DETAILED ACTION
Applicant's Remarks, filed 05/27/2025, have been fully considered. The following rejections and/or objections are either reiterated or newly applied in view of instant application amendments. They constitute the complete set presently being applied to the instant application. Herein, "the previous Office action" refers to the Non-Final rejection of 02/26/2025. Upon further consideration of claim amendments, newly recited portions/rejections are discussed below.
Notice of Pre-AIA or AIA Status
The present application, filed on or after 16 March 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Status
Disposition of claims:
Claims 1, 3-10, 12-19, and 21-25 are currently pending and under examination herein.
Claims 2, 11, and 20 are previously cancelled.
Claims 1, 3-10, 12-19, and 21-25 are rejected.
Priority
Applicant’s invention, filed 14 December 2020, does not claim for the benefit of priority, therefore, effective filing date is 14 December 2020.
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.
Step 1: Statutory categories
Claims 1, 3-10, 12-19, and 21-25, are rejected under 35 U.S.C. 101 because the claimed invention is directed to judicial exceptions (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more, in accordance with MPEP § 2106.
Claims 19 and 21-25 are rejected under 35 USC 101, for reciting “computer program product.” Said instant claim limitation read on carrier waves, which read on transitory propagating signals which are not proper patentable subject matter because they do not fit within any of the four statutory categories of invention (In re Nuijten, Federal. Circuit, 2006). It is noted that the recitation of a "non-transitory computer-readable medium" (NTCRM) would overcome this portion of the rejection with support in the specification [0086-0087] which limits said computer program product as non-transitory signals. For compact prosecution, instant claims 19 and 21-25 limitation, computer program product, is interpreted as “non-transitory computer media,” the preferred terminology for the Applicant to apply to any future claim amendments.
Appropriate correction is requested.
Regarding claims 1, 3-10, and 12-18, limitations are found to recite statutory subject matter (Step 1: YES). Claims are directed to a process (method claims 1, and 3-9) and machines (system claims 10, and 12-18) with processors.
Step 2A Prong 1: Judicial Exceptions
The instant claims are then analyzed to determine if the claims recite any concepts that equate to an abstract idea, law of nature, or natural phenomenon (Step 2A, Prong 1). Abstract ideas include mathematical concepts (mathematical formulas or equations, mathematical relationships, and mathematical calculations), and mental processes (including procedures for collecting, observing, evaluating, and organizing information (see MPEP 2106.04(a)(2)). In the instant application, the claims recite the following limitations that equate to an abstract idea, with mental steps and mathematical concepts. The claims directing to abstract ideas (mental processes and mathematical concepts) are as follows:
Claims 1, 10, and 19 recite generating…a real-time social movement model of the plurality of people moving in the particular area based on the movement data, the interarrival data, and the meeting data of the plurality of people mapped to a bounded grid of the particular area in response to detecting, by the processor, a permission input to capture cohort data associated with a user device of the use… to generate a user-specific real-time social movement model for the user… response to the user request, generating, by the processor, a plurality of routes for a user to travel from the first location to the second location in the particular area at the particular time based on the user-specific real-time social movement model, wherein the plurality of routes includes a prediction of a number of people the user will encounter in a corresponding route, wherein the user-specific real-time social movement model generates the plurality of routes for the user based in part on a first likelihood of intersecting with the one or more known associates of the user and a second likelihood of the user stopping to interact with the one or more known associate; dynamically updating, by the processor, the user-specific real-time social movement model based on real-time contact data captured by the processor while the user travels on a particular route of the plurality of routes, wherein the real-time contact data is based on real-time communication between the user device and other user devices associated with actual people encountered in the particular route; Andin response to determining, by the processor, that a number of the actual people encountered in the particular route is greater than the prediction, modifying the user-specific real- time social movement model using the real-time contact data received from the user for generating future routes.
Claims 3, 12, and 21 recite analyzing regional data of the particular area.
Claims 4, 13, and 22 recite constructing a sparsity grid of the particular area; performing a queue analysis on the real-time congregation data; and modeling one or more random walks based, at least in part, on the real-time congregation data.
Claims 5, 14, and 23 recite producing a matrix using a set of input feeds, wherein the set of input feeds are based on the results of the real-time congregation data that was statistically analyzed.
Claims 6, 15. and 24 recite overlaying the matrix over a sparsity grid of the particular area.
Claims 7, 16, and 25 recite generating the plurality of routes for the user to travel… generating an alternative route from the first location to the second location to avoid the user intersecting with the one or more known associates of the user responsive to determining that user would stop to interact with the one or more known associates based on the cohort data.
Claims 8 and 17 recite generating a predicted level of exposure…
Claims 9 and 18 generating an actual level of exposure from the particular route based on the real-time communication between the user device and the other user devices associated with the actual people encountered in the particular route.
Hence, the claims explicitly recite elements that, individually and in combination, constitute abstract ideas. Also, potentially include the judicial exception of organizing human behavior in claims 1, 10, and 19 which recite “captured by the user device responsive to the user stopping to interact with one or more known associates.”
With respect to step (2A), under the broadest reasonable interpretation (BRI), the instant claims recite a system, NTCRM, and method for route planning that relies on mental decision-making with applied mathematical relationships--all judicial exceptions of abstract groupings and covering performance either in the mind and/or performance by mathematical operation.
