DETAILED ACTION
This is in reference to communication received 24 September 2025. Claims 1 – 2, 4 – 8 and 10 – 11 are pending for examination. 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 § 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 1 – 2, 4 – 8 and 10 – 11 are 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.
Independent claim 10 and represented claims 1 and 11 recites the limitation(s):
acquire the datasets by, based on a user of the one or more users transmitting, from a user terminal, a request for displaying a webpage of the one or more webpages that provide a search result screen, storing a time of request and at least one of: …. However, these limitation are confusing. Applicant has not positively claimed which player acquires the datasets.
Dependent claims 2 and 4 – 8 inherit the deficiencies of parent claim 1 they claim dependency from and are also rejected under 35 U.S.C. 112(b) or (for pre-AIA ) 35 U.S.C. 112, second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor, a joint inventor, or (for pre-AIA ) the applicant regards as the invention.
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 – 2, 4 – 8 and 10 – 11 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.
Independent claim 10, representative claims 1 and 11, in part is directed toward a statutory category of invention, the claim appears to be directed toward a judicial exception namely an abstract idea. Claim 10 recites invention directed to evaluation of search ranking. When a request content is received from a user, based on the user who made the request for the content, datasets are acquired including content identification information identifying displayed search result at the time of the request, or selection information indicating whether the user selected a search result from among the displayed search results, which, pursuant to MPEP 2106.04, is aptly categorized as a method of organizing human activity (i.e. advertising). Therefore, under Step 2A, Prong One, the claims recite a judicial exception.
In addition, based on a plurality of changes in at least one requirement related to the first search policy, acquired datasets are segmented into plurality of pieces, wherein the at least one requirement is based on an order to display the one or more search results, a second search policy is generated for each of the segmented data, off-policy evaluation is performed and prediction result indication performance of the second search policy is provided and second search policy is used for responding to new request for content from users, which, pursuant to MPEP 2106.04, is aptly categorized as a method of organizing human activity (i.e. advertising). Therefore, under Step 2A, Prong One, the claims recite a judicial exception.
Next, the aforementioned claims recite additional functional elements that are associated with the judicial exception, including: generating a learning model related to a second policy for each of segments by training a machine learning algorithm using each of the plurality of pieces of the segmented data; performing an off-policy evaluation for the second policy using an estimated value approximated from each of the plurality of pieces of the segmented data, indicate a performance of the second policy based on the off- policy evaluation; and implementing the second policy based on the prediction result. Not only do these features fail to integrate the abstract idea into a practical application (see below), but it can also reasonably be seen as the conventional application of well-known machine learning concepts to build and train a model to implement the abstract idea on a computer, and merely uses a computer as a tool to perform the abstract idea. See MPEP 2106.05(f).
Represented claims 1 and 11, which do recite statutory categories (machine, product of manufacture, for example), the same analysis as above applies to these claims since the method steps are the same. However, the judicial exception is not integrated into a practical application. These claims add the generic computer components (additional elements) of a system comprising one or more hardware processors and a memory (claim 1), and a non-transitory machine-readable medium comprising instructions that when executed by a processor of a machine cause the machine to perform the method addressed above (claim 11).
The processor, memory, and non-transitory machine-readable medium are recited at a high-level of generality such that they amount to no more than mere instructions to apply the exception using a generic computer component. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea.
The claims do 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 element of the processor, memory, and non-transitory machine-readable medium amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claims are not patent eligible.
Additionally claim 11 is directed to claiming a transitory computer-readable medium. Examiner suggests to amend the “computer-readable medium” to “non-transitory computer-readable medium”.
When taken as an ordered combination, nothing is added that is not already present when the elements are taken individually. When viewed as a whole, the marketing activities amount to instructions applied using generic computer components.
As for dependent claims 2 and 4 – 8, these claims recite limitations that further define the same abstract idea of defining additional limitations that further limit the abstract idea with details regarding descriptions of various data, utilization of the ML model, determining the strength of data associations, data constraints, and suggested modes of communication. Thus, the dependent claims merely provide additional non-structural (and predominantly non-functional) details that fail to meaningfully limit the claims or the abstract idea(s).
Therefore, claims 1 – 2, 4 – 8 and 10 – 11 are not drawn to eligible subject matter, as they are directed to an abstract idea without significantly more.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 4, 6 and 10 – 11 are rejected under 35 U.S.C. 103 as being unpatentable over Kagian et al. US Publication 2018/0232660 in view of Li et al. US Publication 2014/0351179.
