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 .
Information Disclosure Statement
The information disclosure statements (IDS) submitted on September 11, 2024; September 14, 2024; May 29, 2025; August 29, 2025; November 5, 2025; and February 24, 2026 were filed in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements are being considered by the examiner.
Status of Claims
This first action on the merits is in response to the application filed on February 27, 2024. Claims 1-20 pending and have been examined.
Priority
Applicant’s claim for the benefit of a prior-filed application under 35 U.S.C. 120 is acknowledged. Applicant has not complied with one or more conditions for receiving the benefit of an earlier filing date under 35 U.S.C. 120 as follows:
The later-filed application must be an application for a patent for an invention which is also disclosed in the prior application (the parent or original nonprovisional application or provisional application). The disclosure of the invention in the parent application and in the later-filed application must be sufficient to comply with the requirements of 35 U.S.C. 112(a), except for the best mode requirement. See Transco Products, Inc. v. Performance Contracting, Inc., 38 F.3d 551, 32 USPQ2d 1077 (Fed. Cir. 1994).
The disclosure of the prior-filed application, Application No. 63/578,457, fails to provide adequate support or enablement in the manner provided by 35 U.S.C. 112(a) for claims 1-20 of this application.
For example, independent claims 1, 15, and 20 recite determining, by one or more processors and using a transformer machine learning model, one or more categorical identifiers for the one or more search queries based on one or more matches of the one or more search queries to one or more categorical descriptions associated with the one or more categorical identifiers; determining, by the one or more processors, one or more distance preferences of the user locale based on one or more mappings of the one or more categorical identifiers to one or more statistical distances associated with the user locale; determining, by the one or more processors, one or more search radii based on the one or more distance preferences and the one or more categorical identifiers; and identifying, by the one or more processors, one or more entities based on (i) one or more entity activity data entries associated with the one or more entities that match the one or more categorical identifiers and (ii) the one or more search radii. These limitations are not supported by prior-filed Application ‘457 because the prior-filed application does not disclose a transformer machine learning model, determining one or more distance preferences, determining search radii based on the one or more distance preferences, nor identifying entities based on a match of categorical identifiers and one or more search radii. Dependent claims 2-14 and 16-19 inherit the deficiencies of claims 1 and 15, respectively. Therefore, Application ‘457 does not provide adequate written description support for the instant application, and the instant claims do not receive the priority benefit of Application ‘457. The priority date for this application is the filing date, which is February 27, 2024.
Claim Rejections - 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 a judicial exception without significantly more. The claims recite an abstract idea. This judicial exception is not integrated into a practical application. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
The steps for determining eligibility under 35 U.S.C. 101 can be found in the MPEP § 2106.03-2106.05.
Under Step 1, the claims are directed to statutory categories. Specifically, the computer-implemented method, as claimed in claims 1-14, is directed to a process. Additionally, the system, as claimed in claims 15-19, is directed to a machine. Furthermore, the one or more non-transitory computer-readable storage media, as claimed in claim 20, is directed to an article of manufacture.
While the claims fall within statutory categories, under Step 2A, Prong 1, the claimed invention recites the abstract idea of searching for an entity. Specifically, representative claim 1 recites the abstract idea of:
receiving one or more search queries from a user;
determining one or more categorical identifiers for the one or more search queries based on one or more matches of the one or more search queries to one or more categorical descriptions associated with the one or more categorical identifiers;
determining a user locale associated with the user;
determining one or more distance preferences of the user locale based on one or more mappings of the one or more categorical identifiers to one or more statistical distances associated with the user locale;
determining one or more search radii based on the one or more distance preferences and the one or more categorical identifiers;
identifying one or more entities based on (i) one or more entity activity data entries associated with the one or more entities that match the one or more categorical identifiers and (ii) the one or more search radii; and
generating one or more responses to the one or more search queries based on the one or more entities.
