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 .
Status of the Claims
Claims 1-8, 16-20, and 29-31 are canceled. Claims 13 and 25 are amended. Claims 34-36 are added as new claims. Claims 9-15, 21-28, and 32-36 are pending.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 04/28/2026 was filed before the mailing of this action. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Response to Arguments
Applicant’s arguments, see pg. 1, filed 04/28/2026, with respect to 35 U.S.C. 112(a) and 35 U.S.C. 112(b) have been fully considered and are persuasive. The 35 U.S.C. 112(a) and 35 U.S.C. 112(b) rejections have been withdrawn.
Applicant's arguments filed 04/28/2026 regarding 35 U.S.C. 101 have been fully considered but they are not persuasive.
The Claims Do NOT Recite A Technical Improvement, And Are Directed To A Judicial Exception.
Applicant argues that claim 9 contains no reference to any human activity, but instead generic computational steps performed on a plurality of objects. Applicant further provides that the object could be hotels, but also could be molecules, network configurations, etc. Examiner disagrees. The computational steps and plurality of objects are for lodging accommodations; not molecules, network configuration, or anything else. The Federal Circuit has explained that "the 'directed to' inquiry applies a stage-one filter to claims, considered in light of the specification, based on whether 'their character as a whole is directed to excluded subject matter."' Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1335 (Fed. Cir. 2016) (quoting Internet Patents Corp. v. Active Network, Inc., 790 F.3d 1343, 1346 (Fed. Cir. 2015)). It asks whether the focus of the claims is on a specific improvement in relevant technology or on a process that itself qualifies as an "abstract idea" for which computers are invoked merely as a tool. Here, it is clear from the Specification (including the claim language) that claim 9 focuses on an abstract idea, and not on an improvement to technology and/or a technical field. The Specification is titled “SYSTEM AND METHOD FOR DETERMINING OPTIMAL PLACEMENT STRATEGY AND BOOKING ACCOMMODATIONS” and observes, in the Background section, that: [0002] The present disclosure generally relates to booking accommodations for users, for example, visitors. More specifically, the present disclosure relates to a computer-implemented system and method for enabling the visitor to find optimal accommodation in terms of customer satisfaction, and for the host to maximize profit. The Specification further describes: [0003] Determining a placement strategy and booking accommodation is a typical step for visitors. Generally, the visitors research for accommodations with respect to the point of interest, or location of interest planned for visit. In addition, criteria such as availability, affordability, dining options, pet care and other criteria need to align with the determined accommodation. [0004] Websites and applications such as Airbnb, VRBO, or Booking.com are some online services that facilitates the visitor to book accommodations. The visitor could browse through a number of accommodations near their point of interest and book accommodations. Further, the service facilitates the visitor to filter accommodation based on criteria such as accommodation date and location. Furthermore, the service facilitates the visitor to filter accommodation based on criteria such as amenities. Thereby, these services enable the visitor to search at least one location proximal to the point of interest, remotely and book accommodations. [0005] As the consumer base continues to expand, their demand and preference also expand. In attempting to address the needs of visitors, it is important to maintain a minimal level of complexity for a given accommodation system. At the same time, effective management of the system by the owners is equally taken into consideration. One of the common accommodation problems is that the owners may have an extremely fragmented schedule for the utilization of premises. This fragmentation leads to the fact that at some time, very often some premises are not occupied. If the premises are empty, this leads to loss for the owners. Since the existent systems can only place the dates of residence in individual units and cannot schedule the transfer between units, they cannot fill in the empty premises and this results in the loss for owners and limiting options for visitors. Since the existing services do not calculate a monetary estimation for different options, visitors could miss optimally suited accommodations. [0006] In addition, customers are only able to indicate their preference for amenities in a simple Yes (mandatory)/No form. For example, having a washing machine is certainly a plus, but it may not be absolutely necessary, as