DETAILED ACTION
Status of Claims
This Final Office Action is responsive to Applicant's reply filed 1/14/2026.
No claims have been amended.
Claims 31-62 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 Arguments
Applicant’s amendments have been fully considered, but do not overcome the previously pending 35 USC 101 rejections.
Response to Arguments
Applicant's arguments have been fully considered but they are not persuasive.
Applicant argues the pending claims integrate the abstract idea into practical application by using AI. The Examiner respectfully disagrees. The Examiner has already set forth a prima facie case under 35 USC 101. The Applicant does not properly identify the additional elements and actually defines what an abstract idea is in the argument. See rejection below for more details. The Examiner further asserts that the Desjardins Memo was taken into consideration and the specification was analyzed. Applicant does not point out what from the specification actually makes the claims eligible. Generically reciting use of a “predictive machine learning model” does not integrate the abstract idea into a practical application because the machine learning is recited at such a high level of generality that it merely adds the words apply it with the judicial exception (See MPEP 2106.05). Applicant’s arguments are not persuasive.
Applicant argues the claims provide a technological improvement to predictive modeling. The Examiner respectfully disagrees. The Applicant does not point out what is actually being improved. The Examiner points to Page 2 of the McRO-Bascom Memo from December 2016, "The McRO court indicated that it was the incorporation of the particular claimed rules in computer animation "that improved [the] existing technological process", unlike cases such as Alice where a computer was merely used as a tool to perform an existing process." The Applicants’ claims are geared toward predictive modeling for analyzing demand, where these techniques are merely being applied/calculated in a computing environment. Simply applying these known concepts to a specific technical environment (e.g. the computers/Internet) does not account for significantly more than the abstract idea because it does not solve a problem rooted in computer technology nor does it improve the functioning of the computer itself because it is merely making a determination based on rules and/or mathematical relationships to output to a user. The Applicant’s claimed limitations do not appear to bring about any improvement in the operation or functioning of a computer per se, or to improve computer-related technology by allowing computer performance of a function not previously performable by a computer (see page 2 of the McRo-Bascom memo). The solution appears to be more of a business-driven solution rather than a technical one. The machine learning is recited at such a high level that it is merely adding the words apply it with the judicial exception (See MPEP 2106.05). In addition, the Examiner notes the claims specifically state how the machine learning is pre-trained meaning that the machine learning model is really just a piece of software being run by a general purpose computer. The Examiner also notes that automating tasks on a general purpose computer does not make the claims eligible. Applicant’s arguments are not persuasive.
The Applicant then mentions the dependent claims, which list off different ways that the predictive modeling can be used like anomaly detection, use of supervised or unsupervised machine learning, use of an API, which all are recited at such a high level of generality that they further narrow the abstract idea and merely add the words apply it with the judicial exception (See MPEP 2106.05). Applicant’s arguments are not persuasive.
Applicant points to Paragraphs 0030, 0096, 0106-0148, and Figures 7A-7B, but does not say what reflects the improvement. The Applicant the copy and pastes the claim limitations, but does not tie the claims to the specification or provide reasoning as to how the claimed limitations are eligible. Applicant’s arguments are not persuasive.
Applicant’s arguments pages 31-32 broadly states what the claims are doing and says its an improvement, but Applicant does not say what is actually improved. The Examiner asserts that use of machine learning, location data, software, and an API does not make the claims eligible. Applicant’s arguments are not persuasive.
The Examiner asserts First, that using ML to predict demand at certain locations is the abstract idea. Applicant is not properly identifying additional elements. Applicant again does not state what is actually being improved. The Examiner strongly recommends reading the 35 USC 101 rejection below. Applicant’s arguments are not persuasive.
Second, Applicant’s arguments are largely copy and pasted case law, where the claims are not actually tied to any reasoning. Applicant argues that there is an improvement, but what is actually being improved? Is it the computer? Is it the machine learning? Or is it the results output by the prediction, which is to be interpreted by a human? The prediction and its results are the abstract idea. See the rejection below. Applicant’s arguments are not persuasive.
Third, again Applicant argues there is an improvement, but does not state what the improvement is or what claim limitations amount to the improvement. Applicant’s arguments are not persuasive.
Fourth, the term vector is mentioned ten times in Applicant’s specification. A vector is a quantity with magnitude and direction and is commonly used in mathematical analysis (See Wikipedia). Applicant’s specification makes no mention of any type of improvement to how the vector is used, but rather that vectors are used in the machine learning mathematical relationships for performing the prediction. The Examiner has clearly analyzed the specification and the Desjardins memo. The Examiner further notes that use of vectors for training machine learning as claimed is specifically reciting what the input data is to be used in training the machine learning. For example, stating I use demand conditions over a period of time (as recited in claims 31 and 41) narrows the abstract idea. Applicant’s arguments are not persuasive.
The Examiner further asserts that each claims has been analyzed independently and as an ordered combination. Please see the rejection below for more details. 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 31-62 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 31-62 are directed toward a process and a system; which are statutory categories of invention.
