target detection network model, and a semantic segmentation network model, wherein each of the plurality of feature extraction network models is a deep neural network model configured to meet…different feature analysis network model,” [Lines 4-9]. Claim 23; recites the limitation, “a plurality of feature extraction network models comprising an image classification network model, a target detection network model, and a semantic segmentation network model, wherein each of the plurality of feature extraction network models is a deep neural network model configured to meet…different feature analysis network model,” [Lines 4-9]. Claim 23; recites the limitation, “a feature analysis network model corresponding to the at least one of the plurality of feature extract network models used to extract the feature information” [Lines 26-28]. Claims 1, 17 and 23, each respectively invokes 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function. The specification is devoid of adequate structure to perform the claimed functions. The specification does not provide sufficient details such that one of the ordinary skill in the art would understand which structure performed(s) the claimed function. Therefore, the claim is indefinite and is rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph. Applicant may: (a) Amend the claim so that the claim limitation will no longer be interpreted as a limitation under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph; (b) Amend the written description of the specification such that it expressly recites what structure, material, or acts perform the entire claimed function, without introducing any new matter (35 U.S.C. 132(a)); or (c) Amend the written description of the specification such that it clearly links the structure, material, or acts disclosed therein to the function recited in the claim, without introducing any new matter (35 U.S.C. 132(a)). If applicant is of the opinion that the written description of the specification already implicitly or inherently discloses the corresponding structure, material, or acts and clearly links them to the function so that one of ordinary skill in the art would recognize what structure, material, or acts perform the claimed function, applicant should clarify the record by either: (a) Amending the written description of the specification such that it expressly recites the corresponding structure, material, or acts for performing the claimed function and clearly links or associates the structure, material, or acts to the claimed function, without introducing any new matter (35 U.S.C. 132(a)); or (b) Stating on the record what the corresponding structure, material, or acts, which are implicitly or inherently set forth in the written description of the specification, perform the claimed function. For more information, see 37 CFR 1.75(d) and MPEP §§ 608.01(o) and 2181. The following is a quotation of the first paragraph of 35 U.S.C. 112(a): (a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention. The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112: The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention. Claim 1, 17 and 23 along with their dependent claims are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for pre-AIA the inventor(s), at the time the application was filed, had possession of the claimed invention. As described above, the disclosure does not provide adequate structure to perform the claimed function in the recited limitation. Claims 1, 17 and 23 with limitations: Claim 1; recites the limitation, “the plurality of feature extraction network models comprises an image classification network model, a target detection network model, and a semantic segmentation network model, and wherein each of the plurality of feature extraction network models is a deep neural network model configured to meet…a respective different feature analysis network model” [Lines 3-7]. Claim 1; recites the limitation, “the plurality of feature extraction network models comprises an image classification network model, a target detection network model, and a semantic segmentation network model, and wherein each of the plurality of feature extraction network models is a deep neural network model configured to meet…a respective different feature analysis network model” [Lines 3-7]. Claim 1; recites the limitation, “the plurality of feature extraction network models comprises an image classification network model, a target detection network model, and a semantic segmentation network model, and wherein each of the plurality of feature extraction network models is a deep neural network model configured to meet…a respective different feature analysis network model” [Lines 3-7]. Claim 1; recites the limitation, “the plurality of feature extraction network models comprises an image classification network model, a target detection network model, and a semantic segmentation network model, and wherein each of the plurality of feature extraction network models is a deep neural network model configured to meet…a respective different feature analysis network model” [Lines 3-7]. Claim 1; recites the limitation, “a feature analysis network model corresponding to the at least one of the plurality of feature extract network models used to extract the feature information” [Lines 24-26]. Claim 17; recites the limitation, “a plurality of feature extraction network models comprising an image classification network model, a target detection network model, and a semantic segmentation network model, wherein each of the plurality of feature extraction network models is a deep neural network model configured to meet…different feature analysis network model,” [Lines 3-7]. Claim 17; recites the limitation, “a plurality of feature extraction network models comprising an image classification network model, a target detection network model, and a semantic segmentation network model, wherein each of the plurality of feature extraction network models is a deep neural network model configured to meet…different feature analysis network model,” [Lines 3-7]. Claim 17; recites the limitation, “a plurality of feature extraction network models comprising an image classification network model, a target detection network model, and a semantic segmentation network model, wherein each of the plurality of feature extraction network models is a deep neural network model configured to meet…different feature analysis network model,” [Lines 3-7]. Claim 17; recites the limitation, “a plurality of feature extraction network models comprising an image classification network model, a target detection network model, and a semantic segmentation network model, wherein each of the plurality of feature extraction network models is a deep neural network model configured to meet…different feature analysis network model,” [Lines 3-7]. Claim 17; recites the limitation, “a feature analysis network model corresponding to the at least one of the plurality of feature extract network models used to extract the feature information” [Lines 31-34]. Claim 23; recites the limitation, “a plurality of feature extraction network models comprising an image classification network model, a target detection network model, and a semantic segmentation network model, wherein each of the plurality of feature extraction network models is a deep neural network model configured to meet…different feature analysis network model,” [Lines 4-9]. Claim 23; recites the limitation, “a plurality of feature extraction network models comprising an image classification network model, a target detection network model, and a semantic segmentation network model, wherein each of the plurality of feature extraction network models is a deep neural network model configured to meet…different feature analysis network model,” [Lines 4-9]. Claim 23; recites the limitation, “a plurality of feature extraction network models comprising an image classification network model, a target detection network model, and a semantic segmentation network model, wherein each of the plurality of feature extraction network models is a deep neural network model configured to meet…different feature analysis network model,” [Lines 4-9]. Claim 23; recites the limitation, “a plurality of feature extraction network models comprising an image classification network model, a target detection network model, and a semantic segmentation network model, wherein each of the plurality of feature extraction network models is a deep neural network model configured to meet…different feature analysis network model,” [Lines 4-9]. Claim 23; recites the limitation, “a feature analysis network model corresponding to the at least one of the plurality of feature extract network models used to extract the feature information” [Lines 26-28]. The specification does not demonstrate that applicant has made an invention that achieves the claimed function because the invention is not described with sufficient detail such that one of ordinary skill in the art can reasonably conclude that the inventor had possession of the claimed invention. 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, 4, 12-13, 16-17, 20-23 and 26-35 are rejected under 35 U.S.C. 101 Regarding independent claim 1 and its dependent claims 4 and 12-13, 16 and 28-35, Step 1 Analysis: Claim 1 is directed to a method/process, which falls within one of the four statutory categories. Step 2A Prong 1 Analysis: Claim 1 recites, in part, “identifying…is based on the target image processing task indicated by the feature extraction instruction; select a feature analysis network model corresponding to the at least one of the plurality of feature extraction network models used to extract the feature information.” These limitations as drafted, are processes that, under broadest reasonable interpretation, covers the performance of the limitation in the mind which falls within the “Mental Processes” grouping of abstract ideas. The limitations of: “identifying…” are steps, under BRI (broadest reasonable interpretation) scope, understood to be mental processes wherein the human mind can observe an image and evaluate its information to identify feature information to obtain identification information and select corresponding feature analysis network model; “select….” which is a mental process abstract idea wherein, the human mind can select a model to use based on some observable desirable condition, criteria such as recited in the claim. Accordingly, the claim recites an abstract idea. Step 2A Prong 2 Analysis: This judicial exception is not integrated into a practical application. particular, the claim recites the following additional element(s) – storing, to a memory of a first device, a plurality of feature extraction network models, wherein the plurality of feature extraction network models comprises an image classification network model, a target detection network model, and a semantic segmentation network model….; receiving, from a sensor of the first device, a to-be-processed image; receiving, by the first device….used to extract feature information; wherein the identification information of the feature information comprises a first identifier of the least one of the plurality of feature extraction network models processing, by one or more processors of the first device and using at least one of the plurality of feature extraction network models, the to-be-processed image to extract feature information of the to-be-processed image…; sending, by the first device and to a second device, the feature information and the identification information to indicate to the second device to select a feature analysis network model of the neural network…; The additional elements are indicated, are steps considering data gathering insignificant extra-solution activities of extracting data/information of a model and an image, and sending these data/information to another device from a first device (generic high level of generality recitation of a device) and generic high level of generality of neural network without further limiting, in details, how the neural network structured and function to arrive at such outcomes. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim as a whole is directed to an abstract idea. Please see MPEP §2106.04.(d).III.C. The additional elements include steps of insignificant extra-solution/post-solution activities of data gathering, data generating, data transmitting, etc. [acquiring data/information, transmitting data/info., outputting data/information, displaying data/info., converting data/info., generating data/info., etc.] . The additional element includes generic neural network/machine learning model(s)/classifier recited at high level of generality without limiting further, in details, on how the neural network/machine learning model(s) function to arrive at such output, these additional elements are recited as a mere attempt to implement the abstract ideas/judicial exceptions using generic neural network/machine learning models. The step of storing, to a memory of a first device… to be of an insignificant data gathering pre-solution activity additional element, under Step 2A Prong 2 and Step 2B, of storing data and information here being data of a plurality feature extraction network models to be stored being deep neural network to meet some feature information requirements, which is still mere data/information even by giving specifics. Same for receiving, from a sensor…to-be-processed image; receiving, by the first device…models to be used to extract…which are all specific steps of receiving data/gathering data insignificant extra-solution activities, even by giving specifics they are still mere data to be stored and received. The step of processing, by one or more processors…the to-be-processed image…, is a step of insignificant extra-solution activity of processing instructions, data, generic function of a processor to perform processing program instructions and data. The step of sending….feature information is an insignificant post-solution activity of sending data/information. Furthermore, the additional elements or a memory of a first device, and a second device, a sensor, processors, the different models and neural network models, are recited at high level of generality to be generic computer and computer components and generic neural network models. Step 2B Analysis: there are no additional elements that amount to significantly more than the judicial exception. Please see MPEP §2106.05. The claim is directed to an abstract idea. For all of the foregoing reasons, claim 1 does not comply with the requirements of 35 USC 101. Accordingly, the dependent claims 4 and 12-13, 16 and 28-35 do not provide elements that overcome the deficiencies of the independent claim 1. Moreover, claims 4 and 12-13, 16, 28-35 recites further additional elements, under Step 2A Prong 2, to be insignificant extra-solution activities for the “obtaining….” steps are merely steps involving obtaining data/information regarding an image, feature information or a model, by BRI, are data gathering steps; “wherein the output layer is one of a convolutional layer, a pooling layer, or a fully connected layer of a neural network” indicates a selection, therefore, only one option is the instant scope of the claim, moreover, any of which is just generic well known neural network and component, hence, not an indication of an integration of the judicial exceptions into a practical application nor considered significantly more; and the step of “using….” are merely steps that, under Step 2A Prong 2, to be using information/data of insignificant post-solution activities, these are not an indication of an integration of the judicial exceptions into a practical application; moreover, the limitation of “wherein…comprises…” are just limitation that provide general further specification to the abstract ideas, additional elements that they each depend on hence, still abstract ideas, additional elements; claims 28-35 recite additional elements of generic neural network and neural network components well-known in the art recited at high level of generality without further limiting, in details, how the neural network work to arrive at such output, the claims also recites additional elements of insignificant extra-solution activities of data gathering, data inputting, data outputting, and generic devices and device components such as a charge coupled device. Accordingly, the dependent claims 4 and 12-13, 16 and 28-35 are not patent eligible under 101. Regarding independent claim 17 and its dependent claims 20-22, The independent claim 17 recites analogous limitations to the independent claim 1 hence, can be analyzed under the same approach as for claim 1 above to be 101 ineligible, moreover, claim 17 recites further additional elements, under Step 2A Prong 2 and Step 2B, of an electronic device, at least one memory configured to store instructions, at least one processor coupled to the at least one memory and configured to execute the instructions to cause the electronic device to perform the steps; however, these additional elements are generic computer and its components to perform generic well-known functions in the art hence, not indicative of an integration of the judicial exceptions into a practical application nor being considered significantly more. Regarding the dependent claims 20-22, these claims carry analogous limitations to the dependent claims 12-16 and 28-35 which can be analyzed under the same approach to be 101 ineligible; these claims recite limitations of “obtain….,” “use….” which were merely obtaining and using of information/data to be insignificantly extra-solution activity additional elements, and the “wherein….comprises…” limitations are limitations of general further specification to the additional elements and abstract ideas hence, still are just insignificant additional elements and abstract ideas. Regarding independent claim 23 and its dependent claims 26-27, The independent claim 23 recites analogous limitations to the independent claim 1 hence, can be analyzed under the same approach as for claim 1 above to be 101 ineligible, moreover, claim 23 recites further additional elements, under Step 2A Prong 2 and Step 2B, of a computer program product comprising instructions that are stored on a computer-readable medium executed by a processor, cause a first electronic device to; however, these additional elements are generic computer and its components to perform generic well-known functions in the art hence, not indicative of an integration of the judicial exceptions into a practical application nor being considered significantly more. Regarding the dependent claims 26-27, these claims carry analogous limitations to the dependent claims 12-16 and 28-33 which can be analyzed under the same approach to be 101 ineligible; these claims recite limitations of “obtain….,” “use….” which were merely obtaining and using of information/data to be insignificantly extra-solution activity additional elements, and the “wherein….comprises…” limitations are limitations of general further specification to the additional elements and abstract ideas hence, still are just insignificant additional elements and abstract ideas. 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. 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. Claims 1, 12, 17, 21, 23, 27-28, 30-31 and 33 are rejected under 35 U.S.C. 103 as being unpatentable over Matthias Feurer et. al. (“Initializing Bayesian Hyperparameter Optimization via Meta-Learning, 2015, Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 29, No. 1 [2015]: Twenty-Nonth AAAI Conference on Artificial Intelligence, AAAI Technical Track: Heuristic Search and Optimization” hereinafter as “Feurer”) in view of Joaquin Vanschoren et. al. (“Meta-Learning and Algorithm Selection Workshop at ECAL, 2014, 21st European Conference on Artificial Intelligence” hereinafter as “Vanschoren”) further in view of Rastislav Lukac et. al. (“US 2019/0325580 A1” hereinafter as “Lukac”) and Ryou Yumiba et. al. (“US 2021/0374403 A1” hereinafter as “Yumiba”) and Ryoma Oami (“US 2020/0034649 A1” hereinafter as “Oami”). Regarding claim 1, Fuerer teaches an image processing method (page 1131, last Par. discloses “OpenML project to be the best source of datasets…for classification” and FIG. 1 illustrates the dataset includes dataset from OpenML and used for classification task such as liver disorders classification which can be understood to include dataset of images, indicating image processing), comprising: storing a plurality of feature extraction network models (Page 1130, 2nd column, discloses a metafeature computation from the input dataset, and further these metafeature of the dataset is calculated a matrix that reflects how similar the datasets are [page 1130, 2nd column, 1st par.] using algorithm 2; Page 1130, 1st col., 2nd to the last Par., discloses “MI-SMBO is agnostic of the SMBO algorithm used, as long as the algorithm’s implementation accepts an initial design as input or can be warmstarted with a given list of performance data…in contrast to exiting approaches that initialize direct search algorithms via meta-learning…SMBO procedure until all configurations…are evaluated”; wherein using the algorithm 2/SMBO algorithm to obtain different model configurations [analogous to the recited plurality of feature extraction network models]); processing using at least one of the plurality of feature extraction network models (Page 1130, 2nd column, discloses a metafeature computation from the input dataset, which is used to compute similarity between data within the dataset [page 1130, 2nd column, 1st par.] using algorithm 2 initializing image search models’ configurations [the plurality of feature extract network models]; Page 1130, 1st col., 2nd to the last Par., discloses “MI-SMBO is agnostic of the SMBO algorithm used, as long as the algorithm’s implementation accepts an initial design as input or can be warmstarted with a given list of performance data…in contrast to exiting approaches that initialize direct search algorithms via meta-learning…SMBO procedure until all configurations…are evaluated”) identified by the feature extraction instruction (Page 1131, 2nd col., 1st Par, discloses “top-level hyperparameter for choosing between classification algorithms, and set all hyperparameters of classification algorithm Ai as conditional on delta_classifier being set to Ai…hyperparameter configurations” wherein, the parameter configuration chosen for a classification algorithm suitable for a certain type of dataset is analogous to the recited feature extraction instruction), the to-be-processed data to extract, the feature information of the to-be-processed data (Page 1130, last Par., “implemented 46 metafeatures…based on their types and underlying assumptions” discloses the different types of feature to be extract from the dataset which is chosen for respective classification algorithm to fir the certain type of dataset); identifying the extracted feature information to obtain identification information of the feature information (Page 1130, 2nd