Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . 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.
DETAILED ACTION
The following NON-FINAL Office Action is in response to communication filed 6/4/2026.
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 06/24/2026 has been entered.
Priority
Receipt is acknowledged of papers submitted under 35 U.S.C. 119(a)-(d), which papers have been placed of record in the file.
Status of Claims
Claims 1, 4-14, 16-21 are currently pending.
Claims 2-3, 15 were previously cancelled.
Claims 1, 7, 16 are currently amended.
Claims 1, 4-14, 16-21 are currently under examination and have been rejected as follows. -----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
Response to Amendment
The previously pending rejections under 35 USC 101 are maintained. The section 101 rejections are updated in view of the amendments.
The previously pending rejections under 35 USC 102/103 were previously withdrawn in view of the amendments and Applicant arguments dated 8/27/2025 pages 12-15 (see Allowable Subject Matter section below).
-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
Response to Arguments
Regarding Applicant’s remarks pertaining to 35 USC 101:
Step 2A Prong 1:
Applicant argues on page 13 of remarks 6/4/2026:
“…The Examiner's characterization of the claims as merely directed to "commercial or legal interactions" or "mathematical relationships" fails to account for the specific technical features recited in the claims.”
Examiner respectfully disagrees. The presence of technical features or additional elements in the claims do not preclude the claims from reciting, describing, or setting forth an abstract idea. The additional elements are considered subsequently in the practical application analysis in Step 2A Prong 2.
Applicant argues on page 14 of remarks 6/4/2026:
“Applicant further notes that the present claims are distinguishable from those found ineligible in Recentive Analytics, Inc. v. Fox Corp…. Unlike Recentive, the present claims do not merely apply a generic technique to a new field of use. Rather, the claims recite a specific multi-step technical pipeline…. The claims delineate specific steps through which the technical improvement is achieved, unlike the functional claims in Recentive, and thus do not recite an abstract idea.”
Examiner respectfully disagrees. Examiner submits the court ruled against Recentive not only because it applied generic machine learning to a new field of use, but also because it could not establish technical improvement in the machine learning itself. The ruling explained that reciting specific algorithmic steps using existing machine learning technology does not necessarily demonstrate an improvement in machine learning, nor demonstrate an improvement beyond performing a task with greater speed and efficiency than humans previously.
Step 2A Prong 2:
Applicant argues on page 14 of remarks 6/4/2026:
“…The specification identifies a specific technical problem: managing and allocating resources that are inherently limited, and improving the geographical distribution of available resources dynamically in time….
“The claims, as amended, provide a specific technical solution to this problem. In particular, step c) as amended, now recites receiving data from two or more different types of data sources (e.g., public administration, sensors, resources consumption, presence indicators), which ties the method to a specific heterogeneous data collection infrastructure. Step d) recites that "based on the processing, the data set is reduced to a sub-set of data, which reduces memory usage of the repository and increases computational capacity of the computation engine." This is an explicit technical improvement to computer functionality. Step f) as amended, now specifies that the resources allocated comprise "at least one of electricity, gas, water, data network bandwidth, or radio frequencies" and that allocation is performed by "configuring and concentrating the resources in the cluster where they are most needed based on the trends." This ties the method to a concrete, real-world outcome.”
Examiner respectfully disagrees. Examiner acknowledges the solution claimed by Applicant; however, Examiner submits that managing and allocating resources and improving timely geographical distribution of resources are entrepreneurial problems, not technological. Limiting the resources from any type to one of electricity, gas, water, bandwidth, or radio frequencies provides some additional specificity; however, it remains unclear from the claims or specification how technologically the control / initiation / stoppage / adjustment of the actual resource allocation is achieved.
Applicant argues on page 15 of remarks 6/4/2026:
“…Similarly [to the Desjardins memo], claim 1 explicitly recites that the processing reduces memory usage and increases computational capacity. These are analogous technical improvements to how the system itself operates.”
