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 .
Status
This action is in response to applicant’s amendment filed on 4/28/2026. Claims 1, 4, 6-12, 15, 17, 18, 20, 21, are pending. Claims 1, 12, 20, are amended. No claims have been added. No claims are currently cancelled.
Response to Arguments
The applicant has amended claims 1, 12, 20 to overcome the previous claim objections. The previous claim objections are withdrawn.
Applicant's arguments filed 4/28/2026 have been fully considered but they are not persuasive. The applicant has argued the previous 101 rejections. Specifically the applicant has argued that the claims cannot be practically performed in the human mind. “Furthermore, assuming, arguendo, the claims contain an abstract idea (which Applicant does not concede), Prong Two of the Revised Step 2A requires the additional claim elements integrate the alleged abstract idea into a practical application, which renders the claims-as a whole-patent eligible. Similar to the holding in Ex parte Desjardins, there may be sufficient evidence of patentable subject matter when the technical improvement is in the manner the model operates or learns, not merely in what the model predicts. See Ex parte Desjardins Appeal No. 2024-000567 (ARP Sept. 26, 2025) (precedential). The claim amendments explicitly dictates an implementation of multiple neural network in the system that facilitates an improvement. "In a cascade model, it is assumed that guests examine the list from the top and quit at the first result that has unsatisfactory relevance. Further, the concept of relevance is not the same as booking probability, which is a combination of relevance and affordability. The challenge with a cascade model is that one doesn't get an explicit label when users quit, so it's difficult to directly train a model" Specification at [0039].” The examiner respectfully disagrees. The claims are directed to the abstract idea of ranking search results based on a weighted combination of an affordability metric and a quality metric the additional elements merely invoke neural networks as a tool to implement this abstract idea rather than reflecting an improvement to neural network technology itself or the functioning of a computer. Although the claims include a neural network the neural networks are utilized as a tool to perform an existing process and to automate manual tasks of being trained to predict affordability and predict quality. The specification and claims do not describe any specific technological improvement. Rather, the focus of applicant’s invention is to use generic computer components (e.g., “a client device”, “memory”, and “processor”) and “neural network” recited in representative claim 12 as a tool to solve a business problem, i.e., optimize a search listing. See SAP Am., Inc. v. InvestPic LLC, 898 F.3d 1161 (Fed. Cir. 2018). The claimed generic computer components are simply the “automation of the fundamental economic concept.” See OIP Techs., 788 F.3d at 1362–63. “[M]erely requir[ing] generic computer implementation” “does not move into [§] 101 eligibility territory.” See buySAFE, 765 F.3d at 1354. The claims do not claim an improvement. The claims do not show how the neural network is functioning. It is not clear the details of what the neural network is doing. There is no functionality meaningfully applied.
Using two separate models instead of one to avoid one input category influencing the weight given to another is not a technical solution to a technical problem. The claims do not recite how the first and second neural networks are trained, configured, or structured such that the invention is technically achieved. The claims simply recite the desired outcome, specifically two separately trained networks whole outputs are later combined. Using networks as a generic tool applied to a business problem. This is analogous to using two separate spreadsheets to keep two categories of data. The segregation is a result of organizational choice, not a technical improvement. Applying a weighted combination to two neural networks is not a technical solution that is significantly more than the abstract idea. see Revised Guidance at 56 (“[I]f a claim has been determined to be directed to a judicial exception under revised Step 2A, examiners should then evaluate the additional elements individually and in combination under Step 2B to determine whether they provide an inventive concept (i.e., whether the additional elements amount to significantly more than the exception itself).” (emphasis added)). The generic description of the networks and the generic functions it performs to implement the abstract idea does not provide significantly more than the abstract idea. see Weisner v. Google LLC, 51 F.4th 1073, 1083–84 (Fed. Cir. 2022) (a generic description of the components supported a finding they are conventional and not inventive); WhitServe LLC v. Donuts Inc., 809 F. App’x 929, 934 (Fed. Cir. 2020) (the district court did not have to look beyond the specification to make its patent-eligibility determination where the specification described the routine use of the components); MPEP § 2106.05(d)(I)(2) (a generic description indicates that no further description is required to satisfy 35 U.S.C. § 112(a)). “If a claim’s only ‘inventive concept’ is the application of an abstract idea using conventional and well-understood techniques, the claim has not been transformed into a patent-eligible application of an abstract idea.” BSG, 899 F.3d at 1290–91. “It has been clear since Alice that a claimed invention’s use of the ineligible concept to which it is directed cannot supply the inventive concept that renders the invention ‘significantly more’ than that ineligible concept.” Id. at 1290; see also Elec. Power Grp.
