DETAILED ACTION
This rejection is in response to Amendments filed 04/06/2026.
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 .
Claim Objection
Claim 4 objected to because of the following informalities:
Claim 4 recites: the baseline pattern the item data, and the metadata comprises:…Examiner recommends amending claim 4 to include a comma after baseline pattern. Appropriate correction or clarification is required.
Response to Arguments
Applicant’s arguments, see page 12, filed 04/06/2026, with respect to 35 U.S.C. 112(b) rejection to claims 1-20 have been fully considered and are persuasive. The 35 U.S.C. 112(b) rejection to claims 1-20 has been withdrawn.
Applicant's arguments filed 04/06/2026 have been fully considered but they are not persuasive.
With respect to applicant’s arguments on pages 12-13 of remarks filed 04/06/2026 that the claims are patent eligible for reasons discussed in the interview, Examiner respectfully disagrees.
Applicant argued during the interview on 04/07/2026 that the claims are patent eligible because the claimed invention improves machine learning. However, Examiner indicated that the specification describes in paragraph [0046-0047] that machine learning is merely used to provide more accurate and complete recommendations rather than improving machine learning. See 35 U.S.C. 101 rejection below.
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 that include standardizing data,…, to obtain standardized data for a plurality of training modules;
generating, based on the standardized data, based on a cluster associated with a plurality of user…, and for a training module of the plurality of training modules, one or more training data sets that include a training data set that groups historical data associated with the plurality of user …, wherein the historical data comprises unit level;
…to identify patterns;
…to learn a plurality of baseline patterns of the first user…;
generating, …, metadata for items based on the unit level data and review data, wherein the metadata includes tags relevant to the items;
… to generate an output… based on a baseline pattern of the plurality of baseline patterns, that corresponds to a type of an upcoming event, in a feed, that will trigger a deviation in patterns for the first user…, the item data, and based on the metadata.
The above-recited limitations amounts to certain methods of organizing human activity associated with sales activities and commercial interactions that recite standardizing data, generating groups historical data of users based on the data, identifying patterns, learning patterns of users, generating tags relevant to items item as metadata based on the user’s historical data and review data to generate an output based on patterns of users that corresponds to an upcoming event that triggers a deviation in patterns of the user, item, and metadata. 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,…; …devices …devices,…; training, based on the one or more training data sets and across the cluster, a machine learning model, of the training module,…; generating, based on training the machine learning model across the cluster, an engine that includes an individualized machine learning model for a first user device of the plurality of user devices, by further training or fine tuning the machine learning model, for the first user device, for the machine learning model…of the first user device; …by the computing system and using the machine learning model,.. ;… by the computing system,…; using the individualized machine learning model …for the first user device…for the first user device…(Claim 1);
by the computing system…training the machine learning model (claims 2 and 9);
…using the individualized machine learning model…for the first user device (Claims 3 and 6);
…interfacing with an intelligent assistant to the first user device (claims 3, 10 and 17);
…using the individualized machine learning model…for the first user device; …using the individualized machine learning model…of the first user device;… of the first user device… the first user device (claims 4 and 18);
… the first user device…of the first user device;…the first user device…(Claim 6);
by the computing system…the first user device…(claims 7 and 14);
…by a computing system…, received from one or more third party systems via an API module,…; … by the computing system…devices…devices…; training, by the computing system, based on the one or more training data sets, and across the cluster, a first machine learning model,…; generating, by the computing system and based on training the first machine learning model across the cluster, an engine that includes a second machine learning model by fine-tuning the first machine learning model…of the first user device for the first machine learning model…;… by the computing system, using the first machine learning model,…;…using the second machine learning model…for the first user device…for the first user device… (claim 8);
…using the second machine learning model…for the first user device (claim 10 and 13);
…using the second machine learning model…for the first user device…by the second machine learning model…of the first user device…of the first user device…the first user device… (claim 11);
by the first machine learning model (claim 12);
…the first user device…of the first user device…;…the first user device…(Claim 13);
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,…;…devices…devices… training, based on the one or more training data sets and across the cluster, a machine learning model,…; generating, based on training the machine learning model across the cluster, an individualized machine learning model a first user device…devices, of the plurality of users, by further training or fine tuning the machine learning model, for the first user device, for the machine learning model…of the first user device; using the machine learning model…; using the individualized machine learning model,…for the first user device…for the first user device;… of the individualized machine learning model … for the first user device…(claim 15);
…the machine learning model… (Claim 16);
…for the first user device (Claim 17);
…for the first user device…the first user device…of the first user device; the first user device…(Claim 20).
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,…; …devices …devices,…; training, based on the one or more training data sets and across the cluster, a machine learning model, of the training module,…; generating, based on training the machine learning model across the cluster, an engine that includes an individualized machine learning model for a first user device of the plurality of user devices, by further training or fine tuning the machine learning model, for the first user device, for the machine learning model…of the first user device; …by the computing system and using the machine learning model,.. ;… by the computing system,…; using the individualized machine learning model …for the first user device…for the first user device…(Claim 1);
by the computing system…training the machine learning model (claims 2 and 9);
…using the individualized machine learning model…for the first user device (Claims 3 and 6);
…interfacing with an intelligent assistant to the first user device (claims 3, 10 and 17);
…using the individualized machine learning model…for the first user device; …using the individualized machine learning model…of the first user device;… of the first user device… the first user device (claims 4 and 18);
… the first user device…of the first user device;…the first user device…(Claim 6);
by the computing system…the first user device…(claims 7 and 14);
…by a computing system…, received from one or more third party systems via an API module,…; … by the computing system…devices…devices…; training, by the computing system, based on the one or more training data sets, and across the cluster, a first machine learning model,…; generating, by the computing system and based on training the first machine learning model across the cluster, an engine that includes a second machine learning model by fine-tuning the first machine learning model…of the first user device for the first machine learning model…;… by the computing system, using the first machine learning model,…;…using the second machine learning model…for the first user device…for the first user device… (claim 8);
…using the second machine learning model…for the first user device (claim 10 and 13);
…using the second machine learning model…for the first user device…by the second machine learning model…of the first user device…of the first user device…the first user device… (claim 11);
by the first machine learning model (claim 12);
…the first user device…of the first user device…;…the first user device…(Claim 13);
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,…;…devices…devices… training, based on the one or more training data sets and across the cluster, a machine learning model,…; generating, based on training the machine learning model across the cluster, an individualized machine learning model a first user device…devices, of the plurality of users, by further training or fine tuning the machine learning model, for the first user device, for the machine learning model…of the first user device; using the machine learning model…; using the individualized machine learning model,…for the first user device…for the first user device;… of the individualized machine learning model … for the first user device…(claim 15);
…the machine learning model… (Claim 16);
…for the first user device (Claim 17);
…for the first user device…the first user device…of the first user device; the first user device…(Claim 20).
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 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.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to LATASHA DEVI RAMPHAL whose telephone number is (571)272-2644. The examiner can normally be reached 11 AM - 7:30 PM (EST).
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
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
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