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 .
Claims 1-9 are pending in the application under prosecution and have been examined.
The specification has not been checked to the extent necessary to determine the presence of all possible minor errors.
The specification should be amended to reflect the status of all related application, whether patented or abandoned. Therefore, applications noted by their serial number and/or attorney docket number should be updated with correct serial number and patent number if patented.
The first instance of all acronyms or abbreviation should be spelled out for clarity, whether or not considered well known in the art.
In the response to this Office action, the Examiner respectfully requests that support be shown for language added to any original claims on amendment and any new claims. That is, indicate support for newly added claim language by specifically pointing to page(s) and line numbers in the specification and/or drawing figure(s). This will assist the Examiner in prosecuting this application.
Response to Arguments
Applicant’s arguments with respect to claims 1-9 have been considered but are moot because of new ground of rejection.
Claim Rejections - 35 USC § 103
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.
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 1-9 are rejected under 35 U.S.C. 103 as being unpatentable over US 20240242731 A1 (IMOTO et al) in view of US 20230109426 A1 (HASHIMOTO).
With respect to claims 1, 6 and 8, IMOTO teaches model generation apparatus comprising: at least one processor that is configured to: acquire information data for learning, acquire document data for learning, determine a rate of match between the information data for learning and each portion of the document data for learning, wherein the rate of match represents a degree of difference between the information data for learning and each portion of the document data for learning, extract, from the document data for learning (information processing method according including: an acquisition process of acquiring first data from a user terminal; a calculation process of calculating a degree of similarity between a first portion of first document content related to the first data and a second portion of second document content related to a second data; generate learning model based on the degree of similarity between a first portion of first document content related to the first data and a second portion of second document content related to a second data on the basis of a fact that the degree of similarity is higher than a first threshold) [abstract; Par. 0008; Par. 0023-0026; Par. 0048-0052; Par. 0091-0095].
IMOTO fails to specifically teach, however, HASHIMOTO teaches extract a first portion and a second portion having a lower rate of match with the information data for learning than the first portion from the document data for learning based on a rate of match between the information data for learning and each portion of the document data for learning (computer configured to execute machine learning of a learning model, the computer acquires a plurality of learning data sets, to accept an input from a first portion of the feature amounts, and execute the first estimation task on the input data based on the input first portion; accept an input from a second portion of the feature amounts, and execute the second estimation task on the input data based on the input second portion wherein: the first estimation task with respect to the training data trained machine learning model has relatively high explainability for computation content, the data trained models having respective matching expectancy or explainability for computation content)) [Fig. 7; Par. 0156-0159; Par. 0166-0167; Par. 0030-0031; Par. 0037-0038), generate a first machine learning model by using first learning data in which first data for learning included in the information data for learning is used as input data and the first portion is used as correct answer data, and generate a second machine learning model by using second learning data in which second data for learning included in the information data for learning is used as input data and the second portion is used as correct answer data (acquiring a plurality of learning data sets, each of the learning data sets being constituted by a combination of training data, first correct answer data that indicates a correct answer of a first estimation task with respect to the training data, and second correct answer data that indicates a correct answer of a second estimation task with respect to the training data, a result of the first estimator executing the first estimation task matches the first correct answer data, and a result of the second estimator executing the second estimation task matches the second correct answer data) [Par. 0015-0018; Par. 0030-0032; Par. 0156-0159; Par. 0166-0167]. Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing of the instant application to modify the learning model created based on the degree of similarity between a first portion of first document content related to the first data and a second portion of second document content related to a second data, as taught by IMOTO, with the two step machine learning, as above and taught by HASHIMOTO, in order to ensure that input data (training data) can be reconstructed from the feature amounts such that the generalization and robustness of the generated and trained machine learning model can be improved, as taught by IMOTO [Par. 0024]. The combination is proper because HASHIMOTO teaches a trained machine learning model that has relatively high explainability for computation content with data acquisition unit configured to acquire a plurality of learning data sets, each of the learning data sets being constituted by a combination of training data, first correct answer data that indicates a correct answer of a first estimation task with respect to the training data, and second correct answer data that indicates a correct answer of a second estimation task with respect to the training data [Par. 0015-0017].
With respect to claims 5, 7 and 9, IMOTO teaches model generation apparatus comprising: at least one processor that is configured to: acquire information data for learning, acquire document data for learning, determine a rate of match between the information data for learning and each portion of the document data for learning, wherein the rate of match represents a degree of difference between the information data for learning and each portion of the document data for learning, extract, from the document data for learning wherein the rate of match represents a degree of difference between the information data for learning and each portion of the document data for learning (information processing method according including: an acquisition process of acquiring first data from a user terminal; a calculation process of calculating a degree of similarity between a first portion of first document content related to the first data and a second portion of second document content related to a second data; generation process of generating third data in which data of the first portion is associated with second data including at least one of data of the second portion and data of a third portion subsequent to the second portion in the second document content on the basis the degree of similarity) [abstract; Par. 0008; Par. 0048-0052; Par. 0091-0095].
