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 .
Applicant’s Reply
Applicant's response of 10/09/25 has been entered. The examiner will address applicant's remarks at the end of this office action. Currently claims 1-6, 8-21 are pending.
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, 8-21 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
The claims recite a method (1-6, 8-11), a system (12-17), and a non-transitory computer readable medium (18-21); therefore, the claims pass step 1 of the eligibility analysis.
For step 2A, the claim(s) recite(s) an abstract idea of providing housing related information in response to a search query that relates to fair housing rule violations. This represents a certain method of organizing human activities and/or a certain method of organizing human activities.
Using claim 1 as a representative example that is applicable to claims 4, 12, 18, the abstract idea is defined by the elements of:
analyzing contents of multiple documents with information related to multiple housing topics, including generating an encoded vector representation for each of the multiple documents of information related to housing in the contents of that document;
obtaining information about multiple defined tools each configured to provide information about at least one housing topic; and
classifies received queries as having violations of fair housing rules or not having violations of the fair housing rules, including:
retrieving a first group of positive query examples that include a plurality of first queries lacking fair housing rule violations generating a second group of negative query examples that include a plurality of second queries each having one or more fair housing rule violations, including supplying input to use a structure of one or more of the first queries and to generate semantic augmentations that create the second queries with fair housing violations based on legally protected classes and on a defined list of non-compliant phrases associated with violations of the fair housing rules: and
using, as training data, fair housing rules, and the defined list of non-compliant phrases as examples of having identified violations of fair housing rules, and the generated negative query examples as further examples of having identified violations of fair housing rules, and the positive query examples as examples of not having identified violations of fair housing rules:
providing, by the one or more computing devices, housing-related responses to the received queries that are based on the generated encoded vector representation for each of the multiple documents and on the multiple tools and on the trained large language model and on the trained machine learning transformer language model
The above limitations are reciting a process by which housing related information is being provided to a user. Providing housing related information such as information related to the Fair Housing Act of 1968 is considered to be a legal obligation type of abstract idea where information about the laws and rules regarding housing is provided to a user where the response can indicate a violation of a fair housing rule. This represents a certain method of organizing human activities type of abstract idea that is simply the act of providing information to a user upon request. Before the invention of modern day computers, humans would ask other humans questions to elicit a response about a particular topic. The concept of receiving a search query and providing a response, when it regards fair housing laws and housing rule violation related information, is something people can do and is a certain method of organizing human activities.
For claims 1, 4, the additional elements are the recitation to the use of one or more computing devices to perform the steps that define the abstract idea, the machine learning transformer model, the training of the machine learning transformer model, and the input into a trained large language model.
For claim 12, the additional elements (in addition to the above that are referred to in the claims) are the one or more hardware processors of one or more computing devices and the claimed memory for storing instructions to perform the recited steps that are considered to define the abstract idea.
Claim 18 recites the additional element of a non-transitory computer readable medium that has stored instructions to perform the claimed steps upon execution by a computing device.
This judicial exception is not integrated into a practical application (2nd prong of eligibility test for step 2A) because the additional elements of the claim when considered individually and in combination with the claim as a whole, amount to the use of a computing device with a processor and memory (non-transitory CRM) that is being used as a tool to execute the abstract idea (MPEP 2106.05(f)), in combination with training of a machine learning model using training data (the training data itself is part of the abstract idea and is information per se) and the use of a large language model that has been trained (which is also machine learning). The claim is simply instructing one to practice the abstract idea by using a generically recited computing device with a processor and memory to perform steps that define the abstract idea and by using a trained machine learning model and by using a trained LLM, both of which are machine learning models that are trained using training data. This does not amount to more than a mere instruction to implement the abstract idea on a computer (MPEP 2106.05(f)) and can be taken as a link to a particular technological environment which is the field of machine learning, see MPEP 2106.05(h). The training of the machine learning transformer model is recited broadly and is not claiming any specific manner of training that would render the claims eligible. While the data that is used to train the model is claimed, the training of the machine learning model is itself recited at a high level of generality and does not amount to more than a general instruction for one to use machine learning that is trained on training data. Claiming the type of training data that is used to train the model does not amount to claiming a specific way to actually perform the training of the model such that the claim would contain more than a general link to training a machine learning model. This is indicative of the fact that the claim has not integrated the abstract idea into a practical application and therefore the claim is found to be directed to the abstract idea identified by the examiner.
