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 .
DETAILED ACTION
Status of Claims
This action is a Final action on the merits in response to communications filed on 08/14/2025.
Claims 1, 9, 10, 16 and 23 have been amended. Claims 7, 8 and 18-22 have been cancelled. Claims 1-6, 9-17 and 23 are currently pending and have been examined in this application.
Response to Amendment
Applicant’s amendment has been considered.
Response to Arguments
Applicant’s remarks have been considered.
Applicant argues, “Like in Ex Parte Hannun, the Applicant's claims do not recite mental processes because the claimed subject matter cannot be practically performed in the human mind.” (pg. 11)
In Hannun the court found that while transcription generally could be performed by a human, the claims recite a specific implementation including steps (normalizing input file, generating a jitter set of audio files, generating spectrogram files) that cannon practically be performed in the human mind (Ex parte Hannun 2018-003323).
Unlike Hannun the generic computer components (e.g. a processor and memory) are performing training AI models to determine sentiment, fine-tuning AI models and retraining AI models which are considered analyzing data using complex mathematics (evaluation of data). The steps of generating patterns, configuring AI models to identify target entities using the patterns, determining a context of the training texts, generating a tonality score, obtaining a plurality of texts and a lexicon (retrieving data from a data source), generating a table including the texts, discarding a set of texts, determining a pattern, identifying a placement of a competitor, etc., involves collecting and analyzing data (e.g. Mental Processes). These steps may reasonably be done as a mental process, performed in the human mind by observation, evaluation, judgement or opinion using a generic computer to perform generic computer functions. Further, the steps of using AI models to make a determination or training using AI models is reflective of mathematical concepts.
Applicant argues, “…the claims in the present case are not directed to an abstract idea because the claims integrate the allegedly recited judicial exception into a practical application by achieving a technological solution to a technological problem specific to the operation of a system for sentiment analysis of a corpus of texts using AI models.” (pgs. 13-14)
Examiner respectfully disagrees. The judicial exception is not integrated into a practical application. The claims recite the additional elements of a system using an API or database, a processor, a memory, a non-transitory computer readable medium, and a device. These are generic computer components recited at a high level of generality as performing generic computer functions (Spec ¶0089, general purpose computer).
For instance, the steps of training AI models to determine sentiments, fine-tuning the AI models and retraining AI models involves analyzing data using complex mathematics. The steps of generating a plurality of patterns of training texts, configuring an AI model (complex mathematics) to identify at least one target entity, identifying a placement of competitor entity and a target entity, determine a context of training texts with reference to target entity, generating a tonality score for training texts and determining sentiment for each training text involves collecting and analyzing data (data gathering activity). The step of the step of obtaining a plurality of texts using an API or a database is data gathering activity. The step of generating a table based on one or more keywords is data manipulation (e.g. data analysis). The step of discarding a set of texts from the plurality of texts in the table involves data analysis. The steps related to determining patterns of texts in the table using AI models (mathematical operations), identifying a placement, determining a tonality score and determining a sentiment involve collecting and analyzing data, which is considered data gathering activity. The step of notifying the sentiment is general sending/receiving data.
Each of the additional limitations is no more than mere instructions to apply the exception using a generic computer components (e.g. a processor). The combination of these additional elements is no more than mere instructions to apply the exception using a generic computer component (e.g. a processor). Therefore, the additional elements do not integrate the abstract ideas into a practical application because it does not impose meaningful limits on practicing the abstract idea. Therefore, the claims are directed to an abstract idea.
Examiner further notes the problem described in the Specification, “ Existing systems are not accurate in determining the sentiment of the multi-competitor referenced texts which consists of multiple sentiments in each text, “ is business problem that is not technically driven. For instance in DDR Holdings the court found that the claims recite a specific way to automate the creation of composite Web page by an outsource provider that incorporates elements from multiple sources in order to solve a problem faced by web sites on the internet, where the claimed solution is necessarily rooted in computer technology.
Unlike DDR Holdings, the instant claim is directed to training AI models to determine sentiments, fine-tuning the AI models, retraining the models (analyzing data using complex mathematics), obtaining a plurality of texts and a lexicon (retrieving data from a data source), generating a table including the texts, discarding a set of texts, determining a patterns using AI models, identifying a placement of a competitor, etc. representative of Mental Processes related to collecting and analyzing data using generic computer components. The claims are directed to an improved business process related to providing more accurate sentiment which does not provide for an improvement in technology or a technical field.
