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 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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea – mental process – without significantly more.
Claims 1, 17, 19 recite identify subtly biased texts within open corpora, the method comprising: a natural language sentence collecting step of collecting natural language sentences from text-based datasets including open conversation datasets and web corpus datasets; a bias candidate dataset obtaining step of comparing semantic vectors between words composing the natural language sentences by using a word-embedding model from the collected natural language sentences, thereby obtaining a bias candidate dataset including seed bias data expected to be socially and ethically biased and derived bias data expanded from the seed bias data; and a bias determining step of determining the bias candidate dataset as one of a surficial bias dataset, a subtle bias dataset, and a non-bias dataset by using multiple large-scale language models. This amounts to a human gathering data from the internet, books and periodicals, assigns a number to each word, compares the number and determines if there is bias and compiles 3 separate sets in his notebook that indicates surficial, subtle or non-bias. When he determines a bias, he notes in the next column a non-biased response. This judicial exception is not integrated into a practical application because the additional elements of “one or more processors” are not used beyond their ordinary capacity and therefore are merely executing the steps of the abstract idea. Further, the LLM performing steps amounts to merely implementing the steps with generic computer functions using routine and conventional commonly understood technology (e.g., neural networks). The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As such, claims 1, 17 and 19 stand rejected under 35 U.S.C. § 101 as directed to a judicial exception without significantly more.
Claims 10, 18 and 20 recite identify subtly biased texts within open corpora and generate a response to the identified subtly biased texts, the apparatus, the apparatus comprising: a natural language sentence collecting unit for collecting natural language sentences from text-based datasets including open conversation datasets and web corpus datasets; a bias candidate dataset obtaining unit for comparing semantic vectors between words composing the natural language sentences by using a word-embedding model from the collected natural language sentences, thereby obtaining a bias candidate dataset including seed bias data expected to be socially and ethically biased and derived bias data expanded from the seed bias data; a bias determining unit for determining the bias candidate dataset, which is obtained from the bias candidate dataset obtaining unit, as one of a surficial bias dataset, a subtle bias dataset, and a non-bias dataset by using multiple large-scale language models; and a response answer generating unit for generating a response answer for the natural language sentence identified as the surficial bias dataset and the subtle bias dataset in the bias determining unit so as to generate a response answer in which bias is mitigated or removed. This amounts to a human gathering data from the internet, books and periodicals, assigns a number to each word, compares the number and determines if there is bias and compiles 3 separate sets in his notebook that indicates surficial, subtle or non-bias. When he determines a bias, he notes in the next column a non-biased response.. This judicial exception is not integrated into a practical application because the additional elements of “one or more processors” are not used beyond their ordinary capacity and therefore are merely executing the steps of the abstract idea. Further, the LLM performing steps amounts to merely implementing the steps with generic computer functions using routine and conventional commonly understood technology (e.g., neural networks). The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As such, claims 1, 17 and 19 stand rejected under 35 U.S.C. § 101 as directed to a judicial exception without significantly more.
Claim 2 recites wherein the bias determining step includes using, as the multiple large-scale language models, heterogeneous large-scale language models having different structures and training mechanisms. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the recitation of the additional limitations of “training mechanism” amounts to insignificant extra-solution activity comprising gathering data for the abstract idea. No additional limitations are present.
Claim 3 recites wherein the bias determining step includes: when each of the multiple large-scale language models performs bias determination on one bias candidate dataset subject to the bias determination and when all of the large-scale language models determine the one bias candidate dataset as a biased dataset, classifying the one bias candidate dataset as the surficial bias dataset. This amounts to the human asking 3 people’s opinion about the bias in a chosen dataset he has created and if each one determines some bias, to label the datasets as surficial biased. No additional limitations are present.
Claim 4recites wherein the bias determining step includes: when each of the multiple large-scale language models performs bias determination on one bias candidate dataset subject to the bias determination and when all of the large-scale language models determine the one bias candidate dataset as a unbiased dataset, classifying the one bias candidate dataset as the non-bias dataset. This amounts to the human asking 3 people’s opinion about the bias in a chosen dataset he has created and if each one determines no bias, to label the dataset as non-biased. No additional limitations are present.
Claim 5 recites when each of the multiple large-scale language models performs bias determination on one bias candidate dataset subject to the bias determination and when at least one of the large-scale language models determines the one bias candidate dataset as a biased dataset, classifying the one bias candidate dataset as the subtle bias dataset. This amounts to the human asking 3 people’s opinion about the bias in the chosen dataset and if one determines bias, to label all the datasets as subtle-biased. No additional limitations are present.
