DETAILED ACTION
Notice to Applicant
1. The following is a FINAL Office action upon examination of application number 19/000,421. Claims 1, 5-11, and 15-23 are pending in this application, and have been examined on the merits discussed below.
2. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Priority
3. Application 19/000,421 filed 12/23/2024 is a Continuation of application 17/672,790, filed 02/16/2022. Application 17/672,790 claims Priority from Provisional Application 63/212,123, filed 06/18/2021.
Response to Amendment
4. In the response filed August 04, 2026, Applicant amended claims 1, 7, 11, and 17, and cancelled claims 2-4 and 12-14. New claims 21-23 were presented for examination.
5. Applicant's amendments to claims 1 and 11 are hereby acknowledged. The amendments are sufficient to overcome the previously issued claim rejection under 35 U.S.C. 112(a); accordingly, this rejection has been withdrawn.
6. Applicant's amendments to claims 1, 7, and 11 are hereby acknowledged. The amendments are sufficient to overcome the previously issued claim rejection under 35 U.S.C. 112(b); accordingly, this rejection has been withdrawn.
7. Applicant's amendments to claims 1 and 11 are hereby acknowledged. The amendments are not sufficient to overcome the previously issued claim rejection under 35 U.S.C. 101; accordingly, this rejection has been maintained.
Response to Arguments
8. Applicant's arguments filed August 04, 2026, have been fully considered.
9. Applicant submits “Claims 1-20 are rejected under 35 U.S.C. §112 (a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as allegedly failing to comply with the written description requirement. Claims 1-20 are rejected under 35 U.S.C. § 112(a) as failing to comply with the written description requirement. Claims 1 and 11 have been amended to recite "validate the AI model output based on received feedback," as suggested in the Office Action. Accordingly, Applicant respectfully requests withdrawal of the rejection of claims 1-20 under 35 U.S.C. § 112(a).” [Applicant’s Remarks, 08/04/2026, page 7]
In response, it is noted that the amendments are sufficient to overcome the previously issued claim rejection under 35 U.S.C. 112(a); accordingly, this rejection has been withdrawn.
10. Applicant submits “Claims 1-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as allegedly being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, or for pre-AIA the applicant regards as the invention. In response, Applicant has amended the claims. Applicant respectfully submits that the claims as amended meet the requirements of 112(b). Accordingly, Applicant respectfully requests withdrawal of the rejection.” [Applicant’s Remarks, 08/04/2026, page 7]
In response, it is noted that the amendments are sufficient to overcome the previously issued claim rejection under 35 U.S.C. 112(b); accordingly, this rejection has been withdrawn.
11. Applicant submits “Independent claims 1 and 11, as amended, recite, among other things, that the set of structured data fields "reflect summarized hypotheses data and include a product field, a product attribute field, a product attribute value field, and an action field," that the first screen is configured to limit data entry to the set of structured data fields "on which the AI model is trained to accept as input to produce one or more hypotheses as output," and that the processor is configured to "input the customer data formatted with the set of structured data fields to an AI model trained to output the one or more hypotheses in response to the input of the customer data formatted to the set of structured data fields." Claims 1 and 11 as amended thus require an AI model that is trained to accept the claimed structured data fields as input and to produce the one or more hypotheses as output, with the processor inputting customer data formatted with that schema to the AI model.” Applicant also submits “Applicant respectfully disagrees with the rejection and submits that the combination of Montero and Brandish, whether considered alone or together, does not teach or suggest the limitations of claims 1 and 11 as amended. More specifically, Applicant submits “As an initial matter, claims 1 and 11 as amended require an AI model that is trained to accept the set of structured data fields - which reflect summarized hypotheses data and include a product field, a product attribute field, a product attribute value field, and an action field - as input, and to produce the one or more hypotheses as output. Neither Montero nor Brandish teaches this integrated input-output relationship.” Applicant further submits “Agarwal does not disclose a customer-feedback data schema, and does not disclose an AI model trained on a product/attribute/value/action schema to produce hypotheses. Accordingly, the "action field" and "summarized hypotheses data" limitations of claims 1 and 11 as amended are not taught by any of the applied references.” Lastly, Applicant submits that “Montero does not teach inputting customer data formatted with a product/attribute/value/action schema to an AI model trained to output hypotheses.” [Applicant’s Remarks, 08/04/2026, pages 8-10]
In response, it is noted that Applicant's amendments are sufficient to overcome the §103
rejection. Reasons for allowance (over the prior art) are provided below.
