DETAILED ACTION
Claims 1, 3-5, 9, 11-13 have been examined.
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 .
Response to Arguments
Applicant's arguments with respect to the claims have been considered but are moot in view of the new ground(s) of rejection.
On 1/27/26, Applicant amended the independent claims. Also, Applicant makes remarks. See the rejection of amended claim 1 below. Note the additional explanations.
Also, on page 21 of Applicant’s 1/27/26 Remarks, Applicant states that the prior art does not disclose real time physical sensor data. However, Pielot discloses automatically detecting patterns of user behavior related to states and proactive recommendations [4] and live information like user context to automatically sense and determine current user state [14], and that sensors of the users mobile device are used to collect current, live info on user to determine current user context and state change [73] and current context and realtime live state sensors (see Table 1 and context at [82], also see [88, 89]).
Also, see using sensor data for context and predicting attention and the additional explanations at the claimed predicting step below.
Also, the 101 is still found to apply to claim 1 as the additional elements are considered to be used in a generic way. See the 101 below.
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.
Independent Claims 1, 9 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims are in a statutory category of invention. However, the claims recite : - receiving data, wherein the data is received in an event a content is provided; - analyzing, data; - predicting in real-time, an attention score for a user of the user device based on the analyzed data, wherein the attention score indicates a probability of the user paying attention to the content; - categorizing, the attention score in an attention category based on a pre- defined attention threshold; and - initiating, an action based on the attention category; wherein there are one or more sensors, wherein the attention score for the user is predicted using one or more temporal probabilistic techniques; wherein the attention category is one of a very high attention category, a high attention category, a medium attention category, a low attention category, and a very low attention category; wherein: - the very high attention category indicates a very high probability of the user paying attention to the content, - the high attention category indicates a high probability of the user paying attention to the content, - the low attention category indicates a lower probability of the user paying attention to the content, and - the very low attention category indicates a very low probability of the user paying attention to the content; the medium attention category indicates a requirement of a data additional to sensor data to determine a specific probability of the user paying attention to the content. This is considered in the Abstract Idea grouping of certain methods of organizing human activity - advertising, marketing or sales activities or behaviors. This judicial exception is not integrated into a practical application because the claim is directed to an abstract idea with additional generic computer elements. The additional elements are considered a transceiver unit [102], a sensor, from one or more sensors configured on a user device, a user device, by a processing unit [104] connected to the transceiver unit [102], a categorization unit [106] connected to the processing unit [104]; sensors at least one of an accelerometer, a gyroscope, a proximity sensor, an orientation sensor, and an audio control integration sensor. These are considered generic. The sensor and sensor data is generic. How the sensor data is collected, what particular sensor data is used in what way, or how sensor data is analyzed is not stated. The generically recited computer elements do not add a practical application or meaningful limitation to the abstract idea because they amount to simply implementing the abstract idea on a computer.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional limitations only perform well-understood, routine, conventional computer functions as recognized by the court decisions listed in MPEP § 2106.05(d). Also, the additional hardware elements are: (i) mere instructions to implement the idea on a computer, and/or (ii) recitation of generic computer structure that serves to perform generic computer functions. Viewed separately or as a whole, these additional claim elements do not provide meaningful limitations to transform the abstract idea into a patent eligible application of the abstract idea such that the claims amounts to significantly more than the abstract idea itself. The claim does not provide significantly more than the identified abstract idea, in that there is no improvement to another technology or technical field, no improvement to the functioning of a computer, no application with, or by use of a particular machine, no transformation or reduction of a particular article to a different state or thing, no specific limitation other than what is well-understood, routing and conventional in the field, no unconventional step that confines the claim to a particular useful application, or meaningful limitations that amount to more than generally linking the use of the abstract idea to a particular technological environment. Therefore, the claims are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Dependent claims 4, 5, 12, 13 are not considered directed to any additional non-abstract claim elements. Rather, these claims offer further descriptive limitations of elements found in the independent claims and addressed above. While these descriptive elements may provide further helpful description for the claimed invention, these elements do not confer subject matter eligibility to the invention since their individual and combined significance is still not more than the abstract concepts identified in the claimed invention. Hence, these dependent claims are also rejected under 101. Claims 3, 11 were found to pass 101.
Please see the 35 USC 101 section at the Examination Guidance and Training Materials page on the USPTO website.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 3-5, 9, 11-13 are rejected under 35 U.S.C. 103 as being unpatentable over Pielot (20170316463).
Claim 1, 9. Pielot discloses a method of initiating an action based on an attention category, the method comprises:
- receiving, at a transceiver unit [102], a sensor data from one or more sensors configured on a user device, wherein the one or more sensors comprise at least one of an accelerometer, a gyroscope, a proximity sensor, an orientation sensor, and an audio control integration sensor (Examiner notes at least one of claim language; see volume at [85] and proximity at [88], see screen orientation at [82] and Table 6, see sensors and context at Table 1 at [82]; note GPS motion sensor at [7]). Also, Pielot discloses automatically detecting patterns of user behavior related to states and proactive recommendations [4] and live information like user context to automatically sense and determine current user state [14], and that sensors of the users mobile device are used to collect current, live info on user to determine current user context and state change [73] and current context and realtime live state sensors (see Table 1 and context at [82], also see [88, 89]).
