DETAILED ACTION
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 02/19/2026 has been entered.
Status of Application
The following is a Non-Final Office Action. In response to Examiner's communication on 10/20/2025, Applicant on 02/19/2026, amended Claims 1, 9, 17 and added new Claim 21. Claims 1-7, 9-15, 17-21 are now pending in this application and have been rejected below.
Response to Amendment
Applicants’ amendments are insufficient to overcome the 35 USC 101 rejections set forth in the previous action, The rejections are maintained below.
Applicants’ amendments are insufficient to overcome the 35 USC 103 rejections set forth in the previous action. Therefore, these rejections have been updated to address the amendments and are maintained below.
Response to Arguments – 35 USC § 101
Applicant's arguments with respect to the 35 USC 101 rejections have been fully considered but they are not persuasive.
Firstly, Applicant asserts that that the training of a machine learning model necessarily does not involve a mental process, and therefore the claims are not directed to a mental process in the last paragraph of Page 2, continuing onto Page 3 of Remarks filed 02/19/2026. Examiner respectfully disagrees. Firstly, Examiner completely agrees that “train a machine learning model based on …”, does not recite a mental process; this is in accordance with previous Office Actions, see in particular Page 10 of the Final Rejection filed 10/20/2025. Abstract ideas recited by the claims pertain to the mentally performable act of observing various streams of data and performing judgments and evaluations to arrive at an estimate of a population to inform the use cases claimed at the end of Claim 1. While there may be advantages to performing this analysis with a machine learning component, market research and population analysis was still practically possible in the human mind by aid of pen and paper prior to the development of machine learning. Examiner cites [0052] of the Specification, noting map features such as “points-of-interest…features…type of location…accessibility” as evidence of the impossibility of performing the claim in the human mind, but none of these escape the bounds of what a human mind could parse and analyze. Put in other words, there is nothing inherently tying the process of arriving at the population estimate nor the estimate itself to the “machine learning model”, and therefore it is appropriate to separately construct the limitations involving the “machine learning model” as additional elements secondary to the abstract ideas of the claims.
A key distinction here between Applicant’s claims and the cited Appeal filed in Serial Number 16/664,452 is the presence of a “bloom filter” and relevant calculations. Citing Page 8 of the attached Patent Board Decision in the aforementioned case, while large quantities of data were relevant to the consideration, it was in light of “coding function(s) on real map area identifiers and map content, encoding such digests to a bloom filter, using the bloom filter and second digests “. As the Board states “there is no evidence here that the calculations necessary here to ultimately render particular map update data can practically be performed entirely in the human mind”. In Applicant’s claims, there is no analogous element to the calculation-related limitations of 16/664,452. To illustrate, data sets being vast can become a factor in escaping the capacity of the human mind if a specific, rigorous, and taxing calculation is demanded with said data, but its mere usage is too broad to be dispositive. Therefore, our consideration of “train a machine learning model based on…” and the downstream uses of the non-mental machine learning model is relevant to Step 2A Prong II and Step 2B, where we consider these abstract ideas in view of the additional elements such as the machine learning model.
Finally, Examiner notes upon reconsideration that the rejection has been updated to clarify the recitation of Certain Methods of Organizing Human Activity by various limitations. As Applicant has not presented arguments on these grounds, Applicant’s arguments have been found to be unpersuasive for at least that reason.
As Applicant’s arguments wholly pertain to analysis under Step 2A Prong One, Examiner notes the updated rejection below.
Response to Arguments – 35 USC § 103
Applicant’s arguments with respect to the rejections under 35 USC 103 have been fully considered but have not been found to be persuasive.
Applicant argues that Terrazas ‘190 fails to teach “receive dynamic mobility data associated with the first region” as recited in amended Claim 1. As Applicant’s amendments alter the scope of the disputed limitation, arguments directed towards the formerly cited portion of Terrazas ‘190 are rendered moot, and Examiner notes the updated rejection as outlined below.
Applicant subsequently asserts that Terrazas ‘190 fails to teach or suggest the use of dynamic mobility data, alleging the purpose of Terrazas ‘190 is to derive static estimates as opposed to the dynamic estimates of Applicant’s invention. Examiner respectfully disagrees. Examiner notes for purpose of clarification in [0029] of Applicant’s specification, “Dynamic data is data that is regularly changing and is updated frequently, such as in real-time or periodically in terms of seconds, minutes, or hours, typically”. Thus, the broadest reasonable interpretation of “dynamic mobility data” encompasses any data relating to movement that is subject to update on some periodic or regular frequency.
