DETAILED ACTION
Notice of Pre-AIA or AIA Status
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Priority Acknowledgment
2. Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). The certified copy has been filed in parent Application CN202410318340.2 filed on 03/19/2024.
Claim Rejections - 35 USC § 102
3. 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)(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.
4. Claims 1, 9 and 17 are rejected under 35 U.S.C. 102(a) (2) as being anticipated by Sarkar et al. (US 2024/0411999 A1.)
With respect to Claim 1, Sarkar et al. disclose
A method for text similarity recognition, comprising:
by an electronic device, obtaining a first text and a second text (Sarkar et al. Fig. 3B elements 330A and 330B describes receiving sentences S1 and S2);
determining a multi-dimensional similarity feature for the first text and the second text, wherein the multi-dimensional similarity feature comprises at least one of a word dimensional similarity feature characterizing similarity between the first text and the second text in a word dimension, a sentence dimensional similarity feature characterizing similarity between the first text and the second text in a sentence dimension (Sarkar et al. [0054] FIG. 3C depicts an example process for determining similarity between two sentence vectors, according to some embodiments of the present disclosure. In the illustrated example, the sentence vector V1 365A (generated by processing sentence S1) and sentence vector V2 365B (generated by processing sentence S2) are projected/mapped into the multidimensional vector space, and different similarity metrics (e.g., cosine similarity, Euclidean distance, and the like) may be used to measure the similarity between the two sentences S1 and S2. The operations for determining similarity between two sentence vectors may be performed by a coherence determination module (e.g., 725 of FIG. 7)), or a full-text dimensional similarity feature characterizing similarity between the first text and the second text in a full-text dimension; and
determining, based on the multi-dimensional similarity feature, a recognition result indicating similarity between the first text and the second text (Sarkar et al. Fig. 3 element 375 coherence score, [0076] At block 625, the system calculates a sentence similarity score (e.g., 375 of FIG. 3) by comparing the first and second sentences, using a similarity metric. In some embodiments, the similarity metric may comprise at least one of an angle similarity metric (e.g., cosine similarity, Jaccard similarity), and/or a distance similarity metric (Euclidean distance, edit distance). In some embodiments, before calculating the sentence similarity score, the system converts the first and second sentences into a first sentence vector and a second sentence vector, respectively. (e.g., 365A-B of FIG. 3B) using one or more transformer language models. In some embodiments, the system may calculate the sentence similarity score by projecting the generated first and second sentence vectors to a multidimensional space (e.g., 365A-B of FIG. 3C). See paragraphs [0054-0055, 0068-0069 and [0076-0077].)
With respect to Claim 9, Claim 9 recites the similar features as Claim 1, thus Claim 9 is rejected as the same ground as Claim 1.
With respect to Claim 17, Claim 17 recites the similar features as Claim 1, thus Claim 17 is rejected as the same ground as Claim 1.
Claim Rejections - 35 USC § 103
5. 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 of this title, 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.
6. Claims 2, 10 and 18 are rejected under 35 U.S.C.103 as being unpatentable over Sarkar et al. (US 2024/0411999 A1) in view of Shen et al. (US 11,947,915 B1.)
With respect to Claim 2, Sarkar et al. disclose
wherein the determining of the multi-dimensional similarity feature comprises:
determining common words in the first text and the second text and a count of the common words based on the first words and the second words (Sarkar et al. [0049] describes calculating the Jaccard similarity between two sentences, which is the ratio of the number of words common to the sentences by the total number of word in the sentences. The values of Jaccard similarity ranges from 0 to 1, where 0 indicates the two sentences are identical while 1 means there is nothing common among the sentences. Sarkar et al. uses total number of words commons to the sentences in calculating the Jaccard similarity);
determining the multi-dimensional similarity feature for the first text and the second text based on multi-dimensional information comprising at least two of: the first words, the count of the first words, the second words, the count of the second words, the common words, the count of the common words, the first sentences, the count of the first sentences, the second sentences, the count of the second sentences, the first total number of the characters contained in the first text, or the second total number of the characters contained in the second text (Sarkar et al. Fig. 3 element 375 coherence score, [0076] At block 625, the system calculates a sentence similarity score (e.g., 375 of FIG. 3) by comparing the first and second sentences, using a similarity metric. In some embodiments, the similarity metric may comprise at least one of an angle similarity metric (e.g., cosine similarity, Jaccard similarity), and/or a distance similarity metric (Euclidean distance, edit distance). In some embodiments, before calculating the sentence similarity score, the system converts the first and second sentences into a first sentence vector and a second sentence vector, respectively. (e.g., 365A-B of FIG. 3B) using one or more transformer language models. In some embodiments, the system may calculate the sentence similarity score by projecting the generated first and second sentence vectors to a multidimensional space (e.g., 365A-B of FIG. 3C).)
