DETAILED ACTION
Status of Claims
This Final Office Action is responsive to Applicant's reply filed 3/5/2026.
Claims 1, 4, 6, 8-9, 12, 14, 16-17, and 20 have been amended.
Claims 1-20 are currently pending and 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 Amendments
Applicant’s amendments have been fully considered, but do not overcome the previously pending 35 USC 103 and 35 USC 101 rejections.
Response to Arguments
Applicant's arguments have been fully considered but they are not persuasive.
The Examiner notes the proposed amendments in the interview are not even close to what was actually filed. Rejections have been applied appropriately in view of BRI.
With regard to the limitations of claims 1-20, Applicant argues that the claims are patent eligible under 35 USC 101 because the pending claims are rooted in technology. The Examiner respectfully disagrees. The Examiner has already set forth a prima facie case under 35 USC 101. The Examiner has clearly pointed out the limitations directed towards the abstract idea, what the additional elements are and why they do not integrate the abstract idea into a practical application, and why the additional elements and remaining limitations do not amount to significantly more than the abstract idea. The Examiner asserts that running the abstract idea on a general purpose computer does not root the claims in technology (See MPEP 2106.05). Applicant’s arguments are not persuasive.
Applicant argues the claims recite a specific arrangement that makes the claims eligible. The Examiner respectfully disagrees. The Applicant merely copy and pastes the claim limitations and does not provide any reasoning. For example, what is being improved? How is it being improved? The Examiner again asserts that merely performing the abstract idea on a general purpose computer merely adds the words apply it with the judicial exception (See MPEP 2106.05). Applicant’s arguments are not persuasive.
With regard to the limitations of claims 1-20, Applicant argues that the claims are allowable over 35 USC 103 because the claim amendments overcome the current art rejection. The Examiner respectfully disagrees. Please see the updated rejection below since amendments by Applicant require additional reference to the Examiner’s art rejection. Applicant’s arguments are not persuasive.
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 non-statutory subject matter;
When considering subject matter eligibility under 35 U.S.C. 101, it must be determined whether the claim is directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter. If the claim does fall within one of the statutory categories, it must then be determined whether the claim is directed to a judicial exception (i.e., law of nature, natural phenomenon, and abstract idea), and if so, it must additionally be determined whether the claim is a patent-eligible application of the exception. If an abstract idea is present in the claim, any element or combination of elements in the claim must be sufficient to ensure that the claim amounts to significantly more than the abstract idea itself.
In the instant case (Step 1), claims 1-8 are directed toward a process, claims 9-16 are directed toward a product, and claims 17-20 are directed toward a system; which are statutory categories of invention. Additionally (Step 2A Prong One), the independent claims are directed toward a method comprising: attributing a conversion event by a user of an online system to one or more previous impressions by the user of content provided to the user by the online system; identifying feature information describing the user, wherein the feature information comprises a feature category; generating a baseline curve for a user classification, wherein the baseline curve describes a long-term incremental conversion value for the user classification; computing, by a machine-learning model, an adjustment metric according to group user feature information, wherein the adjustment metric corresponds to relevance of the group user feature information to a baseline curve modifier, wherein the group user feature information comprises the feature category, wherein the machine- learning model is trained based on the group user feature information describing previous conversions and long-term conversions associated with the previous conversions; generating the baseline curve modifier by combining the feature information and the adjustment metric; transforming the baseline curve to a modified long-term incremental conversion value for the user by applying the baseline curve modifier to the baseline curve; and outputting the modified long-term incremental conversion value for the user (Organizing Human Activity), which are considered to be abstract ideas (See MPEP 2106). The steps/functions disclosed above and in the independent claims are directed toward the abstract idea of Organizing Human Activity because the claimed limitations are analyzing how users interact with an online system to determine features describing the user and comparing those features to a baseline curve to see how valuable (conversion value) the user is, which is a commercial interaction.