a customer can go to a laundry for washing, spending extra time and money on it. Existing systems do not allow specifying requirements to filters that are useful but not absolutely necessary. [0007] In light of the above-mentioned problems, there is a need for a computer-implemented system and method for determining optimal placement strategy and booking accommodations taking into account. Claim 9 thus focuses on generating a training corpus for fine-tuning a LLM that includes a plurality of textual parameters and numerical parameters, receiving a query from a user comprising required constraints, transforming the query from the user into a formal constraint satisfaction problem (CSP), solving the CSP to determine objects that satisfy the CSP, and providing the one or more objects to the user. The text an numerical parameters of objects are given as an example of accommodations ([0114] of Spec.). The objects are also given as accommodation related objects ([0097] and [0114] of Spec.). The invention and claims are drawn towards enabling a visitor to find optimal accommodation for customer satisfaction while also allowing the host to maximize profit, and the claim limitations, when given their broadest reasonable interpretation, correspond to certain methods of organizing human activity (managing personal interactions; commercial interactions, business relations), such as receiving a query from a user, wherein the query comprises a one or more required constraints and one or more preferences regarding possible matches the plurality of objects, wherein the query is formed in natural language; transforming with use of [the LLM], the query from the user into a formal constraint satisfaction problem (CSP); solving the CSP to determine one or more objects of the plurality of objects that satisfy the CSP; and providing the one or more objects to the user. The claims also correspond to mental processes (observation, evaluation, judgment, opinion), as shown by the limitations generating a training corpus for fine-tuning an LLM, fine-tuning the LLM based on the training corpus, transforming the query from the user into a formal constraint satisfaction problem (CSP), and solving the CSP to determine one or more objects of the plurality of objects that satisfy the CSP. These limitations directly involved the observation and evaluation of data and making a decision (judgment/opinion) based on the observed and evaluated data. Additionally, the claims correspond to mathematical concepts (mathematical relationships) as evidenced by the limitations pertaining to the transformation of the query into a formal constraint satisfaction problem and solving the constraint satisfaction problem. At best, any alleged improvement, is an improvement to the judicial exception itself (optimal placement and booking strategy) which is a business process, and not an improvement to computers or technology. It is important to keep in mind that an improvement in the judicial exception itself (e.g., a recited fundamental economic concept) is not an improvement in technology (emphasis added). For example, in Trading Technologies Int’l v. IBG LLC, the court determined that the claim simply provided a trader with more information to facilitate market trades, which improved the business process of market trading but did not improve computers or technology. Similarly, the Applicant’s claim recitations are an improvement in the judicial exception, not an improvement in technology.
Applicant argues that the claims improve the operation of LLM-based search systems. Examiner disagrees. The claims merely apply an LLM to the search to obtain results. This amounts to apply it or merely using a computer (which has the LLM) as tool to implement the judicial exception, and generally linking the judicial exception to a particular field of use (finding optimal booking accommodations for users/customers). Applicant provides no improvement to LLM models, for example, nor machine learning technology. An LLM is a special type of machine learning model designed to understand, process, and generate human language. Applicant’s claims merely uses an LLM to implement the judicial exception. "[P]atents that do no more than claim the application of generic machine learning to new data environments, without disclosing improvements to the machine learning models to be applied, are patent ineligible under § 101." Recentive Analytics, Inc. v. Fox. Corp., Fed Cir. No. 2023-2437 (Apr. 18, 2025) (slip op. at 18). "Finally, the claimed methods are not rendered patent eligible by the fact that (using existing machine learning technology) they perform a task previously undertaken by humans with greater speed and efficiency than could previously be achieved." Recentive Analytics, Inc. v. Fox. Corp., Fed Cir. No. 2023-2437 (Apr. 18, 2025), slip op. at 15.
