DETAILED ACTION
This final Office action is responsive to Applicant’s amendment filed January 29, 2026. A new examiner has taken over prosecution of the instant application.
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . Claims 1, 12 and 16 have been amended. Claim 20 is canceled. Thus, Claims 1-19 and 21 are pending and have been rejected.
Response to Amendments
Applicant’s amendment filed on January 29, 2026 has overcome the previously-presented claim objections and rejections under 35 U.S.C. § 112(b).
Additionally, Applicant’s claim amendments have overcome the prior art rejections.
Response to Arguments
9. Applicant’s 35 U.S.C. § 101 arguments, filed with respect to Claims 1-19 and 21 have been fully considered, but they are found to not be persuasive.
Argument A:
Applicant argues that Claims 1-19 and 21 do not recite an abstract idea (pages 2-4 of Applicant’s response). The Examiner respectfully disagrees.
In response to Applicant’s Remarks, Examiner notes that when factoring the additional elements (e.g., “machine learning model” & “door sensor” & “seat belt sensor”) when viewed individually and as an ordered combination of the claim limitation of “receiving a first estimated number of people within a geographic boundary based on a prediction generated by a machine learning model taking as an input door sensor data indicating a duration of a door of a vehicle is open and seat belt sensor data indicating a change in status of a seat belt sensor” is indeed an abstract idea under “Mathematical Concepts” or “Mental Processes”. For instance, the described claim limitation receives an estimated number of people based on a machine learning model that takes specific inputs. Examiner considers the claim directed to a mathematical concept or a mental process, triggering an abstract idea rejection. The machine learning model is, at its core, a mathematical algorithm for processing data. Merely applying a known ML technique to new inputs (door sensor, seat belt data) to get a result is often not enough to escape an abstract idea finding. The claim could be framed as a human mental process of estimating the number of people in a vehicle by observing when doors open and close and noting when seat belts are used.
Secondly, Examiner notes that when factoring the additional elements (e.g., “user interface”) when viewed individually and as an ordered combination of the claim limitation of “causing the first visual indicator to be displayed on a map displayed in a user interface, the first visual indicator displayed above a visualization of the POI on the map” is indeed an abstract idea under “Mental Processes” or “Certain Methods of Organizing Human Activities”. For instance, the claim describes the process of observing and analyzing information—in this case, displaying a color-coded visual indicator on a map. This is a classic example of a mental process, which is considered an abstract idea. A person could theoretically perform this task mentally or with a pen and paper. For instance, a person could draw a map, mark a POI, and draw a color-coded indicator over it. Moreover, Examiner discloses that this is based on the reasoning that merely presenting information in a new format on a computer screen is often seen as a mental process or a method of organizing human activity. Also, there are no details about a specific technical improvement or a change in how the computer or map software operates. Merely displaying information differently, without a technical advance in the underlying computer technology, is not enough to avoid being classified as abstract. The claim describes a different way of presenting data (a visual indicator above a POI on a map). Courts have consistently found that claims limited to identifying, analyzing, and presenting data to a user are directed to an abstract idea, especially when there is no technical improvement to the computing technology itself.
Furthermore, according to MPEP § 2106.07 (a) III: “The court does not require "evidence" that a claimed concept is a judicial exception, and generally decides the legal conclusion of eligibility without resolving any factual issues. FairWarning IP, LLC v. Iatric Sys., 839 F.3d 1089, 1097, 120 USPQ2d 1293, 1298 (Fed. Cir. 2016) (citing Genetic Techs. Ltd. v. Merial LLC, 818 F.3d 1369, 1373, 118 USPQ2d 1541, 1544 (Fed. Cir. 2016)); OIP Techs., 788 F.3d at 1362, 115 USPQ2d at 1092; Content Extraction & Transmission LLC v. Wells Fargo Bank, N.A., 776 F.3d 1343, 1349, 113 USPQ2d 1354, 1359 (Fed. Cir. 2014). There is no requirement for the examiner to rely on evidence, such as publications or an affidavit or declaration under 37 CFR 1.104(d)(2), to find that a claim recites a judicial exception. Cf. Affinity Labs of Tex., LLC v. Amazon.com Inc., 838 F.3d 1266, 1271-72, 120 USPQ2d 1210, 1214-15 (Fed. Cir. 2016) (affirming district court decision that identified an abstract idea in the claims without relying on evidence); OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 1362-64, 115 USPQ2d 1090, 1092-94 (Fed. Cir. 2015) (same); Content Extraction & Transmission LLC v. Wells Fargo Bank, N.A., 776 F.3d 1343, 1347, 113 USPQ2d 1354, 1357-58 (Fed. Cir. 2014). Therefore, in conclusion, Examiner maintains that Claims 1-19 and 21 are directed to abstract ideas under “Mental Processes” or “Certain Methods of Organizing Human Activities” or “Mathematical Concepts” Groupings under 35 U.S.C. § 101 Step 2A Prong 1.
Argument B:
Applicant argues that Claims 1-19 and 21 recite additional elements that integrate the judicial exception into a practical application (pages 4-6 of Applicant’s response). The Examiner respectfully disagrees.
Specifically, Applicant argues that due to the amended claim limitations recited in Independent Claims 1, 12 and 16, the claims are directed to a specific improvement in computer technology related to user safety, travel logistics, and/or map visualization and therefore recite additional elements that integrate the judicial exception into a practical application under revised step 2a prong two of the 35 U.S.C. § 101 analysis. Examiner respectfully disagrees.
