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 1-20 were rejected in the Non-Final Office action mailed on 08/28/2025. Applicant’s amended claimset, entered on 11/28/2025, amended Claims 1, 11, and 20. Herein this Final Office Action, Claims 1-20 are rejected.
Response to Arguments
Applicant’s arguments filed 11/28/2025, with respect to Rejections under 35 U.S.C. 101 for Claims 1-20, have been fully considered and are not persuasive.
On Pages 9-10, Applicant summarizes the previous rejection and entered amendments. Examiner does not materially disagree.
On Page 10, Applicant argues “For representative independent claim 1, Applicant respectfully submits that: the amended claim 1 explicitly recites a "A computer-implemented method for delivery management in a data delivery application" executed "by a computing device". These limitations ensure claim 1 (a computer-implemented method) is not an intangible product. The steps involve machine learning (training the prediction model and determining the predicted resource cost according to the trained prediction model), which cannot be performed without computational hardware and thus do not belong to the category of an abstract idea.” Examiner does not agree.
Examiner responds that Step 2A is a two-prong analysis. MPEP 2106.04. Step 2A Prong-One first determines whether the claim merely “recites” (i.e. “sets forth” or “describes”) a judicial exception. MPEP 2106.04.II.A.1. Then, Step 2A Prong-Two determines if the claim “recites” “additional elements” that integrate the recited judicial exception into a practical application (e.g. if the recited additional elements do not “integrate the recited judicial exception into a practical application,” then, Step 2A would conclude that the claim is “directed to” the recited judicial exception.). MPEP 2106.04.II.A.2.
Examiner concedes that the computer, application, computing device, and training of a model, as claimed, are not apart of the recited abstract idea, but are “additional elements” to be analyzed at Step 2A Prong Two and Step 2B. However, the determination of cost using a model (even if the model was created with a computer), is apart of the recited abstract idea (i.e. mathematical concepts and certain methods of organizing human activity) in Step 2A Prong One.
On Pages 10-11, Applicant argues: Step 2A, Prong One: claim 1 includes elements related to "data delivery application," "data being delivered to users of the data delivery application at the plurality of delivery time points," and training and using "the prediction model." The claim is not directed solely to mathematical concepts. For example, claim 1 is directed to a specific computer-implemented method for delivery management in a data delivery application and goes beyond abstract concepts by reciting: i) obtaining, ... from the data delivery application, delivery data, the delivery data comprising: a resource cost ... , and a contribution ... , data being delivered to users of the data delivery application at the plurality of delivery time points; determining, by the computing device, a second time window ... (the relationship between the delivery data, cost, time windows and the like is not mathematical relationship, but relates to a relationship among various data collected from the data delivery application, which is structured training data for training the prediction model); ii) training, by the computing device, a prediction model based on the delivery data, and the first and second time windows, the prediction model indicating ... (a training step for training a machine learning model with the structured training data, but not merely the mathematical formulas or equations); and iii) determining, by the computing device, a predicted resource cost ... according to the prediction model (an inference step that uses the trained prediction model to process the real world input "the previous delivery data" to output the consistency result "the predicted resource cost"). The above steps are not mathematical concepts, instead, these steps are specific to machine learning training and interference and are implemented by the computing device. Further, amended claim 1 is irrelevant to managing personal behavior by following rules and interacting between people. Actually, the claim relates to managing the data delivery application by a prediction model, which may facilitate the application to delivery data to appropriate users who are interested in the data under the condition of resource costs specified by the delivery purpose. Therefore, data is delivered to the users of the data delivery application to meet the users' real interests, it is irrelevant to predicting a price and thus does not belong to "marketing or sales activities or behaviors.” Examiner does not agree.
Examiner responds that Step 2A Prong One involves determining if the claim “recites” an abstract idea. Thus, the identification of non-abstract idea limitations, does not prove that the claim does not “recite” an abstract idea, as other limitation may recite the abstract idea.
In limitation (i) referenced by applicant, the “obtaining” of information related to an event or occurrence (i.e. delivery) is apart of the recited abstract idea (i.e. Step 2A Prong One) that is being implemented using the additional element of the application, which is analyzed in Step 2A Prong Two and Step 2B. Similarly, determining a time is an abstract idea, and the computing device is the additional element applying that abstract idea. In limitation (ii) referenced by applicant, the training of a model is an additional element, but the use of that model, as claimed, is apart of the abstract idea. Although training a model is tied to computer technology, the broadest reasonable interpretation of the resulting model, as claimed, includes very simple models, e.g. a single linear equation. In limitation (iii) referenced by applicant, the determining of the predicted cost “according to” the model is apart of the abstract idea, which is implemented by the additional element of the computing device.
The rejection section below provides a detailed mapping of the recited abstract idea to at least one of the non-mutually-exclusive enumerated grouping per MPEP 2106.04. However, to address Applicant’s arguments, Examiner responds that, at least the limitation of, determining “a predicted resource cost” using a “prediction model” is a mathematical concept and a commercial or legal interaction that can occur without machine learning or a computer. Additionally, the scheduling of an event (i.e. delivery of data) based on certain rules (i.e. prediction outcome) is at least “following rules or instructions” (i.e. “managing personal behavior or relationships or interactions between people”) which is a “certain method of organizing human activity.” MPEP 2106.04(a). See also MPEP 2106.04(a)(2)II (“. . . thus, certain activity between a person and a computer (for example a method of anonymous loan shopping that a person conducts using a mobile phone) may fall within the ‘certain methods of organizing human activity’ grouping.”).
