Detailed Action
Status of Claims
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This Action is in reply to the Amendment filed on 10/31/2025. Claims 1-20 are pending. Claims 1-20 have been amended. Claim amendments have overcome the claim objections.
Priority
Applicant’s Claim of priority to Provisional Application 63531903 is acknowledged. The claims are therefore afforded an effective filing date of 8/10/2023.
Claim Rejection - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
First, it is determined whether the claims are directed to a statutory category of invention. In the instant case, claims 1-10 are directed to a process, and claims 11-20 are directed to a machine. Therefore, claims 1-20 are directed to statutory subject matter under Step 1 as described in MPEP 2106 (Step 1: YES).
The claims are then analyzed to determine whether the claims are directed to a judicial exception. In determining whether the claims are directed to a judicial exception, the claims are analyzed to evaluate whether the claims recite a judicial exception (Prong One of Step 2A), as well as analyzed to evaluate whether the claims recite additional elements that integrate the judicial exception into a practical application of the judicial exception (Prong Two of Step 2A).
Claims 1 and 11 recite at least the following limitations that are believed to recite an abstract idea:
receiving a user query corresponding to a business;
receiving data related to the business and data related to a user associated with the user query;
generating a curated profile corresponding to the business using a model based on the data related to the business and the data related to the user associated with the query, the curated profile comprising information about the business in a formatted layout that is particular to the user, the model accounting for patterns or commonalities in the data related to the user to determine the formatted layout for the information about the business that is particular to the user; and
displaying the curated profile to the user.
The above limitations recite the concept of query/search support. These limitations, under their broadest reasonable interpretation, fall within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas, enumerated in MPEP 2106, in that they recite commercial interactions, e.g. sales activities/behaviors, and managing personal behavior or relationships or interactions between people, e.g., following rules or instructions. Accordingly, under Prong One of Step 2A, claims 1-20 recite an abstract idea (Step 2A, Prong One: YES).
Prong Two of Step 2A is the next step in the eligibility analyses and looks at whether the abstract idea is integrated into a practical application. This requires an additional element or combination of additional elements in the claims to apply, rely on, or user the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the exception.
In this instance, the claims recite the additional elements of:
One or more processors
A machine learning model
A system comprising one or more computing devices
However, these elements do not amount to an improvement in the functioning of a computer or any other technology or technical field; apply the judicial exception with, or by use of, a particular machine; or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort to monopolize the exception.
In addition, the recitations are recited at a high level of generality and also do not amount to an improvement in the functioning of a computer or any other technology or technical field; apply the judicial exception with, or by use of, a particular machine; or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort to monopolize the exception.
The dependent claims also fail to recite elements which amount to an improvement in the functioning of a computer or any other technology or technical field; apply the judicial exception with, or by use of, a particular machine; or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort to monopolize the exception. For example, claims 6-7 & 16-17 are directed to the abstract idea itself and do not amount to an integration according to any one of the considerations above.
As for claims 2-5, 8-10, 12-15, and 18-20, these claims are similar to the independent claims except that they recite the further additional elements of an application comprising any one or more of a search engine, mapping application, email application, social application, video application or website, or app store; a website; browsing history; a link to a website or social media account; web content; an application. These additional elements are recited at a high level of generality and also do not amount to an improvement in the functioning of a computer or any other technology or technical field; apply the judicial exception with, or by use of, a particular machine; or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort to monopolize the exception. Therefore, the dependent claims do not create an integration for the same reasons.
Step 2B is the next step in the eligibility analyses and evaluates whether the claims recite additional elements that amount to an inventive concept (i.e., “significantly more”) than the recited judicial exception. According to Office procedure, revised Step 2A overlaps with Step 2B, and thus, many of the considerations need not be re-evaluated in Step 2B because the answer will be the same.
In Step 2A, several additional elements were identified as additional limitations:
One or more processors
A machine learning model
A system comprising one or more computing devices
These additional limitations, including the limitations in the dependent claims, do not amount to an inventive concept because they were already analyzed under Step 2A and did not amount to a practical application of the abstract idea. Therefore, the claims lack one or more limitations which amount to an inventive concept in the claims.