col., 1st par., discloses a metric is calculated for the dataset based on the metafeatures, the measure information used to obtain the metric is analogous to the identification information as claimed), wherein the identification information of the feature information comprises a first identifier (The metric’s measures obtained from the algorithm 2 is the identification information as disclosed in page 1130, 2nd col.; wherein the metric is analogous to the recited first identifier) the at least one of the plurality of feature extraction network models (Page 1130, 2nd column, discloses a metafeature computation from the input dataset, which is used to compute similarity between data within the dataset [page 1130, 2nd column, 1st par.] using algorithm 2 initializing image search models’ configurations [the plurality of feature extract network models]) used to extract the feature information (Page 1130, last Par., “implemented 46 metafeatures…based on their types and underlying assumptions” discloses the different types of feature to be extract from the dataset which is chosen for respective classification algorithm to fir the certain type of dataset); and sending the feature information and the identification information to indicate (page 1129, 2nd col., discloses algorithm 1 to select the model optimal for performing the function most suitable for the dataset based on the metric’s measure information obtained and the metafeature) to select a feature analysis network model corresponding to the at least one of feature extraction network models used to extract (Page 1130, last Par., “implemented 46 metafeatures…based on their types and underlying assumptions” discloses the different types of feature to be extract from the dataset which is chosen for respective classification algorithm to fir the certain type of dataset). However, Fuerer does not explicitly teach and wherein each of the plurality of feature extraction network models is a deep neural network model configured to meet feature information requirements of a respective different feature analysis network model. Vanschoren teaches and wherein each of the plurality of feature extraction network models is a deep neural network model (Algorithm 2, and the selected models as disclosed in section 3, 1st 4 paragraphs, are analogous to the algorithm 2 of Fuerer, moreover, Vanschoren further teaches that the algorithm 2 and the processing of the selection is for neural network task such as disclosed in section 1, 1st Par. of “hyperparameter optimization is crucial step in the process of applying machine learning algorithms in practice” as further explained in section 2 of page 25, 1st 3 Pars., of “the construction of machine learning models typically gives rise to two optimization problems...selecting a neural network”) configured to meet feature information requirements of a respective different feature analysis network model (Section 2, 1st 2 Pars, disclose “machine learning models typically gives rise to two optimization problems. The first is internal optimization, such as selection a neural network’s likelihood-maximizing weights; the second is tuning the method’s hyperparameters” indicating the feature information requirements to be met). Therefore, it would have been obvious to one or ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teaches of Fuerer of having an image processing method, comprising: storing a plurality of feature extraction network models; processing using at least one of the plurality of feature extraction network models identified by the feature extraction instruction, the to-be-processed data to extract, the feature information of the to-be-processed data; identifying the extracted feature information to obtain identification information of the feature information, wherein the identification information of the feature information comprises a first identifier the at least one of the plurality of feature extraction network models used to extract the feature information; and sending the feature information and the identification information to indicate, with the teachings of Vanschoren of having wherein each of the plurality of feature extraction network models is a deep neural network model configured to meet feature information requirements of a respective different feature analysis network model. Wherein having Fuerer’s method of wherein each of the plurality of feature extraction network models is a deep neural network model configured to meet feature information requirements of a respective different feature analysis network model. The motivation behind the modification would have been to improve machine learning initialization based on hyperparameter optimization that shows mild improvements for low dimensional hyperparameter optimization and substantially improves for complex combined algorithm selection and hyperparameter optimization, and further to improve performance of machine learning based on hyperparameter optimization according to certain types of data. Since both Fuerer and Vanschoren both perform hyperparameter optimization for machine learning selection. Wherein Fuerer’s system improves machine learning initialization based on hyperparameter optimization that shows mild improvements for low dimensional hyperparameter optimization and substantially improves for complex combined algorithm selection and hyperparameter optimization, (see Fuerer’s Abstract) and Vanschoren’s system improves performance of machine learning based on hyperparameter optimization according to certain types of data, (see Vanschoren’s Abstract). However, Fuerer in view of Vanschoren does not explicitly teach storing, to a memory of a first device; processing, by one or more processors of the first device; identifying, by the first device; sending, by the first device; wherein the plurality of feature extraction network models comprises an image classification network model, a target detection network model, and a semantic segmentation network model; receiving, from a sensor of the first device, a to-be-processed image. Lukac teaches storing, to a memory of a first device (Par. [0028] discloses “the resulting image is then displayed on a monitor…or stored in memory by module”); processing, by one or more processors of the first device (Par. [0029] discloses “the flow may be performed by a processor”; moreover, Par. [0095] discloses “vehicles within the environment…roadside computing devices” wherein these relevant use computer is analogous to the recited first device); identifying, by the first device (Par. [0123] discloses “processor is an example of a type of hardware device that can be used in connection with the implementations above”); sending, by the first device (Par. [0123] discloses “processor is an example of a type of hardware device that can be used in connection with the implementations above”); wherein the plurality of feature extraction network models comprises an image classification network model (Par. [0076] discloses a refinement process to use one or more of feature extraction…image registration, trained classifier [image classification network] to adaptively maximize similarities in the overlapping region in images from two neighboring cameras [which is used to maximize similarities between two datasets]), a target detection network model (Par. [0076] discloses a refinement process to use one or more of feature extraction, matching, corner detection, template matching, semantic segmentation [semantic segmentation network]), object detection [target detection network]), and a semantic segmentation network model (Par. [0076] discloses a refinement process to use one or more of feature extraction, matching, corner detection, template matching, semantic segmentation [semantic segmentation network]); receiving, from a sensor of the first device, a to-be-processed image (Par. [0075] discloses the image are obtained from a camera of a device). Therefore, it would have been obvious to one or ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teaches of Fuerer in view of Vanschoren of having an image processing method, comprising: storing a plurality of feature extraction network models; processing using at least one of the plurality of feature extraction network models identified by the feature extraction instruction, the to-be-processed data to extract, the feature information of the to-be-processed data; identifying the extracted feature information to obtain identification information of the feature information, wherein the identification information of the feature information comprises a first identifier the at least one of the plurality of feature extraction network models used to extract the feature information; and sending the feature information and the identification information to indicate, with the teachings of Lukac of having storing, to a memory of a first device; processing, by one or more processors of the first device; identifying, by the first device; sending, by the first device; wherein the plurality of feature extraction network models comprises an image classification network model, a target detection network mode, and a semantic segmentation network model; receiving, from a sensor of the first device, a to-be-processed image. Wherein having Fuerer’s method of storing, to a memory of a first device; processing, by one or more processors of the first device; identifying, by the first device; sending, by the first device; wherein the plurality of feature extraction network models comprises an image classification network model, a target detection network mode, and a semantic segmentation network model; receiving, from a sensor of the first device, a to-be-processed image. The motivation behind the modification would have been to improve machine learning initialization based on hyperparameter optimization that shows mild improvements for low dimensional hyperparameter optimization and substantially improves for complex combined algorithm selection and hyperparameter optimization, and further to improve performance of machine learning based on hyperparameter optimization according to certain types of data. Since both Fuerer and Lukac both perform determining of dataset similarity based on distance measurement. Wherein Fuerer’s system improves machine learning initialization based on hyperparameter optimization that shows mild improvements for low dimensional hyperparameter optimization and substantially improves for complex combined algorithm selection and hyperparameter optimization, (see Fuerer’s Abstract) and Lukac’s system improves using different models to perform projection refinement to enhance similarities between datasets for detection to perform alignment processing more efficiently (see Lukac’s Pars. [0075-0077]). However, Fuerer in view of Vanschoren and Lukac does not explicitly teach the to-be-processed data being a to-be-processed image; receiving, by the first device and from a second device communicatively coupled to the first device via a network, a feature extraction instruction indicating a target image processing task to be performed on the to-be-processed image and identifying at least one of the plurality of feature extraction network models to be used to extract feature information; sending to a second device, indicate to the second device to select a feature analysis network model; the identifier that uniquely identified the at least one of the plurality of feature extraction network models used to extract the feature information, and wherein the first identifier is based on the target image processing task indicated by feature extraction instruction. Yumiba teaches the to-be-processed data being a to-be-processed image (Par. [0095] discloses “the learning model reselection unit may be further provided after the learning model suitability determination unit. when the learning model suitability determination unit determines that the learning model is incompatible, the learning model reselection unit takes the input sample as an input…the operation in the image recognition”); receiving, by the first device and from a second device (Par. [0097] discloses “the learning model suitability determination unit can be installed between the data-to-data recall unit and the learning model selection unit” wherein, the learning model selection unit is analogous to the recited first device and the learning model suitability determination unit is analogous to the recited second device) communicatively coupled to the first device via a network (Figure 14 illustrates the communication between the feature extraction selection unit and the data-to-data recall unit, moreover, Par. [0094] discloses “alternatively, it may be output to a file or notified an external computer via the network”), a feature extraction instruction indicating a target image processing task to be performed on the to-be-processed image (Par. [0105] discloses “by operating a button on the screen, the operator may switch the plurality of feature amounts and recall results to select one appropriate for generating a category in the screen. In this way the learning sample creation support unit may perform at least one of obtaining an input place using the plurality of feature amounts and recall results” indicating an instruction for target imaging processing task to select the appropriate feature extraction model) and identifying at least one of the plurality of feature extraction network models to be used to extract feature information (Par. [0095] discloses “the learning model reselection unit may be further provided after the learning model suitability determination unit. when the learning model suitability determination unit determines that the learning model is incompatible, the learning model reselection unit takes the input sample as an input and selects the learning model for feature amount extraction”); sending to a second device, indicate to the second device to select a feature analysis network model; the identifier that uniquely identifies (Par. [0086] discloses “the learning model selection unit may output information that uniquely determines the feature extraction learning model, such as the actual state and file name of the file of the feature extraction learning model specified by the symbol” wherein, the symbol is analogous to the recited identifier) the at least one of the plurality of feature extraction network models (Par. [0044] discloses “a learning model selection unit, which are the processes realized by the arithmetic functions…composed of a feature extraction learning model group and a recall learning model group realized by the database”) used to extract the feature information (Par. [0086] discloses “the learning model selection unit may output information that uniquely determines the feature extraction learning model, such as the actual state and file name of the file of the feature extraction learning model specified by the symbol”), and wherein the first identifier is based on the target image processing task (Par. [0048] discloses “the learning model selection unit selects the learning model that minimizes the difference between the feature amount output by the feature amount extraction unit and the feature amount output by the data-to-data recall unit, and outputs the symbol assigned to the learning model” wherein, the symbol is assigned according to the feature extraction [image processing task as claimed]) indicated by feature extraction instruction (Par. [0045] discloses “the feature extraction learning model group stores two or more feature extraction learning models in the database….share the assigned symbols of the feature extraction” wherein, the symbol is based on the data storing for feature extraction and recall learning models [analogous to the recited feature extraction instruction]). Therefore, it would have been obvious to one or ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teaches of Fuerer in view of Vanschoren and Lukac of having an image processing method, comprising: storing a plurality of feature extraction network models; processing using at least one of the plurality of feature extraction network models identified by the feature extraction instruction, the to-be-processed data to extract, the feature information of the to-be-processed data; identifying the extracted feature information to obtain identification information of the feature information, wherein the identification information of the feature information comprises a first identifier the at least one of the plurality of feature extraction network models used to extract the feature information; and sending the feature information and the identification information to indicate, with the teachings of Yumiba of having the to-be-processed data being a to-be-processed image; receiving, by the first device and from a second device, a feature extraction instruction indicating a target image processing task to be performed on the to-be-processed image and identifying at least one of the plurality of feature extraction network models to be used to extract feature information; sending to a second device, indicate to the second device to select a feature analysis network model; the identifier that uniquely identified the at least one of the plurality of feature extraction network models used to extract the feature information, and wherein the first identifier is based on the target image processing task indicated by feature extraction instruction. Wherein having Fuerer’s method of the to-be-processed data being a to-be-processed image; receiving, by the first device and from a second device, a feature extraction instruction indicating a target image processing task to be performed on the to-be-processed image and identifying at least one of the plurality of feature extraction network models to be used to extract feature information; sending to a second device, indicate to the second device to select a feature analysis network model; the identifier that uniquely identified the at least one of the plurality of feature extraction network models used to extract the feature information, and wherein the first identifier is based on the target image processing task indicated by feature extraction instruction. The motivation behind the modification would have been to improve machine learning initialization based on hyperparameter optimization that shows mild improvements for low dimensional hyperparameter optimization and substantially improves for complex combined algorithm selection and hyperparameter optimization, and further to improve performance of machine learning based on hyperparameter optimization according to certain types of data. Since both Fuerer and Yumiba both perform machine learning model selection based on type of dataset. Wherein Fuerer’s system improves machine learning initialization based on hyperparameter optimization that shows mild improvements for low dimensional hyperparameter optimization and substantially improves for complex combined algorithm selection and hyperparameter optimization, (see Fuerer’s Abstract) and Yumiba’s system improves feature extraction efficiently by accurately selecting appropriate feature extraction model based on database (see Yumiba’s Par. [0014]). However, Fuerer in view of Vanschoren further in view of Lukac and Yumiba and does not explicitly teach first device and from a second device that is a distinct device. Oami teaches first device and from a second device that is a distinct device (Par. [0154] discloses “a system including a plurality of devices…the present invention is also applicable to a case where an information processing program that achieves functions of the example embodiments is directly or remotely supplied to a system or a device”; Par. [0177] discloses “an object tracking system including a first object feature extraction device and a second object feature extraction device each of which is the object feature extraction device” wherein, first object extraction device is analogous to the recited first device and the second feature extraction device is analogous to the recited second device; furthermore, Par. [0181] also include an intelligent imaging device; Pars. [0258-0261] teach “object feature extraction device (unit)…” wherein, the device is also analogous to unit, analogous to Yumiba’s different feature extraction selection unit and the data-o-data recall unit here to be distinct devices in Oami). Therefore, it would have been obvious to one or ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teaches of Fuerer in view of Vanschoren further in view of Lukac and Yumiba of having an image processing method, comprising: storing a plurality of feature extraction network models; processing using at least one of the plurality of feature extraction network models identified by the feature extraction instruction, the to-be-processed data to extract, the feature information of the to-be-processed data; identifying the extracted feature information to obtain identification information of the feature information, wherein the identification information of the feature information comprises a first identifier the at least one of the plurality of feature extraction network models used to extract the feature information; and sending the feature information and the identification information to indicate, with the teachings of Oami of having first device and from a second device that is a distinct device Wherein having Fuerer’s method of first device and from a second device that is a distinct device The motivation behind the modification would have been to improve machine learning initialization based on hyperparameter optimization that shows mild improvements for low dimensional hyperparameter optimization and substantially improves for complex combined algorithm selection and hyperparameter optimization, and further to improve feature extraction and object tracking with less error and higher accuracy. Since both Fuerer and Oami both perform feature extraction machine learning model with different external devices. Wherein Fuerer’s system improves machine learning initialization based on hyperparameter optimization that shows mild improvements for low dimensional hyperparameter optimization and substantially improves for complex combined algorithm selection and hyperparameter optimization, (see Fuerer’s Abstract) and Oami’s system improves feature extraction and object tracking with less error and higher accuracy (see Oami’s Pars. [0004-0005]). Regarding claim 12, Fuerer in view of Vanschoren further in view of Lukac and Yumiba and Oami teaches the image processing method of claim 1, Fuerer teaches wherein identifying the feature information comprises: obtaining the identification information (page 1130, 2nd column, of the obtaining of the metafeatures and the metric information, which is according to the classification task to optimize relevant machine learning framework [page 1131, 1st col, “ML Algorithm and Hyperparameters” Section, 1st Par.]). However, Fuerer in view of Vanschoren further in view of Lukac and Oami does not explicitly teach according to the target image processing task indicated by