DETAILED ACTION
Summary and Status of Claims
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This Office Action is in response to Applicant’s reply filed 5/20/2026.
Claims 1-20 are pending.
Claims 1-20 are rejected under 35 U.S.C. 101.
Claims 1-3, 5-8, 11, 13-15, 18, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Lee et al. (US Patent Pub 2022/0012773) in view of Haas et al. (US Patent Pub 2019/0340700).
Claims 4 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Lee et al. (US Patent Pub 2022/0012773) in view of Haas et al. (US Patent Pub 2019/0340700), further in view of Rabkin (US Patent Pub 2014/0172544).
Claims 9, 10, and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Lee et al. (US Patent Pub 2022/0012773) in view of Haas et al. (US Patent Pub 2019/0340700), Del Vecchio et al. (US Patent Pub 2015/0193868).
Claims 12 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Lee et al. (US Patent Pub 2022/0012773) in view of Haas et al. (US Patent Pub 2019/0340700), further in view of Ju et al. (US Patent Pub 2015/0370798).
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claim Objections
Claim 10-13 are objected to because of the following informalities:
In claim 10, last line “with events” should be “with the first events”.
In claims 11-13, all occurrences of “the events” should be “the first events”.
Appropriate correction is required.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. Determining whether claims are statutory under 35 U.S.C. 101 involves a two-step analysis. Step 1 requires a determination of whether the claims are directed to the statutory categories of invention. Step 2 requires a determination of whether the claims are directed to a judicial exception without significantly more. Step 2 is divided into two prongs, with the first prong having a part 1 and part 2. See MPEP 2106.
Claim 1
Pursuant to Step 2A, part 1, claims are analyzed to determine whether they are directed to an abstract idea. Pursuant to MPEP 2106, claims are deemed to be directed to an abstract idea if, under their broadest reasonable interpretation, they fall within one of the enumerated categories of (a) mathematical concepts, (b) certain methods of organizing human activity, and (c) mental processes. Under the broadest reasonable interpretation, the terms of the claim are presumed to have their plain meaning consistent with the specification as it would be interpreted by one of ordinary skill in the art. See MPEP 2111.
Claim 1 recites the limitations of: (1) receiving a data stream including events associated with at least a first user and second user assigned to a token, (2) processing the data stream using a first machine learning model to extract a first substream including first events associated with the first user and conducted using the token and a second substream including second events associated with the second user and conducted using the token, by determining, using the first machine learning model, that the first events are associated with the first user based on the first events occurring within a first period of time and that the second events are associated with the second user based on the second events occurring within a second period of time, (3) generating a first profile for the first user based on the first substream and a second profile for the second user based on the second substream, (4) processing the first profile using a second machine learning model to determine that the first profile is associated with a first cluster of profiles, (5) processing the first substream using a third machine learning model to determine that the first substream includes events associated with the first cluster of profiles and is above a threshold related to the first cluster of profiles to trigger a notification related to the first cluster of profiles, (6) based on determining that the first substream includes events above the threshold, generating the notification to a first user device associated with the first user.
Courts consider a mental process if it “can be performed in the human mind, or by a human using a pen and paper.” The mental process grouping covers concepts performed in the human mind, including observation, evaluation, judgment, and opinion. MPEP 2016(a)(2)(III). Limitations can also be deemed insignificant extra-solution activity (IESA). The term "extra-solution activity" can be understood as activities incidental to the primary process or product that are merely a nominal or tangential addition to the claim. Extra-solution activity includes both pre-solution and post-solution activity. An example of pre-solution activity is a step of gathering data for use in a claimed process, e.g., a step of obtaining information about credit card transactions, which is recited as part of a claimed process of analyzing and manipulating the gathered information by a series of steps in order to detect whether the transactions were fraudulent. An example of post-solution activity is an element that is not integrated into the claim as a whole, e.g., a printer that is used to output a report of fraudulent transactions, which is recited in a claim to a computer programmed to analyze and manipulate information about credit card transactions in order to detect whether the transactions were fraudulent. MPEP 2106.05(g).