The additional elements, e.g. application and computer, are used to implement the recited abstract idea. This interaction is analyzed in Step 2A Prong Two and Step 2B. Examiner maintains that the claims recite an abstract idea under Step 2A Prong One.
On Pages 11-12, Applicant argues: Step 2A, Prong Two: Even assuming, arguendo, the claim 1 does recite an abstract idea, the claim integrates the alleged abstract idea into a practical application, as evidenced by multiple aspects recited in the claim itself Amended claim 1 recites a computer-implemented method for delivery management in a data delivery application. Specifically, claim 1 expressly recites: i) computer-implemented processing with machine learning model " ... by a computing device ... " " ... training ... a prediction model ... " Claim 1 clearly recites implementation by a computing device, and defines the prediction model-a specific machine learning architecture. This distinguishes the claim from mathematical concepts or generic abstract processes. ii) use of structured training data collected from the data delivering application "obtaining ... delivery data associated, ... ; determining ... a second time window ... ;" This defines a specific data structure that supports improved performance of the prediction model. iii) training the machine learning model "training ... a prediction model based on the delivery data, and the first and second time windows ... " This adds technical specificity to the training process and improves stability and accuracy of the model. iv) deployment of trained model for real-world inference "determining ... a predicted resource cost based on previous delivery data in the data delivery application according to the prediction model ... " This is a functional deployment of the trained model to new inputs to produce a specific output: a predicted resource cost for a subsequent time window after the previous time window. This shows the model is not theoretical but part of a real computer-implemented pipeline with tangible results. Examiner does not agree.
Examiner responds that the computer implementation and training aspects of the claim are additional elements. However, the specific machine learning architecture is not claimed, but instead, the limitation merely limits what the data represents (i.e. based on the delivery data, and the first and second time windows). Further the specification merely mentions that training occurs, disclosing to a person of ordinary skill in the art that any type of training or machine learning would enable the invention. Similarly, the claim (nor the specification, see ¶131) does not define a specific data structure, e.g. stack or tree, used, but instead limits what the data represents, i.e. the data must contain “delivery data” and “the first and second time windows.” The claim does not provide any specifics related to the training of the model, and therefore does not provide a patent eligible improvement, similar to PEG Example 47 Claim 2, which includes training an artificial neural network and is ineligible. Finally, as discussed above, the use of a model to predict a cost is apart of the abstract idea. Similar to PEG Example 47 Claim 2, the claim merely uses the model to output data. The instant claim is distinguishable from PEG Example Claim 3, where the computer system performs remedial action such as dropping malicious data packets and blocking future traffic from the detected source address. Therefore, the rejection remains.
On Pages 12-13, Applicant argues: Step 2B: Claim 1 recites significantly more than the alleged judicial exception. In particular, the claim includes one or more elements-individually and in combination-that represent an inventive concept. The proposed method is not conventional, and distinguishes from the traditional solution. The specification as originally filed in the present application discloses the following drawbacks of the prior art in paragraphs [0002], [0034], [0037], and [0085]: . . . In view of the above, Applicant respectfully submits that amended claim 1 is directed to a specific, computer-implemented method that utilizes structured training data, a machine learning model, specific training and inference steps to yield a technical improvement in machine learning model performance-specifically, in determining a predicted resource cost that considers the user data security and effects of data delivery. The claimed invention is not directed to a mathematical concept or abstract idea, but rather to a practical application implemented by computing hardware and tailored for real-world data delivery application management. Moreover, even assuming, arguendo, that the claim were directed to an abstract idea, the claim nonetheless recites significantly more, including non-conventional structured training data, specific training and inference techniques that improves the functionality of the underlying data delivery application. Examiner does not agree.
MPEP 2106.05(a) states “If it is asserted that the invention improves upon conventional functioning of a computer, or upon conventional technology or technological processes, a technical explanation as to how to implement the invention should be present in the specification. That is, the disclosure must provide sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement. The specification need not explicitly set forth the improvement, but it must describe the invention such that the improvement would be apparent to one of ordinary skill in the art. Conversely, if the specification explicitly sets forth an improvement but in a conclusory manner (i.e., a bare assertion of an improvement without the detail necessary to be apparent to a person of ordinary skill in the art), the examiner should not determine the claim improves technology. An indication that the claimed invention provides an improvement can include a discussion in the specification that identifies a technical problem and explains the details of an unconventional technical solution expressed in the claim, or identifies technical improvements realized by the claim over the prior art.” (Emphasis added).
MPEP 2106.05(a)II states “However, it is important to keep in mind that an improvement in the abstract idea itself (e.g. a recited fundamental economic concept) is not an improvement in technology.”