IMOTO fails to specifically teach, however, HASHIMOTO teaches document generation apparatus (model generation apparatus and model generation method generating machine learning model in two training steps) [Par. 0030-0031; Par. 0037-0038] comprising: a first machine learning model generated by using first learning data in which first data for learning included in information data for learning is used as input data and a first portion extracted from document data for learning based on a rate of match between the information data for learning and each portion of the document data for learning is used as correct answer data (training data constituting a first data learning set wherein a first estimator configured to accept an input data and a first portion of feature data matching the first correct answer data, the feature data being converted or extracted input data, (corresponding to document data) the learning data sets being constituted by a combination of input data and first correct answer data that indicates a correct answer of a first estimation task with respect to the training data trained machine learning model that having relatively high explainability for computation content) [Par. 0015-0018; Par. 0030-0032; Par. 0156-0159; Par. 0166-0167]; a second machine learning model generated by using second learning data in which second data for learning included in the information data for learning is used as input data and a second portion, which is extracted from the document data for learning and has a lower rate of match with the information data for learning than the first portion, is used as correct answer data (training data constituting a second data learning set wherein a second estimator configured to accept an input data and a second portion of feature data matching second correct answer data, the feature data being converted or extracted input data, the learning data sets being constituted by a combination of training data, second correct answer data that indicates a correct answer of a second estimation task with respect to the training data trained machine learning model that having relatively lower explainability for computation content) [Par. 0015-0018; Par. 0030-0032; Par. 0156-0159; Par. 0166-0167]. Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing of the instant application to modify the learning model created based on the degree of similarity between a first portion of first document content related to the first data and a second portion of second document content related to a second data, as taught by IMOTO, with the two step machine learning, as above and taught by HASHIMOTO, in order to ensure that input data (training data) can be reconstructed from the feature amounts such that the generalization and robustness of the generated and trained machine learning model can be improved, as taught by IMOTO [Par. 0024]. The combination is proper because HASHIMOTO teaches a trained machine learning model that has relatively high explainability for computation content with data acquisition unit configured to acquire a plurality of learning data sets, each of the learning data sets being constituted by a combination of training data, first correct answer data that indicates a correct answer of a first estimation task with respect to the training data, and second correct answer data that indicates a correct answer of a second estimation task with respect to the training data [Par. 0015-0017].
HASHIMOTO teaches computer configured to execute machine learning of a learning model, the computer acquires a plurality of learning data sets, accept an input from a first portion of the feature amounts, and execute the first estimation task on the input data based on the input first portion; accept an input from a second portion of the feature amounts, and execute the second estimation task on the input data based on the input second portion wherein: the first estimation task with respect to the training data trained machine learning model has relatively high explainability for computation content, the data trained models having respective matching expectancy or explainability for computation content [Fig. 7; Par. 0156-0159; Par. 0166-0167; Par. 0030-0031; Par. 0037-0038].
With respect to claim 2, the combination HASHIMOTO and IMOTO teach the model generation apparatus, wherein the document data for learning is patient data related to a specific patient with which first date information is associated, and the information data for learning includes a plurality pieces of document data which are patient data related to the specific patient with which the first date information or second date information indicating a date earlier than a date indicated by the first date information is associated
(receive an image input from an imaging device, wherein the image input comprises one or more images obtained by the imaging device; receive patient health data as input; encode the patient health data to convert the patient health data to encoded patient health data; the encoded patient health data is embedded into at least one image of the image input at or before a time that the machine learning algorithm analyzes the image input, such that the machine learning algorithm analyzes the image input together with the encoded patient health data embedded in the at least one image of the image input) [EVANS Par. 0030-0031; Par. 0037].
With respect to claim 3, the combination HASHIMOTO and IMOTO teach the model generation apparatus, wherein the at least one processor is configured to: generate a third machine learning model that uses the document data for learning as input, and outputs at least one of the first portion or the second portion through reinforcement learning in which performance of the first machine learning model and performance of the second machine learning model are used as rewards, and extract the first portion and the second portion from the document data for learning by using the third machine learning model
(IMOTO teaches machine learning algorithms generation process of generating third data in which data of the first portion is associated with second data including at least one of data of the second portion on the basis of the degree of similarity) [Par. 0030-0031];
HASHIMOTO teaches second training by alternately and repeatedly executing a first step of training the first estimator and the second adversarial estimator so that a result of the estimator executing the second estimation task matches the correct answer data in order improve the accuracy in the estimation) [Par. 0020-0023].
With respect to claim 4, the combination HASHIMOTO and IMOTO teach the model generation apparatus, wherein the second machine learning model is a machine learning model that includes a machine learning model outputting a prediction result based on the information data for learning, and outputs a combination of the prediction result and a template
HASHIMOTO teaches machine learning to execute tasks of estimation including prediction such as classification given training data to make inference task) [Par. 0007-0008].
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 20220197935 A1 (YAMAMOTO et al) teaching document search performed using a document search system in which database document data is stored, wherein, after first document data and second document data are input to the document search system, the document search system extracts a plurality of terms from the first document data.
US 20200151591 A1 (LI) teaching method including sending a first document to a GUI, and receiving at a classification and extraction engine (CEE) from the GUI an input indicating first document data for the first document, the input forming a portion of a dataset, a prediction is generated at the CEE of second document data for a second document using a machine learning model (MLM) configured to receive an input and generate a predicted output, the MLM trained using the dataset.
G. Desjardins, R. Proulx and R. Godin, "An Auto-Associative Neural Network for Information Retrieval," The 2006 IEEE International Joint Conference on Neural Network Proceedings, Vancouver, BC, Canada, 2006, pp. 3492-3498.
R. Yasdi, "Learning user model by neural networks," ICONIP'99. ANZIIS'99 & ANNES'99 & ACNN'99. 6th International Conference on Neural Information Processing. Proceedings (Cat. No.99EX378), Perth, WA, Australia, 1999, pp. 48-53 vol.1.
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.
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to PIERRE MICHEL BATAILLE whose telephone number is (571)272-4178. The examiner can normally be reached Monday - Thursday 7-6 ET.
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/PIERRE MICHEL BATAILLE/Primary Examiner, Art Unit 2138
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