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 .
Status of Claims
This action is in reply to the claims filed on 06/10/2024.
Claims 1-20 are currently pending and have been examined.
Information Disclosure Statement
Information Disclosure Statement received 06/10/2024 has been reviewed and considered.
Allowable Subject Matter
Claims 2, 4-6, 9, 12, 14-16, and 19 would be allowable if rewritten to overcome the rejection(s) under 35 U.S.C. 101 set forth in this Office action and to include all of the limitations of the base claim and any intervening claims.
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 1-20 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to an abstract idea without significantly more.
Under Step 1 of the subject matter eligibility (SME) analysis described in MPEP 2106.03, the instant claims fall within the four statutory categories of invention identified by 35 U.S.C. 101. In the instant case, claims 1-10 are directed to a method and claims 11-20 are directed to a system. Claims 1 and 11 are parallel in nature, therefore, the analysis will use claim 1 as the representative claim.
In Step 2A Prong One, it must be considered whether the claims recite a judicial exception. Claim 1, as exemplary, recites abstract concepts including: transmitting ... at least one questionnaire for scoring the products for at least one user to at least one user device associated with the at least one user; receiving... at least one response for the at least one questionnaire from the at least one user device; analyzing... the at least one response and the at least one questionnaire; determining ... at least one preference associated with the at least one user based on the analyzing of the at least one response and the at least one questionnaire; obtaining... at least one product data associated with each of a plurality of products providable to the at least one user, wherein the at least one product data comprises at least one product information and at least one product characteristic of each of the plurality of products; analyzing... the at least one product characteristic of each of the plurality of products based on the at least one preference; determining ... a degree of match for each of the plurality of products to the at least one user based on the analyzing of the at least one product characteristic, wherein the degree of match ranges from a minimum degree of match and a maximum degree of match; generating ... a product-preference score for each of the plurality of products based on the degree of match; transmitting ... the product-preference score corresponding to each of the plurality of products and the at least one product information of each of the plurality of products to the at least one user device; and storing ... the at least one preference.
These identified limitations recite the abstract idea of “determining users’ product preferences based on questionnaire responses”, which falls within the “Certain Methods of Organizing Human Activities” grouping of abstract ideas as it relates to commercial interactions of sales activities or behaviors. Accordingly, claims 1 and 11 recite an abstract idea. See MPEP 2106.04.
In Step 2A Prong Two, examiners evaluate integration into a practical application by: (1) identifying whether there are any additional elements recited in the claim beyond the judicial exception(s); and (2) evaluating those additional elements individually and in combination to determine whether they integrate the exception into a practical application.
Instant claims 1 and 11 recite additional elements including: a communication device; at least one user device; a processing device; a storage device; and a storage device communicatively coupled with the processing device. The communication device, user device, processing device, and storage device are each recited at a high-level of generality and used in their ordinary capacity to transmit data, store data, and perform the identified abstract sales activity. Implementing an abstract idea on a generic computer, does not integrate the abstract idea into a practical application in Step 2A Prong Two similar to how the recitation of the computer in the claim in Alice amounted to mere instructions to apply the abstract idea of intermediated settlement on a generic computer. See MPEP 2106.05(f). The combination of these additional elements is no more than mere instruction to apply an exception with a generic computer. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. See MPEP 2106.05. Claims 1 and 11 are thus directed to an abstract idea.
Under Step 2B of the SME analysis, if it is determined that the claims recite a judicial exception that is not integrated into a practical application of that exception, it is then necessary to evaluate the additional elements individually and in combination to determine whether they provide an inventive concept (i.e., whether the additional elements amount to significantly more than the exception itself).
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, as discussed above with respect to integration of the abstract idea into a practical application, the additional element(s) individually and in combination are merely being used to apply the abstract idea to a general computer components. For the same reason, the elements are not sufficient to provide an inventive concept. As explained in MPEP 2106.05(f), implementing an abstract idea with a generic computer does not add significantly more in Step 2B. Moreover the courts have repeatedly characterized receiving or transmitting data over a network, and storing information in memory to be well-understood, routine, and conventional functions when they are claimed in a generic manner (as they are here). See MPEP 2106.05(d)(II)(i) and (iv). Therefore, the additional elements, alone or in ordered combination, there is no inventive concept in the claim, and thus claims 1 and 20 are not patent eligible.
Dependent claim(s) 2-7 and 11-17 do not aid in the eligibility of the independent claims. These claims merely further define the abstract idea without reciting any further additional elements. Thus dependent claims 2-7 and 11-17 are also ineligible.
Dependent claims 8-9 and 18-19 recites additional elements including: at least one machine learning model and at least one generative machine learning model. Similar to the additional elements identified above, the recited machine learning models are described in ordinary terms and merely used as a tool in performance of the abstract idea. Claiming only the idea of a solution or outcome (“analyzing ... using at least one machine learning model”), without no description of how the model achieves this result does not integrate a judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words “apply it”. Accordingly, claim(s) 8-9 and 18-19, considered both individually and as a combination, are ineligible.
