DETAILED ACTION
1. This is a Non-Final Office Action in response to the request for continued examination filed 03/11/2026.
Notice of Pre-AIA or AIA Status
2. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Continued Examination Under 37 CFR 1.114
3. A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 03/11/2026 has been entered.
Status of Claims
11. Claims 1-20 are currently pending in the application and have been examined.
Response to Amendment
4. The amendment filed 03/11/2026 has been entered.
Response to Arguments
Claim Rejections 35 U.S.C. § 101:
Applicant submits that the claims have been amended and do not recite certain methods of organizing human activity. Examiner respectfully disagrees and notes that under step 2A of the analysis of claims per the Alice framework, if a claim limitation covers managing personal behavior or relationships or interactions between people, then it falls within the “certain methods of organizing human activity” grouping of abstract ideas.
Applicant submits that at least the amended claims elements provide a practical application of a smart feedback system to provide more targeted and effective recommendations. Examiner respectfully disagrees and notes that the present claims do not integrate the judicial exception into a practical application in a matter that imposes meaningful limit to the judicial exception.
Claim Rejections 35 U.S.C. § 102:
Applicant’s arguments with respect to claim(s) have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Claim Rejections - 35 USC § 101
5. 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.
6. Claim(s) 1-20 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-patentable subject matter. The claims are directed to an abstract idea without significantly more.
7. With respect to claims 1-20, the independent claims (claims 1, 8 and 15) are directed, in part, to a method, a computer-readable medium and a system for smart feedback. Step 1 – First pursuant to step 1 in the eligibility analysis, claims 1-7 are directed to a method comprising a series of steps which falls under the statutory category of a process, claims 8-14 are directed to a computer-readable storage medium which falls under the statutory category of an article of manufacture and claims 15-20 are directed to a system which falls under the statutory category of a machine. However, these claim elements are considered to be abstract ideas because they are directed to a method of organizing human activity which includes managing relationships or interactions between people.
As per Step 2A - Prong 1 of the subject matter eligibility analysis, the claims are directed, in part, to transmitting…a first personalized feedback request… wherein the first personalized feedback request is transmitted during a first visualization of a first personalized avatar associated with a first user of a plurality of users provided to the first device based on a first user profile of a plurality of user profiles associated with the first user, wherein the first personalized feedback request includes a first set of feedback requests associated with a first offer associated with a first participant of a plurality of participants, wherein each user profile is associated with a corresponding user and a corresponding personalized avatar, and wherein each user profile comprises user information, device information, a set of interaction patterns, and a set of preferences; receiving…first feedback of the first user… in response to the first personalized feedback request, wherein the first feedback of the first user includes emotion of the first user captured through facial emotion recognition or audio vocal recognition; transmitting… a visualization of the first feedback of the first user during the first visualization of the first personalized avatar associated with the first user, wherein the visualization of the first feedback of the first user is generated by analyzing the first feedback of the first user based on the first user profile and the first personalized feedback request associated with the first offer associated with the first participant; transmitting… a first recommendation during the first visualization of the first personalized avatar associated with the first user, wherein the first recommendation is determined based on the first user profile, the first feedback of the first user, the first offer associated with the first participant, and a second user profile of the plurality of user profiles of an associated second user of the plurality of users; and transmitting… a second recommendation during a second visualization of a second personalized avatar associated with the associated second user based on the second user profile, wherein the second recommendation is determined using a machine learning algorithm based on the first user profile, the first feedback of the first user, the first offer associated with the first participant, and the second user profile. If a claim limitation, under its broadest reasonable interpretation covers managing relationships between people, then it falls within the “certain methods of organizing human activity” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
8. As per Step 2A - Prong 2 of the subject matter eligibility analysis, this judicial exception is not integrated into a practical application. In particular, the independent claims recite additional elements: processor, computing device, distributed computing system, first device, second device, a network, non-transitory computer-readable medium, system, memory. The dependent claims recite a smart display device and a smart phone. These additional elements are recited at a high-level of generality (i.e., as a generic device performing a generic computer function of receiving and storing data) such that these elements amount no more than mere instructions to apply the exception using a generic computer component. Examiner looks to Applicant’s specification in at least figures 1,8 and related text and [0118-0121] to understand that the invention may be implemented in a generic environment that “FIG. 8 is a block diagram illustrating an example of a computer-implemented System 800 used to provide computational functionalities associated with described algorithms, methods, functions, processes, flows, and procedures, according to an implementation of the present disclosure. In the illustrated implementation, System 800 includes a Computer 802 and a Network 830. The illustrated Computer 802 is intended to encompass any computing device, such as a server, desktop computer, laptop/notebook computer, wireless data port, smart phone, personal data assistant (PDA), tablet computer, one or more processors within these devices, or a combination of computing devices, including physical or virtual instances of the computing device, or a combination of physical or virtual instances of the computing device. Additionally, the Computer 802 can include an input device, such as a keypad, keyboard, or touch screen, or a combination of input devices that can accept user information, and an output device that conveys information associated with the operation of the Computer 802, including digital data, visual, audio, another type of information, or a combination of types of information, on a graphical-type user interface (UI) (or GUI) or other UI. The Computer 802 can serve in a role in a distributed computing system as, for example, a client, network component, a server, or a database or another persistency, or a combination of roles for performing the subject matter described in the present disclosure. The illustrated Computer 802 is communicably coupled with a Network 830. In some implementations, one or more components of the Computer 802 can be configured to operate within an environment, or a combination of environments, including cloud-computing, local, or global. At a high level, the Computer 802 is an electronic computing device operable to receive, transmit, process, store, or manage data and information associated with the described subject matter. According to some implementations, the Computer 802 can also include or be communicably coupled with a server, such as an application server, e-mail server, web server, caching server, or streaming data server, or a combination of servers.” As described, the machine learning system is just being applied as a generic computer component. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they are mere instructions to implement the abstract idea on a computer.
