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 October 2, 2025. Claims 1-3, 9, 15 and 20-21 have been amended. Claims 1-7, 9-21 are currently pending and have been examined in the 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 1-7, 9-21 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.
In Claims 1, 9 and 15 the term “a most relevant” is a relative term which renders the claim indefinite. The term “a most relevant” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. Whether a subset of data is most relevant or not, in the absence of a clear definition or a quantification method, is a matter of opinion. Reasonable people would reasonably disagree as to what is most relevant; see MPEP 2173.05(b). Claims 2-7, 21; 10-14; and 16-20 by being dependents of claims 1, 9 and 15 respectively are also rejected.
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 1-7, 9-21 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.
Claims 1-7, 9-21 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to non-statutory subject matter. Specifically, claims 1-7, 9-21 are directed toward at least one judicial exception without significantly more. In accordance with MPEP 2106.04, the rationale for this determination is explained below:
Representative claim 1 is directed towards a system, claim 9 is directed towards a method, claim 15 is directed towards a non-transitory computer-readable medium, which are statutory categories of invention.
Although, claim 1 is directed toward a statutory category of invention, the claim appears to be directed toward a judicial exception namely an abstract idea. The limitations that set forth the abstract idea recites: generate a plurality of clusters of entities, wherein each cluster of entities includes a set of entities that share similar characteristics; receive, based on monitoring user interactions with a computing device, user interaction data from multiple data sources;
process a most relevant subset of the user interaction data to generate a set of values, for a corresponding set of features, to predict a purchase process stage and a set of likelihoods that a user will perform the action in connection with a corresponding set of clusters of entities, wherein the set of values includes one or more values that are indicative of one or more interactions of the user associated with one or more entities included in the plurality of clusters of entities; generate, based on using the set of values, a set of outputs indicative of the purchase process stage and the set of likelihoods, wherein the set of outputs includes a respective output for each cluster of entities of the plurality of clusters of entities; and wherein each respective output is associated with a likelihood of a visit associated with a respective cluster of entities, and a likelihood of a purchase or trade associated with the respective cluster of entities; identify a cluster of entities, of the plurality of clusters of entities, based on the set of outputs; assign the user to a cluster of users based on the set of outputs; identify an entity, included in the cluster of entities, based on user profile information associated with the user; and provide to the user and based on the cluster of users the user is assigned to, information to facilitate communication associated with the entity based on identifying the entity; wherein the communication is established based on an interaction with the information. These limitations, describe commercial interactions including, marketing or sales activities or behavior; business relations; as well as managing personal behavior including following rules or instructions. As such, the limitations are 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). Alternatively, the limitations to generate a plurality of clusters of entities, wherein each cluster of entities includes a set of entities that share similar characteristics; input a set of values, for a corresponding set of features, to predict a set of likelihoods that a user will perform the action in connection with a corresponding set of clusters of entities; generate, based on using the set of values, a set of outputs indicative of the set of likelihoods; identify a cluster of entities, of the plurality of clusters of entities, based on the set of outputs; assign the user to a cluster of users based on the set of outputs; identify a cluster of entities, of the plurality of clusters of entities, based on the set of outputs; identify an entity, included in the cluster of entities, based on user profile information associated with the user; can also be grouped under Mental Processes because they include observation, evaluation, judgment which can be performed in the human mind, or by using pen and paper. (See MPEP 2106.04(a)(2) III).
This judicial exception is not integrated into a practical application because, when analyzed as a whole under prong two of step 2A of the Alice/Mayo test (See MPEP 2106.04(d)), the additional elements provided by the claim are recited at a high level of generality and amount to insignificant extra-solution activity and merely using a computer as a tool to perform an abstract idea. In particular the claim recites the additional element of wherein the set of values includes one or more values that are formatted for input into the trained machine learning model, which amounts to insignificant extra-solution activity because such activities are merely tangential gathering a particular data source or type of data to be manipulated. See MPEP 2106.05(g). While, the limitations of, one or more memories; and one or more processors, communicatively coupled to the one or more memories, configured to; to input into a trained machine learning model that is trained to; with one or more user interfaces; executing the trained machine learning model, a first device associated with, electronic, an electronic, between the first device and a second device; electronic, electronic; which are recited at a high level of generality and are the mere use of a computer as a tool to perform the abstract ideas. See MPEP 2106.05(f). Simply adding insignificant extra-solution activities and applying 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 claim does not apply the abstract idea with, or by use of, a particular machine (MPEP 2106.05(b)), the claim does not effect a transformation or reduction of a particular article to a different state or thing (MPEP 2106.05(c)), and the claim does 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 claim does not, for example, purport to improve the functioning of a computer. Nor does it 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 claim is directed to abstract ideas.
