CTNF 18/574,470 CTNF 89187 DETAILED ACTION 07-03-aia AIA 15-10-aia The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. This Action is non-final and is in response to the claims filed December 27, 2023 via preliminary amendment. Claims 1-12 and 14 are currently pending, of which claims 1-12 and 14 are currently amended. Claims 13 and 15 have been cancelled. Claim Rejections - 35 USC § 101 07-04-01 AIA 07-04 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. Claim 1-12 and 14 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea(s) without significantly more. As per claim 1 , at Step 1 the claim is directed to a statutory category (method/process). At Step 2A, Prong 1, the abstract ideas are identified in the reproduced claim language below: A method for scheme recommendation, the method comprising: obtaining a technical request for a factory production, wherein the technical request comprises a candidate and job recommendation request for a production task, a technical scheme request for a type of work and specific requirement, or a technical expert and solution request for a technical problem ( mental process – evaluation and judgment; method of organizing human activity – economic practice and commercial interactions ); performing analysis and feature extraction on the technical request and obtaining corresponding current request description information ( mental process – evaluation and judgment; method of organizing human activity – economic practice and commercial interactions ); according to the current request description information, adopting a neural network algorithm for deep learning by using historical searching cases in a case database to obtain at least one searching algorithm from multiple searching algorithms based on case-based reasoning ( mental process – evaluation and judgment; mathematical concepts and relationships ); and obtaining corresponding recommendation scheme after an information database established with industrial technology information, technical characteristics information of workers, and technical expertise information of engineering experts is searched by the at least one searching algorithm ( mental process – evaluation and judgment; method of organizing human activity – economic practice and commercial interactions ). That is, a user/operator could be designing a factory or manufacturing task to solve a problem using their mind with the assistance of pen and paper. There could be a design (or engineering) firm that specializes in certain types of production, with specific employees in those firms that have specific expertise. At Steps 2 Prong 2, the claim does not recite any additional elements that integrate the abstract idea(s) into a practical application. Specifically, the databases are recited at a high level of generality and are merely adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. See MPEP 2106.05(f). At Step 2B, there are no additional elements that amount to significantly more than the abstract idea(s). As per claim 2 , the claim is directed to the mental process of searching and recommending (evaluation and judgment). At Steps 2 Prong 2, the claim does not recite any additional elements that integrate the abstract idea(s) into a practical application. At Step 2B, there are no additional elements that amount to significantly more than the abstract idea(s). As per claim 3 , the claim is directed to the mental process of searching and recommending with the assistance of pen and paper (evaluation and judgment). At Steps 2 Prong 2, the claim does not recite any additional elements that integrate the abstract idea(s) into a practical application. Specifically, the storage and case database are generic computer components that amount to a mere “apply it” scenario. See MPEP 2106.05(f). At Step 2B, there are no additional elements that amount to significantly more than the abstract idea(s). As per claim 4 , the claim is directed to the mental process of searching, scoring, and recommending with the assistance of pen and paper (evaluation and judgment). At Steps 2 Prong 2, the claim does not recite any additional elements that integrate the abstract idea(s) into a practical application. Specifically, the storage and case database are generic computer components that amount to a mere “apply it” scenario. See MPEP 2106.05(f). At Step 2B, there are no additional elements that amount to significantly more than the abstract idea(s). As per claim 5 , the claim is directed to the process of searching and the various ways to do it with general algorithms (mental process – evaluation and judgment; mathematical relationships and formulas). At Steps 2 Prong 2, the claim does not recite any additional elements that integrate the abstract idea(s) into a practical application. At Step 2B, there are no additional elements that amount to significantly more than the abstract idea(s). As per claim 6 , the claim is directed to the mental process of recommending ideas with the assistance of pen and paper (evaluation and judgment). At Steps 2 Prong 2, the claim does not recite any additional elements that integrate the abstract idea(s) into a practical application. Specifically, the storage and results database are