Detailed Action
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Notice to Applicant
The following is a Final Office action to Application Serial Number 18/730,027, filed on July 18, 2024. In response to Examiner’s Non-Final Office Action of October 1, 2025, Applicant, on December 11, 2025, amended claims 1, 4, and 6; and cancelled claims 2, 3, 5, and 7-12. Claims 1, 4 and 6 are pending in this application and have been rejected below.
Response to Amendment
Applicant’s amendments are acknowledged.
The 35 U.S.C. § 112 rejections have been withdrawn.
Regarding 35 U.S.C. § 101 rejection, the amended claims have been considered
and are insufficient to overcome the rejection. Please refer to the 35 U.S.C. § 101 rejection for further explanation and rationale.
Response to Arguments
Applicant’s arguments filed December 11, 2025 have been fully considered but they are not persuasive and/or are moot in view of the revised rejections. Applicant’s arguments will be addressed herein below in the order in which they appear in the response filed December 11, 2025.
On Pgs. 7-11, regarding the 35 U.S.C. § 101 rejection, Applicant states amended claims improve prediction accuracy of a return area regarding a visitor of an event scheduled to be held using machine learning while maintaining accuracy and preventing a specific overtraining problem, and the claims clearly reflect this improvement . In response. Examiner finds the present claims improve an existing business process of return area prediction analysis and there are currently no functional advancement to any technology or technological field, in order for the claim elements to be considered significantly more than the abstract idea itself. Utilizing computer structure and technology to analyze return area data are all, both individually and in combination, generic computer functions such as receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network) and storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015). Regarding the PTO Guidance example 47, the general use of a machine learning analysis does not provide a meaningful limitation to transform the abstract idea into a practical application. Therefore, currently, the machine learning is solely used a tool to perform the instructions of the abstract idea.
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 and 6 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Claims 1, 4 and 6 are directed to return area prediction.
Claim 1 recites an apparatus for return area prediction, which includes acquiring a regression result of prediction of a number of visitors in each return area for a target event from event information of the target event using a regularized regression method, specify, based on the acquired regression result, a nearest-neighbor cluster closest to the regression result among event group clusters previously clustered, and predicting the number of visitors in each return area for the target event based on at least a position of a center of gravity of the nearest-neighbor cluster; acquiring event information regarding a past event group and specify visitors to an event according to the acquired event information based on location information stored in a location information database storing location information of various users; obtaining a return area of each visitor after the event from a movement history of each visitor on a day of the event obtained based on the location information of the specified visitors; and acquiring, as statistical visitor information, statistical information of a number of return area people obtained by statistically processing the number of visitors in each return area and visitor movement history aggregate information before a start of the event on the day of the event.
As drafted, this is, under its broadest reasonable interpretation, within the Abstract idea grouping of “Mental Processes” – evaluation. The recitation of “device”, “processing circuitry”; and “database” , provide nothing in the claim elements to preclude the step from being “Mental Processes”- evaluation. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. The claims primarily recite the additional element of using computer components to perform each step. The “device”; “processing circuitry”; and “database” is recited at a high-level of generality, such that it amounts no more than mere instructions to apply the exception using a computer component. See MPEP 2106.05(f). Furthermore, the claim 1 recites using one or more machine learning analysis techniques. The specification discloses the machine learning analysis at a high-level of generality, providing examples of different techniques that may be applied. The general use of a machine learning analysis does not provide a meaningful limitation to transform the abstract idea into a practical application. Therefore, currently, the machine learning processing is solely used a tool to perform the instructions of the abstract idea. Examiner recommends including the practical application of the use of the results of the machine learning analysis in the claim language.
Accordingly, the additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claims also fail to recite any improvements to another technology or technical field, improvements to the functioning of the computer itself, use of a particular machine, effecting a transformation or reduction of a particular article to a different state or thing, and/or an additional element applies or uses the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception. See 84 Fed. Reg. 55. In particular, there is a lack of improvement to a computer or technical field in prediction analysis.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements when considered both individually and as an ordered combination do not amount to significantly more than the abstract idea. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of “device”; “processing circuitry”; and “database” is insufficient to amount to significantly more. (See MPEP 2106.05(f) – Mere Instructions to Apply an Exception – “Thus, for example, claims that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea eligible.” Alice Corp., 134 S. Ct. at 235). Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
The claim fails to recite any improvements to another technology or technical field, improvements to the functioning of the computer itself, use of a particular machine, effecting a transformation or reduction of a particular article to a different state or thing, adding unconventional steps that confine the claim to a particular useful application, and/or meaningful limitations beyond generally linking the use of an abstract idea to a particular environment. See 84 Fed. Reg. 55. Viewed individually or as a whole, these additional claim element(s) do not provide meaningful limitation(s) to transform the abstract idea into a patent eligible application of the abstract idea such that the claim(s) amounts to significantly more than the abstract idea itself. With regards to receiving data and step 2B, it is M2106.05(d)- Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information) and Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015). With regards to the “machine learning “and step 2B- the machine learning is used as a tool to perform the abstract idea.
Examiner concludes that the additional elements in combination fail to amount to significantly more than the abstract idea based on findings that each element merely performs the same function(s) in combination as each element performs separately. The claim is not patent eligible. Thus, taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception (the abstract idea). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually.