12. Applicant submits with respect to “Practical Application Recited in Claims” “Applicant respectfully submits that claim 1, as amended, includes (1) specific properties of the user interface coupled with (2) specific properties of a claimed data model (3) improved operation based on summarized data in the data model, and (4) an AI trained on the specific data model to produce a specific output in response to input of the attributes defined in the data model, improving operation thereof. Each of these properties taken alone provides for a particular machine and provides for the improvement of the functioning of the computer system on which they are implemented. Applicant respectfully submits that when these properties are considered collectively, the improved operation is readily apparent to the person of ordinary skill in the art, as required by MPEP $2106.05(a).” [Applicant’s Remarks, 08/04/2026, page 10]
The Examiner respectfully disagrees. Under Step 2A, Prong Two of the eligibility inquiry, Applicant argues “that claim 1, as amended, includes (1) specific properties of the user interface coupled with (2) specific properties of a claimed data model (3) improved operation based on summarized data in the data model, and (4) an AI trained on the specific data model to produce a specific output in response to input of the attributes defined in the data model, improving operation thereof. Each of these properties taken alone provides for a particular machine and provides for the improvement of the functioning of the computer system on which they are implemented. Applicant respectfully submits that when these properties are considered collectively, the improved operation is readily apparent to the person of ordinary skill in the art.” The additional elements in exemplary claim 1 are: a frontend system communicatively connected to a backend system and configured to: tailor a visual display of a first user interface, display a first screen of the first user interface, on which the Al model is trained, the backend system comprising at least one processor, and the Al model trained, which merely serves to tie the abstract idea to a particular technological environment (computer-based operating environment) via generic computing hardware, software/instructions, which is not sufficient to amount to a practical application, as noted in MPEP 2106.05. Applicant has provided no facts/evidence, cited any portion of the Specification, nor provided a persuasive line of reasoning showing how the additional elements are integrated with the abstract idea to integrate the abstract idea into a practical application.
Furthermore, it is noted that the claims are devoid of any discernible change, transformation, or improvement to a computer (software or hardware) or any existing technology. Applicant has not shown that any specific technological improvement is achieved within the scope of the claims. It bears emphasis that no frontend system, backend system, user interface, or technological elements are modified or improved upon in any discernible manner. Instead, the result produced by the claims is simply information relating to at least one action, which is not a technical result or improvement thereof.
The additional elements fail to integrate the abstract idea into a practical application because they fail to provide an improvement to the functioning of a computer or to any other technology or technical field, fail to apply the exception with a particular machine, fail to apply the judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, fail to effect a transformation of a particular article to a different state or thing, and fail to apply/use the abstract idea in a meaningful way beyond generally linking the use of the judicial exception to a particular technological environment.
Moreover, it is note that the claimed user interface fields and structure data model merely organize and format customer dt afro input into the AI model. The claimed use of summarized hypotheses data and the recited training and input relationship does not recite an improvement to the functioning of the computer or the AI model itself, but rather applies the model to customer data to generate hypotheses. Further, transmitting the identified action to the frontend “for execution” does not require the claimed system to actually execute the action. Therefore, the limitations, individually and in combination, do not integrate the abstract idea into a practical application. For the reasons above, this argument is found unpersuasive.
13. Applicant submits “that as amended, the claim should not be read as organizing human activity, but, for example, claiming a specific user interface implementation, specific data model, and specific AI that improves operations of the system.” [Applicant’s Remarks, 08/04/2026, page 11]
The Examiner respectfully disagrees. In response to Applicant’s argument “that as amended, the claim should not be read as organizing human activity, but, for example, claiming a specific user interface implementation, specific data model, and specific AI that improves operations of the system,” it is noted that although claim 1 recites a user interface implementation, structured data fields, and an AI model, these limitations are used to facilitate the collection ad evaluation of customer feedback and to determine an action based on that feedback. The Specification indicates that the claimed system is directed to collecting and utilizing customer feedback in a store environment. This, the claimed concept remains directed to managing interaction between people and commercial activities, which falls within the “Certain Methods of Organizing Human Activity” abstract idea grouping. The recitation of a specific user interface, data structure, and AI model does not, by itself, remove the underlying customer feedback and business activity concept from the abstract idea grouping. For the reasons above, this argument is found unpersuasive.
14. Applicant submits “In another example, the specific data model contributes to the processing efficiencies achieved over other conventional approaches (see e.g., para. [0036], and example data structures [0037]- [0039]). Enfish details that data structures (e.g., a self-referential table) that improve operation are subject matter eligible. In Enfish, the general-purpose computer disclosed (e.g., Fig. 1 of Pat. No. 6,151,604) was improved by implementation of the claimed data structure (e.g., self-referential table).² Similarly, implementing the inventors' UI, data model, and AI improves the efficiency of the system over known approaches.” [Applicant’s Remarks, 08/04/2026, page 11]
Applicant argues against the §101 rejection by seeking to analogize to Enfish. Here, unlike Enfish, the claims do not provide any device with an improvement in technology or the functioning of the computer, but rather amount to an application of the abstract idea using the ordinary functions of the computer. The claim limitations are broad, directed to implementation, and not technologically specific, so as indicated in Versata Development Group, Inc. v. SAP America, Inc., No. 2014‐1194, F.3d (Fed. Cir. July 9, 2015), “even if the invention required the use of a computer, the claim did not constitute a technological invention. As we are now instructed, the presence of a general purpose computer to facilitate operations through uninventive steps does not change the fundamental character of an invention. See Alice Corp. Pty. Ltd. v. CLS Bank Int’l, 134 S. Ct. 2347 (2014).” Furthermore, the courts have said the 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 provide significantly more. MPEP 2106.05(f)(2).
Moreover, the Examiner points out that the focus of the claimed invention is on the details of the abstract ideas and not on the details of a database construct, much less how a database construct interacts with the processor to actively improve operations of the processor. The focus of the invention is on the algorithms that have been identified as abstract ideas (as opposed to an improvement to operations of the additional elements, improvement to another technology or technical field). Furthermore, as recited, the claim limitations do not rise to the level of the self-referential table in Enfish. Also, in the Enfish decision, the court found that there was no abstract idea in the Enfish claims, which is not the case in the instant application.
