DETAILED ACTION
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
2. The Amendment filed on May 28, 2026 has been entered. The examiner acknowledges the amendments to claims 1, 15, 21, 22, and the addition of claim 26.
Rejections under 35 U.S.C § 112(b): Applicant’s amendments to claims 1, 21, and 22 have rendered those claims not indefinite, and are not rejected under 35 U.S.C § 112(b). By virtue of their dependency on the independent claims, dependent claims 2-6, 10-18, and 23-25 are not rejected under 35 U.S.C § 112(b).
Applicant’s amendments have now rendered the claims in compliance with the definiteness requirements of 35 U.S.C. § 112 and those rejections are withdrawn.
Rejections under 35 U.S.C. § 101: Applicant argues that the claims are directed to patent eligible subject matter because they include elements that implement or use that judicial exception in conjunction with a particular machine or manufacture integral to the claim, specifically the generative transformer model. The Examiner notes that a generative transformer model is an AI architecture capable of processing data, developing contextual understanding, and predicting a next logical piece of information. It is a generic tool that can be specifically applied and trained to specific applications. In this application, it appears that the generative transformer model is not specifically designed for product prediction and may not be a “particular machine or manufacture integral to the claim,” rather it is trained, programmed or loaded to fit the specific purpose of product prediction. The Examiner interprets this as an instance of “Apply It,” loading the generative model on a processor and then training the model via software input.
Applicant further argues the improvement to the functionality of generative transformer models. The arguments cite a plurality of encoder layers, the Examiner notes that encoder layers are fundamental building blocks for these models and knows of no correlation between the number of encoder layers and model improvements. The argument appears circumstantial and is not compelling.
Still on the argument of improvement to the generative models, (GPT for brevity), neither the claims nor the specification claim or cite specific technical improvements to the GPT model. Examiner notes the following from the claims and specification: The claims cite training a GPT, using a GPT to generate data, using a GPT to output data and providing datasets to the GPT. The specification makes some 50-odd references to the GPT, under the overarching heading of using a computer system configured to generate product predictions using a GPT, with those GPT references categorized as, implementing, [a GPT], configuring, providing data sets to, obtaining data sets from, output a probability metric from, the product prediction engine includes, determining predicted products using, generalized architecture description of, additional information about, and example processes illustrating training of- a GPT. Evidence of an improvement to the model is absent in the claims and not apparent in the specification. Improving the model does not appear to be a goal or even a bi-product of the invention, on the contrary, evidence supports the earlier presumption that the model is software executing on a processor and providing output to a graphical user interface or transmitted on a network.
Additionally, there is no evidence that the invention directly controls another machine or process involving product management or inventory or employs a feedback loop of real world data to validate, update, and continue to train the model.
For these reasons, the Examiner finds that the invention does not demonstrate a practical application and as a result, the invention the claimed invention is directed to non-statutory subject matter. The rejections under 35 U.S.C. § 101 will not be withdrawn.
Rejections under 35 U.S.C. § 103: Applicant’s amendments to independent claims 1, 21 and 22 have overcome the prior art. None of the prior art alone or in combination reach the claimed invention as recited in the claims. Dependent claims 2-6, 10-18, and 23-25 are not rejected by virtue of their dependency on independent claims 1, 21, and 22. New claim 26 recites substantially similar limitations as claim 1, therefore claim 26 is not rejected by prior art with the same rationale, reasoning and motivation as claim 1.
Claim Rejections – 35 U.S.C. § 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 1-6, 10-18, 21-26, are rejected under 35 U.S.C. § 101 because the claimed
invention is directed to non-statutory subject matter. The claims, 1-6, 10-18, 21-26 are directed to a judicial exception (i.e., law of nature, natural phenomenon, abstract idea) without providing significantly more.
Step 1
Step 1 of the subject matter eligibility analysis per MPEP § 2106.03, required the claims to be a process, machine, manufacture or a composition of matter. Claims 1-6, 10-18, 21-26 are directed to a process (method), machine (system), and product/article of manufacture, which are statutory categories of invention.
Step 2A
Claims 1-6, 10-18, 21-26 are directed to abstract ideas, as explained below.
Prong one of the Step 2A analysis requires identifying the specific limitation(s) in the claim under examination that the examiner believes recites an abstract idea, and determining whether the identified limitation(s) falls within at least one of the groupings of abstract ideas of mathematical concepts, mental processes, and certain methods of organizing human activity.