generic computer components that amount to a mere “apply it” scenario. See MPEP 2106.05(f). At Step 2B, there are no additional elements that amount to significantly more than the abstract idea(s). As per claim 7 , the claim is directed to the mental process of searching, scoring, and recommending with the assistance of pen and paper (evaluation and judgment). At Steps 2 Prong 2, the claim does not recite any additional elements that integrate the abstract idea(s) into a practical application. Specifically, the storage and case database are generic computer components that amount to a mere “apply it” scenario. See MPEP 2106.05(f). At Step 2B, there are no additional elements that amount to significantly more than the abstract idea(s). As per claims 8-11 and 12 , the claims are directed to a device that implements the same features as the method of claims 1-4 and 6, respectively, and are therefore rejected for at least the same reasons therein. At Steps 2 Prong 2, the claims do not recite any further additional elements that integrate the abstract idea(s) into a practical application. At Step 2B, there are no further additional elements that amount to significantly more than the abstract idea(s). As per claim 14 , the claim is directed to a system that implements the same features as the method of claim 1, and is therefore rejected for at least the same reasons therein. At Steps 2 Prong 2, the claim does not recite any further additional elements that integrate the abstract idea(s) into a practical application. At Step 2B, there are no further additional elements that amount to significantly more than the abstract idea(s). Claim Objections 07-29-01 AIA Claim s 7 and 11 are objected to because of the following informalities: Claim 7 recites a “wherein” clause preceded by a semi-colon (“;”). Semi-colons delineate claim limitations, and wherein clauses are simply further clarifications to claim limitations. Therefore, the preceding semi-colon should be removed and replaced with a comma (“,”). Claim 11 recites a full run-on claim with a semi-colon (“;”) breaking up parts of the claim language. Applicant should instead split the claim limitations up that are separated by the semi-colon and add an “and” to indicate the final limitation of the claim (as written in similar claim 4) . Appropriate correction is required. Claim Interpretation – 112(f) 07-30-03 AIA 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. 07-30-05 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 limitations 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. 07-30-06 This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: “data obtaining module”, “analysis module”, “algorithm matching module”, “scheme recommendation module”, “scheme integration module”, “case determination module”, and “offline recommendation module” in claims 8-12 and 14 . 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. 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 07-30-02 AIA 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. 07-34-01 Claims 2-12 and 14 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 applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. Claim 2 recites “obtained by at least one searching algorithm” and it is unclear what searching algorithm Applicant is referring to. It could be either one searching algorithm or the two or more searching algorithms. Claim 9 recites similar language and is rejected for at least the same reasons therein. Claim 3 recites “obtained by each of the at least one searching algorithm” and “at least one searching algorithm” and it is unclear what searching algorithm Applicant is referring to. Claim 10 recites similar language and is rejected for at least the same reasons therein. Claim 4 recites “storing the current request description information and each of at least one preferred searching algorithm” and it is unclear if this is a new preferred searching algorithm or the same one introduced in the prior limitation. Claim 11 recites similar language and is rejected for at least the same reasons therein. Claim 6 recites “in offline mode”, and it is unclear if this is meant to be applicable to both the searching the results database according to a technical request and a request description information, or if it offline applies only to the request description information. 07-34-03 Claim 7 recites “large number” and the term “large” is a relative term which renders the claim indefinite. The term “large” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. 