For these reasons, the claims are rejected under 35 U.S.C. 101.
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.
Claim Rejection – 35 USC § 102
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)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-5, 8-15, 18-20 are rejected under 35 U.S.C. 102 as being anticipated by Maiman et al (US 20230010964 A1), hereinafter Maiman.
Regarding Claim 1, Maiman discloses a method for generating curated content comprising:
receiving, by one or more processors (Maiman: [0025]), a user query corresponding to a business [restaurant] (Maiman: “At step 303, as part of the search process, the search system 101 may receive a query from a client device.” [0044] – “provides a restaurant search… query terms (e.g. “thai”)” [0052] – See also Figure 5, which illustrates the query corresponding to “steakhouses”);
receiving, by the one or more processors, data related to the business [merchant information] and data related to a user [user data] associated with the user query (Maiman: “At step 307, information about one or more of the set of merchants found at step 306 may be retrieved …(e.g., a restaurant may be assigned a type of cuisine such as “Thai”, a type of service such as “take-out,” a type of location such as “downtown,” a user price rating such as “$$,” etc.) …retrieve (e.g., from database 131) product costs for the merchant (e.g., a menu of items and corresponding prices) ” [0054] – “At step 305, the search system 101 may retrieve and/or process user data from a user profile that is associated with the user identifier to obtain user data inputs … the user data of the user profile may include transaction data” [0047]);
generating, by the one or more processors, a curated profile [search result] corresponding to the business using a machine learning model based on the data related to the business and the data related to the user associated with the query (Maiman: “At step 308, the search system 101 may use the inputs to generate a custom price rating for one or more merchants using the machine learning model. The search system 101 may supply the inputs as generated and/or retrieved at steps 305 and 307 to the machine learning model to generate an output for each of the one or more merchants. For example, the search system 101 may use the same user data for each rating generated, but may use the corresponding merchant data inputs to generate a customized rating for the corresponding merchants ” [0057] – “At step 309, the search system 101 may generate search results based on the customized ratings generated at step 308. The search system 101 may generate display information for each of the merchants for which customized ratings were generated at step 308. …if the machine learning model outputs a score of 15 out of 100 for a first merchant, the search system 101 may generate a search result including a display icon indicating a single dollar sign ($) to indicate that the restaurant is relatively inexpensive for that user.” [0061]),
the curated profile comprising information about the business in a formatted layout that is particular to the user (Maiman: “the search result 502 includes a customized price rating of “$$$” based on a corresponding score …generated by the machine learning model based on inputs corresponding to a first merchant …and inputs corresponding to the user who submitted the query. …The first search result 502 may also list and show a price for a predicted dish for the user (e.g., the ribeye steak) based on transaction data indicating that the user commonly orders this dish, and menu data indicating the price of the dish, as discussed above. It may also include a relevant excerpt of a review based on transaction data indicating a preference of the user (e.g., that the user likes wine),” [0067] – See also [0062].),
the machine learning model accounting for patterns or commonalities in the data related to the user to determine the formatted layout for the information about the business that is particular to the user (Maiman: “the search result 502 includes a customized price rating of “$$$” based on a corresponding score …generated by the machine learning model based on inputs corresponding to a first merchant …and inputs corresponding to the user who submitted the query” [0067] – “The machine learning model may generally map the one or more inputs to a given output indicating a customized price rating, and thus may be able to predict a customized price rating for every user-merchant pairing based on the inputs corresponding to the user and the inputs corresponding to the merchant. For example, the trained machine learning model may tend to increase a customized price rating (e.g., relative to another user's customized price rating) when a user tends to spend less money on restaurants …whether a user might find a restaurant to be relatively cheap or expensive based on the user's tastes, preferences, and habits. Because the machine learning model takes in merchant data (e.g., product costs) and user data (e.g., spending habits data) as input, it may, in general, indicate a comparison between the average cost of a merchant relative to an average past expenditure by a user at various other merchants.” [0058-0059]); and
displaying, by the one or more processors, the curated profile to the user (Maiman: “At step 310, the search system 101 may send the search results information to the client. The search results information may include some or all of the information generated at step 309, as well as additional information such as the name and address of the merchant, an image representing the merchant, a link to a website of the merchant, and the like. The sent information may be formatted in such a way that the client may render it for display” [0064] – See Figure 5.).