Limitation (1) is directed to a step of IESA in the form of mere data gathering. Limitation (2) is directed to a mental step extracting sub-streams of data from a data stream corresponding to a first and second user, respectively. The broadest reasonable interpretation of a data stream is a series of events, which can be as little as two events. Therefore, it can reasonably be performed by a person in the mind with the aid of pen and paper or with the aid of a computer as a tool. Here, the machine learning model is recited at a high level of generality and is merely utilized to apply the mental steps of extracting. A person can reasonably analyze a data stream, evaluate the events to determine whether they occur within a particular period of time associated with a particular user, and judging that those events are associated with a particular user based on when the events occur. Limitation (3) is directed to a mental step of generating profiles for respective users. Limitation (4) is directed to processing a profile to associate it with a cluster of profiles, which is a mental step that can be reasonably performed by a person. The machine learning model here is also recited at a high level of generality and is merely used to apply the mental steps of processing. Limitation (5) is directed to a mental step of processing that involve determinations, which are mental steps that can be reasonably performed by a person. The limitation also recites a machine learning model that is recited at a high level of generality and is merely used to apply the mental steps of processing. Limitation (6) is directed a mental step of generating a notification, which can reasonably be performed by a person with the aid of pen and paper or with a computer as a tool.
The recited processors and non-transitory computer readable medium is/are recited at a high level of generality, i.e., as a generic components performing generic computer functions. As further noted above, each of the recited models are also recited at a high level of generality.
For at least these reasons, claim 1 is directed to an abstract idea categorized under mental processes.
Pursuant to Step 2A, part 2, claims are analyzed to determine whether the claim as a whole integrates the recited judicial exception into a practical application of the exception. This evaluation is performed by (1) identifying whether there are any additional elements recited in the claim beyond the judicial exception, and (2) evaluating those additional elements individually and in combination to determine whether the claim as a whole integrates the exception into a practical application. See MPEP 2106.04(d). One way to determine integration into a practical application is when the claimed invention improves the functioning of a computer or improves another technology or technical field. To evaluate an improvement to a computer or technical field, the specification must set forth an improvement in technology and the claim itself must reflect the disclosed improvement. See MPEP 2106.04(d)(1).
In this case, as explained above, claim 1 merely recites an abstract idea categorized under mental processes. Limitation (1) is directed to IESA and cannot integrate the abstract idea on its own. Limitations (2) through (5) are directed to mental steps that are recited without sufficient specificity in order to provide meaningful limits on the abstract idea. Moreover, limitations (2), (4), and (5) recite the mental steps as being performed using a machine learning model. These limitations provide nothing more than mere instructions to implement an abstract idea on a generic computer. See MPEP 2106.05(f). MPEP 2106.05(f) provides the following considerations for determining whether a claim simply recites a judicial exception with the words “apply it” (or an equivalent), such as mere instructions to implement an abstract idea on a computer: (1) whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished; (2) whether the claim invokes computers or other machinery merely as a tool to perform an existing process; and (3) the particularity or generality of the application of the judicial exception. Here, the claim recites no details about a particular models, which are used to generally apply the abstract idea without placing any limitation on how the models operate to each of the limitations. In addition, the limitations would cover every mode of implementing the recited abstract idea using a models. The claim omits any details as to how the models solve a technical problem and instead recites only the idea of a solution or outcome. See MPEP 2106.05(f). Limitations (2) through (5) also do not recite specific limitations that adequately demonstrate an improvement to the relevant technology. Limitation (6) also does not recite specifics that would adequately demonstrate an improvement to the technology. While claim 1 recite additional components in the form of processors and a non-transitory computer readable medium, these components are recited at a high level of generality, which do not add meaningful limits on the recited abstract idea to integrate it into a practical application by providing an improvement to the functioning of a computer or technology, implementing the abstract idea with a particular machine or manufacture that is integral to the claim, effecting a transformation or reduction of a particular article to a different state or thing, nor applying the abstract idea in some meaningful way beyond linking its use to computer technology. See MPEP 2106.04(d).
