DETAILED ACTION
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 .
Status of Claims
Claims 21-22 and 24-40 pending.
Claims 21 and 39 amended.
Claims 1-20 and 23 cancelled.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 5/21/2025, 6/13/2025, 7/23/2025, 8/27/2025, 10/15/2025, 12/18/2025, 3/11/2026, 4/14/2026, and 5/1/2026. The submissions are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements are being considered by the examiner.
Terminal Disclaimer
The terminal disclaimer filed on 1/29/2026 disclaiming the terminal portion of any patent granted on this application which would extend beyond the expiration date of 12/2/2042 has been reviewed and is accepted. The terminal disclaimer has been recorded.
Response to Arguments
Applicant’s arguments with respect to claims 21-22 and 24-40 have been considered but are moot because the new ground of rejection.
Claim Rejections - 35 USC § 103
5. 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.
6. 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.
7. The factual inquiries 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.
8. Claims 21-22 and 24-40 are rejected under 35 U.S.C. 103 as being unpatentable over Zhou (US 2021/0035159), hereinafter referred to as Zhou, in view of Galron (US 2017/0337612), hereinafter referred to as Galron.
9. Regarding claims 21 and 39, Zhou discloses a computer-implemented method of determining an intention to perform an action, the method, comprising: determining an intention to perform an action based on at least one of the affinity profile of the user or a change in the affinity profile of the user, wherein the affinity categories are selected from a stored set of affinity categories, each representing an affinity of the user for a respective subject area (paragraph 33 and 42 wherein system may use machine learning system may utilize a variant of classical field-aware factorization machine (“FFM”) to process the data and predict purchase intent based on the provided data);
transmitting, by a content distribution system, television content or other content to each of the plurality of users, wherein each user device displays an electronic programme guide (EPG) or other user interface that is operable by a user to select one or more items of television or other content (paragraph 47 wherein targeted content for the digital content selected and may be transmitted to and displayed on personal devices associated with users on the list output);
in response to the selections, the distribution system distributes the selected items of television content or other content to the user devices for viewing by the users during content viewing sessions (paragraph 148 wherein targeted content for the digital content selected and may be transmitted to and displayed on personal devices associated with users on the list output),
wherein the method further comprises categorizing the users into one or more of a plurality of affinity segments based on their affinity profile, each affinity segment corresponding to an area of interest (paragraph 60 wherein a male user may be denoted by a 1 in a gender category, and a female user may be denoted by a 0);
and for at least one of the affinity segments, classifying each of a plurality of the users that are categorized into that affinity segment into one of a plurality of classifications based on the amount of content they viewed over a period of time, wherein the period of time comprises at least one day (paragraph 87 wherein the terms found in the user's movie history metadata may be split into different categories of related terms. The categories may include as genres, actors, directors, studios, composers, etc., and each category may include related terms).
However, Zhou is silent in regards to disclosing affinity profiles associated with monitored user activity.
Galron discloses for each of a plurality of user devices, monitoring user activity including identifying at least one of television, other content selection or viewing by the user using at least one user device (paragraph 25 wherein database tuning assistant may receive recommendations and statistics regarding a set of one or more queries produced via an account (e.g., using database query interfaces on one or more client devices),
generating a user record for the user, the user record representing the user activity (paragraph 34 wherein system identifies the categories of items that the user selects to view);
processing the user record to generate an affinity profile for the user based on the user record, the generated affinity profile comprising at least one selected affinity category and an affinity category score for each of the at least one selected affinity category, wherein each affinity category score represents a level of interest in the subject area for the respective affinity category (paragraphs 36-37 wherein profile information may include but not limited to the login information of the respective users as well as a personal affinity of the respective users to particular categories of interests);
wherein, based on the selections, categories, and classifications, the distribution system distributes the selected items of television content or other content to the user devices for viewing by the users during content viewing sessions (paragraphs 56-57 wherein the user's categories used to calculate affinity scores are limited (or not limited) the embodiment might use a limited number of user's categories, a threshold level of interest in user's categories, or all of a user's categories when selecting collections for display.).
