DETAILED ACTION
Status of Claims
This action is in reply to the response received on 13 April 2026.
Claims 1, 7, 10, 14, and 17 have been amended.
Claims 2-3, 11-12, and 18 have been canceled.
Claims 1, 4-10, 13-17, and 19-20 are pending and have been examined.
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 .
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, 4-10, 13-17, and 19-20 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea without significantly more).
Under step 1, it is determined whether the claims are directed to a statutory category of invention (see MPEP 2106.03(II)). In the instant case, claims 1 and 4-9 are directed to a product of manufacture (non-transitory computer-readable medium), claims 10 and 13-16 are directed to a method, and claims 17 and 19-20 are directed to a system.
While the claims fall within statutory categories, under revised Step 2A, Prong 1 of the eligibility analysis (MPEP 2106.04), the claimed invention recites an abstract idea of providing a response related to beauty topics. Specifically, representative claim 10 recites the abstract idea of:
transmitting user input and contextual information for the user input to a model,
requesting to provide a confirmation that the user input relates to one or more beauty topics, wherein generates a first classification identifying the user input as a beauty topic request, and wherein based on the first classification, a second classification identifying a category of the beauty topic request;
receiving the confirmation that the user input relates to the one or more beauty topics from the model;
based on the first classification and the second classification, requesting to provide a response to the user input to be presented to a user, wherein the response relates to the identified category of the beauty topic request and is based at least in part on the user input and the contextual information; and
receiving the response from the model.
Under revised Step 2A, Prong 1 of the eligibility analysis, it is necessary to evaluate whether the claim recites a judicial exception by referring to subject matter groupings articulated in 2106.04(a) of the MPEP. Even in consideration of the analysis, the claims recite an abstract idea. Representative claim 10 recites the abstract idea of providing a response related to beauty topics, as noted above. This concept is considered to be a method of organizing human activity. Certain methods of organizing human activity include “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)(II). In this case, the abstract idea recited in representative claim 10 is a certain method of organizing human activity because it relates to sale activities since the claims specifically recite transmitting user input and contextual information for the user, requesting to provide a confirmation that the user input relates to one or more beauty topics, receiving the confirmation that the user input relates to the one or more beauty topics, and based on the confirmation, requesting to provide a response to the user input to be presented to au ser where the response is related to the one or more beauty topics based on the user input and contextual information, generating a first classification identifying the user input as a beauty topic request, and wherein based on the first classification, a second classification identifying a category of the beauty topic request, and receiving the response from the model, thereby making this a sales activity or behavior.
Thus, representative claim 10 recites an abstract idea.
Under Step 2A, Prong 2 of the eligibility analysis, 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. MPEP 2106.04(d). The courts have identified limitations that did not integrate a judicial exception into a practical application include limitations merely reciting the words “apply it” (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP 2106.05(f). MPEP 2106.04(d). In this case, representative claim 10 includes additional elements: a computer, a computer system, a large language model (LLM), and the LLM.
Although reciting such 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 10 merely recites a commonplace business method (i.e., providing a response related to beauty topics) being applied on a general-purpose computer using general purpose computer technology. MPEP 2106.05(f). While the claims recite a large language model, the recitations are results based in nature and do not include details as to how the model actually functioning beyond known functions. 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.
Under Step 2B of the eligibility 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). MPEP 2106.05. In this case, as noted above, the additional elements of a computer, a computer system, a large language model (LLM), and the LLM, recited in independent claim 10 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] [v]iewed as a whole…[the] claims simply recite intermediated settlement as performed by a generic computer.” Alice Corp. Pty. Ltd. v. CLS Bank Int'l, 573 U.S. 208, 217, (2014) (citing Mayo, 566 U.S. at 79, 101 USPQ2d at 1972). Similarly, when viewed as a whole, representative claim 10 simply conveys the abstract idea itself facilitated by generic computing components. Therefore, under Step 2B of the Alice/Mayo test, there are no meaningful limitations in representative claim 10 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 10 is ineligible.