With respect to Independent Claims 1, 12 and 16, certain/particular limitations shown recite (1) mere data gathering and (2) mere data outputting/displaying in which each of these claim limitations reflects mere insignificant extra-solution activities (see MPEP § 2106.05 (g)). Independent Claims 1, 12 and 16: With respect to reliance on “door sensor” & “seat belt sensor” & “machine learning model” as additional elements shown in Independent Claims 1, 12 and 16 when considered individually and as an ordered combination (as a whole) in view of these claim limitations, these additional elements do not provide limitations that are indicative of integration into a practical application under step 2a prong 2 due to the following: (1) recites mere instructions to implement an abstract idea on a computer or using a computer as a tool to “apply” the recited judicial exceptions by providing the results to the user on a computer (see MPEP § 2106.05 (f)) or (2) limiting a particular field of use or technological environment pertaining to estimating a number of people at a POI using vehicle sensor data and generating visual indications of the population of people using a computer in the field of estimating populations at a point of interest such as at a concert hall, a conference hall, a stadium, a restaurant, a hotel, a park or a residential home (see MPEP § 2106.05(h)). For Independent Claims 1, 12 and 16, the additional elements of (e.g., “at least one processor” & “memory” & “user interface”) have been considered individually and as an ordered combination (as a whole), but fail to integrate the abstract idea into a practical application because they amount to using generic computing elements or instructions (software) to perform the abstract idea, similar to adding the words “apply it” (or an equivalent), which merely serves to link the use of the judicial exception to a particular technological environment. See MPEP § 2106.05(f) and MPEP § 2106.05(h).
Examiner notes that when factoring the additional elements (e.g., “machine learning model” & “door sensor” & “seat belt sensor”) when viewed individually and as an ordered combination of the claim limitation of “receiving a first estimated number of people within a geographic boundary based on a prediction generated by a machine learning model taking as an input door sensor data indicating a duration of a door of a vehicle is open and seat belt sensor data indicating a change in status of a seat belt sensor” does not recite additional elements that integrate the judicial exception into a practical application under 35 U.S.C. § 101 step 2a prong 2. Examiner notes that while a machine learning model is a computer-implemented tool, relying on it alone does not satisfy the "specific machine" requirement. The courts have held that a generic computer or a machine configured to perform abstract steps is not sufficient. The model is an abstract concept and must be implemented on a specific, non-generic machine to pass this test. The claim includes using "door sensor data" and "seat belt sensor data." These sensors are physical devices, but the claim describes receiving the data from them, not the sensors themselves as a specific, integral part of the claimed invention. Receiving data from a sensor can be considered a generic computing step. The claim describes a process of receiving data, running a model, and receiving a result. The hardware components, such as the sensors and the processing unit running the model, are either described generically (e.g., "a machine learning model") or are merely the source of generic input data (e.g., "door sensor data"). Therefore, the claim, as described, does not recite a specific machine in a non-generic manner. Examiner notes that there is no physical transformation shown. For instance, the claim involves calculating an estimated number of people. This is an informational output, not a physical transformation of matter to a different state or thing. The sensors collect data about physical objects (the door, the seatbelt), but the claim is not for a process that physically changes those objects. Data transformation is not enough: Transforming data from one form (sensor data) to another (an estimated number) is a core function of computing. The courts have consistently ruled that this kind of data manipulation is not a patent-eligible "transformation."
Based on the analysis, the step of "receiving a first estimated number of people..." would not satisfy the requirements of Step 2A, Prong 2 of the 35 U.S.C. § 101 analysis. The claim does not recite a specific, non-generic machine, nor does it effect a particular transformation. This step is a prime example of an abstract idea (calculating a number from information) being implemented using generic computing components (sensors, machine learning model).
Secondly, Examiner notes that when factoring the additional elements (e.g., “user interface”) when viewed individually and as an ordered combination of the claim limitation of “causing the first visual indicator to be displayed on a map displayed in a user interface, the first visual indicator displayed above a visualization of the point of interest on the map” does not recite additional elements that integrate the judicial exception into a practical application under 35 U.S.C. § 101 step 2a prong 2.
The element "causing the first visual indicator to be displayed on a map displayed in a user interface, the first visual indicator displayed above a visualization of the point of interest on the map" (including with color-coded information) is generally considered a conventional, user-interface function and not enough to demonstrate a practical application. Reasons this element is likely insufficient: A "mere presentation" of data: A claim that simply identifies, analyzes, and presents data to a user is likely to be found ineligible. In this case, the abstract idea is merely being displayed on a map, which is a generic application of data visualization. Lacks a technical improvement: The element does not describe an improvement to computer capabilities, network performance, or data processing accuracy. The Federal Circuit has repeatedly held that arguments about improving a user's experience are not enough to confer patent eligibility. "Apply it" with a generic computer: Simply instructing a computer to display information on a map, without a specific technological improvement, is not sufficient to qualify as a practical application. The map and user interface are considered generic computer components for this purpose.
With respect to “Mental Processes” category, Examiner refers Applicant to MPEP § 2106.04 (a) (2) (III) (C): “Claims can recite a mental process even if they are claimed as being performed on a computer. The Supreme Court recognized this in Benson, determining that a mathematical algorithm for converting binary coded decimal to pure binary within a computer’s shift register was an abstract idea. The Court concluded that the algorithm could be performed purely mentally even though the claimed procedures "can be carried out in existing computers long in use, no new machinery being necessary." 409 U.S at 67, 175 USPQ at 675. See also Mortgage Grader, 811 F.3d at 1324, 117 USPQ2d at 1699 (concluding that concept of "anonymous loan shopping" recited in a computer system claim is an abstract idea because it could be "performed by humans without a computer").” “For instance, the Examiner has reviewed Applicant’s Specification and determined that the claimed invention is described as concepts that are performed in the human mind and applicant is merely claiming that concept performed 1) on a generic computer (see Applicant’s Specification ¶ [0158]: “In addition, some aspects of the disclosure may be practiced within a general purpose computer or in any other circuits or systems.”), or 2) in a computer environment (see Applicant’s Specification ¶ [0153]: “FIGS. 10-13 and the associated descriptions provide a discussion of a variety of operating environments in which aspects of the disclosure may be practiced.”), or 3) is merely using a computer as a tool to perform these concepts.” Thus, based on these 3 factors, Examiner maintains that the claims still recite a mental process.