Deriving mobile location data in [0105] of Terrazas ‘190, “Additionally or alternatively, the example consumer data collector 236 of FIG. 2 collects electronic device data for consumer devices, such as location data from GPS devices, mobile phones, and/or any other devices for which location data may be measured and/or deduced”. It is provided that this data corresponds to trends over time, in [0106] of Terrazas ‘190, “In some examples, the consumer data collector 236 of FIG. 2 may obtain and use the location data (and/or corresponding timestamps of the location data) to determine the relative usage, visitation, and/or popularity of particular location(s) within the specified geographic area based on a number of occurrences of devices being identified as located at the particular location(s)”, and in [0107] of Terrazas ‘190, “For example, the consumer data collector 236 may collect location data corresponding…to public highway routes (e.g., to estimate the number of people in the specified geographic area who drive at specified times of day).
Accordingly, the rejections under 35 USC 103 have been maintained and updated to address the amendments as outlined below.
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.
Claims 1-7, 9-15, 17-21 are 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.
Independent Claims 1 and 9 recite the limitation "receive dynamic mobility data associated with the first region, wherein dynamic mobility data comprises data…”. As the latter recitation separately recites “dynamic mobility data”, the claim is rendered indefinite, as it is unclear whether the latter recitation intends to invoke a separate instance of “dynamic mobility data”, or the first instance “associated with the first region”. For purpose of examination, the limitation will be interpreted as “receive dynamic mobility data associated with the first region, wherein the dynamic mobility data comprises data…”.
Independent Claim 17 recites the limitation “determining map features associated with the first region, wherein dynamic mobility data comprises…receiving dynamic mobility data associated with the region”. As the latter recitation separately recites “dynamic mobility data”, the claim is rendered indefinite, as it is unclear if the latter recitation intends to refer to the first instance of dynamic mobility data that “comprises…” or invoke a separate instance of dynamic mobility data. For purpose of examination, the limitation will be interpreted as “determining map features associated with the first region, wherein dynamic mobility data comprises…receiving the dynamic mobility data associated with the region.
Independent Claims 1, 9, and 17 further recite the limitation “data from mobile devices within the first region associated with the movement of respective mobile devices within the first region”. As there is a lack of antecedent basis for “the movement of respective mobile devices”, the claim is rendered indefinite. For purpose of examination, the limitation will be interpreted as “data from mobile devices within the first region associated with a movement of respective mobile devices within the first region”.
Dependent Claims 2-7, 10-15, 18-21 inherit the deficiencies from Independent Claims 1, 9, and 17, and therefore are similarly rejected under 35 USC 112(b). Appropriate correction is required.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-7, 9-15, 17-21 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
101 Analysis – Step 1
The claims are directed to an apparatus and method. Therefore, the claims are directed to at least one of the four statutory categories.
101 Analysis – Step 2A
Regarding Prong 1 of the Step 2A analysis in the MPEP, the claims are to be analyzed to determine whether they recite subject matter that is directed to a judicial expectation, namely a law of nature, a natural phenomenon, or one of the follow groups of abstract ideas: a) mathematical concepts, b) certain methods of organizing human activity, and/or c) mental processes.
Independent Claim 1 includes limitations that recite an abstract idea and will henceforth be used as a representative claim for the 101 rejection until otherwise noted. Claim 1 recites:
An apparatus comprising at least one processor and at least one memory including computer program code, the at least one memory and the computer program code configured to, with the at least one processor, cause the apparatus to at least: receive ground truth population data corresponding to a first region comprising at least dynamic ground truth population data that changes at least daily; determine map features associated with the first region; receive dynamic mobility data associated with the first region, wherein dynamic mobility data comprises data from mobile devices within the first region associated with the movement of respective mobile devices within the first region; train a machine learning model based on the ground truth population data corresponding to the first region, the map features associated with the first region, and the dynamic mobility data associated with the first region; receive dynamic mobility data associated with a second region; determine map features associated with the second region; process the dynamic mobility data associated with the second region, the map features associated with the second region, and a target time bin using the machine learning model; receive, from the machine learning model, a population estimate for the second region during the target time bin: and provide the population estimate for the second region during the target time bin to a third party service provider to guide at least one of targeted advertisement timing and placement, transportation planning, or emergency management planning.
The examiner submits that the foregoing bolded limitation(s) constitute an abstract idea because under its broadest reasonable interpretation, the claim covers a mental process. “Receiving…data”, “determining…features”, and “receiving…a population estimate” are mental processes that could be performed by a human with a pen and paper, per the MPEP, merely adapting them into the context of a technological environment with computing parts does not preclude them from being abstract. Accordingly, the claim recites at least one abstract idea. Further, “provide the population estimate…to guide at least one of targeted advertisement timing and placement, transportation planning, or emergency management planning” is a certain method of organizing human activity, namely under the category of Commercial or Legal Interactions and Managing Personal Behavior or Relationships or Interactions Between People. As the various analyses are performed in light of marketing research, the limitations of “receiving..data”, “determining…features”, and “receiving…a population estimate” are also Certain Methods of Organizing Human Activity.