For step 2B, the claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception when considered individually and in combination with the claim as a whole because they do not amount to more than simply instructing one to practice the abstract idea by use of a computing device with a processor and memory (non-transitory CRM) that is being used as a tool to execute the abstract idea (MPEP 2106.05(f)), in combination with training of a machine learning model using training data (the training data itself is part of the abstract idea and is information per se) and the use of a large language model that has been trained (which is also machine learning). The claim is simply instructing one to practice the abstract idea by using a generically recited computing device with a processor and memory to perform steps that define the abstract idea and by using a trained machine learning model and by using a trained LLM. This does not amount to more than a mere instruction to implement the abstract idea on a computer (MPEP 2106.05(f)) and can be taken as a link to a particular technological environment which is the field of machine learning, see MPEP 2106.05(h). The training of the machine learning transformer model is recited broadly and is not claiming any specific manner of training that would render the claims eligible. While the data that is used to train the model is claimed, the training of the machine learning model is itself recited at a high level of generality and does not amount to more than a general instruction for one to use machine learning that is trained on training data. Claiming the type of training data that is used to train the model does not amount to claiming a specific way to actually perform the training of the model such that the claim would contain more than a general link to training a machine learning model. What is claimed by way of the additional elements that are not part of the abstract idea does not amount to reciting significantly more.
For the above reasons claims 1, 4, 12, and 18 are not found to be eligible.
For claims 2, the abstract idea is being further defined by the elements of:
receiving a first query about at least one first housing topic from a first user;
determining based at least in part on output that indicates the first query is associated with a violation of fair housing rules, to use information in a first response to the first query indicating an inability to provide further information due to the fair housing rules;
presenting the first response;
receiving a second query about at least one second housing topic from the first user;
determining a second response to the second query, including:
receiving and in response to supplying the second query, an indication that the second query is not associated with a violation of the fair housing rules;
identifying at least one of the multiple documents whose encoded vector representation differs from an encoded version of the second query by at most a defined threshold distance using a defined similarity metric;
generating using one of the multiple defined tools that is selected as being associated with the second query, additional information from the selected one defined tool;
generating a prompt to supply the second query and the identified at least one document and the additional information and one or more predetermined query-response examples;
and generating the second response based at least in part on output of the trained large language model to the generated second prompt; and
presenting the determined second response to the second query.
The above elements are claiming more about the receipt of a query and the providing of a response that indicates if any fair housing rules are violated. This is what defines the abstract idea of the claims. The additional elements are the one or more computing devices and the use of a machine learning transformer model as was addressed for claim 1. These elements are treated in the same manner as set forth for claim 1 to which the applicant is referred.
For claim 3 the receiving of a third query, determining that the query is associated with a violation and the claimed presenting step are all elements that serve to further define the abstract idea of the claims. The additional elements of the one or more computing devices has been treated in the same manner as set forth for claim 1 to which the applicant is referred.
For claims 5, 6, 20, the following elements are further defining the same abstract idea of claim 4:
Claim 5:
receiving the query about a housing-related topic;
determining based on a comparison of the query to the defined list of non-compliant phrases, that the query does not include any of the non- compliant phrases of the defined list;
determining based on output that the query is associated with a violation of the fair housing rules;
providing a response for the query that indicates the violation of the fair housing rules;
receiving a second query about a housing-related topic;
determining based on a comparison of the second query to the defined list of non-compliant phrases, that the second query does include at least one of the non-compliant phrases of the defined list; and
providing based on the second query including the at least one non-compliant phrase, a second response for the second query that indicates a violation of the fair housing rules
Claims 6 (cl. 20): receiving a second query about a second housing-related topic;
determining based on a comparison of the second query to the defined list of non-compliant phrases, that the second query does not include any of the non-compliant phrases of the defined list;
determining based on output from supplying the second query to the trained transformer language model, that the second query is not associated with a violation of fair housing rules;
generating a second response to the second query with information about the second housing-related topic; and
providing the second response for the second query
The above steps are part of the abstract idea and are fully capable of being performed by people that are making comparisons (including mentally) and providing a response. The claimed additional element of the one or more computing devices have been treated in the same manner that was set forth for claim 4. The claims do not recite any additional elements that provide for integration at the 2nd prong or that provide significantly more at step 2B. Therefore the claims are not considered to be eligible.
For claim 8, the claimed using few-shot examples in the one or more prompts for the generation of the negative query example is an element that is part of the abstract idea. The manner in which the negative query examples are generated is part of the abstract idea and is fully capable of being performed by a human, including mentally.