Regarding claim 10 and representative claims 1 and 11, Kagian teaches system and method for generating an updated model related to advertisement selection. Kagian teaches a search ranking evaluation system and evaluation method comprising [Kagian, 0041, Fig. 4 and associated disclosure].
store, based on the one or more users accessing a website and registering thereon from the one or more user terminals (Kagian, The ad log database 510 in this example may continuously receive and store ad related data for online training) [Kagian, 0054], feature information comprising:
attributes of the one or more users [Kagian, see at least, 0110]
Kagian does not explicitly teach acquiring historical behavior information. However, Li teaches system and method for acquiring historical behavior information of user(s) for a social data source [Li, --72] by using a crawler server 2 collects basic information of a user, historical behavior information of the user, and the like from the social networking site 1 (that is, a social data source such as MicroBlog, Facebook, or Twitter). The basic information of the user includes profile data, such as a user name, a nickname, and a school; and the historical behavior information of the user refers to a related operation of the user in a website, such as information browsing, information reposting, original information publishing, and information commenting) [Li, 0060]
Therefore, at the time of filing, it would have been obvious to one of ordinary skill in the art to modify Kagian by adopting teachings of Li and user crawler server to gather online user traffic for improve target advertising.
Kagian in view of Li teaches system and method further comprising:
acquire history data of one or more users that includes datasets, the history data being a record obtained when a first search policy was implemented, and the datasets each comprising multiple pieces of data acquired each time the one or more users make a search request on a search screen via one or more user terminals (Li, the crawler server 2 collects basic information of a user, historical behavior information of the user, and the like from the social networking site 1 (that is, a social data source such as MicroB!og, Facebook, or Twitter). The basic information of the user includes profile data, such as a user name, a nickname, and a school; and the historical behavior information of the user refers to a related operation of the user in a website, such as information browsing, information reposting, original information publishing, and information commenting) [Li, 0060];
store, based on the one or more users accessing a website and registering thereon from the one or more user terminals (Kagian, The ad log database 510 in this example may continuously receive and store ad related data for online training) [Kagian, 0054], feature information comprising:
attributes of the one or more users [Kagian, see at least, 0110], and
the history data, wherein the history data comprises at least one of browsing histories of the one or more users or usage histories of the one or more users
(Li, The basic information of the user includes profile data, such as a user name, a nickname, and a school; and the historical behavior information of the user refers to a related operation of the user in a website, such as information browsing, information reposting, original information publishing, and information commenting) [Li, 0060];
transmit first website display information to the one or more user terminals to cause the one or more user terminals to display one or more webpages on the website comprising one or more search results (Kagian, An advertisement is selected at 1414 based on the model. The advertisement is provided at 1416 with a presentation instruction.) [Kagian, 0112];
acquire the datasets by, based on a user of the one or more users transmitting, from a user terminal, a request for displaying a webpage of the one or more webpages that provide a search result screen, storing a time of request and at least one of:
content identification information identifying displayed search result at the time of the request, or
selection information indicating whether the user selected a search result from among the displayed search results
(Kagian, the ad related information may include preferred ad type of the user and current time, date and environment with respect to the ad request. The ad request analyzer 1310 may send all of the information to the model based ad selector 1320 for ad selection and to the ad presentation instruction generator 1340 for generating ad presentation instructions; User interactions with the web content or advertisement may be achieved via the I/O devices 1550 and provided to the adaptive model training engine 140 and/or other components of systems 100 and 200, e.g., via the network 120.) [Kagian, 0107, 0114];
generate a plurality of pieces of segmented data by segmenting the datasets based on a plurality of changes in at least one requirement related to the first search policy such that a set of search targets or a set of search results are unchanged in each of a plurality of segments (Kagian, ad related models 410 may include e.g. models for predicting a probability of clickthrough rate for an advertisement 412; models for predicting a probability of ad conversion rate for an advertisement 414; models for predicting total ad revenue for an advertisement 416; and models for predicting user experience for an advertisement 418) [Kagian, 0050], wherein the at least one requirement is based on an order to display the one or more search results (Kagian, The user activities may be used to select and/or rank advertisements based on a model stored in the ad related model database 150.) [Kagian, 0041];