Independent Claims 9 and 17 recite abstract ideas by virtue of presenting substantially similar limitations as Claim 1.
Dependent Claims 2-7, 10-15, 18-20 recite abstract ideas by virtue of their dependency on independent Claims 1,9, and 17.
101 Analysis – Step 2A, Prong II
Regarding Prong II of the Step 2A analysis in the MPEP, the claims are to be analyzed to determine whether the claim, as a whole, integrates the abstract into practical application. As noted in the MPEP, it must be determined whether any additional elements in the claim beyond the judicial exception integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception. The courts have indicated that additional elements, such as merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application.
In the present case, the additional limitations beyond the above-noted abstract idea are as follows (where the underlined portions are the “additional limitations” while the bolded portions continue to represent the “abstract idea”):
An apparatus comprising at least one processor and at least one memory including computer program code, the at least one memory and the computer program code configured to, with the at least one processor, cause the apparatus to at least: receive ground truth population data corresponding to a first region comprising at least dynamic ground truth population data that changes at least daily; determine map features associated with the first region; receive dynamic mobility data associated with the first region; train a machine learning model based on the ground truth population data corresponding to the first region, the map features associated with the first region, and the dynamic mobility data associated with the first region; receive dynamic mobility data associated with a second region, wherein dynamic mobility data comprises data from mobile devices within the first region associated with the movement of respective mobile devices within the first region; determine map features associated with the second region; process the dynamic mobility data associated with the second region, the map features associated with the second region, and a target time bin using the machine learning model; receive, from the machine learning model, a population estimate for the second region during the target time bin: and provide the population estimate for the second region during the target time bin to a third party service provider to guide at least one of targeted advertisement timing and placement, transportation planning, or emergency management planning.
For the following reason(s), the examiner submits that the above identified additional limitations do not integrate the above-noted abstract idea into a practical application.
As it pertains to Claim 1, the additional elements in the claims include “an apparatus”, including “computer code, a processor, and memory”. Further detailed is the training and application of a “machine learning model” and the specification of the dynamic mobility data that it uses. When considered in view of the claim as a whole, the additional elements do not integrate the abstract idea into a practical application because the additional elements are generic computing components that are merely used as a tool to perform the recited abstract idea and/or do no more than generally link the use of the recited abstract idea to a particular technological environment or field of use under Step 2A Prong Two.
Thus, taken alone, the additional elements do not integrate the abstract idea into a practical application. Further, looking at the additional limitation(s) as an ordered combination or as a whole, the limitation(s) add nothing that is not already present when looking at the elements taken individually. For instance, there is no indication that the additional elements, when considered as a whole, reflect an improvement in the functioning of a computer or an improvement to another technology or technical field, apply or use the above-noted judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, implement/use the above-noted judicial exception with a particular machine or manufacture that is integral to the claim, effect a transformation or reduction of a particular article to a different state or thing, or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is not more than a drafting effort designed to monopolize the exception (MPEP § 2106.05).
It is important to keep in mind that an improvement in the abstract idea itself (e.g. a recited fundamental economic concept) is not an improvement in technology" and courts have indicated that mere "automation of manual processes, such as using a generic computer" is not sufficient to show an improvement in computer technology. MPEP 2106.05(a); what is employed is a generic machine learning model that effectuates the otherwise abstract ideas recited by the claims.
Accordingly, the additional limitation(s) does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing an abstract idea.
Claim 17 recites substantially similar limitations as Claim 1 and therefore does not integrate the abstract idea into a practical application.
Claims 7, 15 further recite “a graphical user interface”.
Claim 9 further recites “at least one non-transitory computer-readable storage medium having computer-executable program code instructions stored therein”.
These limitations do not integrate the abstract into a practical application by analogous reasoning as above.
Claims 2-6, 10-14, 18-21 do not recite additional elements beyond those found in claims upon which they are dependent and therefore do not integrate the abstract idea into a practical application.
101 Analysis – Step 2B
Regarding Step 2B of the MPEP, representative independent claim 1 does not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for the same reasons to those discussed above with respect to determining that the claim does not integrate the abstract idea into a practical application.
Claim 17 recites substantially similar limitations as Claim 1 and therefore does not integrate the abstract idea into a practical application or amount to significantly more.
Claims 7, 15 further recite “a graphical user interface”.
Claim 9 further recites “at least one non-transitory computer-readable storage medium having computer-executable program code instructions stored therein”.
These limitations do not integrate the abstract into a practical application or amount to significantly more by analogous reasoning as above.
Claims 2-6, 10-14, 18-21 do not recite additional elements beyond those found in claims upon which they are dependent and therefore do not integrate the abstract idea into a practical application or amount to significantly more.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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.