Examiner responds that Specification ¶2 (“However, the reliance of real-time and precise user data has become increasingly controversial, triggering widespread concerns about data protection.” (Emphasis added).) clearly demonstrates that the problem asserted as being solved is a customer satisfaction problem, i.e. addressing “controversial” “concerns.” This is not a technical problem. Additionally, Specification ¶37 (“. . . the problem that the present disclosure defines to solve is to maximize gross merchandise volume (GMV) with the ROI and spend constraints (i.e., the delivery purpose).”) shows that the problem being solved is a mathematical optimization, i.e. abstract idea, which cannot provide an improvement to technology under MPEP 2106.05(a).
Similarly, the specification does not provide a technical basis for the “delay” in ¶¶34-37, but instead merely concludes better protection of user data. Finally, Specification ¶85 (“With these implementations of the present disclosure, a stable, lightweight and effective prediction model may be proposed.”) merely concludes an improvement to the model (i.e. a single linear equation for the model can be more “stable, lightweight and effective” than a larger more complex model, which is apart of the abstract idea. To the extent that the “training” created the model, such improvement would be considered conclusory under MPEP 2106.05(a).
As discussed above, the scope of the “training” and the actual structure of the data, i.e. not the contents, includes any machine learning or data structure. Therefore, these additional elements are used to merely apply the abstract idea and do not provide an improvement. Thus, the rejection remains.
On Pages 13-14, Applicant concludes that the rejection under 35 U.S.C. 101 should be withdrawn. Examiner does not agree. As discussed in greater detail above and below, the claims remain rejected under 35 U.S.C. 101.
Claim Interpretation
Regarding Claim 1, Claim 1 states “training, by the computing device, a prediction model based on the delivery data, and the first and second time windows, the prediction model indicating an association relationship between delivery data associated with a plurality of previous delivery time points in a third time window and a predicted resource cost, the predicted resource cost indicating a total resource cost corresponding to a fourth time window that follows the third time window, a length of the third time window being smaller than the first length, and a length of the fourth time window being equal to the second length; and determining, by the computing device, a predicted resource cost based on previous delivery data in the data delivery application according to the prediction model, the previous delivery data being associated with a plurality of previous delivery time points in a previous time window, and the predicted resource cost being associated with a subsequent time window after the previous time window.” (Emphasis added). It is clear that the claim recites “a predicted resource cost . . . indicating a total resource cost corresponding to a fourth time window that follows the third time window” and “a predicted resource cost . . . associated with a subsequent time window after the previous time window” are two separate values. Although the drafting style may be unconventional, the claim has clearly defined two separate “cost[s],” and therefore the claim is not indefinite under 35 U.S.C. 112(b).
Regarding the interpretation of Claim 6, Claim 1 recites, in the last paragraph, “determining, by the computing device, a predicted resource cost based on previous delivery data in the data delivery application according to the prediction model, the previous delivery data being associated with a plurality of previous delivery time points in a previous time window, and the predicted resource cost being associated with a subsequent time window after the previous time window,” and Claim 6 recites “The method according to claim 1, further comprising: obtaining previous delivery data associated with a plurality of previous delivery time points in a previous time window, the previous delivery data comprising: . . .” (Emphasis added). The limitation of Claim 6 of “obtaining previous delivery data associated with a plurality of previous delivery time points in a previous time window” is referencing the “previous delivery data,” which is “a plurality of previous delivery time points in a previous time window.” The further language in Claim 6 of “associated with a plurality of previous delivery time points in a previous time window” is not a limitation on the “previous delivery data,” which would likely yield a rejection under 35 U.S.C. 112(b), but an explanatory reference that the “previous delivery data” obtained in Claim 6 is the “previous delivery data” of Claim 1, which is “associated with a plurality of previous delivery time points in a previous time window.” Because the language of “associated with a plurality of previous delivery time points in a previous time window” in Claim 6 merely clarifies the reference to the “previous delivery data” of Claim 1, the scope of Claim 6 is the same as if Claim 6 recited “The method according to claim 1, further comprising: obtaining the previous delivery data, the previous delivery data comprising: . . .”
A similar interpretation to the interpretation of Claims 1 and 6 discuss above is extended to the entire claimset.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
Claims 1-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1 recites “. . . , data being delivered to users of the data delivery application at the plurality of delivery time points;” at the end of the second paragraph. It is unclear as to whether the “data being delivered to users of the data delivery application,” which is limited to occurring “at the plurality of delivery time points” is referencing (1) the “delivery data” that is obtained from the “data delivery application,” or (2) the data delivered at “a delivery time point.” Additionally, under the second (2) determination it is unclear as to whether “. . . to users of the data delivery application” is intended to (1) limit all users that receive data from the “data delivery application,” to receiving the data at one of the plurality of delivery time points, or (2) explain that the data delivered at the delivery time points is delivered to users of the data delivery application. Therefore, the claim is rejected under 35 U.S.C. 112(b) as indefinite. In service of compact prosecution, and solely for examination purposes herein, this limitation is interpreted as analogous to “. . . wherein the plurality of delivery time points is a time point that the data delivery application delivers advertising data to a user.”
Claims 2-10 are rejected via dependency.
Claim 11 recites substantially similar language to the limitation rejected in Claim 1 above, and therefore Claim 11 is rejected under 35 U.S.C. 112(b) with similar justification to the rejection of Claim 1 above.
