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 .
DETAILED ACTION
2. This communication is responsive to the amendment filed on 09/17/2025.
3. Claims 15-20 are cancelled; claims 21-23 are newly added. Claims 1-14 and 21-24 are currently pending in this Office Action. This action is made Final.
Claim Objections
4. Claim 23 is objected to because of the following informalities:
Two “claim 23” are filed in the amendment and “claim 22” is missing. For the examining purpose in this Office action, the first “claim 23” will be treated as “claim 22” by the examiner. Appropriate correction is required.
Claim Rejections - 35 USC § 101
5. Applicant’s arguments [Remarks filed on 09/17/2025] are not persuasive and the examiner maintains the rejection as set forth below:
6. Claims 1-14 and 21-23 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The judicial exception is not integrated into a practical application. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The eligibility analysis in support of these findings is provided below, in accordance with the 2019 Revised Patent Subject Matter Eligibility Guidance, hereinafter 2019 PEG.
Step 1. In accordance with Step 1 of the eligibility inquiry (as explained in MPEP 2106), it is noted that the method(s) of claims 1-14 and 21-23 are directed to one of the eligible categories of subject matter and therefore satisfy Step 1.
Step 2A. In accordance with Step 2A, prog one of the 2019 PEG:
In claim 1-7 and 21-23, the limitations directed to additional elements include: an electronic display; in claims 8-14, the limitations directed to additional elements include: one or more processor; an electronic display; a non-transitory computer-readable medium.
In exemplary claim 1, limitations reciting the abstract idea are as follows:
receiving, by a BERT model, a plurality of question-answer pairs derived from a natural language query; (2) receiving, by a classifier model, an output of the BERT model; and (3) displaying, in an electronic display, each satisfactory answer.
These limitations, under the broadest reasonable interpretation, recite mental processes because these limitations can be performed in the human mind or using pen and paper. The examiner believes that the steps disclosed in claim 1 [receiving data, displaying an answer] can be performed by a human, using observation, evaluation, and judgment, because the steps involve making identifications and determinations, which are mental tasks humans routinely perform in the course of producing and performing queries.
Claim 8 and 21 recite the similar limitations as claim 1. Thus, claims 8 and 21 are rejected due to the similar reasons set forth regarding claim 1.
A claim recites a mental process when the claim encompasses acts the person can perform using the mind or pen and paper [determining that a claim whose ‘steps can be performed in the human mind, or by a human using a pen and paper’ is directed to an unpatentable mental process]. This is true even if the claim recites, as they do here, that a generic computer component performs the acts.
For example, a person can perform the “receiving” step by simply looking at and reading the item and evaluating the items selected by using pen and paper to note relevance information. Finally, a person can perform the “displaying” step by manually passing the information to one another using paper.
As noted above, if a claim, under its broadest reasonable interpretation, covers performance in the mind but for the recitation of generic computer components, then it is still in the mental processes category unless the claim cannot practically be performed in the mind. Here, the examiner is not persuaded that the aforementioned steps in claims 1, 8 or 21 cannot practically be performed in the human minds, or using pen and paper, but for the generic computing device.
Step 2A. In accordance with Step 2A, prog two of the 2019 PEG:
With respect to Step 2A, prog two, the judicial exception is not integrated into a practical application. The additional elements are directed to an electronic display and/or one or more processor and a non-transitory computer readable medium. However, these elements do not (1) improve the functioning of a computer or other technology; (2) are not applied with any particular machine (except for a generic computer); (3) do not effect a transformation of a particular article to a different state; and (4) are not applied in any meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception. In other words, the aforementioned additional element (or combination of elements) recited in the claims do not integrate the judicial exception into a practical application.
In other words, the claimed processes fail to improve the functioning of either the electronic display and/or one or more processor and the non-transitory computer readable medium. Rather, these additional elements merely link the underlying abstract idea (i.e., mental processes or using pen and paper) to a particular technological environment, i.e., search query processing. Thus, the claimed process uses conventional computers to automate tasks that would have otherwise been very labor intensive by a human searcher. Such claims are not patent eligible. See OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363 (Fed. Cir. 2015) (“relying on a computer to perform routine tasks more quickly or more accurately is insufficient to render a claim patent eligible”).
Since the analysis of Step 2A prong one and prong two results in the conclusion that the claims are directed to an abstract idea, additional analysis under Step 2B of the eligibility inquiry must be conducted in order to determine whether any claim element or combination of elements amount to significantly more than the judicial exception.