Instant claims direct to mental processes when receiving…generating…receiving… generating… receiving… updating… a social movement model…predict… routes having a least number of people… determining … likely user locations, and choosing low exposure routes. Under the broadest reasonable interpretation, a person of ordinary skill in the art could simply perform by mentally observing said route metrics (through previous/ongoing experiences or mobile device updates), glean the salient information, and decide the best route to drive (see MPEP § 2106.04(a)(2), subsection III). The courts do not distinguish between mental processes that are performed entirely in the human mind and mental processes that require a human to use a physical aid (e.g., pen and paper or a slide rule) to perform the claim limitation (see, e.g., Benson, 409 U.S. at 67, 65, 175 USPQ at 674-75, 674 (noting that the claimed "conversion of [binary-coded decimal] numerals to pure binary numerals can be done mentally," i.e., "as a person would do it by head and hand."); Synopsys, Inc. v. Mentor Graphics Corp., 839 F.3d 1138, 1139, 120 USPQ2d 1473, 1474 (Fed. Cir. 2016) (holding that claims to a mental process of "translating a functional description of a logic circuit into a hardware component description of the logic circuit" are directed to an abstract idea, because the claims "read on an individual performing the claimed steps mentally or with pencil and paper")). Despite being performed in an environment with computer components (FIG 1, [00034-00039]) (system architecture/network: FIG 3-4, modules FIG 1, user devices FIG 3B), these steps (FIG 2) can be performed by an epidemiologist or public health official of ordinary skill in the art, in her own head, with public health data and existing interventions (social distancing), albeit in a time-consuming process. The courts do not distinguish between claims that recite mental processes performed by humans and claims that recite mental processes performed on a computer. As the Federal Circuit has explained, "[c]ourts have examined claims that required the use of a computer and still found that the underlying, patent-ineligible invention could be performed via pen and paper or in a person’s mind." Versata Dev. Group v. SAP Am., Inc., 793 F.3d 1306, 1335, 115 USPQ2d 1681, 1702 (Fed. Cir. 2015); Mortgage Grader, Inc. v. First Choice Loan Servs. Inc., 811 F.3d 1314, 1324, 117 USPQ2d 1693, 1699 (Fed. Cir. 2016) (holding that computer-implemented method for "anonymous loan shopping" was an abstract idea because it could be "performed by humans without a computer").
Instant claims direct to mathematical concepts from applied mathematics with a social movement model using probability to predict… routes having a least number of people, “statistically analyze” (claims 5, 14, and 23)/qualitative/relational methods (FIG 1-2 100,101, 200: data matrix, queue analysis, random walk/route modeling, sparsity grids (claim 4, 13, and 22 and [0021-0022]) with Voronoi Function [0020]). These recitations are similar to the concepts of collecting information, analyzing it and displaying certain results of the collection and analysis (Electric Power Group, LLC, v. Alstom (830 F.3d 1350, 119 USPQ2d 1739 (Fed. Cir. 2016)), organizing and manipulating information through mathematical correlations (Digitech Image Techs., LLC v Electronics for Imaging, Inc. (758 F.3d 1344, 111 U.S.P.Q.2d 1717 (Fed. Cir. 2014)) and comparing information regarding a sample or test to a control or target data in (Univ. of Utah Research Found. v. Ambry Genetics Corp. (774 F.3d 755, 113 U.S.P.Q.2d 1241 (Fed. Cir. 2014) and Association for Molecular Pathology v. USPTO (689 F.3d 1303, 103 U.S.P.Q.2d 1681 (Fed. Cir. 2012)) that the courts have identified as concepts that can be practically performed in the human mind with pen and paper, and can include mathematical concepts. As a whole, instant claims are focused on predicting and updating route parameters by calculating a real-time social movement model from mathematically modeling congregation data as matrices of sparsity grids/queue analyses/walks. Therefore, these limitations fall under the “mental process” and ”mathematical concepts” groupings of abstract idea and are directed to a judicial exception.
As such, claims 1, 3-10, 12-19, and 21-25 recite elements which constitute judicial exceptions: abstract ideas with both mathematical concepts and mental processes (Step 2A, Prong 1: YES).
Step 2A Prong 2: Integration into Practical Application
Claims found to recite a judicial exception under Step 2A, Prong 1 are then further analyzed to determine if the claims as a whole integrate the recited judicial exception into a practical application or not (Step 2A, Prong 2). The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the claims recite additional elements that equate to mere instructions and data use to apply the recited exception in a generic way or in a generic computing environment. Alice Corp., 573 U.S. at 223, 110 USPQ2d at 1983. See also 573 U.S. at 224, 110 USPQ2d at 1984.
With respect to the instant recitations, the claims recite the following additional elements considered for practical application:
Claims 1, 10, and 19 recite processor, system, memory; and a processor in communication with the memory, computer program product, GPS-enabled devices, IoT sensor.
Claim 1, 10, and 19 recites transforming… Global Positioning System (GPS) data collected from a plurality of GPS-enabled devices in a particular area and Information of Things (IoT) data collected from at least one IoT sensor in the particular area into, real- time congregation data associated with the from a plurality of devices indicating a plurality of people located in a particular area, wherein the real-time congregation data includes movement data, interarrival data, and meeting data of a plurality of people located in the particular area…capturing the cohort data including electronic communication patterns between the user device and one or more known associate devices and proximity duration data between the user device and the one or more known associate devices, wherein the proximity duration data is captured by the user device responsive to the user stopping to interact with one or more known associates corresponding to the one or more known associate devices such that the user device detects the one or more known associate devices in proximity of the user device for a duration of time; feeding, by the processor, the cohort data into the real-time social movement model…receiving… a user request to travel from a first location to a second location in the particular area at a particular time… contact data
Claim 10 recites receiving…real-time congregation data from a plurality of devices indicating a plurality of people located… receiving, by the processor, a user request to travel… receiving, by the processor, contact data associated with actual people encountered by the user traveling on the at least one route predicted to have the least number of people…
These additional elements do not reflect an improvement to technology or use the recited judicial exception to effect a particular outcome (see MPEP § 2106.05(a)), but are insignificant extra-solution activity that do not integrate into practical applications (MPEP § 2106.05(g)). The instant claims recite additional elements that amount to mere sequential instructions for using collected data in the judicial exceptions, and using generic computer systems of processor/memory of stored instructions to collect parameters without additional structure for the hardware components (system architecture/networked environment: FIG 3-4, with generic modules FIG 1, devices FIG 3B), or defining features (see MPEP § 2106.05(f)). These additional elements do not provide practical integration, as they are generically reciting computing components without improvement or particularity added to a machine—using them as a calculator or data depot, nor do they impose any meaningful limitation on the judicial exceptions (see MPEP 2106.05(a and b).
