CTNF 18/724,285 CTNF 79991 Notice of Pre-AIA or AIA Status 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. DETAILED ACTION This action is response to the application filed on 06/26/2024. Claims 1-6, 8-9 and 12-13 stand rejected, and are pending in this Office Action. Claims 1, 8, 9 and 12-13 are independent claims. Information Disclosure Statement The information disclosure statements filed 06/26/2024 and 08/08/2025 are in compliance with 37 CFR 1.97(c) and herein have been considered. Its corresponding PTO-1449 have been electronically signed as attached. Claim Rejections - 35 USC § 112 07-30-01 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. 07-31-02 Claim 12-13 are rejected under 35 U.S.C. 112(a) as failing to comply with the enablement requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to enable one skilled in the art to which it pertains, or with which it is most nearly connected, to make and/or use the invention. As per claim 12 , the claim recites “a non-transitory computer-readable storage medium storing therein a program for causing a computer to function as a record pair identity output apparatus according to claim 1, …”. The claim recites a passive object “the medium storing therein a program for causing a computer to act”. While being enabling for the program causing an action, however, the claim does not reasonably enable the program causing the act. Further, claim 12 is recited as a non-transitory computer-readable storage medium, however, the claim is further qualified as “according to claim 1” in which claim 1 recites an apparatus, a totally different category. If claim 12 is an independent claim reciting a non-transitory computer-readable storage medium, the claim should be written independently without causing misleading as a dependent claim of 1. As per claim 13 , the program recites a non-transitory computer-readable storage medium, however, the medium is further qualified as “according to claim 8” in which claim 8 is an apparatus. As such, the claim seems to mislead as a dependent claim of claim 8. Should claim 13 needs to recite some subject matters recited in claim 8, the subject matters should be described independently without referring as “according to claim 8”. Claim Rejections - 35 USC § 102 07-07-aia AIA 07-07 The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action: A person shall be entitled to a patent unless – 07-12-aia AIA (a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention. 07-08-aia AIA (a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention. 07-15 AIA Claim 8 and 13 are rejected under 35 U.S.C. § 102( a)(1 ) as being clearly anticipated by Lin (林勝悟, Lin is the Examiner’s translation of 林) et al.: “INFORMATION PROCESSING DEVICE, INFORMATION PROCESSING METHOD, MANUFACTURING METHOD, AND PROGRAM” (Japan Patent Application Publication JP 7775896 B2, DATE PUBLISHED 2025-11-26; and DATE FILED 2022-01-06, hereafter “Lin”). As per claim 8 , Lin teaches a parameter generation apparatus comprising at least one processor, the at least one processor (Page 33, Computer C has at least one processor C1) carrying out: an acquisition process for acquiring training data including a plurality of sets of a record pair and a label regarding identity between the record pair (See Abstract and Page 7, calculating the similarity between record pairs using multiple similarity functions that calculate the similarity between record pairs, and learns the similarity weights through machine learning using training data; and calculating multiple similarities for a record pair using multiple similarity functions, and to perform identity prediction for the record pair by referencing the record pair and the multiple similarities and using importance determined for the record pair); and a parameter generation process for generating, with reference to the training data, at least one parameter selected from the group consisting of one or more parameters that are possessed by each of a plurality of similarity functions for calculating a plurality of similarities for a record pair to be subjected to prediction (See Lin: Page 10, the parameter generation unit 22 generates, by referring to the training data, at least one of the following parameters: (i) one or more parameters of each of a plurality of similarity functions for calculating a plurality of similarities for the record pair to be predicted; and (ii) one or more parameters of an importance calculation model used for calculating importance by a prediction means that performs identity prediction for the record pair to be predicted by referring to the record pair to be predicted and the plurality of similarities and using importance determined according to the record pair to be predicted), and one or more parameters that are possessed by an importance level calculation model which is used by a prediction apparatus to calculate an importance level determined in accordance with the record pair to be subjected to prediction (See Lin: Page 18, the importance calculation model g is a model for calculating the importance g .sub.i for each of a plurality of similarities s .sub.i . For example, the importance calculation model g is In other words, the sum of the k importance levels {g(e, e')} .sub.i calculated by the importance level calculation model g is 1.), the prediction apparatus referring to the record pair to be subjected to prediction and the plurality of similarities (See Lin: Page 10, the parameter generation unit 22 generates, by referring to the training data, at least one of the