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 claim recites additional limitations that amount to insignificant extra-solution activity and using the computer as a tool. Viewing the limitation individually, the set of values includes one or more values that are formatted for input into the trained machine learning model is tangentially added and used only for providing the data source or type of data, to implement the aforementioned abstract concepts, see MPEP 2106.05(g). Additionally, it is well known, routine and conventional to format data used as input to an algorithm/machine learning model. See at least Wang et al. (US 20220222688 A1); Toffey et al. (US 20220051331 A1); Semeniuk et al. (US 11777874 B1); Yue et al. (US 20210192134 A1); Tiruveedhula et al. (US 20210042786 A1); Atcheson et al. (US 20200160229 A1); Gao et al. (US 20170017886 A1).
Moreover, the limitations generically referring to of one or more memories, one or more processors, a trained machine learning model, user interfaces, a device, electronic communication, which 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 additional elements implement the abstract idea with a high-level of generality, executing basic computer functions applied by generic computer devices. Merely applying an exception using a generic computer cannot provide 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 2-7, 21, likewise, do not recite any limitations that would remedy the deficiencies outlined above. The claims only further add to the abstract idea, with no elements which integrate the abstract idea into a practical application or constitute significantly more. For instance; claims 2-7, add extra-solution activities and nonfunctional descriptive material in furtherance of the abstract idea; claim 21 provides extra-solution data outputting activity directed to managing user behavior. Thus, 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, claims 9-20 suffer from substantially the same deficiencies as outlined with respect to claims 1-7, 21 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, 3-6 and 9-20 are rejected under 35 U.S.C. 103 as being unpatentable over O’Keeffe (US Publication US 2019/0043071) in view of Obrecht (US Publication 2010/0063898) in further view of Malhotra (US Publication 2020/0082294) and Wang (US Publication 2022/0222688).
A. In regards to Claims 1, 9 and 15, O’Keeffe discloses a system, method and non-transitory computer-readable medium comprising:
one or more memories; O’Keeffe [0127];
and one or more processors, communicatively coupled to the one or more memories, O’Keeffe [0127], configured to:
generate a plurality of clusters of entities, wherein each cluster of entities includes a set of entities that share similar characteristics, O’Keeffe [0014: vehicle data system generates an eligibility table which contains data that identifies a set of eligible dealers; 0029: example, when a user location is determined a set of eligible dealers are determined where each eligible dealer is characterized by a set of features];
receive, based on monitoring user interactions with a computing device, user interaction data from multiple data sources; O’Keeffe [0040: utilizing one or more interfaces configured to for example, receive queries from users at computing devices or dealer computer; 0098: a user may specify a vehicle configuration by defining values for a certain set of vehicle attributes (make, model, trim, power train, options, etc.); 0026: a logistic regression model or the like may be created to score a dealer based on data on the set of features collected from a number of sources in a distributed computer network];
process a most relevant subset of the user interaction data to generate a set of values, for a corresponding set of features, to input into a trained machine learning model that is trained to predict a purchase process stage and a set of likelihoods that a user will perform the action in connection with a corresponding set of clusters of entities; O’Keeffe [0008: utilizing a set of inputs to predict the likelihood of a user purchasing a car from a dealer; 0098: a user may specify a vehicle configuration by defining values for a certain set of vehicle attributes (make, model, trim, power train, options, etc.) or other relevant information such as a geographical location; 0019: the front-end components and back-end components may be used to facilitate a vehicle research and purchase process for users, during which information regarding user interactions with the servers may be recorded for analysis; such information may be processed by the server to generate reports of user data interaction of users or predict probabilities of user actions 0026: a logistic regression model or the like may be created to score a dealer based on data on the set of features collected from a number of sources in a distributed computer network 0028: it will be apparent that there is a wide variety of uses for such a model and algorithms. For example, in one embodiment, such models and algorithms can be used in a “Dealer Scoring Algorithm” (DSA), which can be used to select, filter or present vendors in response to a user-submitted search or user information];
wherein the set of values includes one or more values that are indicative of one or more interactions of the user with one or more user interfaces associated with one or more entities included in the plurality of clusters of entities; O’Keeffe [0015: vehicle data system generates a user interface and presents the interface to a user on a second computing device that is communicatively connected to the first computing device; when the vehicle data system receives a user request, it determines a set of eligible dealers corresponding to the user request];