Claims 1-7, 9-15, 17-21 are rejected under 35 U.S.C. 103 as being unpatentable over Terrazas(US 20160358190 A1), hereinafter Terrazas ‘190, in view of Terrazas(US 20160063516 A1), hereinafter Terrazas.
Claims 1, 9, 17
Terrazas ‘190 teaches:
An apparatus comprising at least one processor and at least one memory including computer program code, the at least one memory and the computer program code configured to, with the at least one processor, cause the apparatus to at least
In [0135], "While example manners of implementing the consumer segment determiner 100 of FIG. 1 are illustrated in FIGS. 2 and 8, one or more of the elements, processes and/or devices illustrated in FIGS. 2 and 8 may be combined, divided, re-arranged, omitted, eliminated and/or implemented in any other way...could be implemented by one or more analog or digital circuit(s), logic circuits, programmable processor(s), application specific integrated circuit(s) (ASIC(s)), programmable logic device(s) (PLD(s)) and/or field programmable logic device(s) (FPLD(s)). When reading any of the apparatus or system claims of this patent to cover a purely software and/or firmware implementation...is/are hereby expressly defined to include a tangible computer readable storage device or storage disk such as a memory, a digital versatile disk (DVD), a compact disk (CD), a Blu-ray disk, etc. storing the software and/or firmware. Further still, the example consumer segment determiner 100 of FIG. 1 may include one or more elements, processes and/or devices in addition to, or instead of, those illustrated in FIGS. 2 and/or 8, and/or may include more than one of any or all of the illustrated elements, processes and devices.
receive ground truth population data corresponding to a first region comprising at least dynamic ground truth population data that changes at least daily; determine map features associated with the first region;
In [0139], "The example measurement collector 106 of FIG. 1 collects first measurements of a set of characteristics for training geographic areas (block 1002). The first geographic area may be a calibration area or a model-generating area, for which population information for a consumer segment of interest is known. The set of characteristics may include, for example, measurements of the specified consumer segment via sampling, surveys, and/or ground truth measurement". Underlining data that one of ordinary skill in the art would reasonably interpret as static and italicizing data that is reasonably dynamic in [0091], "For example, the economic data collector 230 may estimate home values in the geographic area 104 based on building densities, building textures, nearby building types, vehicle traffic, distances to designated locations, and/or landmarks. In the example of FIG. 2, the object feature determiner 218 provides descriptions of economic-related features to the aerial image analyzer 214 and/or the ground level image analyzer 216, obtains measurements of features in the aerial images and/or ground level images from the aerial image analyzer 214 and/or the ground level image analyzer 216, and provides the resulting measurements to the economic data collector 230. Example features that may indicate higher home values in some locations include: shorter distances to parks, bodies of water (e.g., lakes, rivers, oceans), and/or transportation features; higher elevations; desirable features on or near the property (e.g., waterfront property); the presence of swimming pools; higher concentrations of parked cars (e.g., on the sides of roads, off the roads, etc.); and/or roofs of a particular color".
receive dynamic mobility data associated with the first region, wherein dynamic mobility data comprises data from mobile devices within the first region associated with the movement of respective mobile devices within the first region;
In [0105], “Additionally or alternatively, the example consumer data collector 236 of FIG. 2 collects electronic device data for consumer devices, such as location data from GPS devices, mobile phones, and/or any other devices for which location data may be measured and/or deduced….may store location data obtained based on IP addresses, connections to wireless access points for which a location is known, self-reporting by devices that can measure their own location, triangulation performed by wireless communications service providers (e.g., using wireless network base stations), and/or any other location measurement techniques”. Noting the dynamic nature of data across time in [0106], “In some examples, the consumer data collector 236 of FIG. 2 may obtain and use the location data (and/or corresponding timestamps of the location data) to determine the relative usage, visitation, and/or popularity of particular location(s) within the specified geographic area based on a number of occurrences of devices being identified as located at the particular location(s)”, and in [0107], “For example, the consumer data collector 236 may collect location data corresponding to public transportation routes (e.g., to estimate a number of people in the specified geographic area who use public transportation to travel rather than personal vehicles) and/or to public highway routes (e.g., to estimate the number of people in the specified geographic area who drive at specified times of day)”.