Claim 11 recites the limitation "determining, by the computing device, a predicted resource cost" in the last paragraph of the Claim. There is insufficient antecedent basis for this limitation in the claim. The claim does not first introduce “a computing device,” before referencing “the computing device.” Therefore, the claim is rejected under 35 U.S.C. 112(b) as indefinite. In service of compact prosecution, and solely for examination purposes herein, this limitation is interpreted as analogous to “determining a predicted resource cost . . ." as this limitation is already “implement[ed]” by “the computer processor.”
Claims 12-19 are rejected via dependency.
Claim 20 recites substantially similar language to the limitation rejected in Claim 11 above, and therefore Claim 20 is rejected under 35 U.S.C. 112(b) with similar justification to the rejection of Claim 11 above.
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.
Overview of Analysis
The subject matter eligibility analysis comprises: Step 1 (i.e. Does the claim fall within one of the four stator categories, e.g. process, machine, manufacture, or composition of matter?), Step 2A (Is the claim “directed to” a judicial exception, e.g. abstract idea, natural phenomena, or law of nature?), and Step 2B (i.e. Does the claim recite “additional elements” that amount to “significantly more” than the judicial exception?). MPEP 2106.III.
Step 2A is a two-prong analysis. MPEP 2106.04. Step 2A Prong-One first determines whether the claim merely “recites” (i.e. “sets forth” or “describes”) a judicial exception. MPEP 2106.04.II.A.1. Then, Step 2A Prong-Two determines if the claim “recites” “additional elements” that integrate the recited judicial exception into a practical application (e.g. if the recited additional elements do not “integrate the recited judicial exception into a practical application,” then, Step 2A would conclude that the claim is “directed to” the recited judicial exception.). MPEP 2106.04.II.A.2.
Step 1
Claims 1-10 recite a method (i.e. a process), Claims 11-19 recite an electronic device (i.e. a machine or manufacture), and Claim 20 recites an electronic device (i.e. a machine or manufacture). Therefore, Claims 1-20 all fall within the one of the four statutory categories of invention of 35 U.S.C. 101.
Step 2A, Prong One
Independent Claim 1 recites the abstract idea of:
“obtaining, . . . delivery data associated with a plurality of delivery time points in a first time window, the delivery data comprising: a resource cost associated with a delivery time point in the plurality of delivery time points, and a contribution that is caused by the resource cost to a delivery purpose, data being delivered to users . . . at the plurality of delivery time points;
determining, . . . , a second time window, the second time window being specified by a data provider to verify whether the contribution meets the delivery purpose, a first length of the first time window being greater than a second length of the second time window; and
[creating] a prediction model based on the delivery data, and the first and second time windows,
the prediction model indicating an association relationship between delivery data associated with a plurality of previous delivery time points in a third time window and a predicted resource cost, the predicted resource cost indicating a total resource cost corresponding to a fourth time window that follows the third time window, a length of the third time window being smaller than the first length, and a length of the fourth time window being equal to the second length; and
determining, . . . , a predicted resource cost based on previous delivery data… according to the prediction model, the previous delivery data being associated with a plurality of previous delivery time points in a previous time window, and the predicted resource cost being associated with a subsequent time window after the previous time window.”
The limitations stated above are processes/ functions that under broadest reasonable interpretation covers (1) obtaining deliver data including cost associated with a delivery time and a contribution, (2) determining a second time window specified by a provider to verify whether the contribution meets the delivery purpose, (3) a prediction model based on the delivery data and time windows, (4) the model indicating relationships between delivery parameters, and (5) determining a predicted resource cost based on certain parameters, all of which are mathematical relationships (i.e. the relationship between delivery data, cost, and time windows) and mathematical formulas or equations, (i.e. the prediction model), and mathematical calculations (i.e. the use of the prediction model and determination of a cost), which are mathematical concepts, an abstract idea, under MPEP 2106.04(a)(2)I, and managing personal behavior by following rules and interacting between people (i.e. performing a delivery at a certain time is at least “following rules or instructions”) and commercial or legal interactions (i.e. scheduling a delivery and predicting a price are at least “marketing or sales activities or behaviors”), which are certain methods of organizing human activity, an abstract idea, under MPEP 2106.04(a)(2)II. The mere the recitation of generic computer components (i.e., “computer” implementing the method, “data delivery application,” “computing device,” and “training . . . a prediction model”) implementing the identified abstract idea does not take the claim out of the mathematical concepts or certain methods of organizing human activity groupings. MPEP 2106.04(d). If a claim limitation, under its broadest reasonable interpretation, covers “mathematical relationships,” “mathematical formulas or equations,” “mathematical calculations,” and “managing personal behavior by following rules and interacting between people,” and “commercial or legal interactions” but for the recitation of generic computer components, then it falls in the mathematical concepts or certain methods of organizing human activity groupings of abstract ideas. MPEP 2106.04. Therefore, Claim 11 recites an abstract idea.