generate, based on executing the trained machine learning model using the set of values, a set of outputs indicative of the purchase process stage and the set of likelihoods wherein the set of outputs includes a respective output for each cluster of entities of the plurality of clusters of entities; O’Keeffe [0056: after the set of eligible dealers has been selected, each of these dealers is scored. The score is an indication of the conditional probability that the dealer will make a sale to the user;];
identify a cluster of entities, of the plurality of clusters of entities, based on the set of outputs; O’Keeffe [0058: after scores have been generated for each of the eligible dealers, these dealers are ranked. The ranking of the eligible dealers is then used to select one or more dealers that will be presented to the user; when the set of dealers has been determined, a presentation of these dealers is generated and is presented to the user];
and provide to a first device associated with the user and based on the cluster of users the user is assigned to, electronic information to facilitate an electronic communication between the first device and a second device associated with the entity based on identifying the entity; O’Keeffe [0040: system may provide a wide degree of functionality including utilizing one or more interfaces configured to receive and respond to queries from users at computing devices; interface with inventory companies, manufacturers, sales data companies, financial institutions or dealers to obtain data; or provide data obtained, or determined, by vehicle data system to any of inventory companies manufacturers sales data companies financial institutions or dealers; 0042: generate interfaces using the selected data set and data determined from the processing, and present these interfaces to the user at the user's computing device; 0015: when the vehicle data system receives a user request, it determines a set of eligible dealers corresponding to the user request by identifying a set of eligible dealers; 0115: upon entering personal information, the identities of the selected dealers are presented to the potential customer via interface];
wherein the electronic communication is established based on an interaction with the electronic information; O’Keeffe [0098: a user at a computing device may access vehicle data system using one or more interface such as a set of web pages provided by vehicle data system; user may specify a vehicle configuration by defining values for a certain set of vehicle attributes; information associated with the specified vehicle configuration may then be presented to the user through the interface];
O’Keeffe does not specifically disclose, identify an entity, included in the cluster of entities, based on user profile information associated with the user; this is old, well known and disclosed by Obrecht [0013: providing a method for purchasing goods or services from a seller by a buyer comprising the steps of (a) receiving a request from a buyer for goods or services with predetermined criteria [i.e. buyer profile] related to the goods or services; (b) selecting at least one seller from a predetermined group of sellers of the goods or services based on the received predetermined criteria; 0070: apparatus automatically selects sellers to contact by analyzing a multitude of seller and buyer information (criteria). This information (criteria for seller selection from a group of prestored sellers) includes, but is not limited to, geographic data, proprietary customer satisfaction index (CSI) ratings, buyer preferences; 0048: example, an inquiry made as to whether or not a consumer satisfaction index (CSI) of the dealer is greater than a predetermined threshold];
additionally and/or alternatively, Obrecht discloses, provide to a first device associated with the user and based on the cluster of users the user is assigned to, electronic information to facilitate an electronic communication between the first device and a second device associated with the entity based on identifying the entity. Obrecht [0073: an individual can request to "watch the market" from their personal computer or other device with internet access. Buyers or sellers merely enter a few parameters into the internet web page of the system and will then be granted access to a "ticker-tape" type of market information on specific goods for particular regions of the world; 0074: buyers and sellers can directly contact each other after the market information has been conveyed to the buyers; buyers will be able to instantly communicate with the seller to purchase the product, to request more information, or to put a hold on the goods etc.];
wherein the electronic communication is established based on an interaction with the electronic information. Obrecht [0071: after automatic payment (interaction), the buyers have access to the system template of buying options; accessibility can be done by facsimile, electronic mail, web page, etc. The buyer selects how he would prefer to receive the data; 0072: seller receive buyer information and submit quotes back to buyers].
it would have been obvious before the effective filing date of the invention for one of ordinary skill in the art to have modified the teachings of O’Keeffe with the teachings from Obrecht with the motivation to match sellers and buyers geographic presence and other criteria automatically, in order to improve both the liquidity and efficiency of the market for the goods desired; resulting in a more competitive market and thus a better shopping situation for buyers. Obrecht [0070].