train a machine learning model based on the ground truth population data corresponding to the first region, the map features associated with the first region, and the dynamic mobility data associated with the first region;
In [0151], "The example measurement collector 106 outputs characteristic measurements for the specified geographic area (block 1116). The example characteristic measurements include counts of the identified instances of determined objects, activities, sales, and/or economic information. The measurement collector 106 provides the characteristic measurements to the segment modeler 108 and/or the segment estimator 110. Upon completion of block 1116, the example instructions 1100 of FIG. 11 end and return control to a calling function, such as block 1002 or block 1006 of FIG. 10". In [0133], "The example segment modeler 108 is described with respect to FIG. 8 as performing supervised machine learning. That is, the example segment modeler 108 of FIG. 8 generates the lifestage model 808, the social model 810, and/or the segment model 802 to calculate a known outcome (e.g., the known segment data 818). However, the example the lifestage model 808, the social model 810, and/or the segment model 802 may additionally or alternatively be implemented to perform unsupervised machine learning. For example, the lifestage model 808, the social model 810, and/or the segment model 802 may attempt to determine patterns and/or changes in consumer segment populations using the characteristic measurements 202 and without having a known outcome to be achieved. In such examples, the segment model 802 may include one or more relationship(s) between object(s), activit(ies), consumer data, economic data, and/or sales data. Examples of such relationship(s) are relationships that indicate lifestage groups, social groups, and/or consumer segment population". Noting the connection between the mobility data collected by the consumer data collector 236 and input data into the machine learning model in [0102], “Returning to FIG. 2, the example measurement collector 106 further includes a consumer data collector 236 that collects consumer data based on the geographic area identifier 104.”.
receive dynamic mobility data associated with a second region;
See [0105] regarding accessing mobility data from consumer devices as outlined above. Note in [0045] our process of engaging the measurement collector 106 for distinct areas and regions, “The example measurement collector 106 of FIG. 1 collects measurements of characteristics for geographic area(s), including area(s) from which a segment model (e.g., a predictive model) is developed and area(s) in which the segment model is to be applied (e.g., to estimate the consumer segment). For example, during a model development phase the measurement collector 106 collects first measurements of a set of characteristics for first areas….Then, during a segment evaluation phase the measurement collector 106 collects second measurements of the set of characteristics for a second area”.
determine map features associated with the second region;
In [0038], "Disclosed example apparatus includes a measurement collector, a segment modeler, and a segment estimator. The measurement collector recognizes, using a first computer vision technique, a first type of object in a first image of a first area, where the first type of object is associated with a consumer segment. The measurement collector also obtains first measurements of a first set of characteristics for the first area. The first set of characteristics is associated with the consumer segment and includes the first type of object. The measurement collector recognizes, using at least one of the first computer vision technique or a second computer vision technique, the first type of object in a second image of a second area. The measurement collector obtains second measurements of a second set of characteristics for the second area, where the second set of characteristics includes the first type of object."
process the dynamic mobility data associated with the second region, the map features associated with the second regio, and … using the machine learning model;
In [0038], "The segment modeler determines a first relationship between a first population of the consumer segment in the first area and the first measurements of the first set of characteristic." In [0133], "The example segment modeler 108 is described with respect to FIG. 8 as performing supervised machine learning. That is, the example segment modeler 108 of FIG. 8 generates the lifestage model 808, the social model 810, and/or the segment model 802 to calculate a known outcome (e.g., the known segment data 818). However, the example the lifestage model 808, the social model 810, and/or the segment model 802 may additionally or alternatively be implemented to perform unsupervised machine learning. For example, the lifestage model 808, the social model 810, and/or the segment model 802 may attempt to determine patterns and/or changes in consumer segment populations using the characteristic measurements 202 and without having a known outcome to be achieved. In such examples, the segment model 802 may include one or more relationship(s) between object(s), activit(ies), consumer data, economic data, and/or sales data. Examples of such relationship(s) are relationships that indicate lifestage groups, social groups, and/or consumer segment population".
and receive, from the machine learning model, a population estimate for the second region …;
In [0038], "The segment modeler determines a first relationship between a first population of the consumer segment in the first area and the first measurements of the first set of characteristic. The segment estimator estimates a second population of the consumer segment in the second area based on applying the first relationship to the second measurements."
and provide the population estimate for the second region during the target time bin to a third party service provider to guide at least one of targeted advertisement timing and placement, transportation planning, or emergency management planning.
In [0030], “Consumer segmentation refers to the classification of consumers into descriptive groups or buckets. As an example of consumer segmentation, the Nielsen PRIZM® lifestyle segmentation system includes 66 demographically and behaviorally distinct types, or “segments,” to help marketers discern those consumers' likes, dislikes, lifestyles and purchase behavior”. The stated goal of this invention is to improve such consumer segmentation techniques, as stated [0031], “Disclosed examples improve consumer segmentation techniques by: identifying geographically-linked information by using computer vision techniques…”. Given the relevancy of this invention to aid the efforts of marketers, we understand this to be an implicit statement of the invention’s relevancy to targeted advertising.
Terrazas ‘190 does not disclose the remaining limitations.