Additionally (Step 2A Prong One), the independent claims are directed toward a system comprising: one or more processors; and one or more non-transitory computer-readable storage devices storing computing instructions configured to run on the one or more processors and cause the one or more processors to execute functions comprising: providing, by the one or more processors, access over a network to a user-sourced analytics platform that includes a predictive machine learning model configured to predict demand metrics in a plurality of channels corresponding to geographic regions; wherein the predictive machine-learning model is pre-trained on a training dataset comprising a plurality of training feature vectors to perform anomaly detection, time-series forecasting, or a combination thereof, each training feature vector including a separate group of training channel features that represent demands conditions in a corresponding channel at a successive point in time, and which collectively model demand conditions over a given time period; receiving, by the user-sourced analytics platform, a first set of channel events comprising location tracking data that identifies locations of computing devices and permits the user-sourced analytics platform to detect the computing devices that are located within a channel being analyzed, the computing devices being operated by or associated with individuals; receiving, by the user-sourced analytics platform, a second set of channel events comprising user-sourced data that is obtained from local applications stored on the computing devices located within the channel, the user-sourced data obtained from the local applications at least indicating propensities or preferences of the individuals with respect to one or more inventory items; correlating, by the one or more processors, the user-sourced data received in the second set of channel events with the location tracking data received in the first set of channel events to determine the propensities or preferences of the individual located in the channel; predicting, via execution of the predictive machine learning model by the one or more processors, one or more demand metrics for the channel wherein the predictive machine-learning model is configured to predict the one or more demand metrics such that: the predictive machine-learning model receives at least one feature vector comprising a set of channel features that are derived from channel events corresponding to current demand conditions in the channel, the channel events at least including the user-sourced data corresponding to the individuals determined to be located in the channel; the predictive machine-learning model analyzes the set of channel features included in the at least one feature vector for performing anomaly detection, time- series forecasting, or a combination thereof to generate the one or more demand metrics based on the at least one feature vector, wherein the one or more demand metrics generated by the predictive machine-learning model predict a demand for the channel: and detecting, by the one or more processors, a demand surge in the channel based, at least in part on the one or more demand metrics predicted by the user-sourced analytics platform; and wherein, in response to detecting the demand surge, a demand adjustment function is executed that adjusts prices or allocations for the one or more inventory items in the channel based, at least in part, on the one or more demand metrics (Organizing Human Activity), which are considered to be abstract ideas (See MPEP 2106.05). 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 receiving user data and local computer data to predict demand for certain channels and adjusting the prices based on demand fluctuations, which is monitoring human behavior for commercial purposes.
Dependent claims 32-40 and 42-62 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 “a system comprising: one or more processors; and one or more non-transitory computer-readable storage devices storing computing instructions configured to run on the one or more processors and cause the one or more processors to execute functions comprising: providing, by the one or more processors, access over a network to a user-sourced analytics platform that includes a predictive machine learning model; wherein the predictive machine-learning model is pre-trained on a training dataset, by the user-sourced analytics platform, the computing devices being operated by or associated with individuals; by the user-sourced analytics platform, obtained from local applications stored on the computing devices located within the channel; via execution of the predictive machine-learning model by the one or more processors (claim 41)”; “implemented via execution of computing instructions by one or more processors and stored on one or more non-transitory computer-readable storage devices; by the one or more processors, access over a network to a user-Sourced analytics platform that includes a predictive machine-learning model configured to; wherein the predictive machine-learning model is pre-trained on a training dataset; by the user-sourced analytics platform, the computing devices being operated by or associated with individuals; by the user-sourced analytics platform, obtained from local applications stored on the computing devices located within the channel; via execution of the predictive machine-learning model by the one or more processors (claim 31)”, 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 2106.05) 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 32-40 and 42-62 further narrow the abstract idea and dependent claims 38, 40, 48, 50, and 62 additionally recite “one or more slider elements (claims 38 and 48)”; “application program interface; a ride hailing application; an accommodation application; a travel application; or a reservation application (claims 40 and 50)”; “a client system; an application programming interface (claim 62)” 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 2106.05).
The Examiner further notes that the claimed “predictive machine learning model”, in the independent and use of in the dependent claims, is not enough to make the claims eligible, where having a “predictive machine learning model” provides nothing more than mere instructions to implement the abstract idea on a generic computer. The “predictive machine learning model” is used generically to apply the abstract idea without limiting how the trained machine learning functions. The “predictive machine learning model” is described at a high level such that it amounts to using a computer with generic machine learning to apply the abstract idea without any details about how the outcomes are accomplished. The Examiner notes that the vectors are just input values into the machine learning model (See MPEP 2106.05). The Examiner further points to claims 51-62 which recite a plurality of uses for the generically claimed machine learning model, where no actual details of the machine learning are claimed, but rather generically recite how the machine learning model is “used” to implement the abstract idea determinations.