The applicant has argued “Further, "In some embodiments, the trained machine learning models may include at least one of: a first trained deep neural network for predicting an affordability metric using affordability features described herein and a second trained deep neural network for predicting a quality metric using quality features described herein. For example, an embodiment of an optimized search system described herein may use two separate networks: (i) an affordability network DNN(affordability), and (ii) a quality network DNN(quality), where DNN represents a deep neural network." See Specification at [0081]. As claimed, the disclosure implements a plurality of deep neural networks that optimize search systems. Similar to the Holding in Ex parte Desjardins, the disclosure implements a model in a defined practical application in particular the at least two deep neural networks implement are trained with distinct data and implemented to optimize a search system. The optimization of the search system with multiple integrated deep neural networks improves implementation of the cascade model in the search system. Therefore, Applicants respectfully request that the Examiner withdraw the rejection of under 35 U.S.C. § 101.” The examiner respectfully disagrees. Claims that recite mathematical calculations and mathematical relationships fall in the Mathematical Concepts grouping even if the claims do not recite a mathematical formula or equation used to make the calculations. Revised Guidance at 52; MPEP § 2106.04(a)(2)(I). Specifically applicant’s claims are computing, estimating, and predicting numerical values. Applicant’s invention is directed to using non-price and price-indicative features for calculating and ranking data related to a probability of booking. Applicant’s invention is directed to the booking of properties. Applicant’s invention is not analogous with McRO as argued by the applicant. The examiner respectfully disagrees. In McRO, Inc. v. Bandai Namco Games Am. Inc., 837 F.3d 1299 (Fed. Cir. 2016), the claimed invention allowed computers to do something they could not do before, i.e., it “allow[ed] computers to produce accurate and realistic lip synchronization and facial expressions in animated characters that previously could only be produced by human animators.” McRO, Inc., 837 F.3d at 1313 (internal quotation marks omitted). No such technological improvement or advance is present here. Claim 1 does not allow a computer to produce higher quality halftone images than were previously able to be produced. Nor does claim 1 allow a computer to produce accurate and realistic lip synchronization and facial expressions in animated characters that were previously only produced by human animators. Claim 1 does not allow computer processors to do something they could not do before. Claim 1 simply employs one or more generic devices (computer processors) to perform generic computer functions using a type of technology.
Looking at applicant’s paragraph 81 the paragraph as quoted does not describe any improvement to the manner in which a neural network operates or learns. It describes only that two networks exist and what each business value each predicts. Using a second, separately trained model to generate a second output is a generic application of machine learning, the use of multiple models for multiple distinct predictions each trained on its own designated on its own designated feature set. Using separate neural networks to perform the task of one network does not appear to be a technical solution to a technical problem it appears to be a business problem. Using separate networks (even neural networks) to compute affordability and quality metrics is not a technological improvement or that it otherwise amounts to a “practical application,” as that term is used in 2019 Revised Guidance. To the extent claim 1 represents an improvement at all, that improvement is, at best, to the abstract idea of marketing and sales activity, which is not enough for patent eligibility. See SAP Am., Inc. v. InvestPic, LLC, 898 F.3d 1161, 1170 (Fed. Cir. 2018) (“[P]atent law does not protect such claims[, i.e., claims to an asserted advance in the realm of abstract ideas], without more, no matter how groundbreaking the advance.”). This is particularly so where, as here, we find no indication in the Specification, nor does the applicant direct us to any indication, that the operations recited in claim 1 require any specialized computer hardware or other inventive computer components, invoke any allegedly inventive programming, or that the claimed invention is implemented using other than generic computer components as tools operating in their ordinary capacity. The applicant is not claiming a specific hardware nor does the applicant claim how the neural network is being trained. The claims merely state that the networks are trained and used to output a value.
The disclosure frames the problem being addressed by the invention in terms of the receiving a search request, generating a set of listings, extracting features, inputting features into trained neural networks, and ranking a set of listings. The Specification, thus, describes that the claimed invention uses known data to compute values to make a ranking determination. It clearly appears from the Specification that the focus of the claimed invention is on achieving a business objective (i.e., creating a ranked set of listings ie homes), and not on any claimed means for achieving that goal that improves technology or a technical field. The concept of using more than one network (even neural networks) is not a technological improvement. It appears that the applicant is merely separating data into networks.