For at least these reasons, claim 1 does not integrate the judicial exception into a practical application.
Pursuant to Step 2B, claims are analyzed to determine whether the claim as a whole amounts to significantly more than the recited exception i.e., whether any additional element, or combination of additional elements, adds an inventive concept to the claim. See MPEP 2106.05. In this case, claim 1 does not recite limitations that amount to significantly more than the abstract idea. Limitation (1) is directed to IESA in the form of receiving data, which is well understood, routine, and conventional and cannot provide significantly more than the abstract idea. Limitations (2), (4), and (5), at best are mere instructions to “apply” the abstract ideas, which cannot provide an inventive concept. See MPEP 2106.05(f). Limitations (3) and (6) are recited without sufficient specificity to provide meaningful limits on the abstract idea that would amount to an inventive step providing significantly more than the abstract idea. Merely generating a profile or a notification is simply creating some form of data without limits on how the data is created or what the data actually is. Even when considered in combination, these additional elements represent mere instructions to implement an abstract idea or other exception on a computer and insignificant extra-solution activity, which do not provide an inventive concept. For at least these reasons, claim 1 is nonstatutory because they are directed to a judicial exception without significantly more.
Claim 2 is essentially the same subject matter as claim 1 in the form of a method. Therefore, it is rejected for the same reasons.
Claim 3
Pursuant to step 2A, part 1, claim 3 depends on claim 2 and therefore recites the same abstract idea. Pursuant to step 2A, part 2, claim 3 recites the additional limitations of wherein processing the data stream using the first machine learning model to extract the first substream comprises validating the first events. The limitation is directed to a mental step of validating and does not provide additional specifics that demonstrate an asserted improvement to the technology. The use of the model is merely a instructions for the model to apply the mental steps. Therefore, these additional limitations do not integrate the abstract idea into a practical application. Pursuant to step 2B, for the same reasons as explained, these additional limitations do not provide an inventive concept. For at least these reasons, claim 3 is directed to a judicial exception without significantly more.
Claim 4
Pursuant to step 2A, part 1, claim 4 depends on claim 2 and therefore recites the same abstract idea. Pursuant to step 2A, part 2, claim 4 recites the additional limitations of (1) receiving a negative feedback response from the first user with respect to the notification, (2) subsequent to receiving the negative feedback response, detecting, in the first substream, new events associated with the first user, (3) updating the first profile for the first user based on the new events to generate an updated first profile, and (4) processing the updated first profile using the second machine learning model to determine that the updated first profile is associated with a new cluster of profiles. Limitation (1) is directed to IESA in the form of receiving data. Limitations (2) through (4) are directed to mental steps of detection, updating, and determination. Detection involves steps of observation and evaluation. Updating involves steps of evaluation and judgment to modify data. Determination involves steps of evaluation and judgment. Additionally, none of these limitations provide additional specific steps that would adequately demonstrate an asserted improvement to the technology. Therefore, these additional limitations do not integrate the abstract idea into a practical application. Pursuant to step 2B, for the same reasons as explained, these additional limitations do not provide an inventive concept. For at least these reasons, claim 4 is directed to a judicial exception without significantly more.
Claim 5
Pursuant to step 2A, part 1, claim 5 depends on claim 2 and therefore recites the same abstract idea. Pursuant to step 2A, part 2, claim 5 recites the additional limitations of wherein processing the data stream using the first machine learning model to extract the first substream including the first events associated with the first user and comprises determining a time stamp when the events occurred. The limitation is directed to a mental step of determining, which involves observation and evaluation. The limitation also recites use of a machine learning model, which amounts to mere instructions to apply the mental steps using the model. The limitation also does not provide additional specifics that adequately demonstrate an asserted improvement. Therefore, these additional limitations do not integrate the abstract idea into a practical application. Pursuant to step 2B, for the same reasons as explained, these additional limitations do not provide an inventive concept. For at least these reasons, claim 5 is directed to a judicial exception without significantly more.