the feature extraction instruction. Yumiba teaches according to the target image processing task (Par. [0048] discloses “the learning model selection unit selects the learning model that minimizes the difference between the feature amount output by the feature amount extraction unit and the feature amount output by the data-to-data recall unit, and outputs the symbol assigned to the learning model” wherein, the symbol is assigned according to the feature extraction [image processing task as claimed]) indicated by feature extraction instruction (Par. [0045] discloses “the feature extraction learning model group stores two or more feature extraction learning models in the database….share the assigned symbols of the feature extraction” wherein, the symbol is based on the data storing for feature extraction and recall learning models [analogous to the recited feature extraction instruction]). Therefore, it would have been obvious to one or ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teaches of Fuerer in view of Vanschoren and Lukac of having an image processing method, comprising: storing a plurality of feature extraction network models; processing using at least one of the plurality of feature extraction network models identified by the feature extraction instruction, the to-be-processed data to extract, the feature information of the to-be-processed data; identifying the extracted feature information to obtain identification information of the feature information, wherein the identification information of the feature information comprises a first identifier the at least one of the plurality of feature extraction network models used to extract the feature information; and sending the feature information and the identification information to indicate, with the teachings of Yumiba of having according to the target image processing task indicated by the feature extraction instruction. Wherein having Fuerer’s method of according to the target image processing task indicated by the feature extraction instruction. The motivation behind the modification would have been to improve machine learning initialization based on hyperparameter optimization that shows mild improvements for low dimensional hyperparameter optimization and substantially improves for complex combined algorithm selection and hyperparameter optimization, and further to improve performance of machine learning based on hyperparameter optimization according to certain types of data. Since both Fuerer and Yumiba both perform machine learning model selection based on type of dataset. Wherein Fuerer’s system improves machine learning initialization based on hyperparameter optimization that shows mild improvements for low dimensional hyperparameter optimization and substantially improves for complex combined algorithm selection and hyperparameter optimization, (see Fuerer’s Abstract) and Yumiba’s system improves feature extraction efficiently by accurately selecting appropriate feature extraction model based on database (see Yumiba’s Par. [0014]). Regarding claim 17, Fuerer discloses a first electronic device a plurality of feature extraction network models (Page 1130, 2nd column, discloses a metafeature computation from the input dataset, and further these metafeature of the dataset is calculated a matrix that reflects how similar the datasets are [page 1130, 2nd column, 1st par.] using algorithm 2; Page 1130, 1st col., 2nd to the last Par., discloses “MI-SMBO is agnostic of the SMBO algorithm used, as long as the algorithm’s implementation accepts an initial design as input or can be warmstarted with a given list of performance data…in contrast to exiting approaches that initialize direct search algorithms via meta-learning…SMBO procedure until all configurations…are evaluated”; wherein using the algorithm 2/SMBO algorithm to obtain different model configurations [analogous to the recited plurality of feature extraction network models); identify, based on the feature extraction instruction Page 1131, 2nd col., 1st Par, discloses “top-level hyperparameter for choosing between classification algorithms, and set all hyperparameters of classification algorithm Ai as conditional on delta_classifier being set to Ai…hyperparameter configurations” wherein, the parameter configuration chosen for a classification algorithm suitable for a certain type of dataset is analogous to the recited feature extraction instruction, at least one of the plurality of feature extraction network models to be used to extract feature information Page 1130, 2nd column, discloses a metafeature computation from the input dataset, which is used to compute similarity between data within the dataset [page 1130, 2nd column, 1st par.] using algorithm 2 initializing image search models’ configurations [the plurality of feature extract network models]; Page 1130, 1st col., 2nd to the last Par., discloses “MI-SMBO is agnostic of the SMBO algorithm used, as long as the algorithm’s implementation accepts an initial design as input or can be warmstarted with a given list of performance data…in contrast to exiting approaches that initialize direct search algorithms via meta-learning…SMBO procedure until all configurations…are evaluated”; process, using at least one of the plurality of feature extraction network models (Page 1130, 2nd column, discloses a metafeature computation from the input dataset, which is used to compute similarity between data within the dataset [page 1130, 2nd column, 1st par.] using algorithm 2 initializing image search models’ configurations [the plurality of feature extract network models]; Page 1130, 1st col., 2nd to the last Par., discloses “MI-SMBO is agnostic of the SMBO algorithm used, as long as the algorithm’s implementation accepts an initial design as input or can be warmstarted with a given list of performance data…in contrast to exiting approaches that initialize direct search algorithms via meta-learning…SMBO procedure until all configurations…are evaluated”) identified by the feature extraction instruction (Page 1131, 2nd col., 1st Par, discloses “top-level hyperparameter for choosing between classification algorithms, and set all hyperparameters of classification algorithm Ai as conditional on delta_classifier being set to Ai…hyperparameter configurations” wherein, the parameter configuration chosen for a classification algorithm suitable for a certain type of dataset is analogous to the recited feature extraction instruction), the to-be-processed data to extract the feature information of the to-be-processed data(Page 1130, last Par., “implemented 46 metafeatures…based on their types and underlying assumptions” discloses the different types of feature to be extract from the dataset which is chosen for respective classification algorithm to fir the certain type of dataset); identify, the feature information, to obtain identification information of the feature information (Page 1130, 2nd col., 1st par., discloses a metric is calculated for the dataset based on the metafeatures, the measure information used to obtain the metric is analogous to the identification information as claimed), wherein the identification information of the feature information comprises a first identifier (The metric’s measures obtained from the algorithm 2 is the identification information as disclosed in page 1130, 2nd col.; wherein the metric is analogous to the recited first identifier) of the at least one of the plurality of feature extraction network models (Page 1130, 2nd column, discloses a metafeature computation from the input dataset, which is used to compute similarity between data within the dataset [page 1130, 2nd column, 1st par.] using algorithm 2 initializing image search models’ configurations [the plurality of feature extract network models]) used to extract the feature information (Page 1130, last Par., “implemented 46 metafeatures…based on their types and underlying assumptions” discloses the different types of feature to be extract from the dataset which is chosen for respective classification algorithm to fir the certain type of dataset); and send the feature information and the identification information to indicate (page 1129, 2nd col., discloses algorithm 1 to select the model optimal for performing the function most suitable for the dataset based on the metric’s measure information obtained and the metafeature) to select a feature analysis network model corresponding to the least one of the plurality of feature extraction network models used to extract(Page 1130, last Par., “implemented 46 metafeatures…based on their types and underlying assumptions” discloses the different types of feature to be extract from the dataset which is chosen for respective classification algorithm to fir the certain type of dataset). However, Fuerer does not explicitly teach and wherein each of the plurality of feature extraction network models is a deep neural network model configured to meet feature information requirements of a respective different feature analysis network model. Vanschoren teaches and wherein each of the plurality of feature extraction network models is a deep neural network model (Algorithm 2, and the selected models as disclosed in section 3, 1st 4 paragraphs, are analogous to the algorithm 2 of Fuerer, moreover, Vanschoren further teaches that the algorithm 2 and the processing of the selection is for neural network task such as disclosed in section 1, 1st Par. of “hyperparameter optimization is crucial step in the process of applying machine learning algorithms in practice” as further explained in section 2 of page 25, 1st 3 Pars., of “the construction of machine learning models typically gives rise to two optimization problems...selecting a neural network”) configured to meet feature information requirements of a respective different feature analysis network model (Section 2, 1st 2 Pars, disclose “machine learning models typically gives rise to two optimization problems. The first is internal optimization, such as selection a neural network’s likelihood-maximizing weights; the second is tuning the method’s hyperparameters” indicating the feature information requirements to be met). Therefore, it would have been obvious to one or ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teaches of Fuerer of having a device, comprising: storing a plurality of feature extraction network models; processing using at least one of the plurality of feature extraction network models identified by the feature extraction instruction, the to-be-processed data to extract, the feature information of the to-be-processed data; identifying the extracted feature information to obtain identification information of the feature information, wherein the identification information of the feature information comprises a first identifier the at least one of the plurality of feature extraction network models used to extract the feature information; and sending the feature information and the identification information to indicate, with the teachings of Vanschoren of having wherein each of the plurality of feature extraction network models is a deep neural network model configured to meet feature information requirements of a respective different feature analysis network model. Wherein having Fuerer’s device of wherein each of the plurality of feature extraction network models is a deep neural network model configured to meet feature information requirements of a respective different feature analysis network model. The motivation behind the modification would have been to improve machine learning initialization based on hyperparameter optimization that shows mild improvements for low dimensional hyperparameter optimization and substantially improves for complex combined algorithm selection and hyperparameter optimization, and further to improve performance of machine learning based on hyperparameter optimization according to certain types of data. Since both Fuerer and Vanschoren both perform hyperparameter optimization for machine learning selection. Wherein Fuerer’s system improves machine learning initialization based on hyperparameter optimization that shows mild improvements for low dimensional hyperparameter optimization and substantially improves for complex combined algorithm selection and hyperparameter optimization, (see Fuerer’s Abstract) and Vanschoren’s system improves performance of machine learning based on hyperparameter optimization according to certain types of data, (see Vanschoren’s Abstract). However, Fuerer in view of Vanschoren does not explicitly teach comprising at least one memory configured to store instructions; and one or more processors coupled to the at least one memory and configured to execute the instructions to cause the first electronic device; store, to a memory of a first device; process, by one or more processors of the first device; identify, by the first device; sending, by the first device; wherein the plurality of feature extraction network models comprises an image classification network model, a target detection network model, and a semantic segmentation network model; receiving, from a sensor of the first device, a to-be-processed image. Lukac teaches comprising at least one memory configured to store instructions (Par. [0099] discloses “a memory device containing instructions”); and one or more processors coupled to the at least one memory (Par. [0099] discloses “include a microprocessor or other processing element operable to execute software instructions”) and configured to execute the instructions to cause the first electronic device to (Par. [0099] discloses “include a microprocessor or other processing element operable to execute software instructions”): store, to the at least one memory (Par. [0028] discloses “the resulting image is then displayed on a monitor…or stored in memory by module”); process, by one or more processors of the first device (Par. [0029] discloses “the flow may be performed by a processor”; moreover, Par. [0095] discloses “vehicles within the environment…roadside computing devices” wherein these relevant use computer is analogous to the recited first device); identify, by the first device (Par. [0123] discloses “processor is an example of a type of hardware device that can be used in connection with the implementations above”); send, by the first device (Par. [0123] discloses “processor is an example of a type of hardware device that can be used in connection with the implementations above”); wherein the plurality of feature extraction network models comprises an image classification network model (Par. [0076] discloses a refinement process to use one or more of feature extraction…image registration, trained classifier [image classification network] to adaptively maximize similarities in the overlapping region in images from two neighboring cameras [which is used to maximize similarities between two datasets]), a target detection network model (Par. [0076] discloses a refinement process to use one or more of feature extraction, matching, corner detection, template matching, semantic segmentation [semantic segmentation network]), object detection [target detection network]), and a semantic segmentation network model (Par. [0076] discloses a refinement process to use one or more of feature extraction, matching, corner detection, template matching, semantic segmentation [semantic segmentation network])); receive, from a sensor of the first device, a to-be-processed image (Par. [0075] discloses the image are obtained from a camera of a device). Therefore, it would have been obvious to one or ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teaches of Fuerer in view of Vanschoren of having a device, comprising: storing a plurality of feature extraction network models; processing using at least one of the plurality of feature extraction network models identified by the feature extraction instruction, the to-be-processed data to extract, the feature information of the to-be-processed data; identifying the extracted feature information to obtain identification information of the feature information, wherein the identification information of the feature information comprises a first identifier the at least one of the plurality of feature extraction network models used to extract the feature information; and sending the feature information and the identification information to indicate, with the teachings of Lukac of having comprising at least one memory configured to store instructions; and one or more processors coupled to the at least one memory and configured to execute the instructions to cause the first electronic device; store, to a memory of a first device; processing, by one or more processors of the first device; identify, by the first device; send, by the first device; wherein the plurality of feature extraction network models comprises an image classification network model, a target detection network mode, and a semantic segmentation network model; receive, from a sensor of the first device, a to-be-processed image. Wherein having Fuerer’s device of comprising at least one memory configured to store instructions; and one or more processors coupled to the at least one memory and configured to execute the instructions to cause the first electronic device; store, to a memory of a first device; process, by one or more processors of the first device; identifying, by the first device; send, by the first device; wherein the plurality of feature extraction network models comprises an image classification network model, a target detection network mode, and a semantic segmentation network model; receive, from a sensor of the first device, a to-be-processed image. The motivation behind the modification would have been to improve machine learning initialization based on hyperparameter optimization that shows mild improvements for low dimensional hyperparameter optimization and substantially improves for complex combined algorithm selection and hyperparameter optimization, and further to improve performance of machine learning based on hyperparameter optimization according to certain types of data. Since both Fuerer and Lukac both perform determining of dataset similarity based on distance measurement. Wherein Fuerer’s system improves machine learning initialization based on hyperparameter optimization that shows mild improvements for low dimensional hyperparameter optimization and substantially improves for complex combined algorithm selection and hyperparameter optimization, (see Fuerer’s Abstract) and Lukac’s system improves using different models to perform projection refinement to enhance similarities between datasets for detection to perform alignment processing more efficiently (see Lukac’s Pars. [0075-0077]). However, Fuerer in view of Vanschoren and Lukac does not explicitly teach the to-be-processed data being a to-be-processed image; receive, by the first device and from a second device communicatively coupled to the first device via a network, a feature extraction instruction indicating a target image processing task to be performed on the to-be-processed image and identifying at least one of the plurality of feature extraction network models to be used to extract feature information; send to a second device, indicate to the second device to select a feature analysis network model; the identifier that uniquely identified the at least one of the plurality of feature extraction network models used to extract the feature information, and wherein the first identifier is based on the target image processing task indicated by feature extraction instruction. Yumiba teaches the to-be-processed data being a to-be-processed image (Par. [0095] discloses “the learning model reselection unit may be further provided after the learning model suitability determination unit. when the learning model suitability determination unit determines that the learning model is incompatible, the learning model reselection unit takes the input sample as an input…the operation in the image recognition”); receive, by the first device and from a second device (Par. [0097] discloses “the learning model suitability determination unit can be installed between the data-to-data recall unit and the learning model selection unit” wherein, the learning model selection unit is analogous to the recited first device and the learning model suitability determination unit is analogous to the recited second device) communicatively coupled to the first device via a network (Figure 14 illustrates the communication between the feature extraction selection unit and the data-to-data recall unit, moreover, Par. [0094] discloses “alternatively, it may be output to a file or notified an external computer via the network”), a feature extraction instruction indicating a target image processing task to be performed on the to-be-processed image (Par. [0105] discloses “by operating a button on the screen, the operator may switch the plurality of feature amounts and recall results to select one appropriate for generating a category in the screen. In this way the learning sample creation support unit may perform at least one of obtaining an input place using the plurality of feature amounts and recall results” indicating an instruction for target imaging processing task to select the appropriate feature extraction model) and identify at least one of the plurality of feature extraction network models to be used to extract feature information (Par. [0095] discloses “the learning model reselection unit may be further provided after the learning model suitability determination unit. when the learning model suitability determination unit determines that the learning model is incompatible, the learning model reselection unit takes the input sample as an input and selects the learning model for feature amount extraction”); send to a second device, indicate to the second device to select a feature analysis network model; the identifier that uniquely identifies (Par. [0086] discloses “the learning model selection unit may output information that uniquely determines the feature extraction learning model, such as the actual state and file name of the file of the feature extraction learning model specified by the symbol” wherein, the symbol is analogous to the recited identifier) the at least one of the plurality of feature extraction network models (Par. [0044] discloses “a learning model selection unit, which are the processes realized by the arithmetic functions…composed of a feature extraction learning model group and a recall learning model group realized by the database”) used to extract the feature information (Par. [0086] discloses “the learning model selection unit may output information that uniquely determines the feature extraction learning model, such as the actual state and file name of the file of the feature extraction learning model specified by the symbol”), and wherein the first identifier is based on the target image processing task (Par. [0048] discloses “the learning model selection unit selects the learning model that minimizes the difference between the feature amount output