Dependent claims 10 and 20 recite additional elements including: wherein the at least one user device comprises at least one sensor, wherein the at least one sensor is configured for generating at least one sensor data. These additional elements do not integrate the abstract idea into a practical application because they merely amount to no more than a general link of the use of the abstract idea to a particular technological environment or field of use. The claims do not describe the sensor or how it is configured for generating sensor data. Even in combination, these additional elements do not act to integrate the abstract idea into a practical application and do not amount to significantly more than the abstract idea itself. Thus claims 10-20 are also ineligible.
Claim Rejections - 35 U.S.C. § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
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 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.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1, 3, 11, and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Koyangi et al. (US 2024/0242263 A1) in view of Perelman et al. (US 2021/0200753 A1).
Claim 1
Koyangi discloses a method of managing shopping of products by users, the method comprising:
transmitting, using a communication device, at least one questionnaire for scoring the products for at least one user to at least one user device associated with the at least one user (¶ [0106] “The server 200 functions as a WEB server that displays the question/answer screen 510 ... on the display device 17 of the user-side terminal 130 that is a client terminal”; FIG. 6 and 7; see ¶ [0164] for scoring);
receiving, using the communication device, at least one response for the at least one questionnaire from the at least one user device (¶ [0160] “A user may perform an operation of selecting an option displayed on the question/answer screen 510 and inputting an answer.”; ¶ [0180] “In a case in which the confirm button 511 is pressed, the answer reception unit 1070 receives an answer of the user to the question.”);
analyzing, using a processing device, the at least one response and the at least one questionnaire (¶ [0122]; ¶ [0165] );
determining, using the processing device, at least one preference associated with the at least one user based on the analyzing of the at least one response and the at least one questionnaire (¶ [0122] “The user preference evaluation value UV for each user ID is generated according to answer content to a question displayed on the question/answer screen 510 of the display device 17 of the user-side terminal 130”);
obtaining, using the processing device, at least one product data associated with each of a plurality of products providable to the at least one user, wherein the at least one product data comprises at least one product information and at least one product characteristic of each of the plurality of products (¶ [0199] “item evaluation value IV stored in the item evaluation value storage unit 1020”; ¶ [0205] “The determination unit 1040 extracts the item evaluation value IV for each of the items IT1 and IT2 in the category of coffee from the item evaluation storage unit 1020”; ¶ [0206] product information could be “bitter taste”, product characteristic could be “sweet tase”);
analyzing, using the processing device, the at least one product characteristic of each of the plurality of products based on the at least one preference (¶ [0199] “compares the extracted user preference evaluation value UV with the corresponding item evaluation value IV stored in the item evaluation value storage unit 1020”; ¶ [0207]);
determining, using the processing device, a degree of match for each of the plurality of products to the at least one user based on the analyzing of the at least one product characteristic (¶ [0199] “ compares the extracted user preference evaluation value UV with the corresponding item evaluation value IV stored in the item evaluation value storage unit 1020, and determines a degree of coincidence between the two values.”), wherein the degree of match ranges from a minimum degree of match and a maximum degree of match (¶ [0208]);
generating, using the processing device, a product-preference score for each of the plurality of products based on the degree of match (¶¶ [0209]-[0210]; ¶ [0216] “the user preference evaluation value UV may be compared with the item evaluation value IV for a corresponding item and the degree of coincidence between the two values may be determined”);
... and
storing, using a storage device, the at least one preference (¶ [0113] “The user preference evaluation value storage unit 1010 stores the user preference evaluation value UV for each of a plurality of preference evaluation topics regarding one or a combination of two or more of the sense of taste, a smell, and a food taste in association with a user ID”).
Koyanagi does not disclose limitations associated with transmitting the user-preference score to the user device. However, Perelman – which like Koyanagi is directed to providing recommendations based on responses to a questionnaire – teaches:
transmitting, using the communication device, the product-preference score corresponding to each of the plurality of products and the at least one product information of each of the plurality of products to the at least one user device (¶ [0129] “ “Match Scores” are used by the strain finder 150 in order to communicate (e.g. via network 110) to user (e.g., customer 120, 130, 140) the likelihood that a particular product would fit the specific requirements of a particular user”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have included the transmission of the product-preference score, as taught by Perelman, in the method of Koyanagi in order to help users identify products that are most likely to provide the desired effects (Perelman ¶ [0129]).
Claim 3
The combination of Koyanagi in view of Perelman teaches the method of claim 1. Koyanagi further discloses, further comprising:
determining, using the processing device, at least one characteristic of the at least one user based on the analyzing of the at least one response and the at least one questionnaire (¶ [0122]);
generating, using the processing device, at least one profile associated with the at least one user based on the at least one characteristic of the at least one user (¶ [0113]); and
storing, using the storage device, the at least one profile (¶ [0123]).
Claims 11 and 13, which are directed to a system, recite limitations that are parallel in nature as those addressed above for method claims 1 and 3. Claim(s) 11 and 13 are therefore rejected for the same reasons as set forth above for claims 1 and 3, respectively.
Claims 7 and 17 rejected under 35 U.S.C. 103 as being unpatentable over Koyangi et al. in view of Perelman, and further in view of Manova et al. (US 2025/0104106 A1).
