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 .
Claim Status
Claims 1-6 are pending.
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 conflicting claims 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); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
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Claims 1-6 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-4 of U.S. Patent No. 12,405,981, application 18/554,311 in view of Oyamada.
This application
Claim 1
An information processing apparatus comprising:
18/554,311
Claim 1
An information processing apparatus comprising:
This application
a memory storing instructions; and
18/554,311
a memory storing instructions; and
This application
at least one processor configured to execute the instructions to
perform processes comprising:
18/554,311
at least one processor configured to execute the instructions to
perform processes comprising:
This application
an acquisition process of acquiring a target text;
18/554,311
an acquisition process of acquiring a target text;
This application
an extraction process of extracting a document related to the target text;
18/554,311
an extraction process of extracting a document related to the target text;
This application
a rewriting process of rewriting the target text with use of the document;
18/554,311
a rewriting process of rewriting the target text with use of the document;
This application
a generation process of generating a text corresponding to the
rewritten target text with use of a machine learning model trained to
generate a text based on an input text;
18/554,311
a generation process of generating a text corresponding to the
rewritten target text with use of a machine learning model trained to
generate a text based on an input text;
This application
a similarity calculation process of calculating similarity between
the text generated in the generation process and the document;
Application 18/554,311does not claim above limitation. However, Sadkin discloses:
Sadkin col 7 lines 48-52, In the preferred embodiment of the cosine distance matching, the following process is followed. Once a scoped down hypothesis has been generated by the matching algorithm the next step in the match algorithm is to look at the next X words of text in the document and attempt to determine a measurement of the similarity between the generated text and the text within the document. The value of X is variable and may be defined in any manner. In the preferred embodiment the value of X is defined based on the font size used and the device screen size.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Application 18/554,311 to obtain above limitation based on the teachings of Sadkin for the purpose of dynamically presenting a prewritten text in a graphical user interface, see abstract.
This application
a calculation process of calculating reliability of the text generated
in the generation process;
18/554,311
a calculation process of calculating reliability of the text generated
in the generation process;
This application
a determination process of determining whether the reliability
exceeds a threshold based on the similarity calculation; and
18/554,311
a determination process of determining whether the reliability exceeds a threshold, see claim 2.
This application
an output process of outputting a result obtained by adding
information identifying the document to the text generated in the
generation process, and of outputting the reliability of the text generated
in the generation process,
18/554,311
an output process of outputting a result obtained by adding
information identifying the document to the text generated in the
generation process, and of outputting the reliability of the text generated
in the generation process,
This application
wherein the calculation process includes referring to a result obtained by inputting, to the machine learning model, the text generated in the generation process, the document extracted in the extraction process, and the target text acquired in the acquisition process.
18/554,311
wherein the calculation process includes referring to a result obtained by inputting, to the machine learning model, the text generated in the generation process, the document extracted in the extraction process, and the target text acquired in the acquisition process.
[Claim 2]
This application
The information processing apparatus according to claim 1, wherein
in a case where the reliability has been determined to exceed the
threshold, the output process includes outputting the result obtained by
adding the information identifying the document as an optimized result.
[Claim 2]
18/554,311
The information processing apparatus according to claim 1, wherein the processes further comprise a determination process of determining whether the reliability exceeds a threshold, in a case where the reliability has been determined to exceed the threshold, the output process includes outputting the result obtained by adding the information identifying the document as an optimized result.
[Claim 3]
This application
The information processing apparatus according to claim 1, wherein the similarity calculation process uses at least one of inter-word distance, inter-document distance, and learning model-based similarity calculation.
[Claim 3]
18/554,311
An information processing method performed by at least one processor and comprising:
an acquisition process of acquiring a target text;
an extraction process of extracting a document related to the target text;
a rewriting process of rewriting the target text with use of the document;
a generating process of generating a text corresponding to the rewritten target text with use of a machine learning model trained to generate a text based on an input text;
a calculation process of calculating reliability of the text generated in the generation process; and
an output process of outputting a result obtained by adding information identifying the document to the text generated in the generation process, and of outputting the reliability of the text generated in the generation process,
wherein the calculation process includes referring to a result obtained by inputting, to the machine learning model, the text generated in the generation process, the document extracted in the extraction process, and the target text acquired in the acquisition process.
[Claim 4]
This application
The information processing apparatus according to claim 1, wherein the at least one processor is further configured to execute the instructions to perform a decision making process of making a decision whether to output based on a quality level and the reliability.
