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 .
Claims 1-9, 11-13, 15-17, 19-22 and 26 are presented for examination. The Examiner acknowledges the preliminary amendments filed on February 3rd, 2025.
This Office action is Non-Final.
Information Disclosure Statement
The information disclosure statement (IDS) filed on 02/03/2025 has been considered by the Examiner and made of record in the application file.
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)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(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 1-9, 11-13, 15-17, 19-22 and 26 are rejected under 35 U.S.C. 102 (a)(1)/(a)(2) as being anticipated by Kim et al. (US 2022/0230253 A1, also cited on the IDS filed on 02/03/2025) hereinafter “Kim” (it is noted that reference is a continuation of application No. 16/779,434 filed on January 31st, 2020, now issued as U.S. Patent No. 11,334,949, as well as, having a provisional filing date of October 11th, 2019).
With respect to claim 1, the Kim reference discloses an automated recommendation system [News recommendation system includes components can be implement in hardware; para [0046]] comprising:
a data source layer which includes customer data from a plurality of data sources [The Ingestion provides extract, transform, load engine that provide central data collection from one or more data stream and store in the database; para [0052]], the data source layer being configured such that the customer data is dynamically or periodically updated [The subscription-based pipeline which has user data such as subscription can be update as needed; para [0051]];
a processing sub-system which is communicatively coupled to the data source layer, wherein the processing sub-system is configured to process the customer data, including generating customer segment data based on at least customer behaviour data and customer lifecycle stage data obtained or derived from the data sources [The Ingestion provides extract, transform, load engine that provide central data collection from one or more data stream such as behavior, subscription from the user and store in the database; para [0052]], wherein the processing sub-system includes or implements a recommendation engine which is configured to generate customer-specific recommendations based on a plurality of machine learning (ML) algorithms or heuristic models, or ML algorithms and heuristic models in combination applied to the processed customer data [The artificial intelligent machine utilize the training data to model and analyze the data to produce the correct output value base on the user data; para [0059]], wherein each customer-specific recommendation is indicative of a proposed best action fora specific customer or customer segment, the proposed best action being either a next offer for a business associated with the system to make to the specific customer or customer segment or a next action for the business to take in respect of the specific customer or customer segment [The news recommendation system store the output of the recommendation in the database where the user can access the subscription portfolio to view or create additional subscription; paras [0069], [0075]]; and
an interface layer which is communicatively coupled to the processing subsystem to receive output indicative of the customer-specific recommendations, and which is further communicatively coupled to a plurality of user devices, thereby allowing the user devices to access the customer-specific recommendations [The clients device can be computers, workstations and the like to communicate with the news recommendation system via LAN, WAN and the like where a GUI can display the recommended information on screen; paras [0039], [0042], [0072]],
wherein the processing sub-system is further configured to receive feedback data indicative of a degree or level of success associated with automated customer specific recommendations generated by the system, the processing sub-system and recommendation engine being configured to utilize the feedback data so as to dynamically or periodically update the customer data and, as a result, the customer specific recommendations [The analysts provide feedback to which the recommendation generated can be adjusted to the new information that being received by clicking the icon to update the data; para [0122]].
With respect to claim 2, Kim discloses the system of claim 1, as referenced above. Kim further discloses wherein the ML algorithms or heuristic models, or ML algorithms and heuristic models in combination are capable of learning each customer's journey, patterns, and behaviour to make the custom er-specific recommendations [The news recommendation of articles may be based on existing subscriptions or new subscription for news business requirement (growth) from the artificial intelligent output. Ingestion provides extract, transform, and load engines that provide central data collection from one or more data stream such as behavior, subscription from the user and store in the database; paras [0052], [0055]].
With respect to claim 3, Kim discloses the system of claim 2, as referenced above. Kim further discloses wherein the customer-specific recommendation includes one or more offers that are made available to each customer over a specific lifecycle stage associated with each customer [The news recommendation system recommends relevant news articles based on user. subscriptions; para [0051]].
With respect to claim 4, Kim discloses the system of claim 3, as referenced above. Kim further discloses wherein the specific lifecycle stage is selected from the group consisting of acquisition, growth, retention, win-back, and combinations thereof [The news recommendation of articles may be based on existing subscriptions or new subscription for news business requirement (growth); para [0055]].