Galron provides motivation to combine the references wherein the method may also infer an interest in a category of items by a user based on categories of items in collections a user chooses to select for viewing (paragraph 35). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Zhou with the affinity determination of Galron (paragraph 35).
10. Regarding claim 22, Zhou discloses the method of claim 21, further comprising for each of a plurality of user devices, monitoring user activity including identifying content selected for viewing by the user of the user device (paragraph 34 wherein system identifies the categories of items that the user selects to view);
obtaining metadata concerning the selected items of content, the metadata representing at least some properties of the selected items of content (paragraph 32 wherein user input data set may include user metadata (e.g., websites visited, frequency of visitation, duration of visitation, click through rate, digital content purchase history, comment history such as terms used, frequency of terms used, timestamps of term usage, etc.) and user demographic data);
and generating or updating a user record for the user, the user record comprising or representing at least one of the user activity or the associated content metadata (paragraph 98 wherein system performs updating the affinities based upon weights supplied by a user and/or administrator of the system);
processing the user record to generate the affinity profile for the user based on the user record (paragraphs 36-37 wherein profile information may include but not limited to the login information of the respective users as well as a personal affinity of the respective users to particular categories of interests).
11. Regarding claim 24, Zhou discloses the method of claim 21, wherein the user interface is operable by a user to select one or more items of content of content types other than television content, the obtaining of metadata, the generating or updating of the user record, and the processing of the user record to generate the affinity profile are performed using the content metadata obtained for both selected items of television content and selected items of content of the other content types (paragraph 98 wherein system performs updating the affinities based upon weights supplied by a user and/or administrator of the system);
and the other content types comprises at least one of computer games, books, music, spoken word content, other audio content, newspapers, or magazines (paragraph 30 wherein the output data set may include affinity-based digital content (e.g., television show, movie, music, advertisements, etc.) to be displayed on the personal device).
12. Regarding claim 25, Zhou discloses the method of claim 21, wherein the intention to perform the action comprises at least one of an intention to purchase a product or service, or an intention to vote (paragraph 33 and 42 wherein system may use machine learning system may utilize a variant of classical field-aware factorization machine (“FFM”) to process the data and predict purchase intent based on the provided data).
13. Regarding claim 26, Zhou discloses the method of claim 21, further comprising determining a change in the affinity profile of the user over time, and wherein the determining of the intention to perform an action is determined based on the change in affinity profile of the user over time (paragraph 91 wherein data may be weighted differently based on the age of the data. As time passes, a user's interests may change).
14. Regarding claim 27, Zhou discloses the method of claim 21, further comprising identifying an occurrence or probability of a past or future life event or change in circumstances of the user in response to a change in the affinity profile of the user, thereby identifying an occurrence or probability of a past or future life event or change in circumstances based on at least one of selection or viewing of television or other content by the user (paragraph 81 wherein an analysis may be performed to identify which features contribute the most to false negative predictions as well as to false positive predictions. An iterative approach may be used where features that are “repeat offenders” (appear often in these lists) are excluded from future iterations of the algorithm).
15. Regarding claim 28, Zhou discloses the method of claim 21, wherein the stored set of affinity categories comprises at least 100 affinity categories or sub-categories (paragraph 189 wherein during affinity prediction analysis, the terms found in the user's movie history metadata may be split into different categories of related terms. The categories may include as genres, actors, directors, studios, composers, etc., and each category may include related terms). Although Zhou does not explicitly disclose 100 affinity categories, it would have been obvious to one of ordinary skill in the art at the time of the invention to have as many categories or sub-categories as necessary.
16. Regarding claim 29, Zhou discloses the method of claim 21, wherein the metadata concerning the selected items of content is obtained from a stored ontology that includes at least 10,000 features that can be used as meta data to represent items of content (paragraph 189 wherein during affinity prediction analysis, the terms found in the user's movie history metadata may be split into different categories of related terms. The categories may include as genres, actors, directors, studios, composers, etc., and each category may include related terms). Although Zhou does not explicitly disclose 10,000 features, it would have been obvious to one of ordinary skill in the art at the time of the invention to have as many features that can be used as metadata as necessary.