07-34-23 Claim limitations “data obtaining module”, “analysis module”, “algorithm matching module”, “scheme recommendation module”, “scheme integration module”, “case determination module”, and “offline recommendation module” invoke 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. More specifically, it is unclear if these modules are hardware, software and/or a combination of both. Therefore, the metes and bounds of the claims cannot be determined and claims 8-12 and 14 are 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. Claim Interpretation – Contingent Language Claims 2-4 and 7 is/are directed to a method that recites various contingent language that affects the interpretation of the method claims. Claim 2 recites “when the at least one searching algorithm is two or more searching algorithms” and the conditional nature of this claim language allow for an interpretation where any prior art meets the broadest reasonable interpretation of the claim without having the conditional language even occurring. Therefore, the prior art only needs to read on the preamble of the claim. Claim 3 recites two separate “when” statements, and the conditional nature of this claim language allow for an interpretation where any prior art meets the broadest reasonable interpretation of the claim with only one of the “when” limitations occurring. Claim 4 recites “after obtaining a corresponding result score”, and the conditional nature of this claim language allow for an interpretation where any prior art meets the broadest reasonable interpretation of the claim without having the conditional language even occurring. Specifically, if no result score is obtained, then this language will not have to occur. Claim 7 recites two separate technical request forms, and the conditional nature of this claim language allow for an interpretation where any prior art meets the broadest reasonable interpretation of the claim with only one of the forms of information limitations occurring. See MPEP 2111.04(II); see also Ex parte Schulhauser . Examiner notes that even though the broadest reasonable interpretation of the method of claim(s) 2-4 requires some or none of these conditions to occur, the prior art cited below reads on the structure for performing all of the functionality in the non-method claims, and thus, in an effort to advance compact prosecution, reads on all of the method claim(s) as well. Examiner’s Note The prior art rejections below cite particular paragraphs, columns, and/or line numbers in the references for the convenience of the applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested that, in preparing responses, the applicant fully consider the references in their entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art. 07-06 AIA 15-10-15 In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status. Claim Rejections - 35 USC § 103 07-20-aia AIA 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. 07-21-aia AIA Claim (s) 1, 2, 4-9, 11, 12, and 14 i is/are rejected under 35 U.S.C. 103 as being unpatentable over Matsuo et al. (U.S. Publication No. 2018/0010986, retrieved from IDS filed March 21, 2025; hereinafter “Matsuo”) and further in view of Kulkarni (U.S. Publication No. 2020/0073953) . As per claim 1 , Matsuo teaches a method for scheme recommendation, the method comprising: obtaining a technical request for a factory production, wherein the technical request comprises a candidate and job recommendation request for a production task, a technical scheme request for a type of work and specific requirement, or a technical expert and solution request for a technical problem (See Matsuo paras. [0069-70] and [0103]: fault diagnosis request of factory machinery); performing analysis [and feature extraction] on the technical request and obtaining corresponding current request description information (See Matsuo paras. [0099-102] and [0106]: “The knowledge system 408 automatically analyzes the machine status according to free text inputted by the user. This includes the matching required items to achieve specified objective for the particular machine); according to the current request description information, [adopting a neural network algorithm for deep learning] by using historical searching cases in a case database to obtain at least one searching algorithm from multiple searching algorithms based on case-based reasonin g (See Matsuo paras. [0077-81], [0114-117], and [0143-144]: obtaining information of fault history (and maintenance history) to be used. Furthermore, “it is possible to reinforce the knowledge system of fault diagnosis and easily improve responses of the responder”); and obtaining corresponding recommendation scheme after an information database established with industrial technology information, technical characteristics information of workers, and technical expertise information of engineering experts is searched by the at least one searching algorithm (See Matsuo Fig. 12 and paras. [0112-117]: maintenance history with searched items about technical characteristics of the machine tool at issue). However, while Matsuo teaches reinforcing the knowledge system (See Matsuo para. [0144]), Matsuo does not explicitly teach a neural network algorithm. Moreover, while Matsuo teaches text analysis, Matsuo does not teach feature extraction. Kulkarni teaches feature extraction , as well as adopting a neural network algorithm for deep learning (See Kulkarni paras. [0054] and [0107-111]: machine learning models can be applied to search results and matching the search query; paras. [0051-52], [0080], [0101-102]: feature extraction from a variety of data sources, including to be used in clustering). It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to combine, with a reasonable expectation of success, the searching and knowledge system of Matsuo with the neural network of Kulkarni. One would have been motivated to combine these