DETAILED ACTION
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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 6/24/2024 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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 1-9 stand rejected:
Claims 1, 8 and 9 correspond to “apparatus”, “method” and “non-transitory recording medium” respectively that are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
The said claims recite “acquiring” a “target data” (Sp. ¶ 0024 S2: “may be” “an article and an editorial”), which is converted to a “target vector”, followed by a “comparison” of the “target vector” with a “reference vector” (Sp. ¶ 0028 S2+: “may be” “data which indicates the tones of past article and editorial created in the mass media” for “correcting” of e.g., “predetermined keyword, predetermined wording” (Sp. Par. 0029 S3+; Sp. ¶ 0034-35: “a process of making correction to an article created in the mass media” “Such that degree of difference between a feature of the article and a feature of data indicating a social trend or the like is within a predetermined range”.
These limitations, under their broadest reasonable interpretation, cover performance of the limitations in the mind but for the recitation of “processor” (claim 1) and “computer” (claim 9). That is, other than reciting by the “processor” (claim 1) and/or “computer” (claim 9), nothing in the claim elements precludes the steps from practically being performed in the mind. For example, a human e.g. an editor of a magazine in which an article (target data) is written could take that and if he sees anything not favored by its base audience (including words, sentences, phrases …etc.) and/or what they call conforming to an acceptable trend, and replace it with something more acceptable. In so doing he could use other similar articles in the past pertaining to the same subject (i.e., claim’s “reference data”) and use them for “comparison”. If a claim limitation and/or limitations, under their broadest reasonable interpretation, cover performance of the limitations in the mind but for the recitation of generic computer components, then they fall within the “Mental Processes” grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claims recite only one additional element, i.e., the “processor” in claim 1 and “computer” in claim 9 to do the “acquiring” “generating” and “correcting” processes. The “processor” and/or the “computer” are recited at a high-level of generality (i.e., as a generic processor and/or computer performing generic computer functions associated with the limitations such that they amount no more than mere instructions to apply the exception using a generic computer component. Furthermore, according to ¶ 0100: “Examples of the processor C1 can include a central processing unit (CPU), a graphic processing unit (GPU), a digital signal processor (DSP), a micro processing unit (MPU), a floating point number processing unit (FPU), a physics processing unit (PPU), a tensor processing unit (TPU), a quantum processor, a microcontroller, and a combination thereof”; and/or ¶ 0099: “The computer C includes at least one processor C1 and at least one memory C2”. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims are thus directed to an abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of using a “processor” (claim 1) and/or “computer” (claim 9) to perform the “acquiring” the “generating of” “vector” and the “correcting process” amount to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
Regarding claims 2, 3 the human editor could take anything not favored by its base audience (including words, sentences, phrases …etc.) and/or what they call conforming to an acceptable trend based on past experience, and replace it (make a correction) with something more acceptable.
Regarding claim 4, the conversion of words in natural language to vector format amount to mapping of text to numerical format which can be achieved by using mapping tables.
Regarding claim 5, the article (target data) would be available as an input to the human editor the moment the human editor begins reading it.
Regarding claim 6, using “machine-learning” to convert “target data” into “target vector” amount to an additional element precluding which will have no impact on the claim; i.e., conversion of the words in natural language to vector format amounts to mapping of text to numerical format which can be achieved by using mapping tables.
Regarding claim 7, the human editor would intuitively search for words, phrases and sentences (reference data) in any category consistent with the word, phrase and or sentence (target data) under investigation including a plurality of categories for each particular target data.
Claim Rejections - 35 USC § 102
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 –
(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.
Claim(s) 1, 5, 8-9 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by SHI CONG et al. (CN114780712A).
Regarding claim 1, SHI CONG et al. do teach an information processing apparatus comprising at least one processor (¶ n0006: “an electronic device” (an information processing apparatus) “including a memory, a processor” (comprising at least one processor) “and a computer program stored in the memory and executable on the processor”),
the at least one processor carrying out:
an acquiring process of acquiring target data (¶ n0051 S1: “suppose the summary embedding vector and keyword embedding vector of news W1” (acquiring a target data) “are E11 and E12”);
a generating process of generating a target vector from the target data (¶ n0051 S1: “suppose the summary embedding vector” (generating a target vector of) “and keyword embedding vector of news W1” (the target data) “are E11” (as the target vector) “and E12” (another target vector of the target data)); and
a correcting process of making a comparison between the target vector and a reference vector generated from reference data, and performing correction-related processing of the target data according to a result of the comparison (¶ n0051 S1-S2 and last S: “suppose the summary embedding vector” “and keyword embedding vector of news W1” “are E11” “and E12” (the target vector) “and the summary embedding vector and keyword embedding vector of news W2” (a reference data) “are E21 and E22” (and their associated reference vectors) “Then, the cosine similarity” (a comparison operation) “between news W1 and W2 can be the cosine similarity between vectors E11 and E21” (between “E11” (the target vector) and “E21” (the reference vector) “can further improve the accuracy” (used for a correcting process and/or correction-related processing of e.g., the target data) “of the similarity between news items”).