The response has already addressed that the claims are directed to certain methods of organizing human activity and mental processes in the first paragraph above. Additionally, examiner notes that claims can recite a mental process even if they are claimed as being performed on a computer. If the claimed invention is described as a concept that is performed in the human mind and applicant is merely claiming that concept performed 1) on a generic computer, or 2) in a computer environment, or 3) is merely using a computer as a tool to perform the concept, the claim is considered to recite a mental process (see MPEP §2106.04(a)(2)(III)). This is the case in the applicant’s claimed invention.
The Claims Do Not Provide An Inventive Concept
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional elements of: a processor (claim 9), a memory (claim 9), one or more non-transitory computer-readable storage media (claims 9 and 21), a compute device, and a large-language model (LLM) amount to no more than mere instructions to apply the exception using a generic computer, and generally linking the judicial exception to a particular field of use. Mere instructions to apply an exception using a generic computer cannot provide an inventive concept. Thus, when viewed as an ordered combination, nothing in the claims add significantly more (i.e. an inventive concept) to the abstract idea. The claims are not patent eligible.
Applicant request that the their claims are not well-understood, routine, or conventional (“WURC”). Examiner disagrees. Further, whether the additional elements are WURC is only one consideration under Step 2B. Limitations that the courts have found not to be enough to qualify as "significantly more" when recited in a claim with a judicial exception include: Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer; Generally linking the use of the judicial exception to a particular technological environment or field of use, both of which applies in the claimed invention. Applicant’s reference to the Berkheimer memo is irrelevant here. At Step 2B, there is no requirement for evidence to support a finding that the exception is not integrated into a practical application or that the additional elements do not amount to significantly more than the exception unless the Examiner asserts that additional limitations are well-understood, routine, conventional activities in Step 2B (MPEP §2106.07(a)). There was no assertion that the additional elements/limitations are well-understood, routine, conventional activities in Step 2B, thus no requirement of evidence of well-understood, routine, and conventional activity according to Berkheimer is necessary.
The 35 U.S.C. 101 rejection is maintained.
Applicant’s arguments, see pg. 5, filed 04/28/2026, with respect to 35 U.S.C. 102 have been fully considered and are persuasive. The 35 U.S.C. 102 and subsequent 35 U.S.C. 103 rejections has 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.
Claims 9-15, 21-28, and 32-36 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e. an abstract idea) without significantly more.
Claims 9-15 and 27, 28, 32-36 recite a device (i.e. machine), and claims 21-26 recite non-transitory computer-readable media (i.e. machine or article of manufacture). Therefore claims 9-15, 21-28, and 32-36 fall within one of the four statutory categories of invention.
Independent claims 9, 21, and 27 recite the limitations: generate a training corpus for fine-tuning [a large-language model (LLM)], wherein the training corpus comprises a plurality of textual parameters and a plurality of numerical parameters for each of a plurality of objects; fine-tune [the LLM based] on the training corpus; receive a query from a user, wherein the query comprises a one or more required constraints and one or more preferences regarding possible matches the plurality of objects, wherein the query is formed in natural language; transform, with use of [the LLM], the query from the user into a formal constraint satisfaction problem (CSP); solve the CSP to determine one or more objects of the plurality of objects that satisfy the CSP; and provide the one or more objects to the user. The invention and claims are drawn towards enabling a visitor to find optimal accommodation for customer satisfaction while also allowing the host to maximize profit, and the claim limitations correspond to certain methods of organizing human activity (managing personal interactions; commercial interactions, business relations), such as receiving a query from a user, wherein the query comprises a one or more required constraints and one or more preferences regarding possible matches the plurality of objects, wherein the query is formed in natural language; transforming with use of [the LLM], the query from the user into a formal constraint satisfaction problem (CSP); solving the CSP to determine one or more objects of the plurality of objects that satisfy the CSP; and providing the one or more objects to the user. The claims also correspond to mental processes (observation, evaluation, judgment, opinion), as shown by the limitations generating a training corpus for fine-tuning an LLM, fine-tuning the LLM based on the training corpus, transforming the query from the user into a formal constraint satisfaction problem (CSP), and solving the CSP to determine one or more objects of the plurality of objects that satisfy the CSP. These limitations directly involved the observation and evaluation of data and making a decision (judgment/opinion) based on the observed and evaluated data. Additionally, the claims correspond to mathematical concepts (mathematical relationships) as evidenced by the limitations pertaining to the transformation of the query into a formal constraint satisfaction problem and solving the constraint satisfaction problem. The claims recite an abstract idea.