Under Step 2A, Prong 1, it is necessary to evaluate whether the claim recites a judicial exception by referring to subject matter groupings articulated in the guidance. When considering MPEP §2106.04(a), the claims recite an abstract idea. For example, representative claim 1 recites the abstract idea of searching for an entity, as noted above. This concept is considered to be a certain method of organizing human activity. Certain methods of organizing human activity are defined in the MPEP as including “fundamental economic principles or practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations); managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions).” MPEP §2106.04(a)(2) subsection II. In this case, the abstract idea recited in representative claim 1 is a certain method of organizing human activity because determining one or more search radii based on the one or more distance preferences and the one or more categorical identifiers; identifying one or more entities based on (i) one or more entity activity data entries associated with the one or more entities that match the one or more categorical identifiers and (ii) the one or more search radii; and generating one or more responses to the one or more search queries based on the one or more entities is a marketing and sales activity. Thus, representative claim 1 recites an abstract idea.
The recited limitations of representative claim 1 also recite an abstract idea because they are considered to be mental processes. As described in the MPEP, mental processes are “concepts performed in the human mind (including an observation, evaluation, judgment, opinion)”. MPEP §2106.04(a)(2) subsection III. In this case, receiving one or more search queries from a user is a type of observation. Additionally, determining one or more categorical identifiers for the one or more search queries based on one or more matches of the one or more search queries to one or more categorical descriptions associated with the one or more categorical identifiers; determining a user locale associated with the user; identifying one or more entities based on (i) one or more entity activity data entries associated with the one or more entities that match the one or more categorical identifiers and (ii) the one or more search radii; and generating one or more responses to the one or more search queries based on the one or more entities are types of judgement. Furthermore, determining one or more distance preferences of the user locale based on one or more mappings of the one or more categorical identifiers to one or more statistical distances associated with the user locale; determining one or more search radii based on the one or more distance preferences and the one or more categorical identifiers are types of evaluation. Thus, representative claim 1 recites an abstract idea.
Under Step 2A, Prong 2, if it is determined that the claims recite a judicial exception, it is then necessary to evaluate whether the claims recite additional elements that integrate the judicial exception into a practical application of that exception. See MPEP §2106.04(d). In this case, representative claim 1 includes additional elements such as a computer, one or more processors, and using a transformer machine learning model.
Although reciting additional elements, the additional elements do not integrate the abstract idea into a practical application because they merely amount to no more than an instruction to apply the abstract idea using a generic computer or merely use a computer as a tool to perform the abstract idea. These additional elements are described at a high level in Applicant’s specification without any meaningful detail about their structure or configuration. Similar to the limitations of Alice, representative claim 1 merely recites a commonplace business method (i.e., searching for an entity) being applied on a general purpose computer. See MPEP §§2106.04(d) and 2106.05(f). Thus, the claimed additional elements are merely generic elements and the implementation of the elements merely amounts to no more than an instruction to apply the abstract idea using a generic computer. Since the additional elements merely include instructions to implement the abstract idea on a generic computer or merely use a generic computer as a tool to perform an abstract idea, the abstract idea has not been integrated into a practical application. As such, representative claim 1 is directed to an abstract idea.
Under Step 2B, 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). See MPEP §2106.05.
In this case, as noted above, the additional elements recited in independent claim 1 are recited and described in a generic manner merely amount to no more than an instruction to apply the abstract idea using a generic computer or merely use a generic computer as a tool to perform an abstract idea.
Even when considered as an ordered combination, the additional elements of representative claim 1 do not add anything that is not already present when they considered individually. In Alice, the court considered the additional elements “as an ordered combination,” and determined that “the computer components ... ‘ad[d] nothing ... that is not already present when the steps are considered separately’ and simply recite intermediated settlement as performed by a generic computer.” Alice Corp. Pty. Ltd. v. CLS Bank Int’l, 573 U.S. 208, 224, 110 USPQ2d 1976, 1983-84 (2014). (citing Mayo, 566 U.S. at 79, 101 USPQ2d at 1972). Also see MPEP §2106.05(f). Similarly, when viewed as a whole, representative claim 1 simply conveys the abstract idea itself facilitated by generic computing components. Therefore, under Step 2B, there are no meaningful limitations in representative claim 1 that transforms the judicial exception into a patent eligible application such that the claims amount to significantly more than the judicial exception itself.
As such, representative claim 1 is ineligible.
Dependent claims 2-14 do not aid in the eligibility of independent claim 1. For example, claims 6, 8, and 11-14 merely further define the abstract limitations of claim 1. Also, claims 2-5, 7, and 9-10 merely provide further embellishments of the abstract limitations recited in independent claim 1.