With respect to “Mental Processes” category, Examiner refers Applicant to MPEP § 2106.04 (a) (2) (III) (B): If a claim recites a limitation that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper, the limitation falls within the mental processes grouping, and the claim recites an abstract idea. The use of a physical aid (e.g., pencil and paper or a slide rule) to help perform a mental step (e.g., a mathematical calculation) does not negate the mental nature of the limitation, but simply accounts for variations in memory capacity from one person to another. For example; the steps of receiving a first estimated number of people within a geographic boundary, generating a first visual indicator indicating the first estimated number of people at the point of interest based on the capacity index and causing a first visual indicator to be displayed on a map showing a visualization of the point of interest on a map, all are interpreted as evaluations or observations that are cognitively performed in the human mind or using pen to paper as a physical aid to collect a number of estimated number of people and displaying the results of the estimated population at a particular point of interest such as at a concert hall, a conference hall, a stadium, a restaurant, a hotel, a park or a residential home which is reflected on a map or graphical visualization which show the population results.
Examiner refers Applicant to MPEP § 2106.04 (a) (2) II which states that: “the sub-groupings encompass both activity of a single person (for example, a person following a set of instructions or a person signing a contract online) and activity that involves multiple people (such as a commercial interaction), and thus, certain activity between a person and a computer may fall within the "Certain Methods of Organizing Human Activities" groupings. It is noted that the number of people involved in the activity is not dispositive as to whether a claim limitation falls within this grouping. Instead, the determination should be based on whether the activity itself falls within one of the sub-groupings.”
With respect to “Mathematical Concepts” category, Examiner refers Applicant to MPEP § 2106.04 (a) (2) (I) (C): “A claim that recites a mathematical calculation, when the claim is given its broadest reasonable interpretation in light of the specification, will be considered as falling within the "mathematical concepts" grouping.” “It is important to note that a mathematical concept need not be expressed in mathematical symbols, because "[w]ords used in a claim operating on data to solve a problem can serve the same purpose as a formula." In re Grams, 888 F.2d 835, 837 and n.1, 12 USPQ2d 1824, 1826 and n.1 (Fed. Cir. 1989). See, e.g., SAP America, Inc. v. InvestPic, LLC, 898 F.3d 1161, 1163, 127 USPQ2d 1597, 1599 (Fed. Cir. 2018) (holding that claims to a ‘‘series of mathematical calculations based on selected information’’ are directed to abstract ideas); Digitech Image Techs., LLC v. Elecs. for Imaging, Inc., 758 F.3d 1344, 1350, 111 USPQ2d 1717, 1721 (Fed. Cir. 2014) (holding that claims to a ‘‘process of organizing information through mathematical correlations’’ are directed to an abstract idea).” Furthermore, see MPEP § 2106.05 (c): “For data, mere "manipulation of basic mathematical constructs [i.e.,] the paradigmatic ‘abstract idea,’" has not been deemed a transformation. CyberSource v. Retail Decisions, 654 F.3d 1366, 1372 n.2, 99 USPQ2d 1690, 1695 n.2 (Fed. Cir. 2011) (quoting In re Warmerdam, 33 F.3d 1354, 1355, 1360, 31 USPQ2d 1754, 1755, 1759 (Fed. Cir. 1994)).”
Turning now to “Enfish”, the Examiner reveals that the Federal Circuit did not find its claims as an eligible improvement, by simple virtue of mere allowing the computer to perform tasks not previously, but instead found a database improvement (see “Elec. Power Grp.p.1482 ¶2-¶3), further explained by configuring a memory according to a logical table that need not be stored contiguously in the computer memory, but instead appended with new columns that are available for immediate use through the creation of new column definition records, with one or more cells defined by the intersection of the rows and columns, and with the object identification number that, acting as a pointer of variable length between databases, to identify each said logical row, corresponding to a record of information, hereinafter “Enfish” case noting providing a trifecta improvements in: increasing flexibility, providing faster search times, and smaller memory requirements. Indeed, as later elucidated by the Federal Circuit in “Elec. Power Grp”: “In Enfish, we applied the distinction to reject the § 101 challenge at stage one because the claims at issue focused not on asserted advances in uses to which existing computer capabilities could be put, but a particular database technique in how computers could carry out one of their basic functions of storage and retrieval of data (“Elec. Power Grp” p.1742 ¶3 last sentence citing “Enfish, 822 F.3d at 1335-36”; “Bascom, 2016 WL 3514158, at *5; cf. Alice, 134 S. Ct. at 2360”. Whereas here, similar to “Elec. Power Grp” p.1742 ¶3 last sentence, the focus of the claims is not on such an improvement in computers as tools, but on certain independently abstract ideas that use computers as tools to allegedly allow the computer to perform the function of estimating the number of people at a point of interest (e.g., in real-time), using real-time vehicle sensor data which could only be performed intuitively by a human cognitively via evaluations or observations in the human mind or using pen to paper as a physical aid or “Mathematical Concepts”.