Examiner respectfully disagrees. The amended claims present the following new additional elements: “public administration data sources”, “distributed sensors”, “resource consumption data sources”, “sources of human or device presence”. These additional elements along with the original ““repository”, “computation engine”, “clustering algorithm”, “mobile communications network”, “base stations”, and “user communication devices” perform functions such as extracting human presence data, selecting observation periods, subdividing observation periods, formatting the data, applying a clustering algorithm to group geographic pixels, selecting a subset of the data, and designating resources to the clusters (Examiner submits that actual physical control or implementation of the distribution of the resources is not evident in the claims). The additional elements are recited at a high level of generality (i.e. as a generic computer performing functions of collecting data; calculating statistics; organizing, evaluating and communicating data, etc.) such that they amount to no more than mere instructions to apply the exception using generic computer components. Therefore, these functions can be viewed as not meaningfully different than a resource allocating business method and its underlying mathematical algorithm being applied on a general-purpose computer as tested per MPEP 2106.05(f)(2)(i).
Applicant argues on page 15 of remarks 6/4/2026:
“…Similarly [to USPTO Example 40], although individual steps of the present claims may be viewed as data gathering or mathematical operations when analyzed individually, the claim as a whole is directed to a particular improvement in processing heterogeneous data from multiple sources to optimize the allocation of physical resources in a geographic area.”
Examiner respectfully finds the argument unpersuasive. Example 40 achieves eligibility by addressing a technological problem, i.e. excess traffic volume on a network and analysis of the cause, therefore integrating the exception into a practical application. The present application by contrast addresses challenges of managing and allocating resources, including water, gas, electricity, etc., and improving timely geographical distribution of resources, rather than a technological problem.
Applicant argues on page 15 of remarks 6/4/2026:
“…Similarly [to USPTO Example 41], the mathematical operations in the present claims are used in a specific manner. Namely, processing data from mobile communication networks and other heterogeneous sources, reducing the data set to improve computational performance, and allocating specific physical resources to geographic clusters.”
Examiner respectfully finds the argument unpersuasive. Example 41 achieves eligibility because the mathematical concepts are integrated into a process that secures private network communications, so that a ciphertext word signal can be transmitted between computers of people who do not know each other or who have not shared a private key between them in advance of the message being transmitted, where the security of the cipher relies on the difficulty of factoring large integers by computers; thus integrating the mathematical concept into a practical application. Examiner submits the mathematical features recited in the present case, processing data to identify quantities of observations breaching a threshold over time and geographic space, falls short of this comparison.
Step 2B:
Applicant argues on page 16 of remarks 6/4/2026:
“…Here, the combination of: (1) receiving data from two or more heterogeneous data source types, (2) transforming data to make it reciprocally coherent, (3) applying specific data quality checks with mathematical tolerances, (4) reducing the data set to improve memory usage and computational capacity, (5) applying clustering algorithms to partition geographic pixels, and (6) configuring and concentrating specific physical resources in clusters where they are most needed, represents a non-conventional and non-generic arrangement that provides a technical improvement in geographic resource allocation.”
Examiner respectfully disagrees. The technical limitations in the claims as amended include mathematical details of how the computation engine sorts human activity observations into similar clusters. The solution to the problem at hand is technical in nature. However, the claims recite this technical solution to an entrepreneurial problem; the computer-based elements apply mathematical calculations to collect and organize human activity data by time and geography, and in turn allocate public resources appropriately. Applicant specification states at page 1 line 21: “In the context of the great effort being made to ensure that these urban areas are transformed from agglomerations of citizens and services into a ‘smart’ environment, the ability to interpret the information gathered from various data sources becomes essential for being able to provide useful services to the City Manager and/or to the individual citizen.” Examiner notes algorithmic improvements indicated by the claims, to include currently amended limitations including retrieving data from further specified/limited data sources, such as public administration data sources, distributed sensors throughout the geographic area, resource consumption data sources, or sources of human or device presence indicators; and allocating further specified/limited resources, such as electricity, gas, water, data network bandwidth, or radio frequencies (Examiner again submits that actual physical control or implementation of the distribution of the resources is not evident in the claims); however, significant improvement to computer technology remains unclear.