9. As per Step 2B of the subject matter eligibility analysis, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional elements are mere instructions to apply the abstract idea on a computer. When considered individually, these claim elements only contribute generic recitations of technical elements to the claims. It is readily apparent, for example, that the claim is not directed to any specific improvements of these elements and the invention is not directed to a technical improvement. When the claims are considered individually and as a whole, the additional elements noted above, appear to merely apply the abstract concept to a technical environment in a very general sense – i.e. a generic computer receives information from another generic computer, processes the information and then sends information back. 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. 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 amount to significantly more than the abstract idea itself. The most significant elements of the claims, that is the elements that really outline the inventive elements of the claims, are set forth in the elements identified as an abstract idea. The fact that the generic computing devices are facilitating the abstract concept is not enough to confer statutory subject matter eligibility. Next, when the “machine learning” is evaluated as an additional element, this feature is recited at a high level of generality and encompasses well-understood, routine, and conventional prior art activity. See, e.g., Balsiger et al., US 2012/0054642, noting in paragraph [0077] that “Machine learning is well known to those skilled in the art.” See also, Djordjevic et al. US 2013/0018651, noting in paragraph [0019] that “As known in the art, a generative model can be used in machine learning to model observed data directly.” See also, Bauer et al., US 2017/0147941, noting at paragraph [0002] that “Problems of understanding the behavior or decisions made by machine learning models have been recognized in the conventional art and various techniques have been developed to provide solutions.” Accordingly, the use of machine learning does not add significantly more to the claims.
10. The dependent claims and the additional elements recited including “a smart phone”, further refine the abstract idea. These claims do not provide a meaningful linking to the judicial exception. Rather, these claims offer further descriptive limitations of elements found in the independent claims and addressed above – such as by describing the nature and content of the data that is received/sent. While these descriptive elements may provide further helpful context for the claimed invention these elements do not serve to confer subject matter eligibility to the invention since their individual and combined significance is still not significantly more than the abstract concepts at the core of the claimed invention.