For claims 9, 10, the recited step/function(s) are considered to be part of the abstract idea of the claims. Generating a one or more structures for use in the negative query examples can be done by a person who is mentally coming up with and manually writing down the one or more structures that is claimed. This includes the claimed types of structures recited in claim 10. Also, for claim 10, as has already been addressed for the independent claims and for dependent claims 2, 3, 7, 8, training of a model is part of the abstract idea. The claimed training of claim 10 is part of the abstract idea for the same reasons already set forth by the examiner for claims 1-3, 7, 8. The claimed additional element of the one or more computing devices have been treated in the same manner that was set forth for claim 4. The claims do not recite any additional elements that provide for integration at the 2nd prong or that provide significantly more at step 2B. Therefore the claims are not considered to be eligible.
For claims 11, 15, 21, using the language from claim 11 below as a representative example, the abstract idea is being further defined by the following elements:
receiving an additional housing-related query;
determining based at least in part on use of the transformer language model with the additional query, that the additional query is not associated with a violation of fair housing rules;
comparing the additional query to multiple prior queries from a plurality of users to determine whether any of the multiple prior queries match the additional query based on one or more defined matching criteria, each of the prior queries being associated with at least one document used in providing a prior response to that prior query, the at least one document being selected for use in responding to that prior query from a group of multiple documents having contents including housing-related information;
selecting one or more documents of the multiple documents to use in responding to the additional query, including, if one of the prior queries is determined to match the additional query, using the at least one document associated with that one prior query as the selected one or more documents, and otherwise determining the one or more documents by matching an encoded representation of the additional query to encoded representations of the contents of the multiple documents;
generating a prompt to supply to a trained large language model that includes the additional query, and one or more predetermined query-response examples, and information about the selected one or more documents, and query-response generation instructions to refuse to provide responses to inputs with references to defined legally protected classes;
generating an additional response to the additional query based at least in part on output of the trained large language model to the generated prompt; and
providing the generated additional response to the additional query with an indication of at least one of the selected one or more documents as a source for the generated additional response
The above elements are simply further defining the abstract idea of claim 4 and are reciting steps that are fully capable of being performed by people. The claimed steps that are taken to provide the response to the user are elements that are part of the abstract idea. The claimed additional element of the one or more computing devices have been treated in the same manner that was set forth for claim 4. The claims do not recite any additional elements that provide for integration at the 2nd prong or that provide significantly more at step 2B. Therefore the claims are not considered to be eligible.
For claims 13, 14, the claims are reciting a further embellishment of the same abstract idea that was found for claim 12. Receiving the housing related query, comparing the query as claimed, the claimed training data of fair housing rules and negative query examples that include fair housing violations and positive query examples, and determining the query is associated with a violation of fair housing rules are elements that are part of the abstract idea. The same is noted for the receiving of the second query, determining that the query is associated with a violation of fair housing rules, and providing the second response of claim 14. These steps are fully capable of being performed by people and are part of the abstract idea. The additional element of the transformer language model that has been trained is an instruction for one to use machine learning as a tool to execute the abstract idea and is also an instruction for one to use a computer to perform the abstract idea. The transformer language model has been treated in the same manner that was set forth for claim 12 and does not provide for integration or significantly more. The claims do not recite any additional elements that provide for integration at the 2nd prong or that provide significantly more at step 2B. Therefore the claims are not considered to be eligible.
For claim 16, as was set forth for claim 12, determining and providing a response that includes whether or not the query violates fair housing rules is an element that is part of the abstract idea. The content of the response is part of the abstract idea and is reciting information per se. The claims do not recite any additional elements that provide for integration at the 2nd prong or that provide significantly more at step 2B. Therefore the claims are not considered to be eligible.
For claim 17, the claimed modifying, selecting, generating of a prompt, generating of a response, and providing the response steps/functions are all elements that are part of the abstract idea. These elements are simply further defining the abstract idea that was recited in claim 12. The transformer language model and the trained large language model have been treated in the same manner as set forth for claim 18 and do not amount to more than an instruction for one to use a computer and machine learning to execute the abstract idea. The claims do not recite any additional elements that provide for integration at the 2nd prong or that provide significantly more at step 2B. Therefore the claims are not considered to be eligible.
For claim 19, the claimed receiving of the housing related query, the training data that is claimed, comparing step, and determining based on an output of the transformer language model that the query is associated with a fair housing violation, and providing the indication of the violation, are elements that are part of the abstract idea. These elements are claiming more about the same abstract idea that was recited in claim 18. The transformer language model and the trained large language model have been treated in the same manner as set forth for claim 18 and do not amount to more than an instruction for one to use a computer and machine learning to execute the abstract idea. See MPEP 2106.05(f). The claims do not recite any additional elements that provide for integration at the 2nd prong or that provide significantly more at step 2B. Therefore the claims are not considered to be eligible.