Regarding claim 5, SHI CONG et al. do teach the information processing apparatus according to claim 1, wherein the at least one processor further carries out a target data generating process of generating the target data from input data (Abstract S2: “The method comprises” “acquiring a first news set, wherein the first news set comprises a plurality of pieces of news” (the target data) “retrieved based on a user input condition” (generated based on input data)).
Regarding claim 8, SHI CONG et al. do teach an information processing method (¶ n0006: “an electronic device” (an information processing apparatus and method) “including a memory, a processor” (comprising at least one processor) “and a computer program stored in the memory and executable on the processor”),
comprising:
acquiring target data (¶ n0051 S1: “suppose the summary embedding vector and keyword embedding vector of news W1” (acquiring a target data) “are E11 and E12”);
generating a target vector from the target data (¶ n0051 S1: “suppose the summary embedding vector” (generating a target vector of) “and keyword embedding vector of news W1” (the target data) “are E11” (as the target vector) “and E12” (another target vector of the target data)); and
making a comparison between the target vector and a reference vector generated from reference data, and performing correction-related processing of the target data according to a result of the comparison (¶ n0051 S1-S2 and last S: “suppose the summary embedding vector” “and keyword embedding vector of news W1” “are E11” “and E12” (the target vector) “and the summary embedding vector and keyword embedding vector of news W2” (a reference data) “are E21 and E22” (and their associated reference vectors) “Then, the cosine similarity” (a comparison operation) “between news W1 and W2 can be the cosine similarity between vectors E11 and E21” (between “E11” (the target vector) and “E21” (the reference vector) “can further improve the accuracy” (used for a correcting process and/or correction-related processing of e.g., the target data) “of the similarity between news items”).
Regarding claim 9, SHI CONG et al. do teach a non-transitory recording medium having recorded thereon a program (¶ n0007: “a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the above-described method”)
For causing a computer to carrying out:
an acquiring process of acquiring target data (¶ n0051 S1: “suppose the summary embedding vector and keyword embedding vector of news W1” (acquiring a target data) “are E11 and E12”);
a generating process of generating a target vector from the target data (¶ n0051 S1: “suppose the summary embedding vector” (generating a target vector of) “and keyword embedding vector of news W1” (the target data) “are E11” (as the target vector) “and E12” (another target vector of the target data)); and
a correcting process of making a comparison between the target vector and a reference vector generated from reference data, and performing correction-related processing of the target data according to a result of the comparison (¶ n0051 S1-S2 and last S: “suppose the summary embedding vector” “and keyword embedding vector of news W1” “are E11” “and E12” (the target vector) “and the summary embedding vector and keyword embedding vector of news W2” (a reference data) “are E21 and E22” (and their associated reference vectors) “Then, the cosine similarity” (a comparison operation) “between news W1 and W2 can be the cosine similarity between vectors E11 and E21” (between “E11” (the target vector) and “E21” (the reference vector) “can further improve the accuracy” (used for a correcting process and/or correction-related processing of e.g., the target data) “of the similarity between news items”).
Claim Rejections - 35 USC § 103
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.
Claim(s) 2-4, 7 is/are rejected under 35 U.S.C. 103 as being unpatentable over SHI CONG et al., and further in view of Goyal et al. (US 2018/0285326).
Regarding claim 2, SHI CONG et al. do not specifically disclose the information processing apparatus according to claim 1, wherein the reference data contains at least one selected from the group consisting of: respective pieces of trend data created by a plurality of persons, the pieces of trend data being at least one selected from the group consisting of sentences, voices, and images; and a plurality of pieces of data each accumulated as the target data of a past time.
Goyal et al. do teach
wherein the reference data contains at least one selected from the group consisting of: respective pieces of trend data created by a plurality of persons, the pieces of trend data being at least one selected from the group consisting of sentences, voices, and images (for a “document-comparison” (¶ 0033 line 2) example, according to ¶ 0034 lines 1+: “FIGS. 1A and 1B respectively illustrate a first digital version 100a” (target data is compared with) “and second digital version 100b” (reference data) “include a plurality of sentences” (consisting of sentences); ¶0040 lines 1+: “FIG. 1C illustrates a document comparison 128 that groups together changes” (makes corrections) “to a repeated term” (i.e., change everywhere in the target data the word “moon” to “Moon” which is based on comparison with reference data, which is a change based on a trend practiced by most people or persons to represent a planet using capital letter as its first letter));
and a plurality of pieces of data each accumulated as the target data of a past time (selection of “Moon” as the correct spelling instead of “moon” is based on common practice from past).
It would have therefore been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the “document-comparison” techniques of Goyal et al. into “news” (document) “similarity” (comparison) techniques of SHI CONG et al. would enable the combined systems and their associated methods to perform in combination as they do separately and to further enable SHI CONG et al. to benefit of an “efficient way to review document changes” as disclosed in Goyal et al. ¶ 0028 last sentence.