Next, responding to the Applicant’s asserted analogy of the current claims to the ones in “McRO”, Examiner resubmits that just because the claims are limited or narrowed to few rules does not make them eligible under Federal Circuit’s decision in “McRO”. Specifically, “McRO” was not found eligible just for applying rules having particular requirements to a data set for manipulating video content as asserted. Instead, what made “McRO” eligible was it capability to provide technological solution that clearly and deliberately improved 3D-lip synchronization in computer animation by automatically setting keyframe at correct point to depict more realistic speech, fine tuning and generate transition parameters and apply such transition parameters to create a final morph weight set. More specifically, “McRO’s” technological improvement “defined output morph weight set stream as a function of phoneme sequence and time of said phoneme sequence” that “adjust for the fact that a phoneme may look different when spoken depending on the phonemes preceding and/or following it” varying by character as, for example, “a swamp monster will use different rules than a tight-lipped cat” (“McRO” supra at p.1098 ¶2-¶4). Accordingly, “McRO’s” 3D technological improvement, of keyframe lip-synchronization in computer animation, produced a final morphology of more accurate and realistic lip synchronization and facial expressions, compensating for differences in mouth positions for similar phonemes based on context.
Examiner submits that while technological improvements need not solely stem from: phoneme to keyframe lip synchronization in facial 3D animation in “McRO” supra, it is still worth noting that of “McRO” raised above, are relevant in providing valuable insight to ascertain what actual technology is, and most importantly, to ascertain what constitutes deliberate improvement to actual technology or the computer itself as opposed to a mere entrepreneurial improvement.
Examiner also cautions Applicant that even requiring the implementation of the computer functions in real time (“Intellectual Ventures I LLC v. Capital One Bank (USA) court case”), or referring to the complexity of the implementing software or the level of detail in the Specification (see Accenture Global Servs., GmbH v. Guidewire Software, Inc. court case) does not transform a claim reciting only an abstract concept into a patent-eligible system or method, and even if such level of detail would be explicitly claimed, it would still remain representative of mere mental limitations reminiscent to at least the ones found ineligible in “Bilski v. Kappos court case.”
Therefore, Examiner maintains that Claims 1-19 and 21 as currently recited do not contain additional elements that integrate the judicial exception into a practical application under step 2a prong 2 of the 35 U.S.C. § 101 analysis and are not-analogous to the Enfish LLC v. Microsoft Corp No. 2015-1244 (Fed. Cir. 2016) and the McRO, Inc. dba Planet Blue v. Bandai Namco Games America, Inc. 120 USPQ2d 1091 (Fed. Cir. 2016) court cases.
Examiner draws Applicant’s attention to Example 39 of the 35 U.S.C. 101 Examples which pertains to a Method for Training a Neural Network for Facial Detection. In Example 39, this claimed invention addresses this issue by using a combination of features to more robustly detect human faces. The first feature is the use of an expanded training set of facial images to train the neural network. This expanded training set is developed by applying mathematical transformation functions on an acquired set of facial images. Applicant’s invention addresses this issue by using a combination of features to more robustly detect human faces. The first feature is the use of an expanded training set of facial images to train the neural network. This expanded training set is developed by applying mathematical transformation functions on an acquired set of facial images. Therefore Example 39 was deemed patent eligible over step 2a prong 1 as not reciting a judicial exception (e.g., “Mental Process” or “Certain Method of Organizing Human Activities”). Examiner notes that Example 39 also provided specific technical improvements with respect to digital images for facial detection. This combination of features provides a robust face detection model that can detect faces in distorted images while limiting the number of false positives which resulted in being more accurate in previous prior art technologies.
In contrast to Example 39 of the 35 U.S.C. 101 Examples, the claim limitations of Independent Claims 1, 12 and 16 recite, for example; “receiving a first estimated number of people within a geographic boundary based on a prediction generated by a machine learning model taking as an input door sensor data indicating a duration of a door of a vehicle is open and seat belt sensor data indicating a change in status of a seat belt sensor” and “causing the first visual indicator to be displayed on a map displayed in a user interface, the first visual indicator displayed above a visualization of the point of interest on the map”. Examiner notes when comparing the “inputs” shown in the initial “training” the machine learning in Independent Claims 1, 12 and 16 to the “inputs” shown in “training” the neural network algorithm in Example 39 they are not the same. The “inputs” fed into “training” step in Independent Claims 1, 11 and 16 are interpreted as numerical values or quantifiable values to estimate population density at a particular point of interest, whereas the inputs for Example 39 pertaining to “creating a first training set comprising the collected set of digital facial images, the modified set of digital facial images, and a set of digital non-facial images” which is rooted in image/digital facial recognition technology. Secondly, the “retraining” the neural network algorithm in Example 39 of the USPTO 101 Examples is rooted in technology for image/digital facial recognition technology through a feedback loop resulting in a robust face detection model that can detect faces in distorted images while limiting the number of false positives. Examiner notes that when looking at Independent Claims 1, 11 and 16, there is no “retraining” of the machine learning algorithm shown at all when compared to Example 39 of the 35 U.S.C. § 101 Examples. Applicant does not contend it invented “machine learning algorithms” in general or any such algorithm in particular. Examiner is not persuaded that “training” steps as recited in Independent Claims 1, 12 and 16 estimate population density at a particular point of interest and displaying the results of the population density on a map depicting the visualization with a color indicator is other than “Certain Methods of Organizing Human Activities” or “Mathematical Concepts” or “Mental Processes” under step 2a prong 1 and therefore also does not integrate the judicial exception into a practical application under step 2a prong 2. In conclusion, Examiner maintains that Claims 1-19 and 21 are deemed non-analogous to Example 39 of the 35 U.S.C. 101 Examples.