Independent claims 1, 12, 20, do not integrate the judicial exception into a practical application. Claim 1 is a method comprising “a client device”, “deep neural network”, “neural network.” Claim 12 is a system that recites limitations performed “one or more processors”, “memory”, “one or more programs”, “a client device”, “deep neural network.” Claim 20 is a “non-transitory computer readable storage medium”, “programs”, “computer system”, “processors”, “memory”, “a client device”, “a display”, “deep neural network.” These additional elements are mere instructions to implement an abstract idea using a computer in its ordinary capacity, or merely uses the computer as a tool to perform the identified abstract idea. Use of a computer or other machinery in its ordinary capacity for performing the steps of the abstract idea or other tasks (e.g., to receive, generate, extract, compute, and rank data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., certain methods of organizing human activity) does not integrate a judicial exception into a practical application. See MPEP 2106.05(f). This limitation does not integrate the judicial exception into a practical application because it is nothing more than generally linking the use of the judicial exception to a particular technological environment. See MPEP 2106.05(h). The previous 101 rejection in maintained.
The applicant has amended the claims to overcome the previous prior art rejections. Specifically the prior art does not specifically teach “the first trained deep neural network is distinct from the second trained deep neutral network, the affordability metric predicted from the first trained deep neural network and the quality metric predicted from the second trained deep neural network are representative of a probability of booking and the quality metric predicted from the second trained deep neural network is representative of an estimate of quality,” and “ranking the set of listings based on the final ranking score weighted combination of the affordability metric predicted from the first trained deep neural network and the quality metric predicted from the second trained deep neural network.” The prior art of record does not teach these limitations. Therefore the previous prior art rejections are 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 1, 4, 6-12, 15, 17, 18, 20, 21, are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter because the claim(s) as a whole, considering all claim elements both individually and in combination, do not amount to significantly more than an abstract idea. The claim(s) is/are directed to the abstract idea of optimizing a search listing. The claimed invention is directed to an abstract idea without significantly more.
Step 2A Prong 1
The claim(s) recite(s) (mathematical relationships/formulas, mental process or certain methods of organizing human activity). Specifically the independent claims recite:
(a) mental process: as drafted, the claim recites the limitations of receiving a search request, generating a set of listings, extracting price-indicative features, inputting features, calculating a metric, and ranking a set of listings which is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting a client device and a processor nothing in the claim precludes the determining step from practically being performed in the human mind. For example, but for the from a client device language, the claim encompasses the user manually receiving data, calculating metrics, and ranking a set of listings. The mere nominal recitation of a generic devices does not take the claim limitation out of the mental processes grouping. This limitation is a mental process.
(b) mathematical formula: The claim recites a mathematical concept (which can include a mathematical relationships, mathematical formulas or equations, and mathematical calculations), and in this case a computing a probability booking by a mathematical model. Thus, the claim recites a mathematical calculation. “Mathematical Calculations” A claim that recites a mathematical calculation will be considered as falling within the “mathematical concepts” grouping. A mathematical calculation is a mathematical operation (such as multiplication) or an act of calculating using mathematical methods to determine a variable or number, e.g., performing an arithmetic operation such as exponentiation. The claim includes the exact words “calculating a weighted combination of the affordability metric.” Further a claim does not have to recite the word “calculating” in order to be considered a mathematical calculation. For example, a step of “determining” a variable or number using mathematical methods or “performing” a mathematical operation may also be considered mathematical calculations when the broadest reasonable interpretation.
(c) certain methods of organizing human activity: The claim as a whole recites a method of organizing human activity. The claimed invention is a method that allows for optimizing a search listing using specified parameters for a user which is a method of managing interactions between people. Thus, the claim recites an abstract idea. Managing Personal Behavior or Relationships or Interactions between People”; According to the 2019 PEG, “managing personal behavior or relationships or interactions between people” includes social activities, teaching, and following rules or instructions. Examples of these sub-groupings include subject matter.
Step 2A Prong 2
Independent claims 1, 12, 20, do not integrate the judicial exception into a practical application. Claim 1 is a method comprising “a client device”, “deep neural network”, “neural network.” Claim 12 is a system that recites limitations performed “one or more processors”, “memory”, “one or more programs”, “a client device”, “deep neural network.” Claim 20 is a “non-transitory computer readable storage medium”, “programs”, “computer system”, “processors”, “memory”, “a client device”, “a display”, “deep neural network.” These additional elements are mere instructions to implement an abstract idea using a computer in its ordinary capacity, or merely uses the computer as a tool to perform the identified abstract idea. Use of a computer or other machinery in its ordinary capacity for performing the steps of the abstract idea or other tasks (e.g., to receive, generate, extract, compute, and rank data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., certain methods of organizing human activity) does not integrate a judicial exception into a practical application. See MPEP 2106.05(f). This limitation does not integrate the judicial exception into a practical application because it is nothing more than generally linking the use of the judicial exception to a particular technological environment. See MPEP 2106.05(h).
Therefore, the additional elements of the independent claims, when considered both individually and in combination, are not sufficient to prove integration into a practical application.