15. Applicant submits “New dependent Claims 21-23 detail additional features and improved functionality. In particular, the claims detail establishing a new communication pathway to improve capture of data that is used to improve operation. Creating additional communication channels, as recited in the claims, is not well-known routine, and conventional. The UI, frontend, and backend systems provide for the capture and return of information. The additional communication pathway provides a new and additional communication channel that improves the system, and ultimately improves the AI model as claimed.” [Applicant’s Remarks, 08/04/2026, pages 12-13]
The Examiner respectfully disagrees. In response, it is noted that the recited “direct communication channel” is nonlimited to a particular technical communication architecture, hardware, protocol, or other specialized mechanism. Rather, the limitation encompasses a pathway through which the backend communicates with a user, including, for example, verbal or written communications. Claims 22 and 23 likewise recite using the communication channel to solicit feedback and evaluate an updated hypothesis based on the received feedback. Therefore, the additional limitations do not establish a specific technological, improvement. Instead, they fryer specify the collection of user feedback and se of that feedback to generate and validate hypothesis, which are activities consistent with the underlying abstract idea of collecting and utilizing customer feedback. For the reasons above, this argument is found unpersuasive.
16. Applicant submits “Applicant respectfully submits that even if the claims are not read to include a practical application, the interaction of the recited claim elements including (1) specific properties of the user interface coupled with (2) specific properties of a claimed data model (3) improved operation based on summarized data in the data model, and (4) an AI trained on the specific data model to produce a specific output in response to input of the attributes defined in the data model provide for functionality not available on conventional systems. Therefore, the claimed ordered combination improves or provides the something more required under Step 2B.” [Applicant’s Remarks, 08/04/2026, page 13]
Applicant alludes to Step 2B of the eligibility inquiry by suggesting that “the interaction of the recited claim elements including (1) specific properties of the user interface coupled with (2) specific properties of a claimed data model (3) improved operation based on summarized data in the data model, and (4) an AI trained on the specific data model to produce a specific output in response to input of the attributes defined in the data model provide for functionality not available on conventional systems. Therefore, the claimed ordered combination improves or provides the something more required under Step 2B.” In response, it is noted that the claims merely produce a result in the form of “at least one action,” which is not an improvement to the frontend system, backend system, user interface, processor, or any other system or technology. Furthermore, with respect to exemplary claim 1, none of the steps, individually or in combination, have been shown to yield an improvement to a computer or to any technology. The claims have not been shown to modify, reconfigure, manipulate, or transform the computer, computer software, or any technology in any discernible manner, much less yield an improvement thereto. There is no indication that any of the additional elements or the combination of elements amount to an improvement to the computer or to any technology. Their individual and collective functions merely provide generic computer implementation. Therefore, these additional claim elements do not amount to significantly more than the abstract idea itself. Accordingly, this argument is found to be unpersuasive.
17. Applicant submits “Applicant respectfully submits that recited and detailed in the claims are the specific properties that are not well known, routine, and conventional, and that provide the something more under the Step 2B analysis. The UI is constrained to limit data entry, the data model is constrained and is claimed as a specific data model, that operates in conjunction with a specific AI model, and the respective components provide for improved operation of the computer system (see e.g., para. [0036]).” [Applicant’s Remarks, 08/04/2026, page 13]
The Examiner respectfully disagrees. In response, it is noted that the claimed user interface limitations merely constrain how customer data is presented and entered into the system. Similarly, training the AI model to receive the structured customer data and generate hypotheses based on that data merely applied the abstract idea using a conventional computer interface an AI processing. The claim does not recite an improvement to the underlying computer technology, the operation of user interface technology itself, or the AI model architecture. Moreover, Applicant’s suggestion that the entire claimed invention must be shown to be well-understood, routine and conventional to support a contention of patent ineligibility is not persuasive. In this instance, the ordered combination of elements add nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide generic computer implementation. Accordingly, the claims considered as a whole fail to amount to significantly more than the abstract idea itself. For the reasons above, this argument is found unpersuasive.
18. Applicant submits “the additional communication channel recited in new dependent claims 21-23 provides for improved operation.” [Applicant’s Remarks, 08/04/2026, page 13]
The Examiner respectfully disagrees. In response, it is noted that the recited “additional communication channel” does not establish an improvement in the operation of the communication technology itself. Claims 21-23 merely recite limitations related to establishing a communication channel with a user, soliciting feedback through that channel, and using the feedback to generate, evaluate, or validate a hypothesis. The are functions directed to obtaining and processing customer feedback rather than improving the underlying communications technology. Furthermore, the claims do not specify any particular communication protocol. Network architecture, hardware configuration, or other technical mechanism by which the alleged communication improvement is achieved. For the reasons above, this argument is found unpersuasive.