Other additional elements of claim limitations recite generating and processing data (receiving and analyzing congregation/regional data/travel/input feeds/user requests) These additional elements equate to steps of mere data gathering (location data for use in algorithm methods 100 and 200) for input/output into the generic routing system 100 (MPEP § 2106.05(g)) and so, merely inform the field of use of the invention without indication these affect or alter the judicial exception (see MPEP § 2106.05(h)). As such, these limitations equate to mere instructions and data usage in mental processes and mathematical concepts with generic computing systems. The courts have stated these do not render an abstract idea eligible in Alice Corp., 573 U.S. at 223, 110 USPQ2d at 1983. See also 573 U.S. at 224, 110 USPQ2d at 1984.
Therefore, these claims do not disclose any additional elements which might integrate into a practical application. The system and methods do not recite “particular” limitations that would have more than a nominal or insignificant relationship to the judicial exception. Rather, these limitations merely apply the exception in a generic way and do no integrate the recited exceptions into a practical application (see MPEP 2106.04(d)(2)). As such, 1, 3-10, 12-19, and 21-25, are directed to judicial exceptions: abstract ideas of mathematical concepts and mental processes (Step 2A, Prong 2: NO).
Step 2B: Inventive Concepts
Claims found to be directed to a judicial exception are then further evaluated to determine if the claims recite an inventive concept that provides significantly more than the judicial exception itself (Step 2B). When making a determination whether the additional elements in a claim amount to significantly more than a judicial exception, the additional element (or combination of elements) are considered for whether they are well-understood, routine, conventional activity in the art.
With respect to the instant recitations, the claims recite the following additional elements considered for inventive concepts:
Claims 1, 10, and 19 recite processor, system, memory; and a processor in communication with the memory, computer program product, GPS-enabled devices, IoT sensor.
Claim 1, 10, and 19 recites transforming… Global Positioning System (GPS) data collected from a plurality of GPS-enabled devices in a particular area and Information of Things (IoT) data collected from at least one IoT sensor in the particular area into, real- time congregation data associated with the from a plurality of devices indicating a plurality of people located in a particular area, wherein the real-time congregation data includes movement data, interarrival data, and meeting data of a plurality of people located in the particular area…capturing the cohort data including electronic communication patterns between the user device and one or more known associate devices and proximity duration data between the user device and the one or more known associate devices, wherein the proximity duration data is captured by the user device responsive to the user stopping to interact with one or more known associates corresponding to the one or more known associate devices such that the user device detects the one or more known associate devices in proximity of the user device for a duration of time; feeding, by the processor, the cohort data into the real-time social movement model…receiving… a user request to travel from a first location to a second location in the particular area at a particular time… contact data
Claim 10 recites receiving…real-time congregation data from a plurality of devices indicating a plurality of people located… receiving, by the processor, a user request to travel… receiving, by the processor, contact data associated with actual people encountered by the user traveling on the at least one route predicted to have the least number of people…
Under the broadest reasonable interpretation, the above recited limitations equate to analyzing received location data, which are then input to mental and mathematical concepts to predict a route with the least number of people. These additional elements do not contribute significantly more to well-known and conventional mental assessments which were routinely performed by users with access to mobile device tools, which were available by the instant effective filing date. For example, Google Apple Exposure Notification (May 2020) application location detection and contact exposure during the COVID-19 pandemic (Bair H et al; IDS cited). Another example is Google’s proximity awareness for a target person to a particular person or member of a user defined group (e.g. emergency contacts group), to a particular place (Gordon et al. US9769610B1, Adaptive location sharing based on proximity), which would entail storing and updating user location information and known contacts/places.
The courts have recognized the following steps as well-understood, routine, conventional activity when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity (see MPEP 2106.05(d)II.): 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 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network). Specifically, the courts have identified limitations of merely data gathering (receiving and analyzing cohort/congregation/regional data/travel/input feeds), see MPEP § 2106.05(g), as well-understood, routine, and conventional (performing repetitive calculations, Flook, 437 U.S. at 594, 198 USPQ2d at 199; recomputing or readjusting alarm limit values (Bancorp Services v. Sun Life, 687 F.3d 1266, 1278, 103 USPQ2d 1425, 1433 (Fed. Cir. 2012)), receiving or transmitting data, utilizing an intermediary computer to forward information: Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 and OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015); updating an activity log (Ultramercial, 772 F.3d at 716, 112 USPQ2d at 1755). A claim reciting a generic computer component (system architecture/networked environment such as instant specification (FIG 3-4, with generic modules FIG 1, devices FIG 3B) performing a generic computer function (instant claims of “receiving data…system/processor/devices/memory… contact/congregation data statistically analyzed…”) is necessarily ineligible (see e.g. Amdocs (Israel), Ltd. v. Openet Telecom, Inc., 841 F.3d 1288, 1316, 120 USPQ2d 1527, 1549 (Fed. Cir. 2016), BASCOM Global Internet Servs. v. AT&T Mobility LLC, 827 F.3d 1341, 1348, 119 USPQ2d 1236, 1241 (Fed. Cir. 2016). MPEP 2106.05(f) teaches mere data gathering and instructions to apply the judicial exception (using location data calculations to plan travel routes) cannot provide an inventive concept to the claims. The additional elements do not comprise an inventive concept, when considered individually or as an ordered combination, that transforms the claimed judicial exception into a patent-eligible application of the judicial exception. Therefore, the claims do not amount to significantly more than the judicial exception itself (Step 2B: No). As such, claims 1, 3-10, 12-19, and 21-25 are not patent eligible.