following parameters: (i) one or more parameters of each of a plurality of similarity functions for calculating a plurality of similarities for the record pair to be predicted; and (ii) one or more parameters of an importance calculation model used for calculating importance by a prediction means that performs identity prediction for the record pair to be predicted by referring to the record pair to be predicted and the plurality of similarities and using importance determined according to the record pair to be predicted.), and using the importance level to carry out identity prediction with respect to the record pair to be subjected to prediction (See Lin: Page 18, the importance calculation model g is a model for calculating the importance g .sub.i for each of a plurality of similarities s .sub.i . For example, the importance calculation model g is In other words, the sum of the k importance levels {g(e, e')} .sub.i calculated by the importance level calculation model g is 1.). As per claim 13 , the claim recites a non-transitory computer-readable storage medium storing therein a program for causing a computer to function as a parameter generation apparatus according to claim 8, the program causing the computer (See Lin, Page 33, a computer that executes program instructions, which is software that realizes each function. An example of such a computer (hereinafter referred to as computer C) is shown in Figure 13. Computer C has at least one processor C1 and at least one memory C2. Memory C2 stores program P for operating computer C as information processing device 1, etc. In computer C, processor C1 reads and executes program P from memory C2, thereby realizing each function of information processing device 1,) to carry out the acquisition process and the parameter generation process as recited as in claim 8 and as rejected above as under 35 U.S.C. § 102(a)(1) as being clearly anticipated by Lin. Accordingly, claim 13 is rejected along the same rationale that rejected claim 8 . Claim Rejections - 35 USC § 103 07-20-aia AIA The following is a quotation of - 35 USC § 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. 07-23-aia AIA The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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 non-obviousness. 07-20-02-aia AIA This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention. 07-21-aia AIA Claim s 1-4, 9, and 12 are rejected under 35 USC § 103 as being unpatentable over GAO et al.: “FULL SCALE MATCHING SEARCH METHOD BASED ON WORD SEGMENTATION, DEVICE, DEVICE AND STORAGE MEDIUM” (China Patent Application Publication CN 112015865 A, DATE PUBLISHED 2020-12-01; and DATE FILED 2020-08-26, hereafter “GAO”), in view of LI et al.: “ENTITY ALIGNING METHOD, DEVICE, STORAGE MEDIUM AND DEVICE OF KNOWLEDGE MAP” (China Patent Application Publication CN 117744778 A, DATE PUBLISHED 2024-03-22; and DATE FILED 2023-12-27, hereafter “LI”). As per claim 1 , GAO teaches a record pair identity output apparatus comprising at least one processor, the at least one processor (See GAO: Page 4, a computer device comprising: one or more processors; a storage device for storing one or more programs, when the one or more programs are executed by the one or more processors, so that the one or more processors) carrying out: an acquisition process for acquiring a record pair including a first record that is input data from a user and a second record that is one of a plurality of records included in target data (See Page 5 and Abstract, determining the target word set corresponding to the obtained search key word; and determining the target participle set corresponding to the obtained search key word according to the preset participle determining rule; based on the corresponding relation between the preset participle and the preset full scale; determining the candidate full scale set matched with the target participle set. Here the obtained search key word teaches the source record and the corresponding target word set or the matched target participle set teaches the set of target records); a similarity calculation process for using a plurality of similarity functions to calculate a plurality of similarities for the record pair (See Page 10, the similarity between each candidate full name and the search key word in the determined candidate full set and similarity can be the candidate full name and the search key word as a pair of input, input to the pre-trained similarity determining model , the output of the similarity value is higher, it can determine the similarity between the candidate full name and the search key word is higher .). GAO does not explicitly teach a prediction process for referring to the record pair and the plurality of similarities. However, LI teaches a prediction process for referring to the record pair and the plurality of similarities (See Page 8, a prediction unit for inputting the candidate knowledge point entity which does not form the first alignment entity pair or the second alignment entity pair with the first knowledge point entity and the first knowledge point entity to the pre-constructed entity alignment classification model to predict whether the two are the alignment entity pair with inclusion relation). It would have been obvious to one having ordinary skill in the art at the time the Applicant’s application was filed to combine LI’s teaching with GAO because GAO is dedicated to retrieval, especially relates to a full name matching search based on word segmentation and LI is dedicated to entity alignment of