19. Applicant submits “Applicant notes that the Office Action cites three references and states that AI is well-understood, routine, and conventional with respect to the prior pending claims, alleged to be recited at a high level of generality. Applicant respectfully submits that the three references each describe respective functions in their Specifications and indicate that the disclosed functionality can be implemented using known artificial intelligence algorithms. Applicant respectfully submits that the references do not teach that AI is generally well known, routine, or conventional. Rather, they indicate that their specific functions may also be implemented as AI. Applicant respectfully submits that there are significant questions as to how that would in fact be done, but the functionality disclosed in each reference (assumed to be implemented in AI) has no bearing on the currently claimed models. (See e.g., Magdon-Ismail 2009/0055270 (describing functions for selection of a targeted ad), Muchkaev 2010/0287011 (describing functions for an artificially controlled player in a game), and Anders 2020/0020015 (describing functions for inferring gift-related patterns)).” [Applicant’s Remarks, 08/04/2026, page 14]
Under Step 2B, Applicant argues the “Applicant notes that the Office Action cites three references and states that AI is well-understood, routine, and conventional with respect to the prior pending claims, alleged to be recited at a high level of generality. Applicant respectfully submits that the three references each describe respective functions in their Specifications and indicate that the disclosed functionality can be implemented using known artificial intelligence algorithms. Applicant respectfully submits that the references do not teach that AI is generally well known, routine, or conventional. Rather, they indicate that their specific functions may also be implemented as AI. Applicant respectfully submits that there are significant questions as to how that would in fact be done, but the functionality disclosed in each reference (assumed to be implemented in AI) has no bearing on the currently claimed models. (See e.g., Magdon-Ismail 2009/0055270 (describing functions for selection of a targeted ad), Muchkaev 2010/0287011 (describing functions for an artificially controlled player in a game), and Anders 2020/0020015 (describing functions for inferring gift-related patterns)).” In response, the Examiner emphasizes that unconventionality of the entire claimed invention, by itself, is insufficient to render a claim as eligible under §101. We may assume that the techniques claimed are “[g]roundbreaking, innovative, or even brilliant,” but that is not enough for eligibility. Ass’n for Molecular Pathology v. Myriad Genetics, Inc., 569 U.S. 576, 591 (2013); accord buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1352 (Fed. Cir. 2014). Nor is it enough for subject-matter eligibility that claimed techniques be novel and nonobvious in light of prior art, passing muster under 35 U.S.C. §§ 102 and 103. See Mayo Collaborative Servs. v. Prometheus Labs., Inc., 566 U.S. 66, 89–90 (2012); Synopsys, Inc. v. Mentor Graphics Corp., 839 F.3d 1138, 1151 (Fed. Cir. 2016) (“[A] claim for a new abstract idea is still an abstract idea. The search for a § 101 inventive concept is thus distinct from demonstrating §102 novelty.”); Intellectual Ventures I LLC v. Symantec Corp., 838 F.3d 1307, 1315 (Fed. Cir. 2016) (same for obviousness) (Symantec). Applicant’s suggestion that the entire claimed invention must be shown to be well-understood, routine and conventional to support a contention of patent ineligibility is not persuasive.
Moreover, it is noted that the Office action does not rely on Magdon-Ismail or Muchkaev as teaching the entirety if the claimed invention, nor is such showing required for the well-understood, routine, or conventional inquiry under Step 2B. Rather, the cited passages are relied upon as evidence that the use of artificial intelligence and machine learning algorithms, as claimed a high level of generality, was well understood in the art. In particular, Magdon-Ismail describes that analysis engines may incorporate artificial intelligence and machine learning techniques that are “well known in the art,” while Munchkaev similarly describes use of an artificial intelligence algorithm, including a search or learning algorithm, as an algorithm “commonly known in the art.” Therefore the references support a finding that applying known artificial intelligence and machine learning techniques to perform analysis or generate outputs was well-understood, routine, and conventional. The claimed AI model is recited without a particular AI architecture, algorithm, training technique, or other technological implementation that would distinguish its operation from the known use of artificial intelligence and machine learning techniques. For the reasons above, this argument is found unpersuasive.
20. Applicant’s remaining arguments either logically depend from the above-rejected arguments, in which case they too are unpersuasive for the reasons set forth above, or they are directed to features which have been newly added via amendment. Therefore, this is now the Examiner's first opportunity to consider these limitations and as such any arguments regarding these limitations would be inappropriate since they have not yet been examined. A full rejection of these limitations will be presented later in this Office Action.
Claim Objections
21. Claim 1 is objected to because of the following informalities: typographical errors: The relevant portion of amended claim 1 is reproduced below:
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Claim 1 was amended to recite “wherein the set of structured data fields reflect the summarized hypotheses data and include the product field, the product attribute field, the product attribute value field, and the action field, on which the Al model is trained to accept as input to produce one or more hypotheses as output access the output of the one or more hypotheses produced from the Al model, and…” The amended claim lacks appropriate punctuation in the phrase “as output access”. The claim should be amended to insert the appropriate punctuation, such as a comma between “output” and “access”, for example. Appropriate correction is required.
Claim Rejections - 35 USC § 112
22. 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.
23. Claims 1, 5-11, and 15-23 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 pre-AIA the applicant regards as the invention.