Dependent Claims 4 and 6 recite predict a number of people represented by the position of the center of gravity of the nearest-neighbor cluster as the number of visitors in each return area for the target event; predict the regression result as the number of visitors in each return area for the target event in a case where a position indicated by the regression result is present within a boundary of the nearest-neighbor cluster, and predict a number of people represented by an intersection point between a straight line, which connects the position indicated by the regression result and the position of the center of gravity of the nearest-neighbor cluster, and a boundary line of the nearest- neighbor cluster as the number of visitors in each return area for the target event in a case where the position indicated by the regression result is absent within the boundary of the nearest-neighbor cluster; and further narrowing the abstract idea. These recited limitations in the dependent claims do not amount to significantly more than the above-identified judicial exceptions in Claim 1. Regarding Claims, 4 and 6, and the additional elements of “prediction device” and “processing circuitry” -it is M2106.05(d)- Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information).
Reasons Claims are Patentably Distinguishable from the Prior Art
Examiner analyzed Claims 1, 4 and 6 in view of the prior art on record and finds not all claim limitations are explicitly taught nor would one of ordinary skill in the art find it obvious to combine these references with a reasonable expectation of success as discussed below.
In regards to Claim 1, the prior art does not teach or fairly suggest:
“A return area prediction device comprising: a prediction unit configured to acquire a regression result of prediction of a number of visitors in each return area for a target event from event information of the target event using a regularized regression method, specify, based on the acquired regression result, a nearest-neighbor cluster closest to the regression result among event group clusters previously clustered, and predict the number of visitors in each return area for the target event based on at least a position of a center of gravity of the nearest-neighbor cluster.”.
Examiner finds that Violos et al.("Predicting Visitor Distribution for Large Events in Smart Cities," 2019 IEEE International Conference on Big Data and Smart Computing (BigComp), Kyoto, Japan, 2019, pp. 1-8) teaches The prediction of the distribution of visitors in large events is a valuable piece of information in the context of smart cities. The organizers of large events leverage it for safety and coordination purposes and the Fog computing infrastructures for cost effective, agile and reliable allocation of the mobile apps and festival services workload along the continuum from edge devices to cloud. In this research we examine two sets of supervised Machine Learning techniques in order to predict the visitors' distribution in the next timesteps and evaluate them using real data from a large music event that took place in 2017 and 2018. To enrich the feature space of the predictive models we use and evaluate open data such as the weather and the popularity of artists. A further added value of the examined Machine Learning techniques, in comparison with the current state of the art in mobility prediction, is that they look into the phenomenon of visitors coming and going from the area of interest. (see Abstract). In particular, Violos discloses methods to be applied to predict whether the visitors density will be increased, decreased or remain stable in the next timestep, in accordance to the density's upper and lower bounds. In order to provide a more precise prediction of the visitor distribution, rather than abstract labels of increase, decrease and stability, we can use a set of regression techniques (see Introduction).
Kim et al, ("Utilizing In-store Sensors for Revisit Prediction," 2018 IEEE International Conference on Data Mining (ICDM), Singapore, 2018, pp. 217-226) teaches predicting revisit intention is very important for the retail industry. Converting first-time visitors to repeating customers is of prime importance for high profitability. However, revisit analyses for offline retail businesses have been conducted on a small scale in previous studies, mainly because their methodologies have mostly relied on manually collected data. With the help of noninvasive monitoring, analyzing a customer's behavior inside stores has become possible, and revisit statistics are available from the large portion of customers who turn on their Wi-Fi or Bluetooth devices (see Abstract). In particular, Kim discloses We designed prediction tasks to explore customers' revisit behaviors. The first task is a binary classification task to predict customers' revisit intention RVbin. The second task is a regression task to predict the revisit interval RVdays between two consecutive visits. For each task, we conducted experiments on two different data subsets. First, we see the performance of our model on the entire customer dataset. Second, we used a dataset consisting of only the first-time visitors to show that our prediction framework is effective in determining the willingness of first-time visitors to revisit. (see Section V).
Shimode et al. (U.S. PG Publication 20190295007) teaches a device has an event information storage unit for pre-storing event information designating an event site position and event date/time. A first processing unit selects a set of event information that is similar to event information designating an event site position and event date/time, which are input out of the event information storage unit and specifies transport facilities from, where a people flow occurs when an event takes place based on the selected set of event information. A second processing unit allocates an event attendance input to specified transport facilities. (Abstract).
Although Violos, Kim and Shimode teaches the predictive elements of the claim, none of the cited prior art, singularly or in combination, teach or fairly suggest, the combination of, the regression result and predicting the number of visitors in each return area for the target event based on at least a position of a center of gravity of the nearest-neighbor cluster .
The dependent claims 4 and 6 are eligible under 35 U.S.C. 102 and 35 U.S.C. 103 because they depend on claim 1 that is determined to be eligible.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure: US Publication No. 20230316186A1 to Miller et al.- Abstract-“ The disclosure is directed to various ways of improving the functioning of computer systems, information networks, data stores, search engine systems and methods, and other advantages. Among other things, provided herein are methods, systems, components, processes, modules, blocks, circuits, sub-systems, articles, and other elements (collectively referred to in some cases as the “platform” or the “system”) that collectively enable, in one or more datastores (e.g., where each datastore may include one or more databases) and systems, the creation, development, maintenance, and use of a set of custom objects for use in a wide range of activities, including sales activities, marketing activities, service activities, content development activities, and others, as well as improved methods and systems for sales, marketing and services that make use of such entity resolution systems and methods as well as custom objects.”
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 extension fee 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 Chesiree Walton, whose telephone number is (571) 272-5219. The examiner can normally be reached from Monday to Friday between 8 AM and 5 PM. If any attempt to reach the examiner by telephone is unsuccessful, the examiner’s supervisor, Patricia Munson, can be reached at (571) 270-5396. The fax telephone numbers for this group are either (571) 273-8300 or (703) 872-9326 (for official communications including After Final communications labeled “Box AF”).
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Sincerely,
/CHESIREE A WALTON/Examiner, Art Unit 3624
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