O’Keeffe does not specifically disclose, wherein each respective output is associated with a likelihood of a visit associated with a respective cluster of entities, and a likelihood of a purchase or trade associated with the respective cluster of entities; this is disclosed by Malhotra [0060: learning sub-system can selectively implement machine learning techniques on the encoded event stream of individual end users, to identify end users who can be influenced to perform or complete a transaction; once such end users are identified, the event handler can implement processes to learn effective engagement actions with respect to individual end users; the event handler can determine a personalized incentive for the end user to visit the store; 0062: the predictive determination can reflect the likelihood that the end user will complete or otherwise convert a transaction (e.g., purchase an item)];
assign the user to a cluster of users based on the set of outputs; this is disclosed by Malhotra [0056: predictive component configured to categorize users in accordance with a set of predictive categories of a categorization schema, where each category of the categorization schema categorizes the end user in accordance with a prediction about the use];
additionally and/or alternatively, Malhotra discloses, provide to a first device associated with the user and based on the cluster of users the user is assigned to, electronic information to facilitate an electronic communication between the first device and a second device associated with the entity based on identifying the entity. Malhotra [0060: the event handler can target select individuals, as identified by the learning sub-system, with personalized messages to invite them to a retail outlet; 0073: communicating message to the end user, where the message includes, for example, a promotional offer or other content. In such examples, the communicated message can be determined by type, based on a predictive determination of the learning sub-system as to the intent of the user];
it would have been obvious before the effective filing date of the invention for one of ordinary skill in the art to have modified the teachings of O’Keeffe with the teachings from Malhotra with the motivation to implement an intelligent process, e.g., using a machine or deep learning technique, to determine a user intent, a current user context, and relevant past user context that can predict an output that engages the end user, in a manner that is likely to influence the end user action. Malhotra [0054].
O’Keeffe does not specifically disclose, wherein the set of values includes one or more values that are formatted for input into the trained machine learning model. This is disclosed by Wang [0046: merging the message features and the user data features to produce an input data set for analysis by the predictive model. The combined feature data may be further prepared as complete and properly formatted input to the predictive mode].
It would have been obvious before the effective filing date of the invention for one of ordinary skill in the art to have modified the teachings of O’Keeffe with the teachings from Wang with the motivation to provide an electronic data system to obtain and process message features and user interaction features, which are combined into an input data set, analyzed by a trained predictive model to generate one or more output values indicative of expected user actions. Wang [0016].
B. In regards to Claim 3, O’Keeffe discloses, wherein the one or more processors are further configured to: identify a contact method for the user based on the user profile information; O’Keeffe [0114: user may enter personal information such as contact information of the user];
O’Keeffe does not specifically disclose, and provide information that identifies the contact method in connection with the electronic information to facilitate electronic communication between the first device and the second device. This is disclosed by Obrecht [0071: accessibility can be done by facsimile, electronic mail, web page, U.S. mail, etc. The buyer selects how he would prefer to receive the data]. The motivation being the same as stated in claim 1.
C. In regards to Claim 4, O’Keeffe discloses, wherein the set of values includes or is determined based on at least one of: clickstream data associated with the one or more user interfaces, or form data input into one or more fields presented via the one or more user interfaces. O’Keeffe [0116: consumer may interact with an embodiment implementing the DSA disclosed herein through a user interface on a client device. Webpage may include forms associated with customer information that may be entered or completed by a user].
D. In regards to Claims 5 and 14, O’Keeffe discloses, wherein set of values includes at least one of: a credit score of the user, a loan amount associated with the user, a maximum loan amount associated with the user, an indication of whether the user has interacted with the one or more user interfaces to prequalify for a loan, an indication of whether the user has interacted with a loan calculator presented via the one or more user interfaces, an indication of whether the user has input a trade-in value for a vehicle via the one or more user interfaces, or an indication of whether the user has interacted with a user interface, of the one or more user interfaces, that presents rebate information, warranty information, or insurance information. O’Keeffe [0005: efficiently identify the consumers more likely to purchase the item in which they expressed interest; 0040: system provide a wide degree of functionality including utilizing one or more interfaces configured to for example, interface with sales data companies, financial institutions; 0455: financial institution may be any entity such as a bank, savings and loan, credit union, etc. that provides any type of financial services to a participant involved in the purchase of a vehicle. For example, when a buyer purchases a vehicle they may utilize a loan from a financial institution, where the loan process usually requires two steps: applying for the loan and contracting the loan. These two steps may utilize vehicle and consumer information in order for the financial institution to properly assess and understand the risk profile of the loan].
