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. Priority Applicant’s claim for the benefit of a prior-filed application under 35 U.S.C. 119(e) or under 35 U.S .C. 120, 121, 365(c), or 386(c) is acknowledged. Applicant has not complied with one or more conditions for receiving the benefit of an earlier filing date under 35 U.S.C. 120 as follows: The later-filed application must be an application for a patent for an invention which is also disclosed in the prior application (the parent or original nonprovisional application or provisional application). The disclosure of the invention in the parent application and in the later-filed application must be sufficient to comply with the requirements of 35 U.S.C. 112(a) or the first paragraph of pre-AIA 35 U.S.C. 112, except for the best mode requirement. See Transco Products, Inc. v. Performance Contracting, Inc. , 38 F.3d 551, 32 USPQ2d 1077 (Fed. Cir. 1994). The disclosure of the prior-filed application, Application No. 63/404,923, fails to provide adequate support or enablement in the manner provided by 35 U.S.C. 112(a) or pre-AIA 35 U.S.C. 112, first paragraph for one or more claims of this application. The examiner is unable to find support for the categorizing and other details of the independent claims. For examination under prior art, the effective filing date will be 8 September 2022, which is the filing date of application 63/424,861. 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 1-9 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Claim 1 recites “categorize, with at least the processor, the real-time data obtained for each of the existing car washes into a plurality of defined categories, the defined categories comprising at least: a. member information about the existing car wash, b. financial information about the existing car wash, and c. traffic information about the existing car wash.” Categorizing data is a mental process that evaluates the data and judges a category to which it belongs. The claim further recites “generate, with at least the processor, a data structure linking the real-time data obtained for the existing car washes with unique location identifiers that indicate respective locations where the existing car washes linked to the real-time data are located.” Generating a data structure is a mental process of judgment or evaluation that considers data and decides on a structured way to show it. Such a process can be performed by a human with pen and paper. The claim further recites “modify, with at least the processor, the data structure to include at least a portion of the real-time data and at least a portion of the demographic information.” Modifying a data structure is similarly a mental process that can be performed with pen and paper by considering the data structure and deciding how to change it. 3 The claim also recites “with a neural network, generate a financial prediction indicating an expected financial performance of the car wash if constructed at the potential future location based on the data structure.” Generating a prediction is a mental process that evaluates information and judges the information to decide on a likely outcome. This judicial exception is not integrated into a practical application because The only product of the claim is a prediction, which is an abstract idea . Nothing practical is done as a result of the prediction. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because all of the additional elements are either generic computing components or mere data gathering, which is insignificant extra-solution activity that does not render the claim significantly more than an abstract idea. A non-transitory computer-readable medium, a processor, a computing system, a communication network, and a neural network are all generic computing components, and are recited at a high degree of generality, so they do not render the claim significantly more than an abstract idea. The steps of “receive, by at least the processor, a geographic location specified by a user as a potential future location of a car wash,” “obtain, over a communication network, real-time data generated for a plurality of existing car washes at or near the geographic location,” and “request, over the communication network, demographic information for the respective locations using the location identifiers” are mere data gathering operations. The examiner notes that Claims 10-17, in contrast to claim 1, recite opening a new car wash or opening a new retail business entity at a location determined by the operations of the claims. Opening a business integrates the recited abstract ideas into a practical application, which renders claims 10-17 statutory under 35 U.S.C. 101. Claim 2 recites additional detail about the step of obtaining real-time data, which is mere data gathering. The recited API and remote data platform are generic computing components recited at a high degree of generality, so they do not render the claim significantly more than an abstract idea. The claim does not recite a practical application. Claims 3-7 recite additional detail about data, which is an abstract idea. The claims does not recite a practical application or any additional elements. Claim 8 recites “limit the expected financial performance to a value that would exceed a realized financial performance of no more than ninety five (95%) percent of the existing car washes.” Limiting a value is a mental process that evaluates the value and judges how it compares to a predetermined value (e.g. 95%). The claim does not recite a practical application or any additional elements. Claim 9 recites “wherein the neural network comprises hyper parameters that are adjusted based on processing of a training data set to minimize an expected error of the financial prediction, the hyper parameters comprising a plurality of: a. a number of nodes used to process the data structure, b. a learning rate, c. a first dropout value, d. a second dropout value, e. a number of epochs, f. a batch size, and g. a validation split.” Hyper parameters are data, which is an abstract idea. The training of the neural network is recited in a generic way, at a high degree of generality—there are no details about how the neural network is trained. So, the recited neural network and training are generic computing components and therefore not significantly more than an abstract idea. The claim does not recite a practical application or