DETAILED ACTION
This communication is in response to application no. 19/204416 filed 09 May 2025
Claims 1-20 are currently pending and have been examined.
Claims 1-20 are rejected as shown in this 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 .
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 (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Step 1
Claims 1-19 recite an apparatus which is considered a machine or manufacture. Claim 20 recites a method which is considered a process.
Step 2A-Prong One
(Claims 1 and 20) These claims recite the concept of receiving an input, using a model to generate an output, determining an advertisement related to the input or output, and displaying the advertisement (see “receive a text input to a generative artificial intelligence/machine learning (AI/ML) model; generate, with the generative AI/ML model, a text output based on the text input; determine an advertisement related to at least one of the text input or the text output; modify at least one of the text input or the text output with the advertisement; and display the advertisement at least one of while receiving the text input or while generating the text output by generating the advertisement for selected text of the at least one of the text input or the text output). This concept falls into the certain methods of organizing human activity grouping of abstract ideas including advertising activities. Thus, these claims recite an abstract idea.
(Claims 2-19) The dependent claims also recite the abstract idea found in the independent claim. They do not recite limitations that take the claims out of the abstract idea grouping. Thus, these claims recite an abstract idea.
Step 2A-Prong Two
This judicial exception is not integrated into a practical application. The claims recite the additional element of an apparatus comprising at least one memory and at least one processor (found in claims 1-19) or a processor (found in claim 20) and includes no more than mere instructions to apply the exception using a generic computer component. The apparatus or processor does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Step 2B
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed previously with respect to Step 2A-Prong Two, the additional element in the claim amounts to no more than mere instructions to apply the exception using a generic computer component. The same analysis applies here in Step 2B, i.e., mere instructions to apply an exception using a generic computer component cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B. See MPEP 2106.05(f). The claims do not provide an inventive concept (significantly more than the abstract idea). The claims are ineligible.
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, 2, 5, 7-9, 13, 17, 19, and 20 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by US 2025/0166021 (“Yoneda”).
Regarding Claims 1 and 20, Yoneda teaches a processor-implemented method and an apparatus, comprising: at least one memory; and at least one processor coupled to the at least one memory (See “The present invention relates to an information processing apparatus, an information processing method, and a non-transitory computer readable storage medium” in ¶ 0002.), the at least one processor configured to:
receive a text input to a generative artificial intelligence/machine learning (AI/ML) model (See “When the user U1 asks a question, the information processing apparatus 100 receives the question from the user U1 (Step S101). For example, the information processing apparatus 100 receives the question as text, from the user U1. The information processing apparatus 100 provides information (input information to be input to the generative AI) to the generative AI server 50 for generation of answer contents including an answer to the user U1, the answer corresponding to the question (Step S102)” in ¶ 0029 and Fig. 2.);
generate, with the generative AI/ML model, a text output based on the text input (See “Upon acquisition of the input information to the generative AI from the information processing apparatus 100, the generative AI server 50 inputs the input information provided from the information processing apparatus 100, to the generative AI (Step S103). The generative AI generates answer contents on the basis of the input information (Step S104). For example, the generative AI generates an answer to the question by the user U1 on the basis of the input information and generates answer contents including the answer generated” in ¶ 0031 and Fig. 2.);
determine an advertisement related to at least one of the text input or the text output; modify at least one of the text input or the text output with the advertisement (See “Furthermore, for example, the generative AI selects an advertisement on the basis of the input information and generates answer contents including the advertisement selected. For example, the generative AI selects an advertisement on the basis of a history of any domain frequently used by the user U1, the history being from a domain history of the user U1, and generates answer contents including the advertisement selected. For example, the generative AI selects an advertisement on the basis of a history selected according to the question by the user U1, the history being from the domain history of the user U1, and generates answer contents including the advertisement selected. … Furthermore, for example, the generative AI selects an advertisement according to the generated answer to the question by the user U1 and generates answer contents including the advertisement selected. The generative AI thus generates answer contents including an advertisement selected according to at least one of the user information on the user U1 and the answer generated as the answer to the question by the user U1” in ¶ 0031.); and
display the advertisement at least one of while receiving the text input or while generating the text output by generating the advertisement for selected text of the at least one of the text input or the text output (See “The generative AI server 50 provides information output from the generative AI, to the information processing apparatus 100 (Step S105). For example, the generative AI server 50 provides answer contents generated by the generative AI. Upon acquisition of the answer contents from the generative AI server 50, the information processing apparatus 100 provides the answer contents to the terminal device 10 (Step S106). Upon receipt of the answer contents from the information processing apparatus 100, the terminal device 10 causes the answer contents to be displayed” in ¶ 0032, “The user U1 operating (for example, clicking or tapping on) the advertisement included in the answer contents displayed on the terminal device 10 generates a conversion” in ¶ 0033, and Fig. 2.).