Claim 7
The combination of Koyanagi in view of Perelman teaches the method of claim 1, but does not disclose limitations associated with updating at least one response to the questionnaire. However, Manova et al. – which like Koyanagi is directed to generating recommended actions using a questionnaire –teaches, further comprising:
receiving, using the communication device, at least one request from the at least one user device (¶ [0012]);
updating, using the processing device, the at least one response for the at least one questionnaire (¶ [0017);
generating, using the processing device, at least one updated response for the at least one questionnaire ( ¶ [0017); and
analyzing, using the processing device, the at least one updated response, wherein the generating of the at least one preference is further based on the analyzing of the at least one updated response (¶ [0017]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have included updating as taught by Manova in the method of Koyanagi in view of Perelman in order to advantageously, have autonomous ongoing decision-making aimed at the facilitation of predetermined business goals (Manova ¶[0019]).
Claim 17, which is directed to a system, recite limitations that are parallel in nature as those addressed above for method claim 7. Claim(s) 17 is therefore rejected for the same reasons as set forth above for claim 7.
Claims 8 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Koyangi in view of Perelman, and further in view Catone et al. (US 2024/0054548 A1).
Claim 8
The combination of Koyanagi in view of Perelman teaches the method of claim 1. Koyanagi further discloses, wherein the at least one questionnaire comprises a plurality of questions for the scoring of the products, wherein the at least one response comprises at least one answer corresponding to at least one of the plurality of questions (FIG.6).
The combination of Koyanagi in view of Perelman does not teach limitations associated with a machine learning model. However, Catone – which like Koyanagi is directed to generating recommendations based on user responses – teaches: wherein the analyzing of the at least one response and the at least one questionnaire comprises analyzing the at least one answer corresponding to at least one of the plurality of questions and the plurality of questions using at least one machine learning model, wherein the determining of the at least one preference is further based on the analyzing of the at least one answer corresponding to at least one of the plurality of questions and the plurality of questions using the at least one machine learning model (Catone [0075] “generation of a matching score may be based at least in part on a machine learning model or algorithm” and “the matching scores can be based on various base scores that are calculated (for example, by the machine learning component 212) based on a comparison of individual attributes associated with a user (including the meeting request) and corresponding attributes associated with a SP (and the related SP business as applicable), which may then be normalized and/or otherwise adjusted, such as to assign respective weights to data fields based on the likely (or indicated) importance to the user”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have included the machine learning model as taught by Catone in the method of Koyanagi in view of Perelman in order to improve different aspects of the processes implemented by the system (Catone ¶ [0070]).
Claim 18, which is directed to a system, recite limitations that are parallel in nature as those addressed above for method claim 8. Claim(s) 18 is therefore rejected for the same reasons as set forth above for claim 8.
Claims 10 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Koyangi in view of Perelman, and further in view Rydin (US 2023/0153869 A1).
Claim 10
The combination of Koyanagi in view of Perelman teaches the method of claim 1. Koyanagi in view of Perelman do not disclose limitations associated with a sensor of the user device, however Rydin – which like Koyanagi is directed to recommending personalized recommendations – teaches: wherein the at least one user device comprises at least one sensor, wherein the at least one sensor is configured for generating at least one sensor data based on detecting at least one of a physical state, a physiological state, and a biometric of the at least one user, wherein the at least one response comprises the at least one sensor data (see “infrared sensor” and “eye direction monitoring system” in ¶¶ [0013]-[0014]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have included the user device sensor as taught by Rydin in the method of Koyanagi to provide a new way to view product and service recommendations (Rydin ¶ [0012]).
Claim 20, which is directed to a system, recite limitations that are parallel in nature as those addressed above for method claim 10. Claim(s) 20 is therefore rejected for the same reasons as set forth above for claim 10.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
NPL Reference U (Liu, Liping) presents a constructive example of a model based on the factors that influence personalized e-commerce information recommendations in the retail sector using questionnaire surveys.
Arazi (US 12,368,911 B1) is directed to a computer-implemented process for evaluating user activity and preferences and providing a situationally targeted content recommendation. Arazi suggests designing surveys for particular users in Col 113, ll. 55-65.
Ichikawa (US 2022/0148064 A1) describes a product characteristic score estimation method capable of estimating a preference degree between products of a user for characteristics of the products.
Li et al. (US 2019/0298088 A1) describes a process for determining which characteristics, of many pillow characteristics, correspond to or meet the needs of an individual user.
Lindgren (US 2022/0020077 A1) sets forth a method for intelligent context-based personalized beauty product recommendation and matching.
Rajaram et al. (US 2023/0325901 A1) is directed to machine learning models for training a recommendation model to select cannabis product that is specific to each user based on what has worked for that particular user.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KENNEDY A GIBSON-WYNN whose telephone number is (571)272-8305. The examiner can normally be reached M-F 8:30-5:30 PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jeffrey Smith can be reached at 571-272-6763. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/K.G.W./Examiner, Art Unit 3688
/Jeffrey A. Smith/Supervisory Patent Examiner, Art Unit 3688