E. In regards to Claims 6, 13 and 19, O’Keeffe discloses, wherein each entity, in the plurality of clusters of entities, is a vehicle dealership; O’Keeffe [0014: system generates an eligibility table which contains data that identifies a set of eligible dealers];
and wherein the plurality of clusters of entities are clustered based on one or more characteristics that include at least one of: a size of a vehicle inventory, a makeup of the vehicle inventory, a dealership score, or a dealership location. O’Keeffe [0017: vehicle data system ranks the eligible dealers based on corresponding dealer scores].
F. In regards to Claims 10 and 17, O’Keeffe discloses, wherein entity is identified based on user profile information associated with the user. O’Keeffe [0028: set of eligible dealers may be identified based on the user's specified geography].
G. In regards to Claims 11 and 17, O’Keeffe discloses, wherein entity is identified based on at least one value of the set of values. O’Keeffe [0098: a user at a computing device may access vehicle data system using one or more interface; user may specify a vehicle configuration by defining values for a certain set of vehicle attributes (make, model, trim, power train, options, etc.)].
H. In regards to Claims 12 and 18, O’Keeffe discloses, applying a clustering model to generate a plurality of clusters of entities, wherein each cluster of entities includes a set of entities that share similar characteristics; O’Keeffe [0014: selecting dealers to present to a user via a vehicle data system based on characteristics of the dealers];
wherein the output indicates a likelihood that the user will perform the action in connection with one or more clusters of the plurality of clusters; O’Keeffe [0056: after the set of eligible dealers has been selected, each of these dealers is scored. The score is an indication of the conditional probability that the dealer will make a sale to the user];
and wherein the entity is selected from a cluster, of the plurality of clusters, associated with a greater likelihood that the user will perform the action as compared to one or more other clusters included in the plurality of clusters. O’Keeffe [0059: vehicle data system can improve the efficiency of the process and increases the likelihood of matching users to dealer based with a high likelihood of the user purchasing a vehicle from the dealer; when the user request is received, the system simply retrieves the coefficients and scores the eligible dealers using these existing coefficients; 0097: the values for the features for each dealer may be truncated or capped; for example, the distance ranking, the cap value may be a ranking of 20th (e.g., if an eligible dealer for a make and ZIP code is ranked as greater than the 20th dealer based on distance the dealer may be assigned a ranking of 20th);the cap value for close rate may be 30% (e.g., if an eligible dealer for a make and ZIP code has a value for close rate greater than 30% the dealer may be assigned a close rate of 30%), while the cap value for market share may be 60% (e.g., if an eligible dealer for a make and ZIP code has a value for market share greater than 60% the dealer may be assigned a market share of 60%); 0098: Information associated with the specified vehicle configuration presented to the user through interface may include pricing data corresponding to the specified vehicle and upfront pricing information or a list of dealers based on the user's geographic location and a score determined for one [dealer] based on the dealer scoring model].
I. In regards to Claim 16, O’Keeffe does not specifically disclose, receive a plurality of sets of historical values corresponding to a plurality of historical users, wherein each set of historical values is based on one or more interactions of a historical user, of the plurality of historical users, with one or more user interfaces associated with the plurality of entities; O’Keeffe [0037: values may be imputed based on appropriate estimates such as using local average of historical data; 0008: utilizing a set of inputs such as pricing, proximity, historical performance to predict the likelihood of a user purchasing a car from a dealer];
receive information indicating whether each of the plurality of historical users performed the action; O’Keeffe [0014: vehicle data system embodied in a first computing device collects dealer location data and historical vehicle sales transaction data from external data sources that are communicatively connected to the computing device; 0037: final model coefficients may be chosen such that the resulting estimate probability of completing a sale is consistent with the actual observed sales actions given the vendors displayed historically];
and generate the trained machine learning model based on the plurality of sets of historical values and the information indicating whether each of the plurality of historical users performed the action. O’Keeffe [0059: further the logistic model that is used to score the dealers is trained based on historical data].