Step 2A, Prong Two
The judicial exception is not integrated into a practical application. Claim 11 as a whole amounts to: (i) merely invoking generic components as a tool to perform the abstract idea or “apply it” (or an equivalent) and (ii) generally links the use of a judicial exception to a particular technological environment or field of use. The claim recites the additional elements of:
(i) “computer” implementing the method,
(ii) “data delivery application,”
(iii) “computing device,” and
(iv) “training . . . a prediction model” based on the delivery data, and the first and second time windows.
The additional elements of (i) “computer” (Fig. 9 and ¶117 shows “computing device 900.”), (ii) “data delivery application” (¶131 shows “A computer program (also known as a program, software, software application, script, or code) may be written in any form of programming language, including compiled or interpreted languages, and it may be deployed in any form, including as a stand-alone program or as a module, component, subroutine, or other unit suitable for use in a computing environment.”), (iii) “computing device” (Fig. 9 and ¶117 shows “computing device 900.”), and (iv) “training . . . a prediction model” (¶75, ¶77, and ¶93 discusses training the model, but does not discuss how, e.g. specific training method or algorithm, the model is trained. Additionally, in light of ¶19 and ¶128 any training known in the art could be used.), are recited at a high-level of generality, such that, when viewed as whole/ordered combination (Fig. 9 shows elements in combination.), they amount to no more than mere instruction to apply the judicial exception using generic computer components or “apply it” (See MPEP 2106.05(f)). Additionally, when viewed with the abstract idea in the claim as a whole, the additional elements do not provide a patent eligible improvement to technology per MPEP 2106.05(a).
The (i) “computer,” (ii) “data delivery application,” (iii) “computing device,” and (iv) “training . . . a prediction model,” when viewed as whole/ordered combination (Fig. 9 shows elements in combination.), does no more than generally link the use of the judicial exception to a particular technological environment or field of use (i.e. computer environment) (See MPEP 2106.05(h)).
Accordingly, these additional elements, when viewed as a whole/ordered combination (Fig. 9 shows elements in combination), do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Thus, the claim is directed to an abstract idea.
Step 2B
As discussed above with respect to Step 2A Prong Two, the additional elements amount to no more than: (i) “apply it” (or an equivalent) and (ii) generally link the use of a judicial exception to a particular technological environment or field of use, and are not a practical application of the abstract idea. The same analysis applies here in Step 2B, i.e., (i) merely invoking the generic components as a tool to perform the abstract idea or “apply it” (See MPEP 2106.05(f)) and (ii) generally linking the use of a judicial exception to a particular technological environment or field of use (See MPEP 2106.05(h)), does not integrate the abstract idea into a practical application at Step 2A or provide an inventive concept at Step 2B.
Therefore, the additional elements of the (i) “computer,” (ii) “data delivery application,” (iii) “computing device,” and (iv) “training . . . a prediction model,” do not integrate the abstract idea into a practical application at Step 2A or provide an inventive concept at Step 2B. Thus, even when viewed as a whole/ordered combination (Fig. 9 shows elements in combination), nothing in the claims adds significantly more (i.e., an inventive concept) to the abstract idea. Thus, the claim is ineligible.
Dependent Claims 2-10 recite the abstract idea of:
“wherein determining the prediction model comprises: with respect to the delivery time point in the plurality of delivery time points in the first time window, determining an intermediate model based on an inverse proportional function that describes a degree of an impact of the resource cost on the contribution; and determine the prediction model based on the intermediate model and the delivery data.” (Claim 2).
“wherein the inverse proportional function is a linear inverse proportional function represented by a group of linear parameters.” (Claim 3).
“wherein determining the prediction model based on the intermediate model and the delivery data comprises: determining a group of candidate values for the group of linear parameters based on the linear inverse proportional function and the delivery data; and representing the prediction model by the group of candidate values.” (Claim 4).
“wherein determining a group of candidate values comprises: with respect to the delivery time point in the plurality of delivery time points in the first time window, determining the group of candidate values by updating the linear inverse proportional function with the resource cost associated with the delivery time point and the contribution caused by the resource cost to the delivery purpose” (Claim 5).
“obtaining previous delivery data associated with a plurality of previous delivery time points in a previous time window, the previous delivery data comprising: a previous resource cost associated with a previous delivery time point in the plurality of previous delivery time points, and a previous contribution caused by the previous resource cost to the delivery purpose; and determining a predicted resource cost associated with the subsequent time window based on the prediction model and the previous delivery data.” (Claim 6).
“wherein determining the predicted resource cost associated with the subsequent time window comprises: obtaining a unit cost and a resource threshold that are specified by the data provider; and determining a predicted resource cost associated with the subsequent time window based on the prediction model and the previous delivery data under constraints of the unit cost and the resource threshold.” (Claim 7).
“wherein determining the predicted resource cost under the constraint of the unit cost comprises: determining a plurality of candidate resource costs based on the prediction model and the previous delivery data; and selecting, from the plurality of candidate resource costs, a candidate resource cost that meets the constraint of the unit cost as the predicted resource cost, the unit cost being represented by the candidate resource costs and a predicted contribution corresponding to the candidate resource cost.” (Claim 8).
“wherein determining the predicted resource cost under the constraint of the resource threshold comprises: determining the plurality of candidate resource costs under the constraint of the resource threshold, the plurality of candidate resource costs being below than the resource threshold. (Claim 9).