Therefore, for the above reasons, claims 1-21 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Response to arguments
With respect to the comments about the interview that occurred on 09/24/25, the examiner disagrees with the applicant’s allegation that an agreement was made that the claims were reciting eligible subject matter. The interview summary from the examiner that is dated 09/29/25 makes it clear that no agreement was reached and did not state that the claims were reciting eligible subject matter.
The traversal of the 35 USC 101 rejection is not persuasive. The applicant argues that the claims are eligible by virtue of the amendments made to the claims. The applicant argues that the claims are reciting a particular way to train a particular type of machine learning model that includes generating training data that is used to train the model. This is not persuasive. The examiner notes that the claims do not recite a particular way that the model is being trained. The extent of the training in the claims is recited by the language “performing by the one or more computing devices, the training of the machine learning transformer language model including using as training data…”, where the applicant simply claims the training data that is used to train the model. This is not claiming a particular way to train the model but is claiming the data that the model is trained with. The claims recite that the training data includes positive query examples that do not have fair housing rule violations and negative query examples that do contain fair housing rule violations; however, specifying the training data as being positive query responses and negative query responses is not claiming a particular way in which the transformer language model is being trained. Machine learning models are trained, that is how they work. Machine learning models can be trained using different training data sets that depends on the field of use that the model is being used where the actual training of the machine learning model is done the same way regardless of the what the training data represents. The argument that the claims recite a particular way to train the transformer language model is not persuasive. This argument is not commensurate with the scope of the claims that only recite the performing of training using the claimed training data.
With respect to the generation of the training data, a person can take the positive query examples and use their format to generate negative query examples that do contain fair housing rule violations. The fact that the claim recites this as being done by a large language model is claiming the use of machine learning for the generation of the training data that can otherwise be generated by a person manually. This does not amount to more than an instruction for one to use machine learning models as a tool to execute the abstract idea. The argument is not persuasive.
With respect to the comments about training of a model not being part of an abstract idea, the examiner notes that this all depends on the claims being examined, and that this can be viewed in two ways. The USPTO has treated a general link to training a machine learning model as being an additional element, whereas if one claims the training as being the use of a specific algorithm, then that algorithm and the training of the model is then considered to be math. This was set forth in the eligibility guidance issued in July 2024, see example 47, claim 2, where the use of machine learning was considered to be an instruction for one to use a computer or is a link to a particular technological environment, see MPEP 2106.05(f) and (h) in this regard. The USPTO does treat the training of machine learning models as being the performance of math in some instances and has treated training of models as being an abstract idea that is directed to math. However, this argument is moot based on the substantial amendment to the claims that has deleted the majority of the claimed invention and rewritten the independent claims, and that necessitated a new grounds of rejection.
The applicant argues that example 39 of the USPTO examples shows that training of a model is eligible subject matter. This is not persuasive because the pending claims do not recite a specific way to train a model and because example 39 did not contain a judicial exception whereas the pending claims do. The pending claims are only claiming the training in a general sense and do not recite an improved manner in which a machine learning model is being trained. Also, the claimed invention recites to a judicial exception so the analysis moves to the 2nd prong and step 2B if necessary, and is more analogous to example 47 from the eligibility guidance than example 39. The pending claims are not similar to example 39 from the USPTO guidance. The applicant is referred to example 47 of the USPTO guidance and specifically to claims 2 and 3 for guidance on how the USPTO treats machine learning claims. The comment that during the interview the examiner agreed that the claims are directly analogous to example 39 is disagreed with because as stated in the interview summary, example 39 did not contain a judicial exception and that was the reason it was considered eligible. Example claim 39 was not found to be eligible due to claiming an improved manner by which a model is trained. To argue such is not supported by the example itself that clearly states that because no judicial exception is recited at step 2A, the claim is eligible. The same cannot be said for the pending claims that do recite a judicial exception and that requires a 2nd prong and step 2B analysis, unlike example 39. The reliance upon example 39 is not persuasive to show that the claims are reciting eligible subject matter.
The arguments are not persuasive; therefore, the 35 USC 101 rejection is being maintained.
Conclusion
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 DENNIS WILLIAM RUHL whose telephone number is (571)272-6808. The examiner can normally be reached M-F 7am-3:30pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jessica Lemieux can be reached at 5712703445. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/DENNIS W RUHL/ Primary Examiner, Art Unit 3626