Regarding Claim 2, Yoneda further teaches the at least one processor is further configured to determine the advertisement related to the at least one of the text input or the text output with a secondary AI/ML model (See ¶ 0031 wherein the answer/output and the advertisement are selected using different criteria/parameters. Thus, the AI/ML model used to determine the advertisement is considered different/secondary to the AI/ML utilized to determine the answer/output.).
Regarding Claim 5, Yoneda further teaches the secondary AI/ML model and the generative AI/ML model reside in a cloud network (See “The following description is on an information processing system 1 illustrated in FIG. 1. As illustrated in FIG. 1, the information processing system 1 includes a terminal device 10, a generative AI server 50, and an information processing apparatus 100. The terminal device 10, the generative AI server 50, and the information processing apparatus 100 are communicably connected to one another by wire or wirelessly via a predetermined communication network (a network N). FIG. 1 is a diagram illustrating an example of a configuration of the information processing system 1 according to the embodiment” in ¶ 0018 and Fig. 2 showing the AI server housing the models being remote from the user U1.).
Regarding Claim 7, Yoneda further teaches the at least one processor is further configured to receive the text output at the secondary AI/ML model and modifying the text output at the secondary AI/ML model (See “Upon acquisition of the input information to the generative AI from the information processing apparatus 100, the generative AI server 50 inputs the input information provided from the information processing apparatus 100, to the generative AI (Step S103). The generative AI generates answer contents on the basis of the input information (Step S104). For example, the generative AI generates an answer to the question by the user U1 on the basis of the input information and generates answer contents including the answer generated. Furthermore, for example, the generative AI selects an advertisement on the basis of the input information and generates answer contents including the advertisement selected. For example, the generative AI selects an advertisement on the basis of a history of any domain frequently used by the user U1, the history being from a domain history of the user U1, and generates answer contents including the advertisement selected” in ¶ 0031 and “By providing the text information acquired, advertisement candidate information, user information, and prompt information to the generative AI server 50, the information processing apparatus 100 causes them to be input to the generative AI and answer contents to be generated by the generative AI (Step S202)” in ¶ 0074.).
Regarding Claim 8, Yoneda further teaches the at least one processor is further configured to: generate, with the secondary AI/ML model, an additional advertisement that is related to the advertisement; and display the additional advertisement along with the advertisement, at least one of while receiving the text input or while generating the text output (See “Furthermore, the generative AI generates answer contents including, in a predetermined frame (a third frame), the answer generated as the answer to the question by the user U1 and including, in a first frame, an advertisement selected from candidates for advertisement. Furthermore, the generative AI generates answer contents including, in a first frame, an advertisement selected from candidates for advertisement, and including, in a second frame, an advertisement selected on the basis of predetermined information, such as a bid price” in ¶ 0031.).
Regarding Claim 9, Yoneda further teaches generate with the generative AI/ML model an additional output; determine, with the secondary AI/ML model, an additional advertisement related to at least one of the text input or the text output; modify the additional output with the additional advertisement; and display the additional advertisement while generating the additional output (See the rejections of claims 1 and 2 above. Functionally, there is no difference between determining an advertisement and determining an additional advertisement. Thus, the apparatus found in Yoneda is capable of performing these functions. Functionally, there is no difference between modifying an output with an advertisement and modifying the additional output with the additional advertisement, etc. Thus, the apparatus found in Yoneda is capable of performing these functions.).