Independent claims 1 and 17 are similar in nature to representative claim 10, and Step 2A, Prong 1 analysis is the same as above for representative claim 10. It is noted that in independent claim 1 includes the additional elements of a non-transitory computer-readable medium having stored thereon instructions configured to, when executed by one or more computing devices of a computer system, cause the computer system to perform operations, and independent claim 17 includes the additional element of a computer system comprising a processor and a non-transitory computer-readable medium having stored thereon instructions configured to, when executed by one or more computing devices of a computer system, cause the computer system to perform operations and a client computing device. The Applicant’s specification does not provide any discussion or description of the claimed additional elements in claims 1 and 17, as being anything other than generic elements. Thus, the claimed additional elements of claims 1 and 17 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. As such, the additional elements of claims 1 and 17 do not integrate the judicial exception into a practical application of the abstract idea. Additionally, the additional elements of claims 1 and 17, considered individually and in combination, do not provide an inventive concept because they merely amount to no more than an instruction to apply the abstract idea using a generic computer.
As such, claims 1 and 17 are ineligible.
Dependent claims 4-9, 13-16, and 19-20, depending from claims 1, 10 and 17 respectively, do not aid in the eligibility of the independent claims nor the representative independent claim 10. The claims of 4-9, 13-16, and 19-20 merely act to provide further limitations of the abstract idea and are ineligible subject matter.
It is noted that dependent claims include the additional elements of vector database (claims 7, 14, & 19), digital model (claims 8, 9, 15, & 20), the client computing device comprises a camera, the camera, and digital images (claim 20). Applicant’s specification does not provide any discussion or description of the claimed additional elements as being anything other than a generic element. The claimed additional elements, individually and in combination do not integrate into a practical application and do not provide an inventive concept because they are merely being used to apply the abstract idea using a generic computer (see MPEP 2106.05(f)). Accordingly, claims 7-9, 14-15, and 20 are directed towards an abstract idea. Additionally, the additional elements of claims 7-9, 14-15, and 20 considered individually and in combination, do not provide an inventive concept because they merely amount to no more than an instruction to apply the abstract idea using a generic computer. It is further noted that the remaining dependent claims 4-6, 8, 13, 16, and 19 do not recite any further additional elements to consider in the analysis, and therefore would not provide additional elements that would integrate the abstract idea into a practical application and would not provide an inventive concept.
As such, the dependent claims 4-9, 13-16, and 19-20 are ineligible.
Claim Rejections - 35 USC § 103
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.
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 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.
Claims 1, 4-10, 13-17, and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Yaqoob, A., et al. (PGP No. US 2025/0245731 A1), in view of Dissanayake, M. (PGP No. US 2025/0166040 A1).
Claim 1-
Yaqoob discloses a non-transitory computer-readable medium having stored thereon instructions configured to, when executed by one or more computing devices of a computer system, cause the computer system to perform operations (Yaqoob, see: paragraph [0054] disclosing “a non-transitory, computer-readable storage medium”) comprising:
transmitting user input and contextual information for the user input to a large language model (LLM) (Yaqoob, see: paragraph [0044] disclosing “the chatbot may treat the question asked by the customer as a user query and call a…large language model, to determine a query entity based on the user query and contextual information of the conversation”);
requesting the LLM to provide a confirmation that the user input relates to one or more topics, wherein the LLM generates a first classification identifying the user input as a topic request, and wherein the LLM generates, based on the first classification, a second classification identifying a category of the topic request (Yaqoob, see: paragraph [0044] disclosing “model is called by the item recommendation computing device 102 to determine the query entity” and “include suggested products or product types [i.e., topics]”; and paragraph [0045] disclosing “customer may continue to ask ‘what to bring as a gift?’[i.e., first classification identifying the user input as a topic request] In this case, the chatbot can utilize the conversational context”; and paragraph [0073] disclosing “product type or product category [i.e., second classification identifying a category of the topic request] based on the general-purpose language understanding of the query and the contextual information”; Also see FIG. 6, demonstrating another example of a user using the chatbot, inputting a query looking for clothes to wear to a wedding, where the chatbot then supplies the responses of recommendations of types of clothing to wear to a wedding, demonstrating an input and a category based on the input of the user.)