knowledge map, the combined teaching of GAO and LI references would have allowed GAO to use accurate alignment of object pair in prediction model improve accuracy of records retrieving. GAO in view of LI further teaches: using an importance level determined in accordance with the record pair to carry out identity prediction with respect to the record pair of the first record and each of the plurality of records included in the target data (See LI: Page 8, a prediction unit for inputting the candidate knowledge point entity which does not form the first alignment entity pair or the second alignment entity pair with the first knowledge point entity and the first knowledge point entity to the pre-constructed entity alignment classification model to predict whether the two are the alignment entity pair with inclusion relation and using the obtained prediction result, the first alignment entity pair and the second alignment entity pair to obtain the entity alignment result of the first knowledge map and the second knowledge map. Here the alignment of the pair of entity points reads on identity); an output process for outputting a prediction result in the prediction process (See LI: Page 8, using the obtained prediction result, the first alignment entity pair and the second alignment entity pair to obtain the entity alignment result of the first knowledge map and the second knowledge map. Here a prediction result obtained reads on the result output); and a search result output process for outputting, with reference to each prediction result output in the output process, a search result which is based on the input data and in which the target data is a search target (See GAO: Page 39, search matching result output device 505 for the link number information in the hit joint number list as the result output, ; and LI: Page 8, using the obtained prediction result, the first alignment entity pair and the second alignment entity pair to obtain the entity alignment result of the first knowledge map and the second knowledge map. Here a prediction result obtained reads on the result output). As per claim 2 , GAO in view of LI teaches the record pair identity output apparatus according to claim 1, wherein in the acquisition process, the at least one processor further acquires auxiliary data (See LI: Page 28, a prediction unit 404 for inputting the candidate knowledge point entity which does not form a first alignment entity pair or a second alignment entity pair with the first knowledge point entity and the first knowledge point entity to a pre-constructed entity alignment classification model, and predicting whether the two are alignment entity pairs with inclusion relation. Here the first alignment and second alignment inclusion is interpreted the auxiliary data acquired), and in the prediction process, the at least one processor refers to the record pair, the plurality of similarities, and the auxiliary data (See Li: Pag 28, a prediction unit 404 for inputting the candidate knowledge point entity which does not form a first alignment entity pair or a second alignment entity pair with the first knowledge point entity and the first knowledge point entity to a pre-constructed entity alignment classification model, and predicting whether the two are alignment entity pairs with inclusion relation; and using the obtained prediction result, the first alignment entity pair and the second alignment entity pair to obtain the entity alignment result of the first knowledge map and the second knowledge map.), and uses an importance level determined in accordance with the record pair and the auxiliary data to carry out identity prediction with respect to the record pair (See LI: Page 8, a prediction unit for inputting the candidate knowledge point entity which does not form the first alignment entity pair or the second alignment entity pair with the first knowledge point entity and the first knowledge point entity to the pre-constructed entity alignment classification model to predict whether the two are the alignment entity pair with inclusion relation and using the obtained prediction result, the first alignment entity pair and the second alignment entity pair to obtain the entity alignment result of the first knowledge map and the second knowledge map. Here the alignment of the pair of entity points reads on identity). As per claim 3 , GAO in view of LI teaches the record pair identity output apparatus according to claim 1, wherein in the prediction process, the at least one processor carries out an importance level calculation process for calculating the importance level with reference to the record pair (See LI: Page 29, calculating the similarity of the first key word and the second key word, and taking the obtained second knowledge point entity related to the second key word corresponding to the similarity higher than the second similarity threshold value as the candidate knowledge point entity corresponding to the first knowledge point entity. Here calculating the pair similarity and similarity threshold teaches the importance level calculation). As per claim 4 , GAO in view of LI teaches the record pair identity output apparatus according to claim 3, wherein in the importance level calculation process, the at least one processor calculates the importance level regarding each of the plurality of similarities (See LI: Page 29, calculating the similarity of the first key word and the second key word, and taking the obtained second knowledge point entity related to the second key word corresponding to