With respect to claim 5, Kim discloses the system of claim 1, as referenced above. Kim further discloses including an analytics layer and a recommendation engine vault forming part of the processing sub-system [The news recommendation system included the artificial intelligent to analyze and train to produce the output; paras [0058], [0063]].
With respect to claim 6, Kim discloses the system of claim 5, as referenced above. Kim further discloses wherein the analytics layer or the vault, or the analytics layer and the vault in combination are implemented as one or more edge nodes [The news recommendation system includes the artificial intelligent to analyze and train to produce the output such as subscriptions for new business requirement; paras [0052], [0058], [0063]].
With respect to claim 7, Kim discloses the system of claim 5, as referenced above. Kim further discloses wherein the analytics layer further includes a staging layer, an aggregated layer, and an output layer [The recommendation system consists of the pipeline where the ingestion (staging), subscription; relevant (aggregate), clustering and ranking(output); para [0051]].
With respect to claim 8, Kim discloses the system of claim 5, as referenced above. Kim further discloses wherein the vault houses the ML algorithms or heuristic models, or ML algorithms and heuristic models in combination [The news recommendation system utilize the artificial intelligent to learn, training, predict, and recommend the result using the ML algorithms; para [0058]].
With respect to claim 9, Kim discloses the system of claim 1, as referenced above. Kim further discloses wherein the feedback data includes data relating to update of customer-specific recommendations and financial data relating thereto [The analysts provide feedback to which the recommendation generated by clicking the icon to update the data where new subscription can bring in more business; paras [0055], [0122]].
With respect to claim 11, Kim discloses the system of claim 5, as referenced above. Kim further discloses wherein the analytics layer or the vault, or the analytics layer and the vault in combination are in communication with each other and a downstream marketing system via a data warehouse [The news recommendation system integrates data stream from different new sources where the centralize data collection of the data stream is stored in the database; para [0052]].
With respect to claim 12, Kim discloses the system of claim 11, as referenced above. Kim further discloses wherein the analytics layer is configured to implement a feedback loop process in terms of which the customer data or customer-specific recommendations, or the customer data and customer-specific recommendations in combination are continuously updated based on feedback data received from the downstream marketing system [The analysts provide feedback to which the recommendation generated by clicking the icon to update the data where new subscription can bring in more business. The analysts provide feedback to which the recommendation generated can be adjusted to the new information that is being received by clicking the icon to update the data; paras [0055], [0122]].
With respect to claim 13, Kim discloses the system of claim 1, as referenced above. Kim further discloses wherein the user interface allows for modification of the customer data to generate a modified customer-specific recommendation for one or more customers [User can create and follow additional subscriptions to have variety of entities from the news recommendation system; para [0075]].
With respect to claim 15, Kim discloses the system of claim 1, as referenced above. Kim is further configured to map available offers to specific customers or segments based on an analysis of the processed data through the recommendation engine [The news recommendation system uses Al to determine clustering and ranking to input in the subscription for user to view; paras [0067], [0068]].
With respect to claim 16, Kim discloses the system of claim 1, as referenced above. Kim is further configured to generate a customer-specific recommendation in the form of a unique product or service offering for a specific customer or segment, based on an analysis of the processed data [The news recommendation system recommends relevant news articles based on user subscriptions; para [0051]].
With respect to claim 17, Kim discloses the system of claim 1, as referenced above. Kim further discloses extending to one or more computer program products for implementation thereof, wherein the computer program product includes at least one computer-readable storage medium having program instructions embodied therewith, the program instructions being executable by at least one non-transitory computer [Program code and computer-readable media form computer program product that is loaded into the data processing system for execution by the processor unit; para [0153]].
With respect to claim 19, the Kim reference discloses an automated recommendation method [News recommendation system includes components can be implement in hardware; para [0046]], the method comprising the steps of:
connecting a data source layer to a processing sub-system, the data source layer including customer data from a plurality of data sources fed into the processing subsystem [The Ingestion provides extract, transform, load engine that provide central data collection from one or more data stream and store in the database; para [0052]].
dynamically or periodically updating the customer data [The subscription-based pipeline which has user data such as subscription can be updated as needed; para [0051]].