Examiner notes that Example 47, Claim 3 was deemed patent-eligible because it includes specific, practical steps beyond the basic anomaly detection of an artificial neural network (ANN). While the claim uses a high-level abstract concept (training an ANN to detect anomalies), it integrates this into a practical application by specifying that the detected anomalies are used to perform remedial actions like dropping malicious packets and blocking their source addresses in real time. These additional steps improve the technical field of network security and address a real-world problem, which makes the claim eligible under the USPTO's guidance.
In contrast to Example 47 Claim 3, Examiner notes that for example; the claim limitations of “receiving a first estimated number of people within a geographic boundary based on a prediction generated by a machine learning model taking as an input door sensor data indicating a duration of a door of a vehicle is open and seat belt sensor data indicating a change in status of a seat belt sensor” and “causing the first visual indicator to be displayed on a map displayed in a user interface, the first visual indicator displayed above a visualization of the point of interest on the map” are deemed non-analogous to Example 47 Claim 3. The method: Receiving sensor data, using a machine learning model to make a prediction, and displaying a visual indicator on a map based on that prediction. Input/Output: The input is vehicle sensor data (door, seatbelt), the prediction is an estimated number of people, and the output is a visual indicator on a map. This method essentially gathers data, analyzes it, and presents the result visually. While useful, it does not represent a sufficient "technological improvement" to qualify for patent eligibility. Moreover, the claim limitations steps recited for Independent Claims 1, 12 and 16 are more closely analogous to the ineligible Example 47 Claim 2 because they describe a process that takes data, analyzes it with a machine learning model, and presents the result. The steps describe the display of information, which is often an "insignificant extra-solution activity" that does not, on its own, confer eligibility. In conclusion, Examiner maintains that Claims 1-19 and 21 are deemed non-analogous to Example 47 Claim 3 of the 35 U.S.C. § 101 Examples.
Argument C:
Applicant comments on USPTO’s August 4th, 2025 35 U.S.C. 101 memorandum to show that the instant claimed invention claims are patent eligible over 35 U.S.C. § 101 (see page 6 of Applicant’s response). The Examiner respectfully disagrees.
In response to Applicant’s remarks here, Examiner notes to Applicant to please refer back to Examiner’s comments under Arguments A-B sections.
Argument D:
Applicant argues that (citing Ex Parte Desjardins) the present claims provide a technical solution to a technical problem by applying machine learning and sensors to solve the technical problem of “real-time, data-driven estimation of crowd density at POIs and dynamic route planning” (page 8 of Applicant’s response).
The Examiner respectfully disagrees. The sensors serve as generic data gathering devices. The machine learning is presented at a high level and as a general link to technology. Determining crowd density at POIs and performing dynamic route planning are not necessarily technical problems. Additionally, a human can visually monitor crowd density at POIs and suggest routes based on this information. The additional elements only serve as generic tools to facilitate this process that could otherwise be performed by a human.
Claim Rejections - 35 USC § 101
13. 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.
14. Claims 1-19 and 21 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Claims 1-19 and 21 are each focused to a statutory category namely, a 1st “method” or a 1st “process” (Claims 1-11) and a 2nd “method” or a 2nd “process” (Claims 12-15), a 3rd “method” or a 3rd “process” (Claims 16-19 and 21).
Step 2A Prong One: Independent Claims 1, 12 and 16 recite limitations that set forth the abstract idea(s), namely (see in bold except where strikethrough):
“” (Claim 20);
“” (Claim 20);
“receiving a first estimated number of people within a geographic boundary based on a prediction generated by a model taking as an input door data indicating a duration a door of a vehicle is open and seat belt data indicating a change in status of a seat belt wherein the prediction is generated by the model trained on door data, seat belt status changes, and seat occupancy data included in a ground truth dataset indicating observed counts of people exiting vehicles at known locations, and wherein the prediction generated by the model includes a number of people who have exited the vehicle within the geographic boundary” (see Independent Claim 1);
“receiving a capacity for a point of interest corresponding to the geographic boundary, wherein the capacity for the point of interest corresponding the geographic boundary is defined by building footprint data, a type associated with the point of interest, and adjacent road segments associated with the point of interest” (see Independent Claim 1);
“calculating a capacity index, the capacity index being a ratio of the first estimated number of people to the capacity, wherein the capacity index indicates a crowd density including low, medium, or high based on a type associated with the point of interest” (see Independent Claim 1);
“generating a first visual indicator indicating the crowd density and the first estimated number of people at the point of interest based on the capacity index, wherein the first visual indicator, the capacity index, and the crowd density are updated in response to obtaining the door sensor data and the seat belt sensor data” (see Independent Claim 1);
“causing the first visual indicator to be displayed on a map displayed , the first visual indicator displayed above a visualization of the point of interest on the map, wherein the first visual indicator includes a color-coded icon determined based on the capacity index” (see Independent Claim 1);
“modifying a route plan based on the crowd density” (see Independent Claim 1);
“receiving a first estimated number of people, outside of vehicles, corresponding to a first point of interest based on a prediction generated by a model taking as an input door data indicating a duration a door of a vehicle is open and seat belt data indicating a change in status of a seat belt , wherein the prediction is generated by the model trained on door data, the seat belt data, and seat occupancy data included in a ground truth dataset indicating observed counts of people exiting vehicles at known locations, and the prediction generated by the model includes a number of people who have exited the vehicle within the first point of interest” (see Independent Claim 12);
“receiving a second estimated number of people, outside of vehicles, corresponding to a second point of interest” (see Independent Claim 12);