by the feature amount extraction unit and the feature amount output by the data-to-data recall unit, and outputs the symbol assigned to the learning model” wherein, the symbol is assigned according to the feature extraction [image processing task as claimed]) indicated by feature extraction instruction (Par. [0045] discloses “the feature extraction learning model group stores two or more feature extraction learning models in the database….share the assigned symbols of the feature extraction” wherein, the symbol is based on the data storing for feature extraction and recall learning models [analogous to the recited feature extraction instruction]). Therefore, it would have been obvious to one or ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teaches of Fuerer in view of Vanschoren and Lukac of having a device, comprising: storing a plurality of feature extraction network models; processing using at least one of the plurality of feature extraction network models identified by the feature extraction instruction, the to-be-processed data to extract, the feature information of the to-be-processed data; identifying the extracted feature information to obtain identification information of the feature information, wherein the identification information of the feature information comprises a first identifier the at least one of the plurality of feature extraction network models used to extract the feature information; and sending the feature information and the identification information to indicate, with the teachings of Yumiba of having the to-be-processed data being a to-be-processed image; receive, by the first device and from a second device, a feature extraction instruction indicating a target image processing task to be performed on the to-be-processed image and identify at least one of the plurality of feature extraction network models to be used to extract feature information; send to a second device, indicate to the second device to select a feature analysis network model; the identifier that uniquely identified the at least one of the plurality of feature extraction network models used to extract the feature information, and wherein the first identifier is based on the target image processing task indicated by feature extraction instruction. Wherein having Fuerer’s device of the to-be-processed data being a to-be-processed image; receive, by the first device and from a second device, a feature extraction instruction indicating a target image processing task to be performed on the to-be-processed image and identifying at least one of the plurality of feature extraction network models to be used to extract feature information; send to a second device, indicate to the second device to select a feature analysis network model; the identifier that uniquely identified the at least one of the plurality of feature extraction network models used to extract the feature information, and wherein the first identifier is based on the target image processing task indicated by feature extraction instruction. The motivation behind the modification would have been to improve machine learning initialization based on hyperparameter optimization that shows mild improvements for low dimensional hyperparameter optimization and substantially improves for complex combined algorithm selection and hyperparameter optimization, and further to improve performance of machine learning based on hyperparameter optimization according to certain types of data. Since both Fuerer and Yumiba both perform machine learning model selection based on type of dataset. Wherein Fuerer’s system improves machine learning initialization based on hyperparameter optimization that shows mild improvements for low dimensional hyperparameter optimization and substantially improves for complex combined algorithm selection and hyperparameter optimization, (see Fuerer’s Abstract) and Yumiba’s system improves feature extraction efficiently by accurately selecting appropriate feature extraction model based on database (see Yumiba’s Par. [0014]). However, Fuerer in view of Vanschoren further in view of Lukac and Yumiba and does not explicitly teach first device and from a second device that is a distinct device. Oami teaches first device and from a second device that is a distinct device (Par. [0154] discloses “a system including a plurality of devices…the present invention is also applicable to a case where an information processing program that achieves functions of the example embodiments is directly or remotely supplied to a system or a device”; Par. [0177] discloses “an object tracking system including a first object feature extraction device and a second object feature extraction device each of which is the object feature extraction device” wherein, first object extraction device is analogous to the recited first device and the second feature extraction device is analogous to the recited second device; furthermore, Par. [0181] also include an intelligent imaging device; Pars. [0258-0261] teach “object feature extraction device (unit)…” wherein, the device is also analogous to unit, analogous to Yumiba’s different feature extraction selection unit and the data-o-data recall unit here to be distinct devices in Oami). Therefore, it would have been obvious to one or ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teaches of Fuerer in view of Vanschoren further in view of Lukac and Yumiba of having a device, comprising: storing a plurality of feature extraction network models; processing using at least one of the plurality of feature extraction network models identified by the feature extraction instruction, the to-be-processed data to extract, the feature information of the to-be-processed data; identifying the extracted feature information to obtain identification information of the feature information, wherein the identification information of the feature information comprises a first identifier the at least one of the plurality of feature extraction network models used to extract the feature information; and sending the feature information and the identification information to indicate, with the teachings of Oami of having first device and from a second device that is a distinct device Wherein having Fuerer’s device of first device and from a second device that is a distinct device The motivation behind the modification would have been to improve machine learning initialization based on hyperparameter optimization that shows mild improvements for low dimensional hyperparameter optimization and substantially improves for complex combined algorithm selection and hyperparameter optimization, and further to improve feature extraction and object tracking with less error and higher accuracy. Since both Fuerer and Oami both perform feature extraction machine learning model with different external devices. Wherein Fuerer’s system improves machine learning initialization based on hyperparameter optimization that shows mild improvements for low dimensional hyperparameter optimization and substantially improves for complex combined algorithm selection and hyperparameter optimization, (see Fuerer’s Abstract) and Oami’s system improves feature extraction and object tracking with less error and higher accuracy (see Oami’s Pars. [0004-0005]). Regarding claim 21, Fuerer in view of Vanschoren further in view of Lukac and Yumiba and Oami teaches the first electronic device of claim 17, Fuerer teaches wherein the first electronic device to identify the feature information by: obtain the identification information according to an image processing task (page 1130, 2nd column, of the obtaining of the metafeatures and the metric information, which is according to the classification task to optimize relevant machine learning framework [page 1131, 1st col, “ML Algorithm and Hyperparameters” Section, 1st Par.]). However, Fuerer in view of Vanschoren further in view of Lukac and Oami does not explicitly teach the one or more processors are further configured to execute the instructions to cause; according to the target image processing task indicated by the feature extraction instruction. Yumiba teaches the one or more processors are further configured to execute the instructions to cause (Par. [0044] discloses “in a computer device, a feature extraction unit” indicating the use of a computer with instructions executed by a processor); according to the target image processing task (Par. [0048] discloses “the learning model selection unit selects the learning model that minimizes the difference between the feature amount output by the feature amount extraction unit and the feature amount output by the data-to-data recall unit, and outputs the symbol assigned to the learning model” wherein, the symbol is assigned according to the feature extraction [image processing task as claimed]) indicated by feature extraction instruction (Par. [0045] discloses “the feature extraction learning model group stores two or more feature extraction learning models in the database….share the assigned symbols of the feature extraction” wherein, the symbol is based on the data storing for feature extraction and recall learning models [analogous to the recited feature extraction instruction]). Therefore, it would have been obvious to one or ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teaches of Fuerer in view of Vanschoren and Lukac of having an image processing method, comprising: storing a plurality of feature extraction network models; processing using at least one of the plurality of feature extraction network models identified by the feature extraction instruction, the to-be-processed data to extract, the feature information of the to-be-processed data; identifying the extracted feature information to obtain identification information of the feature information, wherein the identification information of the feature information comprises a first identifier the at least one of the plurality of feature extraction network models used to extract the feature information; and sending the feature information and the identification information to indicate, with the teachings of Yumiba of having the one or more processors are further configured to execute the instructions to cause; according to the target image processing task indicated by the feature extraction instruction. Wherein having Fuerer’s method of the one or more processors are further configured to execute the instructions to cause; according to the target image processing task indicated by the feature extraction instruction. The motivation behind the modification would have been to improve machine learning initialization based on hyperparameter optimization that shows mild improvements for low dimensional hyperparameter optimization and substantially improves for complex combined algorithm selection and hyperparameter optimization, and further to improve performance of machine learning based on hyperparameter optimization according to certain types of data. Since both Fuerer and Yumiba both perform machine learning model selection based on type of dataset. Wherein Fuerer’s system improves machine learning initialization based on hyperparameter optimization that shows mild improvements for low dimensional hyperparameter optimization and substantially improves for complex combined algorithm selection and hyperparameter optimization, (see Fuerer’s Abstract) and Yumiba’s system improves feature extraction efficiently by accurately selecting appropriate feature extraction model based on database (see Yumiba’s Par. [0014]). Regarding claim 23, Fuerer discloses a computer program product cause a first electronic device to (abstract discloses the processing is for machine learning indicating a program product of a computer: storing a plurality of feature extraction network models (Page 1130, 2nd column, discloses a metafeature computation from the input dataset, and further these metafeature of the dataset is calculated a matrix that reflects how similar the datasets are [page 1130, 2nd column, 1st par.] using algorithm 2; Page 1130, 1st col., 2nd to the last Par., discloses “MI-SMBO is agnostic of the SMBO algorithm used, as long as the algorithm’s implementation accepts an initial design as input or can be warmstarted with a given list of performance data…in contrast to exiting approaches that initialize direct search algorithms via meta-learning…SMBO procedure until all configurations…are evaluated”; wherein using the algorithm 2/SMBO algorithm to obtain different model configurations [analogous to the recited plurality of feature extraction network models); process, using at least one of the plurality of feature extraction network models (Page 1130, 2nd column, discloses a metafeature computation from the input dataset, which is used to compute similarity between data within the dataset [page 1130, 2nd column, 1st par.] using algorithm 2 initializing image search models’ configurations [the plurality of feature extract network models]; Page 1130, 1st col., 2nd to the last Par., discloses “MI-SMBO is agnostic of the SMBO algorithm used, as long as the algorithm’s implementation accepts an initial design as input or can be warmstarted with a given list of performance data…in contrast to exiting approaches that initialize direct search algorithms via meta-learning…SMBO procedure until all configurations…are evaluated”) identified by the feature extraction instruction (Page 1131, 2nd col., 1st Par, discloses “top-level hyperparameter for choosing between classification algorithms, and set all hyperparameters of classification algorithm Ai as conditional on delta_classifier being set to Ai…hyperparameter configurations” wherein, the parameter configuration chosen for a classification algorithm suitable for a certain type of dataset is analogous to the recited feature extraction instruction), the to-be-processed data to extract the feature information of the to-be-processed data (Page 1130, last Par., “implemented 46 metafeatures…based on their types and underlying assumptions” discloses the different types of feature to be extract from the dataset which is chosen for respective classification algorithm to fir the certain type of dataset); identify, the feature information, to obtain identification information of the feature information (Page 1130, 2nd col., 1st par., discloses a metric is calculated for the dataset based on the metafeatures, the measure information used to obtain the metric is analogous to the identification information as claimed), wherein the identification information of the feature information comprises a first identifier (The metric’s measures obtained from the algorithm 2 is the identification information as disclosed in page 1130, 2nd col.; wherein the metric is analogous to the recited first identifier) of the at least one of the plurality of feature extraction network models (Page 1130, 2nd column, discloses a metafeature computation from the input dataset, which is used to compute similarity between data within the dataset [page 1130, 2nd column, 1st par.] using algorithm 2 initializing image search models’ configurations [the plurality of feature extract network models]) used to extract the feature information (Page 1130, last Par., “implemented 46 metafeatures…based on their types and underlying assumptions” discloses the different types of feature to be extract from the dataset which is chosen for respective classification algorithm to fir the certain type of dataset); and send the feature information and the identification information to indicate (page 1129, 2nd col., discloses algorithm 1 to select the model optimal for performing the function most suitable for the dataset based on the metric’s measure information obtained and the metafeature) to select a feature analysis network model corresponding to the least one of the plurality of feature extraction network models used to extract(Page 1130, last Par., “implemented 46 metafeatures…based on their types and underlying assumptions” discloses the different types of feature to be extract from the dataset which is chosen for respective classification algorithm to fir the certain type of dataset). However, Fuerer does not explicitly teach and wherein each of the plurality of feature extraction network models is a deep neural network model configured to meet feature information requirements of a respective different feature analysis network model. Vanschoren teaches and wherein each of the plurality of feature extraction network models is a deep neural network model (Algorithm 2, and the selected models as disclosed in section 3, 1st 4 paragraphs, are analogous to the algorithm 2 of Fuerer, moreover, Vanschoren further teaches that the algorithm 2 and the processing of the selection is for neural network task such as disclosed in section 1, 1st Par. of “hyperparameter optimization is crucial step in the process of applying machine learning algorithms in practice” as further explained in section 2 of page 25, 1st 3 Pars., of “the construction of machine learning models typically gives rise to two optimization problems...selecting a neural network”) configured to meet feature information requirements of a respective different feature analysis network model (Section 2, 1st 2 Pars, disclose “machine learning models typically gives rise to two optimization problems. The first is internal optimization, such as selection a neural network’s likelihood-maximizing weights; the second is tuning the method’s hyperparameters” indicating the feature information requirements to be met). Therefore, it would have been obvious to one or ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teaches of Fuerer of having a system, comprising: storing a plurality of feature extraction network models; processing using at least one of the plurality of feature extraction network models identified by the feature extraction instruction, the to-be-processed data to extract, the feature information of the to-be-processed data; identifying the extracted feature information to obtain identification information of the feature information, wherein the identification information of the feature information comprises a first identifier the at least one of the plurality of feature extraction network models used to extract the feature information; and sending the feature information and the identification information to indicate, with the teachings of Vanschoren of having wherein each of the plurality of feature extraction network models is a deep neural network model configured to meet feature information requirements of a respective different feature analysis network model. Wherein having Fuerer’s system of wherein each of the plurality of feature extraction network models is a deep neural network model configured to meet feature information requirements of a respective different feature analysis network model. The motivation behind the modification would have been to improve machine learning initialization based on hyperparameter optimization that shows mild improvements for low dimensional hyperparameter optimization and substantially improves for complex combined algorithm selection and hyperparameter optimization, and further to improve performance of machine learning based on hyperparameter optimization according to certain types of data. Since both Fuerer and Vanschoren both perform hyperparameter optimization for machine learning selection. Wherein Fuerer’s system improves machine learning initialization based on hyperparameter optimization that shows mild improvements for low dimensional hyperparameter optimization and substantially improves for complex combined algorithm selection and hyperparameter optimization, (see Fuerer’s Abstract) and Vanschoren’s system improves performance of machine learning based on hyperparameter optimization according to certain types of data, (see Vanschoren’s Abstract). However, Fuerer in view of Vanschoren does not explicitly teach comprising instructions that are stored on a computer-readable medium and that; comprising at least one memory configured to store instructions; and one or more processors coupled to the at least one memory and configured to execute the instructions to cause the first electronic device; store, to a memory of a first device; process, by one or more processors of the first device; identify, by the first device; sending, by the first device; wherein the plurality of feature extraction network models comprises an image classification network model, a target detection network model, and a semantic segmentation network model; receiving, from a sensor of the first device, a to-be-processed image. Lukac teaches comprising instructions that are stored on a computer-readable medium and that (Par. [0126] discloses “a program sequence of instructions indicated by code”), when executed by one or more processors; comprising at least one memory configured to store instructions (Par. [0099] discloses “a memory device containing instructions”); and one or more processors coupled to the at least one memory (Par. [0099] discloses “include a microprocessor or other processing element operable to execute software instructions”) and configured to execute the instructions to cause the first electronic device to (Par. [0099] discloses “include a microprocessor or other processing element operable to execute software instructions”): store, to the at least one memory (Par. [0028] discloses “the resulting image is then displayed on a monitor…or stored in memory by module”); process, by one or more processors of the first device (Par. [0029] discloses “the flow may be performed by a processor”; moreover, Par. [0095] discloses “vehicles within the environment…roadside computing devices” wherein these relevant use computer is analogous to the recited first device); identify, by the first device (Par. [0123] discloses “processor is an example of a type of hardware device that can be used in connection with the implementations above”); send, by the first device (Par. [0123] discloses “processor is an example of a type of hardware device that can be used in connection with the implementations above”); wherein the plurality of feature extraction network models comprises an image classification network model (Par. [0076] discloses a refinement process to use one or more of feature extraction…image registration, trained classifier [image classification network] to adaptively maximize similarities in the overlapping region in images from two neighboring cameras [which is used to maximize similarities between two datasets]), a target detection network model (Par. [0076] discloses a refinement process to use one or more of feature extraction, matching, corner detection, template matching, semantic segmentation [semantic segmentation network]), object detection [target detection network]), and a semantic segmentation network model (Par. [0076] discloses a refinement process to use one or more of feature extraction, matching, corner detection, template matching, semantic segmentation [semantic segmentation network])); receive, from a sensor of the first device, a to-be-processed image (Par. [0075] discloses