references because both references disclose search result databases, and Kulkarni enhances the system of Matsuo by improving the search relevance in subsequent searches while also increasing their accuracy (See Kulkarni para. [0018]). As per claim 2 , Matsuo/Kulkarni teaches the method according to claim 1 , However, while Matsuo teaches a searching algorithm(s) and search results, Matsuo does not score and weigh the search results. Kulkarni teaches when the at least one searching algorithm is two or more searching algorithms, sorting and integrating searched items in the recommendation scheme obtained by at least one searching algorithm according to the coincidence rate and weight of each searched item in each recommendation scheme, and obtaining the integrated recommendation scheme (See Kulkarni paras. [0052-58]: weights of features associated with relevance score of search results. This is then used for improving result ranking as well as how the results are sorted and presented to a user). It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to combine Matsuo with the teachings of Kulkarni for at least the same reasons as discussed above in claim 1. As per claim 4 , Matsuo/Kulkarni teaches the method according to claim 3 . However, while Matsuo stores the information and search results, Matsuo does not score and store the scores of the search results. Kulkarni teaches before storing the current request description information and at least one preferred searching algorithm as a searching cases in the case database, providing the recommendation scheme obtained by each of the at least one preferred searching algorithm to a user for result scoring; and after obtaining a corresponding result score, storing the current request description information and each of at least one preferred searching algorithm and a result score thereof in the case database as a searching case (See Kulkarni paras. [0053] and [0061-63]: scores based on user activity that are used to determine relevance of search results for ranking them. Subsequently, the search log module stores information regarding the search results and the user’s interaction(s) therewith; paras. [0071-78]: ranking models used). It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to combine Matsuo with the teachings of Kulkarni for at least the same reasons as discussed above in claim 1. As per claim 5 , Matsuo/Kulkarni teaches the method according to claim 1 . However, Matsuo does not explicitly teach or suggest the specific types of searching algorithms. Kulkarni teaches wherein the multiple searching algorithms each comprise: at least one or more of self-organizing maps algorithm, singular value decomposition algorithm, K-means clustering algorithm, and a prior algorithm for mining association rules (See Kulkarni paras. [0064-65]: clustering modules, including k-means clustering). It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to combine Matsuo with the teachings of Kulkarni for at least the same reasons as discussed above in claim 1. As per claim 6 , Matsuo/Kulkarni further teaches the method according to claim 1, further comprising storing the technical request, the current request description information and corresponding recommendation scheme as a recommendation case in a result database, so as to obtain the corresponding recommendation scheme by searching the result database according to a technical request or a request description information in offline mode (See Matsuo paras. [0070], [0102], and [0113]: know-how database where the case details can be used to recommend a result). As per claim 7 , Matsuo/Kulkarni teaches the method according to claim 1 . However, while Matsuo teaches reinforcement learning, Matsuo does not teach semantic analysis or feature extraction. Kulkarni teaches wherein performing analysis and feature extraction on the technical request comprises: for a technical request in the form of structured information or structured information in a technical request, using a semantic analysis module to perform analysis and feature extraction on the structural information based on pre-determined analytical rules (See Kulkarni paras. [0067-69]: semantic tagging used to classify/cluster queries; para. [0042]: “Each entity has a well-defined schema describing its fields”); and for a technical request in the form of unstructured information or unstructured information in a technical request, adopting an information analysis model to perform analysis and feature extraction on the unstructured information (See Kulkarni paras. [0051-52], [0080], [0101-102]: feature extraction from a variety of data sources, including to be used in clustering); wherein the information analysis model is trained by taking a large number of historical unstructured information as input samples and corresponding historical request description information as output samples (See Kulkarni paras. [0107] and [0110-111]: training dataset, including the use of historical data). It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to combine Matsuo with the teachings of Kulkarni for at least the same reasons as discussed above in claim 1. As per claims 8, 9, 11, and 12 , the claims are directed to a device that implements the same features as the method of claims 1, 2, 4, and 6, respectively, and are therefore rejected for at least the same reasons therein. Furthermore, Matsuo teaches a device for scheme recommendation implementing said method (See Matsuo para. [0003]). As per claim 14 , the claim is directed to a system that implements the same features as the method of claim 1, and is therefore rejected for at least the same reasons therein. Furthermore, Matsuo teaches a system for scheme recommendation implementing said method (See Matsuo para. [0003]) . 