Additionally, it is noted that claim 2 includes further additional elements of wherein the transformer machine learning model comprises a universal sentence encoder; claim 3 includes further additional elements of wherein the transformer machine learning model comprises an N-layer sentence/word textual transformer machine learning model; claim 4 includes further additional elements of wherein the transformer machine learning model comprises a last layer N+1; claim 5 includes further additional elements of wherein the transformer machine learning model comprises one or more (N-1)th layers configured to encode; claim 6 includes further additional elements of embeddings; and claim 8 includes further additional elements of training the transformer machine learning model. However, these additional elements do not integrate the abstract idea into a practical application because they merely amount to an instruction to apply the abstract idea using a generic computer or merely use a generic computer as a tool to perform an abstract idea. These additional elements are merely generic elements and are likewise described in a generic manner in Applicant’s specification. Additionally, the additional elements do not amount to significantly more because they merely amount to an instruction to apply the abstract idea using a generic computer or merely use a generic computer as a tool to perform an abstract idea.
Furthermore, it is noted that claims 7 and 9-14 do not include further additional elements. Therefore, the claims do not integrate the abstract idea into a practical application because they merely amount to an instruction to apply the abstract idea using a generic computer or merely use a generic computer as a tool to perform an abstract idea. The claims also do not amount to significantly more than the abstract idea because they merely amount to an instruction to apply the abstract idea using a generic computer or merely use a generic computer as a tool to perform an abstract idea.
Thus, dependent claims 2-14 are also ineligible.
Lastly, the analysis above applies to all statutory categories of invention. Although literally invoking a machine and article of manufacture, respectively, claims 15-19 and 20 remain only broadly and generally defined, with the claimed functionality paralleling that of claims 1, 6-8, 14; and 1, respectively. It is noted that claim 15 includes further additional elements of A computing system comprising memory and one or more processors communicatively coupled to the memory, the one or more processors configured to perform, and claim 20 includes further additional elements of One or more non-transitory computer-readable storage media including instructions that, when executed by one or more processors, cause the one or more processors to perform. However, these additional elements do not integrate the abstract idea into a practical application because they merely amount to an instruction to apply the abstract idea using a generic computer or merely use a generic computer as a tool to perform an abstract idea. These additional elements are merely generic elements and are likewise described in a generic manner in Applicant’s specification. Additionally, the additional elements do not amount to significantly more because they merely amount to an instruction to apply the abstract idea using a generic computer or merely use a generic computer as a tool to perform an abstract idea. As such, claims 15-19 and 20 are rejected for at least similar rationale as discussed above.
Claim Rejections - 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)(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.
Claims 1, 12-15, and 19-20 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Pineau (US 11308538 B1, herein referred to as Pineau).
Claim 1:
Pineau discloses:
A computer-implemented method comprising {Pineau: fig 2, mobile device 100; [Col. 2, ln. 63-64] a method of automatically finding cosmetic service providers}:
receiving, by one or more processors, one or more search queries from a user {Pineau: fig 2, processor 110; [Col. 6, ln. 45-47] mobile device thus receives the user input specifying a search query for a cosmetic service.};
determining, by the one or more processors and using a transformer machine learning model, one or more categorical identifiers for the one or more search queries based on one or more matches of the one or more search queries to one or more categorical descriptions associated with the one or more categorical identifiers {Pineau: fig 2, processor 110; [Col. 6, ln. 51-57] user interface element enables the user to specify the name of the cosmetic service that the user is seeking (i.e., categorical identifier). The search query may be expressed in plain language or keywords. Some examples of search queries for beauty and cosmetic services are: hair, nails, skin, lashes, brows, lips, face, tan, teeth, collagen, dermal fillers, liftings, implants (i.e., categorical description); [Col. 9, ln. 6-12] app or server, as the case may be, compares before photograph 400 with the after photograph 401 and detects that a woman has had her eyebrows 404 enhanced (i.e., categorical description). Image comparison may be done by an AI or machine-learning algorithm or deep-learning algorithm such as a convolutional neural network (CNN). The app then attempts to identify the cosmetic service provider that performed the procedure (i.e., categorical identifier) by correlating the times and locations of the before and after photographs 400, 402 with a location history of the woman who posted the photographs. Examiner interprets a deep-learning convolutional neural network as a transformer machine learning model};