any additional elements. Claim Rejections - 35 USC § 103 The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action: A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made. Claim s 1- 2, 6-11, and 13-17 are rejected under 35 U.S.C. 103 as being unpatentable over Xu, Yanan , et al. (“ AR2Net: An attentive neural approach for business location selection with satellite data and urban data ,” ACM Transactions on Knowledge Discovery from Data (TKDD) 14.2 (2020): 1-28 ; hereinafter “Xu”). Regarding Claim 1 , Xu teaches a non-transitory computer-readable medium storing computer-executable instructions (section 5.4 and p. 20:17, algorithm 2—the neural network training and machine learning algorithms imply computer-executable instructions that are executed by a processor ) that, when executed by at least a processor of a computing system, cause the computing system to: receive, by at least the processor, a geographic location specified by a user as a potential future location of a car wash ( Introduction and p. 20:6, Problem statement—L locations are considered, i.e. specified by a user as potential future locations for a business. Xu considers many types businesses, so a car wash is an obvious variation ) ; obtain, over a communication network, real-time data generated for a plurality of existing car washes at or near the geographic location (section 3—the satellite imagery and urban data comprise multiple types of real-time data obtained for a plurality of existing businesses, such as car washes . Section 5.1 further describes the real-time data ) ; categorize, with at least the processor, the real-time data obtained for each of the existing car washes into a plurality of defined categories, the defined categories comprising at least: a. member information about the existing car wash, b. financial information about the existing car wash, and c. traffic information about the existing car wash (section 3 and fig. 1—feature extraction categorizes data into categories for density and entropy of POIs, number of intersections for roads, visiting times, average speed for taxis, etc. This includes traffic information for existing businesses. Categories such as member information and financial information are obvious variations of urban data) ; generate, with at least the processor, a data structure linking the real-time data obtained for the existing car washes with unique location identifiers that indicate respective locations where the existing car washes linked to the real-time data are located (section 4—dividing the city into a grid generates a data structure with unique location identifiers that are linked to location popularity, as determined by real-time data such as check-ins) ; request, over the communication network, demographic information for the respective locations using the location identifiers (section 4—crawling the ground truth data comprises requesting demographic information) ; modify, with at least the processor, the data structure to include at least a portion of the real-time data and at least a portion of the demographic information (section 4.1—the data structure is modified by calculating values for each grid; also by correlation with urban data for each grid as described in section 4.2) ; and with a neural network, generate a financial prediction indicating an expected financial performance of the car wash if constructed at the potential future location based on the data structure (section 5.2—a neural network predicts popularity of the business for each location, which directly correlates with expected financial performance) . Regarding Claim 10 , Xu teaches a process of opening a new car wash at a new geographic location (Abstract and Introduction—a geographic location is selected for opening a new business. Xu considers many types businesses, so a car wash is an obvious variation), the process comprising: establishing the geographic location for consideration for placement of the new car wash in a computing system (Introduction and p. 20:6, Problem statement—L locations are considered, i.e. specified by a user as potential future locations for a business); with the computing system, obtaining real-time data generated for a plurality of existing car washes at or near the geographic location over a communication network (section 3—the satellite imagery and urban data comprise multiple types of real-time data obtained for a plurality of existing businesses, such as car washes. Section 5.1 further describes the real-time data); with the computing system, categorizing the real-time data obtained for each of the existing car washes into a plurality of defined categories, the defined categories comprising at least: a. member information about the existing car wash, b. financial information about the existing car wash, and c. traffic information about the existing car wash (section 3 and fig. 1—feature extraction categorizes data into categories for density and entropy of POIs, number of intersections for roads, visiting times, average speed for taxis, etc. This includes traffic information for existing businesses. Categories such as member information and financial information are obvious variations of urban data); with the computing system, generating a data structure linking the real-time data obtained for the existing car washes with unique location identifiers that indicate respective locations where the existing car washes linked to the real-time data are located (section 4—dividing the city into a grid generates a data structure with unique location identifiers that are linked to location popularity, as determined by real-time data such as check-ins); with the computing system, accessing demographic information for the respective locations using the location identifiers, and modifying the data structure to include at least a portion of the real-time data and at least a portion of the demographic information (section 4—crawling the ground truth data comprises requesting demographic information); generating, with a neural network configured on the computing system, a financial prediction indicating an expected financial performance of the car wash if constructed at the geographic location based on the data structure (section 5.2—a neural network predicts popularity of the business for each location, which directly correlates with expected financial performance); making a determination that the expected financial performance of the