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 .
Response to Amendment
This Action is in response to the Amendment filed November 25, 2025. Claims 1-2, 6-7, 11-12 and 16-20 have been amended. Claims 1-20 are currently pending and have been examined in this application.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 2 and 12 rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, or for pre-AIA the applicant regards as the invention.
Claims 2 and 12 recite the limitation “the external apparatus” in lines 6 and 7 respectively. There is insufficient antecedent basis for these limitation in the claims.
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.
Specifically, claims 1-20 are directed toward at least one judicial exception without significantly more. In accordance with MPEP 2106, the rationale for this determination is explained below:
Representative claim 1 is directed towards an apparatus, claim 11 is directed towards a method, which are statutory categories of invention.
Although, claim 1 is directed toward a statutory category of invention, the claim however, is directed towards an abstract idea. The limitations that recite the abstract ideas are: obtain viewing group information of a user by inputting context information including profile information of the user and use history information, wherein the viewing group information corresponds to at least one viewing group representing the user among a plurality of pre-stored viewing groups;
display an advertisement content identified based on the obtained viewing group information, obtain feedback information of the user related to the displayed advertisement content.
These limitations, comprise commercial interactions including, advertising, marketing or sales activities or behavior; business relations; and managing personal behavior including following rules or instructions. And are thus, directed towards the abstract grouping of Certain Methods of Organizing Human Activity in prong one of step 2A of the Alice/Mayo test (see MPEP 2106.04(a)(2) II).
This judicial exception is not integrated into a practical application because, when analyzed under prong two of step 2A of the Alice/Mayo test (see MPEP 2106.04(d)), the additional elements provided by the claim as a whole are recited at a high level of generality and amounts to generally “apply” the abstract by computer components. In particular the claim recites the additional elements of: a display; a memory storing a trained first artificial intelligence model; of the electronic apparatus into the first artificial intelligence model; control the display to; and update the trained first artificial intelligence model to a second artificial intelligence model retrained based on the input context information and the obtained feedback information, which merely uses the computer as a tool to perform the abstract ideas, see MPEP 2106.05(f), and/or a field of use in which to apply the abstract idea, see MPEP 2106.05(h). Simply implementing the abstract idea by computer components is not a practical application of the abstract idea. The additional elements do not involve improvements to the functioning of a computer, or to any other technology or technical field (MPEP 2106.05(a)), the claims do not apply the abstract idea with, or by use of, a particular machine (MPEP 2106.05(b)), the claims do not effect a transformation or reduction of a particular article to a different state or thing (MPEP 2106.05(c)), and the claims do not apply or use the abstract idea in some other meaningful way beyond generally linking the use of the abstract idea to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception (MPEP 2106.05(e). Therefore, the claims do not, for example, purport to improve the functioning of a computer. Nor do they effect an improvement in any other technology or technical field. Accordingly, the additional elements do not impose any meaningful limits on practicing the abstract idea, and the claims are directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements amount to applying the abstract idea on a computer. Viewing these limitations individually, the limitations generically referring to a display, a memory, a trained first artificial intelligence model, at least one processor, an electronic apparatus, a second artificial intelligence model, do not constitute significantly more because they are simply an attempt to limit the abstract idea to a particular technological environment1. Viewing these limitations as a combination, the claims merely instruct the practitioner to implement the abstract idea with a high-level of generality executing data processing functions. Merely applying an exception using generic computer components cannot provide an inventive concept. See TLI Communications LLC v. AV Automotive LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (mere recitation of concrete or tangible components is not an inventive concept). Therefore, the limitations of the claim as a whole, when viewed individually and as an ordered combination, do not amount to significantly more than the abstract idea.
A review of dependent claims 1-10, likewise, do not recite any limitations that would remedy the deficiencies outlined above as they do not add any elements which integrate the abstract idea into a practical application or constitute significantly more. While they may slightly narrow the abstract idea by further describing it, they do not make it less abstract and are rejected accordingly. Further still, claim 11-20 suffer from substantially the same deficiencies as outlined with respect to claim 1-10 and are also rejected accordingly.
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.
Claims 1-9 and 11-19 are rejected under 35 U.S.C. 103 as being unpatentable over Khidekel (US Publication 2021/0209641) in view of Arora (US Publication 2017/0061528).