24. Claim 1 was amended to recites to accept as input to produce one or more hypotheses as output; and receive a response from a customer based on the one or more hypotheses produced by the backend system;…calculate the one or more hypotheses defined by a structured data model including at least the set of structured data fields; input the customer data formatted with the set of structured data fields to the Al model trained to output the one or more hypotheses in response to the input of the customer data formatted to the set of structured data fields, wherein the set of structured data fields reflect the summarized hypotheses data and include the product field, the product attribute field, the product attribute value field, and the action field, on which the Al model is trained to accept as input to produce one or more hypotheses as output access the output of the one or more hypotheses produced from the Al model…” The relationship between “the one or more hypotheses” and “the output” of the AI model is unclear. claim 1 first recites that the AI model is trained “to accept as input to produce one or more hypotheses as output.” Therefore, the one or more hypotheses are identified as the output of the AI model. However, the claim subsequently recites that the processor is configured to “access the output OF the one or more hypotheses produced from the AI model.” It is unclear from this claim language whether the “one or more hypotheses” constitute the output of the AI model, “the one or more hypotheses” themselves produce a separate output, or “the output OF the one or more hypotheses” is intended to refer to the one or more hypotheses produced by the AI model. The claim scope is ambiguous because the claim uses “one or more hypotheses as output” and subsequently refers to “the output OF the one or more hypotheses”. Therefore, the claim is rendered indefinite. If the intended meaning is that the hypotheses constitute the AI model output, Examiner suggests amending the claim to recite “access the one or more hypotheses produced by the AI model.” Independent claim 11 recites similar limitations as those discussed above and are therefore found to be indefinite for the same reasons as claim 1. Appropriate correction is required.
25. Claim 1 was amended to recite “tailor a visual display of a first user interface to limit data entry of customer data to a set of structured data fields, wherein the set of structured data fields reflects summarized hypotheses data and includes a product field, a product attribute field, a product attribute value field, and an action field; display a first screen of the first user interface tailored to capture the customer data in the respective ones of the set of structured data fields, the first screen configured to limit data entry of customer data to the set of structured data fields, on which the Al model is trained to accept as input to produce one or more hypotheses as output…” The claim initially recites “limit data entry of customer data to a set of structured data fields.” The claim subsequently recites “display a first screen of the first user interface tailored to capture the customer data” and further recites “the first screen configured to limit data entry of customer data to the set of structured data fields.” It is unclear whether the subsequent recitation of “customer data” refers to the same customer data previously introduced in the claim or to different customer data. The sue of “customer data” without the define article cerates ambiguity as to whether the limitation refers or the previously introduced customer data or to a separate category of customer data. If the intended meaning is that the first screen limits entry of the same customer data previously introduced, the limitation should be amended to recite “the first screen configured to limit data entry of the customer data to the set of structured data fields.” Independent claim 11 recites similar limitations as those discussed above and are therefore found to be indefinite for the same reasons as claim 1. Appropriate correction is required.
26. All claims dependent from above rejected claims are also rejected due to dependency.
Claim Rejections - 35 USC § 101
27. 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.
28. Claims 1, 5-11, and 15-23 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The eligibility analysis in support of these findings is provided below, in accordance with MPEP 2106.
With respect to Step 1 of the eligibility inquiry (as explained in MPEP 2106), it is first noted that the system (claims 1, 5-10, 21-23) and method (claims 11, 15-20) are directed to at least one potentially eligible category of subject matter (i.e., machine and process, respectively). Thus, Step 1 of the Subject Matter Eligibility test for claims 1, 5-11, and 15-23 is satisfied.
With respect to Step 2A Prong One, it is next noted that the claims recite an abstract idea that falls into the “Certain Methods of Organizing Human Activity” abstract idea set forth in MPEP 2106 because the claims recite steps for collecting customer feedback data from a customer regarding the product or service, which encompasses activity for managing personal behavior or relationships or interactions, as well as commercial interactions (e.g., advertising, marketing or sales activities or behaviors). With respect to independent claim 1, the limitations reciting the abstract idea are indicated in bold below: a frontend system communicatively connected to a backend system and configured to: tailor a visual display of a first user interface to limit data entry of customer data to a set of structured data fields, wherein the set of structured data fields reflects summarized hypotheses data and includes a product field, a product attribute field, a product attribute value field, and an action field; display a first screen of the first user interface tailored to capture the customer data in the respective ones of the set of structured data fields, the first screen configured to limit data entry of customer data to the set of structured data fields, on which the Al model is trained to accept as input to produce one or more hypotheses as output; and receive a response from a customer based on the one or more hypotheses produced by the backend system; and the backend system comprising at least one processor, where the backend system is configured to store system data, wherein the system data comprises, at least, data related to a plurality of alternative products or services; wherein the at least one processor is configured to: in response to receiving the customer data, calculate the one or more hypotheses defined by a structured data model including at least the set of structured data fields; input the customer data formatted with the set of structured data fields to the Al model trained to output the one or more hypotheses in response to the input of the customer data formatted to the set of structured data fields, wherein the set of structured data fields reflect the summarized hypotheses data and include the product field, the product attribute field, the product attribute value field, and the action field, on which the Al model is trained to accept as input to produce one or more hypotheses as output access the output of the one or more hypotheses produced from the Al model, and transmit the one or more hypotheses to the frontend system; analyze the response from the customer to determine at least one action to be taken; validate the Al model output based on received feedback; and transmit the at least one action to the frontend system for execution.
Considered together, these steps set forth an abstract idea of managing personal behavior/relationships/interactions via rules or instructions that simply manage customer feedback at a point-of sale location (See Specification at paragraph [0004]: “A system for managing customer feedback regarding a product or service at a point-of- sale location is provided.”), and also set forth an abstract idea of managing marketing efforts (See Specification at paragraph 0005: “The action to be taken comprises one or more of: optimizing offerings related to the product or service; generating insight reports about the product or service; optimizing marketing efforts related to the product or service; optimizing affinity models related to the product or service; and optimizing inventory management tasks related to the product or service.”), which falls under the realm of managing commercial interactions (e.g., marketing or sales activities or behaviors), thus falling under the “Certain methods of organizing human activity” grouping set forth in MPEP 2106.
