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 .
Priority
Receipt is acknowledged of papers submitted under 35 U.S.C. 119(a)-(d), which papers have been placed of record in the file.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 3/10/2025 in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Status of Claims
Claims 1-20 are pending in this application.
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.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Liu et al. (Chinese Patent Application Publication 112232086, listed in IDS dated 3/10/2025) in view of Nagpal et al. (U.S. Patent Application Publication 20240154941) in further view of Zeng et al. (Chinese Patent Application Publication).
As per claims 1, 13 and 18, Liu et al. discloses:
An electronic device, comprising a processor and a memory, the memory having a computer program stored therein, and the processor, when executing the computer program, performing the operations of a text recognition method, performed by an electronic device and comprising (Paragraphs [0018-0019] – the invention is embodied as a processor and memory containing instructions):
performing at least one training iteration on a text recognition model to be trained based on a pre-constructed text sample set and a reference model, to obtain a trained text recognition model; and
inputting text to be recognized into the trained text recognition model, and recognizing a named entity in the text to be recognized, to obtain text content corresponding to the named entity, wherein each training iteration comprising:
respectively inputting a text sample selected from the text sample set into the reference model and the text recognition model, the reference model comprising at least one first transformer configured to extract a feature, the text recognition model comprising at least one second transformer configured to extract a feature;
obtaining an output difference between each second transformer and the corresponding first transformer of the reference model based on a transformer mapping relationship;
obtaining a prediction difference between the text recognition model and the reference model for mask information in the text sample; and
adjusting parameters of the text recognition model based on the output differences and the prediction difference (Paragraphs [0041-0170]).
Liu et al. fails to disclose but Nagpal et al. in the same field of endeavor teaches:
Recognizing a named entity in a text to be recognized, so as to obtain text content corresponding to the named entity (Paragraph [0085] – the natural language processing is named entity recognition)
It would be obvious for a person having ordinary skill in the art at the effective failing date of the invention to modify the method, device and computer readable medium of Liu et al with the named entity recognition capabilities of Nagpal et al. because it is a case of simple substitution of one known element for another to obtain predictable results.
The combination of Liu et al. and Nagpal et al. fail to disclose, but Zeng et al. in the same field of endeavor teaches:
A prediction difference being a prediction difference about mask information contained in a text sample. (Paragraph [0024] - predicting the target word replaced by the mask information in the input text, obtaining the prediction result; using the difference between the prediction result and the target word, adjusting the parameter of the initial extraction model, until the training end condition is reached, obtaining the pre-training model.)
It would be obvious for a person having ordinary skill in the art at the effective failing date of the invention to modify the method, device and computer readable medium of Liu et al and Nagpal et al. with the prediction difference capabilities of Zeng et al. because it is a case of simple substitution of one known element for another to obtain predictable results.
Claim 1 is directed to the method of using the device of claim 13, so is rejected for similar reasons.
Claim 18 is directed to a computer readable medium containing instructions to cause a device to act as the device of claim 13, so is rejected for similar reasons.
As per claims 2, 14 and 19, the combination of Liu et al., Nagpal et al. and Zeng et al. discloses all of the limitations of claims 1, 13 and 18 above. Liu et al. in the combination further discloses:
a quantity of the first transformers being greater than that of the second transformers, and each transformer comprises a self-attention sub-layer and a feedforward neural network (FNN) sub-layer, and the transformer mapping relationship is a mapping relationship between each second transformer of the text recognition model and a first transformer of the reference model; and
the output difference comprises a first output difference corresponding to the self-attention sub-layer, and a second output difference corresponding to the FNN sub-layer; and the obtaining an output difference between each second transformer and the corresponding first transformer of the reference model based on a transformer mapping relationship comprises performing the following operations for each second transformer of the text recognition model: determining the first transformer, corresponding to the second transformer, of the reference model as a target transformer based on the transformer mapping relationship;
taking a difference between outputs of the self-attention sub-layer of the second transformer and the self-attention sub-layer of the target transformer as the first output difference; and taking a difference between outputs of the FNN sub-layer of the second transformer and the FNN sub-layer of the target transformer as the second output difference (Paragraphs [0041-0170]).
As per claims 3, 15 and 20, the combination of Liu et al., Nagpal et al. and Zeng et al. discloses all of the limitations of claims 2, 14 and 19 above. Liu et al. in the combination further discloses:
the taking a difference between outputs of the self-attention sub-layer of the second transformer and the self-attention sub-layer of the target transformer as the first output difference comprises: obtaining a degree of first correlation of every two characters in the text sample based on the self-attention sub-layer of the second transformer; obtaining a degree of second correlation of every two characters in the text sample based on the self-attention sub-layer of the target transformer; and obtaining the first output difference based on a difference between the degree of first correlation and the degree of second correlation of every two characters (Paragraphs [0041-0170]).