car wash at the geographic location satisfies a minimum performance threshold or fails to satisfy the minimum performance threshold (section 3, last two paragraphs, and section 5.2—ranking locations in order of popularity comprises determining that the expected financial performance for each location satisfies a minimum performance threshold); based at least in part on the determination that the expected financial performance of the car wash at the geographic location satisfies the minimum performance threshold, constructing the car wash at the geographic location; and based at least in part on the determination that the expected financial performance of the car wash at the geographic location fails to satisfy the minimum performance threshold, establishing a different geographic location for consideration for placement of the new car wash in the computing system (section 3, last two paragraphs, and section 5.2—ranking locations in order of popularity implies constructing the business at the chosen location if it satisfies the expected financial performance, or establishing a different location if the chosen location does not satisfy the expected performance). Regarding Claims 2 and 11 , Xu teaches wherein the real-time data is obtained through operation of an API that establishes a communication channel between the computing system and a remote data platform that aggregates the real-time data for a plurality of the existing car washes (section 6.1) . Regarding Claim 6 , Xu teaches wherein the real-time data to be categorized as the traffic information comprises at least one of: a. retail traffic, b. retail traffic percentage, c. member traffic, d. member traffic percentage, and e. total traffic (p. 20:11) . Regarding Claims 7 and 13 , Xu teaches wherein the demographic information comprises population information about residents who reside within a defined distance from the potential future location (p. 20:2, second full paragraph and section 6.10) . Regarding Claims 8 and 14 , Xu teaches computer-executable instructions that, when executed by at least the processor of the computing system, cause the computing system to: limit the expected financial performance to a value that would exceed a realized financial performance of no more than ninety five (95%) percent of the existing car washes (section 3—popularity {which is proportional to financial performance} of each location is predicted and ranked. Limiting the prediction to a value is an obvious variation) . Regarding Claims 9 and 15 , Xu teaches wherein the neural network comprises hyper parameters that are adjusted based on processing of a training data set to minimize an expected error of the financial prediction, the hyper parameters comprising a plurality of: a. a number of nodes used to process the data structure, b. a learning rate, c. a first dropout value, d. a second dropout value, e. a number of epochs, f. a batch size, and g. a validation split (section 5.4—an output layer may be added, indicating a hyperparameter for a number of nodes, and hyperparameters including dropout ratio are adjusted to reduce an error and improve generalization of the model) . Regarding Claim 16 , Xu teaches a process of opening a retail business entity at a new geographic location (Abstract and Introduction—a geographic location is selected for opening a new business) , the process comprising: establishing the geographic location for consideration for placement of the retail business entity in a computing system (Introduction and p. 20:6, Problem statement—L locations are considered, i.e. specified by a user as potential future locations for a business); with the computing system, obtaining real-time data generated for a plurality of existing retail business entities at or near the geographic location over a communication network; with the computing system, categorizing the real-time data obtained for each of the existing retail business entities into a plurality of defined categories (section 3—the satellite imagery and urban data comprise multiple types of real-time data obtained for a plurality of existing businesses, such as car washes. Section 5.1 further describes the real-time data); with the computing system, generating a data structure linking the real-time data obtained for the existing retail business entities with unique location identifiers that indicate respective locations where the existing retail business entities linked to the real- time data are located (section 4—dividing the city into a grid generates a data structure with unique location identifiers that are linked to location popularity, as determined by real-time data such as check-ins); with the computing system, accessing demographic information for the respective locations using the location identifiers, and modifying the data structure to include at least a portion of the real-time data and at least a portion of the demographic information (section 4—crawling the ground truth data comprises requesting demographic information); generating, with a neural network configured on the computing system, a financial prediction indicating an expected financial performance of the retail business entity if constructed at the geographic location based on the data structure (section 5.2—a neural network predicts popularity of the business for each location, which directly correlates with expected financial performance); making a determination that the expected financial performance of the retail business entity at the geographic location satisfies a minimum performance threshold or fails to satisfy the minimum performance threshold (section 3, last two paragraphs, and section 5.2—ranking locations in order of popularity comprises determining that the expected financial performance for each location satisfies a minimum performance threshold); based at least in part on the determination that the expected financial performance of the retail business entity at the geographic location satisfies the minimum performance threshold, constructing the retail business entity at the geographic location; and based at least in part on the determination that the expected financial performance of the retail business entity at the geographic location fails to satisfy the minimum performance threshold, establishing a different geographic location for consideration for placement of the retail business entity in the computing system (section 3, last two paragraphs, and section 5.2—ranking locations in order of popularity implies constructing the business at the chosen location if it satisfies the expected financial performance, or establishing a different location if the chosen location does not satisfy the expected performance). Regarding Claim 17 , Xu teaches wherein the retail business entity comprises at least one of a convenience store and a mobile convenience store (section 6.6—a convenience store is an obvious type of retail shop) . Claim s 3 and 12 are rejected under 35 U.S.C. 103 as being unpatentable over Xu, as applied to claims 2 and 11, above, in view of García-Castro, José Daniel, and Josefa Mula (“ Decision Model to Locate a Franchisee Applied to a Fast-Food Restaurant ,” Design and Management of Interfirm Networks: Franchise Networks, Cooperatives and Alliances. Cham: Springer International Publishing, 2019. 