Claim Rejections - 35 USC § 102
12. In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
13. The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
14. Claim(s) 1-20 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by US Pub. No. 2019/0325498 (hereinafter; Clark).
Regarding claims 1/8/15, Clark discloses:
A computer-implemented method, the method comprising; A non transitory, computer-readable medium storing one or more instructions executable by a computer system to perform operations, the operations comprising; A computer-implemented system, comprising: transmitting, by a computing device in a distributed computing system (DCS) (Clark [0081] discloses a distributed computing system), a first personalized feedback request to a first device, (Clark [0143] discloses The flow chart continues at 245, when NOWW shopping application 1180 sends the user the determined product specifics, i.e., feedback from the vendor information and the User product request or specifics based upon the user profile and vendor information.) wherein the DCS includes the computing device, the first device, and a second device that communicate across a network, wherein the first personalized feedback request is transmitted during a first visualization of a first personalized avatar associated with a first user of a plurality of users provided to the first device based on a first user profile of a plurality of user profiles associated with the first user, (Clark [0010] discloses instructions to receive a (i) product request; (ii) identify a product based on the product request; (iii) obtain, based on the identified product and a user profile associated with a user account or a vendor profile, product information from a plurality of vendors, wherein the user profile includes a modelling an object using an avatar or dimensionally accurate image for contextual view of an object[…]) wherein the first personalized feedback request includes a first set of feedback requests associated with a first offer associated with a first participant of a plurality of participants, wherein each user profile is associated with a corresponding user and a corresponding personalized avatar, and wherein each user profile comprises user information, device information, a set of interaction patterns, and a set of preferences; (Clark [0143] discloses NOWW shopping application 1180 sends the user the determined product specifics, i.e., feedback from the vendor information and the User product request or specifics based upon the user profile and vendor information. The user may then confirm or override one or more of NOWW shopping application 1180's determined product specifics. For example, in one embodiment, the user may choose a different product size or color than suggested, confirm the determined payment method and vendor, or request customization; [0243] discloses “Inventors Studio” or “Empire” where a user may: create new product ideas, create new service ideas, or become an idea generator where brand vendors get consumer ideas and feedback for freebies, coupons or create or suggest adaptions of existing products via feedback forum where feedback or other methodology where feedback goes directly to the vendor (not just in a review), and they request where new features or explain non-working aspects (changes needed) on existing products or services; do focus groups for brands with friends for free product, etc., (virtual parties, surveys, chats, etc.); and where if adopted as a new product or feature, the consumer can share profits with Vendor, brand, retailer, company, etc.) receiving, by the computing device and from the first device, first feedback of the first user captured by the first device in response to the first personalized feedback request, (See at least Clark [0143]; [0243]) wherein the first feedback of the first user includes emotion of the first user captured through facial emotion recognition or audio vocal recognition; (Clark [0071] discloses One example of this may be obtaining audio input representative of “I would like those shoes.” Through application of NLP techniques this audio input may be used to drive the image analysis to “search” the image for shoes[…]) transmitting, by the computing device and to the first device, a visualization of the first feedback of the first user during the first visualization of the first personalized avatar associated with the first user, wherein the visualization of the first feedback of the first user is generated by analyzing the first feedback of the first user based on the first user profile and the first personalized feedback request associated with the first offer associated with the first participant; (Clark discloses a modelled object used with an avatar in at least [0010]; [0013-0014]. transmitting, by the computing device and to the first device, a first recommendation during the first visualization of the first personalized avatar associated with the first user, wherein the first recommendation is determined based on the first user profile, the first feedback of the first user, the first offer associated with the first participant, and a second user profile of the plurality of user profiles of an associated second user of the plurality of users; (Clark [0014] discloses Extraction of the recommended product specifics may be limited based on the vendor white-list, black-list, or some combination of the two. The recommended product specifics (e.g. dress size) may be presented by display on an electronic device viewed by the user, e.g., desktop computer, laptop, tablet, smart phone, watch, etc. The instructions to initiate purchase of the particular product from the particular vendor may ensure a third party initiates the purchase for the benefit of the user and using the user's profile. This may be a gift purchase. The modelled object may include one or more models of an individual, e.g. user, an environment or an object. The modelled object may be used in conjunction with an avatar of a third persons, e.g., family member.) and transmitting, by the computing device and to the second device, a second recommendation during a second visualization of a second personalized avatar associated with the associated second user based on the second user profile, wherein the second recommendation is determined using a machine learning algorithm based on the first user profile, the first feedback of the first user, the first offer associated with the first participant, and the second user profile. (Clark [0101] discloses recommending products and possible suitable substitute items; Where an authentic product is too expensive for the user, NOWW shopping application 1180 may show similar products in other price ranges. For example, if the user makes a product request for a Louboutin® pump but decides an authentic product is too expensive, upon user request, the NOWW shopping application 1180 may show other pumps in similar colors, heel height, and the like in a less expensive price range. (LOUBOUTIN is a registered trademark of Christian Louboutin.) Where an authentic product is no longer available and NOWW shopping application 1180 is unable to find the exact product, such as an antique or a product no longer manufactured, NOWW shopping application 1180 may recommend similar products to the user. These similar products may be identified by the NOWW shopping application utilizing an image search function in conjunction with the cloud or a network such as the Internet. NOWW shopping application 1180 may retrieve a user's profile. The user's profile may use user input or user profile, artificial intelligence, e.g., machine learning of NOWW shopping application, or proprietary shopping algorithm (PSA) to identify possible suitable substitute items.)
Regarding claims 2/9/16, Clark discloses:
The computer-implemented method of claim 1; The non-transitory, computer-readable medium of claim 8; The computer-implemented system of claim 15, wherein the first device is a smart display device associated with a retailer at a first location, and the second device is a smart phone associated with the second user at the first location. (Clark discloses the NOWW application can be used at a retail location in a smart display and a smart phone. See at least [0347-0361]; [0012-0014].)