Claim 6 recites determination criterion querying step, when the one bias candidate dataset is classified as the subtle bias dataset by performing the bias determining step, of querying a determination criterion to the large-scale language model having obtained the classification results as the subtle bias dataset; a response validity determining step of, when a response returned by the large-scale language model is present by performing the determination criterion querying step, determining validity of the returned response; and a learning dataset storing step of classifying the subtle bias dataset, which is obtained as the valid response in the response validity determining step, as a learning dataset for self-learning to store the classified the subtle bias dataset in a learning database. This amounts to the human asking the one person if he has validated that data as subtle-bias and on confirmation mark that dataset as a dataset from which future determinations can be made for bias. Learning is an insignificant extra solution step for data gathering for the abstract idea. No additional limitations are present.
Claim 7 recites wherein the response validity determining step includes determining the response as a valid response when a factor containing at least one of keywords and ideas containing social and ethical issues is obtained from the response returned by the large-scale language model. This amounts to the human asking the one person if he has validated that data as subtle-bias and he repeats words that have social and ethical connotations in his response. No additional limitations are present.
Claim 8 recites a self-learning step, after the bias determining step, of performing self-learning by providing subtle bias-related learning data to the multiple large-scale language models by using the learning data sets stored in the learning database. Learning is an insignificant extra solution step for data gathering for the abstract idea. No additional limitations are present.
Claim 9 recites when each of the multiple large-scale language models performs bias determination on one bias candidate dataset subject to the bias determination and when the multiple large-scale language models provide different results on the bias determination, classifying the one bias candidate dataset as the subtle bias dataset when there are a majority of determinations on 'biased' and classifying the one bias candidate dataset as the non-bias dataset when there are a majority of determinations on 'non-bias', based on a majority rule. This amounts to the human asking 3 people’s opinion about the bias in one of the datasets and labelling it based on majority vote. No additional limitations are present.
Claim 11 recites wherein the response answer generating step includes generating the response answer to the natural language sentence having identified bias in a format that includes at least one of a summary format and a detailed description format. This amounts to the human replacing a biased entity in his notebook and adding a summary of the replacement. No additional limitations are present.
Claim 12 recites a bias determination result receiving step of receiving bias determination results for the one bias candidate dataset from the multiple large-scale language models; a bias score calculating step of calculating a bias score for the one bias candidate dataset based on the majority rule by aggregating the received bias determination results; and a bias intensity defining step of defining a bias intensity for the one bias candidate dataset based on the calculated bias score. This amounts to the human asking 3 people’s opinion about the bias on a dataset, averaging their scores on a scale of 1-10 and assigning that score to the dataset. No additional limitations are present. No additional limitations are present.
Claim 13 recites wherein a trust level is defined for each of the large-scale language models based on a trust level management model preset for the multiple large-scale language models, and the bias score calculating step includes calculating the bias score by giving a highest weight to the bias determination result provided by the large-scale language model defined with a highest trust level. This amounts to the human assigning trust level “high” “medium” and “low” to the 3 people and gives the highest weight to score of the person’s score. No additional limitations are present. No additional limitations are present.
Claim 14 recites wherein the bias intensity defining step includes, based on a preset threshold bias score, defining the one bias candidate dataset as a first bias level when the calculated bias score is less than the threshold bias score, and defining the one bias candidate dataset as a second bias level when the calculated bias score is greater than or equal to the threshold bias score. This amounts to the human labeling the dataset as surficial bias if the weighted score is above 5 and subtle bias if below 5. No additional limitations are present. No additional limitations are present.
Claim 15 recites wherein the response answer generating step, when the bias intensity defined for the one bias candidate dataset is the first bias level, includes generating a first response answer, which is a response answer composed of correction information that corrects the bias. This amounts to the human writing a corrected response for surficial bias in his notebook when the score is above 5 indicating a surficial bias in the dataset. No additional limitations are present. No additional limitations are present.
Claim 16 recites wherein the response answer generating step, when the bias intensity defined for the one bias candidate dataset is the second bias level, includes generating a second response answer, which is a response answer composed of warning information that warns of the bias, together with the correction information that corrects the bias. This amounts to the human writing a corrected response for subtle bias in his notebook when the score is below 5 indicating a subtle bias in the dataset. No additional limitations are present. No additional limitations are present.
Conclusion
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/ATHAR N PASHA/Primary Examiner, Art Unit 2657