determining, by the one or more processors, a user locale associated with the user {Pineau: fig 2, processor 110; [Col. 6, ln. 66-67; Col. 7, ln. 1-3] server 270 determines the current location of the user computing device. This may be done by receiving the location data from the mobile device, which may accompany the search request transmitted from the mobile device to the server 270.};
determining, by the one or more processors, one or more distance preferences of the user locale based on one or more mappings of the one or more categorical identifiers to one or more statistical distances associated with the user locale {Pineau: fig 2, processor 110; [Col. 6, ln. 4-8] cosmetic server 270 has a memory and a processor configured to search for all cosmetic service providers offering the cosmetic service within a prescribed geographical area based on the search query and the current location received from the user computing device.};
determining, by the one or more processors, one or more search radii based on the one or more distance preferences and the one or more categorical identifiers {Pineau: fig 2, processor 110; [Col. 7, ln 14-19] prescribed geographical area may be user-defined or based on the locations of one's social media contacts. For example, the user may prescribe that the search is to be limited to the user's home city or to within a certain radius of the user's current location, e.g. 5 km, 10 km, 25 km, 50 km, etc.};
identifying, by the one or more processors, one or more entities based on (i) one or more entity activity data entries associated with the one or more entities that match the one or more categorical identifiers and (ii) the one or more search radii {Pineau: fig 2, processor 110; [Col. 7, ln. 16-25] user may prescribe that the search is to be limited to the user's home city or to within a certain radius of the user's current location, e.g. 5 km, 10 km, 25 km, 50 km, etc. In addition, the search may be further constrained by time, e.g. the search query may be limited to cosmetic service providers that are presently open or which are open for business on a particular day or time. For example, the user may specify a search for a cosmetic search provider that is open on Saturdays and within 10 km of the user's current location; [Col. 9, ln. 8-12] The app then attempts to identify the cosmetic service provider that performed the procedure (i.e., categorical identifier) by correlating the times and locations of the before and after photographs 400, 402 with a location history of the woman who posted the photographs}; and
generating, by the one or more processors, one or more responses to the one or more search queries based on the one or more entities {Pineau: fig 2, processor 110; [Col. 11, ln. 45-54] graphical user interface 1000 in this example provides a search field (“find”) 1010 and a distance field 1015. The search results 1020 are presented on the graphical user interface. A user interface element (virtual button) 1030 is provided to view results on a map. The locations of the three cosmetic service providers 1121, 1122, 1123 may be presented using pinpoint icons or any other suitable graphical representation.}.
Claim 12:
Pineau discloses the method of claim 1. Pineau further discloses:
determining the user locale based on one or more user activity data entries associated with the user {Pineau: [Col. 7, ln. 37-41] Visits to cosmetic service providers may also be determined by check-ins (e.g. on Facebook®) or any equivalent location-determining function provided by another application or by a social media platform.}.
Claim 13:
Pineau discloses the method of claim 1. Pineau further discloses:
ranking the one or more entities based on amount of the one or more entity activity data entries matching the one or more categorical identifiers {Pineau: [Col. 17, ln. 7-15] app comprises non-transitory computer-readable code that is programmed, in some embodiments, to dynamically rank the cosmetic service providers based on trustworthiness factors such as feedback, reviews or endorsements from celebrities or public figures, cosmetic service providers with the most reviews and/or the best reviews, those who have received the most likes or who have the most followers, etc.}.