Accordingly, the rejections under 35 USC 101 are maintained. The 101 rejection section below is updated in view of the amendments.
-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1, 4-14, 16-21 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claims 1, 4-13, 16-21 are directed to a methods or processes which is a statutory category.
Claim 14 is directed to a system or machine which is also a statutory category.
Step 2A Prong One: The claims recite, describe, or set forth a judicial exception of an abstract idea (see MPEP 2106.04(a)). Specifically:
I. The claims recite, describe, or set forth commercial or legal interactions including: “subdividing the geographic area into a number of pixels”, “selecting… an observation period, and subdividing… the observation period into a number of predefined time sub-intervals”, “receiving, from the two or more of: public administration data sources… resource consumption data sources, or sources of human… presence indicators, a data set associated with the pixels during the observation period, the data set comprising, for each pixel, a set of observations related to presence of people in the geographic area, wherein each observation is derived from an estimate or a measurement of a resource consumption or a presence indicator”, “processing… the data set to identify one or more typical time sub-intervals during the observation period”, and “wherein the resources comprise at least one of electricity, gas, water, data network bandwidth, or radio frequencies, and wherein the allocation optimizes resource allocation to match effective needs of users in the cluster by configuring and concentrating the resources in the cluster where they are most needed based on the trends.” Furthermore, the claims are read in light of Applicant specification line 20 of page 39: “Thanks to the method of the present invention, it is possible to better understand the social and human dynamics of a certain area, the changes that take place in the context (e.g. increase/decrease in the resident population), profile the customers based on consumption data, identify their habits and therefore be able to provide improved or additional services (for instance, greater telephone coverage at certain times of the year or greater quantities of water at certain hours/days of the week) or more suitable commercial offers,” which can be clearly viewed as commercial or legal actions under the larger abstract grouping of Certain Methods of Organizing Human Activity (MPEP 2106.04(a)(2) II).
II. Complimenting the commercial or legal interactions above, the claims recite, describe or set forth mathematical formulas or equations including:
“wherein the processing the data set further comprises checking whether a number of acceptable observations, does not differ from a number of observations of the received data set of more than a predefined tolerance, Ɛ, the number of observations of the received data set being equal to N x ITI x M, where N is the number of observations within a time sub-interval, M is the number of pixels, and ITI is a number of sub-intervals of the observation period, T” at independent claims 1, 16, and similarly at dependent claim 4;
“wherein the processing the data set comprises identifying pixels where (xmax(i) - xmin(i)) ≤ Slow, where xmax(i) is the maximum value of the observations associated with a i-th pixel over a considered time period, xmin(i) is the minimum value of the observations associated with the i-th pixel over the considered time period, and Slow is a pre-defined threshold” at dependent claim 6;
“computing a normalized value, xNorm(i,t), for each value of an observation, x(i,t), within the sub-set of data associated with a i-th pixel, wherein:
PNG
media_image1.png
115
282
media_image1.png
Greyscale
where
PNG
media_image2.png
82
284
media_image2.png
Greyscale
and N' is an integer number representing the number of observations of the sub-set of data for the i-th pixel” at independent claim 7, and similarly dependent claim 17;
“-applying a functional data analysis technique to transform the normalized values in a time variable XNorm(i, t) by rewriting them as a linear combination of a set of basis functions as:
PNG
media_image3.png
121
390
media_image3.png
Greyscale
where I is an integer index ranging from 1 to an integer number L representing a total number of basis functions and the basis functions are:
PNG
media_image4.png
332
494
media_image4.png
Greyscale
while coefficients ci(i) are real-valued coefficients;
“-applying a principal component analysis technique as follows:
PNG
media_image5.png
112
588
media_image5.png
Greyscale
where
PNG
media_image6.png
202
728
media_image6.png
Greyscale
k' is a total number of principal components where 1<=k'<=L; and
“-determining a matrix, Y, of dimension equal to M x k', M being the number of pixels, wherein an i-th row of the matrix, Y, comprises the coefficients, pj(i), of the functional principal components for the i-th pixel” at dependent claim 8 and similarly at dependent claim 18, each of which can be clearly viewed as mathematical relationships, formulas, equations, or calculations under the larger abstract grouping of Mathematical Concepts (MPEP 2106.04(a)(2) I).