Response to 101 Remarks
The Applicant's remarks (p.14-17), filed 05/27/2025, have been fully considered. The Applicant asserts:
[Remarks, p.16-17] “Applicant notes that paragraph [0014] states, "While people can make general decisions based on their previous experiences (e.g., regarding times when a service station or other public space is servicing a high number or low number of people), such generalizations can often be inaccurate." (Emphasis added). The claims are not simply directed to automation. Instead, the claimed invention provides routes that are accurate and tailored specifically to each user based on their cohort data”… Even assuming that the tools listed by the Office could be used (which is not conceded by the Applicant), the mere fact a user can implement Applicant's claim as a single source for the solution without having to use a patchwork of different tools is in itself a technological improvement… Applicant's specification in paragraph [0093] discloses "special purpose hardware-based systems that perform the specified functions or acts or carry out combinations of special purpose hardware and computer instructions."
However, it is respectfully submitted that Applicant’s assertion is not persuasive. Under the broadest reasonable interpretation, the instant disclosure is a method, system, and non-transitory computer program product for travel route planning with GPS/IoT sensor data. There is no indication that the claimed route data analysis is so complex that it cannot be practically performed in the human mind with the existing tools at the time of the filing of instant application. The features which Applicant highlights, including user-specific, real-time social movement model from GPS/IoT sensor data and cohort data, is embodied in Jain, as well as Yadev, Tineo, Sheha, Altman, as discussed in the prior office action. Huang’s article shows how a common user device, an Apple cellular phone, contains GPS sensors coupled to a location tracking application, “Find My Friends” can equip with the data to make a real-time decision to avoid walking into a phone contact, whether his ex-girlfriend, his boss, or his mother-in law. Further, the standard for the judicial exception of mental process is not the level of accuracy, but the ability to be a human mental process, of which route planning is a convention mental process as disclosed by Applicant [Specification [0014-0015].
Thus the claims do not amount to significantly more than the judicial exceptions of abstract ideas (both mental processes and mathematical concepts), and as such, claims 1, 3-10, 12-19, and 21-25 are not patent eligible.
Claim Rejections - 35 USC § 112(a)
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1, 10, and 19 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. This rejection is newly recited.
The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.. Specifically, the claims 1, 10, and 19 recite the limitation of “a permission input” from “ in response to detecting, by the processor, a permission input to capture cohort data associated with a user device of the user, capturing the cohort data” which is not defined or described in the specification in order initiate cohort data collection.
Claim Rejections - 35 USC § 112(b)
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 1, 10, and 19 rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. This rejection is newly recited.
Claims 1, 10, and 19 recite “permission input” from “ in response to detecting, by the processor, a permission input to capture cohort data associated with a user device of the user, capturing the cohort data” which is indefinite. The instant specification does not include a description of the term, and there is no standard definition in the art. For compact prosecution, the term is interpreted as equivalent to a data collection trigger.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Note: references to instant claims are italicized in the following section regarding USC § 103.
Claims 1, 3, 5-6, 8-10, 12, 14-15, 17-19, 21, and 23-24 are rejected under 35 USC 103 as unpatentable over Jain (US11056242B1, cited on the 1/18/2024 Form 892; herein referred to as Jain).
Jain in ‘242 teaches predicting a level of disease exposure from user activity by correlating visits to different locations, and subsequent contraction of the disease by context data for individuals that visited the location (congregation data), incorporating multiple properties of location, community (regional data), and user behavior (claim 1, Col 103 L34-39).
Regarding instant method (claim 1), with a system (claim 10), computer-program product (claim 19) to perform the method steps, Jain teaches the instant independent claims 1, 10, and 19:
A system 110 (FIG 3) with machine learning model is configured to receive input data indicating activities, location of different users and corresponding instances of disease infection (reference claim 1, Col 103 L34-39). The passive sensing data 1452 represents data collected through passive sensing techniques, such as without requiring user action to initiate the data capture and often without any output or indication to the user signaling that measurement or data capture has occurred. An example is the automatic capture of sensor data from sensors of a device, e.g., a measurement or output from a GPS sensor, compass, accelerometer, a inertial measurement unit (IMU), a light sensor, a heart rate sensor, a camera, and so on… can be part of the device that runs the application for data collection and user interaction, e.g., a user's smartphone… application initiating sensor data capture automatically [Col 78 L31-45]… (transforming, by a processor, Global Positioning System (GPS) data collected from a plurality of GPS-enabled devices in a particular area and Information of Things (IoT) data collected from at least one IoT sensor in the particular area into real-time congregation data from a plurality of devices indicating a plurality of people located in a particular area).
A disease exposure prevention option for the user is selected or customized for the user based on at least one of the user data or the response data. Content is provided to cause the mobile device associated with the user to present the disease exposure prevention option [reference Abstract; FIG 17-18: Traffic Pattern Analysis with high/medium/low traffic routes, high/low risk/occupancy areas on map] (generating, by the processor, a real-time social movement model of the plurality of people moving in the particular area based on the real-time congregation data…).