Claim 6
Pursuant to step 2A, part 1, claim 6 depends on claim 2 and therefore recites the same abstract idea. Pursuant to step 2A, part 2, claim 6 recites the additional limitations of wherein the threshold to trigger the notification is based on information for the first user extracted from the first substream with an event tracker. The limitation is directed to specifying the type of data to process or manipulate, which is IESA. Therefore, these additional limitations do not integrate the abstract idea into a practical application. Pursuant to step 2B, for the same reasons as explained, these additional limitations do not provide an inventive concept. For at least these reasons, claim 6 is directed to a judicial exception without significantly more.
Claim 7
Pursuant to step 2A, part 1, claim 7 depends on claim 6 and therefore recites the same abstract idea. Pursuant to step 2A, part 2, claim 7 recites the additional limitations of wherein the threshold comprises a total number of events conducted by the first user with the token and associated with a first event type. The limitation is directed to specifying the type of data that is being processed or manipulated, which is IESA. Therefore, these additional limitations do not integrate the abstract idea into a practical application. Pursuant to step 2B, for the same reasons as explained, these additional limitations do not provide an inventive concept. For at least these reasons, claim 7 is directed to a judicial exception without significantly more.
Claim 8
Pursuant to step 2A, part 1, claim 8 depends on claim 2 and therefore recites the same abstract idea. Pursuant to step 2A, part 2, claim 8 recites the additional limitations of wherein generating the notification to the first user device associated with the first user comprises determining a user device from a plurality of user devices associated with the token. The limitation is directed to a mental step of determining and also does not provide additional specifics to adequately demonstrate an asserted improvement to the technology. Therefore, these additional limitations do not integrate the abstract idea into a practical application. Pursuant to step 2B, for the same reasons as explained, these additional limitations do not provide an inventive concept. For at least these reasons, claim 8 is directed to a judicial exception without significantly more.
Claim 9
Pursuant to step 2A, part 1, claim 9 depends on claim 8 and therefore recites the same abstract idea. Pursuant to step 2A, part 2, claim 9 recites the additional limitations of wherein determining the first user device comprises determining the first user device within a threshold distance to the first events. The limitation is directed to a mental step of determining and also does not provide additional specifics to adequately demonstrate an asserted improvement to the technology. Therefore, these additional limitations do not integrate the abstract idea into a practical application. Pursuant to step 2B, for the same reasons as explained, these additional limitations do not provide an inventive concept. For at least these reasons, claim 9 is directed to a judicial exception without significantly more.
Claim 10
Pursuant to step 2A, part 1, claim 10 depends on claim 9 and therefore recites the same abstract idea. Pursuant to step 2A, part 2, claim 10 recites the additional limitations of wherein determining the first user device within the threshold distance to the first events comprises (1) determining the first user device is within the threshold distance, and (2) determining the first user device is associated with events within the first substream. The limitations are directed to mental steps of determining and also does not provide additional specifics to adequately demonstrate an asserted improvement to the technology. Therefore, these additional limitations do not integrate the abstract idea into a practical application. Pursuant to step 2B, for the same reasons as explained, these additional limitations do not provide an inventive concept. For at least these reasons, claim 10 is directed to a judicial exception without significantly more.
Claim 11
Pursuant to step 2A, part 1, claim 11 depends on claim 2 and therefore recites the same abstract idea. Pursuant to step 2A, part 2, claim 11 recites the additional limitations of wherein processing the data stream using the first machine learning model to extract the first substream including the events associated with the first user comprises (1) transmitting a request to a first database for the data stream, wherein the first database includes event data from one or more event trackers, (2) receiving the data stream from the first database, (3) identifying the events associated with the first user within the data stream, (4) generating the first substream including the events associated with the first user, and (5) transmitting the first substream including the events associated with the first user to a second database, wherein the second database includes event data associated with the token. Limitations (1), (2), and (5) are directed to IESA in the form of receiving and transmitting data, which are well understood, routine, and conventional and cannot integrate the abstract idea nor provide an inventive concept. Limitations (3) and (4) are directed to mental steps but do not recite specific steps to sufficiently demonstrate an asserted improvement to the technology. Therefore, these additional limitations do not integrate the abstract idea into a practical application. Pursuant to step 2B, for the same reasons as explained, these additional limitations do not provide an inventive concept. For at least these reasons, claim 11 is directed to a judicial exception without significantly more.