However, Terrazas ‘516 teaches:
a target time bin, during the target time bin
The example computer vision analyzer 202 identifies newly-constructed buildings (or other features) by comparing the aerial image with another aerial image of the same geographic area from a previous time period (block 832). For example, the image retriever 116 of FIG. 1 may request and receive multiple aerial images for a geographic area that correspond to images taken at different times. In some examples, the received images are taken at least 6 months apart. However, other intervals may be used.” We understand this to implicitly be a time bin, namely in the form of time intervals in the past up to and including the present, within which trends can be analyzed to estimate relevant populations.
Terrazas ‘190 discloses a system for estimating the population of a specific consumer segment in a given area. Terrazas ‘516 discloses a system meant to estimate commercial characteristics of a given area from aerial images. Each reference discloses analyzing various streams of data to identify features of a given geographic area. Extending the system as recorded in Terrazas ‘190 to include the road analysis of Terrazas ‘516 is applicable to Terrazas ‘190 as they are both concerned with analyzing data to identify features of a region.
It would have been obvious to one having ordinary skill in the art at the effective filling date of the invention to apply the road analysis of Terrazas ‘516 and apply that to the system as taught in Terrazas ‘190. Motivation to do so comes from the fact that the claim is plainly directed to the predictable result of combining known items in the prior art, with the expected benefit that adopting the analysis of roads of Terrazas ‘516 would enable users to enrich their analysis by paying attention to a relevant, additional feature of the landscape.
Claims 9 and 17 are rejected as disclosing substantially similar limitations as Claim 1.
Claims 2, 10, 18
As to Claim 2, Terrazas ‘190 combined with Terrazas ‘516 teaches all the limitations of Claim 1.
Terrazas ‘190 teaches:
The apparatus of claim 1, wherein the population estimate for the second region is determined by the machine learning model using map features within a predefined degree of similarity of the map features associated with the second region.
In [0060], "In some examples, the aerial image analyzer 214 and/or the ground level image analyzer 216 identify collections of objects that are highly similar in shape, size, color, geographic distribution, and/or other observable attributes. The example aerial image analyzer 214 may identify that the collection of objects is homogenous; that is, that the collection of objects has a high similarity metric. For example, if a collection of houses in an area appears to be highly similar based on size (from aerial and ground level views), facade, and spacing, the example segment modeler 108 may apply objects identified near the collection of similar houses to other collections of houses that are similar to the observed similar collection. That is, while objects may not be identifiable from images of the second collection of houses, the homogeneity of both collections and their similarities with each other may permit the segment modeler 108 to weight the observed objects similarly and/or to impute the presence and/or count of observed objects to the second collection of houses at which the objects were not observed". It is clarified that these analyzers are under the umbrella of the measurement collector whose data is used for the machine learning model in [0062], "The example measurement collector 106 of FIG. 2 further includes a ground level image analyzer 216". In [0059], "The example measurement collector 106 of FIG. 2 further includes an aerial image analyzer 214".
Claims 10 and 18 are rejected as disclosing substantially similar limitations as Claim 2.
Claim 3
As to Claim 3, Terrazas ‘190 teaches all the limitations of Claim 1 as discussed above.
Terrazas ‘190 does not teach:
The apparatus of claim 1, wherein the first region comprises a first road segment, wherein the map features used to train the machine learning model include one or more of a functional classification of the first road segment, a speed classification, a number of lanes, a direction of travel, an environmental context, points-of-interest proximate the first road segment, or first road segment length.
However, Terrazas ‘516 teaches:
The apparatus of claim 1, wherein the first region comprises a first road segment, wherein the map features used to train the machine learning model include one or more of a functional classification of the first road segment, a speed classification, a number of lanes, a direction of travel, an environmental context, points-of-interest proximate the first road segment, or first road segment length.
In [0025] of Terrazas ‘190, "From image-based data sources, disclosed examples extract visually observable features such as the presence of identifiable objects. Some disclosed examples extract visually observable features from satellite imagery and extract visually observable features from digital photos such as Google Street View photos and/or other publicly available photos having geographic metadata. The presence and/or quantities of visually observable features are used as characteristics to describe the geographic areas in which the features are observed (or not observed)". Now looking at Terrazas ‘516, On [0123], "The example computer vision analyzer 202 of FIG. 2 further identifies roads in the aerial images 502-518 such as a local road 528 (e.g., a lower traffic and/or lower speed roadway) and a highway 530 (e.g., a higher traffic and/or higher speed roadway). In the example of FIG. 5, the computer vision analyzer 202 identifies the local road 528 and/or the highway 530 using computer vision techniques and based on respective widths of the roadways 528, 530, average numbers of cars identified on respective lengths of the roadways 528, 530, and/or the respective colors of the roadways 528, 530". In [0060], "Example road types may include major highways (e.g., roads having at least a threshold number of lanes". In [0142], "Additionally or alternatively, the example distance meter 210 determines distance(s) between features 1502-1506, such as the distance between the commercial building 1502 and the road 1504. Features such as distances and/or dimensions provide spatial information that may be useful for matching images. In some examples, the computer vision analyzer 202 determines directional bearings (e.g., North, South, East, West, and/or intermediate bearings) between pairs of the features 1502-1506". In [0153], "The example feature identifier 118 of FIG. 1 selects an aerial image (e.g., one of the aerial images 502-518) (block 706) and identifies contextual feature(s) present in the selected aerial image (block 708). For example, the feature identifier 118 may use computer vision and/or supplemental data to identify contextual features. Example contextual features identifiable via computer vision include, but are not limited to, public parks, buildings having designated type(s) (e.g., commercial, retail, residential, industrial, transportation, etc.), road(s), transportation features (e.g., railroad tracks, bus stops, etc.), observable vehicle(s), vehicle parking areas (e.g., parking lots), and/or fueling stations. In some cases, the feature identifier 118 derives contextual features from computer vision and/or supplemental data".
