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 .
This communication is in response to Amendment filed on June 22, 2026.
Claims 1-20 are pending. Claim 7 is amended.
Response to Arguments
Referring to the 35 USC 112(b) rejection of claim 7, Applicant’s amendments are acknowledged. As such, the 35 USC 112(b) rejection of the claim is withdrawn.
Referring to the 35 USC 101 rejection of claims 1-20, as amended, Applicant’s arguments have been considered but are not found persuasive.
Applicant argues that the claims provide a technical solution to the problem of wastage of computing, network and power resources by utilizing a trained machine learning model to estimate user interactions and/or affirmative user actions with respect to a digital component of a new media type. However, Examiner respectfully disagrees. While the claims recite training a machine learning model, the training of the machine learning model is merely recited as being achieved through the input data to generate an output does not show a technological solution to the problem because the claims do not recite specific steps used within the training process that would enable one to realize an improvement other than merely inputting data into a model and outputting it for analysis. The mere recitation of the machine learning model is an additional element that does not integrate the judicial exception into a practical application or teach significantly more. As such, Examiner submits that the claims do not recite an improvement to technology.
For at least the reasons stated above, the claims remain rejected under 35 USC 101.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claims 1, 9 and 17 recite:
training a machine learning model on (i) historical user interaction data for digital components of a first media type provided by a plurality of content providers and (ii) corresponding data for affirmative user actions relating to the digital components of the first media type;
determining that a specific content provider has not previously provided a first digital component of the first media type;
obtaining a first set of user interaction data representative of interactions by a plurality of users with digital components provided by the specific content provider; obtaining, using the machine learning model and in response to input of the first set of user interaction data to the machine learning model, result data for expected affirmative user actions related to the first digital component of the first media type;
determining, based on the result data for expected affirmative user actions related to the first digital component of the first media type, a recommendation specifying whether the specific content provider should provide the first digital component of the first media type; and
providing, to the specific content provider, the recommendation specifying whether the specific content provider should provide the digital component of the first media type.
Step 1: The claims as a whole fall within one or more statutory categories.
Step 2A prong 1: At least claims 1, 9 and 17 recite limitations that are abstract ideas.
Furthermore, the limitation “determining that a specific content provider has not previously provided a first digital component of the first media type” is also a mental step. A user can mentally determine whether or not a content provider has previously provided a digital component. Thus, the claimed limitation can be performed by the human mind.
Furthermore, the limitation “determining, based on the result data for expected affirmative user actions related to the first digital component of the first media type, a recommendation specifying whether the specific content provider should provide the first digital component of the first media type” is also a mental step. A user can mentally determine whether to recommend that a content provider provide a digital component based on a set of data as a criteria measure. Thus, the claimed limitation can be performed by the human mind.
Step 2A prong 2:
Claims 1, 9 and 17 recite the limitations “obtaining a first set of user interaction data representative of interactions by a plurality of users with digital components provided by the specific content provider”, “obtaining, and in response to input of the first set of user interaction data to the machine learning model, result data for expected affirmative user actions related to the first digital component of the first media type”. These limitations are additional elements and are insignificant extra-solution activity as retrieval/receiving of data (i.e. mere data gathering) such as 'obtaining information' as identified in MPEP 2106.05(g) and does not provide integration into a practical application.
Claims 1, 9 and 17 recite the limitation “providing, to the specific content provider, the recommendation specifying whether the specific content provider should provide the digital component of the first media type”. This limitation is also an additional element and is insignificant extra-solution activity as selecting and outputting information for display, as identified in MPEP 2106.05(g) in Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354-55, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016, and does not provide integration into a practical application.
Claims 1, 9 and 17 recite the limitation “training a machine learning model on (i) historical user interaction data for digital components of a first media type provided by a plurality of content providers and (ii) corresponding data for affirmative user actions relating to the digital components of the first media type”. This limitation is an additional element is enabling inputting the historical user interaction data for digital components of a first media type provided by a plurality of content providers and corresponding data for affirmative user actions relating to the digital components of the first media type into a machine learning model. Furthermore, Claims 1, 9 and 17 recite the following additional elements “system”, “one or more data processing apparatus”, “one or more memory devices storing instructions” and “a machine learning model”, note that these recited additional elements are a high-level recitation of generic computer components to perform the mental process and applied on a computer as in MPEP 2106.05(f), which does not provide integration into a practical application.