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 2106.05). Further, method; and System Independent claims 31 and 41 recite “a system comprising: one or more processors; and one or more non-transitory computer-readable storage devices storing computing instructions configured to run on the one or more processors and cause the one or more processors to execute functions comprising: providing, by the one or more processors, access over a network to a user-sourced analytics platform that includes a predictive machine learning model; wherein the predictive machine-learning model is pre-trained on a training dataset, by the user-sourced analytics platform, the computing devices being operated by or associated with individuals; by the user-sourced analytics platform, obtained from local applications stored on the computing devices located within the channel; via execution of the predictive machine-learning model by the one or more processors (claim 41)”; “implemented via execution of computing instructions by one or more processors and stored on one or more non-transitory computer-readable storage devices; by the one or more processors, access over a network to a user-Sourced analytics platform that includes a predictive machine-learning model configured to; wherein the predictive machine-learning model is pre-trained on a training dataset; by the user-sourced analytics platform, the computing devices being operated by or associated with individuals; by the user-sourced analytics platform, obtained from local applications stored on the computing devices located within the channel; via execution of the predictive machine-learning model by the one or more processors (claim 31)”; 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 0151 and Figures 1A-1B. 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 32-40 and 42-62 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 38, 40, 48, 50, and 62 additionally recite “one or more slider elements (claims 38 and 48)”; “application program interface; a ride hailing application; an accommodation application; a travel application; or a reservation application (claims 40 and 50)”; “a client system; an application programming interface (claim 62)” 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 2106.05). 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.
Allowable over 35 USC 103
Claims 31-62 are allowable over the prior art, but remain rejected under §101 for the reasons set forth above. Independent claims 31-50 disclose a system and method for receiving user data and local computer data to predict demand for certain channel based inventory items using preferences, location tracking data, and local application data and adjust the prices based on the predicted demand fluctuations using defined input variables.
Regarding a possible 103 rejection: The closest prior art of record is:
Zhou et al. (US 2020/0402058 A1) – which discloses real time processing of data streams.
Jones et al. (US 2017/0308914 A1) – which discloses forecasting analysis in sales using consumer data.
Schroeder et al. (US 2005/0273380 A1) – which discloses analyzing sales of items during certain promotion periods.
The prior art of record neither teaches nor suggests all particulars of the limitations as recited in claims 31-62, such as receiving user data and local computer data to predict demand for certain channel based inventory items using preferences, location tracking data, and local application data and adjust the prices based on the predicted demand fluctuations. While individual features may be known per se, there is no teaching or suggestion absent applicants’ own disclosure to combine these features other than with impermissible hindsight and the combination/arrangement of features are not found in analogous art. Specifically the claimed “a system comprising: one or more processors; and one or more non-transitory computer-readable storage devices storing computing instructions configured to run on the one or more processors and cause the one or more processors to execute functions comprising: providing, by the one or more processors, access over a network to a user-sourced analytics platform that includes a predictive machine learning model configured to predict demand metrics in a plurality of channels corresponding to geographic regions; wherein the predictive machine-learning model is pre-trained on a training dataset comprising a plurality of training feature vectors to perform anomaly detection, time-series forecasting, or a combination thereof, each training feature vector including a separate group of training channel features that represent demands conditions in a corresponding channel at a successive point in time, and which collectively model demand conditions over a given time period; receiving, by the user-sourced analytics platform, a first set of channel events comprising location tracking data that identifies locations of computing devices and permits the user-sourced analytics platform to detect the computing devices that are located within a channel being analyzed, the computing devices being operated by or associated with individuals; receiving, by the user-sourced analytics platform, a second set of channel events comprising user-sourced data that is obtained from local applications stored on the computing devices located within the channel, the user-sourced data obtained from the local applications at least indicating propensities or preferences of the individuals with respect to one or more inventory items; correlating, by the one or more processors, the user-sourced data received in the second set of channel events with the location tracking data received in the first set of channel events to determine the propensities or preferences of the individual located in the channel; predicting, via execution of the predictive machine learning model by the one or more processors, one or more demand metrics for the channel wherein the predictive machine-learning model is configured to predict the one or more demand metrics such that: the predictive machine-learning model receives at least one feature vector comprising a set of channel features that are derived from channel events corresponding to current demand conditions in the channel, the channel events at least including the user-sourced data corresponding to the individuals determined to be located in the channel; the predictive machine-learning model analyzes the set of channel features included in the at least one feature vector for performing anomaly detection, time- series forecasting, or a combination thereof to generate the one or more demand metrics based on the at least one feature vector, wherein the one or more demand metrics generated by the predictive machine-learning model predict a demand for the channel: and detecting, by the one or more processors. a demand surge in the channel based, at least in part on the one or more demand metrics predicted by the user-sourced analytics platform; and wherein, in response to detecting the demand surge, a demand adjustment function is executed that adjusts prices or allocations for the one or more inventory items in the channel based, at least in part, on the one or more demand metrics (as required by independent claims 31-62)”, thus rendering claims 31-62 as allowable over the prior art.
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