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 .
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the claims at issue are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); and In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
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Claims 21-40 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-17 of U.S. Patent No. 12,411,876.
Although the claims at issue are not identical, they are not patentably distinct from each other because the claims of the present application are anticipated by the claims of the parent patent, USPN 12,411,876. For example, claim 21 of the present application and corresponding claim 1 of the parent is compared below.
USPN 12,411,876 – Claim 1
Application No. 19/294,992 - Claim 21
An answer information generation method based on a large language model, comprising:
obtaining, in response to receiving a question text from a user, a semantic vector of the question text and event information related to a specific field, wherein the event information includes an event category concerning the question text and at least one piece of argument information in the question text;
obtaining a plurality of candidate documents from a document library of the specific field based on at least two of the semantic vector of the question text, the at least one piece of argument information and the event category; determining, based on the event category, at least one document evaluation dimension corresponding to the event category, wherein:
the at least one document evaluation dimension corresponds to at least one aspect of document quality, each document evaluation dimension of the at least one document evaluation dimension corresponds to a plurality of document categories, and the plurality of document categories are determined based on an aspect of document quality that a corresponding document evaluation dimension focuses on;
determining, based on the event category, a quality score corresponding to each document category of the plurality of document categories of each document evaluation dimension;
determining quality evaluation information for each candidate document in the plurality of candidate documents based on a document category corresponding to each of the at least one document evaluation dimension of the candidate document and a quality score corresponding to the event category for the document category; and
determining at least one target document from the plurality of candidate documents based on the quality evaluation information of each candidate document and a correlation between each candidate document and the question text, to obtain, based on the at least one target document, answer information used to answer the question text.
An answer information generation method based on a large language model, comprising:
obtaining, in response to receiving a question text from a user, a semantic vector of the question text and event information related to a specific field, wherein the event information includes an event category concerning the question text and at least one piece of argument information in the question text;
obtaining a plurality of candidate documents from a document library of the specific field based on at least two of the semantic vector of the question text, the at least one piece of argument information and the event category; determining quality evaluation information for each candidate document in the plurality of candidate documents based on the event category; and determining at least one target document from the plurality of candidate documents based on the quality evaluation information of each candidate document and a correlation between each candidate document and the question text, to obtain, based on the at least one target document, answer information used to answer the question text.
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 21-40 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
As to claim 21,
Step 1: Statutory Category
The claim as a whole fall within one or more statutory categories.
Step 2A – Prong One: Judicial Exception Recited
At least claim 21 recites limitations that are abstract ideas. The limitation “obtaining, in response to receiving a question text from a user, a semantic vector of the question text and event information related to a specific field, wherein the event information includes an event category concerning the question text and at least one piece of argument information in the question text” is a mental process, this limitation can be performed by the human mind. A human can obtain “a semantic vector of the question text and event information related to a specific field, wherein the event information includes an event category concerning the question text and at least one piece of argument information in the question text”. A human can relate words in a question text with a concept in order to obtain a semantic vector. A human can also obtain an event information related to a specific field by a meaning of a specific field in an input question text. A piece of argument information in the question can also be obtained by a human from the meaning of words in the question text. A human can mentally obtain words, arguments, semantic vector from an input question text.
Step 2A - Prong Two: Integrated into a Practical Application
Claim 21 recites the limitation “obtaining a plurality of candidate documents from a document library of the specific field based on at least two of the semantic vector of the question text, the at least one piece of argument information and the event category”. This limitation is an additional element and is insignificant extra-solution activity as retrieval/receiving of data (i.e. mere data gathering) such as 'obtaining information' as identified in MPEP 2106.05(g) and does not provide integration into a practical application.
Additionally, claim 21 recites “determining quality evaluation information for each candidate document in the plurality of candidate documents based on the event category”. The quality evaluation information is determined based on the event category. This limitation is an additional element and is insignificant extra-solution activity as retrieval/receiving of data (i.e. mere data gathering) such as 'obtaining information' as identified in MPEP 2106.05(g) and does not provide integration into a practical application.
Step 2B: Claim provides an Inventive Concept
The conclusion for the additional elements representing mere information gathering and do not provide significantly more. The limitations “determining at least one target document from the plurality of candidate documents based on the quality evaluation information of each candidate document and a correlation between each candidate document and the question text, to obtain, based on the at least one target document, answer information used to answer the question text” describe steps of data gathering based on predetermined criteria an data correlation.
Therefore, the claim as a whole does not change this conclusion and the claim is ineligible.
As to claim 22, the claim recites “wherein the document library includes a plurality of preset documents, wherein each preset document of the plurality of preset documents includes a corresponding document semantic vector, at least one document event category and at least one piece of document argument information, and wherein the obtaining the plurality of candidate documents from the document library of the specific field based on at least two of the semantic vector of the question text, the at least one piece of argument information, and the event category comprises: retrieving, based on the semantic vector of the question text and the corresponding document semantic vector of each preset document in the document library a plurality of first candidate documents with highest semantic similarities from the document library; obtaining a plurality of second candidate documents from the document library, wherein each second candidate document of the plurality of second candidate documents meets at least one of following conditions: a document event category of the second candidate document matches the event category; and the second candidate document includes at least one piece of document argument information that matches one or more pieces of argument information in the at least one piece of argument information; and obtaining the plurality of candidate documents based on the plurality of first candidate documents and the plurality of second candidate document”. The judicial exception is not integrated into a practical application. In particular, this additional limitation covers data gathering/comparison and does not amount to significantly more than the above-identified judicial exception.