Step 2B. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional limitations are directed to an electronic display and/or one or more processor and a non-transitory computer readable medium, at a very high level of generality and without imposing meaningful limitations on the scope of the claim. Such general-purpose computing device, high-level, and nominal involvement of a computer or computer-based elements for carrying out the invention merely serves to tie the abstract idea to a particular technological environment, which is not enough to render the claims patent-eligible.
The additional elements are broadly applied to the abstract idea at a high level of generality and they operate in a well-understood, routine, and conventional manner. Courts have held computer-implemented processes not to be significantly more than an abstract idea (and thus ineligible) where the claim as a whole amount to nothing more than generic computer functions merely used to implement an abstract idea, such as an idea that could be done by a human analog.
The dependent claims have been fully considered, however, similar to the findings for claims above, these claims are similarly directed to the “Mental Processes” grouping of abstract ideas set forth in the 2019 PEG, without integrating it into a practical application and with, at most, a general-purpose computer that serves to tie the idea to a particular technological environment, which does not add significantly more to the claims. The ordered combination of elements in the dependent claims (including the limitations inherited from the parent claim(s)) add nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. The collective functions merely provide conventional computer implementation. Accordingly, the subject matter encompassed by the dependent claims fail to amount to significantly more than the abstract idea.
Claim Rejections - 35 USC § 112
7. The 35 U.S.C. 112, second paragraph rejections made in the prior Office action are withdrawn.
Claim Rejections - 35 USC § 103
8. 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.
9. 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.
10. Claims 1-14 and 21-23 are rejected under 35 U.S.C. 103 as being unpatentable over CN 112115238 (hereinafter Liao; machine translated) in view of non-patent literature “Attention Mechanism with BERT for Content Annotation and Categorization of Pregnancy-Related Questions on a Community Q&A Site”, Proceedings (IEEE Int Conf Bioinformatics Biomed). 2020 December; 2020: 1077–1081 [total 9 pages] (hereinafter Luo), and further in view of U.S. 2023/0385558 (hereinafter Oh).
Regarding claims 1 and 8, Liao discloses a method of displaying answers of question-answer pairs in response to a natural language search query, the method comprising:
receiving, by a Bidirectional Encoder Representations from Transformers (BERT) model, a plurality of question-answer pairs derived from a natural language query ([0042-0045]; A. obtaining the question and answer linguistic data for constructing knowledge base and for BERT downstream task training, and pre-processing; B, according to the question and answer corpus pre-processed in step A, constructing question and answer knowledge base; C, according to the question and answer corpus pre-processed in step A, constructing a language model based on BERT; D, obtaining the training question and answer corpus data of BERT language model according to step C; marking to form the label corpus”),
receiving, by a classifier model, an output of the BERT model, wherein the classifier model classifies each question-answer pair as one of answer based on a probability ([0047-0049 and 0084-0085]; “F, constructing the text similarity two classification model based on BERT and language model according to the BERT language model obtained in step C and the pre-processed marking language material in step D; G; using the BERT-CRF (Conditional Random Fields) model obtained in step E and the text attribute two classification model of BERT and language model obtained in step F; respectively training by using marked corpus; respectively obtaining BERT-CRF language model with parameter weight and BERT text similarity two classification model; H, using the step E, F to obtain the BERT-CRF language model with parameter weight and BERT text similarity two classification model; then combining the question and answer knowledge base obtained in step B; processing the question language material to be answered; obtaining the correct answer of the problem, and automatically rewriting the answer….”; and “P calculating probability formula represents the original sequence based on the corresponding probability of the predicted sequence. F, constructing the text similarity two classification model based on BERT and language model according to the BERT language model obtained in step C and the pre-processed marking language material in step D”).
