DETAILED ACTION
This rejection is in response to Request for Continued Examination filed 11/03/2025.
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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 11/03/2025 has been entered.
Response to Arguments
Applicant's arguments filed 11/03/2025 have been fully considered but they are not persuasive.
With respect to applicant’s arguments on pages 12-14 of remarks filed 11/03/2025 that the claims are not directed to certain methods of organizing human activity because the claims do not recite any sales activities or commercial interactions, Examiner respectfully disagrees.
One of the enumerated groupings is defined as certain methods of organizing human activity that includes fundamental economic principles or practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations); managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions). See MPEP § 2106.04(a)(2).
The claims are directed towards certain methods of organizing human activity associated with sales activities and commercial interactions because the claim are directed towards recommending transactions based on identifying spending patterns associated with upcoming events, inventory data, datasets, and metadata by learning historical transaction data. Recommending transactions is a commercial interaction and sales activity that uses data to suggest or direct a user towards a transaction Therefore, the claims are directed towards recommending transactions which is directed towards certain methods of organizing human activity.
With respect to applicant’s arguments on pages 14-17 of remarks filed 11/03/2025 that the claims are directed to a practical application because the claims improve machine learning by minimizing computing resources, training time, and avoiding sparse-data problems by first training a machine learning model for a cluster of similar users and then fine-tuning that model into an individualized model for the user, Examiner respectfully disagrees.
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. See MPEP § 2106.05(a).
To show that the involvement of a computer assists in improving the technology, the claims must recite the details regarding how a computer aids the method, the extent to which the computer aids the method, or the significance of a computer to the performance of the method. Merely adding generic computer components to perform the method is not sufficient. Thus, the claim must include more than mere instructions to perform the method on a generic component or machinery to qualify as an improvement to an existing technology. See MPEP § 2106.05(a).
Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more. See MPEP § 2106.05(f).
It is unclear to one of ordinary skill in the art how training a machine learning model and fine-tuning the model improves machine learning. The specification fails to show how machine learning is improved or how computing resources or training time are minimized. Applicant’s specification in paragraphs [0031-0032] describe solving a commercial problem of individuals purchasing unnecessary items or quantities of items due to an unforeseen event which leads to high volumes of return and waste for merchants rather than solving a problem rooted in technology. Applicant’s specification in paragraph [0057] states that fine tuning the machine learning model for each individual user allows the machine learning model to learn a user’s baseline spending habits. The claims merely use the computing device using machine learning as a tool to analyze historical data and identify spending patterns associated with upcoming events and relationships to recommend transactions to user. Therefore, the claims are not directed to a practical application because the claims merely adding computer components to perform the method is not sufficient.
Claim Rejections - 35 USC § 112(b)
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.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his 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.
Claims 1, 8, and 15 recite: wherein the historical transaction data comprises stock-keeping unit (SKU) level data;… generating, …metadata for items based on SKU level data, rendering said claims indefinite because it is unclear whether stock-keeping unit (SKU) level data is the same or different from the subsequent recitation of SKU level data. Appropriate correction or clarification is required.
Claim 17 recites a recommendation, rendering said claim indefinite because it is unclear whether a recommendation is the same or different from the recommendation recited in independent claim 15. Appropriate correction or clarification is required.
Claim 15 recites the limitation "the recommendation." There is insufficient antecedent basis for this limitation in the claim. Appropriate correction or clarification is required.
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 a judicial exception (an abstract idea) without significantly more.
Under Step 1 of the Subject Matter Eligibility Test, it must be considered whether the claims are directed to one of the four statutory classes of invention. See MPEP § 2106. In the instant case, claims 1-7 are directed to a non-transitory medium, claims 8-14 are directed to a method, and claims 15-20 are directed to a system each of which falls within one of the four statutory categories of inventions (process/apparatus). Accordingly, the claims will be further analyzed under revised step 2:
Under step 2A (prong 1) of the Subject Matter Eligibility Test, it must be considered whether the claims are “directed to” an abstract idea by referring to the groupings of subject matter. One of the enumerated groupings is defined as certain methods of organizing human activity that includes fundamental economic principles or practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations); managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions). See MPEP § 2106.04(a)(2).