Regarding Claim 2, Maiman discloses the method of claim 1, wherein the user query is received from a service application comprising one or more of a search engine [search application], mapping application, email application, social application, video application or website, or app store (Maiman: “a user of the device may input one or more search terms into a search field of a search application running on the client device” [0044]).
Regarding Claim 3, Maiman discloses the method of claim 1, wherein the data related to the business comprises one or more of data from a website, social media, or advertisement associated with the business (Maiman: “At step 307, information about one or more of the set of merchants found at step 306 may be retrieved …(e.g., a restaurant may be assigned a type of cuisine such as “Thai”, a type of service such as “take-out,” a type of location such as “downtown,” a user price rating such as “$$,” etc.) …retrieve (e.g., from database 131) product costs for the merchant (e.g., a menu of items and corresponding prices) ” [0054] – certain data may generated and/or updated at search time based on dynamic data (e.g., based on a menu that is updated daily from a merchant's website, based on merchant ratings information that may be updated at any time” [0045]).
Regarding Claim 4, Maiman discloses the method of claim 1, wherein the data related to the user comprises one or more of demographics, location, or browsing history associated with the user (Maiman: “send a query including the search terms, a GPS location of the client device, a user identifier, and/or other metadata. ” [0044] – “the transaction history may indicate that the user has certain preferences (e.g., the user is vegetarian)” [0056]).
Regarding Claim 5, Maiman discloses the method of claim 1, wherein the information about the business includes one or more of a logo of the business, a link to a website of the business, or a link to a social media account of the business (Maiman: “At step 310, the search system 101 may send the search results information to the client. The search results information may include some or all of the information generated at step 309, as well as additional information such as the name and address of the merchant, an image representing the merchant, a link to a website of the merchant, and the like. The sent information may be formatted in such a way that the client may render it for display” [0064] – See Figure 5.).
Regarding Claim 8, Maiman discloses the method of claim 1, wherein the curated profile further comprises personalized content, including one or more of links to web content, images, or multimedia that is particular to the user (Maiman: “At step 310, the search system 101 may send the search results information to the client. The search results information may include some or all of the information generated at step 309, as well as additional information such as the name and address of the merchant, an image representing the merchant, a link to a website of the merchant, and the like. The sent information may be formatted in such a way that the client may render it for display” [0064] – See also [0057] “customized price rating”- See Figure 5.).
Regarding Claim 9, Maiman discloses the method of claim 8, wherein the personalized content is further generated based on the information about the business (Maiman: “At step 310, the search system 101 may send the search results information to the client. The search results information may include some or all of the information generated at step 309, as well as additional information such as the name and address of the merchant, an image representing the merchant, a link to a website of the merchant, and the like. The sent information may be formatted in such a way that the client may render it for display” ” [0064] – See also [0057] “customized price rating”- See Figure 5.).
Regarding Claim 10, Maiman discloses the method of claim 1, wherein the user query is a request received from a service application (Maiman: “a user of the device may input one or more search terms into a search field of a search application running on the client device” [0044]).
Regarding claims 11-15, 18-20, the limitations of claims 11-15 and 18-20 are closely parallel to the limitations of claims 1-5, 8-10, with the additional limitation of one or more computing devices (Maiman: [0025]), and are rejected on the same basis.
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.
Claim Rejection – 35 USC § 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.
The factual inquiries 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 non-
obviousness.
Claims 6-7 and 16-17 are rejected under 35 U.S.C. 103 as being unpatentable over Maiman, in view of Mediratta et al (US 20170132676 A1), hereinafter Mediratta.
Regarding Claim 6, Maiman discloses the method of claim 1, including that the format of the curated profile comprises information about the business that are particular to the user (Maiman: [0062], [0067], Figure 5), but does not specifically teach that the format of the curated profile comprises personalized tabs, the personalized tabs corresponding to categories of information about the business that are particular to the user.