A. In regards to Claims 1 and 11, Khidekel discloses an electronic apparatus and method comprising:
a display; Khidekel [0080];
a memory storing a trained first artificial intelligence model; Khidekel [0079];
and at least one processor configured to: obtain viewing group information of a user by inputting context information including profile information of the user and use history information of the electronic apparatus into the first artificial intelligence model, wherein the viewing group information corresponds to at least one viewing group representing the user among a plurality of pre-stored viewing groups; Khidekel [0035: a machine learning model can be trained based on training inputs including multiple sets of characteristics describing a target audience; 0019: content providing servers may also gather characteristics about target audiences of the client devices; 0020: content providing servers can also provide the corresponding characteristics associated with the client devices (which may include characteristics of users of such client devices); 0027: outputs of multiple machine learning models may be compared, and an optimal (based on current information) set of content item components may be determined. The Optimal set of content item components together may form a content item in accordance with an associated content template, specific to a target with the input set of characteristics; 0036: After the initial training, a processing device of the present disclosure can group the machine learning models into the first subset based on a reliability criterion, as well as any correlation to KPIs; 0045: the processing device, for the group of requests to be processed using sufficiently stable trained machine learning models [saved group], inputs respective set of characteristics associated with a request in the first group into each trained machine learning model in the first subset of the trained machine learning models. In one implementation, the processing device can determine from the request the set of characteristics such as demographic characteristics (such as age or gender), contextual characteristics (such as device brand, operating system, user time, geographic location), historical or user behavioral characteristics];
control the display to display an advertisement content identified based on the obtained viewing group information; Khidekel [0018: content item includes a personalized advertisement; 0058: machine learning model is trained to output a probability that a target associated with an input set of characteristics would perform a target action responsive to being presented with a content item; 0050: content providing server can present the content item to a client device],
obtain feedback information of the user related to the displayed advertisement content; Khidekel [0044: processing device can receive responses of the targets when content items are presented to the targets];
additionally, and/or alternatively Arora discloses, obtain feedback information of the user related to the displayed advertisement content; Arora [0023: provide an electronic survey to the user in which the user can input feedback related to the content item; 0049: feedback signals can be received via an electronic feedback interface presented along with content items (e.g., advertisements) displayed];
Khidekel does not specifically disclose, and update the trained first artificial intelligence model to a second artificial intelligence model retrained based on the input context information and the obtained feedback information. This is disclosed by Arora [0005: generate machine learning model using historical signals and corresponding features; 0049: to generate the model, the machine learning engine can receive feedback signals associated with content impressions from computing devices; 0068: processing system can generate a second model based on feedback signals received for content items selected using the first model. In some implementations, data processing system can base the second model on a click through rate or conversion rate associated with content items selected using the first model; 0024: obtain features associated with content item or the impression of the content item; features can include, e.g., time of day, subject matter of the web page on which the content item is displayed, keywords used to select the content item, information about the computing device (e.g., type of computing device, operating system, geographic location), content provider, etc. The system can input the feedback received from the user and the features associated with the content item impression into a machine learning model].
before the effective filing date of the claimed invention it would have been obvious for those skilled in the art to modify the teachings of Khidekel with the teachings from Arora with the motivation of to improve content selection (e.g., content items such as electronic documents or online electronic advertisements) based on survey feedback; for example, by adjusting the ranking and pricing of advertisements in an online auction based on a user feedback received from a survey presented via an historical advertisement impression. Arora [0017].
additionally, and/or alternatively Hong discloses, wherein the viewing group information corresponds to at least one viewing group representing the user among a plurality of pre-stored viewing groups. Hong [0047: an audience group identifier is stored in the user profile store and associated with user identifying information of users in the corresponding audience group].
Before the effective filing date of the claimed invention it would have been obvious for those skilled in the art to modify the teachings of Khidekel with the teachings from Hong with the motivation to provide information of entities sponsored content to effectively identify sponsored content to for presentation to various users and to identify which group of users is the optimal to target with sponsored content. Hong [0003].
B. In regards to Claims 2 and 12, Khidekel discloses wherein the profile information comprises: at least one of age, sex, nationality, and a residential area of the user, and the use history information comprises: at least one of use information for a function of the electronic apparatus, use information for an application installed in the electronic apparatus, and use information for the external apparatus that communicates with the electronic apparatus. Khidekel [0019: characteristics can include demographic information such as, an age or a gender; contextual information such as, a brand of the client devices, an operating system of the client devices; historical features such as, a number of impressions, time since the last impression, a number of clicks; 0022: client devices may each include a web browser and/or a client application e.g., a mobile application or a desktop application) for viewing contents].