the image are obtained from a camera of a device). Therefore, it would have been obvious to one or ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teaches of Fuerer in view of Vanschoren of having a system, comprising: storing a plurality of feature extraction network models; processing using at least one of the plurality of feature extraction network models identified by the feature extraction instruction, the to-be-processed data to extract, the feature information of the to-be-processed data; identifying the extracted feature information to obtain identification information of the feature information, wherein the identification information of the feature information comprises a first identifier the at least one of the plurality of feature extraction network models used to extract the feature information; and sending the feature information and the identification information to indicate, with the teachings of Lukac of having comprising instructions that are stored on a computer-readable medium and that; comprising at least one memory configured to store instructions; and one or more processors coupled to the at least one memory and configured to execute the instructions to cause the first electronic device; store, to a memory of a first device; processing, by one or more processors of the first device; identify, by the first device; send, by the first device; wherein the plurality of feature extraction network models comprises an image classification network model, a target detection network mode, and a semantic segmentation network model; receive, from a sensor of the first device, a to-be-processed image. Wherein having Fuerer’s system of comprising instructions that are stored on a computer-readable medium and that; comprising at least one memory configured to store instructions; and one or more processors coupled to the at least one memory and configured to execute the instructions to cause the first electronic device; store, to a memory of a first device; process, by one or more processors of the first device; identifying, by the first device; send, by the first device; wherein the plurality of feature extraction network models comprises an image classification network model, a target detection network mode, and a semantic segmentation network model; receive, from a sensor of the first device, a to-be-processed image. The motivation behind the modification would have been to improve machine learning initialization based on hyperparameter optimization that shows mild improvements for low dimensional hyperparameter optimization and substantially improves for complex combined algorithm selection and hyperparameter optimization, and further to improve performance of machine learning based on hyperparameter optimization according to certain types of data. Since both Fuerer and Lukac both perform determining of dataset similarity based on distance measurement. Wherein Fuerer’s system improves machine learning initialization based on hyperparameter optimization that shows mild improvements for low dimensional hyperparameter optimization and substantially improves for complex combined algorithm selection and hyperparameter optimization, (see Fuerer’s Abstract) and Lukac’s system improves using different models to perform projection refinement to enhance similarities between datasets for detection to perform alignment processing more efficiently (see Lukac’s Pars. [0075-0077]). However, Fuerer in view of Vanschoren and Lukac does not explicitly teach the to-be-processed data being a to-be-processed image; receive, by the first device and from a second device communicatively coupled to the first device via a network, a feature extraction instruction indicating a target image processing task to be performed on the to-be-processed image and identifying at least one of the plurality of feature extraction network models to be used to extract feature information; send to a second device, indicate to the second device to select a feature analysis network model; the identifier that uniquely identified the at least one of the plurality of feature extraction network models used to extract the feature information, and wherein the first identifier is based on the target image processing task indicated by feature extraction instruction. Yumiba teaches the to-be-processed data being a to-be-processed image (Par. [0095] discloses “the learning model reselection unit may be further provided after the learning model suitability determination unit. when the learning model suitability determination unit determines that the learning model is incompatible, the learning model reselection unit takes the input sample as an input…the operation in the image recognition”); receive, by the first device and from a second device (Par. [0097] discloses “the learning model suitability determination unit can be installed between the data-to-data recall unit and the learning model selection unit” wherein, the learning model selection unit is analogous to the recited first device and the learning model suitability determination unit is analogous to the recited second device) communicatively coupled to the first device via a network (Figure 14 illustrates the communication between the feature extraction selection unit and the data-to-data recall unit, moreover, Par. [0094] discloses “alternatively, it may be output to a file or notified an external computer via the network”), a feature extraction instruction indicating a target image processing task to be performed on the to-be-processed image (Par. [0105] discloses “by operating a button on the screen, the operator may switch the plurality of feature amounts and recall results to select one appropriate for generating a category in the screen. In this way the learning sample creation support unit may perform at least one of obtaining an input place using the plurality of feature amounts and recall results” indicating an instruction for target imaging processing task to select the appropriate feature extraction model) and identify at least one of the plurality of feature extraction network models to be used to extract feature information (Par. [0095] discloses “the learning model reselection unit may be further provided after the learning model suitability determination unit. when the learning model suitability determination unit determines that the learning model is incompatible, the learning model reselection unit takes the input sample as an input and selects the learning model for feature amount extraction”); send to a second device, indicate to the second device to select a feature analysis network model; the identifier that uniquely identifies (Par. [0086] discloses “the learning model selection unit may output information that uniquely determines the feature extraction learning model, such as the actual state and file name of the file of the feature extraction learning model specified by the symbol” wherein, the symbol is analogous to the recited identifier) the at least one of the plurality of feature extraction network models (Par. [0044] discloses “a learning model selection unit, which are the processes realized by the arithmetic functions…composed of a feature extraction learning model group and a recall learning model group realized by the database”) used to extract the feature information (Par. [0086] discloses “the learning model selection unit may output information that uniquely determines the feature extraction learning model, such as the actual state and file name of the file of the feature extraction learning model specified by the symbol”), and wherein the first identifier is based on the target image processing task (Par. [0048] discloses “the learning model selection unit selects the learning model that minimizes the difference between the feature amount output by the feature amount extraction unit and the feature amount output by the data-to-data recall unit, and outputs the symbol assigned to the learning model” wherein, the symbol is assigned according to the feature extraction [image processing task as claimed]) indicated by feature extraction instruction (Par. [0045] discloses “the feature extraction learning model group stores two or more feature extraction learning models in the database….share the assigned symbols of the feature extraction” wherein, the symbol is based on the data storing for feature extraction and recall learning models [analogous to the recited feature extraction instruction]). Therefore, it would have been obvious to one or ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teaches of Fuerer in view of Vanschoren and Lukac of having a system, comprising: storing a plurality of feature extraction network models; processing using at least one of the plurality of feature extraction network models identified by the feature extraction instruction, the to-be-processed data to extract, the feature information of the to-be-processed data; identifying the extracted feature information to obtain identification information of the feature information, wherein the identification information of the feature information comprises a first identifier the at least one of the plurality of feature extraction network models used to extract the feature information; and sending the feature information and the identification information to indicate, with the teachings of Yumiba of having the to-be-processed data being a to-be-processed image; receive, by the first device and from a second device, a feature extraction instruction indicating a target image processing task to be performed on the to-be-processed image and identify at least one of the plurality of feature extraction network models to be used to extract feature information; send to a second device, indicate to the second device to select a feature analysis network model; the identifier that uniquely identified the at least one of the plurality of feature extraction network models used to extract the feature information, and wherein the first identifier is based on the target image processing task indicated by feature extraction instruction. Wherein having Fuerer’s system of the to-be-processed data being a to-be-processed image; receive, by the first device and from a second device, a feature extraction instruction indicating a target image processing task to be performed on the to-be-processed image and identifying at least one of the plurality of feature extraction network models to be used to extract feature information; send to a second device, indicate to the second device to select a feature analysis network model; the identifier that uniquely identified the at least one of the plurality of feature extraction network models used to extract the feature information, and wherein the first identifier is based on the target image processing task indicated by feature extraction instruction. The motivation behind the modification would have been to improve machine learning initialization based on hyperparameter optimization that shows mild improvements for low dimensional hyperparameter optimization and substantially improves for complex combined algorithm selection and hyperparameter optimization, and further to improve performance of machine learning based on hyperparameter optimization according to certain types of data. Since both Fuerer and Yumiba both perform machine learning model selection based on type of dataset. Wherein Fuerer’s system improves machine learning initialization based on hyperparameter optimization that shows mild improvements for low dimensional hyperparameter optimization and substantially improves for complex combined algorithm selection and hyperparameter optimization, (see Fuerer’s Abstract) and Yumiba’s system improves feature extraction efficiently by accurately selecting appropriate feature extraction model based on database (see Yumiba’s Par. [0014]). However, Fuerer in view of Vanschoren further in view of Lukac and Yumiba and does not explicitly teach first device and from a second device that is a distinct device. Oami teaches first device and from a second device that is a distinct device (Par. [0154] discloses “a system including a plurality of devices…the present invention is also applicable to a case where an information processing program that achieves functions of the example embodiments is directly or remotely supplied to a system or a device”; Par. [0177] discloses “an object tracking system including a first object feature extraction device and a second object feature extraction device each of which is the object feature extraction device” wherein, first object extraction device is analogous to the recited first device and the second feature extraction device is analogous to the recited second device; furthermore, Par. [0181] also include an intelligent imaging device; Pars. [0258-0261] teach “object feature extraction device (unit)…” wherein, the device is also analogous to unit, analogous to Yumiba’s different feature extraction selection unit and the data-o-data recall unit here to be distinct devices in Oami). Therefore, it would have been obvious to one or ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teaches of Fuerer in view of Vanschoren further in view of Lukac and Yumiba of having a system, comprising: storing a plurality of feature extraction network models; processing using at least one of the plurality of feature extraction network models identified by the feature extraction instruction, the to-be-processed data to extract, the feature information of the to-be-processed data; identifying the extracted feature information to obtain identification information of the feature information, wherein the identification information of the feature information comprises a first identifier the at least one of the plurality of feature extraction network models used to extract the feature information; and sending the feature information and the identification information to indicate, with the teachings of Oami of having first device and from a second device that is a distinct device Wherein having Fuerer’s system of first device and from a second device that is a distinct device The motivation behind the modification would have been to improve machine learning initialization based on hyperparameter optimization that shows mild improvements for low dimensional hyperparameter optimization and substantially improves for complex combined algorithm selection and hyperparameter optimization, and further to improve feature extraction and object tracking with less error and higher accuracy. Since both Fuerer and Oami both perform feature extraction machine learning model with different external devices. Wherein Fuerer’s system improves machine learning initialization based on hyperparameter optimization that shows mild improvements for low dimensional hyperparameter optimization and substantially improves for complex combined algorithm selection and hyperparameter optimization, (see Fuerer’s Abstract) and Oami’s system improves feature extraction and object tracking with less error and higher accuracy (see Oami’s Pars. [0004-0005]). Regarding claim 27, Fuerer in view of Vanschoren further in view of Lukac and Yumiba and Oami teaches the computer program product of claim 23, Fuerer teaches wherein cause the first electronic device to (as discussed above in claim 23): obtaining the identification information according to an image processing task (page 1130, 2nd column, of the obtaining of the metafeatures and the metric information, which is according to the classification task to optimize relevant machine learning framework [page 1131, 1st col, “ML Algorithm and Hyperparameters” Section, 1st Par.]). However, Fuerer in view of Vanschoren further in view of Lukac and Oami does not explicitly teach the instructions, when executed by the one or more processors, further; according to the target image processing task indicated by the feature extraction instruction. Yumiba teaches the instructions, when executed by the one or more processors, further (Par. [0044] discloses “in a computer device, a feature extraction unit” indicating the use of a computer with instructions executed by a processor); according to the target image processing task (Par. [0048] discloses “the learning model selection unit selects the learning model that minimizes the difference between the feature amount output by the feature amount extraction unit and the feature amount output by the data-to-data recall unit, and outputs the symbol assigned to the learning model” wherein, the symbol is assigned according to the feature extraction [image processing task as claimed]) indicated by feature extraction instruction (Par. [0045] discloses “the feature extraction learning model group stores two or more feature extraction learning models in the database….share the assigned symbols of the feature extraction” wherein, the symbol is based on the data storing for feature extraction and recall learning models [analogous to the recited feature extraction instruction]). Therefore, it would have been obvious to one or ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teaches of Fuerer in view of Vanschoren and Lukac of having an image processing method, comprising: storing a plurality of feature extraction network models; processing using at least one of the plurality of feature extraction network models identified by the feature extraction instruction, the to-be-processed data to extract, the feature information of the to-be-processed data; identifying the extracted feature information to obtain identification information of the feature information, wherein the identification information of the feature information comprises a first identifier the at least one of the plurality of feature extraction network models used to extract the feature information; and sending the feature information and the identification information to indicate, with the teachings of Yumiba of having the instructions, when executed by the one or more processors, further according to the target image processing task indicated by the feature extraction instruction. Wherein having Fuerer’s method of the instructions, when executed by the one or more processors, further; according to the target image processing task indicated by the feature extraction instruction. The motivation behind the modification would have been to improve machine learning initialization based on hyperparameter optimization that shows mild improvements for low dimensional hyperparameter optimization and substantially improves for complex combined algorithm selection and hyperparameter optimization, and further to improve performance of machine learning based on hyperparameter optimization according to certain types of data. Since both Fuerer and Yumiba both perform machine learning model selection based on type of dataset. Wherein Fuerer’s system improves machine learning initialization based on hyperparameter optimization that shows mild improvements for low dimensional hyperparameter optimization and substantially improves for complex combined algorithm selection and hyperparameter optimization, (see Fuerer’s Abstract) and Yumiba’s system improves feature extraction efficiently by accurately selecting appropriate feature extraction model based on database (see Yumiba’s Par. [0014]). Regarding claim 28, Fuerer in view of Vanschoren further in view of Lukac and Yumiba and Oami teaches the image processing method of claim 1, Feurer teaches wherein the identification information further comprises a second identifier of an output layer of the at least one of the plurality of feature extraction network models (Page 1130, 2nd col., 1st Par., discloses the metric calculated represent measure between the data, moreover, the measures are calculated for all data, such as shown in equation 5 therefore, any of which of these calculated measures can be understood to be the second identifier as claimed of the output of the calculation of algorithm 2 based on the metafeature [output layer of the feature information as claimed by BRI]), and wherein the output layer is a layer at which the feature information is output from the at least one of the plurality of feature extraction network models (The measures are output of algorithm 2, is analogous to the claimed limitation wherein the algorithm 2 is analogous to the pre-stored feature extraction network model as claimed, e). Regarding claim 30, Fuerer in view of Vanschoren further in view of Lukac and Yumiba and Oami teaches the image processing method of claim 1, Feurer teaches wherein the first device comprises a photographing apparatus, a camera, or a mobile terminal (“or” indicates a selection, the examiner selects “a camera” as disclosed in [0075] discloses the image are obtained from a camera of a device, of Lukac). Regarding claim 31, Fuerer in view of Vanschoren further in view of Lukac and Yumiba and Oami teaches the image processing method of claim 1. However, Fuerer in view of Vanschoren further in view of Yumiba and Oami does not teach wherein sending the feature information and the identification information comprises wirelessly transmitting the feature information and the identification information from the first device to the second device. Lukac teaches wherein sending the feature information and the identification information comprises wirelessly transmitting the feature information and the identification information from the first device to the second device (Lukac’s [0095] discloses wireless transmitting signals within communication channels to share data and coordinate movement within an autonomous driving environment, among other examples communications including sharing data of the cameras of these devices, which is analogous to transmitting feature information and identification information from one device to another). Therefore, it would have been obvious to one or ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teaches of Fuerer in view of Vanschoren further in view of Yumiba and Oami of having an image processing method, comprising: storing a plurality of feature extraction network models; processing using at least one of the plurality of feature extraction network models identified by the feature extraction instruction, the to-be-processed data to extract, the feature information of the to-be-processed data; identifying the extracted feature information to obtain identification information of the feature information, wherein the identification information of the feature information comprises a first identifier the at least one of the plurality of feature extraction network models used to extract the feature information; and sending the feature information and the identification information to indicate, with the teachings of Lukac of having wherein sending the feature information and the identification information comprises wirelessly transmitting the feature information and the identification information from the first device to the second device. Wherein having Fuerer’s method of wherein sending the feature information and the identification information comprises wirelessly transmitting the feature information and