07-21-aia AIA Claim s 3 and 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Matsuo/Kulkarni as applied above, and further in view of Hall et al. (U.S. Publication No. 2023/0148321; hereinafter “Hall”) . As per claim 3 , Matsuo/Kulkarni teaches the method according to claim 2, further comprising: storing the current request description information and at least one preferred searching algorithm as a searching case in the case database (See Matsuo paras. [0070] and [0113]: failure know-how database). However, while Matsuo teaches the searching algorithm, Matsuo doesn’t explicitly teach a preferred searching algorithm. Kulkarni teaches when the at least one searching algorithm is one searching algorithm, determining the one searching algorithm as a preferred searching algorithm (See Kulkarni paras. [0057] and [0123]: different models and selecting best user cluster). It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to combine Matsuo with the teachings of Kulkarni for at least the same reasons as discussed above in claim 1. Furthermore, while Matsuo/Kulkarni perform individual models as well as the searching/retrieval algorithms themselves, and Kulkarni has two level models to choose from (See Kulkarni paras. [0076-78]), neither Matsuo or Kulkarni teach similarity matching between the schemes to select the models. Hall teaches when the at least one retrieval algorithm is two or more searching algorithms, performing a similarity matching between the integrated recommendation scheme and recommendation scheme obtained by each of the at least one searching algorithm, and at least one searching algorithm with the highest matching degree or at least one searching algorithm with the matching degree reaching a set threshold is regarded as preferred searching algorithm (See Hall paras. [0202-203]: “the performance of each model at each training epoch is assessed on their shared validation set, using a primary metric, and we then select two or more (or all) of the trained AI models for inclusion in the ensemble model based on the stored best primary metrics”) It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to combine, with a reasonable expectation of success, the knowledge system and searching models of Matsuo/Kulkarni with the combined model selection of Hall. One would have been motivated to combine these references because both references disclose search result databases, and Hall enhances the learning models of Matsuo/Kulkarni because “Selecting a model based on a confidence metric may indeed lead to improved performance in not only that metric, but also other metrics…such as Accuracy” (See Hall para. [0084]). As per claim 10 , the claims is directed to a device that implements the same features as the method of claim 3, and is therefore rejected for at least the same reasons therein . Conclusion 07-96 AIA The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Any inquiry concerning this communication or earlier communications from the examiner should be directed to Nicholas Klicos whose telephone number is (571)270-5889. The examiner can normally be reached Mon-Fri 9:00 AM-5:00 PM. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Scott Baderman can be reached at (571) 272-3644. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent- center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /NICHOLAS KLICOS/Primary Examiner, Art Unit 2118 Application/Control Number: 18/574,470 Page 2 Art Unit: 2118 Application/Control Number: 18/574,470 Page 3 Art Unit: 2118 Application/Control Number: 18/574,470 Page 4 Art Unit: 2118 Application/Control Number: 18/574,470 Page 5 Art Unit: 2118 Application/Control Number: 18/574,470 Page 6 Art Unit: 2118 Application/Control Number: 18/574,470 Page 7 Art Unit: 2118 Application/Control Number: 18/574,470 Page 8 Art Unit: 2118 Application/Control Number: 18/574,470 Page 9 Art Unit: 2118 Application/Control Number: 18/574,470 Page 10 Art Unit: 2118 Application/Control Number: 18/574,470 Page 11 Art Unit: 2118 Application/Control Number: 18/574,470 Page 12 Art Unit: 2118 Application/Control Number: 18/574,470 Page 13 Art Unit: 2118 Application/Control Number: 18/574,470 Page 14 Art Unit: 2118 Application/Control Number: 18/574,470 Page 16 Art Unit: 2118 Application/Control Number: 18/574,470 Page 18 Art Unit: 2118 Application/Control Number: 18/574,470 Page 19 Art Unit: 2118 Application/Control Number: 18/574,470 Page 20 Art Unit: 2118 Application/Control Number: 18/574,470 Page 21 Art Unit: 2118 Application/Control Number: 18/574,470 Page 22 Art Unit: 2118 Application/Control Number: 18/574,470 Page 23 Art Unit: 2118
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