“wherein the first length of the first time window is greater than a time delay between a time point when the contribution is received and the time point.” (Claim 10).
Dependent Claims 2-10, have been given the full two-prong analysis including analyzing the further elements and limitations, both individually and in combination. When analyzed individually and in combination, these claims are also held to be patent ineligible under 35 U.S.C. 101. The further limitation of Claims 2-10 fail to establish claims that are not directed to an abstract idea because the further limitations merely limit the scope of the abstract idea (i.e. mathematical concepts and certain methods of organizing human activity), and do not introduce further “additional elements.” Thus, the elements of Claims 2-10 (i.e. elements of Claim 1) fails to establish claims that are not directed to an abstract idea because the elements merely recite additional generic computer and generally link the abstract idea to a particular technology or field of use just as in Claim 1. The organization of the further limitations of Claims 2-10 fail to integrate an abstract idea into a practical application just as discussed above for Claim 1. Additionally, performing the abstract idea of Claim 1 as recited in each of the further limitations of Claims 2-10, individually or in combination, does not (1) impose any meaningful limits on practicing the abstract ideas, or (2) provide improvements to the functioning of computing systems or to another technology or technical field, just as discussed above regarding Claim 1. Therefore, Claims 2-10 amount to mere instructions to implement the abstract idea (1) using generic computer components—using the computer, in its ordinary capacity, as a tool to perform the abstract idea, and (2) generally linked to a particular technology or field of use. Because the claims merely use a computer, in its ordinary capacity in a particular field of use, as a tool to perform the abstract idea cannot provide an inventive concept, the elements and limitations of Claims 2-10 fail to establish that the claims provide an inventive concept, just as in Claim 1. Therefore, Claims 2-10 fails the Subject Matter Eligibility Test and are consequently rejected under 35 U.S.C. 101.
Step 2A, Prong One
Independent Claim 11 recites the abstract idea of:
“. . . obtaining, . . . , delivery data associated with a plurality of delivery time points in a first time window, the delivery data comprising: a resource cost associated with a delivery time point in the plurality of delivery time points, and a contribution that is caused by the resource cost to a delivery purpose data being delivered to users of . . . at the plurality of delivery time points;
determining, . . . , a second time window, the second time window being specified by a data provider to verify whether the contribution meets the delivery purpose, a first length of the first time window being greater than a second length of the second time window; [[and]]
obtaining a prediction model based on the delivery data, and the first and second time windows, the prediction model indicating an association relationship between delivery data associated with a plurality of previous delivery time points in a third time window and a predicted resource cost, the predicted resource cost indicating a total resource cost corresponding to a fourth time window that follows the third time window, a length of the third time window being smaller than the first length, and a length of the fourth time window being equal to the second length; and
determining, . . . , a predicted resource cost based on previous delivery data … according to the prediction model, the previous delivery data being associated with a plurality of previous delivery time points in a previous time window, and the predicted resource cost being associated with a subsequent time window after the previous time window.”
The limitations stated above are processes/ functions that under broadest reasonable interpretation covers (1) obtaining deliver data including cost associated with a delivery time and a contribution, (2) determining a second time window specified by a provider to verify whether the contribution meets the delivery purpose, (3) obtaining a prediction model based on the delivery data and time windows, (4) the model indicating relationships between delivery parameters, and (5) determining a predicted resource cost based on certain parameters, all of which are mathematical relationships (i.e. the relationship between delivery data, cost, and time windows) and mathematical formulas or equations, (i.e. the prediction model), and mathematical calculations (i.e. the use of the prediction model and determination of a cost), which are mathematical concepts, an abstract idea, under MPEP 2106.04(a)(2)I, and managing personal behavior by following rules and interacting between people (i.e. performing a delivery at a certain time is at least “following rules or instructions”) and commercial or legal interactions (i.e. scheduling a delivery and predicting a price are at least “marketing or sales activities or behaviors”), which are certain methods of organizing human activity, an abstract idea, under MPEP 2106.04(a)(2)II. The mere the recitation of generic computer components (i.e., the “An electronic device, comprising a computer processor coupled to a computer-readable memory unit, the memory unit comprising instructions that when executed by the computer processor implements a method for delivery management in a data delivery application”) implementing the identified abstract idea does not take the claim out of the mathematical concepts or certain methods of organizing human activity groupings. MPEP 2106.04(d). If a claim limitation, under its broadest reasonable interpretation, covers “mathematical relationships,” “mathematical formulas or equations,” “mathematical calculations,” and “managing personal behavior by following rules and interacting between people,” and “commercial or legal interactions” but for the recitation of generic computer components, then it falls in the mathematical concepts or certain methods of organizing human activity groupings of abstract ideas. MPEP 2106.04. Therefore, Claim 11 recites an abstract idea.
Step 2A, Prong Two
The judicial exception is not integrated into a practical application. Claim 11 as a whole amounts to: (i) merely invoking generic components as a tool to perform the abstract idea or “apply it” (or an equivalent) and (ii) generally links the use of a judicial exception to a particular technological environment or field of use. The claim recites the additional elements of:
(i) An electronic device,
(ii) a computer processor, and
(iii) computer-readable memory unit, and
(iv) data delivery application.