receiving the confirmation that the user input relates to the one or more topics from the LLM (Yaqoob, see: paragraph [0044] disclosing “large language model, to determine a query entity” and “include suggested products or product types [i.e., topics]”; and see: paragraph [0069] disclosing “item categories 348 identifying a product type (or category) [i.e., relates to one or more topics] of each item”; and paragraph [0073] disclosing “product type or product category based on the general-purpose language understanding of the query and the contextual information” and “large language model…used to determine [i.e., confirmation] at least one query entity based on a query and contextual information”);
based on the first classification and the second classification, requesting the LLM to provide a response to the user input to be presented to a user, wherein the response relates to the identified category of the topic request and is based at least in part on the user input and the contextual information (Yaqoob, see: paragraph [0044] disclosing “include suggested products or product types [i.e., topics]”; and see: paragraph [0045] disclosing “customer may continue to ask ‘what to bring as a gift?’[i.e., first classification identifying the user input as a topic request] In this case, the chatbot can utilize the conversational context”; and paragraph [0073] disclosing “product type or product category [i.e., second classification identifying a category of the topic request] based on the general-purpose language understanding of the query and the contextual information”; and paragraph [0080] disclosing “receives a search request 310 from the server 104. The search request 310 may be associated with a query and contextual information obtained from a conversation” and “the item recommendation computing device 102 transmits the item recommendation 312”; Also see FIG. 6, demonstrating another example of a user using the chatbot, inputting a query looking for clothes to wear to a wedding, where the chatbot then presents via the interface, the responses of recommendations of types of clothing to wear to a wedding, demonstrating an input and a category based on the input of the user” and see: [0090]-[0091]); and
receiving the response from the LLM (Yaqoob, see: paragraph [0044] disclosing “a large language model, to determine a query entity based on the user query and contextual information” and “model is called by the item recommendation computing device 102 to determine the query entity, which include suggested products or product types” and “generate a ranked list of recommended items” and “may transmit some or all of the recommended items…to be displayed to the customer”; See paragraph [0088], also describing that the system provides the customer a response including recommendations of items via the user interface.).
Although Yaqoob discloses the suggested products or product types that are of interest that is determined by the input and contextual information, Yaqoob does not disclose that the topics are related to beauty topics. Yaqoob does not disclose:
input relates to one or more beauty topics;
identifying the input as a beauty topic;
response relates to the beauty topic request;
Dissanayake, however, does teach:
input relates to one or more beauty topics (Dissanayake, see: paragraph [0041] teaching “pigmented spots (e.g., hemoglobin and/or melanin) may be identified by the user” and “Through a conversation, the user can describe these features”; and see: paragraph [0117] teaching “interface 602 may also include or render a user-specific skin issue 610” and “comprises a message 610m”; Also see FIG. 6 depicting the user interface allowing the user to indicate the topics of concern, such as pigmented spots of the skin.);
identifying the input as a beauty topic (Dissanayake, see: paragraph [0041] teaching “pigmented spots (e.g., hemoglobin and/or melanin) may be identified by the user” and “Through a conversation, the user can describe these features”; and see: paragraph [0117] teaching “interface 602 may also include or render a user-specific skin issue 610” and “comprises a message 610m”; Also see FIG. 6 depicting the user interface allowing the user to indicate the topics of concern, such as pigmented spots of the skin.);
response relates to the beauty topic request (Dissanayake, see: paragraph [0118] teaching “message 612m indicates to a user that the user-specific issue and/or condition is mild…issue results from pigmented spots at the indicated region of the user’s skin” and “recommends to the user to use a night face cream to help reduce the pigmented spots”; Also see: FIG. 6).
This step of Dissanayake is applicable to the product of manufacture of Yaqoob, as they both share characteristics and capabilities, namely, they are directed to recommending items to customers based on conversations. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify product of manufacture of Yaqoob, to include the features of input relates to the beauty topic, and response relates to the beauty topic request, as taught by Dissanayake. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify the reference of Yaqoob to improve the responses regarding beauty products to customers in order to provide improved product recommendations (Dissanayake, see: paragraph [0007]).
Claim 4-
Yaqoob in view of Dissanayake teach the computer-readable medium of Claim 1, as described above.