Therefore, because the limitations above set forth activities falling within the “Certain methods of organizing human activity” abstract idea grouping described in MPEP 2106, the additional elements recited in the claims are further evaluated, individually and in combination, under Step 2A Prong Two and Step 2B below. Independent claim 11 recites similar limitations as those discussed above and are therefore found to recite the same or substantially the same abstract idea as claim 1.
With respect to Step 2A Prong Two, the judicial exception is not integrated into a practical application. With respect to the independent claims, the additional elements are: a frontend system communicatively connected to a backend system and configured to: tailor a visual display of a first user interface, display a first screen of the first user interface, on which the Al model is trained, the backend system comprising at least one processor, and the Al model trained (claim 1), tailoring, by at least one processor, a visual display of a first user interface; displaying, by the at least one processor, a first screen of the first user interface, on which the Al model is trained, and the Al model trained (claim 11). These additional elements have been evaluated, but fail to integrate the abstract idea into a practical application because they amount to using generic computing elements or computer-executable instructions (software) to perform the abstract idea, similar to adding the words “apply it” (or an equivalent), and merely serve to link the use of the judicial exception to a particular technological environment. See MPEP 2106.05(f) and 2106.05(h). Even if the “receive,” step is evaluated as an additional element, this step amounts at most to insignificant extra-solution data gathering activity, which is not indicative of a practical application, as noted in MPEP 2106.05(g). Similarly, even if the “transmit” and “output” steps are interpreted as being conducted by a computer or network, this activity amounts to insignificant extra-solution activity accomplished via receiving/transmitting data, which is not enough to amount to a practical application. See MPEP 2106.05(g).
In addition, these limitations fail to provide an improvement to the functioning of a computer or to any other technology or technical field, fail to apply the exception with a particular machine, fail to apply the judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, fail to effect a transformation of a particular article to a different state or thing, and fail to apply/use the abstract idea in a meaningful way beyond generally linking the use of the judicial exception to a particular technological environment.
Accordingly, because the Step 2A Prong One and Prong Two analysis resulted in the conclusion that the claims are directed to an abstract idea, additional analysis under Step 2B of the eligibility inquiry must be conducted in order to determine whether any claim element or combination of elements amount to significantly more than the judicial exception.
With respect to Step 2B of the eligibility inquiry, it has been determined that the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. With respect to the independent claims, the additional elements are: a frontend system communicatively connected to a backend system and configured to: tailor a visual display of a first user interface, display a first screen of the first user interface, on which the Al model is trained, the backend system comprising at least one processor, and the Al model trained (claim 1), tailoring, by at least one processor, a visual display of a first user interface; displaying, by the at least one processor, a first screen of the first user interface, on which the Al model is trained, and the Al model trained (claim 11). These elements have been considered individually and in combination, but fail to add significantly more to the claims because they amount to using generic computing elements or instructions (software) to perform the abstract idea, similar to adding the words “apply it” (or an equivalent), and merely serves to link the use of the judicial exception to a particular technological environment and does not amount to significantly more than the abstract idea itself. Notably, Applicant’s Specification acknowledges that the claimed invention relies on nothing more than a general purpose computer executing instructions to implement the invention (Specification at paragraph [0024]: e.g., “The backend system 110, includes a processor 112 such as a computer or microcontroller configured to perform processing operations related to the received feedback from the customer...”). Accordingly, the generic computer involvement in performing the claim steps merely serves to generally link the use of the judicial exception to a particular technological environment, which does not add significantly more to the claim. See, e.g., Alice Corp., 134 S. Ct. 2347, 110 USPQ2d 1976.).
With respect to the “receive,” “transmit,” and “output,” steps, it is noted that receiving/transmitting data has been recognized as well-understood, routine, and conventional, and thus insufficient to add significantly more to the abstract idea. See MPEP 2106.05(d) - Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network).
Even if the AI model was evaluated as an element beyond software/code for a generic computer to execute, it is noted that that the claimed use of Artificial Intelligence (AI) is recited at a high level of generality these elements amount to well-understood, routine, and conventional activity in the art, which fails to add significantly more to the claims. See, e.g., Magdon-Ismail et al., US 2009/0055270 (paragraph 39: “Both local and central engines may incorporate analysis techniques, such as artificial intelligence, machine learning and other techniques, which are well known in the art”). See also, Muchkaev, US 2010/0287011 (paragraph 47: “artificial intelligence algorithm such as a search algorithm, a learning algorithm, or any other artificial intelligence algorithm commonly known in the art”).
In addition, when taken as an ordered combination, the ordered combination adds nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements integrate the abstract idea into a practical application. Their collective functions merely provide generic computer implementation. Therefore, when viewed as a whole, these additional claim elements do not provide meaningful limitations to transform the abstract idea into a practical application of the abstract idea or that, as an ordered combination, amount to significantly more than the abstract idea itself.