the similarity higher than the second similarity threshold value as the candidate knowledge point entity corresponding to the first knowledge point entity. Here calculating the pair similarity and similarity threshold teaches the importance level calculation), and in the prediction process, the at least one processor carries out the identity prediction with use of a linear sum regarding the plurality of similarities, the linear sum using, as a weighting factor, the importance level regarding each of the plurality of similarities (See LI: Page 29, a fourth calculating sub-unit for calculating the similarity between the text feature vector of the first knowledge point entity and the text feature vector of the candidate knowledge point entity, and the weighted sum result of the similarity between the text feature vector of the parent node entity of the first knowledge point entity and the text feature vector of the candidate parent node entity; and forming a first aligned entity pair by the candidate knowledge point entity corresponding to the weighted sum result higher than the first similarity threshold value and the first knowledge point entity.). As per claim 9 , the claim recites a record pair identity output method comprising operations recited as steps in the claim 1 and rejected above under 35 USC § 103 as being unpatentable over GAO in view of LI . Therefore, claim 9 is rejected along the same rationale that rejected claim 1. As per claim 12 , the claim recites a non-transitory computer-readable storage medium storing therein a program for causing a computer to function as a record pair identity output apparatus according to claim 1, the program causing the computer to carry out the acquisition process, the similarity calculation process, the prediction process, the output process, and the search result output process (See GAO: Page 4, a computer device comprising: one or more processors; a storage device for storing one or more programs, when the one or more programs are executed by the one or more processors, so that the one or more processors) recited as steps in the claim 1 and rejected above under 35 USC § 103 as being unpatentable over GAO in view of LI . Therefore, claim 12 is rejected along the same rationale that rejected claim 1 . 07-22-aia AIA Claim s 5-6 are rejected under 35 USC § 103 as being unpatentable over GAO in view of LI , as applied to claim s 1-4 above, and further in view of Lin (林 勝悟) et al.: “INFORMATION PROCESSING DEVICE, INFORMATION PROCESSING METHOD, MANUFACTURING METHOD, AND PROGRAM” (Japan Patent Application Publication JP 7775896 B2, DATE PUBLISHED 2025-11-26; and DATE FILED 2022-01-06, hereafter “Lin”). As per claim 5 , GAO in view of LI does not explicitly teach the record pair identity output apparatus according to claim 3, wherein in the acquisition process, the at least one processor further acquires training data including a plurality of sets of the record pair and a label regarding identity between the record pair. However, Lin teaches the record pair identity output apparatus according to claim 3, wherein in the acquisition process, the at least one processor further acquires training data including a plurality of sets of the record pair and a label regarding identity between the record pair (See Abstract and Page 7, calculating the similarity between record pairs using multiple similarity functions that calculate the similarity between record pairs, and learns the similarity weights through machine learning using training data; and calculating multiple similarities for a record pair using multiple similarity functions, and to perform identity prediction for the record pair by referencing the record pair and the multiple similarities and using importance determined for the record pair). It would have been obvious to one having ordinary skill in the art at the time the Applicant’s application was filed to combine LI’s teaching with GAO in view of LI because GAO is dedicated to retrieval, especially relates to a full name matching search based on word segmentation, LI is dedicated to entity alignment of knowledge map and Lin is dedicated to predicting identity between record pairs, and the combined teaching of GAO and LI references would have allowed GAO in view of LI to use the machine learning method for the importance calculation model based on neural network to improve prediction accuracy. GAO in view of LI , and further in view of Lin further teaches the following: the at least one processor further carries out a parameter generation process for generating, with reference to the training data, at least one parameter selected from the group consisting of one or more parameters that are possessed by each of the plurality of similarity functions which are used in the similarity calculation process to calculate the similarities (See Lin: Page 10, the parameter generation unit 22 generates, by referring to the training data, at least one of the following parameters: (i) one or more parameters of each of a plurality of similarity functions for calculating a plurality of similarities for the record pair to be predicted; and (ii) one or more parameters of an importance calculation model used for calculating importance by a prediction means that performs identity prediction for the record pair to be predicted by referring to the record pair to be predicted and the plurality of similarities and using importance determined according to the record pair to be predicted.), and one or more parameters that are possessed by an importance level