DETAILED ACTION
This action is responsive to the communication filed on 06/25/2025.
Claims 1-22 were canceled by preliminary amendment.
New claims 23-42 were added.
Claims 23-42 are pending.
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 .
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.
Claim(s) 23-42 is/are rejected under 35 U.S.C. 103 as being unpatentable over Jones et al (US Pub. No. 2022/0027953 herein after “Jones”) and Poncz et al (US Pat. No. 11418919 herein after “Poncz”) and further in view of Kaul et al (US Pub. No. 2019/0236652 herein after “Kaul”).
As per claim 23, and similarly claims 33 and 41, Jones discloses a method comprising:
assigning a data set drawn from a population of mobile devices, wherein the assigned data set include socioeconomic data (Jones, para[0061] data about ideal consumers…a variety of data about users and user behavior);
generating behavior profiles associated with each mobile device of the population of mobile devices, wherein the behavior profiles include semantic data associated with locations visited by each mobile device in the population of mobile devices (Jones, para[0061,0069] customer profiles contains collected information about a customer or consumer; data can relate to…physical locations visited);
determining a seed audience based on the behavior profiles and the socioeconomic data of the population of mobile devices (Jones, para[0075] a retailer has a list of ideal customers…the list forming the seed audience);
identifying a behave-alike audience to the seed audience from the population of mobile devices, said identifying is based on correlative similarity in respective behavior profiles and socioeconomic data of the seed audience and the behave-alike audience (Jones, para[0075] the encoded candidates are compared with the aggregate seed representative on a number of characteristics, the system determines a threshold of similarity…in this way the system suggest candidates similar to those known to be ideal).
Jones does not disclose, however, Poncz discloses semantic data associated with locations visited by each mobile device in the population of mobile devices (Poncz, col. 5 lines 6-66: metric data related to audiences at particular locations; col. 7 lines 18-27).
Therefore, it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to incorporate Poncz’z teaching of semantic data into Jones’ teaching of consumer profiles because one of the ordinary skill in the art would have been motivated to provide recommendation based on known audience interactions.
Jones nor Poncz discloses, however, Kaul discloses generating a score of a candidate of the behave alike-audience to seed audience comparison (Kaul, para[0016] the lookalike model computes a similarity score).
Therefore, it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to incorporate Kaul’s teaching of a similarity score into Jones and Poncz’ teaching because one of the ordinary skill in the art would have been motivated to provide a similarity score for every pair of the retailer’s customers.
As per claim 24 and similarly claim 35, Jones discloses the method of claim 23, wherein the behavior profiles indexed by mobile device and including semantic locations visited by each respective mobile device over a common time period, wherein a semantic location indicates a purpose tied to a respective location, the semantic locations visited are attributed to categories based structures at each given location, and said identifying is further based on a relationship to the categories (Jones, para[0079])(Poncz, col. 9 line 61-col. 10 line 15).
As per claim 25 and similarly claim 34, Jones discloses the method of claim 23, further comprising: generating the seed audience from the behavior profiles of the population of mobile devices based on a search query, wherein the search query is based on a filterable attribute of the behavior profiles (Jones, para[0023,0078-0079] criteria).
As per claim 26 and similarly claim 36, Jones discloses the method of claim 25, wherein filterable attributes include any of: whether a device owner is married; flagged interests of the device owner from a social media profile; registered political party of the device owner; a household income of the device owner; an economic net worth of the device owner; a workplace location of the device owner; a household location of the device owner; a category of a workplace of the device owner; a device's last seen date of location data; categories of locations visited by the device; distinct commercial businesses visited by the device; or a number of visits to any particular location by the device (Jones, para[0023,0079]).
As per claim 27 and similarly claim 37, Jones discloses the method of claim 24, wherein the correlative similarity is based on one of: having visited matching categories of the semantic locations; recency of visits to the matching categories of the semantic locations; a number of visits to the matching categories of the semantic locations; having visited a same exact location; or matching demographic characteristics between the behavior profiles of the population of mobile devices and the seed audience of mobile devices (Jones, para[0075-0081] characteristics).
As per claim 28 and similarly claim 38, Jones nor Poncz disclose, however, Kaul discloses the method of claim 24, wherein the score is averaged to generate an average similarity of the candidate to seed audience score (Kaul, para[0016]) while not explicitly disclosed averaging a score is well within the scope of the claimed invention and would be obvious distinction or matter of design choice and thus cannot be considered an inventive concept).
As per claim Jones does not discloses, however, Kaul discloses the method of claim 28 and similarly claims 39 and 42, comprising: applying a decision threshold on whether to include the candidate in the behave-alike audience, wherein any candidate with a score above the decision threshold is include in the behave-alike audience (Kaul, para[0022]).
As per claim 30 and similarly claim 40, Jones discloses the method of claim 24, wherein the candidate is a first and second order social connection to the seed audience (Jones, para[0023,0078-0079]).
As per claim 31 and similarly claim 42, Jones discloses the method of claim 24, comprising: using the score to tailor marketing strategies or content delivery to the behave-alike audience (Jones, para[0004,0018])(Kaul, para[0013,0022]).
As per claim 32, Jones discloses the method of claim 24, where the correlative similarity of the seed audience is compared against a filtered subset of the population of mobile devices, the filtered subset of the population of mobile devices is based on any combination of: geographic filters applied to the population of mobile devices; degrees of separation in a social network relationship from a seed audience filter applied to the population of mobile devices; socioeconomic filters applied to the population of mobile devices; recency of location data filter applied to the population of mobile devices; or conversion of marketing campaign filter applied to the population of mobile devices (Jones, para[0003,0075-0081]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure. See form 892.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Natisha Cox whose telephone number is (571)270-7167. The examiner can normally be reached on Monday to Friday, 10:00 am - 6:00pm EST.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Umar Cheema can be reached on (571)270-3037. The fax phone number for the organization where this application or proceeding is assigned is (571) 273-8000.
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/NATISHA D COX/ Primary Examiner, Art Unit 2458
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