C. In regards to Claims 3 and 13, Khidekel discloses wherein the obtained viewing group information comprises: at least one of a viewing group representing the user and a probability value corresponding to the at least one of a viewing group representing the user. Khidekel [0035: training outputs indicating whether or not target actions (e.g., a click on the content item, a viewing of the content item) are performed by the targets responsive to being presented with content items].
D. In regards to Claims 4 and 14, Khidekel discloses, further comprising: a communication interface configured to communicate with a server, wherein the at least one processor is configured to: transmit the obtained viewing group information to the server through the communication interface, Khidekel [0024: communication module can communicate with the content providing servers to receive requests for a content item, corresponding sets of characteristics to the requests, and any responses e.g., a viewing of the content item],
receive the advertisement content identified based on the obtained viewing group information from the server through the communication interface, Khidekel [0019: content providing servers may provide a web page or any other medium that contains various contents, e.g., a personalized advertisement, to the client devices; 0044: processing device can assign a small portion, such as 5%, of the requests to another group and provide a randomly generated content item to the respective targets]
and control the display to display the received advertisement content. Khidekel [0050: the content providing server can present the content item to a client device].
E. In regards to Claims 5 and 15, Khidekel discloses, further comprising: a communication interface configured to communicate with a server, wherein the at least one processor is configured to: identify viewing groups in a predetermined number based on probability values corresponding to each of a plurality of viewing groups; Khidekel [0043: once the processing device determines the ratio, the processing device can determine a number of requests to assign to the first group and a number of requests to assign to the second group based on the ratio];
transmit the identified viewing groups in the predetermined number to the server through the communication interface; Khidekel [0019: content providing servers may also gather characteristics about target audiences of the client devices; 0044: processing device can assign a small portion, such as 5%, of the requests to another group and provide a randomly generated content item to the respective targets]
and based on receiving at least one advertisement content corresponding to the identified viewing groups in the predetermined number from the server through the communication interface, control the display to display the at least one advertisement content based on probability values corresponding to each of the identified viewing groups in the predetermined number. Khidekel [0050: the content providing server can present the content item to a client device: 0025: the trained machine learning models can each predict a probability of a target audience having a respective set of input characteristics performing a target action (such as, a click) in response to being presented with a respective content item; 0046: the output can be a numerical value between 0 and 1—the higher the number, the higher the probability].
F. In regards to Claims 6 and 16, Khidekel does not specifically disclose, wherein the obtained feedback information comprises at least one: of information on whether an advertisement content is displayed, information on whether the advertisement content was selected, information on whether a service corresponding to the advertisement content was used, information on an accumulated number of times of display of the advertisement content, information on an accumulated number of times of selection of the advertisement content, information on an accumulated number of times of use of the service corresponding to the advertisement content, and information on contribution of the advertisement content. This is disclosed by Arora [0023: provide the survey responsive to user interaction with the content item, responsive to the impression; 0017: ranking and pricing of advertisements in an online auction (service) based on a user feedback; 0027: selecting content based on feedback signals using electronic content selection infrastructure;]. The motivation being the same as stated in claim 1.
G. In regards to Claims 7 and 17, Khidekel discloses, wherein the at least one processor is configured to: identify at least one label information based on the input context information; Khidekel [0051: the processing device can use the set of characteristics associated with the indication as a training input and the indication as a label to further train the machine learning model];
wherein update the first artificial intelligence model to a second artificial intelligence model retrained based on the input context information, the at least one label information, and the obtained feedback information; Khidekel [0068: processing system can generate a second model based on feedback signals received for content items selected using the first model. In some implementations, data processing system can base the second model on a click through rate or conversion rate associated with content items selected using the first model; 0024: obtain features associated with content item or the impression of the content item; features can include, e.g., time of day, subject matter of the web page on which the content item is displayed, keywords used to select the content item, information about the computing device (e.g., type of computing device, operating system, geographic location), content provider, etc. The system can input the feedback received from the user and the features associated with the content item impression into a machine learning model; 0051: use the indication as a training input and the indication as a label to further train the machine learning model];
and the first artificial intelligence model being a model trained based on a plurality of training context information, a plurality of training label information, and a plurality of training feedback information for a plurality of advertisement contents. Khidekel [0035: machine learning model can be trained based on training inputs including multiple sets of characteristics describing a target (e.g., a target audience of the content item), and corresponding training outputs indicating whether or not target actions (e.g., a click on the content item, a viewing of the content item, a conversion, after having seen or clicked the content item, purchasing an item presented in the content item or any other type of conversion) are performed by the targets responsive to being presented with content items; 0051: use the indication as a label to further train the machine learning model].