Regarding claims 3/10/17, Clark discloses:
The computer-implemented method of claim 1; The non-transitory, computer-readable medium of claim 8, The computer-implemented system of claim 15 comprising: prior to transmitting the first recommendation, determining that the associated second user is associated with the first user based on one or more of friendship between the associated second user and the first user or a second offer similar to the first offer associated with the second user profile. (Clark [0106-0107] disclose the processor 1150 may select a recommended product size or dimension of a particular brand compared with the appropriate modelled object and any purchasing history as well as any stored preferences to assess the appropriateness of a product for the user; the user may confirm or override the appropriate modelled object, the recommended product size, and other recommendations, such as color, shipping method, and the like; NOWW shopping application 1180 may also send or provide the user with product reviews, e.g., tiered reviews and verified offers or reviews such as potentially prioritizing reviews by the user's friends over the general public.)
Regarding claims 4/11/18, Clark discloses:
The computer-implemented method of claim 1; The non-transitory, computer-readable medium of claim 8; The computer-implemented system of claim 15, comprising: updating the second user profile associated with the associated second user including the set of interaction patterns and the set of preferences in the second user profile using a machine learning algorithm based on the first user profile, the analyzed first feedback of the first user, the first offer associated with the first participant, and the second user profile of the associated second user. (Clark [0157] discloses The user profile can be updated by the NOWW shopping application from user input using machine learning software or other technology.)
Regarding claims 5/12/19, Clark discloses:
The computer-implemented method of claim 1; The non-transitory, computer-readable medium of claim 8; The computer-implemented system of claim 15, comprising: updating the first user profile associated with the first user including the set of interaction patterns and the set of preferences in the first user profile using a machine learning algorithm based on the first user profile, the analyzed first feedback of the first user, the first offer associated with the first participant, and the second user profile of the associated second user. (Clark [0157] discloses The user profile can be updated by the NOWW shopping application from user input using machine learning software or other technology.)
Regarding claims 6/13/20, Clark discloses:
The computer-implemented method of claim 1; The non-transitory, computer-readable medium of claim 8; The computer-implemented system of claim 15, wherein the first feedback of the first user captured by the first device comprises at least one of a tone of voice, a facial expression, a heart rate, an emotion, content of speech, a gesture, or content of a textual or written response, and wherein analyzing the first feedback of the first user captured by the first device comprises mapping at least one of the tone of voice, the facial expression, the emotion, the content of speech, the gesture, or the content of the textual or written response to meaning using a machine learning algorithm based on the first personalized feedback request, and the set of interaction patterns and the set of preferences in the first user profile associated with the first user. (Clark [0071] discloses the image may be analyzed in accordance with any one or more of a number of known image analysis techniques. In yet other embodiments additional input may be used to refine or drive the image analysis. For example, a digital advertisement for product “X” may be used to drive the image analysis to search for product “X”. In another example, audio input may be obtained and analyzed using natural language processing (NLP) techniques to identify a product in an image. One example of this may be obtaining audio input representative of “I would like those shoes.” Through application of NLP techniques this audio input may be used to drive the image analysis to “search” the image for shoes. Once one or more shoes are identified, further analysis of those objects may be used to uniquely identify the desired shoes (e.g., shoes from manufacturer “Y”). This information could then, for example, be combined with a user profile's information (e.g., the user's shoe size) to obtain information from one or more vendors about the availability and cost of shoes from manufacturer “Y” in the proper size.)
Regarding claims 7/14, Clark discloses:
The computer-implemented method of claim 1; The non-transitory, computer-readable medium of claim 8, comprising: determining a contextual and actionable response using a machine learning algorithm based on the first user profile, the analyzed first feedback of the first user, the first offer associated with the first participant, the set of preferences in the first user profile and the second user profile of the associated second user; and providing the determined contextual and actionable response to the first device. (Clark [0009-0010] disclose the use of machine learning and deep learning with an embodiment for causing a processor to receive a (i) product request; (ii) identify a product based on the product request; (iii) obtain, based on the identified product and a user profile associated with a user account or a vendor profile, product information from a plurality of vendors, wherein the user profile includes a modelling an object using an avatar or dimensionally accurate image for contextual view of an object; (iv) determine, based on the product information and the modelled object, recommended product specifics; present the recommended product specifics to the user; (v) receive, in response to the presented recommended product specifics, and typically prior to purchase decision, a confirmation regarding a particular product and a particular vendor from the application; (vi) initiate, in response to the confirmation, purchase of the particular product from the particular vendor; and (vii) present, after completion of the purchase, purchase confirmation information. Product specifics may include product sizes, color options, product specifications, ingredients, availability, etc.)
Conclusion
15. Any inquiry concerning this communication or earlier communications from the examiner should be directed to FRANCIS Z SANTIAGO-MERCED whose telephone number is (571)270-5562. The examiner can normally be reached M-F 7am-4:30pm EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, BRIAN EPSTEIN can be reached at 571-270-5389. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/FRANCIS Z. SANTIAGO MERCED/Examiner, Art Unit 3625
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