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
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Election/Restrictions
Applicant's election without traverse of Group I, covered by claims 1-5 in the reply filed on 23 August 2025 is acknowledged.
Status of Claims
Claims 1-5 are currently pending and have been examined.
Claims 6 and 7 have been withdrawn.
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.
Claim(s) 1-5 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Claim(s) 1 recite(s) a system and series of steps for predicting a price trend, which under broadest reasonable interpretation, is analogous to concepts performed in the human mind, such as observation, evaluation, and judgement, and mathematical concepts, such as mathematical relationships and calculations. These concepts are grouped as mental processes and mathematical concepts. Accordingly, the claim(s) recite(s) an abstract idea.
The limitation(s) of, ‘collecting historical data and merging the historical data…’; ‘extract sentiment data and trend data from market news and social media’; ‘inputting the historical data, the sentiment data, and the trend data…for data preprocessing and feature extraction’; ‘inputting a feature…for time series analysis, and outputting a predicted price trend’; ‘adjust a learning rate adaptively…’, as drafted, recite a process that, under broadest reasonable interpretation, is/are certain methods of organizing human activity. Accordingly, the claim(s) recite(s) an abstract idea.
The judicial exception is not integrated into a practical application. In particular, the claim(s) recite(s) the additional element(s) of ’natural language processing technology’, ‘a convolutional neural network’, ‘a recurrent neural network’, ‘an Adam algorithm’, ‘a momentum method’, ‘RMSProp algorithm’. These additional elements are recited at a high-level of generality such that in conjunction with the abstract limitations, they amount to no more than generally linking the use of the judicial exception to a particular technological environment or field of use.
Claim(s) 2-5 further recite(s) the system and series of steps for predicting a price trend, which under broadest reasonable interpretation, is analogous to concepts performed in the human mind, such as observation, evaluation, and judgement, and mathematical concepts, such as mathematical relationships and calculations. These concepts are grouped as mental processes and mathematical concepts. Accordingly, the claim(s) recite(s) an abstract idea.
The judicial exception is not integrated into a practical application. In particular, the claim(s) recite(s) the additional element(s) of ‘a sentiment analysis model’, ‘long short-term memory layer’, ‘a dense connection layer’, ‘a machine learning algorithm’, ‘a classification algorithm’, ‘a clustering algorithm’, ‘a CNN layer’, ‘an RNN layer’, ‘an Adam optimization algorithm’. These additional elements are recited at a high-level of generality such that in conjunction with the abstract limitations, they amount to no more than generally linking the use of the judicial exception to a particular technological environment or field of use.
The limitations as an ordered combination, are merely collecting, analyzing, and optimizing data using generic data collection and generic natural language techniques. In addition, the claims do not improve functionality of a computer or improve any other technology. Thus, claims 1-5 are ineligible as the claims do not recite additional elements which result in significantly more than the abstract idea itself.
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) 1 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wellmann (U.S. Patent App. Pub. No. 20230206058), in view of Natarajan (U.S. Patent App. Pub. No. 20150019295).
In regards to claim 1, although Wellmann teaches A price elasticity analysis and prediction method based on deep learning (Wellmann: ¶3-19 disclose processing time series data using neural networks and, more specifically, convolutional neural networks to generate predicted outputs characterizing entities), the reference does not explicitly state collecting and merging historical data into a time series dataset.
However, Wellmann and Natarajan together teach:
S1, collecting historical data and merging the historical data into a multi-dimensional time series dataset; the historical data comprise a historical price change and a sales volume change (Natarajan: ¶6-8, ¶22, ¶31, ¶61 disclose obtaining and compiling historical price data and sales data into a time series; Wellmann: ¶32-37, ¶96, ¶108-114 disclose collecting and organizing input data, e.g., historical data, over a time window);
S2, using natural language processing technology to extract sentiment data and trend data from market news and social media (Wellmann: ¶35, ¶95-100 disclose using natural language processing techniques to extract sentiment data of news articles, social media posts, and other communications during a given time period);
S3, inputting the historical data, the sentiment data, and the trend data obtained by S1 and S2 into a convolutional neural network (CNN) for data preprocessing and feature extraction to capture key information (Wellmann: ¶35, ¶48-56, ¶95-100 disclose inputting the input data into a CNN to generate feature vectors);
S4, inputting a feature extracted by the CNN into a recurrent neural network (RNN) for time series analysis, and outputting a predicted price trend (Wellmann: ¶19, ¶57-59, ¶104-106 disclose processing the outputted feature vectors using a aggregation neural network 120, e.g., a recurrent neural network, to generate an aggregated feature vector 122 that characterizes the entity over the entire time window);
S5, training and optimizing model: using an Adam algorithm to adjust a learning rate adaptively, and combining a momentum method and RMSProp algorithm to improve a generalization ability and prediction accuracy of the model (Wellmann: ¶81-85, ¶107-124 disclose training and fine-tuning the prediction neural network using an appropriate optimizer, e.g., Adam, rmsProp, or Adafactor).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the historical data time series, as taught by Natarajan, into the system and method of Wellmann. One of ordinary skill in the art would have been motivated to make this modification in order to obtain “more reliable forecast estimates…thereby significantly improving the retail price prediction that is provided to a variety of retail decision support applications used for supply chain, inventory management and pricing” (Wellmann: ¶28).
Novel/Non-Obvious Subject Matter
The subject matter of claims 2-5 is not taught by the cited prior art and is considered novel/non-obvious. However, claims 2-5 remain rejected under 35 U.S.C. 101 as described above.
The closest prior art of record is Wellmann (U.S. Patent App. Pub. No. 20230206058), Natarajan (U.S. Patent App. Pub. No. 20150019295), Sun (U.S. Patent App. Pub. No. 20180096420), Ivanov (U.S. Patent App. Pub. No. 20030177103), Bateni (U.S. Patent App. Pub. No. 20080133313).
The cited prior art, taken either individually or in combination, fails to teach or suggest data cleaning and preprocessing: comprising a removal of stop words, and irrelevant punctuations, and performing a simplified processing through stemming and word vectorization; performing a sentiment prediction through a sentiment analysis model: inputting preprocessed data, processing sequence data through a long short-term memory (LSTM) layer, capturing a time-dependent characteristic in text data, and finally outputting a result of sentiment classification through a dense connection layer; trend analysis: using text mining and a machine learning algorithm to identify a trend and pattern in a market; wherein based on the preprocessed data, using a classification algorithm to identify sentiment tendencies, using a clustering algorithm to find different types of market behaviors, and using a time series analysis method to predict the price trend or a market trend; feature extraction: according to a result of sentiment and trend analysis, extracting useful features from the text data; and feature fusion: fusing extracted feature data with traditional quantitative market data to form a feature set.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to WAYNE S MURRAY whose telephone number is (571)272-4306. The examiner can normally be reached M-F 8am-5pm.
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/Wayne S. Murray/Examiner, Art Unit 3628