Claim 14:
Pineau discloses the method of claim 1. Pineau further discloses:
generating the one or more mappings by:
extracting one or more user locales from one or more user activity data entries associated with a plurality of users {Pineau: [Col. 1, ln. 57-60] user computing device also has a location-determining subsystem (e.g. GPS in the case of a mobile device) for determining a current location of the user computing device; [Col. 7, ln. 37-41] Visits to cosmetic service providers may also be determined by check-ins (e.g. on Facebook®) or any equivalent location-determining function provided by another application or by a social media platform.};
determining one or more entity locales and one or more respective categorical identifiers associated with the one or more entity locales for respective one or more entities present in the one or more user activity data entries {Pineau: [Col. 7, ln. 37-41] Visits to cosmetic service providers may also be determined by check-ins (e.g. on Facebook®) or any equivalent location-determining function provided by another application or by a social media platform; [Col. 6, ln. 53-57] search query may be expressed in plain language or keywords. Some examples of search queries for beauty and cosmetic services are: hair, nails, skin, lashes, brows, lips, face, tan, teeth, collagen, dermal fillers, liftings, implants; [Col. 7, ln. 5-8] search for all cosmetic service providers offering the cosmetic service within a prescribed geographical area based on the search query and the current location received from the user computing device.};
determining, for an entity locale of the one or more entity locales, an approximate distance between the user locale and the entity locale {Pineau: [Col. 11, ln. 43-44] user can specify a category or keyword and a distance by providing user input to the mobile device 100; [Col. 16, ln. 44-54] app can enable the user to search by: fields of beauty (e.g. lash extensions, nails, etc.); professionals (e.g. technicians, hairstylists, estheticians, cosmeticians, beauticians, nurses, etc.); cosmetic service provider (clinic, salon, etc.). Searches may also be performed based on location, distance, etc. Searches can be performed using a Nearby function with the possibility to pick one or various fields such as current promotions, hotspots (e.g. places where celebrities or public figures have been), newest (professional, places), date and time of desired appointment or other such criteria; [Col. 7, ln. 23-25] user may specify a search for a cosmetic search provider that is open on Saturdays and within 10 km of the user's current location.}; and
mapping the approximate distance to a respective one of the one or more respective categorical identifiers associated with the entity locale {Pineau: fig 10 allows user to enter search criteria, including distance radius, in the user interface and fig 11 displays the results in a map; [Col. 11, ln. 50-61] a graphical user interface 1100 of the mobile device 100 showing the search results on a map. The locations of the three cosmetic service providers 1121, 1122, 1123 may be presented using pinpoint icons or any other suitable graphical representation. In one implementation, the pinpoint icons may be user-selectable to provide additional contact information about the cosmetic service provider that has been selected. Optionally, the pinpoint icons may be user-selectable to cause display of a user interface element to call a ride-sharing service like Uber or Lyft with the destination (address of the cosmetic service provider) already pre-populated by the app.}.
Regarding claims 15, 19, and 20, claims 15 and 19 are directed to a system, while claim 20 is directed to one or more non-transitory computer-readable storage media. Claims 15 and 19; and 20 recite limitations that are parallel in nature to those addressed above for claims 1 and 14; and 1, respectively, which are directed towards a method. Therefore, claims 15, 19; and 20 are rejected for the same reasons as set forth above for claims 1, 14; and 1, respectively.
It is noted that claim 15 includes additional elements of:
A computing system comprising memory and one or more processors communicatively coupled to the memory, the one or more processors configured to.
Pineau discloses:
A computing system comprising memory and one or more processors communicatively coupled to the memory, the one or more processors configured to {Pineau: fig 2, mobile device 100 has microprocessor 110 and RAM 130}.
It is further noted that claim 20 includes additional elements of:
One or more non-transitory computer-readable storage media including instructions that, when executed by one or more processors, cause the one or more processors to.
Pineau discloses:
One or more non-transitory computer-readable storage media including instructions that, when executed by one or more processors, cause the one or more processors to {Pineau: [Col. 15, ln. 30-41] a non-transitory computer-readable medium comprising computer-readable instructions in software code. The non-transitory computer-readable medium or machine-readable medium is in most instances a computer-readable memory. When stored in a memory and executed by a processor of a user computing device, the app causes the user computing device to receive user input specifying a search query for a cosmetic service, determine a current location of the user computing device, and then search for all cosmetic service providers within a prescribed geographical area based on the search query and the current location.}.
Claim Rejections - 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 nonobviousness.
Claims 2-7 and 16-17 are rejected under 35 U.S.C. 103 as being unpatentable over Pineau (US 11308538 B1, herein referred to as Pineau), in view of Batina et. al. (US 20240289361 A1, herein referred to as Batina).
Claim 2:
Pineau discloses the method of claim 1. Pineau does not disclose:
wherein the transformer machine learning model comprises a universal sentence encoder.
Pineau does disclose utilizing a deep-learning convolutional neural network (Pineau: [Col. 9, ln. 6-12]).
However, Batina teaches:
wherein the transformer machine learning model comprises a universal sentence encoder {Batina: [0082] pre-trained embedding models which may be implemented include: Universal Sentence Encoder}.