As to claim 23, the claim recites “wherein the determining the quality evaluation information for each candidate document in the plurality of candidate documents based on the event category comprises: determining, based on the event category, at least one document evaluation dimension corresponding to the event category, wherein the at least one document evaluation dimension corresponds to at least one aspect of document quality, wherein each document evaluation dimension of the at least one document evaluation dimension corresponds to a plurality of document categories, and wherein the plurality of document categories are determined based on an aspect of document quality that a corresponding document evaluation dimension focuses on; determining, based on the event category, a quality score corresponding to each document category of the plurality of document categories of each document evaluation dimension; and determining the quality evaluation information for each candidate document in the plurality of candidate documents based on a document category corresponding to each of the at least one document evaluation dimension of the candidate document and a quality score corresponding to the event category for the document category”. The recitation of a quality score determination is an additional limitation which can be performed in human mind, and does not amount to significantly more than the above-identified judicial exception.
As to claim 24, the claim recites “wherein the determining the at least one target document from the plurality of candidate documents based on the quality evaluation information of each candidate document and a correlation between each candidate document and the question text comprises: determining a comprehensive score for each candidate document in the plurality of candidate documents based on the quality evaluation information of each candidate document and the correlation between each candidate document and the question text; and determining, as the at least one target document, at least one candidate document whose comprehensive score meets a preset condition in the plurality of candidate documents”. The recitation of a comprehensive score determination is an additional limitation which can be performed in human mind, and does not amount to significantly more than the above-identified judicial exception.
As to claim 25, the claim recites “wherein the quality evaluation information includes a quality score corresponding to each of at least one document evaluation dimension of a corresponding candidate document, and wherein the determining the comprehensive score for each candidate document in the plurality of candidate documents based on the quality evaluation information of each candidate document and the correlation between each candidate document and the question text comprises: performing, for each candidate document in the plurality of candidate documents, following operations: inputting at least the question text, the candidate document, and the quality evaluation information for the candidate document into a trained ranking model; determining the correlation between the candidate document and the question text by using the ranking model based on at least the question text and the candidate document; and determining the comprehensive score for the candidate document by using the ranking model based on the quality evaluation information for the candidate document and the correlation between the candidate document and the question text”. The recitation of a ranking model is an additional limitation describing ranking feature in general, and thus it can be performed in human mind. As a result, it does not amount to significantly more than the above-identified judicial exception.
As to claim 26, the claim recites “wherein obtaining the document semantic vector of each preset document in the document library comprises: performing, for each preset document in the document library, following operations: paragraphing the preset document to obtain at least one document paragraph; obtaining at least one paragraph semantic vector corresponding to the at least one document paragraph; and obtaining the document semantic vector of the preset document based on the at least one paragraph semantic vector”. Paragraphing is to divide a text into paragraphs, and thus can be performed in a human mind. Obtaining a paragraph semantic vector can also be performed in a human mind as to relate words in a paragraph with a corresponding concept. Thus, the additional limitations do not amount to significantly more than the above-identified judicial exception.
As to claim 27, it recites “wherein obtaining, based on the at least one target document, the answer information used to answer the question text comprises: organizing the question text and the at least one target document into an instruction text based on a preset instruction template; and inputting the instruction text into an answer information generation model to obtain the answer information output by the answer information generation model”. Making an instruction text by organizing the question text and the at least one target document can be performed in a human mind. The additional limitation of an answer information generation model in general does not amount to significantly more than the above-identified judicial exception.
As to claim 32, it recites “wherein the quality evaluation information includes a quality score corresponding to each of at least one document evaluation dimension of a corresponding candidate document, and wherein the determining the comprehensive score for each candidate document in the plurality of candidate documents based on the quality evaluation information of each candidate document and the correlation between each candidate document and the question text comprises: performing, for each candidate document in the plurality of candidate documents, following operations: inputting at least the question text, the candidate document, and the quality evaluation information for the candidate document into a trained ranking model; determining the correlation between the candidate document and the question text by using the ranking model based on at least the question text and the candidate document; and determining the comprehensive score for the candidate document by using the ranking model based on the quality evaluation information for the candidate document and the correlation between the candidate document and the question text”. The addition limitation of a trained ranking model, using ranking model, in general without details of how the trained ranking model is being implemented does not amount to significantly more than the above-identified judicial exception.
Claims 28-31 are rejected for similar reason as claims 21-24 respectively.
Claims 33-34 are rejected for similar reason as claims 26-27 respectively.
Claims 35-40 are rejected for similar reason as claims 21-26 respectively.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Loc Tran whose telephone number is 571-272-8485. The examiner can normally be reached on Mon-Fri. 7:30am-5pm; First Fri Off.
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/LOC TRAN/
Primary Examiner, Art Unit 2164
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