“receiving a first capacity for the first point of interest and a second capacity for the second point of interest, wherein the first capacity for the first point of interest is defined by footprint data and adjacent road segments associated with the first point of interest” (see Independent Claim 12);
“calculating a first capacity index for the first point of interest and a second capacity index for the second point of interest, wherein the first capacity index and the second capacity index indicates a crowd density including low, medium, or high” (see Independent Claim 12);
“generating a first indicator, based on the first capacity index indicating the first capacity index, and a second indicator, based on the second capacity index, wherein the first indicator, the first capacity index, and the crowd density are updated in response to obtaining the door sensor data and the seat belt sensor data” (see Independent Claim 12);
“causing the first indicator to be displayed on a map displayed , the first indicator displayed above a visualization of the point of interest on the map, where the first indicator includes a color-coded icon determined based on the first capacity index” (see Independent Claim 12);
“modifying a route plan based on the crowd density” (see Independent Claim 12);
“receiving an estimated number of people, outside of vehicles, within one or more geographic boundaries, based on a prediction generated by a model taking as an input door data indicating a duration a door of a vehicle is open and seat belt data indicating a change in status of a seat belt wherein the model is trained on data included in a ground truth dataset indicating observed counts of people exiting vehicles at known locations, and the prediction indicates a number of people who have exited the vehicle within the one or more geographic boundaries” (see Independent Claim 16);
“receive a capacity for a point of interest corresponding to the one or more geographic boundaries, wherein the capacity for the point of interest corresponding to the one or more geographic boundaries is defined by building footprint data and adjacent road segments associated with the point of interest” (see Independent Claim 16);
“calculating a capacity index, the capacity index being a ratio of the estimated number of people to the capacity, wherein the capacity index indicates a crowd density including low, medium, or high based on a type associated with the point of interest” (see Independent Claim 16);
“generating a visual indicator indicating the estimated number of people at the point of interest based on the capacity index” (see Independent Claim 16);
“wherein the estimated number of people, the capacity index, and the visual indicator are updated periodically or in response to obtaining the door sensor data and the seat belt sensor data” (see Independent Claim 16);
“causing the visual indicator to be displayed on a map displayed , the visual indicator displayed above a visualization of the point of interest on the map, where the visual indicator includes an icon determined based on the capacity index” (see Independent Claim 16);
“modifying a route plan based on the crowd density” (see Independent Claim 16);
These abstract idea limitations (as identified above in bold), under their broadest reasonable interpretation of the claims as a whole, cover performance of their limitations as “Mental Processes” which pertains to (1) concepts performed in the human mind (including observations or evaluations or judgments) or (2) using pen and paper as a physical aid, which in order to help perform these mental steps does not negate the mental nature of these limitations. The use of "physical aids" in implementing the abstract mental process, does not preclude the claim from reciting an abstract idea. See MPEP § 2106.04(a) III C.
Additionally, or alternatively, these abstract idea limitations (as identified above in bold), under their broadest reasonable interpretation of the claims as a whole, cover performance of their limitations as “Certain Methods of Organizing Human Activities” which pertains to (3) managing personal behavior or relationships or interactions between people (which includes teachings or following rules or instructions) or “Mathematical Concepts” which pertains to (4) mathematical calculations.
That is, other than reciting (e.g., “at least one processor” & “memory storing instructions” & “user interface” & “door sensor” & “sensor data” & “seat belt sensor”, etc…), nothing in the claim elements precludes the steps from being performed as “Mental Processes” which pertains to (1) concepts performed in the human mind (including observations or evaluations or judgments) or (2) using pen and paper as a physical aid, and additionally or alternatively as “Certain Methods of Organizing Human Activities” which pertains to (3) managing personal behavior or relationships or interactions between people (which includes teachings or following rules or instructions) or “Mathematical Concepts” which pertains to (4) mathematical calculations.
Therefore, at step 2a prong 1, Yes, Claims 1-19 and 21 recite an abstract idea. We proceed onto analyzing the claims at step 2a prong 2.
Step 2A Prong Two: With respect to Step 2A Prong Two of the eligibility inquiry (as explained in MPEP § 2106.04(d)), the judicial exception is not integrated into a practical application. Independent Claims 1, 12 and 16 recites additional elements directed to: (e.g., “at least one processor” & “memory storing instructions” & “user interface”). These additional elements have been considered individually and in combination, but fail to integrate the abstract idea into a practical application because they amount to using generic computing elements or instructions (software) to perform the abstract idea, similar to adding the words “apply it” (or an equivalent), which merely serves to link the use of the judicial exception to a particular technological environment. See MPEP § 2106.05(f) and MPEP § 2106.05(h).
Independent Claims 1, 12 and 16: With respect to reliance on “door sensor” & “seat belt sensor” & “sensor data” & “machine learning model” (including “as an output of the machine learning”) as additional elements shown in Independent Claims 1, 12 and 16 when considered individually and as an ordered combination (as a whole) in view of these claim limitations, these additional elements do not provide limitations that are indicative of integration into a practical application under step 2a prong 2 due to the following: (1) recites mere instructions to implement an abstract idea on a computer or using a computer as a tool to “apply” the recited judicial exceptions by providing the results to the user on a computer (see MPEP § 2106.05 (f)) or (2) limiting a particular field of use or technological environment pertaining to estimating a number of people at a point of interest using vehicle sensor data and generating visual indications of the population of people using a computer in the field of estimating populations at a point of interest such as at a concert hall, a conference hall, a stadium, a restaurant, a hotel, a park or a residential home (see MPEP § 2106.05(h)).