As per claims 4 and 16, the combination of Liu et al., Nagpal et al. and Zeng et al. discloses all of the limitations of claims 3 and 15 above. Liu et al. in the combination further discloses:
obtaining the first output difference based on a difference between the degree of first correlation and the degree of second correlation of every two characters comprises: taking a sum of differences of squares of the degree of first correlations and the corresponding degree of second correlations as the first output difference (Paragraphs [0041-0170]).
As per claims 5 and 17, the combination of Liu et al., Nagpal et al. and Zeng et al. discloses all of the limitations of claims 2 and 14 above. Liu et al. in the combination further discloses:
he taking a difference between outputs of the FNN sub-layer of the second transformer and the FNN sub-layer of the target transformer as the second output difference comprises: obtaining a first output vector of each character in the text sample based on the FNN sub-layer of the second transformer; obtaining a second output vector of each character in the text sample based on the FNN sub-layer of the target transformer; performing dimension transformation on the first output vector of each character, to obtain a target vector having a same dimension as the second output vector of the character; and obtaining the second output difference based on differences between the target vectors and the second output vectors of the characters (Paragraphs [0041-0170]).
As per claim 6, the combination of Liu et al., Nagpal et al. and Zeng et al. discloses all of the limitations of claim 5 above. Liu et al. in the combination further discloses:
the performing dimension transformation on the first output vector of each character, to obtain a target vector having a same dimension as the second output vector of the character comprises: performing dimension transformation on the first output vector based on a pre-set parameter matrix and parameter vector, to obtain the target vector, the parameter matrix and the parameter vector being determined based on the dimension of the second output vector; and the method further comprises: adjusting parameters of the parameter matrix and the parameter vector based on the output differences and the prediction difference (Paragraphs [0041-0170]).
As per claim 7, the combination of Liu et al., Nagpal et al. and Zeng et al. discloses all of the limitations of claim 5 above. Liu et al. in the combination further discloses:
the obtaining the second output difference based on the differences between the target vectors and the second output vectors of the characters comprises: taking a sum of differences of squares of the target vectors and the corresponding second output vectors as the second output difference (Paragraphs [0041-0170]).
As per claim 8, the combination of Liu et al., Nagpal et al. and Zeng et al. discloses all of the limitations of claim 1 above. Liu et al. in the combination further discloses:
the obtaining a prediction difference between the text recognition model and the reference model for mask information in the text sample comprises: acquiring a first probability distribution corresponding to each first prediction result based on the first prediction result of the text recognition model for each piece of mask information in the text sample; acquiring a second probability distribution corresponding to each second prediction result based on the second prediction result of the reference model for each piece of mask information in the text sample; and obtaining the prediction difference based on the first probability distributions and the corresponding second probability distributions (Paragraphs [0041-0170]).
As per claim 9, the combination of Liu et al., Nagpal et al. and Zeng et al. discloses all of the limitations of claim 8 above. Liu et al. in the combination further discloses:
the obtaining the prediction difference based on the first probability distributions and the corresponding second probability distributions comprises: taking a sum of inverse numbers of relative entropies between the first probability distributions and the corresponding second probability distributions as the prediction difference (Paragraphs [0041-0170]).
As per claim 10, the combination of Liu et al., Nagpal et al. and Zeng et al. discloses all of the limitations of claim 2 above. Liu et al. in the combination further discloses:
the adjusting parameters of the text recognition model based on the output differences and the prediction difference comprises: performing weighted summation on a sum of the first output differences, a sum of the second output differences, and the prediction difference based on pre-set coefficients, to obtain a target loss function; and adjusting the parameters of the text recognition model based on the target loss function (Paragraphs [0041-0170]).
As per claim 12, the combination of Liu et al., Nagpal et al. and Zeng et al. discloses all of the limitations of claim 1 above. Nagpal et al. in the combination further discloses:
performing feature extraction on the text to be recognized based on the trained text recognition model, to obtain a text feature of the text to be recognized; and recognizing the named entity in the text to be recognized based on the text feature, to obtain the text content corresponding to the named entity (Paragraphs [0065-0069], [0081], [0085] and [0088]).
Allowable Subject Matter
Claim 11 is 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.
Examiner Notes
The Examiner cites particular columns and line numbers in the references as applied to the claims above for the convenience of the Applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested that, in preparing responses, the Applicant fully considers the references in its entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or as disclosed by the Examiner.
Communications via Internet e-mail are at the discretion of the applicant and require written authorization. Should the Applicant wish to communicate via e-mail, including the following paragraph in their response will allow the Examiner to do so:
“Recognizing that Internet communications are not secure, I hereby authorize the USPTO to communicate with me concerning any subject matter of this application by electronic mail. I understand that a copy of these communications will be made of record in the application file.”
Should e-mail communication be desired, the Examiner can be reached at Edwin.Leland@USPTO.gov
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to EDWIN S LELAND III whose telephone number is (571)270-5678. The examiner can normally be reached 8:00 - 5:00 M-F.
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/EDWIN S LELAND III/Primary Examiner, Art Unit 2654
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