Liao does not explicitly disclose the features of wherein each question-answer pair as one of a satisfactory answer and an unsatisfactory answer based on a probability; wherein the BERT model generates an array of attention matrices for each question-answer pair of the plurality of question-answer pairs, wherein each attention matrix of the array of attention matrices produces an array of attribution values; and displaying each satisfactory answer, wherein one or more words of each satisfactory answer is highlighted based at least in part on the array of attribution values. However, Luo discloses that “The attention mechanism of BERT works as Query (Q), Key (K), and Value (V) that start a linear transformation to “dynamically” generate weights for different connections, and then feed them into the scaling dot product...” (pgs. 3-4, [A. Content Annotation using the Self-Attention Extraction from the BERT]). Luo continue to discloses that “The main objective of this research is to explore whether the attention mechanisms can be used to annotate the relevant words that drive the classification. In this section, we demonstrate a few questions with terms that have high attention weights. Dark green and light green were used to highlight the words identified by self-attention of BERT and BERT-attention, respectively. Red was used to highlight the words identified by base BERT or BERT-attention if they are misclassified. Color grey was used to highlight the words that are identified by both attention mechanisms” (pgs. 6-7, [D. Contention Annotation using Attentions]) and it would have been obvious for one with ordinary skill in the art to utilize the teachings of Luo in the system of Liao in view of the desire to enhance the question-answering system by utilizing the specific processing scheme resulting in improving the accuracy of extracting an answer.
While Liao in view of Luo discloses the feature of utilizing the hardware computer components for performing the above functions, the references do not explicitly disclose the feature of displaying in an electronic display. However, such feature is well known in the art as disclosed by Oh (figs. 21-22) and it would have been obvious for one with ordinary skill in the art to utilize the teachings of Oh in the modified system of Liao in view of the desire to enhance the question-answering system by utilizing the computing hardware components resulting in improving the accuracy of extracting an answer. In addition, the references disclose a system comprising: one or more processors; an electronic display; and a non-transitory computer-readable medium storing instructions (Oh: fig. 22).
Regarding claims 2 and 9, Liao in view of Luo and Oh disclose the method further comprising, for each satisfactory answer, generating a total attribution value for each word of the satisfactory answer from an individual array of attention matrices associated with the satisfactory answer (Luo: pgs. 3-5, [Content Annotation using the Self-Attention Extraction from the BERT] and [Content Annotation by Adding an External Attention Layer to BERT (BERT-Attention Model)]). Therefore, the limitations of claims 2, 9 and 16 are also rejected in the analysis of claims 1, 8 or 15, and the claims are rejected on that basis.
Regarding claims 3, 10 and 22, Liao in view of Luo and Oh disclose the method wherein the total attribution value for each word of the satisfactory answer is generated by:
selecting a sub-set of attention matrices of the array of attention matrices; and for each word of the satisfactory answer, summing attribution values of the sub-set of attention matrices (Luo: pgs. 3-5, [Content Annotation using the Self-Attention Extraction from the BERT] and [Content Annotation by Adding an External Attention Layer to BERT (BERT-Attention Model)]). Therefore, the limitations of claims 3, 10 and 17 are also rejected in the analysis of claims 1, 8 or 15, and the claims are rejected on that basis.
Regarding claims 4 and 11, Liao in view of Luo and Oh disclose the method wherein the sub-set of attention matrices is selected by a loss function [i.e., the error function] and an optimization algorithm [i.e., the fine-tuning of BERT] (Luo: pgs. 3-4, [Content Annotation using the Self-Attention Extraction from the BERT]). Therefore, the limitations of claims 4, 11 and 18 are also rejected in the analysis of claims 1, 8 or 15, and the claims are rejected on that basis.
Regarding claims 5, 12 and 23, Liao in view of Luo and Oh disclose the method wherein the array of attention matrices comprises a plurality of heads and a plurality of layers that are provided as input to the optimization algorithm (Luo: pgs. 3-4, [A. Content Annotation using the Self-Attention Extraction from the BER]; pgs. 6-7, [D. Contention Annotation using Attentions]). Therefore, the limitations of claims 5, 12 and 19 are also rejected in the analysis of claims 1, 8 or 15, and the claims are rejected on that basis.
Regarding claims 6 and 13, Liao in view of Luo and Oh disclose the method further comprising applying a post-processing process that does one or more of the following: adds highlighting to one or more words being adjacent on both sides of word having highlighting, and removes highlighting from one or more words having a lowest total attribution when a maximum number of highlighted phrases is exceeded (Luo: pgs. 3-5, [Content Annotation using the Self-Attention Extraction from the BERT] and [Content Annotation by Adding an External Attention Layer to BERT (BERT-Attention Model)]). Therefore, the limitations of claims 6, 13 and 20 are also rejected in the analysis of claims 1, 8 or 15, and the claims are rejected on that basis.
Regarding claims 7 and 14, Liao in view of Luo and Oh disclose the method wherein the classifier is a layer of the BERT model (pgs. 3-4, [A. Content Annotation using the Self-Attention Extraction from the BER]). Therefore, the limitations of claims 7 and 14 are also rejected in the analysis of claims 1 or 8, and the claims are rejected on that basis.