Step 2B: the conclusions for the additional elements representing mere implementation using a computer are carried over and do not provide significantly more.
With respect to the “obtaining” and “providing” limitations identified as insignificant extra-solution activity above when re-evaluated these elements are well-understood, routine, and conventional as evidenced by the court cases in MPEP 2106.05(d)(II), "i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); … OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network);" and thus remains insignificant extra-solution activity that does not provide significantly more.
Therefore, the claims as a whole do not change this conclusion and the claims are ineligible.
Claims 2, 5, 7, 10, 13, 15 and 18 depend from claims 1, 9 and 17 and thus include all the limitations of claims 1, 9 and 17, therefore claims 2, 5, 7, 10, 13, 15 and 18 recite the same abstract ideas of "mental processes".
Claims 2, 5, 7, 10, 13, 15 and 18 furthermore recite:
(claims 2, 10, 18) that the first media type comprises one of video, audio, image, or text;
(claims 5 and 13): that the machine learning model corresponds to one of a plurality of different machine learning models that each correspond to a different media type; and
(claims 7, 15): that determining the recommendation includes: determining whether (1) the first data item satisfies a first threshold and (2) the second data item satisfies a second threshold; and generating the recommendation based on whether the (1) the first data item satisfies the first threshold and (2) the second data item satisfies the second threshold, including: generating a recommendation specifying that the specific content provider should provide the first digital component of the first media type when (1) the first data item satisfies the first threshold and (2) the second data item satisfies the second threshold; and generating a recommendation specifying that the specific content provider should not provide the first digital component of the first media type when (1) the first data item does not satisfy the first threshold and (2) the second data item does not satisfy the second threshold.
Step 1: Claims 2, 5, 7, 10, 13, 15 and 18 as a whole fall within one or more statutory categories.
Step 2A prong 1: Claims 2, 7, 10, 15 and 18 recite limitations that are abstract ideas.
The limitation “the first media type comprises one of video, audio, image, or text” in claims 2, 10 and 18 further define the first media type in the ‘training’ step in claims 1, 9 and 17 which is considered a mental step. As such, this limitation is also a mental step.
The limitations “determining whether (1) the first data item satisfies a first threshold and (2) the second data item satisfies a second threshold; and generating the recommendation based on whether the (1) the first data item satisfies the first threshold and (2) the second data item satisfies the second threshold, including: generating a recommendation specifying that the specific content provider should provide the first digital component of the first media type when (1) the first data item satisfies the first threshold and (2) the second data item satisfies the second threshold; and generating a recommendation specifying that the specific content provider should not provide the first digital component of the first media type when (1) the first data item does not satisfy the first threshold and (2) the second data item does not satisfy the second threshold” in claims 7 and 15 are mental steps. One can mentally determine whether specific data satisfies a threshold criteria. Furthermore one can mentally generate a recommendation based on whether an items meets or does not meet the threshold. Thus, the claimed limitations can be performed by the human mind.
Step 2A prong 2:
Furthermore, Claims 5 and 13 recite the following additional elements “a machine learning model”, note that these recited additional elements are a high-level recitation of generic computer components to perform the mental process and applied on a computer as in MPEP 2106.05(f), which does not provide integration into a practical application.
Step 2B: the conclusions for the additional elements representing mere implementation using a computer are carried over and do not provide significantly more.
Therefore, claims 2, 5, 7, 10, 13, 15 and 18 as a whole are ineligible.
Claims 3, 4, 11, 12, 19 and 20 depend from claims 1, 9 and 17 and thus include all the limitations of claims 1, 9 and 17, therefore claims 3, 4, 11, 12, 19 and 20 recite the same abstract ideas of "mental processes".
Claims 3, 4, 11, 12, 19 and 20 furthermore recite:
(claims 3, 11, 19); that the first set of user interaction data includes data indicating to one or more of the following: one or more characteristics of the plurality of users, one or more characteristics of a plurality of client devices correspond to the plurality of users, a number of user interactions with one or more digital components of the first media type, and a duration of user interactions with one or more digital components of the first media type;
(claims 4, 12, 20): that the machine learning model uses gradient boosting ensemble techniques.
Step 1: Claims 3, 4, 11, 12, 19 and 20 as a whole fall within one or more statutory categories.
Step 2A prong 1: Claims 3, 4, 11, 12, 19 and 20 recite limitations that are mental concepts because they depend from claims 1, 9 and 17.