The additional elements of (i) An electronic device (Fig. 9 and ¶117 shows “computing device 900.”), (ii) a computer processor (Fig. 9 and ¶117-18 shows “processing unit 910.”), (iii) computer-readable memory unit (Fig. 9 and ¶¶117-19 shows “memory 920.”), and (iv) “data delivery application” (¶131 shows “A computer program (also known as a program, software, software application, script, or code) may be written in any form of programming language, including compiled or interpreted languages, and it may be deployed in any form, including as a stand-alone program or as a module, component, subroutine, or other unit suitable for use in a computing environment.”), are recited at a high-level of generality, such that, when viewed as whole/ordered combination (Fig. 9 shows elements in combination.), they amount to no more than mere instruction to apply the judicial exception using generic computer components or “apply it” (See MPEP 2106.05(f)). Additionally, when viewed with the abstract idea in the claim as a whole, the additional elements do not provide a patent eligible improvement to technology per MPEP 2106.05(a).
The (i) electronic device, (ii) computer processor, (iii) computer-readable memory unit, and (iv) data delivery application, when viewed as whole/ordered combination (Fig. 9 shows elements in combination.), does no more than generally link the use of the judicial exception to a particular technological environment or field of use (i.e. computer environment) (See MPEP 2106.05(h)).
Accordingly, these additional elements, when viewed as a whole/ordered combination (Fig. 9 shows elements in combination), do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Thus, the claim is directed to an abstract idea.
Step 2B
As discussed above with respect to Step 2A Prong Two, the additional elements amount to no more than: (i) “apply it” (or an equivalent) and (ii) generally link the use of a judicial exception to a particular technological environment or field of use, and are not a practical application of the abstract idea. The same analysis applies here in Step 2B, i.e., (i) merely invoking the generic components as a tool to perform the abstract idea or “apply it” (See MPEP 2106.05(f)) and (ii) generally linking the use of a judicial exception to a particular technological environment or field of use (See MPEP 2106.05(h)), does not integrate the abstract idea into a practical application at Step 2A or provide an inventive concept at Step 2B.
Therefore, the additional elements of the (i) electronic device, (ii) computer processor, (iii) computer-readable memory unit, and (iv) data delivery application, do not integrate the abstract idea into a practical application at Step 2A or provide an inventive concept at Step 2B. Thus, even when viewed as a whole/ordered combination (Fig. 9 shows elements in combination), nothing in the claims adds significantly more (i.e., an inventive concept) to the abstract idea. Thus, the claim is ineligible.
Dependent Claims 12-19 recite the abstract idea of:
. . . wherein determining the prediction model comprises: with respect to the delivery time point in the plurality of delivery time points in the first time window, determining an intermediate model based on an inverse proportional function that describes a degree of an impact of the resource cost on the contribution; and determine the prediction model based on the intermediate model and the delivery data. (Claim 12).
. . . wherein the inverse proportional function is a linear inverse proportional function represented by a group of linear parameters. (Claim 13).
. . . wherein determining the prediction model based on the intermediate model and the delivery data comprises: determining a group of candidate values for the group of linear parameters based on the linear inverse proportional function and the delivery data; and representing the prediction model by the group of candidate values. (Claim 14).
. . . wherein determining a group of candidate values comprises: with respect to the delivery time point in the plurality of delivery time points in the first time window, determining the group of candidate values by updating the linear inverse proportional function with the resource cost associated with the delivery time point and the contribution caused by the resource cost to the delivery purpose. (Claim 15).
. . . the method further comprising: obtaining previous delivery data associated with a plurality of previous delivery time points in a previous time window, the previous delivery data comprising: a previous resource cost associated with a previous delivery time point in the plurality of previous delivery time points, and a previous contribution caused by the previous resource cost to the delivery purpose; and determining a predicted resource cost associated with the subsequent time window based on the prediction model and the previous delivery data. (Claim 16).
. . . wherein determining the predicted resource cost associated with the subsequent time window comprises: obtaining a unit cost and a resource threshold that are specified by the data provider; and determining a predicted resource cost associated with the subsequent time window based on the prediction model and the previous delivery data under constraints of the unit cost and the resource threshold. (Claim 17).
. . . wherein determining the predicted resource cost under the constraint of the unit cost comprises: determining a plurality of candidate resource costs based on the prediction model and the previous delivery data; and selecting, from the plurality of candidate resource costs, a candidate resource cost that meets the constraint of the unit cost as the predicted resource cost, the unit cost being represented by the candidate resource costs and a predicted contribution corresponding to the candidate resource cost. (Claim 18).
. . . wherein determining the predicted resource cost under the constraint of the resource threshold comprises: determining the plurality of candidate resource costs under the constraint of the resource threshold, the plurality of candidate resource costs being below than the resource threshold. (Claim 19).