…application initiating sensor data capture automatically, for example, on an ongoing basis ( e.g., periodically at a determined interval) or in response to the application or the computer system 110 detecting a condition (e.g., in response to a change in behavior, location, etc.) Many different types of data can be captured using passive sensing, including location data, environmental data, physiological data, behavior data ( e.g., regarding sleep, exercise, physical activity, movement, movement profiles for different tasks or activities, etc.) The passive sensing data 1452 includes the recorded data streams from the various users. The sensor data for each user can be associated with metadata indicating the context in which the data capture occurred (e.g., a timestamp, a location, etc.), as well as a user identifier for the associated user and a community identifier for the user's community [Col 78 L45-54]… location type (e.g., a category or classification for the characteristics and/or use of the location) of visited locations is one of several factors that the computer system 110 can use to determine the disease exposure risk that individuals face given to their individual activities and travel [Col 79 L18-21]… Each location tag can have a corresponding record that associates a unique location tag identifier with descriptive data about the visit, such as the location of the visit ( e.g., an address, GPS coordinates, etc.), a classification or category for the location ( e.g., a business type), beginning time and end times of the visit (e.g., arrival and departure times) and/or the duration of the visit, a path traveled or area covered during the visit, an activity or movement pattern that occurred during the visit, a task performed during the visit, conditions present during the visit (e.g., current occupancy or traffic level during the visit [Col 81 L50-63] (wherein the real-time congregation data includes movement data, interarrival data, and meeting data of a plurality of people located in the particular area…).
User data indicating a prospective action of a user of a mobile device is received [Abstract] (user request) and can be integrated with location/navigation tools [Col 123-124 L61-L5; FIG 1 Col 30 L48-55]. Content is provided to cause the mobile device associated with the user to present a prompt for user input regarding the prospective action of the user (user to travel from the first location to the second location). Potential future exposure of the user to a disease is evaluated based on response data indicating a response to the prompt [Col 38 L3-10: models analyze modes/routes of transportation/locations visited] (receiving, by the processor, a user request to travel from a first location to a second location in the particular area at a particular time; and in response to the user request, generating, by the processor, a plurality of routes for a user to travel from the first location to the second location in the particular area at the particular time based on the real-time social movement model).
measuring potential future exposure of the user to the disease to predict the level of disease exposure for the user with community movement data (a real-time social movement model of the plurality of people moving in the particular area …) by processing activity and location input with a trained machine learning model as in claim 1 (social movement model). Location tag data based on location tag with descriptive data about the visit, such as the location of the visit ( e.g., an address, GPS coordinates, etc.), a classification or category for the location ( e.g., a business type), beginning time and end times of the visit (e.g., arrival and departure times) and/or the duration of the visit, a path traveled or area covered during the visit, an activity or movement pattern that occurred during the visit, a task performed during the visit, conditions present during the visit (e.g., current occupancy or traffic level during the visit [Col 81 L50-63] and can employ geofencing [FIG 22, Col 83 L15-22: geofencing indicating a geographic region estimated to be affected by the exposure area…based on the path of movement of the user visiting…assigned a timestamp indicating the time the exposure event occurred] (based on the based on the movement data, the interarrival data, and the meeting data of the plurality of people mapped to a bounded grid of the particular area).
The process 2000 can be used in an ongoing, iterative way to respond to changing conditions in a community and tailor the data collection in the community. This allows the computer system 110 to customize the level of interaction and disease monitoring performed for individuals in a community ( e.g., through capture of sensor data and though capture 15 of user responses to surveys and other prompts). The computer
system 110 can collect data about individuals and/or communities (user/known associates). When certain characteristics of the collected data are detected (e.g., certain values, patterns, trends, etc.), this trigger changes in the data collection process used…expanding data collection a larger set of individual or communities, changing the types of data collected…[Col 106: L8-25]… FIG. 20 shows the process detecting one of the data collection triggers (permission input) indicated by the trigger data (step 2006). The computer system (processor) 110 can detect that a particular data collection trigger occurs by determining that the one or more criteria for the particular data collection trigger are satisfied...the detection can be done in substantially real time, such as in response to receiving collected data. Additionally or alternatively, the analysis to detect triggers can be done periodically, e.g., hourly, daily, (duration of time). [FIG 20; Col 108: L45-60] (in response to detecting, by the processor, a permission input to capture cohort data associated with a user device of the user, capturing the cohort data including electronic communication patterns between the user device and one or more known associate devices and proximity duration data between the user device and the one or more known associate devices, wherein the proximity duration data is captured by the user device responsive to the user stopping to interact with one or more known associates corresponding to the one or more known associate devices such that the user device detects the one or more known associate devices in proximity of the user device for a duration of time; feeding, by the processor, the cohort data into the real-time social movement model to generate a user-specific real-time social movement model for the user;).
Reference claim 13 recites confirm[ing] whether the user intends to perform the prospective activity or another activity; indicate a destination or mode of travel of the user; or describe conditions for the prospective activity, including at least one of a location type for the prospective activity, a location of the prospective activity, characteristics of a location for the prospective activity, a number of people at the location… (reference claims 1, 13, and 14; FIG.14) (wherein the plurality of one or more routes includes a prediction of a number of people the user will encounter in a corresponding route, and wherein the prediction indicates at least one route of the plurality of one or more routes having a least number of people the user will encounter relative to the plurality of one or more routes).
generating disease transmission scores that indicates the degree to which entry into the corresponding defined or geofenced area (first/second locations/particular area) increases exposure potential (Col 70 L55-63), and selecting, by the one or more computers (processor), a disease exposure prevention option for the user that is predicted to reduce or avoid exposure of the user to the disease, by selecting or customizing for the user based on the user’s measure of potential future exposure (reference claim 1) and wherein the context data indicates a location (first location) or path of movement (route) for the user…has entered or is approaching a particular location visited (likely located in the particular area), within a predetermined period of time (particular time), by a person (second location/user) classified as having COVID-19 [reference claim 5; FIG 14-17: FIG 15A shows Av Occupancy/Traffic Level/Disease Transmission Score, which is congregation data and a prediction of a number of people the user will encounter in a corresponding route] (generating one or more routes from the first location to the second location in the particular area based on the social movement model, one or more routes includes a prediction of a number of people the user will encounter in a corresponding route, and wherein the prediction indicates at least one route of the one or more routes having a least number of people the user will encounter relative to the one or more routes).