Step 2A-Prong 1
The claims recite the following limitations that are directed to abstract ideas, which can be summarized as being directed to a method, the abstract idea, of analyzing a shopper’s purchasing actions and identifying trends, correlating products and purchases to both suggest additional products, and plan inventory based on predictions.
Claim 1 discloses a method, comprising:
receiving, a first data set representing a plurality of first purchases of a plurality of first products by a plurality of first users, (following rules or instructions, observation, evaluation, judgement, opinion, advertising, marketing, sales activities),
wherein the first data set comprises a plurality of first data strings, each
having a respective sequence of first tokens, (following rules or instructions, observation, evaluation, judgement, opinion),
wherein for each of the first data strings:
the first data string represents a respective one of the first purchases, (following rules or instructions, observation, evaluation,
judgement, opinion, advertising, marketing, sales activities), and
each of the first tokens of the first data string represents a respective one of the first products purchased in the respective one of the first purchases in a single transaction,
wherein for each of the first data strings, the first tokens of that first data string are arranged sequentially according to at least one of:
a price of a respective one of the first products,
a purchase frequency of a respective one of the first products by the first users, or
a purchase frequency of a respective one of the first products by a respective one of the first users; (following rules or
instructions, observation, evaluation, judgement, opinion, advertising, marketing, sales activities),
training, a model using the first data set as an input, and based on the sequence of the first token for each of the first data strings, (following rules or instructions, observation, evaluation, judgement, opinion, advertising, marketing, sales activities),
to process the input iteratively for each of the first tokens, the encoding position
information indicating that a respective one of the first products was purchased in a respective one of the first products was purchased in a respective one of the first purchases, (following rules or instructions, observation, evaluation, judgement, opinion, sales activities),
and representing at least one of the price of the respective one of the first products, the purchase frequency of the respective one of the first products by the first users, or the purchase frequency of the respective one of the first products by a respective one of the first users, (following rules or instructions, observation, evaluation, judgement, opinion, sales activities),
wherein the second data string comprises one or more second tokens, and
wherein each of the one or more second tokens represents a respective one of the
one or more second products; (following rules or instructions, observation, evaluation, judgement, opinion, advertising, marketing, sales activities),
providing, the second data set to the model; (following rules or instructions, observation, evaluation, judgement, opinion),
outputting, a third data set generated by the model based on the second data set, wherein the third data set represents a prediction of one or more third products for purchase by the second user; (following rules or instructions, observation, evaluation, judgement, opinion, advertising, marketing, sales activities),
causing, a message to be presented to a user, the message representing at least some of the one or more third products; storing, the third data set (following rules or instructions, observation, evaluation, judgement, opinion, advertising marketing, sales activities).
Additional limitations employ the method with the plurality of first users comprises the second user, (following rules or instructions, observation, evaluation, judgement, opinion, advertising, marketing, sales activities - claim 2), where the third data set comprises the third data string and is comprised of third tokens each representing a third product, (following rules or instructions, observation, evaluation, judgement, opinion, advertising, marketing, sales activities – claim 3), where at least one of the first, second or third tokens is a SKU associated with one of the first, second, or third products, (following rules or instructions, observation, evaluation, judgement, opinion, advertising, marketing, sales activities – claim 4), where at least one of the first, second or third tokens indicates the beginning of at least one of the first, second, or third strings of data, (following rules or instructions, observation, evaluation, judgement, opinion, advertising, marketing, sales activities – claim 5), where one of the first, second or third tokens is a respective token indicating the end of at least one of the first, second, or third data strings, (following rules or instructions, observation, evaluation, judgement, opinion – claim 6), wherein the first data set is first embedded data of the time when each of the first products was purchased by the first users, (following rules or instructions, observation, evaluation, judgement, opinion – claim 10), where the first tokens are the embedded data, (following rules or instructions, observation, evaluation, judgement, opinion – claim 11), where the embedded data and the first tokens have different respective data structures, (following rules or instructions, observation, evaluation, judgement, opinion – claim 12), and the second data set represents one or more second products chosen by the second user for purchase on-line, (following rules or instructions, observation, evaluation, judgement, opinion, advertising, marketing, sales activities – claim 13), and the second data set represents one or more second products chosen by the second user for purchase at a physical location, (following