H. In regards to Claims 8 and 18, Khidekel discloses, further comprising: a communication interface configured to communicate with a server, wherein the at least one processor is configured to: Khidekel [0022: client devices may each include a web browser and/or a client application application for viewing contents provided by the content providing servers via user interfaces]
transmit first viewing group information obtained through the first artificial intelligence model to the server through the communication interface; Khidekel [0019: content providing servers may also gather characteristics about target audiences of the client devices; 0044: processing device can assign a small portion, such as 5%, of the requests to another group and provide a randomly generated content item to the respective targets; 0040: content items can later be included, by the content providing servers, in a web page to be loaded by a web browser of the client devices. Examples of a content item can include a personalized advertisement, media (a video or music)];
Khidekel discloses, receiving a first advertisement content corresponding to the transmitted first viewing group information from the server through the communication interface, but does not specifically disclose, control the display to display the first advertisement content in a first style; this is disclosed by Arora [0042: webpage feature data structure can further include information about the content slot for which a content item is requested, such as type of content slot (e.g., banner advertisement, popup window, embedded advertisement, search advertisement)]; the motivation being the same as stated in claim 1.
transmit second viewing group information obtained through the second artificial intelligence model to the server through the communication interface; Khidekel [0022: client devices may each include a web browser and/or a client application application) for viewing contents provided by the content providing servers via user interfaces; 0048: for each request in the second group, generates a content item based on a content template associated with one of the second subset of the trained machine learning models]
Khidekel discloses, based on receiving a second advertisement content corresponding to the second viewing group information from the server through the communication interface, but does not specifically disclose, control the display to display the second advertisement content in a second style. This is disclosed by Arora [0083: data processing system can also determine a second feature of the second candidate content item. Example features associated with a content item can include keywords, type of content item (e.g., text, image, video, audio, search advertisement, popup window, banner advertisement, mobile advertisement, desktop advertisement)]. The motivation being the same as stated in claim 1.
I. In regards to Claims 9 and 19, Khidekel discloses, wherein the at least one processor is configured to: obtain updated viewing group information of the user by inputting the context information into the second artificial intelligence model; Khidekel [0051: train the second subset of the plurality of trained machine learning models using the characteristics, the content items generated based on the characteristics, and the received indications associated with the content items. In one implementation, each trained machine learning model of the second subset of the trained machine learning models is trained for a respective content template using a) respective sets of characteristics of targets (groups) associated with requests for which the respective content template was selected; 0062: processing device inputs the respective set of characteristics associated with the request into each of the first subset of the second plurality of trained machine learning models associated with elements for the respective component of selected content template; characteristics including demographic characteristics (such as age or gender), contextual characteristics (such as device brand, operating system, user time, geographic location)];
and based on obtaining an advertisement content on a basis of the updated viewing group information, control the display to display the advertisement content and an indicator corresponding to the updated second artificial intelligence model. Khidekel [0027: outputs of multiple machine learning models may be compared, and an optimal (based on current information) set of content item components may be determined; the optimal set of content item components together may form a content item in accordance with an associated content template, specific to a target with the input set of characteristics; 0064: processing device can select any one of the elements to be included in the content item in accordance with the selected content template; 0068: processing device can receive an indication as to whether the associated respective target performed a target action responsive to presentation of the respective content item].
Claims 10 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Khidekel (US Publication 2021/0209641) in view of Arora (US Publication 2017/0061528). In further view of Atcheson (US Publication 2020/0160229).
A. In regards to Claims 10 and 20, Khidekel does not specifically disclose, wherein the indicator comprises: at least one of icon information indicating update information, version information of the second artificial intelligence model, and text information indicating an update. This is disclosed by Atcheson [0039: first output may be supplied by the user experience system to a different machine learning model to generate a second output, and the process may be repeated for as many different machine learning models; 0095: current progress is represented in the user interface. In addition, the user interface includes an icon indicating the selected data source to be used in training the selected classifier model].