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 a universal sentence encoder as taught by Batina in the cosmetic services search method of Pineau in order to improve accuracy of outputs (Batina: [0049]).
Claim 3:
Pineau discloses the method of claim 1. Pineau does not disclose:
wherein the transformer machine learning model comprises an N-layer sentence/word textual transformer machine learning model.
Pineau does disclose utilizing a deep-learning convolutional neural network (Pineau: [Col. 9, ln. 6-12]).
However, Batina teaches:
wherein the transformer machine learning model comprises an N-layer sentence/word textual transformer machine learning model {Batina: [0047] A plurality of neurons may be organized into a neural network layer (or simply “layer”) and there may be multiple such layers in a neural network. The output of one layer may be provided as input to a subsequent layer. Thus, input to a neural network may be processed through a succession of layers until an output of the neural network is generated by a final layer; [0059] language model may be trained to model how words relate to each other in a textual sequence}.
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 a multilayer textual transformer model as taught by Batina in the cosmetic services search method of Pineau in order to improve accuracy of outputs (Batina: [0049]).
Claim 4:
Pineau and Batina teach the method of claim 1. Pineau does not disclose:
wherein the transformer machine learning model comprises a last layer N+1 configured to classify the one or more search queries with respect to the one or more categorical identifiers.
Pineau does disclose utilizing a deep-learning convolutional neural network for finding cosmetic services available at cosmetic service providers (Pineau: [Col. 9, ln. 6-12]).
However, Batina teaches:
wherein the transformer machine learning model comprises a last layer N+1 configured to classify the one or more search queries with respect to the one or more categorical identifiers {Batina: [0056] a fully connected layer 18 processes the set of feature maps 16 in order to perform a classification of the image, based on the features encoded in the set of feature maps 16. The fully connected layer 18 contains learned parameters that, when applied to the set of feature maps 16, outputs a set of probabilities representing the likelihood that the image 12 belongs to each of a defined set of possible classes. The class having the highest probability may then be outputted as the predicted classification for the image 12.}.
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 using a fully connected N+1 output layer for classification as taught by Batina in the cosmetic services search method of Pineau in order to improve accuracy of outputs (Batina: [0049]).
Claim 5:
Pineau and Batina teach the method of claim 3. Pineau does not disclose:
wherein the transformer machine learning model comprises one or more (N-1)th layers configured to encode the one or more search queries and the respective ones of the one or more categorical descriptions.
Pineau does disclose utilizing a deep-learning convolutional neural network for finding cosmetic services available at cosmetic service providers (Pineau: [Col. 9, ln. 6-12]).
However, Batina teaches:
wherein the transformer machine learning model comprises one or more (N-1)th layers configured to encode the one or more search queries and the respective ones of the one or more categorical descriptions {Batina: [0047] there may be multiple such layers in a neural network. The output of one layer may be provided as input to a subsequent layer. Thus, input to a neural network may be processed through a succession of layers; [0056] Each feature map 16 generally has smaller width and height than the image 12. The set of feature maps 16 encode image features that may be processed by subsequent layers of the CNN 10}.
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 using intermediate layers as taught by Batina in the cosmetic services search method of Pineau in order to improve accuracy of outputs (Batina: [0049]).
Claim 6:
Pineau discloses the method of claim 1. Pineau does not disclose:
wherein determining the one or more categorical identifiers further comprises:
generating one or more search query embeddings based on the one or more search queries;
generating one or more categorical description embeddings based on the one or more categorical descriptions; and
matching the one or more search queries to the one or more categorical descriptions based on a comparison of the one or more search query embeddings associated with respective ones of the one or more search queries with the one or more categorical description embeddings associated with respective ones of the one or more categorical descriptions.
Pineau does disclose utilizing a deep-learning convolutional neural network for finding cosmetic services available at cosmetic service providers (Pineau: [Col. 9, ln. 6-12]).
