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 .
Claims 1-20 are pending and examined in this office action.
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 a judicial exception (i.e., a law of nature, a natural phenomenon, mathematical relationship or an abstract idea) without significantly more.
Statutory Category: Claim 1 recites a method, comprising: obtaining an assigned feature name for an application feature of a target application; selecting, using respective vector encodings of an application feature name, a set of application feature names comprising the assigned feature name; filtering a collection of feedback using a set of application feature names of an application feature to obtain a filtered set of feedback; generating a sentiment request prompt requesting a sentiment score and comprising the filtered set of feedback and the set of application feature names; processing, by a large language model (LLM), the filtered set of feedback with the set of application feature names to generate the sentiment score for the application feature; selecting a recommended action based on the sentiment score; and updating the target application according to the recommended action.
Step 2A – Prong 1: Claim 1 recites: selecting, using respective vector encodings of an application feature name, a set of application feature names comprising the assigned feature name (a user can mentally or manually select a set of application feature names); filtering a collection of feedback using a set of application feature names of an application feature to obtain a filtered set of feedback; (a user can mentally or manually performs filtering); generating a sentiment request prompt requesting a sentiment score and comprising the filtered set of feedback and the set of application feature names (a user can mentally or manually generates a prompt); processing the filtered set of feedback with the set of application feature names to generate the sentiment score for the application feature (a user can mentally process data and generate a score); selecting a recommended action based on the sentiment score (a user can mentally selects a recommended action). That is, nothing in the claim elements precludes the steps from practically being performed mentally or using pen and paper. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the mental process grouping of abstract idea. Accordingly, the claim recites an abstract idea under step 2A prong 1.
This judicial exception is not integrated into a practical application. In particular, the claim 1 recites additional elements such as obtaining an assigned feature name for an application feature of a target application. Examiner would like to point out that with the broad reasonable interpretation, these elements amount to mere data gathering for a mental process, which do not impose any meaningful limits on practicing the mental process (insignificant additional element and an extra-solution activity). Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to insignificant additional elements under Step 2B.
This judicial exception is not integrated into a practical application. In particular, the claim 1 recites additional elements such as using a large language model (LLM). The additional elements in the claim amounts to no more than generic software component to apply the exception, which cannot integrate a judicial exception into a practical application or provide an inventive concept. Thus, the claim is directed to an abstract idea under Prong II step 2A and 2B.
This judicial exception is not integrated into a practical application. In particular, the claim 1 recites additional elements such as updating the target application according to the recommended action, which is a post solution activity of applying a recommended action, that is a Well-Understood, Routine, Conventional (WURC) Activity, as evidenced in Cabrera (paragraph [0071]; implement a recommended action from a model, including updating and modifying the application to improve an application). Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea under Prong II step 2B.
Dependent claims 2-9 do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the dependent claims 2-9 recite more steps of a mental process (such as switching an application feature (modifying a code), expanding an application feature (modifying a code), disabling an application feature (modifying a code), detecting, generating, aggregating, selecting, relating) which can be performed mentally or using pen and paper. The additional element of dependent claims 2-9 recite more extra-solution activities (receiving, transmitting, deploying), which do not impose any meaningful limits on practicing the mental process (insignificant additional element that are well known in the field of the art). Therefore, these claims are not patent eligible.
Independent claim 10 (a system with memory and a processor to perform the method of claim 1) with dependent claims 11-18 are rejected under the similar rational as claims 1-9. The additional elements in the claim amounts to no more than generic hardware component with instructions to apply the exception, which cannot integrate a judicial exception into a practical application or provide an inventive concept.
Independent claim 19 (a storage medium storing instructions to perform the method similar to claim 1) with dependent claim 20 are rejected under the similar rational as claims 1-2. The additional elements in the claim amounts to no more than generic hardware component with instructions to apply the exception, which cannot integrate a judicial exception into a practical application or provide an inventive concept.
Relevant Prior Art
Per claim 1, Osuala et al. (US PGPUB 2024/0111794) suggest a method comprising: obtaining an assigned feature name for an application feature of a target application; selecting, using respective vector encodings of an application feature name, a set of application feature names comprising the assigned feature name (claim 1; receiving a query, generating a set of embeddings comprising an embedding of the received query; searching, in the data object embeddings, for a subset of the data object embeddings that match the set of embeddings, resulting in search result embeddings; i.e. searching for a set of object embeddings that are similar to the input query).
Cheng et al. (US PGPUB 2022/0237386) further suggest filtering a collection of feedback using a set of application feature names of an application feature to obtain a filtered set of feedback (claims 1-5; extracting aspect-sentiment pairs from an input text, extraction includes filtering the candidate aspect-sentiment pairs to exclude pairs that do not include an aspect from a predetermined set of sentiment terms); processing, by a large language model (LLM), the filtered set of feedback to generate the sentiment score for the application feature; selecting a recommended action based on the sentiment score (claims 1-5; paragraph [0029]; using a trained model, and the filtered aspect-sentiment pairs to estimate an attention-property-aware rating for the input text; performing a response (selecting a recommended action) to the input text based on the estimated rating).
Cabrera et al. (US PGPUB 2015/0347759) further suggests updating the target application according to the recommended action (paragraph [0071]; implement a recommended action from a model, including updating and modifying the application to improve an application).
Olivier et al. (US PGPUB 2024/0378654) further suggests generating a sentiment request prompt requesting a sentiment score and comprising the set of feedback (claim 1; obtaining user feedback data (comment), generating a prompt for input to a machine-learned language model, the prompt specifying a request to infer a sentiment on the comment; generate a sentiment score for the comment).
However, discovered prior art do not teach the rest of the limitations of claim 1 with the context from the other limitations in claim 1.
Independent claims 10 and 19 recite similar limitations as claim 1.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. See PTO-892 form.
Hosseini et al. (US PGPUB 2022/0366145) disclose a method for generating a sentiment analysis, comprising: receiving, at an aspect-based sentiment analysis (ABSA) generative language model stored in a memory, a sentence expressing a sentiment of a user; and generating, using the ABSA generative language model, a plurality of sentiment pairs, wherein at least one pair includes an aspect term from the sentence and a polarity of a sentiment associated with the aspect term, and at least one pair includes an aspect category associated with the sentence and a polarity of a sentiment associated with the aspect category.
Prendki (US PGPUB 2021/0224817) discloses a method for recommendations based on user intent and sentiment data. The method can include receiving from the user the respective intent weights for the plurality of features, and selecting one or more first items from among a plurality of items in the category of items based at least in part on: (a) the respective intent weights for the plurality of features for the user, and (b) sentiment data comprising a respective sentiment score for each respective feature for each of the plurality of items.
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/HANG PAN/Primary Examiner, Art Unit 2193
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