Yaqoob discloses wherein the user input includes text input or voice input (Yaqoob, see: paragraph [0057] disclosing “data input…can include one or more of a keyboard…a touchscreen… speaker, a microphone”; and paragraph [0074] disclosing “transform the text of a query into a query embedding vector”).
Claim 5-
Yaqoob in view of Dissanayake teach the computer-readable medium of Claim 1, as described above.
Yaqoob discloses the operations further comprising requesting the LLM to provide a summary of the user input (Yaqoob, see: paragraph [0074] disclosing “model 393 may be used to encode a query or a product item into an embedding” and “used to transform each query entity name (which may be a product name, product type name, product category name, product family name, or product department name) into an embedding vector in the vector space”; and paragraph [0075] disclosing “In some embodiments, the grouping model 394 is used to compare embedding vectors of two query entities” and “model 394 includes an LLM configured to determine whether two query entities should be organized into a same group for display”).
Claim 6-
Yaqoob in view of Dissanayake teach the computer-readable medium of Claim 5, as described above.
Yaqoob discloses:
the operations further comprising: receiving the summary of the user input from the LLM; generating a vector representation of the user input (Yaqoob, see: paragraph [0102] disclosing “sentence encoder 430 performs sentence embedding to transform textual data 910 into a numerical vector 912” and “the textual data 910 can be replaced by any product name or any query entity name, e.g. a name of the query entity X 810 or the query entity Y 820, to generate a corresponding embedding vector”; Also see: FIG. 9).
Claim 7-
Yaqoob in view of Dissanayake teach the computer-readable medium of Claim 6, as described above.
Yaqoob discloses the operations further comprising:
comparing the vector representation of the user input with other vector representations in a vector database (Yaqoob, see: paragraph [0077] disclosing “can compare these vector embeddings with an embedding of the enhanced query to determine, among the most commonly searched queries” and “a nearest neighbor index can be utilized in the vector embedding database for fast search and retrieval”); and
identifying a near-neighbor match for the vector representation of the user input among the other vector representations in the vector database (Yaqoob, see: paragraph [0077] disclosing “a nearest neighbor index can be utilized in the vector embedding database for fast search and retrieval” and “then map the most similar query back to a set of product items”).
Claim 8-
Yaqoob in view of Dissanayake teach the computer-readable medium of Claim 1, as described above.
Yaqoob discloses the operations further comprising: requesting the LLM to generate a product recommendation (Yaqoob, see: paragraph [0044] disclosing “large language model…is called by the item recommendation computing device 102 to determine the query entity, which include suggested products”).
Yaqoob does not disclose:
obtaining a digital model of a face of the user ; and
a care routine recommendation based at least in part on the digital model of the face.
Dissanayake, however, does teach:
obtaining a digital model of a face of the user (Dissanayake, see: paragraph [0095] disclosing “Digital twin image 202a-dt is generated based on the 202a’s information”); and
a care routine recommendation based at least in part on the digital model of the face (Dissanayake, see: paragraph [0118] teaching “recommends to the user to use a night face cream to help reduce the pigmented spots” and “output by AI models based on the natural language data and/or digital twin image” and “recommendation can be correlated to the identified feature within the pixel data”).
This step of Dissanayake is applicable to the product of manufacture of Yaqoob, as they both share characteristics and capabilities, namely, they are directed to recommending items to customers based on conversations. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify product of manufacture of Yaqoob, to include the features of obtaining a digital model of a face of the user and a care routine recommendation based at least in part on the digital model of the face, as taught by Dissanayake. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify the reference of Yaqoob to improve the responses regarding beauty products to customers in order to provide improved product recommendations (Dissanayake, see: paragraph [0007]).
Claim 9-
Yaqoob in view of Dissanayake teach the computer-readable medium of Claim 8, as described above.
Yaqoob does not disclose wherein the digital model of the face includes a plurality of skin features including blemish information, hyper-pigmentation information, clinical signs, skin concern information, skin texture information, skin tone information, or a combination thereof.