Step 2A prong 2:
The limitation “the first set of user interaction data includes data indicating to one or more of the following: one or more characteristics of the plurality of users, one or more characteristics of a plurality of client devices correspond to the plurality of users, a number of user interactions with one or more digital components of the first media type, and a duration of user interactions with one or more digital components of the first media type” in claims 3, 11 and 19 are insignificant extra-solution activity because they further define the ‘obtaining a first set of user interaction data’ step in claims 1, 9 and 17. As such, this limitation does not provide integration into a practical application.
The limitation “the machine learning model uses gradient boosting ensemble techniques” in claims 4, 12 and 20 does not integrate the judicial exception into a practical application because it merely ties the machine learning model to the technical field of ensemble techniques in machine learning.
Step 2B:
As applied in claims 1 and 9, with respect to the "obtaining” limitation identified as insignificant extra-solution activity above, when re-evaluated these elements are also well-understood, routine, and conventional as evidenced by the court cases in MPEP 2106.05(d)(II), "i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); … OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network);" and thus remain insignificant extra-solution activity that does not provide significantly more.
Therefore, claims 3, 4, 11, 12, 19 and 20 as a whole do not change this conclusion and the claims are ineligible.
Claims 6, 8, 14 and 16 depend from claims 1 and 9 and thus include all the limitations of claims 1 and 9, therefore claims 6, 8, 14 and 16 recite the same abstract ideas of "mental processes".
Claims 6, 8, 14 and 16 furthermore recite:
(6 and 14) that data relating to affirmative user actions that are expected for the first digital component of the first media type comprises at least one of: a first data item specifying an expected number of resources consumed in obtaining an expected affirmative user action relating to the first digital component of the first media type; or a second data item representing an expected number of affirmative user actions relating to the first digital component of the first media type relative to the expected number of resources consumed; and
(claims 8 and 16): that the recommendation regarding whether the specific content provider should provide the first digital component of the first media type includes providing the first and second data items.
Step 1: Claims 6, 8, 14 and 16 as a whole fall within one or more statutory categories.
Step 2A prong 1: Claims 6, 8, 14 and 16 recite limitations that are mental concepts because they depend from claims 1 and 9.
Step 2A prong 2:
The limitation “data relating to affirmative user actions that are expected for the first digital component of the first media type comprises at least one of: a first data item specifying an expected number of resources consumed in obtaining an expected affirmative user action relating to the first digital component of the first media type; or a second data item representing an expected number of affirmative user actions relating to the first digital component of the first media type relative to the expected number of resources consumed” in claims 6 and 14 are insignificant extra-solution activity because they further define the ‘obtaining result data for expected affirmative user actions’ step in claims 1 and 9. As such, this limitation does not provide integration into a practical application.
The limitation “that the recommendation regarding whether the specific content provider should provide the first digital component of the first media type includes providing the first and second data items” in claims 8 and 16 are insignificant extra-solution activity because they further define the ‘providing’ step in claims 1 and 9. As such, this limitation does not provide integration into a practical application.
Step 2B:
As applied in claims 1 and 9, with respect to the "obtaining” and “providing” limitations identified as insignificant extra-solution activity above, when re-evaluated these elements are also well-understood, routine, and conventional as evidenced by the court cases in MPEP 2106.05(d)(II), "i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); … OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network);" and thus remain insignificant extra-solution activity that does not provide significantly more.
Therefore, claims 6, 8, 14 and 16 as a whole do not change this conclusion and the claims are ineligible.
To expedite a complete examination of the instant application, the claims rejected under 35 U.S.C. 101 (nonstatutory} above are further rejected as set forth below in anticipation of applicant amending these claims to place them within the four statutory categories of the invention.
Novel and/or non-obvious Subject Matter
Claims 1-20 were indicated as reciting novel and/or non-obvious subject matter for the reasons stated in the Final rejection dated October 10, 2025.
Conclusion
The art made of record and not relied upon is considered pertinent to applicant's disclosure:
Brown et al (US 20260099866) directed to: creating and delivering digital media assets [entire document].
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 CHERYL M SHECHTMAN whose telephone number is (571)272-4018. The examiner can normally be reached on M-F: 10am-6:30pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Amy Ng can be reached on 571-270-1698. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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CHERYL M SHECHTMANPatent Examiner
Art Unit 2164
/C.M.S//AMY NG/Supervisory Patent Examiner, Art Unit 2164
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