Accordingly, the character as a whole of the claims is abstract.
Step 2A Prong Two:
Independent claims 1, 7, 16 recite the following computer-based additional elements: “repository”, “computation engine”, “clustering algorithm”, “mobile communications network”, “base stations”, and “user communication devices”. These additional elements merely provide an abstract-idea-based-solution implemented with computer hardware and software components which fail to integrate the abstract idea into a practical application. The capabilities of the additional elements include: “data corresponding to the geographic area is stored”, “extracts the data in the repository and subdivides the geographic area into the number of pixels based on the data”, “selecting an observation period”, “subdividing the observation period into a number of time sub-intervals”, “wherein the data in the repository is retrieved from one or more data sources, and wherein the data is transformed to make the data reciprocally coherent for the computation engine”, “identify one or more typical time sub-intervals”, “applying a clustering algorithm to partition the pixels into a number of clusters, each cluster comprising a respective group of pixels associated with similar trends in the resource consumption and/or in the presence indicator”, “wherein, based on the processing, the data set is reduced to a sub-set of data”, and “allocating resources to a cluster”. The additional elements are recited at a high level of generality (i.e. as a generic computer performing functions of collecting data; calculating statistics; organizing, evaluating and communicating data, etc.) such that they amount to no more than mere instructions to apply the exception using generic computer components. Therefore, these functions can be viewed as not meaningfully different than a resource allocating business method and its underlying mathematical algorithm being applied on a general-purpose computer as tested per MPEP 2106.05(f)(2)(i). The claims are directed to an abstract idea and the judicial exception does not integrate the abstract idea into a practical application.
Step 2B:
According to MPEP 2106.05(f)(1), considering whether the claim recites only the idea of a solution or outcome i.e., the claims fail to recite the technological details of how the actual technological solution to the actual technological problem is accomplished. The recitation of claim limitations that attempt to cover an entrepreneurial and thus abstract solution to an entrepreneurial problem with no technological details on how the technological result is accomplished and no description of the mechanism for accomplishing the result do not provide significantly more than the judicial exception.
Dependent claim 14 recites the additional elements “A non-transitory computer readable medium,” “a computer program comprising computer-executable instructions”, and “computer”. The additional elements are also recited at a high level of generality (i.e. as a generic computer performing functions of collecting data; calculating statistics; organizing, evaluating and communicating data, etc.) such that they amount to no more than mere instructions to apply the exception using generic computer components.
Further, dependent claims 4-13, 17-21 merely incorporate the additional elements recited in claims 1, 7, 16 along with further narrowing of the abstract idea of claims 1, 7, 16 along with their execution of the abstract idea. Claim 10 narrows the clustering algorithm to a centroid-based clustering algorithm and claim 11 narrows the clustering algorithm to a fuzzy k-means algorithm. The other additional computer-based elements are narrowed to capabilities such as checking, identifying, acquiring, discarding, transforming, rewriting, and quantifying various forms of data such as time intervals, pixels, observations, thresholds, values, clusters, etc. which, when evaluated per MPEP 2106.05(f)(2) represent mere invocation of computers to perform an existing process. Therefore, the additional elements recited in the claimed invention individually and in combination fail to integrate a judicial exception into a practical application (Step 2A prong two) and for the same reasons they also fail to provide significantly more (Step 2B). Thus, claims 1, 4-14, 16-21 are reasoned to be patent ineligible.
