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 .
DETAILED ACTION
Status of Claims
This action is a first action on the merits in response to the application filed on 01/14/2025.
Claims 1 – 14 are currently pending and have been examined in this application.
Claim Objection
The following claim is objected to for the following limitations.
Claim 9 recites, “ wherein active network events comprise Call Detail Records, CDRs…and Extended Detail Records, XDRs…” at lines 2-3. The abbreviations should be within parentheses. Appropriate correction is required.
Claim Rejections - 35 USC § 112
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 1-14 are rejected under 35 U.S.C. 112, (b)/second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which applicant regards as the invention.
The following claims are rejected under insufficient antecedent basis issues.
Claim 1 recites “active and/or passive network events” at line 12. Claim 13 is rejected based on the same rationale. Claims 2-12 and 14 are rejected based on their dependency on Claims 1 and 13 respectively.
Claim 1 recites “the general population” at line 16. Claim 13 is rejected based on the same rationale. Claims 2-12 and 14 are rejected based on their dependency on Claims 1 and 13 respectively.
Claim 2 recites “the national scope” at line 6.
Claim 3 recites “ the second dataset” at line 4. A second dataset is introduced in Claim 2, while Claim 3 depends on Claim 1.
Claim 4 recites “the daily mobility of resident users” at line 4.
Claim 4 recites “the determination of when and where an overnight stay…” at lines 4-5.
Claim 4 recites “the unique identifier” at line 6. The unique identifier is introduced in claim 2, while Claim 4 depends on Claim 1.
Claim 4 recites “the travel route of resident users” at line 11.
Claim 4 recites “the domestic territory” at line 12.
Claim 4 recites “the web browsing of resident users” at line 18.
Claim 4 recites “the total web browsing time” at lines 21-22.
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 1-14 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Claim 1 recites:
- accessing user data from a mobile network operator, said data being associated with active and/or passive network events from the connections established between user mobile devices and mobile network operator towers;
- calculating a set of user parameters using said accessed data, calculating the set of parameters comprising: calculating a visit parameter, calculating points of interest of each user, and obtaining user web browsing data from the mobile devices based on obtaining network traffic from each mobile device in the network;
- determining a characterisation profile of a geographic area by carrying out the following steps:
- calculating a socio-demographic profile, including gender, age group and/or income level, of temporary and permanent resident users in said geographic area using said data associated with active and/or passive network events;
- assigning a statistical weight to each resident user using information from a first dataset relating to the census of the geographic area for domestic resident users and information obtained from external sources for international resident users, combining the assigned statistical weights, and extrapolating to the general population;
- aggregating the information extrapolated by micro-segments based on a geographic area location, age group, gender and income level of resident users.
The limitation under its broadest reasonable interpretation covers Mental Process related to observation and evaluation of data but for the recitation of generic computer components (e.g. a processor and memory). For example, accessing user data from a mobile network operator, calculating a set of user parameters and determining a characterization profile of a geographic area involve collecting and analyzing data which can be performed in the human mind or using a pen/paper. Accordingly, the claim recites an abstract idea of Mental Processes.
In addition, the claim could be seen as Mathematical Concepts as calculating parameters and statistical analysis is performed.
Independent Claim 13 substantially recite the subject matter of Claim 1 and also include the abstract ideas identified above. The dependent claims encompass the same abstract ideas. For instance, Claim 2 is directed to filtering a second data set including information form a unique identifier (analyzing data); Claim 3 is directed to identifying anomalous behaviors of residents using ML, classifying non anomalous residents based on income level (analyzing data using complex math); Claim 4 is directed to calculating resident user behavior based on overnight stays (analyzing data); Claim 5 is directed to one or more sources of information related to connectivity variables; Claim 6 is directed to calculating the visit parameter (analyzing data); Claim 7 is directed to calculating points of interest using ML; Claim 8 is directed to determining user residence; Claim 9 is directed to active network events; Claim 10 is directed to passive network events; Claim 11 is directed to detecting and eliminating flickering/intermittency events; Claim 12 is directed to micro segments and Claim 14 is directed to a computer program product.
The judicial exceptions are not integrated into a practical application. Claim 1 recites the additional elements of a mobile device and a mobile network operator tower. Claim 13 recites the additional elements of a computing unit including a memory or database and at least one processor, a mobile device an a mobile network operator tower. These are generic computer components recited at a high level of generality as performing generic computer functions (see Spec ¶0009).
For instance, the steps of accessing user data from a mobile network operator where data is associated with active/passive network events is data gathering activity. The steps of calculating a set of user parameters using accessed data, determining a characterization profile of a geographic area based on calculating a socio-demographic profile, assigning a statistical weight to each resident user and aggregating the information extrapolated by micro-segments involves analyzing data.