Dependent claims 2-8, 10-16, and 18-20 further narrow the abstract idea identified in the independent claims, where any additional elements introduced are discussed below.
Step 2A Prong Two: In this application, even if not directed toward the abstract idea, the independent claims additionally recite “by a user of an online system; by a machine-learning model; wherein the machine- learning model is trained (claim 1)”; “a computer program product comprising a non-transitory computer-readable storage medium having instructions encoded thereon that, when executed by a processor, cause the processor to perform steps comprising; by a user of an online system; by a machine-learning model; wherein the machine- learning model is trained (claim 9)”; “computer system comprising: a processor that executes instructions; and a non-transitory computer-readable storage medium having instructions executable by the processor for; by a user of an online system; by a machine-learning model; wherein the machine- learning model is trained (claim 17)”, which are additional elements that do not integrate the judicial exception (e.g. abstract idea) into a practical application because the claimed structure merely adds the words to apply it with the judicial exception and mere instructions to implement an abstract idea on a computer (See MPEP 2106) and are recited at such a high level of generality. These limitations can also be viewed as nothing more than an attempt to generally link the use of the judicial exception to the technological environment of a computer. Even when viewed in combination, the additional elements in the claims do no more than use the computer components as a tool. There is no change to the computer or other technology that is recited in the claim, and thus the claims do not improve computer functionality or other technology.
In addition, dependent claims 2-8, 10-16, and 18-20 further narrow the abstract idea and dependent claims 5, 7, 13, and 15 additionally recite “a bidding system (claims 5 and 13); an administrator of the online system or an administrator of a third-party system (claims 7 and 15)” which do not account for additional elements that integrate the judicial exception (e.g. abstract idea) into a practical application because the claimed structure merely adds the words to apply it with the judicial exception and mere instructions to implement an abstract idea on a computer (See MPEP 2106).
Step 2B: When analyzing the additional element(s) and/or combination of elements in the claim(s) other than the abstract idea per se the claim limitations amount(s) to no more than: a general link of the use of an abstract idea to a particular technological environment and merely amounts to the application or instructions to apply the abstract idea on a computer (See MPEP 2106). Further, Method; System; and Product Independent claims 1, 9, and 17 recite “by a user of an online system; by a machine-learning model; wherein the machine- learning model is trained (claim 1)”; “a computer program product comprising a non-transitory computer-readable storage medium having instructions encoded thereon that, when executed by a processor, cause the processor to perform steps comprising; by a user of an online system; by a machine-learning model; wherein the machine- learning model is trained (claim 9)”; “computer system comprising: a processor that executes instructions; and a non-transitory computer-readable storage medium having instructions executable by the processor for; by a user of an online system; by a machine-learning model; wherein the machine- learning model is trained (claim 17)”; however, these elements merely facilitate the claimed functions at a high level of generality and they perform conventional functions and are considered to be general purpose computer components which is supported by Applicant’s specification in Paragraphs 0087-0088 and Figures 1-2. The Applicant’s claimed additional elements are mere instructions to implement the abstract idea on a general purpose computer and generally link of the use of an abstract idea to a particular technological environment. When viewed as a whole, these additional claim element(s) do not provide meaningful limitation(s) to transform the abstract idea into a patent eligible application of the abstract idea such that the claim(s) amounts to significantly more than the abstract idea itself.
In addition, claims 2-8, 10-16, and 18-20 further narrow the abstract idea identified in the independent claims. The Examiner notes that the dependent claims merely further define the data being analyzed and how the data is being analyzed. Similarly, claims 5, 7, 13, and 15 additionally recite “a bidding system (claims 5 and 13); an administrator of the online system or an administrator of a third-party system (claims 7 and 15)” which do not account for additional elements that amount to significantly more than the abstract idea because the claimed structure merely amounts to the application or instructions to apply the abstract idea on a computer and does not move beyond a general link of the use of an abstract idea to a particular technological environment (See MPEP 2106). The additional limitations of the independent and dependent claim(s) when considered individually and as an ordered combination do not amount to significantly more than the abstract idea. The examiner has considered the dependent claims in a full analysis including the additional limitations individually and in combination as analyzed in the independent claim(s). Therefore, the claim(s) are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
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.