However, Batina teaches:
wherein determining the one or more categorical identifiers further comprises:
generating one or more search query embeddings based on the one or more search queries {Batina: [0035] generated enhancement data may include the search query and/or additional keywords or synonyms. Additionally, or alternatively, the generated enhancement data may be in a format associated with the embedding space. For example, if the embedding space is a sentence embedding space, the generated enhancement data may include full sentences based on the search query; [0082] An embeddings module 116 creates the vector representations of data. Embeddings are computed using machine learning models. The embeddings module 116 is configured to implement one or more embedding models for processing different types of data; [0105] computing system may initiate a vector search using a text (e.g., word, sentence, etc.) embedding of the user-inputted query.};
generating one or more categorical description embeddings based on the one or more categorical descriptions {Batina: [0039] enhancement text that is in the structure and style of a product description may then be embedded for use in the vector search of a library of product descriptions; [0082] search engine 114 indexes queries and searchable objects (e.g., text, image, documents, data records, etc.) of a library with vector embeddings.}; and
matching the one or more search queries to the one or more categorical descriptions based on a comparison of the one or more search query embeddings associated with respective ones of the one or more search queries with the one or more categorical description embeddings associated with respective ones of the one or more categorical descriptions {Batina: [0083] The search engine 114 computes similarity between vectors in the embedding space. In particular, the search engine 114 may use one or more metrics for calculating vector similarity; [0100] a vector similarity search may be performed to determine which of the searchable objects is similar to the selected object and are associated with at least one of the preferred object attributes}.
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 using intermediate layers as taught by Batina in the cosmetic services search method of Pineau in order to improve accuracy of outputs (Batina: [0049]).
Claim 7:
Pineau and Batina teach the method of claim 6. Pineau does not disclose:
wherein matching the one or more search queries to the one or more categorical descriptions further comprises determining a cosine similarity or a K-nearest neighbor between the one or more search query embeddings and the one or more categorical description embeddings.
Pineau does disclose utilizing a deep-learning convolutional neural network for finding cosmetic services available at cosmetic service providers (Pineau: [Col. 9, ln. 6-12]).
However, Batina teaches:
wherein matching the one or more search queries to the one or more categorical descriptions further comprises determining a cosine similarity or a K-nearest neighbor between the one or more search query embeddings and the one or more categorical description embeddings {Batina: [0032] finding related data for a search query amounts to searching for nearest neighbors of an embedding, i.e., vector representation, of a query object in the relevant embedding space; [0083] The search engine 114 computes similarity between vectors in the embedding space. In particular, the search engine 114 may use one or more metrics for calculating vector similarity such as, but not limited to cosine similarity. Various algorithms for vector similarity search may be implemented by the search engine. Examples include k-nearest neighbor (kNN)}.
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 using cosine similarity or k-nearest neighbors as taught by Batina in the cosmetic services search method of Pineau in order to improve accuracy of outputs (Batina: [0049]).
Regarding claims 16-17, claims 16-17 are directed to a system. Claims 16-17 recite limitations that are parallel in nature to those addressed above for claims 6-7, which are directed towards a method. Therefore, claims 16-17 are rejected for the same reasons as set forth above for claims 6-7, respectively.
Claims 8-10 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Pineau (US 11308538 B1, herein referred to as Pineau), in view of Neumann (US 20240070693 A1, herein referred to as Neumann).
Claim 8:
Pineau discloses the method of claim 1. Pineau does not disclose:
training the transformer machine learning model based on one or more time-stamped directed graphs associated with the user, wherein one of the one or more time-stamped directed graphs comprises (i) one or more historical query response interactions associated with the user and (ii) one or more user activity data entries comprising one or more confirming actions of the user.
Pineau does disclose utilizing a deep-learning convolutional neural network for finding cosmetic services available at cosmetic service providers (Pineau: [Col. 9, ln. 6-12]), and that the system tracks bookings made by the user for the cosmetic services (Pineau: [Col. 19, ln. 62-67]) and the user’s location history (Pineau: [Col. 9, ln. 24-26]).
However, Neumann teaches:
training the transformer machine learning model based on one or more time-stamped directed graphs associated with the user, wherein one of the one or more time-stamped directed graphs comprises (i) one or more historical query response interactions associated with the user and (ii) one or more user activity data entries comprising one or more confirming actions of the user {Neumann: [0021] “machine learning process,” as used in this disclosure, is a process that automatedly uses a body of data known as “training data” and/or a “training set” (described further below) to generate an algorithm that will be performed by a computing device/module to produce outputs given data provided as inputs; [0047] Data elements are listing in immutable sequential listing 500; [0051] Immutable sequential listing 500 may be incorporated in directed acyclic graph or the like. In some embodiments, the timestamp of an entry is cryptographically secured and validated via trusted time, either directly on the chain or indirectly by utilizing a separate chain; [0027] datum of user activity as used herein, includes any data describing a user's current and/or previous interaction with apparatus 100 and/or the metaverse 116 related to the user. A datum of user activity may include data describing a user's previously selected and/or purchased products, currently selected and/or purchased products. Datum of user activity data may include historical data such as browsing and/or purchasing history that occurred at any time in the past. In yet another example, a datum of user activity data may include current real time data describing current browsing and/or purchasing history that user is actively engaged upon at the present moment.}.