calculation model which is used in the importance level calculation process to calculate the importance level (See Lin: Page 18, the importance calculation model g is a model for calculating the importance g .sub.i for each of a plurality of similarities s .sub.i . For example, the importance calculation model g is In other words, the sum of the k importance levels {g(e, e')} .sub.i calculated by the importance level calculation model g is 1. ). As per claim 6 , GAO in view of LI and further in view of Lin teaches the record pair identity output apparatus according to claim 1, wherein in the acquisition process, the at least one processor acquires first data including a first record included in the record pair and second data including a second record included in the record pair (See Lin: Page 14, the output unit 14 outputs the prediction result by the prediction unit 13. For example, the prediction result includes information indicating whether the records included in the record pair are identical. The prediction result may also include information indicating the degree of similarity between the records included in the record pair. The output unit 14 may output the prediction result by writing it to the storage unit 20A or an external storage device, or may output it to an output device (such as a display device or a printer) connected to the input/output unit 40A. The output unit 14 may also output the prediction result by transmitting it to another device via the communication unit 30A.), and the at least one processor carries out an integration process for referring to the prediction result output in the output process (See Lin: Page 7, the output unit 14 outputs the prediction result by the prediction unit 13. The prediction result includes, for example, information indicating whether the records included in the record pair are identical or information indicating the similarity between the records included in the record pair.), and generating integrated data from the first data and the second data (See Lin: Page 9, the parameter generation unit 22 generates, by referring to the training data, at least any of the following parameters: (i) one or more parameters of each of a plurality of similarity functions φ .sub.i for calculating a plurality of similarities for the record pair to be predicted; and (ii) one or more parameters of an importance calculation model used by the prediction unit 13, which refers to the record pair to be predicted and the plurality of similarities and performs identity prediction for the record pair to be predicted using an importance determined according to the record pair to be predicted). Related Prior Arts 07-96 AIA The prior art made of record and not relied upon is considered pertinent to applicant's disclosure can be found in the PTO-892 Notice of Reference Cited . Conclusion Examiner has cited particular columns and line numbers in the references applied to the claims above for the convenience of the applicant. Although the specified citations are representative of the teachings of the art and are applied to specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested from the applicant in preparing responses, to fully consider the references in 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 or disclosed by the Examiner. SEE MPEP 2141.02 [R-5] VI. PRIOR ART MUST BE CONSIDERED IN ITS ENTIRETY, INCLUDING DISCLOSURES THAT TEACH AWAY FROM THE CLAIMS: A prior art reference must be considered in its entirety, i.e., as a whole, including portions that would lead away from the claimed invention. W.L. Gore & Associates, Inc. v. Garlock, Inc., 721 F.2d 1540, 220 USPQ 303 (Fed. Cir. 1983), cert. denied, 469 U.S. 851 (1984) In re Fulton, 391 F.3d 1195, 1201, 73 USPQ2d 1141, 1146 (Fed. Cir. 2004). >See also MPEP §2123. In the case of amending the Claimed invention, Applicant is respectfully requested to indicate the portion(s) of the specification which dictate(s) the structure relied on for proper interpretation and also to verify and ascertain the metes and bounds of the claimed invention. Contact Information Any inquiry concerning this communication or earlier communications from the examiner should be directed to KUEN S LU whose telephone number is (571)272-4114. The examiner can normally be reached on M-F, 8-19, Mid-Flex 2 hours. 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, Mr. Aleksandr Kerzhner can be reached on 571-270-1760. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see http://pair-direct.uspto.gov. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. KUEN S LU /Kuen S Lu/ Art Unit 2165 Primary Patent Examiner May 29, 2026 Application/Control Number: 18/724,285 Page 2 Art Unit: 2165 Application/Control Number: 18/724,285 Page 3 Art Unit: 2165 Application/Control Number: 18/724,285 Page 4 Art Unit: 2165 Application/Control Number: 18/724,285 Page 5 Art Unit: 2165 Application/Control Number: 18/724,285 Page 6 Art Unit: 2165 Application/Control Number: 18/724,285 Page 7 Art Unit: 2165 Application/Control Number: 18/724,285 Page 8 Art Unit: 2165 Application/Control Number: 18/724,285 Page 9 Art Unit: 2165 Application/Control Number: 18/724,285 Page 10 Art Unit: 2165 Application/Control Number: 18/724,285 Page 11 Art Unit: 2165 Application/Control Number: 18/724,285 Page 12 Art Unit: 2165 Application/Control Number: 18/724,285 Page 13 Art Unit: 2165 Application/Control Number: 18/724,285 Page 14 Art Unit: 2165 Application/Control Number: 18/724,285 Page 15 Art Unit: 2165
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