Sarkar et al. fail to explicitly teach
performing word segmentation on the first text and the second text to obtain first words of the first text, a count of the first words, second words of the second text, and a count of the second words;
performing sentence segmentation on the first text and the second text to obtain first sentences contained in the first text, a count of the first sentences, second sentences contained in the second text, and a count of the second sentences;
obtaining a first total number of characters contained in the first text and a second total number of characters contained in the second text; and
However, Shen et al. teach
performing word segmentation on the first text and the second text to obtain first words of the first text, a count of the first words, second words of the second text, and a count of the second words (Shen et al. col. 4 lines 12-38 describes the document is divided into smaller parts and the number of words is counted);
performing sentence segmentation on the first text and the second text to obtain first sentences contained in the first text, a count of the first sentences, second sentences contained in the second text, and a count of the second sentences (Shen et al. col. 4 lines 12-38 describes the document is divided into smaller parts and the number of sentences is counted);
obtaining a first total number of characters contained in the first text and a second total number of characters contained in the second text (Shen et al. col. 4 lines 12-38 describes the document is divided into smaller parts and the number of characters is counted); and
Sarkar et al. and Shen et al. are analogous art because they are from a similar field of endeavor in the Signal Processing techniques and applications. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the steps of determining the similarity between sentences as taught by Sarkar et al., using teaching of dividing the document(s) and counting a number of characters, words, and sentences as taught by Shen et al for the benefit of facilitating determining of portions of a document that correspond to a received query (Shen et al. col. 4 lines 12-38.)
With respect to Claim 10, Claim 10 recites the similar features as Claim 2, thus Claim 10 is rejected as the same ground as Claim 2.
With respect to Claim 18, Claim 18 recites the similar features as Claim 2, thus Claim 18 is rejected as the same ground as Claim 2.
Allowable Subject Matter
7. Claims 3-8, 11-16 and 19-20 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
The following is a statement of reasons for the indication of allowable subject matter: the prior art(s) taken alone or in combination fail(s) to teach the following element(s) in combination with the other recited elements in the claim(s).
“wherein the multi-dimensional similarity feature comprises a first word feature representing the word dimensional similarity feature; and
the determining of the multi-dimensional similarity feature for the first text and the second text based on the multi-dimensional information comprises:
obtaining a first absolute difference between the count of the first words and the count of the second words, and a first product of the count of the first words and the count of the second words;
obtaining a second product of the first absolute difference and the count of the common words; and
determining the first word feature based on a ratio of the second product to the first product, wherein a value of the first word feature is negatively correlated with the similarity between the first text and the second text in the word dimension.” as recited in Claim 3.
Claims 11 and 19 recite the similar features as Claim 3.
“wherein the multi-dimensional similarity feature comprises a second word feature representing the word dimensional similarity feature; and
the determining of the multi-dimensional similarity feature for the first text and the second text based on the multi-dimensional information comprises:
determining a first number of occurrences of the common words in the first text and a second number of occurrences of the common words in the second text;
obtaining a second absolute difference between the first number of occurrences and the second number of occurrences, and determining a maximum value among the first number of occurrences and the second number of occurrences; and
determining the second word feature based on a ratio of the second absolute difference to the maximum value, wherein a value of the second word feature is negatively correlated with the similarity between the first text and the second text in the word dimension.” as recited in Claim 4.
Claims 12 and 20 recite the similar features as Claim 4.