Dependent claims 5-10 and 15-23 recite the same abstract idea as recited in the independent claims, and when evaluated under Step 2A Prong One are found to merely recite details that serve to narrow the same abstract idea recited in the independent claims accompanied by the same generic computing elements or software as those addressed above in the discussion of the independent claims, which is not sufficient to amount to a practical application or add significantly more, or other additional elements that fail to amount to a practical application or add significantly more, as noted above. In particular, dependent claims 5-10 and 21-23 recite “transmit collected customer responses including the set of structured data fields,” “wherein the one or more hypotheses are indicative of a question or action regarding a target product or service based on the customer data,” “determine the at least one action including an automatic identification of new and non-existing inventory associated with the customer data,” “output a hypothesis that specifies criteria for a new product,” “output a hypothesis that identifies a missing product from inventory,” “identify whether a current product arrangement at a retail location associated with the frontend system matches a consumer demand based, at least in part, on the collected customer data consisting of the structured data model,” “generate a direct communication channel with a user,” “solicit feedback from the user via the direct communication channel and hypothesis generation based on received feedback,” “evaluate or validate an updated hypothesis generated subsequent to receiving feedback via the direct communication channel,” however these limitations cover organizing human activity since they flow directly from the customer feedback involving human interaction, which encompasses activity for managing personal behavior or relationships or interactions (e.g., following rules or instructions), which is part of the same abstract idea as addressed in the independent claims that falls within the “Certain Methods of Organizing Human Activity” abstract idea grouping. Accordingly, these steps are part of the same abstract idea(s) set forth in the independent claims. Dependent claims 15-20 have been evaluated as well, but are subject to similar findings as claims 5-10 and 21-23. The dependent claims recite additional elements of: the Al model is trained (claims 8-10 and 18-20) and the backend system (claims 21-23). However, when evaluated under Step 2A Prong Two and Step 2B, these additional elements do not amount to a practical application or significantly more since they merely require generic computing devices (or computer-implemented instructions/code) which as noted in the discussion of the independent claims above is not enough to render the claims as eligible.
Even if the AI model was evaluated as an element beyond software/code for a generic computer to execute, it is noted that that the claimed use of Artificial Intelligence (AI) is recited at a high level of generality these elements amount to well-understood, routine, and conventional activity in the art, which fails to add significantly more to the claims. See, e.g., Magdon-Ismail et al., US 2009/0055270 (paragraph 39: “Both local and central engines may incorporate analysis techniques, such as artificial intelligence, machine learning and other techniques, which are well known in the art.”). See also, Anders et al., US 2020/0020015 (paragraph 101: “inferences may be performed by any combination of means known in the art, such as by pattern-matching, text analytics, semantic analytics, statistical methods, artificial intelligence, Bayesian analysis, machine learning, or keyword searching”).
The ordered combination of elements in the dependent claims (including the limitations inherited from the parent claim(s)) add nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide generic computer implementation. Accordingly, the subject matter encompassed by the dependent claims fails to amount to a practical application or significantly more than the abstract idea itself.
For more information, see MPEP 2106.
Allowable Subject Matter
29. Claims 1, 5-11, and 15-23 are allowable over prior art. With respect to independent claim 1, the closest prior art, Montero et al. (US 2021/0103945 A1), Brandish et al. (US 2017/0178205 A1), and Agarwal et al. (US 2017/0140007 A1), collectively teach features for tailoring a visual display of a first user interface to limit data entry to a set of structured data fields; display a first screen of the first user interface tailored to capture respective ones of the set of structured data fields; receive a response from a customer based on the one or more hypotheses; storing system data, wherein the system data comprises, at least, data related to a plurality of alternative products or services; in response to receiving the customer data, calculating the one or more hypotheses defined by a structured data model including at least the set of structured data fields; accessing the output of the one or more hypotheses produced from the Al model, transmitting the one or more hypotheses to the frontend system; analyzing the response to determine at least one action to be taken; and transmit the at least one action to the frontend system for execution [See Office Action mailed 02/09/2026 for prior art citations pertinent to the above-noted subject matter].