Before the effective filing date of the claimed invention it would have been obvious for those skilled in the art to modify the teachings of Khidekel with the teachings from Atcheson with the motivation to provide a system that ensures different machine learning models input data that is of an appropriate type and format before being applied to a machine learning model, so that outputs generated using disparate models and data sources, are able to leverage different data sets in generating a user experience. Atcheson [0004].
Response to Arguments
Applicant's other filed arguments have been fully considered but have not been found persuasive.
A. Regarding the 35 U.S.C. § 101 rejection Applicant argues that the claims are not directed to an abstract idea because they are directed to a specific technical process for dynamically training and updating an artificial intelligence (Al) model based on context information and user feedback to improve the accuracy of advertisement selection in an electronic apparatus. The Examiner respectfully disagrees. The claims are directed to an abstract idea, grouped under Certain Methods of Organizing Human Activity because they entail commercial interactions including, advertising, marketing or sales activities or behavior; business relations; (using user feedback to improve advertisement selection accuracy) and managing personal behavior including following rules or instructions (obtaining user provided profile, use history and feedback). Applying the claims via an AI model does not remove them from the realm of the abstract idea. See SAP America, Inc. v. InvestPic, LLC, 898 F.3d 1161, 1163, 127 USPQ2d 1597, 1599 (Fed. Cir. 2018) (holding that claims to a ‘‘series of mathematical calculations based on selected information and the presentation of the results of those calculations’’ are directed to abstract ideas);
Applicant submits that the claims are integrated into a practical application by steps tied to a specific machine, which is an electronic apparatus having a display, a memory storing a trained first artificial intelligence model, and at least one processor configured to execute the claimed functions. The Examiner respectfully disagrees. Merely applying the abstract idea by a computer apparatus using artificial intelligence algorithm does not constitute a practical application integrated by the additional elements. See TLI Communications LLC v. AV Automotive LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (“It is well-settled that mere recitation of concrete, tangible components is insufficient to confer patent eligibility to an otherwise abstract idea”). That the retraining process is a technological operation that enhances the performance of the Al model in identifying appropriate advertisement content based on dynamically changing input parameters, does not amount to a technological improvement but rather solving an entrepreneurial problem.
Applicant argues that the claims include an inventive concept that transforms the abstract idea into a patent-eligible subject matter. The Examiner respectfully disagrees. As, shown above, merely applying the abstract idea by use of a model/algorithm is not an inventive concept. See id. Furthermore, the specification provides no technical evidence/technical support that an AI model is improved. Indeed, the improvement proffered by the specification is that “as update is repeated, the user’s behavior is applied more, and thus the user’s satisfaction for output data can be improved, and the target performance of providing an appropriate advertisement can be improved” [133]. Even assuming that the algorithms claimed are groundbreaking, innovative or even brilliant, the claims are ineligible because their innovation is an innovation in ineligible subject matter because they are nothing but a series of mathematical algorithms based on selected information and the presentation of the results of those algorithms. Thus, the advance lies entirely in the realm of abstract ideas, with no plausible alleged innovation in the non-abstract application realm. An advance of this nature is ineligible for patenting. See SAP America, Inc. v. InvestPic, LLC, 898 F.3d 1161, 1163, 127 USPQ2d 1597, 1599 (Fed. Cir. 2018).
As such, the claims as a whole, in view of Alice, do not connote an improvement to another technology or technical field; the claims do not amount to an improvement to the functioning of a computer itself; and the claims do not move beyond a general link of the use of the abstract idea to a particular technological environment. Therefore, the Examiner considers that the current 35 U.S.C. § 101 rejection has not been overcome by the Applicant.
B. In regards to to the 35 U.S.C. §103 rejection Applicant’s arguments are moot in light of the new grounds of rejection.
C. Applicant’s arguments relating to the dependent claims are rejected accordingly to independent claims 1 and 11.
Conclusion
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 extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Errol CARVALHO whose telephone number is (571)272-9987. The examiner can normally be reached on M-F 9:30-7:00 Alt Fri.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Ilana Spar can be reached on 571-270-7537. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/E CARVALHO/
Primary Examiner, Art Unit 3622
1 See, Alice Corp. Pty Ltd. v. CLS Bank lnt'l, 134 S. Ct. 2347, 2360 (2014) (noting that none of the hardware recited “offers a meaningful limitation beyond generally linking ‘the use of the [method] to a particular technological environment,’ that is, implementation via computers” (citing Bilski v. Kappos, 561 U.S. 593, 610-11 (2010))).