the identification information from the first device to the second device. The motivation behind the modification would have been to improve machine learning initialization based on hyperparameter optimization that shows mild improvements for low dimensional hyperparameter optimization and substantially improves for complex combined algorithm selection and hyperparameter optimization, and further to improve performance of machine learning based on hyperparameter optimization according to certain types of data. Since both Fuerer and Lukac both perform determining of dataset similarity based on distance measurement. Wherein Fuerer’s system improves machine learning initialization based on hyperparameter optimization that shows mild improvements for low dimensional hyperparameter optimization and substantially improves for complex combined algorithm selection and hyperparameter optimization, (see Fuerer’s Abstract) and Lukac’s system improves using different models to perform projection refinement to enhance similarities between datasets for detection to perform alignment processing more efficiently (see Lukac’s Pars. [0075-0077]). Regarding claim 33, Fuerer in view of Vanschoren further in view of Lukac and Yumiba and Oami teaches the image processing method of claim 1, Fuerer teaches further comprising storing, in the memory of the first device, a plurality of feature analysis identifiers, wherein each feature analysis identifier corresponds to a different type of image processing task, and wherein the first identifier is one of the plurality of feature analysis identifiers selected based on a target image processing task (page 1129, “Initializing SMBO With Configurations Suggested by Meta-Learning” section, discloses initializing with configurations by meta-learning, each configuration is initialized specifically for each meta learning performed by algorithm 2, therefore, different configurations are being output based on the different metalearning initializations, therefore, each initialization configuration can be understood to be an identifier for that specific suggestion by meta learning of algorithm 2). Claims 4, 13, 16, 20, 22 and 26 are rejected under 35 U.S.C. 103 as being unpatentable over Matthias Feurer et. al. (“Initializing Bayesian Hyperparameter Optimization via Meta-Learning, 2015, Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 29, No. 1 [2015]: Twenty-Nonth AAAI Conference on Artificial Intelligence, AAAI Technical Track: Heuristic Search and Optimization” hereinafter as “Feurer”) in view of Joaquin Vanschoren et. al. (“Meta-Learning and Algorithm Selection Workshop at ECAL, 2014, 21st European Conference on Artificial Intelligence” hereinafter as “Vanschoren”) further in view of Rastislav Lukac et. al. (“US 2019/0325580 A1” hereinafter as “Lukac”) and Ryou Yumiba et. al. (“US 2021/0374403 A1” hereinafter as “Yumiba”) and Ryoma Oami (“US 2020/0034649 A1” hereinafter as “Oami”) and Yann N. Dauphin et. al. (“MetaInit: Initializing learning by learning to initialize, 2019, Advances in Neural Information Processing Systems 32, Curran Associates, Inc., pp. 12624-12636” hereinafter as “Dauphin”). Regarding claim 4, Fuerer in view of Vanschoren further in view of Lukac and Yumiba and Oami teaches the image processing method of claim 1, Fuerer teaches wherein identifying the feature information comprises: obtaining the first identifier of the at least one of the plurality of feature extraction network models (The metric obtained can be understood to be analogous to the first identifier as claimed, which is the output of algorithm 2, is analogous to “first identifier of the at least one pre-stored feature extraction network model” as claimed); obtaining a second identifier of an output layer of the at least one of the plurality of feature extraction network models (Page 1130, 2nd col., 1st par., discloses the metric calculated represent measure between the data, moreover, the measures are calculated for all data, such as shown in equation 5, any of which of these calculated measures is the second identifier as claimed of the output of the calculation of algorithm 2 based on the metafeature [output layer of the feature information]), wherein the output layer is a layer at which the feature information is output in the at least one of the plurality of feature extraction network models (The measures are output of algorithm 2, in other words, is analogous to the claimed limitation wherein the algorithm 2 is analogous to the pre-stored feature extraction network model as claimed); and using the first identifier and the second identifier as the identification information of the feature information (the measures are used as metric is analogous to using the first identifier and the second identifier as the identification information of the metafeatures as claimed). Yumiba teaches the identifier that uniquely identifies (Par. [0086] discloses “the learning model selection unit may output information that uniquely determines the feature extraction learning model, such as the actual state and file name of the file of the feature extraction learning model specified by the symbol” wherein, the symbol is analogous to the recited identifier) the at least one of the plurality of feature extraction network models (Par. [0044] discloses “a learning model selection unit, which are the processes realized by the arithmetic functions…composed of a feature extraction learning model group and a recall learning model group realized by the database”). Therefore, it would have been obvious to one or ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teaches of Fuerer in view of Vanschoren and Lukac of having an image processing method, comprising: storing a plurality of feature extraction network models; processing using at least one of the plurality of feature extraction network models identified by the feature extraction instruction, the to-be-processed data to extract, the feature information of the to-be-processed data; identifying the extracted feature information to obtain identification information of the feature information, wherein the identification information of the feature information comprises a first identifier the at least one of the plurality of feature extraction network models used to extract the feature information; and sending the feature information and the identification information to indicate, with the teachings of Yumiba of having the identifier that uniquely identified the at least one of the plurality of feature extraction network models. Wherein having Fuerer’s method of the identifier that uniquely identified the at least one of the plurality of feature extraction network models. The motivation behind the modification would have been to improve machine learning initialization based on hyperparameter optimization that shows mild improvements for low dimensional hyperparameter optimization and substantially improves for complex combined algorithm selection and hyperparameter optimization, and further to improve performance of machine learning based on hyperparameter optimization according to certain types of data. Since both Fuerer and Yumiba both perform initialization machine learning model through parameter setting. Wherein Fuerer’s system improves machine learning initialization based on hyperparameter optimization that shows mild improvements for low dimensional hyperparameter optimization and substantially improves for complex combined algorithm selection and hyperparameter optimization, (see Fuerer’s Abstract) and Yumiba’s system improves feature extraction efficiently by accurately selecting appropriate feature extraction model based on database (see Yumiba’s Par. [0014]). However, Fuerer in view of Vanschoren further in view of Lukac and Yumiba and Oami does not explicitly teach wherein the output layer is one of a convolutional layer, a pooling layer, or a fully connected layer of a neural network. In the same field of Meta Learning (title, Dauphin) Dauphin discloses wherein the output layer is one of a convolutional layer, a pooling layer, or a fully connected layer of a neural network (“is one of…or…” indicates a selection, only one of these options is the instant scope of the claim, the examiner selects “a convolutional layer” which is taught in Dauphin’s page 7, “CIFAR” section which includes using convolutional layers to perform the metalearning initialization which is analogous to algorithm 2 of Fuerer, therefore, the it can be understood the output layer of algorithm 2 of Fuerer can be performed using a convolutional layer of Dauphin). Therefore, it would have been obvious to one or ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teaches of Fuerer in view of Fuerer in view of Vanschoren further in view of Lukac and Yumiba and Oami of having an image processing method, comprising: storing a plurality of feature extraction network models; processing using at least one of the plurality of feature extraction network models identified by the feature extraction instruction, the to-be-processed data to extract, the feature information of the to-be-processed data; identifying the extracted feature information to obtain identification information of the feature information, wherein the identification information of the feature information comprises a first identifier the at least one of the plurality of feature extraction network models used to extract the feature information; and sending the feature information and the identification information to indicate, with the teachings of Dauphin of having wherein the output layer is one of a convolutional layer, a pooling layer, or a fully connected layer of a neural network. Wherein having Fuerer’s method of wherein the output layer is one of a convolutional layer, a pooling layer, or a fully connected layer of a neural network. The motivation behind the modification would have been to improve machine learning initialization based on hyperparameter optimization that shows mild improvements for low dimensional hyperparameter optimization and substantially improves for complex combined algorithm selection and hyperparameter optimization, and further to improve machine learning initialization with starting in regions that look locally linear so that can automatically receive from a class of bad initializations to perform machine learning more efficiently. Since both Fuerer and Dauphin both perform machine learning model selection based on type of dataset. Wherein Fuerer’s system improves machine learning initialization based on hyperparameter optimization that shows mild improvements for low dimensional hyperparameter optimization and substantially improves for complex combined algorithm selection and hyperparameter optimization, (see Fuerer’s Abstract) and Dauphin’s system improves machine learning initialization with starting in regions that look locally linear so that can automatically receive from a class of bad initializations to perform machine learning more efficiently (see Dauphin’s Par. [0014]). Regarding claim 13, Fuerer in view of Vanschoren further in view of Lukac and Yumiba and Oami and Dauphin teaches the image processing method of claim 4, Fuerer teaches wherein the identification information comprises a first field and a second field, and wherein the first field indicates the first identifier and the second field indicates the second identifier (Page 1130, 2nd col., 1st Par., discloses the measures are calculated in equation 5 for all of the data, therefore, any of which can be understood to be the first identifier as claimed and any of the others can be understood to be the second identifier as claimed, moreover, in equation 5 the d(DN+1,Dj) can be understood to include the field from D-N+1 to Dj which can be understood to be the field as claimed, which as being respective to the first identifier and the second identifier’s metrics can be understood to be the first field and the second field, respectively). Regarding claim 16, Fuerer in view of Vanschoren further in view of Lukac and Yumiba and Oami teaches the image processing method of claim 1, Fuerer teaches wherein the identification information further comprises a second identifier of an output layer of the at least one of the plurality of feature extraction network models (Page 1130, 2nd column, discloses a metafeature computation from the input dataset, and further these metafeature of the dataset is calculated a matrix that reflects how similar the datasets are [page 1130, 2nd column, 1st par.] using algorithm 2; Page 1130, 1st col., 2nd to the last Par., discloses “MI-SMBO is agnostic of the SMBO algorithm used, as long as the algorithm’s implementation accepts an initial design as input or can be warmstarted with a given list of performance data…in contrast to exiting approaches that initialize direct search algorithms via meta-learning…SMBO procedure until all configurations…are evaluated”; wherein using the algorithm 2/SMBO algorithm to obtain different model configurations [analogous to the recited plurality of feature extraction network models), wherein the output layer is a layer at which the feature information is output in the at least one of the plurality of feature extraction network models (page 1130, 2nd col., 1st par., discloses the metric calculated represent measure between the data, moreover, the measures are calculated for all data, such as shown in equation 5 therefore, any of which of these calculated measures can be understood to be the second identifier as claimed of the output of the calculation of algorithm 2 based on the metafeature [output layer of the feature information as claimed by BRI of the feature network model]). However, Fuerer in view of Vanschoren further in view of Lukac and Yumiba and Oami does not explicitly disclose wherein the output layer is one of a convolutional layer, a pooling layer, or a fully connected layer of a neural network. In the same field of Meta Learning (title, Dauphin) Dauphin discloses wherein the output layer is one of a convolutional layer, a pooling layer, or a fully connected layer of a neural network (“is one of…or…” indicates a selection, only one of these options is the instant scope of the claim, the examiner selects “a convolutional layer” which is taught in Dauphin’s page 7, “CIFAR” section which includes using convolutional layers to perform the metalearning initialization which is analogous to algorithm 2 of Fuerer). Therefore, it would have been obvious to one or ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teaches of Fuerer in view of Fuerer in view of Vanschoren further in view of Lukac and Yumiba and Oami of having an image processing method, comprising: storing a plurality of feature extraction network models; processing using at least one of the plurality of feature extraction network models identified by the feature extraction instruction, the to-be-processed data to extract, the feature information of the to-be-processed data; identifying the extracted feature information to obtain identification information of the feature information, wherein the identification information of the feature information comprises a first identifier the at least one of the plurality of feature extraction network models used to extract the feature information; and sending the feature information and the identification information to indicate, with the teachings of Dauphin of having wherein the output layer is one of a convolutional layer, a pooling layer, or a fully connected layer of a neural network. Wherein having Fuerer’s method of wherein the output layer is one of a convolutional layer, a pooling layer, or a fully connected layer of a neural network. The motivation behind the modification would have been to improve machine learning initialization based on hyperparameter optimization that shows mild improvements for low dimensional hyperparameter optimization and substantially improves for complex combined algorithm selection and hyperparameter optimization, and further to improve machine learning initialization with starting in regions that look locally linear so that can automatically receive from a class of bad initializations to perform machine learning more efficiently. Since both Fuerer and Dauphin both perform machine learning model selection based on type of dataset. Wherein Fuerer’s system improves machine learning initialization based on hyperparameter optimization that shows mild improvements for low dimensional hyperparameter optimization and substantially improves for complex combined algorithm selection and hyperparameter optimization, (see Fuerer’s Abstract) and Dauphin’s system improves machine learning initialization with starting in regions that look locally linear so that can automatically receive from a class of bad initializations to perform machine learning more efficiently (see Dauphin’s Par. [0014]). Regarding claim 20, Fuerer in view of Vanschoren further in view of Lukac and Yumiba and Oami teaches the first electronic device of claim 17, Fuerer teaches wherein cause the first electronic device to identify the feature information by: obtaining a first identifier of the at least one of the plurality of feature extraction network models (The metric obtained can be understood to be analogous to the first identifier as claimed, which is the output of algorithm 2, is analogous to “first identifier of the at least one pre-stored feature extraction network model” as claimed) identified by the feature extraction instruction (Page 1131, 2nd col., 1st Par, discloses “top-level hyperparameter for choosing between classification algorithms, and set all hyperparameters of classification algorithm Ai as conditional on delta_classifier being set to Ai…hyperparameter configurations” wherein, the parameter configuration chosen for a classification algorithm suitable for a certain type of dataset is analogous to the recited feature extraction instruction); obtaining a second identifier of an output layer (Page 1130, 2nd Col., 1st Par., discloses the metric calculated represent measure between the data, moreover, the measures are calculated for all data, such as shown in equation 5, any of which of these calculated measures can be understood to be the second identifier as claimed of the output of the calculation of algorithm 2 based on the metafeature [output layer of the feature information as claimed]) of the at least one of the plurality of feature extraction network models (Page 1130, 2nd column, discloses a metafeature computation from the input dataset, which is used to compute similarity between data within the dataset [page 1130, 2nd column, 1st par.] using algorithm 2 initializing image search models’ configurations [the plurality of feature extract network models]; Page 1130, 1st col., 2nd to the last Par., discloses “MI-SMBO is agnostic of the SMBO algorithm used, as long as the algorithm’s implementation accepts an initial design as input or can be warmstarted with a given list of performance data…in contrast to exiting approaches that initialize direct search algorithms via meta-learning…SMBO procedure until all configurations…are evaluated”), wherein the output layer of the feature information is a layer at which the feature information is output in the at least one pre-stored feature extraction network model (The measures are output of algorithm 2, in other words, is analogous to the claimed limitation wherein the algorithm 2 is analogous to the pre-stored feature extraction network model as claimed); and using the first identifier and the second identifier as the identification information of the feature information (The measures are used as metric is analogous to using the first identifier and the second identifier as the identification information of the metafeatures as claimed). Fuerer in view of Vanschoren further in view of Lukac and Oami does not explicitly teach the one or more processors are further configured to execute the instructions; the identifier that uniquely identifies the at least one of the plurality of feature extraction network models. Yumiba teaches the one or more processors are further configured to execute the instructions (Par. [0044] discloses “a computer device, a feature amount extraction unit” indicating a computer including processor executing the instructions); the identifier that uniquely identifies (Par. [0086] discloses “the learning model selection unit may output information that uniquely determines the feature extraction learning model, such as the actual state and file name of the file of the feature extraction learning model specified by the symbol” wherein, the symbol is analogous to the recited identifier) the at least one of the plurality of feature extraction network models (Par. [0044] discloses “a learning model selection unit, which are the processes realized by the arithmetic functions…composed of a feature extraction learning model group and a recall learning model group realized by the database”). Therefore, it would have been obvious to one or ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teaches of Fuerer in view of Vanschoren and Lukac of having a device, comprising: storing a plurality of feature extraction network models; processing using at least one of the plurality of feature extraction network models identified by the feature extraction instruction, the to-be-processed data to extract, the feature information of the to-be-processed data; identifying the extracted feature information to obtain identification information of the feature information, wherein the identification information of the feature information comprises a first identifier the at least one of the plurality of feature extraction network models used to extract the feature information; and sending the feature information and the identification information to indicate, with the teachings of Yumiba of having the one or more processors are further configured to execute the instructions; the identifier that uniquely identified the at least one of the plurality of feature extraction network models. Wherein having Fuerer’s device of the one or more processors are further configured to execute the instructions; the identifier that uniquely identified the at least one of the plurality of feature extraction network models. The motivation behind the modification would have been to improve machine learning initialization based on hyperparameter optimization that shows mild improvements for low dimensional hyperparameter optimization and substantially improves for complex combined algorithm selection and hyperparameter optimization, and further to improve performance of machine learning based on hyperparameter optimization according to certain types of data. Since both Fuerer and Yumiba both perform initialization machine learning model through parameter setting. Wherein Fuerer’s system improves machine learning initialization based on hyperparameter optimization that shows mild improvements for low dimensional hyperparameter optimization and substantially improves for complex combined algorithm selection and hyperparameter optimization, (see Fuerer’s Abstract) and Yumiba’s