Regarding Claim 13, Yoneda further teaches the at least one processor is further configured to receive, at the generative AI/ML model, the advertisement in addition to the text input (See “By providing the text information acquired, advertisement candidate information, user information, and prompt information to the generative AI server 50, the information processing apparatus 100 causes them to be input to the generative AI and answer contents to be generated by the generative AI (Step S202)” in ¶ 0074 and Fig. 9.).
Regarding Claim 17, Yoneda further teaches the at least one processor is further configured to determine the advertisement based on user spatio-temporal context (See “By means of such text of an instruction, an advertisement to be included in the answer contents is selected on the basis of the user information on the user U1, the advertisement candidate information, and the text information on the question” in ¶ 0029, “For example, the user information storage unit 121 stores information indicating attributes, interests and tastes, and behavior histories, of users” in ¶ 0056, and ““User information” represents user information. FIG. 6 illustrates an example where conceptual information, such as “user information #1” and “user information #2”, is stored as “user information”, but information indicating the attributes, such as ages, sexes, and places of residence of the users, information indicating the interests and tastes, and information indicating the behavior histories, are actually stored, for example” in ¶ 0057.).
Regarding Claim 19, Yoneda further teaches the at least one processor is further configured to track usage of the advertisement (See “The user U1 operating (for example, clicking or tapping on) the advertisement included in the answer contents displayed on the terminal device 10 generates a conversion. Upon generation of the conversion, the information processing apparatus 100 memorizes for which advertiser and which advertisement the conversion has been generated. The information processing apparatus 100 then determines cost the advertiser is to be charged, according to the number of conversions counted in a predetermined time period (Step S107)”in ¶ 0033.).
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 of this title, 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.
Claim 3 is rejected under 35 U.S.C. 103 as being unpatentable over Yoneda in view of US 2022/0180190 (“Verma”).
Regarding Claim 3, Yoneda does not expressly teach the generative AI/ML model includes the secondary AI/ML model.
However, Verma teaches the generative AI/ML model includes the secondary AI/ML model (See ¶¶ 0020-0021 and Fig. 2 disclosing a system in which the generated AI/ML model includes an additional ML model (a “Head Discriminator” appended to an adapted GAN), the adapted GAN including a Generator, a Discriminator, and the Head Discriminator, each being an ML module/neural network/computational module.).
It would have been obvious to one having ordinary skill in the art at the time of filing to combine the teachings of Yoneda and Verma to have one model included with the other. The claimed invention is merely a combination of old elements, in the combination each element merely performs the same function as it does separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Claims 4 and 6 are rejected under 35 U.S.C. 103 as being unpatentable over Yoneda in view of US 2016/0092932 (“Bharath”).
Regarding Claim 4, Yoneda teaches the generative AI/ML model resides in a cloud network (See “The following description is on an information processing system 1 illustrated in FIG. 1. As illustrated in FIG. 1, the information processing system 1 includes a terminal device 10, a generative AI server 50, and an information processing apparatus 100. The terminal device 10, the generative AI server 50, and the information processing apparatus 100 are communicably connected to one another by wire or wirelessly via a predetermined communication network (a network N). FIG. 1 is a diagram illustrating an example of a configuration of the information processing system 1 according to the embodiment” in ¶ 0018 and Fig. 2 showing the AI server housing the model being remote from the user U1.).
Yoneda does not expressly teach the secondary AI/ML model resides on an edge device.
However, Bharath teaches the secondary AI/ML model resides on an edge device (See “The client devices 110, the advertisers 120, and the content server 130 are connected via a network 140” in ¶ 0027, “The ad selection server 137 provides advertisements to a client device 110 receiving audio content. In one embodiment, the application on the client device 110 is configured to request advertisements between items of audio content. The application queries the ad selection server 137, which selects an advertisement. The selected advertisement may be an advertisement with pre-recorded audio received from an advertiser 120, or an advertisement based on a personalized text ad from the ad construction server 135” in ¶ 0033, and Fig. 6 showing the layout of the devices wherein the ad selection server 137 is considered an edge device where ad selection takes place.).