Dependent Claims 12-19, have been given the full two-prong analysis including analyzing the further elements and limitations, both individually and in combination. When analyzed individually and in combination, these claims are also held to be patent ineligible under 35 U.S.C. 101. The further limitation of Claims 12-19 fail to establish claims that are not directed to an abstract idea because the further limitations merely limit the scope of the abstract idea (i.e. mathematical concepts and certain methods of organizing human activity), and do not introduce further “additional elements.” Thus, the elements of Claims 12-19 (i.e. elements of Claim 11) fails to establish claims that are not directed to an abstract idea because the elements merely recite additional generic computer and generally link the abstract idea to a particular technology or field of use just as in Claim 11. The organization of the further limitations of Claims 12-19 fail to integrate an abstract idea into a practical application just as discussed above for Claim 11. Additionally, performing the abstract idea of Claim 11 as recited in each of the further limitations of Claims 12-19, individually or in combination, does not (1) impose any meaningful limits on practicing the abstract ideas, or (2) provide improvements to the functioning of computing systems or to another technology or technical field, just as discussed above regarding Claim 11. Therefore, Claims 12-19 amount to mere instructions to implement the abstract idea (1) using generic computer components—using the computer, in its ordinary capacity, as a tool to perform the abstract idea, and (2) generally linked to a particular technology or field of use. Because the claims merely use a computer, in its ordinary capacity in a particular field of use, as a tool to perform the abstract idea cannot provide an inventive concept, the elements and limitations of Claims 12-19 fail to establish that the claims provide an inventive concept, just as in Claim 11. Therefore, Claims 12-19 fails the Subject Matter Eligibility Test and are consequently rejected under 35 U.S.C. 101.
Claim 20 recite elements and limitations that are substantially similar to Claim 11. Therefore, Claim 20 is rejected under 35 U.S.C. 101 just as Claim 11 is rejected under 35 U.S.C. 101 as discussed above.
Reasons for No Art Rejection
Claims 1-20 are not rejected over the prior art of record.
The Closest prior art of record is:
US-20240422235-A1 (“Okuno”);
US-20100191600-A1 (“Sideman”);
US-20080022301-A1 (“Aloizos”);
US-20030172165-A1 (“Xu”);
US-20230103048-A1 (“Eberstein”);
CN-114092125-A (“Ji”);
CN-115204922-A (“Song”);
CN-109003140-A (“Shi”);
WO-2018055506-A1 (“Yellin”); and
“Online banner advertisement scheduling for advertising effectiveness” (“Kim” Computers & Industrial Engineering Volume 140, February 2020, 106226, https://doi.org/10.1016/j.cie.2019.106226).
The Following is an examiner’s statement of reasons for no art rejection:
Okuno shows determining the time frame for delivering a printable advertisement based on a desired delivery schedule, and provisioning the advertisements to printers to satisfy the delivery requirement.
Sideman shows simulating a series of advertisement auctions for a future series of available time slots in a delivery network. The simulation can be used to advise customers (i.e. advertisers) on an optimal “per exposure” bid price to achieve the desired range of target users. Although the customer can verify the auction information, Sideman does not explicitly teach what auction information is verified.
Aloizos shows matching advertisements to time slots with varying prices for different times. The time slot and advertisement are matched based on a weighted budget based on the time slot, the offered price, the asking price, and a comparison between the rate-determinative data of the advertiser and the rate-determinative data of the television station. The actual price of the advertisement can be based on supply and demand, demographic that is being targeted, day part, number of impressions, and the like for a particular time.
Xu shows calculating a cost of receiving multicast data which is dependent on start and end times.
Eberstein shows receiving actual advertisement log including specific time slots and costs, and actual traffic to advertiser’s web page, then correlating the data to determine actual conversion performance and peak exposure times.
Ji shows receiving target release information and corresponding release effect parameter (i.e. click rate/ conversion), and determining future target release information.
Song shows predicting conversion rate based on historical content conversion data for a target time period comprising the current time period and a future time period to create a delivery index.
Shi shows using the expected and actual advertising of n time window of the ith time window to predict future time window such that the budget control of the advertiser is more accurate, further improving the ROI of the advertiser. Although Shi solves a similar problem to the instant claims, Shi does not explicitly teach much of the details of the instant claims.
Yellin shows an optimized content delivery network for the wireless “last mile” by scheduling and delaying transmission of data based on, in part, hardware and software costs of the data transmission and predicted user activity.
Kim shows that the effectiveness of advertisement (i.e. click through rate) can be dependent on the timeslot of advertisement delivery.
Generally, the closest prior art teaches (1) scheduling transmission of data (Okuno, Yellin, and Kim), (2) predicting future time slots (Sideman, Ji, Song, and Shi), (3) allocating data transmissions to time slots (Aloizos, Shi, and Yellin), or (4) pricing transmission of data (Sideman, Aloizos, Xu, Eberstein, and Yellin).
With respect to independent Claims 1, 11, and 20, the closest prior art, taken individually and in an ordered combination, does not explicitly or implicitly disclose the specific ordered combination of elements. Although the prior art of record teaches related concepts, the art does not teach the specific configuration of the independent claims.
Dependent Claims 2-10 depend on Claim 1, and Dependent Claims 12-19 depends on Claim 11, and therefore are also not rejected via dependency.
Conclusion
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.
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/MATTHEW PARKER GOODMAN/Examiner, Art Unit 3628
/JESSICA LEMIEUX/Supervisory Patent Examiner, Art Unit 3626