“wherein the multi-dimensional similarity feature comprises a first sentence feature representing the sentence dimensional similarity feature; and
the determining of the multi-dimensional similarity feature for the first text and the second text based on the multi-dimensional information comprises:
determining a first total number of ones of the first sentences each satisfying a preset condition, and a second total number of ones of the second sentences each satisfying the preset condition, wherein the preset condition is that a ratio of a sum of lengths of character strings occupied by the common words in a respective sentence of the first sentences and the second sentences to a total length of character strings of the respective sentence is greater than or equal to a preset threshold; and
determining the first sentence feature based on an absolute difference between the first total number of ones of the first sentences and the second total number of ones of the second sentences, wherein a value of the first sentence feature is negatively correlated with the similarity between the first text and the second text in the sentence dimension.” as recited in Claim 5.
Claim 13 recite the similar features as Claim 5.
“wherein the multi-dimensional similarity feature comprises a second sentence feature representing the sentence dimensional similarity feature; and
the determining of the multi-dimensional similarity feature for the first text and the second text based on the multi-dimensional information comprises:
determining a first maximum number of consecutive ones of the first sentences each satisfying a preset condition, and a second maximum number of consecutive ones of the second sentences each satisfying the preset condition, wherein the preset condition is that a ratio of a sum of lengths of character strings occupied by the common words in a respective sentence of the first sentences and the second sentences to a total length of character strings of the respective sentence is greater than or equal to a preset threshold;
obtaining a first difference between the first maximum number of the consecutive ones of the first sentences and the second maximum number of the consecutive ones of the second sentences;
obtaining a second difference between the count of the first sentences and the count of the second sentences, the second difference representing a first length adjustment factor; and
determining the second sentence feature based on an absolute value of a product of the first difference and the second difference, wherein a value of the second sentence feature is negatively correlated with the similarity between the first text and the second text in the sentence dimension.” as recited in Claim 6.
Claim 14 recite the similar features as Claim 6.
“wherein the multi-dimensional similarity feature comprises a first text feature representing the full-text dimensional similarity feature; and
the determining of the multi-dimensional similarity feature for the first text and the second text based on the multi-dimensional information comprises:
determining a first total number of characters occupied by the common words in the first text and a second total number of characters occupied by the common words in the second text;
obtaining an absolute difference between a ratio of the first total number of the characters occupied by the common words in the first text to the first total number of the characters contained in the first text and a ratio of the second total number of the characters occupied by the common words in the second text to the second total number of the characters contained in the second text;
determining a minimum value and a maximum value among the first total number of the characters contained in the first text and the second total number of the characters contained in the second text, and obtaining a ratio of the maximum value to the minimum value representing a second length adjustment factor; and
determining the first text feature based on the ratio of the maximum value to the minimum value and the absolute difference, wherein a value of the first text feature is negatively correlated with the similarity between the first text and the second text in the full-text dimension.” as recited in Claim 7.
Claim 15 recites the similar features as Claim 7.
“wherein the multi-dimensional similarity feature comprises a second text feature representing the full-text dimensional similarity feature; and
the determining of the multi-dimensional similarity feature for the first text and the second text based on the multi-dimensional information comprises:
determining common character strings in the first text and the second text, and determining a first total number of occurrences of ones of the common character strings, each having a length within a first range, in the first text and the second text, a second total number of occurrences of ones of the common character strings, each having a length within a second range, in the first text and the second text, and a third total number of occurrences of ones of the common character strings, each having a length within a third range, in the first text and the second text; and
determining the second text feature based on the first total number of occurrences, a first weight, the second total number of occurrences, a second weight, the third total number of occurrences and a third weight, wherein a value of the second text feature is positively correlated with the similarity between the first text and the second text in the full-text dimension.” as recited in Claim 8.
Claim 16 recites the similar features as Claim 8.
Conclusion
8. The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure. See PTO-892.
a. Qin et al. (US 2025/0363296 A1.) In this reference, Qin et al. disclose determining a similarity between the first text string and the second text string.
b. Kobayashi et al. (US 2024/0012998 A1.) Kobayashi et al. disclose calculating the cosine similarity between vectors.
c. Liu et al. (US 2021/0209311 A1.) In this reference, Liu et al. disclose calculating a distance between two sentences.
9. Any inquiry concerning this communication or earlier communications from the examiner should be directed to THUYKHANH LE whose telephone number is (571)272-6429. The examiner can normally be reached Mon-Fri: 9am-5pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Andrew C. Flanders can be reached on 571-272-7516. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/THUYKHANH LE/Primary Examiner, Art Unit 2655
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