Jain teaches another embodiment for real-time congregation/movement data, such as current location tracking data that indicates the user is currently on route to the shopping mall (the first location to the second location in the particular area at the particular time based on the real-time social movement model). Tracking movement data includes the user’s phone travel in a direction toward the shopping mall and the user’s phone being located within a certain level of proximity of the shopping mall ( e.g., within half a mile). The user’s phone is tracked along a route or path previously used to visit the shopping mall, which is being set as a destination for a navigation application, etc. (generating, by the processor, a plurality of routes for a user to travel). The process 2300 can be used to provide just-in-time interventions (real-time) responsive to the context of the user to generate or customize tailor different interactions for different recipients [Col 108: L59-67 and Col 109: 2-9; Col 123-124 L61-L5; FIG 1 Col 30 L48-55] (in response to the user request, generating, by the processor, a plurality of one or more routes for a user to travel from the first location to the second location in the particular area at the particular time based on the real-time social movement model).
The techniques… enable a variety of features including geofencing, contact tracing, and proximity tracing…[and] enable better communication with individuals, including those who have been officially diagnosed with COVID-19 to identify all individuals with whom they have had close contact ("close contacts") during the time period in which they may have been infectious. Communication can also be provided with those who are identified to be in close contact officially diagnosed individuals, e.g., a family member, a friend, a person living with them, or a random engagement. Communication can also be provided to those who are identified to visit the same area as another person within a set period of time, such as 16 hours, or events by individuals) to have different sizes and shapes of geofence areas and for the location tags to have transmission scores (e.g., indicating the intensity or risk of disease transmission) to be different and to vary over time or any defined amount of time [Col 3: l51-65] ] (based on real-time contact data captured by the processor while the user travels on a particular route of the plurality of routes, wherein the real-time contact data is based on real-time communication between the user device and other user devices associated with actual people encountered in the particular route).
Tracking movement data includes current travel route and previously used routes by the navigation application [Col 123-124 L61-L5]. The recommendations and interactions that the system provides avoid disease exposure and may vary significantly based on type of activity predicted through the trained machine learning algorithm. Every time the user leaves the house, the system can predict a location and/or activity for a future user action ( e.g., grocery shopping at a certain store) and provide customized interactions for that predicted action’s risk profile (e.g., “Store X [and its route] is safer than Store Y [and its route] for grocery shopping”) [Col 123 L11-22] The process 2300 can be used to provide just-in-time interventions (modifying the user-specific real- time social movement model) responsive to the context of the user to generate or customize tailor different interactions for different recipients [Col 108: L59-67 and Col 109: 2-9 and L50-61: ”communicating with devices of the selected set of individuals to cause presentation of the selected content that prompts user input (step 2012). For example, the computer system 110 can send a message or configuration data to the user device of each individual in the selected set of individuals. The message can instruct the user devices to perform the needed data collection… can store data indicating the user devices or user accounts for different users and use this to target content delivery to the right individuals (and in response to determining, by the processor, that a number of the actual people encountered in the particular route is greater than the prediction, modifying the user-specific real- time social movement model using the real-time contact data received from the user for generating future routes).
Regarding instant system and computer-program product claims 10 and 19, Jain teaches a system (instant claims 10-18) with a processor (instant claims 1 and 10), memory (instant claim 10), computer program product (instant claims 19-25), computer readable storage medium (instant claim 19):
a system 110 (FIG 3) with a processor, memory, computer program product and computer readable storage medium (reference claim 1 and 19, Col 131-132: L60-55).
Regarding instant method, system, computer-program product claims 3, 12, and 21, Jain teaches (analyzing regional data of the particular area)
The community data can include information such as demographic data for the community, mapping data for the community, traffic data indicating movement patterns and traffic flows, economic data indicating the types of businesses or industries present in the community, and more. The community data 1112 also indicates disease measures for the community, such as results of COVID-19 testing, COVID-19 predictions for the community, transmission rate metrics, and more (FIG 3 and 11, Col 70 L5-15).
Regarding instant method, system, computer-program product claims 5, 14, and 23 and dependent claims 6, 15, 24, Jain teaches (producing a matrix using a set of input feeds, wherein the set of input feeds are based on the results of the congregation data that was statistically analyzed and then overlaying the matrix over a sparsity grid of the particular area):
FIG 15C Location overlays for matrix (matrix and sparsity grid) of COVID-19 exposure risks of FIG 1 and 5 tables, and 16C Risk factor combination graphical representation (matrix of input feeds from tags for location/COVID 19 exposure contacts overlaying on sparsity grids = population volume), showing the predictive models 612 for predicting infection likelihood and/or other items may receive indicators of community characteristics and community exposure levels, and the training of the models 612 may automatically account for the variation in the predicted item due to factors such as user locations, user activities, user exposure level, community characteristics, community exposure levels, etc. In other words, the predictive models 612 can be trained to receive and process input feature values indicative of any of these factors, and potentially any or all of the data items collected for the user. Through the model training process, the model 612 learns the relative importance and predictive value of each type of input, as well as how different combinations of input values increase or decrease likelihoods, so that the trained models 612 automatically generate a prediction using the relationships implicitly learned through machine learning training from a training data set showing many examples of other users (Col 53-54 L53-5).