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 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Davin et al. (US 2018/0150883 A1) in view of Bao et al. (US 2021/0326233 A1).
Regarding Claim 1: Davin et al. teach a method comprising (See Claim 1):
attributing a conversion event by a user of an online system to one or more previous impressions by the user of content provided to the user by the online system (See Paragraph 0038 – “Conversion actions are received for members of the impression group 330 and the control group 320”);
identifying feature information describing the user, wherein the feature information comprises a feature category (See Paragraph 0025 – “user profile include biographic, demographic, and other types of descriptive information”, Paragraph 0039 – “For each of the target users 360, a response likelihood prediction 370 is determined by applying a user's characteristics 365 to the machine learned model 340, and a baseline likelihood prediction 375 is determined by applying the user's characteristics 365 to the machine learned model 350. The difference in predicted likelihoods for each of the target users 360 is identified as the predicted incremental likelihood 380”, and Paragraph 0052 – “Attributes may include demographic characteristics of users”);
generating a baseline for a user classification, wherein the baseline curve describes a long-term incremental conversion value for the user classification (See Figure 6, Paragraph 0036 – “a control group 320 of users and an impression group 330 of users from the online system”, Paragraph 0039 – “For each of the target users 360, a response likelihood prediction 370 is determined by applying a user's characteristics 365 to the machine learned model 340, and a baseline likelihood prediction 375 is determined by applying the user's characteristics 365 to the machine learned model 350. The difference in predicted likelihoods for each of the target users 360 is identified as the predicted incremental likelihood 380”, Paragraph 0057, and Paragraph 0061);
computing, by a machine-learning model, an adjustment metric according to group user feature information, wherein the adjustment metric corresponds to relevance of the group user feature information to a baseline modifier, wherein the group user feature information comprises the feature category, wherein the machine- learning model is trained based on the group user feature information describing previous conversions and long-term conversions associated with the previous conversions (See Figure 3, Figure 6, Paragraph 0030 – “record information about actions users perform on a third party system, including webpage viewing histories, conversion actions for content items, purchases made, and other patterns from user interactions across various external systems”, Paragraph 0036, Paragraph 0039 – “For each of the target users 360, a response likelihood prediction 370 is determined by applying a user's characteristics 365 to the machine learned model 340, and a baseline likelihood prediction 375 is determined by applying the user's characteristics 365 to the machine learned model 350. The difference in predicted likelihoods for each of the target users 360 is identified as the predicted incremental likelihood 380”, Paragraph 0054 – “The training module 414 constructs one or more machine-learned models based on the training data 440 that predict, for a given set of attributes for a user, a response likelihood indicating the likelihood of conversion actions when the user is presented with content items, and a baseline likelihood indicating the likelihood of user actions when the user is not presented with the content items. The machine-learned models predict the baseline likelihoods by identifying the relationship between conversion responses and user attributes in the training data of the control group, and predict the response likelihoods by identifying the relationship in the training data of the impression group”, Paragraph 0057, and Paragraph 0061);
generating the baseline modifier by combining the feature information and the adjustment metric (See Figure 3, Figure 6, Paragraph 0037 – “an initial set of target users 300 (e.g., as determined by targeting criteria)”, Paragraph 0039 – “For each of the target users 360, a response likelihood prediction 370 is determined by applying a user's characteristics 365 to the machine learned model 340, and a baseline likelihood prediction 375 is determined by applying the user's characteristics 365 to the machine learned model 350. The difference in predicted likelihoods for each of the target users 360 is identified as the predicted incremental likelihood 380”, Paragraph 0054, Paragraph 0057, and Paragraph 0061);
transforming the baseline to a modified long-term incremental conversion value for the user by applying the baseline curve modifier to the baseline curve (See Figure 3 – “300, 360, 390”, Figure 6, Figure 8, Paragraph 0037, Paragraph 0040 – “identifying modified target users 390 who have characteristics similar to those users with certain incremental likelihoods”, Paragraph 0042 – “users of the online system who have been identified to like baseball, as the frequency of the conversion actions among these users are likely to be higher than other users”, Paragraph 0047, and Paragraph 0061 – “users are ranked according to the value of their incremental likelihood”);
and outputting the modified long-term incremental conversion value for the user (See Figure 3, Figure 6, Figure 8, Paragraph 0061 – “users are ranked according to the value of their incremental likelihood”, Paragraph 0069, and Claim 1 – “providing the content item for display to one or more of the modified set of target users”).