It would have been obvious to one having ordinary skill in the art at the time of filing to combine the teachings of Yoneda and Bharath to have ad selection take place at an edge device. The claimed invention is merely a combination of old elements, in the combination each element merely performs the same function as it does separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Regarding Claim 6, Yoneda does not expressly teach the secondary AI/ML model modifies the text input to include the advertisement before the text input is received at the generative AI/ML model.
However, Bharath teaches the secondary AI/ML model modifies the text input to include the advertisement before the text input is received at the generative AI/ML model (See the Abstract, Figs. 1, 2, 7, and 9 wherein the system contains an ad-construction server that generates a personalized text ad according to a template “specifying an ordered combination of text components,” where the personalized text ad includes “the received advertisement text, user information text selected from the obtained user information, and template text,” and an audio advertisement is provided “based on an audio version of the personalized text ad” generated by a text-to-speech algorithm (i.e., a secondary model constructs/updates the text input by incorporating the advertisement text along with other input before that text is provided to the generative text-to-speech model).).
It would have been obvious to one having ordinary skill in the art at the time of filing to combine the teachings of Yoneda and Bharath to modify the text before passing it along. The claimed invention is merely a combination of old elements, in the combination each element merely performs the same function as it does separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Claims 10 and 11 are rejected under 35 U.S.C. 103 as being unpatentable over Yoneda in view of US 11,334,750 (“Arilla”).
Regarding Claim 10, Yoneda does not expressly teach the at least one processor is further configured to receive training input for training the secondary AI/ML model, the training input comprising a brand name and an associated set of at least one of terms or phrases for training the secondary AI/ML model.
However, Arilla teaches the at least one processor is further configured to receive training input for training the secondary AI/ML model, the training input comprising a brand name and an associated set of at least one of terms or phrases for training the secondary AI/ML model (See the abstract, column 1 lines 49-60, column 3 lines 34-49, Figs. 6A and 6B, and Fig. 8 describing a machine learning system that is “trained using attributes that represent each of a plurality of training images” from a “brand entity,” where each training image for the brand entity is represented by imagery attributes and textual attributes such as caption words, character counts, and hashtags (i.e., brand-associated images together with associated textual terms/phrases used as the training input for the model).).
It would have been obvious to one having ordinary skill in the art at the time of filing to combine the teachings of Yoneda and Arilla to train based on brand name and associated terms/phrases. The claimed invention is merely a combination of old elements, in the combination each element merely performs the same function as it does separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Regarding Claim 11, Yoneda does not expressly teach the training input further comprises weights of each term or phrase of the set of at least one of terms or phrases.
However, Arilla teaches the training input further comprises weights of each term or phrase of the set of at least one of terms or phrases (See “In some arrangements, neural network techniques may be implemented using the data representing the images (e.g., a vector of numerical values to represent each attribute, etc.) to invoke training algorithms for automatically learning the images and related information. Such neural networks typically employ a number of layers. Once the layers and number of units for each layer is defined, weights and thresholds of the neural network are typically set to minimize the prediction error through training of the network. Such techniques for minimizing error can be considered as fitting a model (represented by the network) to training data” in column 8 lines 6-16 and “One or more techniques may be employed to assist the image machine learning system 408 in identifying positive feedback (e.g., data representing top performing images) and negative feedback (e.g., data representing poor performing images); for example one more weighting techniques may be employ to highlight some feedback and reduce the effects of other feedback” in column 10 line 63-column 11 line 3.).
It would have been obvious to one having ordinary skill in the art at the time of filing to combine the teachings of Yoneda and Arilla to utilize weights. The claimed invention is merely a combination of old elements, in the combination each element merely performs the same function as it does separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Claims 12 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Yoneda in view of US 2015/0161672 (“Jung”).
Regarding Claim 12, Yoneda does not expressly teach the at least one processor is further configured to prevent displaying of the advertisement in response to detecting a blacklisted topic in the at least one of the text input or the text output
However, Jung teaches the at least one processor is further configured to prevent displaying of the advertisement in response to detecting a blacklisted topic in the at least one of the text input or the text output (See ¶¶ 0033 and 0069.).