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 directed acyclic graph as taught by Neumann in the cosmetic services search method of Pineau in order to provide a best predicted output/actual output fit (Neumann: [0043]).
Claim 9:
Pineau and Neumann teach the method of claim 8. Pineau further discloses:
wherein the one or more historical query response interactions comprises one or more selections of one or more search results associated with one or more prior search queries from the user {Pineau: [Col. 19, ln 57-63] The user may use the app to accept to book a last-minute appointment. Reminder module may learn from analysis of past bookings}.
Claim 10:
Pineau and Neumann teach the method of claim 8. Pineau further discloses:
wherein the one or more historical query response interactions and the one or more user activity data entries have occurred with an observation time-window {Pineau: fig 19; [Col. 23, ln. 7-15] Once the user-acceptable appointment times are determined by the app, the scheduling module of the app then transmits one or more appointment requests to the one or more cosmetic service providers for the one or more user-acceptable appointment times. The scheduling module of the app receives appointment confirmations from the cosmetic service providers and transfers these into the calendar application of the mobile device as new appointments; [Col. 14, ln. 64-67; Col. 15, ln. 1-3] user computing device 100 automatically tags a photograph of the user (e.g. selfies) with a keyword identifying the cosmetic service when the photograph is taken within a predetermined time of a calendar appointment at the cosmetic service provider and/or when the photograph is taken within a predetermined distance of the cosmetic service provider}.
Regarding claim 18, claim 18 is directed to a system. Claim 18 is limitations that are parallel in nature to those addressed above for claim 8, which is directed towards a method. Therefore, claim 18 is rejected for the same reasons as set forth above for claim 8.
Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable over Pineau (US 11308538 B1, herein referred to as Pineau), in view of Bahnsen et. al. (US 20240386454 A1, herein referred to as Bahnsen).
Claim 11:
Pineau discloses the method of claim 1. Pineau further discloses:
determining the one or more statistical distances between a plurality of user locales associated with a plurality of aggregate users and a plurality of entity locales {Pineau: [Col. 6, ln. 4-8] cosmetic server 270 has a memory and a processor configured to search for all cosmetic service providers offering the cosmetic service within a prescribed geographical area based on the search query and the current location received from the user computing device; [Col. 1, ln. 57-60] user computing device also has a location-determining subsystem (e.g. GPS in the case of a mobile device) for determining a current location of the user computing device; [Col. 7, ln. 37-41] Visits to cosmetic service providers may also be determined by check-ins (e.g. on Facebook®) or any equivalent location-determining function provided by another application or by a social media platform.}.
Although disclosing that social media contacts can narrow the available cosmetic provider based on distance, Pineau does not disclose:
determining the one or more statistical distances based on an average distance between a plurality of user locales and a plurality of entity locales.
However, Bahnsen teaches:
determining the one or more statistical distances based on an average distance between a plurality of user locales and an entity locale {Bahnsen: [0069] the average computed distance to the food store from the geographic region may be 8 miles, the average distance traveled to the food store from the geographic region may be 8.3 miles, the number of queries for the food store may be 51, and the number of visits to the food store may be 1.342.}.
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 an average distance as taught by Bahnsen in the cosmetic services search method of Pineau in order to determine which services are optimal for a given geographical region (Bahnsen: [0019]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Maschmeyer et. al. (US 20250166037 A1) was used to understand other methods for employing embeddings and similarity searching for items.
Rao Karikurve et. al. (US 20240428315 A1) was used to understand other methods for recommending retailers to users based on the user’s location.
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/KATHERINE A BARLOW/Examiner, Art Unit 3689
/VICTORIA E. FRUNZI/Primary Examiner, Art Unit 3689 4/16/2026
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