processing, by the processing sub-system, the customer data, including generating customer segment data based on at least customer behaviour data and customer lifecycle stage data obtained or derived from the data sources [The Ingestion provides extract, transform, load engine that provide central data collection from one or more data stream such as behavior, subscription from the user and store in the database; para [0052]];
generating, by a recommendation engine forming part of or implemented by the processing sub-system, customer-specific recommendations based on a plurality of ML algorithms or heuristic models, or ML algorithms and heuristic models in combination applied to the processed customer data [The artificial intelligent machine utilize the training data to" model and analyze the data to produce the correct output value base on the user data; para [0059]], wherein each customer-specific recommendation is indicative of a proposed best action for a specific customer or customer segment, the proposed best action being either a next offer for a business to make to the specific customer or customer segment, or a next action for the business to take in respect of the specific customer or customer segment [The news recommendation system store the output of the recommendation in the database where the user can access the subscription portfolio to view or create additional subscription; paras [0069], [0075]];
connecting the processing sub-system to a plurality of user devices via an interface layer, the interface layer being communicatively coupled to the processing sub-system to receive output indicative of the customer-specific recommendations and communicatively coupled to the user devices thereby allowing the user devices to access the customer-specific recommendations [The clients device can be computers, workstations and the like to communicate with the news recommendation system via LAN, WAN and the like where a GUI can display the recommended information on-screen; paras [0039], [0042], [0072]];
receiving, by the processing sub-system, feedback data indicative of a degree or level of success associated with automated customer-specific recommendations generated by the system [The analysts provide feedback to which the recommendation generated can be adjusted to the new information that being received by clicking the icon to update the data; para [0122]]; and
updating the customer data and customer-specific recommendations by the processing sub-system and recommendation engine, using the feedback data [The analysts provide feedback to which the recommendation generated can be adjusted to the new information that being received by clicking the icon to update the data; para [0122]].
With respect to claim 20, Kim discloses the method of claim 19, as referenced above. Kim further discloses wherein the step of generating customer-specific recommendations includes receiving, by the recommendation engine, input data from an analytics layer of the processing subsystem [During the ingestion of date, the news recommendation system integrates heterogenous data stream from a number of different news sources to extract and provide recommendation output; para [0052]].
With respect to claim 21, Kim discloses the method of claim 19, as referenced above. Kim further discloses including the step of the analytics layer performing a feedback loop process in terms of which the customer data or customer-specific recommendations, or customer data or customer-specific recommendations in combination are continuously updated based on feedback data received from a downstream marketing system [The analysts provide feedback to which the recommendation generated by clicking the icon to update the data where new subscription can bring in more business. The analysts provide feedback to which the recommendation generated can be adjusted to the new information that is being received by clicking the icon to update the data; paras [0055], [0122]].
With respect to claim 22, Kim discloses the method of claim 21, as referenced above. Kim further discloses wherein the customer behaviour data includes data indicative of all, or the majority of, interactions of a customer with the business across a plurality of products and services lines [The Ingestion provides extract, transform, load engine that provide central data collection from one or more data stream such as behavior, subscriptions, and different news source from the user and store in the database; para [0052]].
With respect to claim 26, Kim discloses the method of claim 21, as referenced above. Kim further discloses a computer-implemented method of providing a customer with an automated recommendation, comprising: receiving an input dataset comprising customer data from a data source layer; carrying out the steps of the method according to claim 19; and producing an output dataset comprising one or more customer-specific recommendations. [During the ingestion of date, the news recommendation system integrates heterogenous data stream from a number of different news sources to extract and provide recommendation output; para [0052]].
Prior Art Made of Record
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Bhawra et al. discloses an advanced data and analytics management platform.
Vijayan et al. discloses customer categorization and customized recommendations for automotive retail.
Furlan et al. discloses a recommendation engine that integrates customer social review-based data to understand preferences and recommend products.
Nathenson et al. discloses leveraging customer and company data to generate recommendations and other forms of interactions with customers.
Makhlouf discloses engagement and distribution of media content.
Chu et al. discloses predicting customer value.
D’Imporzano et al. discloses a consumer and shopper analysis system.
Itani et al. discloses intelligent customer retention and offer/customer matching.
Cheng et al. discloses customer lifecycle definition and categorization.
Young et al. discloses a customer relationship management business method.
Reed et al. discloses multi-dimensional segmentation for use in a customer interaction.
Conclusions/Points of Contacts
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JORGE A CASANOVA whose telephone number is (571)270-3563. The examiner can normally be reached M-F: 9 a.m. to 6 p.m. (EST).
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, Aleksandr Kerzhner can be reached at (571) 270-1760. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/JORGE A CASANOVA/Primary Examiner, Art Unit 2165
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