Each of the additional limitations is no more than mere instructions to apply the exception using a generic computer components (e.g. a processor). The combination of these additional elements is no more than mere instructions to apply the exception using a generic computer component (e.g. a processor). Therefore, the additional elements do not integrate the abstract ideas into a practical application because it does not impose meaningful limits on practicing the abstract idea. Therefore, the claims are directed to an abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As stated above, the additional elements of a processor, a memory, a crm, etc. are considered generic computer components performing generic computer functions that amount to no more than instructions to implement the judicial exception. Mere, instructions to apply an exception using generic computer components cannot provide an inventive concept.
The dependent claims when analyzed both individually and in combination are also held to be ineligible for the same reason above and the additional recited limitations fail to establish that the claims are not directed to an abstract. The additional limitations of the dependent claims when considered individually and as an ordered combination do not amount to significantly more than the abstract idea.
Looking at these limitations as an ordered combination and individually adds nothing additional that is sufficient to amount to significantly more than the recited abstract idea because they simply provide instructions to use generic computer components, to "apply" the recited abstract idea. Thus, the elements of the claims, considered both individually and as an ordered combination, are not sufficient to ensure that the claim as a whole amounts to significantly more than the abstract idea itself. Therefore, Claims 1-14 are not patent eligible.
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 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.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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.
Claims 1, 2,5, 8, 13 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Schewel et al. (US 2015/0005007) further in view of Milton et al. (US 2016/0253689).
Claim 1:
Schewel discloses:
A computer-implemented method for characterising geographic areas, comprising: (see at least Abstract, determining a probability that the device is associated with a location of interest; see also ¶0020)
- accessing user data from a mobile network operator, said data being associated with active and/or passive network events from the connections established between user mobile devices and mobile network operator towers; (see at least ¶0021, collection cellular phone data including device check ins, location, uncertainty radius and other metrics; ¶0025, mobile service carrier network that receives raw data regarding location devices associated with a network)
- calculating a set of user parameters using said accessed data, calculating the set of parameters comprising: calculating a visit parameter, calculating points of interest of each user, and obtaining user web browsing data from the mobile devices based on obtaining network traffic from each mobile device in the network; (see at least ¶0026-¶0028, determine probability device is associated with a location of interest; see also ¶0043-¶0044, determining visit frequency and visit unusualness metrics)
- determining a characterisation profile of a geographic area by carrying out the following steps:- calculating a socio-demographic profile, including gender, age group and/or income level, of temporary and permanent resident users in said geographic area using said data associated with active and/or passive network events; (see at least ¶0036-¶0037, demographic probability distribution includes user type data such as shopper data, stay at home, commuter, shopper, mobility patterns, etc.; see also ¶0025, demographic data includes census data, age, income ethnicity, etc.; see also ¶0041, demographic probability distribution comprises census data scaled by appropriate scaling function; see also ¶0035, demographic data includes resident data and other users of a location which may include non-residents; see also ¶0065, residents and non-residents of bay area)
- assigning a statistical weight to each resident user using information from a first dataset relating to the census of the geographic area for domestic resident users and information obtained from external sources for international resident users, combining the assigned statistical weights, and extrapolating to the general population; (see at least ¶0035, demographic probability destruction comprises census or census like data scaled (weighted) by appropriate scaling function and where the data comprises resident and non-resident data; see also ¶0031-¶0034)
- aggregating the information extrapolated by micro-segments based on a geographic area location, age group, gender and income level of resident users. (see at least ¶0038; aggregate demographics include scaling demographics at various time and include home location probability distribution, demographic data probability distribution or other appropriate distributions; see also ¶0050, subset of visitors based on demographic data; see also ¶0025)
While Schewel discloses the above limitations, Schewel does not explicitly disclose the following limitations; however, Milton does disclose:
calculating a set of user parameters using said accessed data, calculating the set of parameters comprising: calculating a visit parameter, calculating points of interest of each user, and obtaining user web browsing data from the mobile devices (see at least ¶0144, log data includes browsing; see also claim 9)
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, to combine the determining of demographic data of mobile devices in cellular network of Schewel with the collecting of browsing network traffic data of Milton provide a means of matching network traffic data to user devices in visited areas to determine effectiveness of potential content (see Abstract).