Davin et al. does not specifically disclose a baseline curve. However, Bao et al. further teach generating a baseline “curve” describing a long-term incremental conversion value for the user; modifying the baseline “curve” based on the modifiers (See Figure 2, Figure 3, Figure 6b, Paragraph 0048, Paragraph 0050, and Paragraph 0075).
The teachings of Davin et al. and Bao et al. are related because both are analyzing users versus baselines to make determinations about content presentation. Therefore, it would have been obvious to one of ordinary skill in the art at the effective filing date of the claimed invention to have modified the impression based conversion analysis system of Davin et al. to incorporate the baseline curve of Bao et al. in order to better visualize how impressions affect conversions.
Regarding Claim 2: Davin et al. in view of Bao et al. teach the limitations of claim 1. Davin et al. further teach wherein generating the baseline comprises: generating a customer type describing the user, the customer type based at least in part on conversion history of the user; and selecting the baseline associated with the customer type (See Paragraph 0025 – “user profile include biographic, demographic, and other types of descriptive information”, Paragraph 0039 – “For each of the target users 360, a response likelihood prediction 370 is determined by applying a user's characteristics 365 to the machine learned model 340, and a baseline likelihood prediction 375 is determined by applying the user's characteristics 365 to the machine learned model 350. The difference in predicted likelihoods for each of the target users 360 is identified as the predicted incremental likelihood 380”, and Paragraph 0052 – “Attributes may include demographic characteristics of users”).
Davin et al. does not specifically disclose a baseline curve. However, Bao et al. further teach baseline curve (See Figure 2, Figure 3, Figure 6b, Paragraph 0048, Paragraph 0050, and Paragraph 0075).
The teachings of Davin et al. and Bao et al. are related because both are analyzing users versus baselines to make determinations about content presentation. Therefore, it would have been obvious to one of ordinary skill in the art at the effective filing date of the claimed invention to have modified the impression based conversion analysis system of Davin et al. to incorporate the baseline curve of Bao et al. in order to better visualize how impressions affect conversions.
Regarding Claim 3: Davin et al. in view of Bao et al. teach the limitations of claim 1. Davin et al. further teach wherein attributing the conversion event comprises: identifying information describing a conversion by the user of the online system; and identifying content viewed by the user before the conversion (See Paragraph 0027 – “a page post, a status update, a photograph, a video, a link, a shared content item, a gaming application achievement, a check-in event at a local business, a brand page, or any other type of content”, Paragraph 0029 – “Users may interact with various objects on the online system 110, and information describing these interactions is stored in the action log 252. Examples of interactions with objects include commenting on posts, sharing links, and checking-in to physical locations via a mobile device, accessing content items, and any other interactions”, Paragraph 0030 – “identify a particular user of the online system 110 to associate with the actions”, Paragraph 0038 – “Conversion actions are received for members of the impression group 330 and the control group 320”, and Paragraph 0042).