Regarding representative independent claim 1, recites the abstract idea of:
performing one or more pre-processing operations on user level data,…, to generate a data set;
identifying, based on the data set and using a clustering model, a plurality of clusters that cluster a plurality of users into a cluster of the plurality of clusters;
generating based on identifying the plurality of clusters and for a training module of a plurality of training modules, one or more training data sets that include a training data set comprising historical transaction data associated with the plurality of users, wherein the historical transaction data comprises stock-keeping unit (SKU) level;
…to identify patterns;
…to learn a plurality of baseline patterns of the first user;
accessing, …, a news feed to identify upcoming events or ongoing events;
identifying, …, an upcoming event in the news feed that will trigger a deviation in typical spending for the first user;
determining, …, a baseline pattern from the plurality of spending patterns that corresponds to a type of event associated with the upcoming event;
generating, …, metadata for items based on SKU level data and review data, wherein the metadata includes tags relevant to the items;
… to recommend a new transaction for the first user based on the baseline pattern associated with the upcoming event, the item data, and the metadata.
The above-recited limitations amounts to certain methods of organizing human activity associated with sales activities and commercial interactions including recommending transactions based on identifying spending patterns associated with upcoming events, inventory data, datasets, and metadata by learning historical transaction data. Such concepts have been considered ineligible certain methods of organizing human activity by the Courts. See MPEP § 2106.
The Step 2A (prong 2) of the Subject Matter Eligibility Test, is the next step in the eligibility analyses and looks at whether the abstract idea is integrated into a practical application. This requires an additional element or combination of additional elements in the claims to apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the exception. See MPEP § 2106.
In this instance, the claims recite the additional elements such as:
One or more non-transitory computer readable media comprising one or more sequences of instructions, which, when executed by a computing system, causes the computing system to perform operations comprising: …, received from one or more third party systems via an API module,…; training, based on the one or more training data sets and across the cluster, a machine learning model, of the training module,…; generating, following the training of the machine learning model across the cluster, a recommendation engine that includes an individualized prediction model for a first user of the plurality of users, by further training or fine tuning the machine learning model, for the first user, for the machine learning model; by the computing system;…; by the computing system; by the computing system and using the machine learning model,.. ;… by the computing system,…; using the individualized prediction model …(Claim 1);
by the computing system…training the machine learning model (claims 2 and 9);
…using the individualized prediction model…(Claims 3 and 6);
…interfacing with an intelligent assistant (claims 3, 10 and 17);
…using the individualized prediction model…; …using the individualized prediction model…(claims 4 and 18);
by the computing system…(claims 7 and 14);
…by a computing system…, received from one or more third party systems via an API module,…; … by the computing system…;… by the computing system…; training, by the computing system, based on the one or more training data sets, and across the cluster, a first prediction model,…; generating, by the computing system and following the training of the first prediction model across the cluster, a recommendation engine that includes a second prediction model by fine-tuning the first prediction model…;… by the computing system…;… by the computing system…;… by the computing system…;… by the computing system, using the first prediction model,…;…using the second prediction model… (claim 8);
…using the second prediction model…(claim 10 and 13);
…using the second prediction model…by the second prediction model (claim 11);
by the first prediction model (claim 12);
A system, comprising: a processor; and a memory having programming instructions stored thereon, which, when executed by the processor, causes the processor to perform operations comprising: received from one or more third party systems via an API module,…; training, based on the one or more training data sets and across the cluster, a machine learning model,…; generating, following the training of the machine learning model across the cluster, an individualized prediction model a first user, of the plurality of users, by further training or fine tuning the machine learning model, for the first user, for the machine learning model…; using the individualized prediction model,…; of the individualized prediction model … (claim 15);
…the machine learning model… (Claim 16).
However, these elements do not amount to an improvement in the functioning of a computer or any other technology or technical field, apply the judicial exception with, or by use of, a particular machine, or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception.