rules or instructions, observation, evaluation, judgement, opinion, advertising, marketing, sales activities – claim 14), causing a message to go to the second user with an indication of a third product, (following rules or instructions, observation, evaluation, judgement, opinion, advertising, marketing, sales activities – claim 15), estimating based on the third data set a future stock level for third products, (following rules or instructions, observation, evaluation, judgement, opinion, advertising, marketing, sales activities – claim 16), receiving a fourth data set representing fourth products, providing fourth data set to the model obtaining a fifth third data set based on the fourth data set, where the fifth data set is a prediction of one or more fifth products that are related to the fourth products and storing the fifth data set, (following rules or instructions, observation, evaluation, judgement, opinion, advertising, marketing, sales activities – claim 17), where one of the fourth products is a subset of the second products, (following rules or instructions, observation, evaluation, judgement, opinion, advertising, marketing, sales activities – claim 18), where each of the first tokens of the first data strings are arranged sequentially according to the purchase frequency of the respective one of the first products by the first users, (following rules or instructions, observation, evaluation, judgement, opinion, advertising, marketing, sales activities – claim 23), where for each of the first data strings, the first tokens of that first data string are arranged sequentially according to the purchase frequency of the respective one of the first products by the first users, (following rules or instructions, observation, evaluation, judgement, opinion, advertising, marketing, sales activities – claim 24), and where for each of the first data strings, the first tokens of that first data string are arranged sequentially according to the purchase frequency of a respective one of the first products by a respective one of the first users, (following rules or instructions, observation, evaluation, judgement, opinion, advertising, marketing, sales activities – claim 25).
Each of these claimed limitations employ: organizing human activity in the form of following rules or instructions, sales activities or behaviors; performing mental processes including, observation, evaluation, judgement, and opinion; as well as organizing human activity – commercial or legal interactions, advertising, marketing or sales activities.
Claims 21-22 and 26 recite similar abstract ideas as those identified with respect to claims 1-6, and 10-18.
Thus, the concepts set forth in claims 1-6, 10-18, 21-26 recite abstract ideas.
Step 2A-Prong 2
As per MPEP § 2106.04, while the claims 1-6, 10-18, 21-26 recite additional limitations which are hardware or software elements such as one or more processors, a generative transformer model, one or more computerized attention mechanisms, a plurality of encoder layers, a plurality of decoder layers, wherein at least one of the encoder layers or decoder layers comprise a feed-forward neural network, a graphical user interface, one or more computer storage devices, a stock keeping unit (SKU), an encoder and a decoder, a memory communicatively coupled to the at least one processor, an electronic message to be transmitted by a communications network, and a non-transitory computer-readable media, these limitations are not sufficient to qualify as a practical application being recited in the claims along with the abstract ideas since these elements are invoked as tools to apply the instructions of the abstract ideas in a specific technological environment. The mere application of an abstract idea in a particular technological environment and merely limiting the use of an abstract idea to a particular technological field do not integrate an abstract idea into a practical application (MPEP § 2106.05 (f) & (h)).
Evaluated individually, the additional elements do not integrate the identified abstract ideas into a practical application. Evaluating the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually.
The claims do not amount to a “practical application” of the abstract idea because they neither (1) recite any improvements to another technology or technical field; (2) recite any improvements to the functioning of the computer itself; (3) apply the judicial exception with, or by use of, a particular machine; (4) effect a transformation or reduction of a particular article to a different state or thing; (5) provide other meaningful limitations beyond generally linking the use of the judicial exception to a particular technological environment.
Accordingly, claims 1-6, 10-18, 21-26 are directed to abstract ideas.
Step 2B
Claims 1-6, 10-18, 21-26 do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements when considered both individually and as an ordered combination, do not amount to significantly more than the abstract idea.
The analysis above describes how the claims recite the additional elements beyond those identified above as being directed to an abstract idea, as well as why identified judicial exception(s) are not integrated into a practical application. These findings are hereby incorporated into the analysis of the additional elements when considered both individually and in combination.
For the reasons provided in the analysis in Step 2A, Prong 1, evaluated individually, the additional elements do not amount to significantly more than a judicial exception. Thus, taken alone, the additional elements do not amount to significantly more than a judicial exception.
Evaluating the claim limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. In addition to the factors discussed regarding Step 2A, prong two, there is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely amount to instructions to implement the identified abstract ideas on a computer.