[Claim 4]
18/554,311
A non-transitory storage medium storing a program executable by a computer to perform processes comprising;
an acquisition process of acquiring a target text;
an extraction process of extracting a document related to the target text;
a rewriting process of rewriting the target text with use of the document;
a generating process of generating a text corresponding to the rewritten target text with use of a machine learning model trained to generate a text based on an input text;
a calculation process of calculating reliability of the text generated in the generation process; and
an output process of outputting a result obtained by adding information identifying the document to the text generated in the generation process, and of outputting the reliability of the text generated in the generation process,
wherein the calculation process includes referring to a result obtained by inputting, to the machine learning model, the text generated in the generation process, the document extracted in the extraction process, and the target text acquired in the acquisition process.
[Claim 5]
This application
An information processing method performed by at least one processor and comprising:
an acquisition process of acquiring a target text;
an extraction process of extracting a document related to the target
text;
a rewriting process of rewriting the target text with use of the document;
a generation process of generating a text corresponding to the
rewritten target text with use of a machine learning model trained to
generate a text based on an input text;
a similarity calculation process of calculating similarity between
the text generated in the generation process and the document;
a calculation process of calculating reliability of the text generated
in the generation process;
a determination process of determining whether the reliability exceeds a threshold based on the similarity calculation; and an output process of outputting a result obtained by adding
information identifying the document to the text generated in the generation process, and of outputting the reliability of the text generated in the generation process,
wherein the calculation process includes referring to a result obtained by inputting, to the machine learning model, the text generated in the generation process, the document extracted in the extraction process, and the target text acquired in the acquisition process.
[Claim 5]
18/554,311
No claim recited
[Claim 6]
This application
A non-transitory storage medium storing a program executable by
a computer to perform processes comprising:
an acquisition process of acquiring a target text;
an extraction process of extracting a document related to the target
text;
a rewriting process of rewriting the target text with use of the
document;
a generation process of generating a text corresponding to the
rewritten target text with use of a machine learning model trained to generate a text based on an input text;
a similarity calculation process of calculating similarity between
the text generated in the generation process and the document;
a calculation process of calculating reliability of the text generated
in the generation process;
a determination process of determining whether the reliability
exceeds a threshold based on the similarity calculation; and
an output process of outputting a result obtained by adding
information identifying the document to the text generated in the
generation process, and of outputting the reliability of the text generated
in the generation process,
wherein the calculation process includes referring to a result
obtained by inputting, to the machine learning model, the text generated in
the generation process, the document extracted in the extraction process,
and the target text acquired in the acquisition process.
[Claim 6]
18/554,311
No claim recited
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Yamaguchi US 2020/0279189 [0138] In a case where data (description data such as Claims, and Abstract) serving as a search source in each document in the to-be-classified document set are acquired by the acquiring unit 22, the character-string searching unit 29 may search the data acquired by the acquiring unit 22 for a search character string.
Roux US 2005/0138000 [0006] Some search engines have tried to preserve a crude representation of the grammatical relationships in the search string while searching documents by returning documents in which the words of the search string are only separated by a user defined number of words, for instance, ten words.
Tuerke US 6,836,886 col 7 lines 25-35 (35) As illustrated in Table 3, the <link> tag was replaced by an inline <style> block, and the individual <script src=> tags were replaced by inline <script> blocks. By converting the HTML as indicated and providing the entire page as a single string, the need for the download object to write to the user's disk is completely eliminated. By rewriting a document as a single string, many advantages may be provided. For example, code-reuse may be facilitated, the size of the download object may be reduced, maintainability of the code may be improved, and/or developers may be provided with reasonable flexibility in designing a Web page.
Chen US 2018/0137434 [0017] Moreover, in an embodiment, a machine learning method for data acquisition includes the character string recognition method in any aforementioned embodiment. When the computer receives the updated content of the character string, the computer executes machine learning according to the updated content of the character string.
Sharp US 2023/0353513 [0021] In some implementations, determining the candidate reply text comprises: providing the at least one message feature as input to a trained machine learning system and receiving an indication of the candidate reply text as output from the trained machine learning system. In some of those implementations, the indication of the candidate reply text is a category of reply and the reply text is determined based on a defined relationship between the reply text and the category of reply.
Scholtes US 2016/0048587 [0034] One or more local computers 121 can provide connectivity to one or more users 123 and 125, for example, via a local-area network (LAN), and the like. Similarly, one or more remote computers 127 can provide connectivity to one or more remote users 117 and 119, for example, via the Internet, an Intranet, a wide-area network (WAN) 115, and the like. Accordingly, the computers 121 and 127 connect to the document storage subsystem 113 and to allow the one or more users 123, 125, 119 and 117 to manually or automatically access the document collection 111, view documents, document groups, document meta information, training documents, training results, machine learning models, document classifications, names, authorships, aliases, and the like.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ETIENNE PIERRE LEROUX whose telephone number is (571)272-4022. The examiner can normally be reached 8:00 am to 4:30 pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Apu Mofiz can be reached at 571 272 4080. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ETIENNE P LEROUX/Primary Examiner of Art Unit 2161
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