Regarding Claim 4: Davin et al. in view of Bao et al. teach the limitations of claim 1. Davin et al. further teach wherein modifying the baseline based on the baseline modifier comprises modifying one or more of: order frequency, order value, order or delivery type, basket size, or order time (See Figure 3, Figure 6, Figure 8, Paragraph 0037, Paragraph 0042 – “users of the online system who have been identified to like baseball, as the frequency of the conversion actions among these users are likely to be higher than other users”, Paragraph 0047, Paragraph 0051 – “a conversion response may be a continuous value in the set of [0, 1], indicating the frequency of conversion actions performed by the user in a predetermined amount of time”, and Paragraph 0061 – “users are ranked according to the value of their incremental likelihood”).
Davin et al. does not specifically disclose a baseline curve. However, Bao et al. further teach baseline curve (See Figure 2, Figure 3, Figure 6b, Paragraph 0048, Paragraph 0050, and Paragraph 0075).
The teachings of Davin et al. and Bao et al. are related because both are analyzing users versus baselines to make determinations about content presentation. Therefore, it would have been obvious to one of ordinary skill in the art at the effective filing date of the claimed invention to have modified the impression based conversion analysis system of Davin et al. to incorporate the baseline curve of Bao et al. in order to better visualize how impressions affect conversions.
Regarding Claim 5: Davin et al. in view of Bao et al. teach the limitations of claim 1. Davin et al. further teach further comprising: providing the modified long-term incremental conversion value for the user to a bidding system (See Paragraph 0042 – “The value for a content item may be represented as a bid amount or a budget for the content item” and Paragraph 0045 – “the content targeting module 402 performs competition, such as an auction process … This value may be represented as a bid in an auction process, or may otherwise represent the desirability of placing the content item to the user requesting the content item”).
Regarding Claim 6: Davin et al. in view of Bao et al. teach the limitations of claim 5. Davin et al. further teach further comprising: receiving an order from the user; identifying updated feature information describing the user; applying the machine-learning model to the updated feature information to generate one or more updated modifiers; modifying the baseline based on the one or more updated modifiers to generate a second modified long-term incremental conversion value for the user; and providing the second modified long-term incremental conversion value for the user to the bidding system (See Figure 3, Figure 6, Figure 8, Paragraph 0037, Paragraph 0039, Paragraph 0042, Paragraph 0045, Paragraph 0047, Paragraph 0054, Paragraph 0057, and Paragraph 0061).
Davin et al. does not specifically disclose a baseline curve. However, Bao et al. further teach baseline curve (See Figure 2, Figure 3, Figure 6b, Paragraph 0048, Paragraph 0050, and Paragraph 0075).
The teachings of Davin et al. and Bao et al. are related because both are analyzing users versus baselines to make determinations about content presentation. Therefore, it would have been obvious to one of ordinary skill in the art at the effective filing date of the claimed invention to have modified the impression based conversion analysis system of Davin et al. to incorporate the baseline curve of Bao et al. in order to better visualize how impressions affect conversions.
Regarding Claim 7: Davin et al. in view of Bao et al. teach the limitations of claim 1. Davin et al. further teach further comprising: providing the modified long-term incremental conversion value for the user to one or more of: an administrator of the online system or an administrator of a third-party system (See Figure 6 and Paragraph 0061).
Regarding Claim 8: Davin et al. in view of Bao et al. teach the limitations of claim 1. Davin et al. further teach wherein the machine-learning model is one or more of: a decision tree model, a random forest model, or a gradient boosting model (See Paragraph 0056 – “the machine-learned models are decision-tree based models, such as gradient-boosted decision trees, random forests”).
Regarding Claims 9-20: Claims 9-20 recite limitations already addressed by the rejections of claims 1-8 above; therefore the same rejections apply.
Conclusion
THIS ACTION IS MADE FINAL. 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.
The prior art made of record, but not relied upon is considered pertinent to applicant's disclosure is listed on the attached PTO-892.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MATTHEW D HENRY whose telephone number is (571)270-0504. The examiner can normally be reached on Monday-Thursday 9AM-5PM.
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/MATTHEW D HENRY/Primary Examiner, Art Unit 3625
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