Therefore, since there are no limitations in the claims 1-6, 10-18, 21-26 that transform the exception into a patent eligible application such that the claims amount to significantly more than the exception itself, the claims are directed to non-statutory subject matter and are rejected under 35 U.S.C. § 101.
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.
Independent claims 1, 21-22, 26, are not rejected by prior art under 35 U.S.C. § 103.
Dependent claims 1-6, 10-18, 23-25 are not rejected because of their inherent dependency on claims 1, 21-22.
The closest prior art to the invention includes Hall, (US 20230376981 A1), "Predictive
Systems and Processes for Product Attribute Research and Development," in view of Sorensen (US20230056742A1), "In Store Computerized Product Promotion system with Product Prediction Model that Outputs a Target Product Message Based on Products Selected in A Current Shopping Session,” in further view of Blohm, (US 8630989 B2), "Systems and Methods for Information Extraction Using Contextual Pattern Discovery." None of the prior art alone or in combination teach the claimed invention as recited in this claim wherein the novelty is in the combination of all the limitations and not in a single limitation.
Regarding claim 1, Hall teaches A method comprising:
receiving, by one or more processors, a first data set representing a plurality of first purchases of a plurality of first products by a plurality of first users,
wherein the first data set comprises a plurality of first data strings, each having a respective sequence of first tokens, and where the one or more first characteristics comprises at least one of a price of a respective one of the first products, (predicting sales for a product, training a model and analyzing point of sale data and key features, including sales volume, revenue profitability transactions, financial data, and indicators; in one or more embodiments, the systems and processes leverage the analyses to generate predictions for a)product sales volume (e.g., at a particular price level, via a particular channel, at a particular location, over a particular time interval, with particular product attributes, or combinations thereof), b) the impact of price and other attribute changes on sales volume, and c) generating recommendations for product development strategy to propel product performance.)
Sorensen teaches each of the first tokens of the first data string represents a respective one of the first products purchased in the respective one of the first purchases in a single transaction; a purchase frequency of a respective one of the first products by the first users, or a purchase frequency of a respective one of the first products by a respective one of the first users,
training, by the one or more processors, a generative transformer model including one or more computerized attention mechanisms using the first data set as an input; (the product prediction model receives as input the one or more products selected by the shopper during the current shopping session and outputs the identity of the at least one target product, and the token embedding indicates the identity of the target product; the embeddings are used as input into a transformer that uses an attention mechanism to learn a contextual relationship between products selected by shoppers).
Blohm teaches wherein for each of the first data strings, the first tokens of that first data string are arranged sequentially according to one or more first characteristics, (the sequence analysis component operates to prepare the information necessary to generate semantic signatures for the context associated with each relation candidate. A context string may be comprised of a sequence of words or tokens. Embodiments provide that the semantic signature of a context string may satisfy the following two conditions: it should be the most informative subsequence(s) of tokens that identifies the relation candidate; and at the same time it is shared by the most number of context strings of other relation candidates).
Neither or Hall, Sorensen or Blohm specifically teach the first tokens of that first data string are arranged sequentially according to at least one of:
a price of a respective one of the first products,
a purchase frequency of a respective one of the first products by the first users, or
a purchase frequency of a respective one of the first products by a respective one of the first users;
Specific token sequences based on price, purchase frequency, or purchase frequency by a respective one of the first users were not taught. These individually or in combination did not teach the complete scope of the claim.
None of the prior art alone or in combination teach the claimed invention as recited in this claim wherein the novelty is in the combination of all the limitations and not in a single limitation.
Independent claims 21-22 and 26 recite substantially similar limitations as claim 1 and are not rejected under as they present similar rationale, reasoning, and motivation as claim 1. Dependent claims 1-6, 10-18, 23-25 are not rejected because of their inherent dependency on claims 1, 21-22.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure or directed to the state of the art is listed on the enclosed PTO-892.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MICHAEL BOROWSKI whose telephone number is (703) 756-1822. The examiner can normally be reached M-F 8-4:30.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jerry O’Connor can be reached on (571) 272-6787. The fax phone number for the organization where this application or proceeding is assigned is (571) 273-8300.
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/MB/
Patent Examiner, Art Unit 3624
/MEHMET YESILDAG/Primary Examiner, Art Unit 3624
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