Dissanayake however, does teach:
wherein the digital model of the face includes a plurality of skin features including blemish information, hyper-pigmentation information, clinical signs, skin concern information, skin texture information, skin tone information, or a combination thereof (Dissanayake, see: paragraph [0104] teaching “spot IDs may comprise a range (e.g., IDs 1-20) for identifying various levels of pigmentations or intensities of the skin (e.g., caused by hemoglobin, melanin, acne, etc.) as identifiable within the pixel data”; Also see: FIG. 4B).
This step of Dissanayake is applicable to the product of manufacture of Yaqoob, as they both share characteristics and capabilities, namely, they are directed to recommending items to customers based on conversations. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify product of manufacture of Yaqoob, to include the features of wherein the digital model of the face includes a plurality of skin features including blemish information, hyper-pigmentation information, clinical signs, skin concern information, skin texture information, skin tone information, or a combination thereof, as taught by Dissanayake. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify the reference of Yaqoob to improve the responses regarding beauty products to customers in order to provide improved product recommendations (Dissanayake, see: paragraph [0007]).
Regarding claim 10, claim 10 is directed to a method. Claim 10 recites limitations that are parallel in nature to those addressed above for claim 1 which is directed towards a product of manufacture. Claim 10 is therefore rejected for the same reasons as set forth above for claim 1.
Regarding claim 13, claim 13 is directed to a method. Claim 13 recites limitations that are parallel in nature to those addressed above for claim 6 which is directed towards a product of manufacture. Claim 13 is therefore rejected for the same reasons as set forth above for claim 6.
Regarding claim 14, claim 14 is directed to a method. Claim 14 recites limitations that are parallel in nature to those addressed above for claim 7 which is directed towards a product of manufacture. Claim 14 is therefore rejected for the same reasons as set forth above for claim 7.
Regarding claim 15, claim 15 is directed to a method. Claim 15 recites limitations that are parallel in nature to those addressed above for claim 8 which is directed towards a product of manufacture. Claim 15 is therefore rejected for the same reasons as set forth above for claim 8.
Regarding claim 16, claim 16 is directed to a method. Claim 16 recites limitations that are parallel in nature to those addressed above for claim 9 which is directed towards a product of manufacture. Claim 16 is therefore rejected for the same reasons as set forth above for claim 9.
Regarding claim 17, claim 17 is directed to a system. Claim 17 recites limitations that are similar in nature to those addressed above for claim 1 which is directed towards a product of manufacture. It is noted that claim 17 also includes a computer system comprising a processor and a non-transitory computer read-able medium, which is disclosed by Yaqoob (Yaqoob, see: paragraph [0054] disclosing “one or more processors 201”). Claim 17 is therefore rejected for the same reasons as set forth above for claim 1.
Regarding claim 19, claim 19 is directed to a system. Claim 19 recites limitations that are similar in nature to those addressed above for claims 7, 13 and 14, which are directed towards a product of manufacture. Claim 19 is therefore rejected for the same reasons as set forth above for claim 7, 13, and 14.
Claim 20-
Yaqoob in view of Dissanayake teach the computer system of Claim 17, as described above.
Yaqoob discloses requesting the LLM to generate a product recommendation (Yaqoob, see: paragraph [0044] disclosing “large language model…is called by the item recommendation computing device 102 to determine the query entity, which include suggested products”).
Yaqoob does not disclose:
wherein the client computing device comprises a camera, the operations further comprising: causing the client computing device to request activation of the camera to capture one or more digital images;
receiving the one or more captured digital images;
generating a digital model of the face of the user based at least in part on the one or more captured digital images; and
a care routine recommendation based at least in part on the digital model of the face.
Dissanayake, however, does teach:
wherein the client computing device comprises a camera, the operations further comprising: causing the client computing device to request activation of the camera to capture one or more digital images (Dissanayake, see: paragraph [0092] teaching “Each of the digital twin images may comprise photorealistic images comprising pixel data, for example, as would have been captured by a digital camera”);
receiving the one or more captured digital images; (Dissanayake, see: paragraph [0095] disclosing “Digital twin image 202a-dt is generated based on the 202a’s information”);
generating a digital model of the face of the user based at least in part on the one or more captured digital images (Dissanayake, see: paragraph [0095] disclosing “Digital twin image 202a-dt is generated based on the 202a’s information”); and
a care routine recommendation based at least in part on the digital model of the face (Dissanayake, see: paragraph [0118] teaching “recommends to the user to use a night face cream to help reduce the pigmented spots” and “output by AI models based on the natural language data and/or digital twin image” and “recommendation can be correlated to the identified feature within the pixel data”).