Applicant argues, “…the claimed method of contextual sentiment analysis improves the efficiency of computing systems processing data.” (pg. 17)
The Federal Circuit has found that "merely adding computer
functionality to increase the speed or efficiency of the process does not
confer patent eligibility on an otherwise abstract idea." Intellectual Ventures
I LLC, 792 F.3d at 1369-70; see also Intellectual Ventures I LLC v. Erie
Indemnity Co., 711 F. App'x 1012, 1017 (Fed. Cir. 2017) (unpublished)
("Though the claims purport to accelerate the process of finding errant files
and to reduce error, we have held that speed and accuracy increases
stemming from the ordinary capabilities of a general-purpose computer
'do[] not materially alter the patent eligibility of the claimed subject
matter."').
Applicant argues, “Because the claims recite technological solutions to technological problems, the claims are not directed to an abstract idea under Step 2A of the two-part test for subject matter eligibility, and in any case amounts to significantly more than an abstract idea under Step 2B of the two-part test.” (pg.17)
Applicant argues, “The disclosure identifies a technical problem encountered in the field of sentiment analysis and provides this invention as a solution to the existing sentiment identification techniques.” (pg. 13)
The problem described in the Specification, “ Existing systems are not accurate in determining the sentiment of the multi-competitor referenced texts which
consists of multiple sentiments in each text, “ is business problem that is not technically driven. For instance in DDR Holdings the court found that the claims recite a specific way to automate the creation of composite Web page by an outsource provider that incorporates elements from multiple sources in order to solve a problem faced by web sites on the internet, where the claimed solution is necessarily rooted in computer technology.
Unlike DDR Holdings, the instant claim is directed to training AI models to determine sentiments, fine-tuning the AI models, retraining the models (analyzing data using complex mathematics), obtaining a plurality of texts and a lexicon (retrieving data from a data source), generating a table including the texts, discarding a set of texts, determining a patterns using AI models, identifying a placement of a competitor, etc. representative of Mental Processes related to collecting and analyzing data using generic computer components. The claims are directed to an improved business process related to providing more accurate sentiment which does not provide for an improvement in technology or a technical field.
Applicant argues, “ Because the claims recite technological solutions to technological problems, the claims are not directed to an abstract idea under Step 2A of the two-part test for subject matter eligibility, and in any case amounts to significantly more than an abstract idea under Step 2B of the two-part test.” (pg. 17)
Examiner respectfully disagrees. As stated above the claims are not reflective of an improvement in a technology or technical field. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As stated above, the additional elements of a processor, a memory, a crm, etc. are considered generic computer components performing generic computer functions ( training, fine-tuning, obtaining, generating) that amount to no more than instructions to implement the judicial exception. Mere, instructions to apply an exception using generic computer components cannot provide an inventive concept.
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 1-6, 9-17 and 23 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Claim 1 recites:
training, …, one or more Artificial Intelligence (Al) models to determine sentiments, using a plurality of training texts, wherein each of the plurality of training texts is tagged with one of a plurality of sentiments, and wherein the training of the one or more AI models comprises:
generating a plurality of patterns for the plurality of training texts, wherein each pattern of the plurality of training texts includes one or more words in the plurality of training texts;
configuring the one or more Al models to: identify at least one target entity using the plurality of patterns for each pattern of the plurality of training texts, identify a placement of at least one competitor entity and the at least one target entity;
determine a context of the plurality of training texts with reference to the at least one target entity;
generate a tonality score for each of the plurality of training texts based on the context; and
determine a sentiment for each of the plurality of training texts based on the tonality score generated and one or more threshold ranges;
fine-tuning the one or more Al models ..., the fine-tuning comprising: comparing, …, a sentiment determined by the one or more Al models for at least one testing text with a sentiment tagged with the at least one testing text, providing, …, feedback to the one or more Al models based on the comparison, and retraining, …, the one or more Al models based on the feedback;
obtaining, …, a plurality of texts and a lexicon comprising one or more keywords, wherein each of the one or more keywords is indicative of at least one competitor entity and at least one target entity;
generating, …, based on the one or more keywords, a table that includes the plurality of texts and a corresponding plurality of competitor reference indicators, wherein the competitor reference indicator for each text is indicative of a presence or an absence of the at least one competitor entity in the text;
discarding, …, a set of texts from the plurality of texts included in the table based on the plurality of competitor reference indicators, wherein the competitor reference indicator for each of the set of texts is indicative of the absence of the at least one competitor entity in the text, and wherein one or more texts of the plurality of texts remain in the generated table after the discarding of the set of texts;
determining, …, a pattern from a plurality of patterns in the one or more texts using one or more Artificial Intelligence (AI) models, wherein the pattern includes one or more words in the one or more texts;
identifying, …, a placement of the at least one competitor entity and the at least one target entity in the one or more texts using the one or more Al models; determining, …, for each of the one or more texts, a tonality score indicating a tone towards the at least one target entity based on the placement of the at least one target entity and the pattern;
determining, …, a sentiment for each of the one or more texts based on the tonality score; …
The limitation under its broadest reasonable interpretation covers Mental Processes related to observation and evaluation of information, but for the recitation of generic computer components (e.g. a processor). For example, obtaining a plurality of texts and a lexicon, generating a table including a plurality of texts and reference indicators, identifying keywords and determining patterns involve collecting and analyzing data. Accordingly, the claim recites an abstract idea of Mental Processes.