However, with respect to amended independent claim 1, while Brandish teaches structured data fields (Brandish, paragraph 0001, discussing that invention relates to electronic systems, methods, apparatus and user interfaces for structuring and obtaining user feedback; paragraph 0028, discussing that configuring the application-specific feedback survey comprises selecting one or more user interface (UI) elements for inclusion in the feedback survey, each UI element related to an aspect of the website or mobile application upon which feedback from users is to be sought; paragraph 0066, discussing a system for managing the solicitation and receipt of structured user feedback...The system comprises an apparatus in the form of a data storage unit, such as a server, having stored therein data relating to one or more application-specific feedback surveys, each survey based on the one or more options for content to be included in the respective application and based on the one or more respective selections; paragraph 0082) and Agarwal teaches attribute fields (Agarwal, paragraph 0039, discussing that user interfaces and controls can be provided in connection with an example model generator allowing human or automated users to input data to populate and be used in an instantiation of a plan model. In some instances, source data can also be collected, requested, retrieved, or otherwise accessed to populate attribute fields, build logic of the plan model, calculate results from the logic within a given plan model, or be otherwise used to generate an instantiation of a particular plan model for addition to the set of plan models; paragraph 0040, discussing that particular instances of a plan model or a particular set of attribute values of a particular plan model can be adopted by an organization as a model of a current working plan, goal, assumption, or approach to be considered by the organization both in its analysis of other business scenarios as well as drive the real world behavior and decision-making of the organization), and newly discovered art, Ito et al., Pub. No.: US 2017/0337171 A1, describes structured data fields (paragraph 0005, discussing that the user interface may be operable to detect input entered into an input field of a form fillable window provided on a user interface display screen; paragraph 0016: “Responsive to detecting any discrepancy/concordance in the data the user is inputting in the form field on a user interface, the system of the present disclosure updates the quality indicator accordingly, for example, indicating that the quality of the form being entered is poor or good, respectively. In some embodiments, the comparison and acting on the data quality indicator (feedback of the quality) may be performed and provided on a field by field basis, for example, as the user enters a field.”; paragraph 0024: “an application such as a user interface or like application running on one or more hardware processors may monitor for input data on a user interface display and detect that an input has been entered. For example, the user interface may detect input entered into an input field of a form fillable window provided on a user interface display screen”), Montero et al., Brandish et al., Agarwal et al., and Ito et al., and the other prior art of record does not teach tailor a visual display of a first user interface to limit data entry of customer data to a set of structured data fields, wherein the set of structured data fields reflects summarized hypotheses data and includes a product field, a product attribute field, a product attribute value field, and an action field; display a first screen of the first user interface tailored to capture the customer data in the respective ones of the set of structured data fields, the first screen configured to limit data entry of customer data to the set of structured data fields, on which the Al model is trained to accept as input to produce one or more hypotheses as output; and input the customer data formatted with the set of structured data fields to the Al model trained to output the one or more hypotheses in response to the input of the customer data formatted to the set of structured data fields, wherein the set of structured data fields reflect the summarized hypotheses data and include the product field, the product attribute field, the product attribute value field, and the action field, on which the Al model is trained to accept as input to produce one or more hypotheses as output, as required by amended claim 1.
The following is a statement of reasons for the indication of allowable subject matter: The claims are directed to allowable subject matter because the prior art of record either individually or in combination does not teach: “A system for determining at least one action using an artificial intelligent ("Al") model, the system comprising: a frontend system communicatively connected to a backend system and configured to: tailor a visual display of a first user interface to limit data entry of customer data to a set of structured data fields, wherein the set of structured data fields reflects summarized hypotheses data and includes a product field, a product attribute field, a product attribute value field, and an action field; display a first screen of the first user interface tailored to capture the customer data in the respective ones of the set of structured data fields, the first screen configured to limit data entry of customer data to the set of structured data fields, on which the Al model is trained to accept as input to produce one or more hypotheses as output; and receive a response from a customer based on the one or more hypotheses produced by the backend system; and the backend system comprising at least one processor, where the backend system is configured to store system data, wherein the system data comprises, at least, data related to a plurality of alternative products or services; wherein the at least one processor is configured to: in response to receiving the customer data, calculate the one or more hypotheses defined by a structured data model including at least the set of structured data fields; input the customer data formatted with the set of structured data fields to the Al model trained to output the one or more hypotheses in response to the input of the customer data formatted to the set of structured data fields, wherein the set of structured data fields reflect the summarized hypotheses data and include the product field, the product attribute field, the product attribute value field, and the action field, on which the Al model is trained to accept as input to produce one or more hypotheses as output access the output of the one or more hypotheses produced from the Al model, and transmit the one or more hypotheses to the frontend system; analyze the response from the customer to determine at least one action to be taken; validate the Al model output based on received feedback; and transmit the at least one action to the frontend system for execution” as recited in amended claim 1, and “A computer implemented method for determining at least one action using an artificial intelligent ("AI") model, the method comprising: tailoring, by at least one processor, a visual display of a first user interface to limit data entry of customer data to a set of structured data fields, wherein the set of structured data fields reflects summarized hypotheses data and includes a product field, a product attribute field, a product attribute value field, and an action field; displaying, by the at least one processor, a first screen of the first user interface tailored to capture the customer data in the respective ones of the set of structured data fields, the first screen configured to limit data entry of customer data to the set of structured data fields, on which the AI model is trained to accept as input to produce one or more hypotheses as output; receiving, by the at least one processor, a response from the customer based on the one or more hypotheses produced by the at least one processor; storing, by the at least one processor, system data, wherein the system data comprises, at least, data related to a plurality of alternative products or services; calculating, by the at least one processor, the one or more hypotheses defined by a structured data model including at least the set of structured data fields in response to receiving the customer data,” as recited in amended claim 11, thus rendering claims 1, 5-11, and 15-23 as allowable over prior art. However, these claims are not allowable because they remain rejected under 35 U.S.C. 112(b) and 35 U.S.C. 101. Claim 1 is objected to due to minor informalities.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Wawrzynowicz, Pub. No.: US 2017/0068976 A1 – with respect to claim 22 limitation “solicit feedback”, Wawrzynowicz describes computer systems using automatically triggered analytics actions for solicited feedback data.
Gacanin, Haris, and Mark Wagner. "Artificial intelligence paradigm for customer experience management in next-generation networks: Challenges and perspectives." Ieee Network 33.2 (2019): 188-194 – elaborates on data analytics and artificial intelligence driven Customer Experience Management and their functional differences.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, 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 extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/Darlene Garcia-Guerra/
Primary Examiner, Art Unit 3625