However, Mediratta teaches business reviews based on user queries (Mediratta: Abstract), including that the format of the curated profile comprises personalized tabs, the personalized tabs corresponding to categories of information about the business that are particular to the user (Mediratta: “the generalized reviews include a plurality of metatags, to allow the user to click for obtaining further information. For example, when a hotel image along with the review, which reads, “Most people spoke awesome about it, and of the opinion that the rooms were refreshing” is displayed, the user is allowed to select the metatag room. When the user selects the metatag room, the application displays the images of the rooms of that hotel, along with the generalized review about the rooms. Furthermore, the generalized review about the rooms include metatags that provide information about the components of the room, such as the dressing table, furniture, bed.” [0029] – “FIG. 6B illustrates the multimedia images of the metatags (in this example, pool images) when the user selects the pool metatag of the generalized reviews. … FIG. 6C illustrates the multimedia images of the selected metatag (in this example, room) when the user of the computing device selects the room or the amenities metatag of the generalized reviews.” [0098-0099]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of invention to combine these references because the results would be predictable. Specifically, Maiman would continue to teach that the curated profile comprising information about the business in a formatted layout that is particular to the user, except that now it would also teach that the format of the curated profile comprises personalized tabs, the personalized tabs corresponding to categories of information about the business that are particular to the user, according to the teachings of Mediratta. This is a predictable result of the combination.
In addition, it would have been obvious to one of ordinary skill in the art before the effective filing date of invention to combine these references because it would result in an improved ability to provide a use4r with relevant content (Mediratta: [0024]).
Regarding Claim 7, Maiman/Mediratta teach the method of claim 6, wherein each of the personalized tabs comprises respective curated content particular to the user (Mediratta: “FIG. 6B illustrates the multimedia images of the metatags (in this example, pool images) when the user selects the pool metatag of the generalized reviews. … FIG. 6C illustrates the multimedia images of the selected metatag (in this example, room) when the user of the computing device selects the room or the amenities metatag of the generalized reviews.” [0098-0099]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Mediratta with Maiman for the reasons identified above with respect to claim 6.
Regarding Claims 16-17, the limitations of claims 16-17 are closely parallel to the limitations of claims 6-7 and are rejected on the same basis.
Response to Arguments
Applicant's arguments filed 10/31/2025 have been fully considered but they are not persuasive.
Claim Rejection – 35 USC §101
Applicant argues that the amended features of claim 1“provide for a practical application of generating improved profiles for a business that provide dynamic and personalized information about the business to the user.” Applicant argues that the claim 1 “thus…improves computer technology, particularly through the use of a machine learning model utilized to generate the curated profile.”
Examiner disagrees. The argued limitations are part of the abstract idea itself, except for computer-related additional elements recited at a high level of generality, e.g. one or more processors, an ML model. These additional elements, rather than integrating the abstract idea into a practical application, such as a technological solution to a technological problem, amount to mere instructions to apply the abstract idea in a technological environment, such that they provide only a general linking to computer technology [MPEP 2106.05(f)]. The argued improvement of providing a user with relevant information about a business without the need to conduct searches is at best a business improvement stemming solely from the abstract idea, rather than a solution rooted in computer technology. At best, the additional elements provide only the improved speed or efficiency inherent to a general purpose computer, which does not amount to a practical application or to significantly more than the abstract idea [MPEP 2106.05(a)].
Applicant argues that parallel claim 11 and dependent claims 2-10 and 12-20 are patent-eligible “for at least the reasons discussed above, as well as on their own merits.”
Examiner disagrees for the reasons addressed in the Rejection and Response above.
Claim Rejection – 35 USC §§102 & 103
Applicant’s arguments with respect to the prior art rejections 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.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Fung et al (US 20190340537 A1) teaches systems for generating a personalized overview of a business, including user-specific information generated by machine learning, and tabs for accessing different categories of data about the business.
Stang et al (US 20240338737 A1) teaches systems for AI-based generation of reviews about businesses for presentation to a user, including consideration of user preferences.
Mimassi (US 20220230122 A1) teaches systems for managing restaurant profiles and recommendations, including personalizing recommendations based on user information using machine learning.
References U-V (NPL – see attached) discuss search result summaries through machine learning, including by using user personal data.
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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/T.J.S./Examiner, Art Unit 3689
/MARISSA THEIN/Supervisory Patent Examiner, Art Unit 3689