Independent claims and dependent claims also fail to recite elements which amount to an improvement in the functioning of a computer or any other technology or technical field, apply the judicial exception with, or by use of, a particular machine, or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception. For example, independent claims and dependent claims are directed to the abstract idea itself and do not amount to an integration according to any one of the considerations above.
Step 2B is the next step in the eligibility analyses and evaluates whether the claims recite additional elements that amount to an inventive concept (i.e., “significantly more”) than the recited judicial exception. According to Office procedure, revised Step 2A overlaps with Step 2B, and thus, many of the considerations need not be re-evaluated in Step 2B because the answer will be the same. See MPEP § 2106.
In Step 2A, several additional elements were identified as additional limitations:
One or more non-transitory computer readable media comprising one or more sequences of instructions, which, when executed by a computing system, causes the computing system to perform operations comprising: …, received from one or more third party systems via an API module,…; training, based on the one or more training data sets and across the cluster, a machine learning model, of the training module,…; generating, following the training of the machine learning model across the cluster, a recommendation engine that includes an individualized prediction model for a first user of the plurality of users, by further training or fine tuning the machine learning model, for the first user, for the machine learning model; by the computing system;…; by the computing system; by the computing system and using the machine learning model,.. ;… by the computing system,…; using the individualized prediction model …(Claim 1);
by the computing system…training the machine learning model (claims 2 and 9);
…using the individualized prediction model…(Claims 3 and 6);
…interfacing with an intelligent assistant (claims 3, 10 and 17);
…using the individualized prediction model…; …using the individualized prediction model…(claims 4 and 18);
by the computing system…(claims 7 and 14);
…by a computing system…, received from one or more third party systems via an API module,…; … by the computing system…;… by the computing system…; training, by the computing system, based on the one or more training data sets, and across the cluster, a first prediction model,…; generating, by the computing system and following the training of the first prediction model across the cluster, a recommendation engine that includes a second prediction model by fine-tuning the first prediction model…;… by the computing system…;… by the computing system…;… by the computing system…;… by the computing system, using the first prediction model,…;…using the second prediction model… (claim 8);
…using the second prediction model…(claim 10 and 13);
…using the second prediction model…by the second prediction model (claim 11);
by the first prediction model (claim 12);
A system, comprising: a processor; and a memory having programming instructions stored thereon, which, when executed by the processor, causes the processor to perform operations comprising: received from one or more third party systems via an API module,…; training, based on the one or more training data sets and across the cluster, a machine learning model,…; generating, following the training of the machine learning model across the cluster, an individualized prediction model a first user, of the plurality of users, by further training or fine tuning the machine learning model, for the first user, for the machine learning model…; using the individualized prediction model,…; of the individualized prediction model … (claim 15);
…the machine learning model… (Claim 16).
These additional limitations, including the limitations in the independent claims and dependent claims, do not amount to an inventive concept because the recitations above do not amount to an improvement in the functioning of a computer or any other technology or technical field, apply the judicial exception with, or by use of, a particular machine, or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception. In addition, they were already analyzed under Step 2A and did not amount to a practical application of the abstract idea.
For these reasons, the claims are rejected under 35 U.S.C. 101.
Allowable Subject Matter
Claims 1-20 are allowable if rewritten to overcome the 35 U.S.C. 101 and 35 U.S.C 112(b) rejection.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure is cited as patent no. Pathiyal (US 10839361 B1) related to automatically providing items based on item preference, patent publication no. Wadell et al. (US 20140095285 A1) related to automating and streamlining consumer shopping purchases, as well as non-patent literature cited as "Item-Based Collaborative Filtering and Association Rules for a Baseline Recommender in E-Commerce," is related to generating recommendations using item-based collaborative filtering and association rule mining.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to LATASHA DEVI RAMPHAL whose telephone number is (571)272-2644. The examiner can normally be reached 11 AM - 7:30 PM (EST).
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jeffrey A Smith can be reached on 5712726763. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/LATASHA D RAMPHAL/Examiner, Art Unit 3688
/Jeffrey A. Smith/Supervisory Patent Examiner, Art Unit 3688