This step of Dissanayake is applicable to the system of Yaqoob, as they both share characteristics and capabilities, namely, they are directed to recommending items to customers based on conversations. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Yaqoob, to include the features of wherein the client computing device comprises a camera, the operations further comprising: causing the client computing device to request activation of the camera to capture one or more digital images, receiving the one or more captured digital images, generating a digital model of the face of the user based at least in part on the one or more captured digital images, and a care routine recommendation based at least in part on the digital model of the face, as taught by Dissanayake. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify the reference of Yaqoob to improve the responses regarding beauty products to customers in order to provide improved product recommendations (Dissanayake, see: paragraph [0007]).
Response to Arguments
With respect to the rejections made under 35 USC § 101, the Applicant’s arguments filed on 13 April 2026, have been fully considered but are not considered persuasive.
In response to the Applicant’s arguments found on page 8 of the remarks stating that “the amended claims include a technical improvement that improves the performance of the system – notably, in terms of its ability to reduce the chance of hallucinations when the LLM responds to the user input” and “the pending claims are eligible at least under Step 2A, Prong Two of the Alice framework because the claims reflect an improvement to the functioning of the computer system in this context, thereby integrating the alleged judicial exception into a practical application,” the Examiner respectfully disagrees. Even when considering the amendments to the claims, under Step 2A, prong one of the eligibility analysis, the claims are still directed the abstract idea of providing a response related to beauty topics. The abstract idea falls into the enumerated sub-grouping of a certain method of organizing human activity, related to sales activities or behaviors. Under Step 2A, Prong two of the eligibility analysis, the claimed additional elements which are beyond the abstract idea, are still described at a high-level of generality, and are not recited in a manner that would be sufficient to integrate the abstract idea into a practical application. The additional elements of the compute and LLM, when considered individually and in combination, are generically recited and are being used to apply the abstract idea with generically recited computing components and generic a computer. Further, the claim limitations do not reflect an improvement to the technology itself, such as improvements to the computer system or to the LLM. The MPEP (2106.05(a)) provides further guidance on how to evaluate whether claims recite an improvement in the functioning of a computer or an improvement to other technology or technical field. For example, as indicated in 2106.05(d)(1) of the MPEP “the specification should be evaluated to determine if the disclosure provides sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement,” and that “[t]he specification need not explicitly set forth the improvement, but it must describe the invention such that the improvement would be apparent to one of ordinary skill in the art.” Looking to the specification is a standard that the courts have employed when analyzing claims as it relates to improvements in technology. For example, in Enfish, the specification provided teaching that the claimed invention achieves benefits over conventional databases, such as increased flexibility, faster search times, and smaller memory requirements. Enfish LLC v. Microsoft Corp., 822 F.3d 1327, 1335-36 (Fed. Cir. 2016). Additionally, in Core Wireless the specification noted deficiencies in prior art interfaces relating to efficient functioning of the computer. Core Wireless Licensing v. LG Elecs. Inc., 880 F.3d 1356 (Fed Cir. 2018). With respect to McRO, the claimed improvement, as confirmed by the originally filed specification, was “…allowing computers to produce ‘accurate and realistic lip synchronization and facial expressions in animated characters…’” and it was “…the incorporation of the claimed rules, not the use of the computer, that “improved [the] existing technological process” by allowing the automation of further tasks”. McRO, Inc. v. Bandai Namco Games America Inc., 837 F.3d 1299, (Fed. Cir. 2016).