Moreover, with respect to Independent Claims 1, 12 and 16, certain/particular limitations shown recite (1) mere data gathering and (2) mere data outputting/displaying in which each of these claim limitations reflects mere insignificant extra-solution activities (see MPEP § 2106.05 (g)). In addition, these limitations fail to provide an improvement to the functioning of a computer or to any other technology or technical field, fail to apply the exception with a particular machine, fail to apply the judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, fail to effect a transformation of a particular article to a different state or thing, and fail to apply/use the abstract idea in a meaningful way beyond generally linking the use of the judicial exception to a particular technological environment. Accordingly, because the Step 2A Prong One and Prong Two analysis resulted in the conclusion that the claims are directed to an abstract idea, additional analysis under Step 2B of the eligibility inquiry must be conducted in order to determine whether any claim element or combination of elements amount to significantly more than the judicial exception. Therefore, at step 2a prong 2, Claims 1-19 and 21 are directed to the abstract idea and do not recite additional elements that integrate into a practical application.
Step 2B: (As explained in MPEP § 2106.05), it has been determined that the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. Independent Claims 1, 12 and 16 recites additional elements directed to: (e.g., “at least one processor” & “memory storing instructions” & “user interface”). These elements have been considered individually and in combination, but fail to add significantly more to the claims because they amount to using generic computing elements or instructions (software) to perform the abstract idea, similar to adding the words “apply it” (or an equivalent), which merely serves to link the use of the judicial exception to a particular technological environment (computing environment) and does not amount to significantly more than the abstract idea itself. Notably, Applicant’s Specification suggests that the claimed invention relies on nothing more than a general-purpose computer executing the instructions to implement the invention (e.g., see at Applicant’s Specification ¶ [0158-0160] & Fig. 10.).
Independent Claims 1, 12 and 16: With respect to reliance on “door sensor” & “seat belt sensor” & “sensor data” & “machine learning model” (including “as an output of the machine learning”) as additional elements shown in Independent Claims 1, 12 and 16 when considered both individually and in combination (as a whole) with these recited claim limitations, these additional elements do not amount to significantly more than the judicial exceptions under step 2B due to the following: (1) recites mere instructions to implement an abstract idea on a computer or using a computer as a tool to “apply” the recited judicial exceptions by providing the results to the user on a computer (see MPEP § 2106.05 (f)) or (2) limiting a particular field of use or technological environment pertaining to estimating a number of people at a point of interest using vehicle sensor data and generating visual indications of the population of people using a computer in the field of estimating populations at a point of interest such as at a concert hall, a conference hall, a stadium, a restaurant, a hotel, a park or a residential home (see MPEP § 2106.05(h)).
Moreover, with respect to Independent Claims 1, 12 and 16, certain/particular limitations shown recite (1) mere data gathering and (2) mere data outputting/displaying in which each of these claim limitations reflects mere insignificant extra-solution activities (see MPEP § 2106.05 (g)). Furthermore, these certain/particular limitations claim limitations as demonstrated above for Independent Claims 1, 12 and 16 reflect Well-Understood, Routine and Conventional Activities (WURC) under MPEP § 2106.05 (d) ii: See 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); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359,1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network).
The additional element of “machine learning model” or “machine learning” in Independent Claims 1, 12 and 16 does not amount to significantly more than the judicial exception under step 2B due to being expressly recognized as Well-Understood, Routine and Conventional (WURC) in the art. See e.g., US PG Pub (US 2020/0286391 A1) – Beaurepaire, et. al. Beaurepaire at ¶ [0078] notes “Determining dynamic population density for calculating risks can be a significant technical challenge. The system 100 introduces a further capability to predict population in a given area at a certain time (e.g., time in the future). FIG. 7 is a diagram of an overview of providing dynamic population density data. The system 100 collects data from and learns from various input data sources 701 to train a machine learning model 703 (e.g., neural network or equivalent) to make a dynamic population density prediction 705.” See e.g., US PG Pub (US 2023/0105099 A1) - Stade-Schuldt, et. al. Stade-Schuldt at ¶ [0025]: “Ground truth population data can be used with aggregated mobility data and map data (e.g., road network features, geographic sub-area features) to train a machine learning model to determine the estimated population density for a geographic sub-area. Combine dynamic input data (mobility data, ground truth population data) and static data (map data) to estimate the population density a geographic sub-area. This concept provides a nanocensus service that solves the prediction problem of how many people are estimated to be in a given area at a given time.” The additional element of “door sensor” and “seat belt sensor” in Independent Claims 1, 12 and 16 does not amount to significantly more than the judicial exception under step 2B due to being expressly recognized as Well-Understood, Routine and Conventional (WURC) in the art. See e.g., Applicant’s Original Specification at ¶ [0066-0067]: “The vehicle data 112 may include speed data, direction data, location data, time data, battery data, fuel level data, door sensor data, seat data, seatbelt data, weather data (e.g., from an in-vehicle thermometer or other weather-related sensor), and/or other vehicle sensor data that may be recognized by those of ordinary skill in the art.” See also US PG Pub (US 2016/0171521 A1) hereinafter Ramirez, et. al. Ramirez at ¶ [0105-0107]: “Sensor 711 also may detect and store data received from the vehicle's 710 internal systems, such as impact to the body of the vehicle, air bag deployment, headlights usage, brake light operation, door opening and closing, door locking and unlocking, cruise control usage, hazard lights usage, windshield wiper usage, horn usage, turn signal usage, seat belt usage, phone and radio usage within the vehicle, autonomous driving system usage, maintenance performed on the vehicle, and other data collected by the vehicle's computer systems, including the vehicle on-board diagnostic systems (OBD).”
In addition, when taken as an ordered combination, the ordered combination adds nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements integrates the abstract idea into a practical application. Therefore, when viewed as a whole, these additional claim elements do not provide meaningful limitations to transform the abstract idea into a practical application of the abstract idea or that, as an ordered combination, amount to significantly more than the abstract idea itself.