system improves feature extraction efficiently by accurately selecting appropriate feature extraction model based on database (see Yumiba’s Par. [0014]). However, Fuerer in view of Vanschoren further in view of Lukac and Yumiba and Oami does not explicitly teach wherein the output layer is one of a convolutional layer, a pooling layer, or a fully connected layer of a neural network. In the same field of Meta Learning (title, Dauphin) Dauphin discloses wherein the output layer is one of a convolutional layer, a pooling layer, or a fully connected layer of a neural network (“is one of…or…” indicates a selection, only one of these options is the instant scope of the claim, the examiner selects “a convolutional layer” which is taught in Dauphin’s page 7, “CIFAR” section which includes using convolutional layers to perform the metalearning initialization which is analogous to algorithm 2 of Fuerer, therefore, the it can be understood the output layer of algorithm 2 of Fuerer can be performed using a convolutional layer of Dauphin). Therefore, it would have been obvious to one or ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teaches of Fuerer in view of Fuerer in view of Vanschoren further in view of Lukac and Yumiba and Oami of having a device, comprising: storing a plurality of feature extraction network models; processing using at least one of the plurality of feature extraction network models identified by the feature extraction instruction, the to-be-processed data to extract, the feature information of the to-be-processed data; identifying the extracted feature information to obtain identification information of the feature information, wherein the identification information of the feature information comprises a first identifier the at least one of the plurality of feature extraction network models used to extract the feature information; and sending the feature information and the identification information to indicate, with the teachings of Dauphin of having wherein the output layer is one of a convolutional layer, a pooling layer, or a fully connected layer of a neural network. Wherein having Fuerer’s device of wherein the output layer is one of a convolutional layer, a pooling layer, or a fully connected layer of a neural network. The motivation behind the modification would have been to improve machine learning initialization based on hyperparameter optimization that shows mild improvements for low dimensional hyperparameter optimization and substantially improves for complex combined algorithm selection and hyperparameter optimization, and further to improve machine learning initialization with starting in regions that look locally linear so that can automatically receive from a class of bad initializations to perform machine learning more efficiently. Since both Fuerer and Dauphin both perform machine learning model selection based on type of dataset. Wherein Fuerer’s system improves machine learning initialization based on hyperparameter optimization that shows mild improvements for low dimensional hyperparameter optimization and substantially improves for complex combined algorithm selection and hyperparameter optimization, (see Fuerer’s Abstract) and Dauphin’s system improves machine learning initialization with starting in regions that look locally linear so that can automatically receive from a class of bad initializations to perform machine learning more efficiently (see Dauphin’s Par. [0014]). Regarding claim 22, Fuerer in view of Vanschoren further in view of Lukac and Yumiba and Oami and Dauphin teaches the first electronic device of claim 20, Fuerer teaches wherein the identification information comprises a first field and a second field, and wherein the first field indicates the first identifier and the second field indicates the second identifier (Page 1130, 2nd Col., 1st Par., discloses the measures are calculated in equation 5 for all of the data, therefore, any of which can be understood to be the first identifier as claimed and any of the others can be understood to be the second identifier as claimed, by BRI, moreover, in equation 5 the d(DN+1,Dj) can be understood to include the field from D-N+1 to Dj which can be understood to be the field as claimed, which as being respective to the first identifier and the second identifier’s metrics can be understood to be the first field and the second field, respectively). Regarding claim 26, Fuerer in view of Vanschoren further in view of Lukac and Yumiba and Oami teaches the computer program product of claim 23, Fuerer teaches wherein, further cause the first electronic device to: obtain the first identifier of the at least one of the plurality of feature extraction network models (as discussed above in claim 23, the metric obtained can be understood to be analogous to the first identifier as claimed, by BRI, which is the output of algorithm 2, in other words, is analogous to “first identifier of the at least one pre-stored feature extraction network model” as claimed); obtain a second identifier of an output layer of the feature information (page 1130, 2nd col., 1st par., discloses the metric calculated represent measure between the data, moreover, the measures are calculated for all data, such as shown in equation 5 therefore, any of which of these calculated measures can be understood to be the second identifier as claimed of the output of the calculation of algorithm 2 based on the metafeature [output layer of the feature information as claimed by BRI]), wherein the output layer is a layer at which the feature information is output in the at least one of the plurality of feature extraction network models (as discussed previously, the measures are output of algorithm 2, in other words, is analogous to the claimed limitation wherein the algorithm 2 is analogous to the pre-stored feature extraction network model as claimed, as already discussed and explained in claim 1 above); and use the first identifier and the second identifier as the identification information of the feature information (the measures are used as metric is analogous to using the first identifier and the second identifier as the identification information of the metafeatures as claimed, by BRI, are analogous). However, Fuerer in view of Vanschoren further in view of Lukac and Oami does not explicitly teach wherein the instructions, when executed by the one or more processors; the identifier that uniquely identifies the at least one of the plurality of feature extraction network models. Yumiba teaches wherein the instructions, when executed by the one or more processors (Par. [0044] discloses “a computer device, a feature amount extraction unit” indicating a computer including processor executing the instructions); the identifier that uniquely identifies (Par. [0086] discloses “the learning model selection unit may output information that uniquely determines the feature extraction learning model, such as the actual state and file name of the file of the feature extraction learning model specified by the symbol” wherein, the symbol is analogous to the recited identifier) the at least one of the plurality of feature extraction network models (Par. [0044] discloses “a learning model selection unit, which are the processes realized by the arithmetic functions…composed of a feature extraction learning model group and a recall learning model group realized by the database”). Therefore, it would have been obvious to one or ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teaches of Fuerer in view of Vanschoren and Lukac of having a system, comprising: storing a plurality of feature extraction network models; processing using at least one of the plurality of feature extraction network models identified by the feature extraction instruction, the to-be-processed data to extract, the feature information of the to-be-processed data; identifying the extracted feature information to obtain identification information of the feature information, wherein the identification information of the feature information comprises a first identifier the at least one of the plurality of feature extraction network models used to extract the feature information; and sending the feature information and the identification information to indicate, with the teachings of Yumiba of having the one or more processors are further configured to execute the instructions; the identifier that uniquely identified the at least one of the plurality of feature extraction network models. Wherein having Fuerer’s system of the one or more processors are further configured to execute the instructions; the identifier that uniquely identified the at least one of the plurality of feature extraction network models. The motivation behind the modification would have been to improve machine learning initialization based on hyperparameter optimization that shows mild improvements for low dimensional hyperparameter optimization and substantially improves for complex combined algorithm selection and hyperparameter optimization, and further to improve performance of machine learning based on hyperparameter optimization according to certain types of data. Since both Fuerer and Yumiba both perform initialization machine learning model through parameter setting. Wherein Fuerer’s system improves machine learning initialization based on hyperparameter optimization that shows mild improvements for low dimensional hyperparameter optimization and substantially improves for complex combined algorithm selection and hyperparameter optimization, (see Fuerer’s Abstract) and Yumiba’s system improves feature extraction efficiently by accurately selecting appropriate feature extraction model based on database (see Yumiba’s Par. [0014]). However, Fuerer in view of Vanschoren further in view of Lukac and Yumiba and Oami does not explicitly teach wherein the output layer is one of a convolutional layer, a pooling layer, or a fully connected layer of a neural network. In the same field of Meta Learning (title, Dauphin) Dauphin discloses wherein the output layer is one of a convolutional layer, a pooling layer, or a fully connected layer of a neural network (“is one of…or…” indicates a selection, only one of these options is the instant scope of the claim, the examiner selects “a convolutional layer” which is taught in Dauphin’s page 7, “CIFAR” section which includes using convolutional layers to perform the metalearning initialization which is analogous to algorithm 2 of Fuerer, therefore, the it can be understood the output layer of algorithm 2 of Fuerer can be performed using a convolutional layer of Dauphin). Therefore, it would have been obvious to one or ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teaches of Fuerer in view of Fuerer in view of Vanschoren further in view of Lukac and Yumiba and Oami of having a system, comprising: storing a plurality of feature extraction network models; processing using at least one of the plurality of feature extraction network models identified by the feature extraction instruction, the to-be-processed data to extract, the feature information of the to-be-processed data; identifying the extracted feature information to obtain identification information of the feature information, wherein the identification information of the feature information comprises a first identifier the at least one of the plurality of feature extraction network models used to extract the feature information; and sending the feature information and the identification information to indicate, with the teachings of Dauphin of having wherein the output layer is one of a convolutional layer, a pooling layer, or a fully connected layer of a neural network. Wherein having Fuerer’s system of wherein the output layer is one of a convolutional layer, a pooling layer, or a fully connected layer of a neural network. The motivation behind the modification would have been to improve machine learning initialization based on hyperparameter optimization that shows mild improvements for low dimensional hyperparameter optimization and substantially improves for complex combined algorithm selection and hyperparameter optimization, and further to improve machine learning initialization with starting in regions that look locally linear so that can automatically receive from a class of bad initializations to perform machine learning more efficiently. Since both Fuerer and Dauphin both perform machine learning model selection based on type of dataset. Wherein Fuerer’s system improves machine learning initialization based on hyperparameter optimization that shows mild improvements for low dimensional hyperparameter optimization and substantially improves for complex combined algorithm selection and hyperparameter optimization, (see Fuerer’s Abstract) and Dauphin’s system improves machine learning initialization with starting in regions that look locally linear so that can automatically receive from a class of bad initializations to perform machine learning more efficiently (see Dauphin’s Par. [0014]). Claim 29 is rejected under 35 U.S.C. 103 as being unpatentable over Matthias Feurer et. al. (“Initializing Bayesian Hyperparameter Optimization via Meta-Learning, 2015, Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 29, No. 1 [2015]: Twenty-Nonth AAAI Conference on Artificial Intelligence, AAAI Technical Track: Heuristic Search and Optimization” hereinafter as “Feurer”) in view of Joaquin Vanschoren et. al. (“Meta-Learning and Algorithm Selection Workshop at ECAL, 2014, 21st European Conference on Artificial Intelligence” hereinafter as “Vanschoren”) further in view of Rastislav Lukac et. al. (“US 2019/0325580 A1” hereinafter as “Lukac”) and Ryou Yumiba et. al. (“US 2021/0374403 A1” hereinafter as “Yumiba”) and Ryoma Oami (“US 2020/0034649 A1” hereinafter as “Oami”) and Yann N. Dauphin et. al. (“MetaInit: Initializing learning by learning to initialize, 2019, Advances in Neural Information Processing Systems 32, Curran Associates, Inc., pp. 12624-12636” hereinafter as “Dauphin”) and Lingqiao Liu et. al. (“Cross-Convolutional-Layer Pooling for Image Recognition, Nov. 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 39, Issue 11” hereinafter as “Liu”). Regarding claim 29, Fuerer in view of Vanschoren further in view of Lukac and Yumiba and Oami teaches the image processing method of claim 28, Fuerer teaches wherein different output layers of the at least one of the plurality of feature extraction network models produce different feature information (Page 1130, 2nd column, discloses a metafeature computation from the input dataset, and further these metafeature of the dataset is calculated a matrix that reflects how similar the datasets are [page 1130, 2nd column, 1st par.] using algorithm 2; Page 1130, 1st col., 2nd to the last Par., discloses “MI-SMBO is agnostic of the SMBO algorithm used, as long as the algorithm’s implementation accepts an initial design as input or can be warmstarted with a given list of performance data…in contrast to exiting approaches that initialize direct search algorithms via meta-learning…SMBO procedure until all configurations…are evaluated”; wherein using the algorithm 2/SMBO algorithm to obtain different model configurations [analogous to the recited plurality of feature extraction network models). However, Fuerer in view of Vanschoren further in view of Lukac and Yumiba and Oami does not explicitly teach wherein each of the plurality of feature extraction network models comprises a plurality of layers including convolutional layers and pooling layers, wherein the output layer comprises an intermediate layer or a final layer of the at least one of the plurality of feature extraction network models. Dauphin teaches wherein each of the plurality of feature extraction network models comprises a plurality of layers including convolutional layers, wherein the output layer comprises an intermediate layer or a final layer of the at least one of the plurality of feature extraction network models (“a convolutional layer” which is taught in Dauphin’s page 7, “CIFAR” section which includes using convolutional layers to perform the metalearning initialization which is analogous to algorithm 2 of Fuerer, therefore, the it can be understood the output layer of algorithm 2 of Fuerer can be performed using a convolutional layer of Dauphin; section 4.2, last par., discloses the each layer of the model, therefore, any last layer is analogous to the final layer since, “or” indicates a selection, and the examiner selects “final layer of the at least one of…”), and wherein different output layers of the at least one of the plurality of feature extraction network models produce different feature information (section 4.2, last par., discloses the different layers produce different feature information, including the output layers). Therefore, it would have been obvious to one or ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teaches of Fuerer in view of Fuerer in view of Vanschoren further in view of Lukac and Yumiba and Oami of having an image processing method, comprising: storing a plurality of feature extraction network models; processing using at least one of the plurality of feature extraction network models identified by the feature extraction instruction, the to-be-processed data to extract, the feature information of the to-be-processed data; identifying the extracted feature information to obtain identification information of the feature information, wherein the identification information of the feature information comprises a first identifier the at least one of the plurality of feature extraction network models used to extract the feature information; and sending the feature information and the identification information to indicate, with the teachings of Dauphin of having wherein each of the plurality of feature extraction network models comprises a plurality of layers including convolutional layers and pooling layers, wherein the output layer comprises an intermediate layer or a final layer of the at least one of the plurality of feature extraction network models. Wherein having Fuerer’s method of wherein each of the plurality of feature extraction network models comprises a plurality of layers including convolutional layers and pooling layers, wherein the output layer comprises an intermediate layer or a final layer of the at least one of the plurality of feature extraction network models. The motivation behind the modification would have been to improve machine learning initialization based on hyperparameter optimization that shows mild improvements for low dimensional hyperparameter optimization and substantially improves for complex combined algorithm selection and hyperparameter optimization, and further to improve machine learning initialization with starting in regions that look locally linear so that can automatically receive from a class of bad initializations to perform machine learning more efficiently. Since both Fuerer and Dauphin both perform machine learning model selection based on type of dataset. Wherein Fuerer’s system improves machine learning initialization based on hyperparameter optimization that shows mild improvements for low dimensional hyperparameter optimization and substantially improves for complex combined algorithm selection and hyperparameter optimization, (see Fuerer’s Abstract) and Dauphin’s system improves machine learning initialization with starting in regions that look locally linear so that can automatically receive from a class of bad initializations to perform machine learning more efficiently (see Dauphin’s Par. [0014]). However, Fuerer in view of Fuerer in view of Vanschoren further in view of Lukac and Yumiba and Oami and Dauphin does not explicitly teach feature extraction network models comprises a plurality of layers including convolutional layers and pooling layers. Liu teaches feature extraction network models comprises a plurality of layers including convolutional layers and pooling layers (section 3.1 discloses adding pooling for convolutional network to improve the functions of the convolutional neural network, to include pooling layers). Therefore, it would have been obvious to one or ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teaches of Fuerer in view of Fuerer in view of Vanschoren further in view of Lukac and Yumiba and Oami and Dauphin of having an image processing method, comprising: storing a plurality of feature extraction network models; processing using at least one of the plurality of feature extraction network models identified by the feature extraction instruction, the to-be-processed data to extract, the feature information of the to-be-processed data; identifying the extracted feature information to obtain identification information of the feature information, wherein the identification information of the feature information comprises a first identifier the at least one of the plurality of feature extraction network models used to extract the feature information; and sending the feature information and the identification information to indicate, with the teachings of Liu of having feature extraction network models comprises a plurality of layers including convolutional layers and pooling layers Wherein having Fuerer’s method of feature extraction network models comprises a plurality of layers including convolutional layers and pooling layers. The motivation behind the modification would have been to improve machine learning initialization based on hyperparameter optimization that shows mild improvements for low dimensional hyperparameter optimization and substantially improves for complex combined algorithm selection and hyperparameter optimization, and further to improve image recognition by using cross-convolutional layer pooling which enables effective image processing and retrieval. Since both Fuerer and Liu both perform image recognition using machine learning model. Wherein Fuerer’s system improves machine learning initialization based on hyperparameter optimization that shows mild improvements for low dimensional hyperparameter optimization and substantially improves for complex combined algorithm selection and hyperparameter optimization, (see Fuerer’s Abstract) and Liu’s system improves image recognition by using cross-convolutional layer pooling which enables effective image processing and retrieval (see Liu’s Par. [0014]). Claim 32 is rejected under 35 U.S.C. 103 as being unpatentable over Matthias Feurer et. al. (“Initializing Bayesian Hyperparameter Optimization via Meta-Learning, 2015, Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 29, No. 1 [2015]: Twenty-Nonth AAAI Conference on Artificial Intelligence, AAAI Technical Track: Heuristic Search and Optimization” hereinafter as “Feurer”) in view of Joaquin Vanschoren et. al. (“Meta-Learning and Algorithm Selection Workshop at ECAL, 2014, 21st European Conference on Artificial Intelligence” hereinafter as “Vanschoren”) further in view of Rastislav Lukac et. al. (“US 2019/0325580 A1” hereinafter as “Lukac”) and Ryou Yumiba et. al. (“US 2021/0374403 A1” hereinafter as “Yumiba”) and Ryoma Oami (“US 2020/0034649 A1” hereinafter as “Oami”) and Montserrat Corbalan-Fuertes et. al. (“Color Image Acquisition by charge-coupled device cameras in polychromatic pattern recognition, March 1996, Optical Engineering, Vol. 35, Issue 3” hereinafter as “Corbalan-Fuertes”). Regarding claim 32, Fuerer in view of Fuerer in view of Vanschoren further in view of Lukac and Yumiba and Oami teaches the image processing method of claim 1 (as discussed above in claim 1). However, Fuerer in view of Fuerer in view of Vanschoren further in view of Lukac and Yumiba and Oami does not explicitly disclose wherein the sensor comprises a charge coupled device (CCD) or a complementary metal-oxide-semiconductor (CMOS) photosensitive element. Corbalan-Fuertes discloses wherein the sensor comprises a charge coupled device (CCD) or a complementary metal-oxide-semiconductor (CMOS) photosensitive element (“or” indicates a selection, the examiner selects “charge coupled device” which is disclosed in section 3 wherein the camera used is a charge coupled device for recognition). Therefore, it would have been obvious to one or ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teaches of Fuerer in view of Fuerer in view of Vanschoren further in view of Lukac and Yumiba and Oami of having an image processing method, comprising: storing a plurality of feature extraction network models; processing using at least one of the plurality of feature extraction network models identified by the feature extraction instruction, the to-be-processed data to extract, the feature information of the to-be-processed data; identifying the extracted feature information to obtain identification information of the feature information, wherein the identification information of the feature information comprises a first identifier the at least one of the plurality of feature extraction network models used to extract the feature information; and sending the feature information and the identification information to indicate, with the teachings of Corbalan-Fuertes of having wherein the sensor comprises a charge coupled device (CCD) or a complementary metal-oxide-semiconductor (CMOS) photosensitive element. Wherein having Fuerer’s method of wherein the sensor comprises a charge coupled device (CCD) or a complementary metal-oxide-semiconductor (CMOS) photosensitive element. The motivation behind the modification would have been to improve machine learning initialization based on hyperparameter optimization that shows mild improvements for low dimensional hyperparameter optimization and substantially improves for complex combined algorithm selection and hyperparameter optimization, and further to improve image acquisition features by performing optical correlation which enable improvement in real-time performance of optical correlators. Since both Fuerer and Corbalan-Fuertes both perform feature extraction machine learning model with different external devices. Wherein Fuerer’s system improves machine learning initialization based on hyperparameter optimization that shows mild improvements for low dimensional hyperparameter optimization and substantially improves for complex combined algorithm selection and hyperparameter optimization, (see Fuerer’s Abstract) and Corbalan-Fuertes’s system improves image acquisition features by performing optical correlation which enable improvement in real-time performance of optical correlators (see Corbalan-Fuertes’s Section 1, 2nd Par.). Claim 34 is rejected under 35 U.S.C. 103 as being unpatentable over Matthias Feurer et. al. (“Initializing Bayesian Hyperparameter Optimization via Meta-Learning, 2015, Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 29, No. 1 [2015]: Twenty-Nonth AAAI Conference on Artificial Intelligence, AAAI Technical Track: Heuristic Search and Optimization” hereinafter as “Feurer”) in view of Joaquin Vanschoren et. al. (“Meta-Learning and Algorithm Selection Workshop at ECAL, 2014, 21st European Conference on Artificial Intelligence” hereinafter as “Vanschoren”) further in view of Rastislav Lukac et. al. (“US 2019/0325580 A1” hereinafter as “Lukac”) and Ryou Yumiba et. al. (“US 2021/0374403 A1” hereinafter as “Yumiba”) and Ryoma Oami (“US 2020/0034649 A1” hereinafter as “Oami”) and Lingqiao Liu et. al. (“Cross-Convolutional-Layer Pooling for Image Recognition, Nov. 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 39, Issue 11” hereinafter as “Liu”). Regarding claim 34, Fuerer in view of Vanschoren further in view of Lukac and Yumiba and Oami teaches the image processing method of claim 1. However, Fuerer in view of Vanschoren further in view of Lukac and Yumiba and Oami does not explicitly teach wherein the feature information comprises a feature map generated by an intermediate layer of the at least one of the plurality of feature extraction network models during processing of the to-be-processed image, wherein the intermediate layer is positioned between an input layer and a final output layer of the at least one of the plurality of feature extraction network models, and wherein different intermediate layers of the at least one of the plurality of feature extraction network models generate different feature information. Liu teaches wherein the feature information comprises a feature map generated by an intermediate layer of the at least one of the plurality of feature extraction network models during processing of the to-be-processed image (Section 3.1 discloses “the convolutional layer in a CNN is embedded…each feature map and D denotes the number of feature maps”), wherein the intermediate layer (“a convolutional layer” which is taught in Dauphin’s page 7, “CIFAR” section which includes using convolutional layers to perform the metalearning initialization which is analogous to algorithm 2 of Fuerer, therefore, the it can be understood the output layer of algorithm 2 of Fuerer can be performed using a convolutional layer of Dauphin; section 4.2, last par., discloses the each layer of the model, therefore, any last layer is analogous to the final layer since, “or” indicates a selection, and the examiner selects “final layer of the at least one of…”; wherein Fig. 1 illustrates the different layers of the Conv, wherein any of the layers between xi, 2 to xi, 3 is analogous to the recited intermediate layer being position in between) is positioned between an input layer and a final output layer of the at least one of the plurality of feature extraction network models (the middle layers of Fig. 1’ Conv. layer indicating the position in between the initial layer and the final layer), and wherein different intermediate layers of the at least one of the plurality of feature extraction network models generate different feature information section 4.2, last par., discloses the different layers produce different feature information, including the output layers. Therefore, it would have been obvious to one or ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teaches of Fuerer in view of Fuerer in view of Vanschoren further in view of Lukac and Yumiba and Oami and Dauphin of having an image processing method, comprising: storing a plurality of feature extraction network models; processing using at least one of the plurality of feature extraction network models identified by the feature extraction instruction, the to-be-processed data to extract, the feature information of the to-be-processed data; identifying the extracted feature information to obtain identification information of the feature information, wherein the identification information of the feature information comprises a first identifier the at least one of the plurality of feature extraction network models used to extract the feature information; and sending the feature information and the identification information to indicate, with the teachings of Liu of having wherein the feature information comprises a feature map generated by an intermediate layer of the at least one of the plurality of feature extraction network models during processing of the to-be-processed image, wherein the intermediate layer is positioned between an input layer and a final output layer of the at least one of the plurality of feature extraction network models, and wherein different intermediate layers of the at least one of the plurality of feature extraction network models generate different feature information. Wherein having Fuerer’s method of wherein the feature information comprises a feature map generated by an intermediate layer of the at least one of the plurality of feature extraction network models during processing of the to-be-processed image, wherein the intermediate layer is positioned between an input layer and a final output layer of the at least one of the plurality of feature extraction network models, and wherein different intermediate layers of the at least one of the plurality of feature extraction network models generate different feature information. The motivation behind the modification would have been to improve machine learning initialization based on hyperparameter optimization that shows mild improvements for low dimensional hyperparameter optimization and substantially improves for complex combined algorithm selection and hyperparameter optimization, and further to improve image recognition by using cross-convolutional layer pooling which enables effective image processing and retrieval. Since both Fuerer and Liu both perform image recognition using machine learning model. Wherein Fuerer’s system improves machine learning initialization based on hyperparameter optimization that shows mild improvements for low dimensional hyperparameter optimization and substantially improves for complex combined algorithm selection and hyperparameter optimization, (see Fuerer’s Abstract) and Liu’s system improves image recognition by using cross-convolutional layer pooling which enables effective image processing and retrieval (see Liu’s Par. [0014]). Claim 35 is rejected under 35 U.S.C. 103 as being unpatentable over Matthias Feurer et. al. (“Initializing Bayesian Hyperparameter Optimization via Meta-Learning, 2015, Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 29, No. 1 [2015]: Twenty-Nonth AAAI Conference on Artificial Intelligence, AAAI Technical Track: Heuristic Search and Optimization” hereinafter as “Feurer”) in view of Joaquin Vanschoren et. al. (“Meta-Learning and Algorithm Selection Workshop at ECAL, 2014, 21st European Conference on Artificial Intelligence” hereinafter as “Vanschoren”) further in view of Rastislav Lukac et. al. (“US 2019/0325580 A1” hereinafter as “Lukac”) and Ryou Yumiba et. al. (“US 2021/0374403 A1” hereinafter as “Yumiba”) and Ryoma Oami (“US 2020/0034649 A1” hereinafter as “Oami”) and Steven Joseph Gardner et. al. (“US 2021/0264287 A1” hereinafter as “Gardner”). Regarding claim 35, Fuerer in view of Vanschoren further in view of Lukac and Yumiba and Oami teaches the image processing method of claim 1, Fuerer teaches further comprising: receiving, by the first device, an update instruction to update at least one of the plurality of feature extraction network models (Page 1130, 1st col., last 4 Pars., disclose “begin the search with hyperparameter configurations…initial design of t configurations initializes SMBO with configurations…initialization and delays the start of the actual SMBO procedure until all configurations…are evaluated” wherein, the initialization and changing of the parameter configuration as the process goes indicating an update instruction to the models); and storing, the first device and in response to the update instruction, an updated feature extraction network model having a distinct first identifier (Page 1130, 2nd col., 1st par., discloses a metric is calculated for the dataset based on the metafeatures, the measure information used to obtain the metric is analogous to the identification information as claimed; the metric’s measures obtained from the algorithm 2 is the identification information as disclosed in page 1130, 2nd col.; wherein the metric is analogous to the recited first identifier). However, Fuerer in view of Vanschoren further in view of Lukac and Oami does not explicitly teach a distinct first identifier that uniquely identifies the updated feature extraction network model. Yumiba teaches a distinct first identifier that uniquely identifies (Par. [0086] discloses “the learning model selection unit may output information that uniquely determines the feature extraction learning model, such as the actual state and file name of the file of the feature extraction learning model specified by the symbol” wherein, the symbol is analogous to the recited identifier) the updated feature extraction network model (Par. [0044] discloses “a learning model selection unit, which are the processes realized by the arithmetic functions…composed of a feature extraction learning model group and a recall learning model group realized by the database”). Therefore, it would have been obvious to one or ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teaches of Fuerer in view of Vanschoren further in view of Lukac and Oami of having an image processing method, comprising: storing a plurality of feature extraction network models; processing using at least one of the plurality of feature extraction network models identified by the feature extraction instruction, the to-be-processed data to extract, the feature information of the to-be-processed data; identifying the extracted feature information to obtain identification information of the feature information, wherein the identification information of the feature information comprises a first identifier the at least one of the plurality of feature extraction network models used to extract the feature information; and sending the feature information and the identification information to indicate, with the teachings of Yumiba of having a distinct first identifier that uniquely identifies the updated feature extraction network model. Wherein having Fuerer’s method of the to-be-processed data being a to-be-processed image; receiving, by the first device and from a second device, a feature extraction instruction indicating a target image processing task to be performed on the to-be-processed image and identifying at least one of the plurality of feature extraction network models to be used to extract feature information; sending to a second device, indicate to the second device to select a feature analysis network model; the identifier that uniquely identified the at least one of the plurality of feature extraction network models used to extract the feature information, and wherein the first identifier is based on the target image processing task indicated by feature extraction instruction. The motivation behind the modification would have been to improve machine learning initialization based on hyperparameter optimization that shows mild improvements for low dimensional hyperparameter optimization and substantially improves for complex combined algorithm selection and hyperparameter optimization, and further to improve performance of machine learning based on hyperparameter optimization according to certain types of data. Since both Fuerer and Yumiba both perform machine learning model selection based on type of dataset. Wherein Fuerer’s system improves machine learning initialization based on hyperparameter optimization that shows mild improvements for low dimensional hyperparameter optimization and substantially improves for complex combined algorithm selection and hyperparameter optimization, (see Fuerer’s Abstract) and Yumiba’s system improves feature extraction efficiently by accurately selecting appropriate feature extraction model based on database (see Yumiba’s Par. [0014]). However, Fuerer in view of Vanschoren further in view of Lukac and Yumiba and Oami does not explicitly teach receiving, by the first device and from the second device; storing, to the memory of the first device; wherein the update instruction is specific to the first device such that other devices communicatively coupled to the second device via the network are not required to store the updated feature extraction network model. Gardner teaches receiving, by the first device and from the second device (Par. [0029] discloses “hyperparameter selection system may include a user system, a selection manager device, a worker system , and a network. Each of user system, selection manager device, and worker system may be composed one or more discrete computing devices in communication through network”; Par. [0050] discloses “selection manager application performs operation associated with selecting sets of parameter configurations to evaluate based on inputs provided by user device. Selection manager application requests that the computing devices of worker system generate a trained classification model for each hyperparameter configuration in the selected sets” indicating receiving request from a user device [first device] and generating the parameter configuration by the computing devices [second device]); storing, to the memory of the first device (Par. [0050] discloses “selection manager application performs operation associated with selecting sets of parameter configurations to evaluate based on inputs provided by user device. Selection manager application requests that the computing devices of worker system generate a trained classification model for each hyperparameter configuration in the selected sets” indicating generating the parameter configuration by the computing devices to be stored there [second device]), wherein the update instruction is specific to the first device (Par. [0050] discloses “selection manager application performs operation associated with selecting sets of parameter configurations to evaluate based on inputs provided by user device. Selection manager application requests that the computing devices of worker system generate a trained classification model for each hyperparameter configuration in the selected sets”; moreover, Par. [0068] discloses “selection manager device is a separate device from user device” indicating the instruction for updating/configuring parameter is specific computing device) such that other devices communicatively coupled to the second device via the network (Par. [0029] discloses “hyperparameter selection system may include a user system, a selection manager device, a worker system , and a network. Each of user system, selection manager device, and worker system may be composed one or more discrete computing devices in communication through network”) are not required to store the updated feature extraction network model (Par. [0179] discloses “the second configuration list is updated based on a comparison of h1 with each hyperparameter configuration included in evaluation cache…if so, the hyperparameter configuration h1 is removed from the second configuration list to avoid expending computing resources” indicating the updated configuration is to be removed if not met certain condition [not required to store the updated feature extraction network model]). Therefore, it would have been obvious to one or ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teaches of Fuerer in view of Vanschoren further in view of Lukac and Yumiba and Oami of having an image processing method, comprising: storing a plurality of feature extraction network models; processing using at least one of the plurality of feature extraction network models identified by the feature extraction instruction, the to-be-processed data to extract, the feature information of the to-be-processed data; identifying the extracted feature information to obtain identification information of the feature information, wherein the identification information of the feature information comprises a first identifier the at least one of the plurality of feature extraction network models used to extract the feature information; and sending the feature information and the identification information to indicate, with the teachings of Gardner of having receiving, by the first device and from the second device; storing, to the memory of the first device; wherein the update instruction is specific to the first device such that other devices communicatively coupled to the second device via the network are not required to store the updated feature extraction network model. Wherein having Fuerer’s method of receiving, by the first device and from the second device; storing, to the memory of the first device; wherein the update instruction is specific to the first device such that other devices communicatively coupled to the second device via the network are not required to store the updated feature extraction network model. The motivation behind the modification would have been to improve machine learning initialization based on hyperparameter optimization that shows mild improvements for low dimensional hyperparameter optimization and substantially improves for complex combined algorithm selection and hyperparameter optimization, and further to improve classification model based on parameter selection according to input data more effectively. Since both Fuerer and Gardner both perform machine learning model selection based on type of dataset. Wherein Fuerer’s system improves machine learning initialization based on hyperparameter optimization that shows mild improvements for low dimensional hyperparameter optimization and substantially improves for complex combined algorithm selection and hyperparameter optimization, (see Fuerer’s Abstract) and Gardner’s system improves classification model based on parameter selection according to input data more effectively (see Gardner’s Par. [0002]). Pertinent Prior Art(s) The prior art made of record and not relied upon is considered pertinent to applicant's disclosure: Koch, Patrick Nathan et. al., “US 2018/0240041 A1”, discloses a computing device automatically selects hyperparameter values based on objective criteria to train a predictive model. Each session of a plurality of sessions executes training and scoring of a model type using an input dataset in parallel with other sessions of the plurality of sessions. Farooq, Hasan et. al., “US 12561611 B2”, discloses a method of a hyperparameter server improves hyper-parameter search efficiency for devices in a self-organizing network (SON) includes sending configuration for data feature collection to at least one edge device in the self-organizing network, receiving hyper-parameter performance data from the at least one edge device, and training a shared hyperparameter machine learning model using a global training database including the hyperparameter performance data to identify optimal hyperparameters for use by the at least one edge device. A further method of an edge device improves hyperparameter search efficiency for devices in a SON includes receiving configuration for data feature collection from a hyperparameter server, training an edge machine learning model using local training data and selected hyperparameters. 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. 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