In this case, Applicant’s specification provides no explanation of an improvement to the functioning of a computer or other technology. Rather, the claims focus “on a process that qualifies as an ‘abstract idea’ for which computers are invoked merely as a tool”. Id citing Enfish at 1327, 1336. This is reflected in pages 16-17 of of Applicant’s specification, which describe Applicant’s claimed invention is directed toward solving problems such as providing accurate product recommendations. Although the claims include computer technology such as a computer-system, a computer, and the LLM, such elements are merely peripherally incorporated in order to implement the abstract idea. This is unlike the improvements recognized by the courts in cases such as Enfish, Core Wireless, and McRO. Unlike precedential cases, neither the specification nor the claims of the instant invention identify such a specific improvement to computer capabilities. The instant claims are not directed to improving the existing technological process but are directed to improving the commercial task of providing responses related to beauty topics. The claimed process, while arguably resulting in improved product recommendations related to beauty topics, is not providing any improvement to another technology or technical field as the claimed process is not, for example, improving the processor and computer components that operate the system. Rather, the claimed process is utilizing different data while still employing the same processor and computer components used in conventional systems to improve providing responses related to beauty topics, e.g. commercial process. As such, the claims do not recite specific technological improvements, do not integrate the abstract idea into a practical application, and thus, the Examiner maintains the 101 rejection.
With respect to the rejections made under 35 USC § 103, the Applicant’s arguments filed on 13 April have been fully considered but are not considered persuasive.
In response to the Applicant’s arguments found on page 9 of the remarks stating that “The applied references do not teach or suggest at least the amended language,” the Examiner respectfully disagrees. The cited references of Yaqoob and Dissanayake teach the amended claim limitations. First, the reference of Yaqoob still discloses the amended claim language requesting the LLM to provide a confirmation that the user input relates to one or more topics, wherein the LLM generates a first classification identifying the user input as a topic request, and wherein the LLM generates, based on the first classification, a second classification identifying a category of the topic request, as Yaqoob describes a method for providing item recommendations for a user, utilizing a chatbot for a user to input a specific query, for a response related to a specific type of product of interest, where the user/customer inputs questions such as “What to bring as a gift?” or questions related to specific types of clothing to wear to a wedding (Yaqoob, paragraphs [0044]-[0045], [0073], as well as FIG. 6). Yaqoob inputs the question or query into the chatbot, where the query is related to a specific type of gift that could be given, or what type of clothes to wear to a wedding, which encompasses the newly amended feature of a first classification identifying the user input as a topic request (Yaqoob, see: paragraphs [0045] and [0073]). Next, the chatbot recognizes the query by contextual information given by the user, and responds, providing a type or category of items that are recommended based on the user input query, such as providing recommendations for types of clothing that would be appropriate to wear to a wedding, encompassing the second classification identifying a category of the topic request (Yaqoob, see: paragraphs [0044]-[0045], and [0073], FIG. 6). Yaqoob also describes that based on the first classification, such as the input query from the user into the chatbot, and the second classification, such as the identified category of the topic request, the chatbot responds with a category type of items to recommend in response to the query, such as the clothing items recommended that would be appropriate to wear to a wedding as a guest, as described in paragraph [0073] of Yaqoob, and further described in detail in paragraphs [0090]-[0091], and rendered in FIG. 6. Next, considering that Yaqoob does not mention that the topics are related to beauty products necessarily, the reference of Dissanayake is merely relied upon to demonstrate that the input of a user, regardless of the type of input, is used and analyzed by the system to provide specific responses to input regarding beauty topics (Dissanayake, see: paragraphs [0041], and [0117]-[0118]). Further, Dissanayake describes that through a conversation of an AI system, the user can describe specific skin conditions or concerns, where the system then recognizes the language of the input using natural language data, to provide a specific response to the user related to beauty topics, such as skin issues (Dissanayake, paragraphs [0041], [0117]-[0118], and further described in paragraphs [0039]-[0040], and FIGS. 2B and 6 rendering the analyzing of the input related to beauty topics and displaying the response with recommended beauty topic products, such as “Night Face Cream”). Therefore, the Examiner maintains that the cited references do teach the amended claim limitations, and thus, maintains the 103 rejection.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Adcock, et al. (Patent No. US 12,517,968 B2), describes Systems and methods for generating user-specific textual and image-based outputs, in response to a user query, for provision of a matching item.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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 nonprovisional extension fee (37 CFR 1.17(a)) 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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ASHLEY PRESTON whose telephone number is (571)272-4399. The examiner can normally be reached M-F 9-5.
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/ASHLEY D PRESTON/Primary Examiner, Art Unit 3688
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