generate a learning model related to a second search policy for each of the plurality of segments in the segmented data by training a machine learning algorithm using each of the plurality of pieces of the segmented data (Kagian, a method for generating an updated model related to advertisement selection is disclosed. A request is obtained for updating a model to be utilized for selecting an advertisement. A plurality of copies of the model is generated. The model is pre-selected based on a performance metric related to advertisement selection. Based on each of the plurality of copies, a candidate model is created by modifying one or more parameters of the copy of the model to create a plurality of candidate models. One of the plurality of candidate models is selected based on the performance metric. The steps of generating, creating, and selecting are repeated until a predetermined condition is met. The model is updated with the latest selected candidate model when the predetermined condition is met) [Kagian, 0006];
perform off-policy evaluation for the second search policy using an estimated value approximated from each of the plurality of pieces of the segmented data (Kagian, In the end of each tuning cycle (e.g., an hour or 4 model training periods), each model version is evaluated and the current best hyperparameters set and resulting model are identified. The training during the next cycle will continue from the best performing model with new generated variations of its hyper-parameters.) [Kagian, 0043];
outputting a prediction result indicating a performance of the second search policy based on the off-policy evaluation (Kagian, ad related models 410 may include e.g. models for predicting a probability of click-through rate for an advertisement 412; models for predicting a probability of ad conversion rate for an advertisement 414; models for predicting total ad revenue for an advertisement 416; and models for predicting user experience for an advertisement 418) [Kagian, 0050]; and
receiving one or more search queries transmitted from the one or more user terminals (Kegian, At 1402, an ad request is received from a user and analyzed.) [Kegian, 0112];
determining one or more subsequent search results of the one or more users based on the one or more search queries (Kegian, An advertisement is selected at 1414 based on the model. The advertisement is provided at 1416 with a presentation instruction.) [Kegian, 0112];
ranking the one or more subsequent search results based on the prediction result (Kegian, The use activities may be used to select and/or rank advertisements based on a model stored in the ad related model database 150) [Kegian, 0041];
transmitting second website display information to the one or more user terminals to cause the one or more user terminals to display the one or more subsequent search results in accordance with the determined ranks of the one or more subsequent search results (Kegian, An advertisement is selected at 1414 based on the model. The advertisement is provided at 1416 with a presentation instruction.) [Kegian, 0112], and
wherein each of the first policy and the second policy define, with respect to at least one of the attributes, at least one of the attributes, an order to display the one or more search result (Kagian, the ad related information may include preferred ad type of the user and current time, date and enviromnent with respect to the ad request. The ad request analyzer 1310 may send all of the information to the model based ad selector 1320 for ad selection and to the ad presentation instruction generator 1340 for generating ad presentation instructions) [Kagian, 0107].
Regarding claim 4, as combined and under the same rationale as above, Kagian in view of Li teaches search ranking evaluation system and method, wherein the first policy and the second policy each include displaying one of one or more candidate images in a display slot of the one or more display slots, and the one or more candidate images are unchanged in each of the plurality of segments (Kagian, User interactions with the web content or advertisement may be achieved via the I/O devices 1550 and provided to the adaptive model training engine 140 and/or other components of systems 100 and 200, e.g., via the network 120)) [Kagian, 0114].
Regarding claim 6, as combined and under the same rationale as above, Kagian in view of Li teaches search ranking evaluation system and method, wherein the first policy and the second policy each include selecting one or more target candidates, and the one or more target candidates are unchanged in each of the plurality of segments (Kagian, The tuning target selector 540 in this example may select one or more tuning targets in the model to be tuned. The one or more tuning targets may include a set of hyperparameters in the model.) [Kagian, 0056].
Claims 2 and 5 are rejected under 35 U.S.C. 103 as being unpatentable over Kagian et al. US Publication 2018/0232660 in view of Li et al. US Publication 2014/0351179 and Yaniv Navot published article “Beyond A/B testing: Multi-armed bandit experiments”.
Regarding claim 2, Kagian in view of Li does not explicitly teach using multi-band algorithm. However, Navot teaches that In traditional A/B testing methodologies, traffic is evenly split between two variations (both get 50%). Multi-armed bandits allow you to dynamically allocate traffic to variations that are performing well while allocating less and less traffic to underperforming variations. Multi-armed bandits are known to produce faster results since there’s no need to wait for a single winning variation [Navot, page 3].
Therefore, at the time of filing, it would have been obvious to one of ordinary skill in the art to modify Kagian in view of Li by adopting teachings of Navot and use Bandit algorithms go beyond classic A/B/n testing, conveying a large number of algorithms to tackle different problems, all for the sake of achieving the best results possible. With the help of a relevant user data stream, multi-armed bandits can become context-based.