17. Regarding claim 30, Zhou discloses the method of claim 29, wherein the ontology includes enriched versions of metadata obtained for items of content (paragraph 62 wherein a number of movie data sources may be used, including electronic transactions and web browsing data, digital content metadata, and third party sources including demographics, social interests and other publicly available content metadata sources. This third party data is added to enrich the connectivity between movies, as additional features enable movies to link to other movies via additional dimensions).
18. Regarding claim 31, Zhou discloses the method of claim 29, wherein the processing of the user record to generate an affinity profile comprises applying a mapping process to map between the metadata in the user record and the affinity categories, the mapping process including applying weightings or confidence scores for mappings between metadata in the user record and items in the affinity categories (paragraph 42 wherein in determining whether a user will click on the advertisement and purchase or view the digital content or not, the machine learning system may also provide a number indicative of the confidence that the predicted result will happen).
19. Regarding claim 32, Zhou discloses the method of claim 21, wherein the user record comprises or represent user activity for different time windows during a day or week, and the method comprises generating different affinity profiles for the user for the different time windows (paragraph 91 wherein data may be weighted differently based on the age of the data. As time passes, a user's interests may change. As such, data that is older may be less relevant to a user's current interests).
20. Regarding claim 33, Zhou discloses the method of claim 21, further comprising categorizing a user into one of a plurality of categories based on the affinity profile (paragraph 87 wherein the terms found in the user's movie history metadata may be split into different categories of related terms. The categories may include as genres, actors, directors, studios, composers, etc., and each category may include related terms).
21. Regarding claim 34, Zhou discloses the method of claim 21, wherein the affinity profile for the user comprises a set of scores, each score being for a respective one of the affinity categories (paragraph 213 wherein based on the set of user metadata, a subset of the user is identified, via a machine learning model. The subset of the users has an affinity for purchasing digital home-entertainment content, wherein the affinity is above a threshold level).
22. Regarding claim 35, Zhou discloses the method of claim 21, wherein the user is a user account (paragraph 34 wherein system identifies the categories of items that the user selects to view).
23. Regarding claim 36, Zhou discloses the method according to claim 35, wherein at least one of a plurality of individuals have access to the user account or a plurality of user devices are associated with the user account, and wherein the affinity profile for the user account is based on selection of content by at least one of the plurality of individuals or using the plurality of user devices (paragraph 47 wherein targeted content for the digital content selected and may be transmitted to and displayed on personal devices associated with users on the list output).
24. Regarding claim 37, Galron discloses the method according to claim 21, further comprising selecting additional television content or other content to push to the user based on the determined affinity profile for the user (paragraphs 56-57 wherein the user's categories used to calculate affinity scores are limited (or not limited) the embodiment might use a limited number of user's categories, a threshold level of interest in user's categories, or all of a user's categories when selecting collections for display.)
25. Regarding claim 38, Zhou discloses the method according to claim 22, further comprising outputting the determined affinity profiles for the plurality of users via an API or operator interface thereby making the affinity profiles available to a third party external to the content distribution system (paragraph 62 wherein a number of movie data sources may be used, including electronic transactions and web browsing data, digital content metadata, and third party sources including demographics, social interests and other publicly available content metadata sources. This third party data is added to enrich the connectivity between movies, as additional features enable movies to link to other movies via additional dimensions).
26. Regarding claim 40, Zhou discloses a non-transitory computer-readable medium that comprises computer-readable instructions that are executable to perform a method according to claim 21 (paragraph 25 wherein database tuning assistant may receive recommendations and statistics regarding a set of one or more queries produced via an account (e.g., using database query interfaces on one or more client devices),
Conclusion
27. 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 nonprovisional extension fee (37 CFR 1.17(a)) 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 mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CHARLES N HICKS whose telephone number is (571)270-3010. The examiner can normally be reached Monday-Friday 10-7 EST.
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, Benjamin Bruckart can be reached on 571-272-3982. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/CHARLES N HICKS/Examiner, Art Unit 2424
/BENJAMIN R BRUCKART/Supervisory Patent Examiner, Art Unit 2424
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