Claim 2 :
Schewel and Milton disclose claim 1, Schewel further discloses:
wherein the gender of the resident users is obtained by: a second dataset including information from a unique identifier of the mobile device of each resident user and parameters associated with the calculated points of interest and from the first dataset for an observation time period and for the national scope; (see at least Figure 8 and associated text; see also ¶0059-¶0060, determine the number of people in the area as a function of time, breakdown data into home locations, work locations of visitors and demographics of visitors (e.g. race, gender , income, etc.) or any other subgroup)
While Schewel discloses the above limitations, Schewel does not explicitly disclose the following limitations ; however, Milton does disclose:
sampling, with or without replacement, the filtered data. (see at least ¶0106, sample device identifiers)
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, to combine the determining of demographic data of mobile devices in cellular network of Schewel with the sampling of device identifiers of Milton to analyze a representative subset of data for efficiency and to gain insights of a larger dataset.
Further, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Claim 5:
Schewel and Milton disclose claim 1. Schewel further discloses:
further comprising using one or more sources of information comprising connectivity variables, including access to public transport, underground, and/or bus; household variables, including average household size, composition, and/or number of households; and/or urbanity variables, including typology of dwellings based on their age, size and type of facilities; typology of the area based on whether it is residential, commercial/leisure or office. (see at least ¶0066, transit demographics (e.g. travel by car, bus, rail, etc.; see also ¶0060, area demographics such as business use; see also ¶0056, census data includes household data)
Claim 8:
Schewel and Milton discloses claim 1. Schewel further discloses:
wherein the calculated points of interest include at least the identification of the user place of residence and place of work. (see at least ¶0038, home location probability)
Claim 14:
Schewel and Milton disclose claim 1. Schewel further discloses:
A computer program product including code instructions which, when executed in a computer system, implement a method according to claim 1. (see at least ¶0017, crm)
Claim 13 for a system (Schewel Figure 1) substantially recites the subject matter of Claim 1 for a method and is rejected based on the same rationale.
Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over Schewel et al. (US 2015/0005007) further in view of Milton et al. (US 2016/0253689) further in view of Li et al. (US 2017/0289593).
Claim 4:
While Schewel and Milton disclose claim 1 and Schewel further discloses:
wherein determining the characterisation profile of the geographic area further comprises calculating the resident user behaviour by performing the following steps: - calculating a set of variables related to the daily mobility of resident users based on the determination of when and where an overnight stay has taken place using a fourth dataset including the unique identifier of the mobile device of each resident user and parameters associated with the calculated visit parameter and configurable parameters indicating hours at which a network event must be found to be considered an overnight stay; (see at least Figure 5 and associated text; see also ¶0054, determining nighttime device locations at a particular time e.g. 9pm-7am)
- calculating a set of variables related to resident user trips based on the calculation of at least two of: identifying the travel route of resident users using at least the second dataset, a fifth dataset including information on overnight stays within the domestic territory, and a sixth dataset relating to roaming,
identifying frequent destinations of resident users using the fourth dataset and the second dataset, identifying outings made by resident users using at least the second dataset, the fourth dataset, the fifth dataset and information from the identified frequent destinations; (see at least ¶0025 and ¶0029, frequently visited location; see also ¶0043, see also ¶0066, visit frequency, shopping locations visited, trip types; see also Figures 10-11)
While Schewel discloses the above limitations, neither Schewel nor Milton explicitly disclose the following limitation; however, Li does disclose:
- calculating a set of variables related to the web browsing of resident users based on: determining an average interest rate per browsing category using a seventh dataset including the unique identifier of the mobile device of each resident user, a time period associated with web browsing, a category of web browsing and the total web browsing time, and on determining an individual interest rate, per resident user and category, by dividing a particular resident user web browsing by the average interest rate determined. (see at least ¶0074, user profile may include a set of parameters about individual user including statistical data, category preference, average viewing pattern and time of access; see also ¶0096, category average viewing patterns can be collected as one profile parameter for individual users then aggregated; see also ¶0108)
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, to combine the resident user data of Schewel and the sampling of device identifiers of Milton with the browsing behavior analysis of Li to determine content category popularity.
Claims 6 and 7 are rejected under 35 U.S.C. 103 as being unpatentable over Schewel et al. (US 2015/0005007) further in view of Milton et al. (US 2016/0253689) further in view of Liang et al. (US 2021/0195366).
Claim 6:
While Schewel and Milton discloses claim 1, and Schewel further discloses visit frequency (see at least ¶0043 and ¶0066), neither Schewel nor Milton explicitly disclose the following limitation; however, Liang does disclose:
wherein calculating the visit parameter is performed by aggregating a certain continuous number of network events at the given geographic location, said continuous number of network events having a predefined minimum duration. (see at least ¶0053, determining the most frequently visited geo blocks for a plurality of mobile devices; see also ¶0083, aggregated location events includes a number of visits, time period, time of last visit and average number of visits)
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, to combine the visit frequency of Schewel and the sampling of device identifiers of Milton with the aggregation of location events of mobile devices of Liang to facilitate location based services by capturing relevant location information (See ¶0003).