-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
Allowable Subject Matter
Claims 1, 4-14, 16-21 overcome prior art, with the following being Examiner’s statement of reasons for overcoming the prior art: The closest prior art is Ainsley et al. US 2013184887 A1. Yet, neither of Ainsley, nor any other prior art on record teaches either alone or, in combination with adequate rationale, teach the mathematical expressions, relationships, formulas and numerical equivalencies, as recited in each of independent claims 1, 7, 16.
The reason for withdrawing the 35 USC 102 rejection of claims 1, 14, 16 in the instant application is because the prior art of record fails to teach the overall combination as claimed. Therefore, it would not have been obvious to one of ordinary skill in the art to modify the prior art to meet the combination above without unequivocal hindsight and one of ordinary skill would have no reason to do so. Upon further searching the examiner could not identify any prior art to teach these limitations. The prior art on record, alone or in combination, neither anticipates, reasonably teaches, not renders obvious the Applicant's claimed invention.
Last but not least, the Examiner reminds Applicant that novelty (35 USC 102) and non-obviousness (35 USC 103) still pertain to features that are mostly abstract that do not render the claims patent eligible (35 USC 101). Simply said the novel and non-obviousness rationale above do not necessarily render the claims patent eligible. See for example MPEP 2106.04 I ¶5, 3rd sentence citing Mayo, 566 U.S. 71, 101 USPQ2d at 1965); Flook, 437 U.S. at 591-92, 198 USPQ2d at 198 “the novelty of the mathematical algorithm is not a determining factor at all”.
----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
Conclusion
The following art is made of record and considered pertinent to Applicant’s disclosure:
Bouet, M. and Conan, V. (2018) "Mobile Edge Computing Resources Optimization: A Geo-Clustering Approach," in IEEE Transactions on Network and Service Management, vol. 15, no. 2, pp. 787-796, June 2018, doi: 10.1109/TNSM.2018.2816263.
https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8318685
Dong et al. US 20110137710 A1, Method and apparatus for outlet location selection using the market region partition and marginal increment assignment algorithm, teaches the use of clustering to partition a geographic region into a fixed number of sub-regions.
Horelik WO 2018039142 A1, Predictive analytics for emergency detection and response management, teaches a method for efficiently deploying emergency response teams based on geographic data.
Jacobs WO 2015058801 A1, Method for performing distributed geographic event processing and geographic event processing system, teaches geographic event processing such as dynamic heat maps, geofencing, and real-time crowd detections.
KRIISK, K. (2019). Distribution of local social services and territorial justice: The case of estonia. Journal of Social Policy, 48(2), 329-350.
Doi: http://dx.doi.org/10.1017/S0047279418000508
Kennedy, L.W., Caplan, J.M. & Piza, E. (2011) Risk Clusters, Hotspots, and Spatial Intelligence: Risk Terrain Modeling as an Algorithm for Police Resource Allocation Strategies. J Quant Criminol 27, 339–362 (2011). https://doi.org/10.1007/s10940-010-9126-2
Kumar et al US 20210103899 A1, Waste management system and method, teaches a system for predicting waste volumes in geographic areas.
Nordstrand US 20140032271 A1, System and method for processing demographic data, teaches the distribution of population data with geographic features.
Reese et al. US 20160196577 A1, Geotargeting of content by dynamically detecting geographically dense collections of mobile computing devices, teaches population clustering analysis based on mobile phone geolocations.
Yang et al. US 20180046652 A1, A definition method for urban dynamic spatial structure circle, teaches steps for tracking human activity through polygonal segments in urban areas.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to REED M. BOND whose telephone number is (571) 270-0585. The examiner can normally be reached Monday - Friday 8:00 am - 5:00 pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Patricia Munson can be reached at (571) 270-5396. The fax phone number for the organization where this application or proceeding is assigned is (571) 273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/REED M. BOND/Examiner, Art Unit 3624 A
September 3, 2026
/HAMZEH OBAID/Primary Examiner, Art Unit 3624
Prosecution Insights