The scoring and classification agent 650 takes the collected input elements (e.g., records and measures of what has occurred, such as physiological readings, user actions and behavior patterns, etc.) and combines them with the predictive input elements and determining overall risks associated, and what recommendations can be shared with the delivery agent (Col 54-55 L63-2)...
Regarding instant method, system, computer-program product claims 8 and 17, Jain teaches (generating a predicted level of exposure associated with each of the plurality of routes):
generating disease transmission scores that indicates the degree to which entry into the corresponding defined or geofenced area (first/second locations/particular area) increases exposure potential (Col 70 L55-63),
Regarding instant method, system, computer-program product claims 9 and 18, Jain teaches (choosing a particular route from the plurality of routes; and generating an actual level of exposure from the particular route):
selecting, by the one or more computers (processor), a disease exposure prevention option (choosing a particular route) for the user that is predicted to reduce or avoid exposure of the user to the disease, by selecting or customizing for the user based on the user’s measure of potential future exposure (claim 1) and wherein the context data indicates a location (first location) or path of movement (route) for the user…has entered or is approaching a particular location visited within a predetermined period of time, by a person (second location/user) classified as having COVID-19 (reference claim 5).
generating disease transmission scores that indicates the degree to which entry into the corresponding defined or geofenced area (first/second locations/particular area) increases exposure potential (Col 70 L55-63), and selecting, by the one or more computers (processor), a disease exposure prevention option for the user that is predicted to reduce or avoid exposure of the user to the disease, by selecting or customizing for the user based on the user’s measure of potential future exposure (reference claim 1) and wherein the context data indicates a location (first location) or path of movement (route) for the user…has entered or is approaching a particular location visited (likely located in the particular area), within a predetermined period of time (particular time), by a person (second location/user) classified as having COVID-19 [reference claim 5; FIG 14-17: FIG 15A shows Av Occupancy/Traffic Level/Disease Transmission Score (and generating an actual level of exposure from the particular route based on the real-time communication between the user device and the other user devices associated with the actual people encountered in the particular route).
B. Claims 4, 13, and 22 are rejected under 35 U.S.C. § 103 as being unpatentable over Jain, as applied above to independent claims 1, 10, and 19, and further in view of in view of Yadav (US20170191843A1, Real-time, crowd-sourced, geo-location based system or enhancing personal safety; previously cited on Form 892; herein Yadav) and Tineo (US 20170352043A1: Queue reduction; previously cited on Form 892; herein Tineo).
The prior art to Jain discloses limitations as apply to independent claims 1, 10, and 19.
Regarding instant method, system, computer-program product claims 4, 13, and 22, Jain teaches further comprising: constructing a sparsity grid of the particular area:
Measuring potential future exposure of the user to the disease is used to predict the level of disease exposure for the user, by processing activity and location input with a trained machine learning model (social movement model) (reference claim 1; FIG.14) incorporating community data 1112, such as demographic data for the community, mapping data for the community, traffic data indicating movement patterns and traffic flows (sparsity grid of the particular area), economic data indicating the types of businesses or industries present in the community with disease measures for the community, such as results of COVID-19 testing, COVID-19 predictions for the community, transmission rate metrics (FIG 3 and 11, Col 70 L5-15).
A disease exposure prevention option for the user is selected or customized for the user based on at least one of the user data or the response data and cause the mobile device to present the disease exposure prevention option [reference Abstract; FIG 17-18: Traffic Pattern Analysis with high/medium/low traffic routes, high/low risk/occupancy areas on map (sparsity grids defined in the Specification as: “a sparsity grid is a mathematical construct that allows for observation of different factors at various locations within a bounded area (e.g. “the particular area of interest)”] (based, at least in part, on the congregation data).
The recommendations and interactions that the system provides to avoid disease exposure may vary significantly based on type of activity predicted (modeling one or more random walks based, at least in part, on the congregation data ) through trained machine learning algorithm (social movement model) and provide customized interactions (weighted random walks—social movement model generated routes for contacting the fewest number of people) for predicted action’s risk profile (e.g., “Store X is safer than Store Y for grocery shopping”) [Col 123 L11-22].
However, Jain does not further disclose using congregation data for queue analysis and modeling one or more random walks in claims 4, 13, and 22.
The prior art to Tineo teaches the instant claim limitation of queue analysis as a system for determining queues and reducing queue times for a location by determining the number of users that are in a queue (queue analysis) based on sensor readings from devices and interactions with a geofence (particular place.. time) to produce queue times and a rate associated with the movement of the queue. These determined queue times may be displayed on user devices that are inquiring about the location [Tineo at Abstract] (input feeds, congregation data).
The prior art to Yadav teaches the instant claim limitation of modeling one or more random walks in a real-time, crowd-sourced, social network and geo-location based, personal safety system for enhancing personal safety via users' mobile devices. Users selectively receive notifications for such events (level of exposure) in a map view based on the events' proximity to users and can be initiated because of another person in a user’s social network. The manager software 104 uses map data stored in the personal safety system database 106 and from other sources to consider all possible navigation routes (modeling one or more random walks) to calculate the cumulative distances for multiple routes, if possible, along streets between the starting and ending locations and selects the routes having, respectively, the shortest cumulative driving and walking distances. The program produces a set of turn-by-turn directions in textual, visual and/or audible form for the shortest routes including map data, alert data, and emergency data for the shortest routes [0177].
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the present application to combine the teachings of Jain, Yadav, and Tineo. Jain teaches a machine learning model for exposure and disease risk prediction based on user activity and travel context data. Yadav teaches another system for real-time, social geo-location for walk modeling for personal safety travel guidance. Tineo teaches an additional system for queue analysis by determining the number of users that are in a queue based on sensor readings from devices and interactions with a geofence.