Further, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Claim 7:
While Schewel, Milton and Liang disclose claim 5, neither Schewel nor Milton explicitly disclose the following limitations; however, Liang does disclose:
wherein calculating the points of interest comprises executing machine learning models on the calculated visit parameters. (see at least ¶0048, machine learning module is configured to train a location prediction model for a location group; see also ¶0055-¶0056, filtering and aggregating the location events across time and space for machine learning)
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, to combine the visit frequency of Schewel and the sampling of device identifiers of Milton with the aggregation of location events of mobile devices of Liang to facilitate location based services by capturing relevant location information (See ¶0003).
Further, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over Schewel et al. (US 2015/0005007) further in view of Milton et al. (US 2016/0253689) further in view of Boyle et al. (US 2022/0116265).
Claim 9:
While Schewel and Milton disclose claim 1, and Milton further disclose including web browsing information from the mobile devices (see at least ¶0144, log data includes browsing; see also claim 9), neither explicitly disclose the following limitations; however, Boyle does disclose:
wherein the active network events comprise Call Detail Records, CDRs, including phone calls made by the mobile devices, and Extended Detail Records, XDRs, including web browsing information from the mobile devices. (see at least ¶0003, call detail records and even data records)
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, to combine the visit frequency of Schewel and web browsing data of Milton with the network data of Boyle to allow all event data, across each protocol to be collected, and analyzed for each call flow allowing richer monitoring and debug capability (see Spec ¶0003).
Claim 10:
While Schewel and Milton disclose claim 1, neither explicitly disclose the following limitations; however, Boyle does disclose:
wherein the passive network events comprise information regarding power-on, coverage recovery, cell change and/or network change of mobile devices. (see at least ¶0127-¶0128, handovers)
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, to combine the visit frequency of Schewel and web browsing data of Milton with the network data of Boyle to allow all event data, across each protocol to be collected, and analyzed for each call flow allowing richer monitoring and debug capability (see Spec ¶0003).
Claims 11 are rejected under 35 U.S.C. 103 as being unpatentable over Schewel et al. (US 2015/0005007) further in view of Milton et al. (US 2016/0253689) further in view of Durgan et al. (US 2015/0049599).
Claim 11:
While Schewel and Milton disclose claim 1, neither explicitly disclose the following limitations; however, Durgan does disclose:
wherein the calculation of the visit parameter further comprises detecting and eliminating flickering/intermittency events between network towers. (see at least ¶0011-¶0012, detect an intermittent IP network failure and reassign the Location Area Code by powering down and removing all associated context of existing mobile)
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, to combine the visit frequency of Schewel and web browsing data of Milton with the detection of network failure and reassigning LAC of Durgan to assist in maintaining mobile device network connection.
Claims 12 are rejected under 35 U.S.C. 103 as being unpatentable over Schewel et al. (US 2015/0005007) further in view of Milton et al. (US 2016/0253689) further in view Arya et al. (US 10984434) .
Claim 12:
While Schewel and Milton disclose claim 1, and Schewel further discloses micro-segments (see at least ¶0050, subset of visitors to a location of interest of a demographic of interest; see also ¶0025, demographic data includes age, income, gender)
wherein the micro-segments comprise at least male and female for gender; 18-29, 30-39, 40-49, 50-59, 60-59 and above or equal to 70 for age group; and low, medium, medium-high and high for income level. (see at lease column 21, lines 55-67-column 22, lines 1-6, using selected demographics including gender, age, income level to determine a peer group of users)
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, to combine the subset of visitors based on location and demographics Schewel and web browsing data of Milton with the selected demographics for a peer group as in Arya to facilitate analyzing customer behavior.
Conclusion
The prior art made of record and not relied upon is considered relevant but not applied:
Fix et al. (US 9967364) discloses generating a plurality of different user profiles based upon demographic data of existing customers, historical utilization data and historical usage data, determines a demographic of a new neighborhood, correlates one of the plurality of different user profiles to the new neighborhood based upon the demographic of the new neighborhood.
Izumori et al. (US 2015/0193821) discloses the number of histories of a predetermined action of a user corresponding to acquired user identification information identifying a requesting user and the associated browsing history.
Any inquiry of a general nature or relating to the status of this application or concerning this communication or earlier communications from the Examiner should be directed to Renae Feacher whose telephone number is 571-270-5485. The Examiner can normally be reached Monday-Friday, 9:00 am - 5:00 pm. If attempts to reach the examiner by telephone are unsuccessful, the Examiner's supervisor, Beth Boswell can be reached at 571-272-6737.
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/Renae Feacher/
Primary Examiner, Art Unit 3625