155-176 ; hereinafter “Garcia-Castro”). Regarding Claims 3 and 12 , Xu does not specifically teach wherein the existing car washes comprise franchise locations that are to be affiliated with the car wash to be constructed at the potential future location. However, Garcia-Castro teaches wherein existing businesses comprise franchise locations that are to be affiliated with the business to be constructed at the potential future location (section 1. See also the case study in section 4). All of the claimed elements were known in Xu and Garcia-Castro and could have been combined by known methods with no change in their respective functions. It therefore would have been obvious to a person of ordinary skill in the art at the time of filing of the applicant’s invention to combine the franchises of Garcia-Castro with the businesses of Xu to yield the predictable result of wherein the existing car washes comprise franchise locations that are to be affiliated with the car wash to be constructed at the potential future location. One would be motivated to make this combination for the purpose of providing investors with better tools to select a suitable location for their businesses (Garcia-Castro, section 3, first paragraph). Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over Xu, as applied to claim 1, above, in view of Soni et al. (U.S. 2017/0228804, hereinafter “Soni”). Regarding Claim 4 , Xu does not specifically teach wherein the real-time data to be categorized as the member information comprises at least one of: a. a number of members of the existing car wash, and b. an average length of membership for the members. However, Soni teaches information that comprises visits over a duration of membership (¶ 0076] —average length of membership is an obvious variation on the collected data ). All of the claimed elements were known in Xu and Soni and could have been combined by known methods with no change in their respective functions. It therefore would have been obvious to a person of ordinary skill in the art at the time of filing of the applicant’s invention to combine the member information of Soni with the real-time data and categorization of Xu to yield the predictable result of wherein the real-time data to be categorized as the member information comprises at least one of: a. a number of members of the existing car wash, and b. an average length of membership for the members. One would be motivated to make this combination for the purpose of improving the ability to predict how many customers are likely to visit a business frequently. Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over Xu, as applied to claim 1, above, in view of Han, Shuihua , et al. (“ Search well and be wise: A machine learning approach to search for a profitable location ,” Journal of Business Research 144 (2022): 416-427 ; hereinafter “Han”). Regarding Claim 5 , Xu does not specifically teach wherein the real-time data to be categorized as the financial information comprises at least one of: a. annual revenue, b. monthly retail revenue, c. retail revenue percentage, d. member revenue, e. member revenue percentage, f. total monthly revenue, g. retail ticket average, h. recharge ticket average, and i . revenue per car. However, Han teaches data to be categorized as the financial information comprises at least one of: a. annual revenue, b. monthly retail revenue, c. retail revenue percentage, d. member revenue, e. member revenue percentage, f. total monthly revenue, g. retail ticket average, h. recharge ticket average, and i . revenue per car (section 2.2—data used to select a location of a business comprises sales data {i.e. revenue} of other stores). All of the claimed elements were known in Xu and Han and could have been combined by known methods with no change in their respective functions. It therefore would have been obvious to a person of ordinary skill in the art at the time of filing of the applicant’s invention to combine the sales data of Han with the real-time data of Xu to yield the predictable result of wherein the real-time data to be categorized as the financial information comprises at least one of: a. annual revenue, b. monthly retail revenue, c. retail revenue percentage, d. member revenue, e. member revenue percentage, f. total monthly revenue, g. retail ticket average, h. recharge ticket average, and i . revenue per car. One would be motivated to make this combination for the purpose of enabling the selection of a location for a new business that has the highest estimated sales (Han, section 1). Conclusion The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure. Oliveira, Marcelo Fernando Felix de, et al. (“ Mapping regional business opportunities using geomarketing and machine learning ,” Gestão & Produção 27 (2020): e4158 ) teaches using machine learning to determine market demand for a product using demographic data, creating a geographic demand map to choose a location for a retail business. Any inquiry concerning this communication or earlier communications from the examiner should be directed to FILLIN "Examiner name" \* MERGEFORMAT HAL W SCHNEE whose telephone number is FILLIN "Phone number" \* MERGEFORMAT (571) 270-1918 . The examiner can normally be reached FILLIN "Work Schedule?" \* MERGEFORMAT M-F 7:30 a.m. - 6:00 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, FILLIN "SPE Name?" \* MERGEFORMAT Michael Huntley can be reached at FILLIN "SPE Phone?" \* MERGEFORMAT 303-297-4307 . The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /HAL SCHNEE/ Primary Examiner, Art Unit 2129