One of ordinary skill in the art would have been motivated to use the exposure modeling from Jain, with congregate data analyses by Yadav walk routing and Tineo queue analysis to more completely evaluate user geolocation risks for travel planning.
Combining these prior art elements would have been obvious because cohort data based route planning “minimizes risk and improves the person's level of safety while traveling by considering information related to each option at the particular instant in time that the person must make the decision” [Yadav at 0004]. One of ordinary skill in the art would predict combining Jain, Yadav, and Tineo with a reasonable expectation of success as each is analogously applicable to teach geolocation data analysis. The invention is therefore prima facie obvious.
C. Claims 7, 16, and 25 are rejected under 35 U.S.C. § 103 as being unpatentable over Jain, as applied above to independent claims 1, 10, and 19, further in view of Yadav (US20170191843A1, Real-time, crowd-sourced, geo-location based system or enhancing personal safety; previously cited on Form 892; herein Yadav).
The prior art to Jain discloses limitations of claims 7, 16, and 25, as apply to independent claims 1, 10, and 19, of "generating the plurality of routes for the user to travel from the first location to the second location in the particular area at the particular time based on the real-time social movement model of the particular area.” The context data indicates a location (first location) or path of movement (route) for the user (one user from a groups of two more users) based on determining that the user (user-specific) has entered or is approaching a particular location visited, within a predetermined period of time (likely located in the particular area), by a person (second location/user) classified as having COVID-19 (level of exposure) [Jain at claim 5]. Utilizing tracking movement data (real-time social movement model) incorporates current travel routes and previously used routes in the navigation application [Col 123-124 L61-L5]. The recommendations and interactions that the system provides avoid disease exposure and may vary significantly based on type of activity predicted through the trained machine learning algorithm. Every time the user leaves the house, the system can predict a location and/or activity for a future user action ( e.g., grocery shopping at a certain store) and provide customized interactions for that predicted action’s risk profile (e.g., “Store X [and its route] is safer than Store Y [and its route] for grocery shopping”) [Col 123 L11-22] (to avoid the user intersecting with the at least one or more known associates of person that is known to the user responsive to determining that user would stop to interact with the one or more known associates based on the cohort data). The process 2300 can be used to provide just-in-time interventions (modifying the user-specific real- time social movement model) responsive to the context of the user to generate or customize tailor different interactions for different recipients [Col 108: L59-67 and Col 109: 2-9 and L50-61: ”communicating with devices of the selected set of individuals to cause presentation of the selected content that prompts user input (step 2012). For example, the computer system 110 can send a message or configuration data to the user device of each individual in the selected set of individuals. The message can instruct the user devices to perform the needed data collection… can store data indicating the user devices or user accounts for different users and use this to target content delivery to the right individuals (and in response to determining, by the processor, that a number of the actual people encountered in the particular route is greater than the prediction, modifying the user-specific real- time social movement model using the real-time contact data received from the user for generating future routes).
However, Jain does not further disclose said limitation of "generating an alternative route from the first location to the second location to avoid the user intersecting with the at least one person that is known to the user” in claims 7, 16, and 25. Yadav teaches awareness and avoidance of the geolocation of a person that is known to the user.
The prior art to Yadav teaches the instant claim limitation in a real-time, crowd-sourced, social network and geo-location based system for enhancing personal safety via users' mobile devices. Users selectively receive notifications for such events (level of exposure) in a map view based on the events' proximity to users or on whether the notification was initiated by a person in the users' social network. The system also shares users' status and geo-location information with previously identified persons or with all persons in their social network (at least one person that is known to the user) to let them know their geo-location and/or when they have reached a destination or to warn a friend or member of a social group away from an unsafe geolocation [0004 and 0006] (generating an alternative route from the first location to the second location to avoid the user intersecting with the at least one or more known associates of person that is known to the user responsive to determining that user would stop to interact with the one or more known associates based on the cohort data.).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the present application to combine the teachings of Jain and Yadav. Jain teaches a machine learning model for exposure and disease risk prediction based on user activity and travel context data. Yadav teaches another system with real-time, social geo-location for walk modeling for personal safety travel guidance.
One of ordinary skill in the art would have been motivated to use the exposure modeling from Jain with Yadav social crowd-sourcing of travel events to share important user geolocation risks among friends and families.
Combining these prior art elements would have been obvious because cohort route planning with crowd-sourced emergency events alerts allows “family members and close friends to know the current geo-location of a person and be able warn them of then-occurring events near their geo-location, thereby increasing their level of knowledge and, hence, of safety” [Yadav at 0006]. One of ordinary skill in the art would predict combining Jain and Yadav, with a reasonable expectation of success as each is analogously applicable to community risk reduction analysis in emergency settings. The invention is therefore prima facie obvious.
Response to 102/103 Remarks
The Applicant’s remarks (p18-19), filed 05/27/2025, have been fully considered but they were not persuasive, as discussed in the above 103 rejection. Any newly recited portions or rejections, as set forth above, are necessitated by instant application amendments.
The Applicant’s assertions, regarding independent claim 1, and its dependent claims, that the prior art to Jain fails to disclose features of amended independent claim 1 including, in response to detecting, by the processor, a permission input to capture cohort data and proximity duration data corresponding to the one or more known associate devices such that the user device detects the one or more known associate devices in proximity of the user device for a duration of time for a user-specific real-time social movement model generates the plurality of routes based on real-time contact data in further view of Tineo and Yadav, were not persuasive as set forth in above rejection.
Conclusion
No claims are allowed.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/VR/
Examiner
Art Unit 1685
/MARY K ZEMAN/Primary Examiner, Art Unit 1686