J. In regards to Claim 20, O’Keeffe discloses, identify a contact method for the user based on at least one of: user profile information associated with the user, or applying the trained machine learning model or another trained machine learning model to the set of values; O’Keeffe [0114: user may enter personal information such as contact information of the user];
O’Keeffe does not specifically disclose, and provide information that identifies the contact method in connection with the electronic information to facilitate the electronic communication between the first device and the second device. This is disclosed by Obrecht [0071: accessibility can be done by facsimile, electronic mail, web page, etc. The buyer selects how he would prefer to receive the data]. The motivation being the same as stated in claim 1.
Claim 2 is rejected under 35 U.S.C. 103 as being unpatentable over O’Keeffe (US Publication 2019/0043071) in view of Obrecht (US Publication 2010/0063898) in in further view of Malhotra (US Publication 2020/0082294) and Wang (US Publication 2022/0222688) and Semeniuk (US Patent 11777874).
A. In regards to Claim 2, O’Keeffe does not specifically disclose, wherein the one or more processors are further configured to: identify a contact method for the user based on applying the trained machine learning model or another trained machine learning model to the set of values; this is disclosed by Semeniuk [Col. 18 Ln 1-6: communications layer may implement rules to select a channel (e.g., an SMS conversation channel, Email conversation channel, messaging app channel) via which to provide a response to a customer; Col. 6 Ln 31-34: AI conversation system is configured to determine a best channel of communication for the user];
and provide information that identifies the contact method in connection with the electronic information to facilitate electronic communication between the first device and the second device. This is disclosed by Semeniuk [Col. 18 Ln 52-54: conversation application selects a communications channel and sends the response to the customer via the communications channel; Col. 6 Ln 64-67 – Col. 7 Ln 1-3: a conversation may be identified by an organization name/id, a user name and contact information (e.g., phone number, email address, messenger id) so that messages from the same user on different channels with respect to the same organization (e.g., retailer) can be identified as being part of the conversation initiated for that user];
It would have been obvious before the effective filing date of the invention for one of ordinary skill in the art to modify the teachings of O’Keeffe with the teachings from Semeniuk with the motivation to provide an artificial intelligence (AI) conversation system that can participate in automated conversations with users, configured to respond to user inquiries or requests and implement conversations to achieve tasks. Semeniuk [Col. 1 Ln 56-61].
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over O’Keeffe (US Publication 2019/0043071) in view of Obrecht (US Publication 2010/0063898) in further view of Malhotra (US Publication 2020/0082294) and Wang (US Publication 2022/0222688) and Rajani (US Patent 11775861).
A. In regards to Claim 7, O’Keeffe does not specifically disclose, wherein the user profile information indicates a preferred user experience associated with an entity visit; this is disclosed by Rajani [Col. 5 Ln 40-42: information stored in the user profile store also include information describing target actions associated with the entities. A target action may correspond to a type of action that may be performed by a user; this is disclosed by Rajani [Col. 5 Ln 47-55: target actions include performing a conversion, having a deep conversation with an entity via a messaging application, expressing a preference for a content item, attending an event, purchasing a product, checking-in to a physical location];
and wherein the one or more processors, to identify the entity, are configured to identify the entity based on the preferred user experience associated with the entity visit. This is disclosed by Rajani [Col. 4 Ln 62-63: user profile in the user profile store also may maintain references to actions by the corresponding user; Col. 7 Ln 21-24: data from the action log is used to infer interests or preferences of a user, augmenting the interests included in the user's user profile and allowing a more complete understanding of user preferences; Col. 16 Ln 29-34: the model may make prediction based on information describing the target action (e.g., retrieved from the user profile store, or received from the entity), such as whether the target action corresponds to expressing a preference for a content item, making a purchase associated with the entity, etc.]
It would have been obvious before the effective filing date of the invention for one of ordinary skill in the art to modify the teachings of O’Keeffe with the teachings from Rajani with the motivation to present content to a viewing user of an online system that accesses a machine-learning model trained to predict a likelihood that an online system user will perform a target action in response to being presented with a link that launches a messaging application and initiates a conversation with an entity via the messaging application, in which the likelihood is predicted based on one or more attributes of the user. Rajani [Col. 2 Ln 17-25].