Regarding claim 3, SHI CONG et al. do not specifically disclose the information processing apparatus according to claim 2, wherein in the correcting process, a correction is made to the target data or making the correction to the target data is suggested, and the target data corrected is acquired.
Goyal et al. do teach:
the information processing apparatus according to claim 2, wherein in the correcting process, a correction is made to the target data or making the correction to the target data is suggested, and the target data corrected is acquired (¶0040 lines 1+: “FIG. 1C illustrates a document comparison 128” (using comparison of target and reference data) “that groups together changes” (makes corrections) “to a repeated term” (i.e., to correct everywhere in the target data the word “moon” to “Moon” consistent with its corresponding reference data)).
For obviousness to combine SHI CONG et al. and Goyal et al. see claim 2.
Regarding claim 4, SHI CONG et al. do teach the information processing apparatus according to claim 3, wherein the target data acquired in the acquiring process contains one or more sentences (¶ 0058: “The sentences” (sentences included in) “in the article” (e.g., “NEWS” “W1” (target data)) “are sorted” “and the top M sentences are selected as the article summary”),
and in the generating process, the target vector is generated with reference to at least one of the one or more sentences (¶ n0040: “the text vector and the vector” (vector generated) “of the sentence” (with reference to at least one or more sentences) “can be the embedding vector of the sentence”).
Regarding claim 7, SHI CONG et al. do not specifically disclose the information processing apparatus according to claim 1, wherein in the correcting process, the target vector is optimized by correcting the target vector with reference to data which is contained in the reference data and which belongs to a first category, and
correcting again the target vector with reference to data which is contained in the reference data and which belongs to a second category.
Goyal et al. do teach
in the correcting process, the target vector is optimized by correcting the target vector with reference to data which is contained in the reference data and which belongs to a first category (¶ 0075 S3: “Specifically, by applying the deterministic classification algorithm, the document-comparison system identifies changes as part of an information-insert category” (using a first category associated with e.g. reference data) “for changes” (to make corrections in the target data with respect to the reference data) “that insert information”; e.g., see Fig. 1A also called “first digital version” (target data) versus Fig. 1B also called “second digital version” (reference data), and Fig. 1C (in which the target data undergoes change using the reference data)), and
correcting again the target vector with reference to data which is contained in the reference data and which belongs to a second category (¶ 0075 lines 13+: “a lexical-paraphrase category”(using a second category) “for changes” (to make corrections again in the target data with respect to reference data) “that replace a term or phrase with a synonym or that modify a style of terms or phrases”); ¶ 0046 S1: “To generate mapped-sentence combinations, in some embodiments, the document-comparison system applies a sentence-alignment algorithm to generate a sentence vector for each sentence within a first version of a document and a sentence vector for each sentence within a second version of a document” (in the “document-comparison” used for making “changes” (corrections) both the “first digital version” (target data) and the “second digital version” (reference data) are converted into “sentence vector” (i.e. target vector and reference vector respectively)).
It would have therefore been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the “document-comparison” techniques of Goyal et al. into “news” (document) “similarity” (comparison) techniques of SHI CONG et al. would enable the combined systems and their associated methods to perform in combination as they do separately and to further enable SHI CONG et al. to benefit of an “efficient way to review document changes” as disclosed in Goyal et al. ¶ 0028 last sentence.
Claim(s) 6 is/are rejected under 35 U.S.C. 103 as being unpatentable over SHI CONG et al., and further in view of CHOSA KATSUKI et al. (JP 2024123669).
Regarding claim 6, SHI CONG et al. do not specifically disclose the information processing apparatus according to claim 5, wherein in the generating process,
a machine-learned model is optimized with reference to training data which contains the target data and a ground truth label associated with the target data, the ground truth label being assigned to the target vector, and the target vector is generated with use of the machine-learned model optimized.
CHOSA KATSUKI et al. do teach disclose the information processing apparatus according to claim 5, wherein in the generating process,
a machine-learned model is optimized with reference to training data which contains the target data and a ground truth label associated with the target data, the ground truth label being assigned to the target vector, and the target vector is generated with use of the machine-learned model optimized (¶ 0120 lines 23+: “the processor takes sentences contained in a given pair of documents as input” (target data) “converts the sentences into sentence vectors” (is converted into target vector) “representing the features of the sentences using a third machine learning model” (with use of a machine learning model) “with a third parameter set, and learn the first parameter, the second parameter, and the third parameter to minimize the error” (which is optimized); ¶ 0101 “training data” “extracted using” “ground truth data” (a ground truth is associated with the target data)).
It would have therefore been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the “machine learning” method for conversion of “sentences” into “sentence vectors” of CHOSA KASUKI et al. into the “embedding vector” procedures of SHI CONG et al. would enable the combined systems and their associated methods to perform in combination as they do separately and to further enable generation of vectors such that it “minimize[ed] the error” in the vector generation as disclosed in CHOSA KATSUKI et al. ¶ 0120 last sentence.
Conclusion
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/Farzad Kazeminezhad/
Art Unit 2653
March 20th 2026.