Dependent claims 4, 6-9, 15, 17, 18 further narrow the abstract idea identified in the independent claims and do not introduce further additional elements for consideration, which does not integrate the judicial exception into a practical application.
Dependent claims 10, 11, 21, introduce additional features of “trained machine learning model.” This limitation provides nothing more than mere instructions to implement an abstract idea on a generic computer. See MPEP 2106.05(f). MPEP 2106.05(f) provides the following considerations for determining whether a claim simply recites a judicial exception with the words “apply it” (or an equivalent), such as mere instructions to implement an abstract idea on a computer: (1) whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished; (2) whether the claim invokes computers or other machinery merely as a tool to perform an existing process; and (3) the particularity or generality of the application of the judicial exception. Therefore, this limitation is not sufficient to prove integration into a practical application.
Therefore, the additional elements of the dependent claims, when considered both individually and in the context of the independent claims, are not sufficient to prove integration into a practical application.
Step 2B:
Independent claims 1, 12, and 20 do not comprise anything significantly more than the judicial exception. As can be seen above with respect to Step 2A, Prong 2, Claim 1 is a method comprising “a client device”, “deep neural network”, “neural network.” Claim 12 is a system that recites limitations performed “one or more processors”, “memory”, “one or more programs”, “a client device”, “deep neural network.” Claim 20 is a “non-transitory computer readable storage medium”, “programs”, “computer system”, “processors”, “memory”, “a client device”, “a display”, “deep neural network.” These additional elements are mere instructions to implement an abstract idea using a computer in its ordinary capacity, or merely uses the computer as a tool to perform the identified abstract idea. Use of a computer or other machinery in its ordinary capacity for performing the steps of the abstract idea or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., certain methods of organizing human activity) is not anything significantly more than the judicial exception. See MPEP 2106.05(f). The claim employs generic computer functions to execute an abstract idea, even when limiting the use of the idea to one particular environment. This type of generally linking is not anything significantly more than the judicial exception. See MPEP 2106.05(h).
The additional elements of the independent claims, when considered both individually and in combination, do not comprise anything significantly more than the judicial exception.
Dependent claims 4, 6-9, 15, 17, 18 further narrow the abstract idea identified in the independent claims and do not introduce further additional elements for consideration, which is not anything significantly more than the judicial exception.
Dependent claims 10, 11, 21, introduce additional features of a “trained machine learning model.” This limitation provides nothing more than mere instructions to implement an abstract idea on a generic computer. See MPEP 2106.05(f). MPEP 2106.05(f) provides the following considerations for determining whether a claim simply recites a judicial exception with the words “apply it” (or an equivalent), such as mere instructions to implement an abstract idea on a computer: (1) whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished; (2) whether the claim invokes computers or other machinery merely as a tool to perform an existing process; and (3) the particularity or generality of the application of the judicial exception. Therefore, this limitation is not anything significantly more than the judicial exception.
The additional elements of the dependent claims, when considered both individually and in the context of the independent claims, are not anything significantly more than the judicial exception.
Accordingly, claims 1, 4, 6-12, 15, 17, 18, 20, 21, are rejected under 35 USC 101.
Therefore based on the above analysis as conducted based on MPEP 2106 from the United States Patent and Trademark Office the claims are viewed as a court recognized abstract idea, are viewed as a judicial exception, does not integrate the claims into a practical application, does not provide significantly more, and does not provide an inventive concept, therefore the claims are ineligible.
Claim Objections
Claims 1, 12, 20, are objected to because of the following informalities: the claim language states “and wherein the first trained deep neural network is distinct from the second trained deep neutral network.” The language of neutral appears to be a typo wherein the applicant meant to type “neural”. Appropriate correction is required.
Pertinent pieces of prior art include Blecharczyk et al. (US 20200019892 A1) disclose an accommodation booking system and in particular to predicting the booking availability of an accommodation on previous booking history. Ward et al. (US 20200250609 A1) discloses an inventory management system having a machine learning engine. Xu et al. (US 20170178036 A1) which discloses modeling listing behavior to improve search results based on unique accommodation listing responses to reservation requests. Lee et al. (US 20210004379 A1 ) which discloses rankings of search results that are returned to a user who inputs a search query for products in an online system. Fotso et al. (US 20220327583 A1) which discloses evaluating data from a business and relating the business to one or more merchant services.
Conclusion
THIS ACTION IS MADE FINAL. 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 JAMIE H AUSTIN whose telephone number is (571)272-7363. The examiner can normally be reached Monday, Tuesday, Thursday, Friday 7am-2pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Brian Epstein can be reached at (571) 270 5389. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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JAMIE H. AUSTIN
Examiner
Art Unit 3625
/JAMIE H AUSTIN/Primary Examiner, Art Unit 3625
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