Claim 21 is rejected under 35 U.S.C. 103 as being unpatentable over O’Keeffe (US Publication 2019/0043071) in view of Obrecht (US Publication 2010/0063898) in further view of Malhotra (US Publication 2020/0082294) and Wang (US Publication 2022/0222688) and Farchy (US Publication 2021/0243162).
A. In regards to Claim 21, O’Keeffe does not specifically disclose, wherein the one or more processors are further configured to: provide a first recommendation when the user is assigned to a first cluster of users; and provide a second recommendation when the user is assigned to a second cluster of users, wherein the first recommendation is different from the second recommendation. This is disclosed by Farchy [0165: in response to a determination that the first group of users includes the second user, cause a suggestion of an alternative statistical query to be provided to the second user; e.g., in response to the received indication that the second user accepted the suggested alternative statistical query, provide a third estimated property of the medical data to the second user; the third estimated property of the medical data may be based on the suggested alternative statistical query; 0163: providing a second estimated property of the medical data, for example to the second user, in response to a determination that the first group of users selected does not include the second user; the second estimated property of the medical data provided may be based on a statistical query; 0124: in response to a first statistical query, a first property of the subgroup of the group of patients may be graphically illustrated, and in response to a second statistical query, a second property of the subgroup of the group of patients may be graphically illustrated (the second property differs from the first property)
It would have been obvious before the effective filing date of the invention for one of ordinary skill in the art to modify the teachings of O’Keeffe with the teachings from Farchy with the motivation to provide a machine learning classification model that may be trained using training examples to determine whether numerical data includes identifying information of individuals, and to analyze at least part of the numerical data to determine whether it is likely to include information that identifies at least one particular individual. Farchy [0243].
Response to Arguments
Applicant's filed arguments have been fully considered but have not been found persuasive.
A. Applicant's argument that the claims are patent eligible under the 35 U.S.C. § 101. The Examiner respectfully disagrees. The claims are directed to the abstract grouping of Certain Methods of Organizing Human Activity (and Mental Processes) with no additional elements that integrate the abstract idea into a practical application. Simply communicating between devices is an abstract idea in and of itself and at most extra solution activity. The additional elements provided merely use a computer as a tool to perform the abstract ideas and/or limit the abstract ideas to a particular technological environment and add insignificant extra-solution activity. See 101 analysis above.
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 35 U.S.C. § 101 rejection is maintained.
B. Regarding the 35 U.S.C. § 103 rejection, Applicant’s argues that the references do not disclose or suggest at least “receive, based on monitoring user interactions with a computing device, user interaction data from multiple data sources; process a most relevant subset of the user interaction data to generate a set of values, for a corresponding set of features, to input into a trained machine learning model that is trained to predict a purchase process stage and a set of likelihoods that a user will perform the action in connection with a corresponding set of clusters of entities," and "generate, based on executing the trained machine learning model using the set of values, a set of outputs indicative of the purchase process stage and the set of likelihoods.” The Examiner respectfully disagrees. O’Keeffe discloses receiving user interaction for input to a machine learning model, in that a user may specify a vehicle configuration by defining values for a certain set of vehicle attributes such as make, model, trim, power train, options, etc., or other relevant information; O’Keeffe [0098], and that a logistic regression model or the like may be created to score a dealer based on data on the set of features collected from a number of sources in a distributed computer network; O’Keeffe [0026]; where front-end components and back-end components may be used to facilitate a vehicle research and purchase process for users, during which information regarding user interactions with the servers may be recorded for analysis; such information may be processed by the server to generate reports of user data interaction of users or predict probabilities [purchase process stage] of user actions. O’Keeffe [0019]. Furthermore, O’Keeffe suggests that such prediction models and algorithms can be used in a Dealer Scoring Algorithm (DSA), which can be used to select, filter or present vendors in response to a user-submitted search or user information; and DSA can then be used to rank eligible dealers and to select the dealers to present based on the ranking; incorporate, for example, features such as historical performance, inventory features, and network features to generate a probability of closing a sale to the user O’Keeffe [0028];
C. Applicant’s arguments regarding the dependent claims are rejected accordingly to independent claims 1, 9 and 15.
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.
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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))).