It would have been obvious to one having ordinary skill in the art at the time of filing to combine the teachings of Yoneda and Jung to consider the content which will be displayed in connection with the advertisement when making a decision on advertisement selection. The claimed invention is merely a combination of old elements, in the combination each element merely performs the same function as it does separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Regarding Claim 15, Yoneda does not expressly teach the at least one processor is further configured to block the advertisement from displaying.
However, Jung teaches the at least one processor is further configured to block the advertisement from displaying (See ¶¶ 0033 and 0069.).
It would have been obvious to one having ordinary skill in the art at the time of filing to combine the teachings of Yoneda and Jung to consider the content which will be displayed in connection with the advertisement when making a decision on advertisement selection. The claimed invention is merely a combination of old elements, in the combination each element merely performs the same function as it does separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Claim 14 is rejected under 35 U.S.C. 103 as being unpatentable over Yoneda in view of US 2015/0012354 (“Pedersen”).
Regarding Claim 14, Yoneda does not expressly teach the at least one processor is further configured to display an indication of the advertisement along with the advertisement.
However, Pedersen teaches the at least one processor is further configured to display an indication of the advertisement along with the advertisement (See “One common example of native advertising appears in the context of internet search engines, where sponsored search results can be displayed in-line with non-sponsored search result, but are typically identified as advertising by visual cues such as a colored background or text stating that the item is an advertisement” in ¶ 0006.).
It would have been obvious to one having ordinary skill in the art at the time of filing to combine the teachings of Yoneda and Pedersen to utilize an indication. The claimed invention is merely a combination of old elements, in the combination each element merely performs the same function as it does separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Claim 16 is rejected under 35 U.S.C. 103 as being unpatentable over Yoneda in view of US 2017/0041648 (“Dalrymple”).
Regarding Claim 16, Yoneda does not expressly teach the at least one processor is further configured to modify at least one of a temperature parameter, a Top P parameter, or a penalty for the advertisement in response to a weight assigned to an advertiser sponsoring the advertisement.
However, Dalrymple teaches the at least one processor is further configured to modify at least one of a temperature parameter, a Top P parameter, or a penalty for the advertisement in response to a weight assigned to an advertiser sponsoring the advertisement (See ¶¶ 0089 and 0091.).
It would have been obvious to one having ordinary skill in the art at the time of filing to combine the teachings of Yoneda and Dalrymple to modify parameters. The claimed invention is merely a combination of old elements, in the combination each element merely performs the same function as it does separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Claim 18 is rejected under 35 U.S.C. 103 as being unpatentable over Yoneda in view of US 2010/0036703 (“Chen”).
Regarding Claim 18, Yoneda teaches modifying the at least one of the text input or the text output (see the rejection of claim 1 above); however, Yoneda does not expressly teach doing this modification based on at least one of a frequency penalty or a presence penalty.
However, Chen teaches based on at least one of a frequency penalty or a presence penalty (See “The ad server 104 includes an ad selection module 122. The ad selection module 122 determines which of the ads 120a-b are sent to the client 108 for presentation with the publisher page 112. For example, the ad selection module 122 can compare targeting information from the client 108 and the publisher page 112 to the ads 120a-b. If the ad selection module 122 finds a match, then the ad selection module 122 schedules the matching ad for delivery to the requesting publisher page. In addition, the ad selection module 122 can compare the bids submitted by the advertisers 106a-b as well as other information, such as the advertiser's remaining budget or the frequency with which an advertiser's ads are placed. The ad selection module 122 determines one or more winners of an ad placement selection process” in ¶ 0021.).
It would have been obvious to one having ordinary skill in the art at the time of filing to combine the teachings of Yoneda and Chen to utilize budget constraints and frequency constraints to modify input/output. The claimed invention is merely a combination of old elements, in the combination each element merely performs the same function as it does separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MEREDITH A LONG whose telephone number is (571)272-3196. The examiner can normally be reached Mon - Fri 9:30 - 6.
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/MEREDITH A LONG/Primary Examiner, Art Unit 3622
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