Independent Claims 12 and 23 substantially recite the subject matter of Claim 1 and also include the abstract ideas identified above. The dependent claims encompass the same abstract ideas. For instance, Claim 2 is directed to keyword types, Claim 3 is directed to alphanumeric content or emotional icon, Claims 4-6 are directed to sentiment, Claim 9 is directed to threshold ranges, Claim 10 is directed to sentiment for emotional icons and Claim 11 is directed to training test. Claims 13 -17 substantially recite the subject matter of Claims 2-6 and encompass the same abstract idea. Thus, the dependent claims further limit the abstract concepts found in the independent claims.
The judicial exception is not integrated into a practical application. Claim 1 recites the additional elements of a system using an API or database. Claim 12 recites the additional elements of a processor, a memory and an API or database . Claim 23 recites a non-transitory computer readable medium, a processor, an API or database and a device. These are generic computer components recited at a high level of generality as performing generic computer functions (Spec ¶0089, general purpose computer).
For instance, the steps of training AI models to determine sentiments, fine-tuning the AI models and retraining AI models involves analyzing data using complex mathematics. The steps of generating a plurality of patterns of training texts, configuring an AI model (complex mathematics) to identify at least one target entity, identifying a placement of competitor entity and a target entity, determine a context of training texts with reference to target entity, generating a tonality score for training texts and determining sentiment for each training text involves collecting and analyzing data (data gathering activity). The step of the step of obtaining a plurality of texts using an API or a database is data gathering activity. Examiner notes that obtaining information from an API or a database is commonly used functionality. The step of generating a table based on one or more keywords is data manipulation (e.g. data analysis). The step of discarding a set of texts from the plurality of texts in the table involves data analysis. The steps related to determining patterns of texts in the table using AI models (mathematical operations), identifying a placement, determining a tonality score and determining a sentiment involve collecting and analyzing data, which is considered data gathering activity. The step of notifying the sentiment is general sending/receiving data.
Each of the additional limitations is no more than mere instructions to apply the exception using a generic computer components (e.g. a processor). The combination of these additional elements is no more than mere instructions to apply the exception using a generic computer component (e.g. a processor). Therefore, the additional elements do not integrate the abstract ideas into a practical application because it does not impose meaningful limits on practicing the abstract idea. Therefore, the claims are directed to an abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As stated above, the additional elements of a processor, a memory, a crm, etc. are considered generic computer components performing generic computer functions that amount to no more than instructions to implement the judicial exception. Mere, instructions to apply an exception using generic computer components cannot provide an inventive concept.
The dependent claims when analyzed both individually and in combination are also held to be ineligible for the same reason above and the additional recited limitations fail to establish that the claims are not directed to an abstract. The additional limitations of the dependent claims when considered individually and as an ordered combination do not amount to significantly more than the abstract idea.
Looking at these limitations as an ordered combination and individually adds nothing additional that is sufficient to amount to significantly more than the recited abstract idea because they simply provide instructions to use generic computer components, to "apply" the recited abstract idea. Thus, the elements of the claims, considered both individually and as an ordered combination, are not sufficient to ensure that the claim as a whole amounts to significantly more than the abstract idea itself. Therefore, Claims 1-6, 9-17 and 23 claim are not patent eligible.
Conclusion
The prior art made of record and not relied upon is considered relevant but not applied:
Curin (US 9959341) discloses processing an input text to identify a plurality of semantic patterns that match the input text for at least one semantic pattern of the plurality of semantic patterns comprising a plurality of semantic entities identified from the at least one input text, and the plurality of semantic entities occur in a common context within the at least one input text.
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 of a general nature or relating to the status of this application or concerning this communication or earlier communications from the Examiner should be directed to Renae Feacher whose telephone number is 571-270-5485. The Examiner can normally be reached Monday-Friday, 9:00 am - 5:00 pm. If attempts to reach the examiner by telephone are unsuccessful, the Examiner's supervisor, Beth Boswell can be reached at 571-272-6737.
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/Renae Feacher/
Primary Examiner, Art Unit 3625