It would have been obvious to one having ordinary skill in the art at the effective filling date of the invention to apply the road analysis of Terrazas ‘516 and apply that to the system as taught in Terrazas ‘190. Motivation to do so comes from the same rationale as outlined above with respect to Claim 1.
Claims 11 and 19 are rejected as presenting substantially similar limitations as Claim 3.
Claim 4
As to Claim 4, Terrazas ‘190 combined with Terrazas ‘516 teaches all the limitations of Claim 3 as discussed above.
Terrazas ‘516 also teaches:
The apparatus of claim 3, wherein the second region comprises a second road segment, wherein the map features used by the machine learning model for the population estimate include one or more of a functional classification of the second road segment, a speed classification, a number of lanes, a direction of travel, an environmental context, points-of-interest proximate the second road segment, or second road segment length, wherein the population estimate for the second region is generated by the machine learning model based on map features of the second road segment.
In [0025] of Terrazas ‘190, "From image-based data sources, disclosed examples extract visually observable features such as the presence of identifiable objects. Some disclosed examples extract visually observable features from satellite imagery and extract visually observable features from digital photos such as Google Street View photos and/or other publicly available photos having geographic metadata. The presence and/or quantities of visually observable features are used as characteristics to describe the geographic areas in which the features are observed (or not observed)". Now looking at Terrazas ‘516, On [0123], "The example computer vision analyzer 202 of FIG. 2 further identifies roads in the aerial images 502-518 such as a local road 528 (e.g., a lower traffic and/or lower speed roadway) and a highway 530 (e.g., a higher traffic and/or higher speed roadway). In the example of FIG. 5, the computer vision analyzer 202 identifies the local road 528 and/or the highway 530 using computer vision techniques and based on respective widths of the roadways 528, 530, average numbers of cars identified on respective lengths of the roadways 528, 530, and/or the respective colors of the roadways 528, 530". In [0060], "Example road types may include major highways (e.g., roads having at least a threshold number of lanes". In [0142], "Additionally or alternatively, the example distance meter 210 determines distance(s) between features 1502-1506, such as the distance between the commercial building 1502 and the road 1504. Features such as distances and/or dimensions provide spatial information that may be useful for matching images. In some examples, the computer vision analyzer 202 determines directional bearings (e.g., North, South, East, West, and/or intermediate bearings) between pairs of the features 1502-1506". In [0153], "The example feature identifier 118 of FIG. 1 selects an aerial image (e.g., one of the aerial images 502-518) (block 706) and identifies contextual feature(s) present in the selected aerial image (block 708). For example, the feature identifier 118 may use computer vision and/or supplemental data to identify contextual features. Example contextual features identifiable via computer vision include, but are not limited to, public parks, buildings having designated type(s) (e.g., commercial, retail, residential, industrial, transportation, etc.), road(s), transportation features (e.g., railroad tracks, bus stops, etc.), observable vehicle(s), vehicle parking areas (e.g., parking lots), and/or fueling stations. In some cases, the feature identifier 118 derives contextual features from computer vision and/or supplemental data".
It would have been obvious to one having ordinary skill in the art at the effective filling date of the invention to apply the road analysis of Terrazas ‘516 and apply that to the system as taught in Terrazas ‘190. Motivation to do so comes from the same rationale as outlined above with respect to Claim 1.
Claims 12 and 20 are rejected as presenting substantially similar limitations as Claim 4.
Claims 5, 13
As to Claim 5, Terrazas ‘190 combined with Terrazas ‘516 teaches all the limitations of Claim 1.
Terrazas ‘190 teaches:
The apparatus of claim 1, wherein the ground truth population data corresponding to the first region additionally comprises static ground truth population data, wherein dynamic ground truth population data comprises population data corresponding to the first region that changes at least daily, wherein static ground truth population data comprises population data corresponding to the first region that remains constant for at least a day.