Regarding claim 21, Liao discloses a method of displaying answers of question-answer pairs in response to a natural language search query, the method comprising:
receiving, by a Bidirectional Encoder Representations from Transformers (BERT) model, a plurality of question-answer pairs derived from a natural language query ([0042-0045]; A. obtaining the question and answer linguistic data for constructing knowledge base and for BERT downstream task training, and pre-processing; B, according to the question and answer corpus pre-processed in step A, constructing question and answer knowledge base; C, according to the question and answer corpus pre-processed in step A, constructing a language model based on BERT; D, obtaining the training question and answer corpus data of BERT language model according to step C; marking to form the label corpus”),
receiving, by a classifier model, an output of the BERT model, wherein the classifier model classifies each question-answer pair as one of answer based on a probability ([0047-0049 and 0084-0085]; “F, constructing the text similarity two classification model based on BERT and language model according to the BERT language model obtained in step C and the pre-processed marking language material in step D; G; using the BERT-CRF (Conditional Random Fields) model obtained in step E and the text attribute two classification model of BERT and language model obtained in step F; respectively training by using marked corpus; respectively obtaining BERT-CRF language model with parameter weight and BERT text similarity two classification model; H, using the step E, F to obtain the BERT-CRF language model with parameter weight and BERT text similarity two classification model; then combining the question and answer knowledge base obtained in step B; processing the question language material to be answered; obtaining the correct answer of the problem, and automatically rewriting the answer….”; and “P calculating probability formula represents the original sequence based on the corresponding probability of the predicted sequence. F, constructing the text similarity two classification model based on BERT and language model according to the BERT language model obtained in step C and the pre-processed marking language material in step D”).
Liao does not explicitly disclose the features of wherein each question-answer pair as one of a satisfactory answer and an unsatisfactory answer based on a probability; wherein the BERT model generates an array of attention matrices for each question-answer pair of the plurality of question-answer pairs, wherein each attention matrix of the array of attention matrices produces an array of attribution values; and displaying each answer that is the first class, wherein one or more words of each answer of the first class is highlighted based at least in part on a total attribution value for each word of the answer of the first class from an individual array of attention matrices associated with the answer of the first class, and wherein each word having a total attribution value above a threshold value is highlighted. However, Luo discloses that “The attention mechanism of BERT works as Query (Q), Key (K), and Value (V) that start a linear transformation to “dynamically” generate weights for different connections, and then feed them into the scaling dot product...” (pgs. 3-5, [A. Content Annotation using the Self-Attention Extraction from the BERT] and [Content Annotation by Adding an External Attention Layer to BERT (BERT-Attention Model)]). Luo continue to discloses that “The main objective of this research is to explore whether the attention mechanisms can be used to annotate the relevant words that drive the classification. In this section, we demonstrate a few questions with terms that have high attention weights. Dark green and light green were used to highlight the words identified by self-attention of BERT and BERT-attention, respectively. Red was used to highlight the words identified by base BERT or BERT-attention if they are misclassified. Color grey was used to highlight the words that are identified by both attention mechanisms” (pgs. 6-7, [D. Contention Annotation using Attentions]) and it would have been obvious for one with ordinary skill in the art to utilize the teachings of Luo in the system of Liao in view of the desire to enhance the question-answering system by utilizing the specific processing scheme resulting in improving the accuracy of extracting an answer.
While Liao in view of Luo discloses the feature of utilizing the hardware computer components for performing the above functions, the references do not explicitly disclose the feature of displaying in an electronic display. However, such feature is well known in the art as disclosed by Oh (figs. 21-22) and it would have been obvious for one with ordinary skill in the art to utilize the teachings of Oh in the modified system of Liao in view of the desire to enhance the question-answering system by utilizing the computing hardware components resulting in improving the accuracy of extracting an answer. In addition, the references disclose a system comprising: one or more processors; an electronic display; and a non-transitory computer-readable medium storing instructions (Oh: fig. 22).
Response to Arguments
11. Applicant’s arguments have been considered but are deemed to be moot in view of the new ground of rejection presented in this Office action.
Conclusion
12. Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MONICA M PYO whose telephone number is (571)272-8192. The examiner can normally be reached Monday-Friday 8am-4pm.
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/MONICA M PYO/Primary Examiner, Art Unit 2161
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