Dependent Claims 2-11, 13-15, 17-19 and 21 recite additional elements directed to: (e.g., “at least one processor” & “memory storing instructions” & “user interface”), which in conjunction with the limitations recite the same abstract idea(s) as shown in Independent Claims 1, 12 and 16 along with further steps/details that reflect “Certain Methods of Organizing Human Activities” Grouping which pertains to (1) managing personal behavior or relationships or interactions between people (including teachings or following rules or instructions) and additionally or alternatively as “Mental Processes” which pertains to (2) concepts performed in the human mind (including observations or evaluations or judgments) or (3) using pen and paper as a physical aid or “Mathematical Concepts” which pertains to (4) mathematical calculations.
Dependent Claims 2-11, 13-15, 17-19 and 21 further narrow the abstract ideas, and are therefore still ineligible for the reasons previously provided in Steps 2A Prong 2 and Step 2B for Independent Claims 1, 12 and 16. The ordered combination of elements in the Dependent Claims (including the limitations inherited from the parent claim(s)) add nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Accordingly, the subject matter encompassed by the dependent claims fails to amount to a practical application or significantly more than the abstract idea itself. Therefore, under Step 2B, Claims 1-19 and 21 do not include additional elements that are sufficient to amount to significantly more than the recited judicial exceptions. Thus, Claims 1-19 and 21 are ineligible with respect to the 35 U.S.C. § 101 analysis.
Allowable Subject Matter
Claims 1-19 and 21 are allowed over the prior art of record. The claims remain rejected under 35 U.S.C. § 101.
The following is a statement of reasons for the indication of allowable subject matter:
US PG Pub (US 2021/0179117 A1) hereinafter Glazman, et. al., in view of US Patent # (US 10,214,118 B1) hereinafter Jain, et. al., in view of US PG Pub (US 2023/0085346 A1) hereinafter Sameer, et. al., and in further view of US PG Pub (US 2022/0138260 A1) hereinafter Koval, et. al. most closely address the various concepts recited in each of the independent claims, as seen in the last art rejection of claims 1, 12, and 15-16 in the Office action dated October 29, 2025. However, the Examiner finds that one of ordinary skill in the art prior to Applicant’s invention would not have, in light of the teachings of the aforementioned references, found it obvious to create the claimed invention with the level of detail and specific manner of integration of operations as they are presented in each of the independent claims.
Additionally, Beaurepaire et al. (US 2015/0219464) determines a number of people within a geographical boundary based on seat belt and door opening/closing activity (Beaurepaire: ¶ 43 – “The information conveyed may include instructions for navigating to the recommended passenger embarkation point as a waypoint on a route to the point of interest. The information may also specify the availability of a recommended passenger embarkation point. For example, in the case where a limited amount of time is available for drop off or pick up to occur, the time availability is presented to the passenger or the driver of the vehicle. In one embodiment, this information may also be conveyed to other users or drivers within proximity of the recommended embarkation point. Per this approach, oncoming vehicles are warned or made aware of the current actions of the passenger at the drop off or pick up point for safety purposes. In one example embodiment, the platform 103 may determine other users and/or other vehicles over the network to gather location information, in order to provide recommendations to the at least one user and/or at least one vehicle, for example, during a concert, the platform 103 may propose an alternative drop off location for the at least one user and/or the at least one vehicle to prevent a blocking of the at least one vehicle by other vehicles. The platform 103 may determine that the at least one recommended drop off location may be crowded as it monitors the number of users navigating to the same location.”; ¶ 42 – “In one example embodiment, the user interface may not point at the one or more locations but may present something similar to a heat map representing a suitable drop off and/or a pick up areas for a point of interest, for example, one or more green areas on the map may depict a suitable drop off and/or pick up regions where traffic is not impacted by such activities, whereas the red areas on the map may depict areas that is inappropriate for drop off and/or pick up because such activity disturbs the traffic.”). Route plans may be suggestion for selection based on this information (Beaurepaire: fig. 5B, 5C; ¶¶ 67-73). Beaurepaire also generates a heat map showing suitable areas for drop-off and/or pick-off by using green to indicate a suitable area since traffic would not be impacted and by using red to indicate an inappropriate area (Beaurepaire: ¶ 42). Beaurepaire does not address all of the details of the independent claims, including the use of machine learning trained on at least the door sensor data and seat belt status changes included in a ground truth dataset or that a capacity for a point of interest corresponds to geographic boundaries defined by building footprint data and adjacent road segments.
Beaurepaire et al. (US 2023/0039738) evaluates a footprint of a building in regard to traffic impact simulations (Beaurepaire ‘738: ¶¶ 39, 91); however, this is not performed within the same context of Applicant’s claimed invention as a whole.
Shoda et al. (US 2020/0209878) determines a degree of congestion based on a ratio of the number of vehicles per unit area (i.e., a density), as seen in ¶ 153 of Shoda; however, Shoda still fails to make up for the deficiencies of the aforementioned references.
Kojo et al. (EP 3 809 393 A1) detects seatbelt and door usage to determine when passengers have boarded or alighted from a vehicle (Kojo: ¶¶ 40-45) and Kojo utilizes deep learning, such as neural networks (Kojo: ¶ 73); however, Kojo fails to make up for the deficiencies of the aforementioned references.
Again, the Examiner finds that one of ordinary skill in the art prior to Applicant’s invention would not have, in light of the teachings of the aforementioned references, found it obvious to create the claimed invention with the level of detail and specific manner of integration of operations as they are presented in each of the independent claims. Therefore, claims 1-19 and 21 are deemed to be allowable over the prior art of record.
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.
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/SUSANNA M. DIAZ/
Primary Examiner
Art Unit 3625A
Prosecution Insights