Note: the features or elements in brackets in the above section are inserted for reading clarity, but are analyzed as “additional elements” under Step 2A Prong Two and Step 2B below.
The judicial exception is not integrated into a practical application simply because the claims recite the additional elements of: a processor (claim 9), a memory (claim 9), one or more non-transitory computer-readable storage media (claims 9 and 21), a compute device, and a large-language model (LLM). The additional elements are computer components recited at a high-level of generality performing the above-mentioned limitations. The combination of the additional elements are no more than mere instructions to apply the judicial exception using a generic computer. Further, the LLM amounts to generally linking the judicial exception to a particular field of use (finding optimal booking accommodations for users/customers). Accordingly, in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. 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 discussed above with respect to integration of the abstract idea into a practical application, the additional elements amount to no more than mere instructions to apply the exception using a generic computer, and generally linking the judicial exception to a particular field of use. Mere instructions to apply an exception using a generic computer cannot provide an inventive concept. Thus, when viewed as an ordered combination, nothing in the claims add significantly more (i.e. an inventive concept) to the abstract idea. The claims are not patent eligible.
Dependent claims 10-15, 22-26, 28, and 32-36 recite additional limitations that are further directed to the abstract idea analyzed in the rejected claims above. The claims also recite additional elements that have been analyzed in the rejected claims above. Thus, claims 10-15, 22-26, 28, and 32-36 are also rejected under 35 U.S.C. 101 and are not patent eligible.
Allowable Subject Matter
Claims 9-15, 21-28, and 32-36 would be allowable if rewritten or amended to overcome the rejection(s) under 35 U.S.C. 101, set forth in this Office action.
The closest patent or patent application found that is relevant to the applicant’s invention includes Lange (2023/0259714), which discloses a conversational agent implementing a state handler and a language model (LM) communicates with a user computing device through a user frontend. Rather than communicating directly with a user with output in natural language, the agent uses a (LM) trained as described herein to navigate a conversation graph. The state handler receives API calls generated by the LM and updates the state of a conversation with a user as indicated in the graph. After the update, the state handler can perform one or more predetermined actions associated with a node indicating the current state of the conversation. The reference seemingly fails to disclose utilizing a large language model to transform a query from the user into a formal constraint satisfaction problem (CSP), and solving the CSP to determine one or more objects of the plurality of objects that satisfy the CSP. The claims appear to overcome the prior art.
The closest non-patent literature prior art reference found that is relevant to the applicant’s invention includes the publication “Cost Effective Accommodation Planning in a Trip by Using Accom[m]odation Advisor Query (AA-Query) in STPF” which discloses a skyline query processing technique that can retrieve the best possible list of accommodations from the accommodation set. The publication implements the accommodation advisor query (AA-Query) based upon the skyline algorithm. The reference seemingly fails to disclose utilizing a large language model to transform a query from the user into a formal constraint satisfaction problem (CSP), and solving the CSP to determine one or more objects of the plurality of objects that satisfy the CSP. The claims appear to overcome the prior art.
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 DIONE N SIMPSON whose telephone number is (571)272-5513. The examiner can normally be reached M-F; 7:30 a.m.-4:30 p.m..
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, Sarah Monfeldt can be reached at (571) 270-1833. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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DIONE N. SIMPSON
Primary Examiner
Art Unit 3628
/DIONE N. SIMPSON/Primary Examiner, Art Unit 3629
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