Pielot further discloses wherein the sensor data is received in an event a content is provided on the user device ([88, 89]; [82] with Table 1);
- analyzing, by a processing unit [104] connected to the transceiver unit [102], the sensor data ([57, 76, 77]);
- predicting in real-time, by the processing unit [104], an attention score using one or more temporal probabilistic techniques (see predict and percentages at [84] and [74-77] which reads on probabilistic techniques) for a user of the user device based on the analyzed sensor data, wherein the attention score indicates a probability of the user paying attention to the content ([34-36] and this reads on score since it discloses minimum, levels, and thresholds, also for score see percentage at [84] and see [60] with likely times level and also see 5 point scales of attention [100]; also see [57, 76, 77]; see predict attention level based on data collected [85] and [74-77] and data collected includes context data [74, 14, 18] which is based on sensor data as shown above);
- categorizing, by a categorization unit [106] connected to the processing unit [104], the attention score in one of an attention category ([34-36] and see category with minimum, levels, and thresholds and deciding to send or not and different prices based on attention level/category; also see high attention level at [69] and see attention level/boredom rating at [74, 77, 85]). Pielot does not explicitly wherein the attention category is one of a very high attention category, a high attention category, a medium attention category, a low attention category, and a very low attention category. However, Pielot discloses different attention levels and different prices based on the levels ([34-36]) including lower and higher levels ([34-36], [100]) and also 5 point scales of attention ([100]) and also see high attention level at [69] and see attention level/boredom rating at [74, 77, 85]. And, the MPEP states that differing ranges, amounts, and proportions are obvious (MPEP 2144.05). Therefore, it would have been obvious to one having ordinary skill in the art at the time the invention was made to add other levels like very high, medium, very low to the levels, thresholds, and minimums and 5 point scales of attention that Pielot already discloses. One would have been motivated to do this in order to better categorize and charge for attention and content presented (as seen in Pielot at [34-36]).
Pielot further discloses based on a pre- defined attention threshold ([34-36] and see category with minimum, levels, and thresholds and deciding to send or not and different prices based on attention level/category; also note high attention level at [69] and see attention level/boredom rating at [74, 77, 85]).
Pielot further renders obvious wherein: - the very high attention category indicates a very high probability of the user paying attention to the content, - the high attention category indicates a high probability of the user paying attention to the content, - the low attention category indicates a lower probability of the user paying attention to the content, and - the very low attention category indicates a very low probability of the user paying attention to the content (see the preceding obviousness statement on different attention levels; and Pielot discloses that attention level correlates with likelihood of paying attention, see likely at [12, 15] and [57, 60]).
Pielot does not explicitly disclose the medium attention category indicates a requirement of a data additional to the sensor data to determine a specific probability of the user paying attention to the content. However, Pielot discloses a answer or and interaction measurement and access measurement ([30, 99]) and also a idleness and click measurement ([82] and Table 5) and a general attention estimation and a “fine-grained” attention estimation based on responses and questions ([100]). Therefore, it would have been obvious to one having ordinary skill in the art at the time the invention was made to add responses or other measurements to Pielot’s attention estimation. One would have been motivated to do this in order to better make a “fine-grained” attention estimation (as seen in Pielot at [100]).
Pielot further discloses initiating, by the processing unit [104], an action based on the attention category ([34-36] and see category with minimum, levels, and thresholds and deciding to send or not and different prices based on attention level/category, and the action is charging what price based on attention level and sending the ad or not).
Claim 3, 11. Pielot further discloses the method as claimed in claim 2, wherein the sensor data comprises at least one of:
- the accelerometer sensor data received from an accelerometer, wherein the accelerometer sensor data indicates one or more changes in at least one of a movement of the user device and an acceleration of the user device (note GPS motion sensor at [7]), - the gyroscope sensor data received from a gyroscope, wherein the gyroscope sensor data indicates one or more changes in at least one of an orientation of the user device and an angular speed of the user device (note GPS motion sensor at [7], see screen orientation at [82] and Table 6 ), - the proximity sensor data received from a proximity sensor, wherein the proximity sensor data indicates one or more changes in a distance of the user device from one or more objects (see proximity at [88]), - the orientation sensor data received from a orientation sensor, wherein the orientation sensor data indicates one or more changes in at least one of an orientation of the user device and a direction of the user device (see screen orientation at [82] and Table 6), and - the integration sensor data received from an audio control integration sensor, wherein the integration sensor data indicates one or more changes in an audio level of the user device (see volume at [85]).
Claim 4, 12. Pielot further discloses the method as claimed in claim 1, wherein the content is one of an advertisement related media content ([34-36]) and a non- advertisement related media content (see content at [22]).
Claim 5, 13. Pielot further discloses the method as claimed in claim 1, wherein the sensor data is analyzed by the processing unit [104] using one or more data analysis techniques (see Fig. 1 item 500 and also [34-36] and machine learning and estimation module at [51]).
Conclusion
The following prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
a) Frank [96], Parra discloses relevant features for sensors and attention score/level.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ARTHUR DURAN whose telephone number is (571)272-6718. The examiner can normally be reached Mon-Thurs, 7-5pm.
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, Ilana Spar can be reached at (571) 270-7537. 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.
/ARTHUR DURAN/Primary Examiner, Art Unit 3621 2/24/26