as combined and under the same rationale as above, Kagian in view of Li and Navot teaches search ranking evaluation system and method, wherein
The machine-learning algorithm is based on a multi-armed bandit algorithm [Navot, page 3], and
the datasets each include data related to a feature, an action corresponding to the feature, and a result of the action (Li, The basic information of the user includes profile data, such as a user name, a nickname, and a school; and the historical behavior information of the user refers to a related operation of the user in a website, such as information browsing, information reposting, original information publishing, and information commenting) [Li, 0060]
Regarding claim 5, as combined and under the same rationale as above, Kagian in view of Li and Navot teaches search ranking evaluation system and method, wherein
the datasets each include multiple pieces of data acquired each time the user transmits the request for displaying the webpage (Kegian, The ad log database 510 in this example may continuously receive and store ad related data for online training.) [Kegian, 0054],
the one or more display slots comprise one or more advertising slots (Kegian, At 1402, an ad request is received from a user and analyzed.) [Kegian, 0112],
the first policy and the second policy each include displaying an advertisement image in each of the one or more advertising slots [Kegian, 0040, 0041, 0114],
the feature is the attribute of the user, the action is each of one or more candidate images displayed in the one or more advertising slots, and the result of the action is the click information, wherein the click information indicates whether the user clicked on a displayed advertisement image in the one or more advertising slots (Kegian, ad related models 410 may include e.g. models for predicting a probability of click through rate for an advertisement 412; models for predicting a probability of ad conversion rate for an advertisement 414; models for predicting total ad revenue for an advertisement 416; and models for predicting user experience for an advertisement 418.) [Kegian, 0051], and
the one or more candidate images are unchanged in each of the plurality of segments (Kegian, A user, e.g., the user 110-1, may send requests to the web service provider 130 via the network 120 and receive web content with one or more advertisement from the web service provider 130, e.g. by accessing a web page hosted by the web service provider 130 or using an application supported by the web service provider 130. The web service provider 130 may provide to the users 110 some online services like web portal, online search, news app, published content, etc. In some embodiment, the web service provider 130 may also provide support or update to some applications installed on a local device of a user. The web service provider 130 may collect user activities related to the online services or applications. The user activities may be used to select and/or rank advertisements based on a model stored in the ad related model database 150.) [Kegian, 0040, 0041].
Claims 7 – 8 are rejected under 35 U.S.C. 103 as being unpatentable over Kagian et al. US Publication 2018/0232660 in view of Li et al. US Publication 2014/0351179 and McElfresh et al. US Publication 2013/0047063.
Regarding claim 7, Kagian in view of Li does not teach determining display order in which images are displayed sequentially. However, McElfresh teaches method and system for placement of graphical objects on a page to optimize the occurrence of an event associated with such objects. The graphical objects might include, for instance, advertisements on a webpage, and the event would include a user clicking on that ad (McElfresh,) [0010]. McElfresh further teaches A click--through-percentage 133 is calculated for each ad based upon the performance stats and the user information. The Rad Server 112 thereafter ranks the ads according to a desired arrangement method 135 [McElFresh, 0042].
Therefore, at the time of filing, it would have been obvious to one of ordinary skill in the art to modify Kagian in view of Li by adopting teachings of McElfresh to increase the amount of click-through traffic on ads presented on a webpage, and thereby increase the revenue generated by a website provider which sells ads on that webpage.
as combined and under the same rationale as above, Kagian in view of Li and McElfresh teaches search ranking evaluation system and method, wherein the first policy and the second policy each include
determining a display order in which images are displayed sequentially in a single display slot (McElfresh, A click--through-percentage 133 is calculated for each ad based upon the performance stats and the user information (segment information). The Rad Server 112 thereafter ranks the ads according to a desired arrangement method 135 [McElFresh, 0042], and
the images are unchanged in each of the plurality of segments (McElfresh, Fig. 2. Fig. 3B and associated disclosure].
Regarding claim 8, as combined and under the same rationale as above,Kagian in view of Li, McElfresh teaches search ranking evaluation system and method,
wherein the first policy and the second policy each include selecting one or more products or services recommended for the user from multiple products or multiple services, and at least one of a price of each of the multiple products or the multiple services (Kagian, the ad related information may include preferred ad type of the user and current time, date and environment with respect to the ad request. The ad request analyzer 1310 may send all of the information to the model based ad selector 1320 for ad selection and to the ad presentation instruction generator 1340 for generating ad presentation instructions) [Kagian, 0107] and an assortment of the multiple products or the multiple services are unchanged in each of the plurality of segments (McElfresh, Fig. 2. Fig. 3B and associated disclosure].
Response to Arguments
Applicant's argument that pending claimed amended invention is eligible for patent under 35 USC 101 is acknowledged and considered.
However, upon further review, it is deemed that the amended claimed invention is not eligible for patent under 35 USC 101, and has been responded to in Rejection under 35 USC 101 section.
Applicant's argument that pending claimed amended invention is eligible for patent because cited prior art does not teach the amended invention.
However, applicant’s arguments are for added limitations which have been responded in Rejection under 35 USC 101 section.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Blom et al. US Publication 2017/0061478 teaches system and method for dynamically varying remarketing based on evolving user interest.
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 Naresh Vig whose telephone number is (571)272-6810. The examiner can normally be reached Mon-Fri 06:30a - 04:00p.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Ilana Spar can be reached at 571.270.7537. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/NARESH VIG/Primary Examiner, Art Unit 3622
December 23, 2025
Prosecution Insights