Underlining data that one of ordinary skill in the art would reasonably interpret as static and italicizing data that is reasonably dynamic in [0091], "For example, the economic data collector 230 may estimate home values in the geographic area 104 based on building densities, building textures, nearby building types, vehicle traffic, distances to designated locations, and/or landmarks. In the example of FIG. 2, the object feature determiner 218 provides descriptions of economic-related features to the aerial image analyzer 214 and/or the ground level image analyzer 216, obtains measurements of features in the aerial images and/or ground level images from the aerial image analyzer 214 and/or the ground level image analyzer 216, and provides the resulting measurements to the economic data collector 230. Example features that may indicate higher home values in some locations include: shorter distances to parks, bodies of water (e.g., lakes, rivers, oceans), and/or transportation features; higher elevations; desirable features on or near the property (e.g., waterfront property); the presence of swimming pools; higher concentrations of parked cars (e.g., on the sides of roads, off the roads, etc.); and/or roofs of a particular color". In [0139], " The example measurement collector 106 of FIG. 1 collects first measurements of a set of characteristics for training geographic areas (block 1002). The first geographic area may be a calibration area or a model-generating area, for which population information for a consumer segment of interest is known. The set of characteristics may include, for example, measurements of the specified consumer segment via sampling, surveys, and/or ground truth measurement...In some examples, the set of characteristics includes economic information for the first geographic area".
Claim 13 are rejected as disclosing substantially similar limitations as Claim 5.
Claim 6
As to Claim 6, Terrazas ‘190 combined with Terrazas ‘516 teaches all the limitations of Claim 5.
Terrazas ‘190 teaches:
The apparatus of claim 5, wherein the dynamic mobility data associated with the first region comprises at least one of: mobile device probe data, vehicle probe data, social media check-in data, traffic data, or camera image data.
In [0053], "The example aerial images obtained by the aerial image collector 204 may include aerially generated images (e.g., images captured from an aircraft such as airplanes, helicopters, and/or drones, which may be operated by governments, commercial organizations, individuals, etc.), satellite-generated images (e.g., images captured from a satellite), and/or drone images (e.g., images captured using drone aircraft by governments, commercial organizations, individuals, etc.)". In [0091], " In the example of FIG. 2, the object feature determiner 218 provides descriptions of economic-related features to the aerial image analyzer 214 and/or the ground level image analyzer 216, obtains measurements of features in the aerial images and/or ground level images from the aerial image analyzer 214 and/or the ground level image analyzer 216, and provides the resulting measurements to the economic data collector 230".
Claim 14 is rejected as disclosing substantially similar limitations as Claim 6.
Claim 7
As to Claim 7, Terrazas ‘190 combined with Terrazas ‘516 teaches all the limitations of Claim 1.
Terrazas ‘190 teaches:
The apparatus of claim 1, wherein the apparatus is further caused to generate a graphical user interface of a geographic region including the second region, wherein the graphical user interface presents the second region of the geographic region and provides an indication of the population estimate for the second region.
In [0130], "FIG. 9 is a graphical heat map 900 representative of an estimated population of one or more specified consumer segments in a geographic area 902, which is generated by the example segment estimator 110 of FIG. 1 using a segment model 802 generated by the example segment modeler 108 of FIGS. 1 and/or 8. The example heat map 900 of FIG. 9 divides the geographic area 902 into blocks representative of sub-regions of the geographic area 902. The example segment estimator 110 of FIG. 1 generates the heat map 900 by applying the segment model 802 generated by the segment modeler 108 to a set of characteristic measurements obtained from the example measurement collector 106".
Claim 15 is rejected as disclosing substantially similar limitations as Claim 7.
Claim 21
As to Claim 21, Terrazas ‘190 combined with Terrazas ‘516 teaches all the limitations of Claim 1 as outlined above.
Terrazas ‘190 further teaches:
The apparatus of claim 1, wherein the first region comprises an area defined by the boundaries of at least one of a city, zip code, or county.
In [0052], “From the geographic area identifier 104, the example aerial image collector 204 identifies the location of the specified geographic area and requests an aerial image of the specified geographic area from an aerial image repository 208. For example, the aerial image collector 204 may interpret a text description of the geographic area identifier 104 (e.g., a 5-digit zip code, a name of a municipality, country, or state, etc.) to a coordinate system (e.g., a set of GPS coordinates indicating a boundary or perimeter of an area) or other system used by the aerial image repository 208 to identify aerial images”.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to THEODORE L XIE whose telephone number is (571)272-7102. The examiner can normally be reached M-F 9-5.
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/THEODORE XIE/Examiner, Art Unit 3623
/WILLIAM S BROCKINGTON III/Primary Examiner, Art Unit 3623
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