Claim 12
Pursuant to step 2A, part 1, claim 12 depends on claim 2 and therefore recites the same abstract idea. Pursuant to step 2A, part 2, claim 12 recites the additional limitations of wherein processing the first profile using the second machine learning model to determine that the first profile is associated with the first cluster of profiles comprises (1) processing the events associated with the first user, using a classification function, to determine an event type that was most frequent, and (2) associating the first profile with the first cluster of profiles associated with the event type that was most frequent. These limitations recite mental steps of determination and association. None of the limitations recite specific steps that adequately demonstrate an asserted improvement to the technology. Therefore, these additional limitations do not integrate the abstract idea into a practical application. Pursuant to step 2B, for the same reasons as explained, these additional limitations do not provide an inventive concept. For at least these reasons, claim 12 is directed to a judicial exception without significantly more.
Claim 13
Pursuant to step 2A, part 1, claim 13 depends on claim 2 and therefore recites the same abstract idea. Pursuant to step 2A, part 2, claim 13 recites the additional limitations of wherein processing the first substream using the third machine learning model to determine that the first substream includes the first events associated with the first cluster of profiles and is above the threshold to trigger the notification comprises (1) determining, for the first cluster of profiles, the threshold to trigger the notification, (2) processing the first substream to identify events, for the first events, associated with the first profile, and (3) determining, for the first profile, whether the events are above the threshold. The limitations involve mental steps of determining and identification, which involve steps of observation, evaluation, and judgment that can be performed by a person. None of the limitations recite specific steps that adequately demonstrate an asserted improvement to the technology. Therefore, these additional limitations do not integrate the abstract idea into a practical application. Pursuant to step 2B, for the same reasons as explained, these additional limitations do not provide an inventive concept. For at least these reasons, claim 13 is directed to a judicial exception without significantly more.
Claim 14
Pursuant to step 2A, part 1, claim 14 depends on claim 2 and therefore recites the same abstract idea. Pursuant to step 2A, part 2, claim 14 recites the additional limitations of (1) wherein processing the data stream using the first machine learning model comprises identifying a first flag for validating events associated with the first user. The limitation is directed to amental step of identification, which can be performed by a person through observation and evaluation. The limitation does not recite specific steps that would render the steps impractical for a person to perform. The limitation also does not recite specific steps that adequately demonstrate an asserted improvement. Therefore, these additional limitations do not integrate the abstract idea into a practical application. Pursuant to step 2B, for the same reasons as explained, these additional limitations do not provide an inventive concept. For at least these reasons, claim 14 is directed to a judicial exception without significantly more.
Claims 15-20 recite essentially the same subject matter as claims 2, 4, 10, 14, 11, and 12, respectively, in the form of non-transitory computer-readable storage medium. Therefore, they are rejected for the same reasons. Claims 15-20 recite the additional component of a non-transitory computer readable storage medium, but it is recited at a high level of generality and does not provide meaningful limits on the abstract idea.
Claims 1-20 are therefore not drawn to eligible subject matter as they are directed to an abstract idea without significantly more.
To expedite a complete examination of the instant application, the claims rejected under 35 U.S.C. 101 (nonstatutory) above are further rejected as set forth below in anticipation of applicant amending these claims to overcome the rejection.
Note on Prior Art Rejections
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-3, 5-8, 11, 13-15, 18, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Lee et al. (US Patent Pub 2022/0012773) (Lee) in view of Haas et al. (US Patent Pub 2019/0340700) (Haas).
In regards to claim 1, Lee discloses a system, comprising:
one or more processors (Lee at para. 0048); and
a non-transitory computer-readable medium storing instructions that when executed by the one or more processors cause operations (Lee at para. 0048) comprising:
receiving a data stream including events associated with at least a first user and second user assigned to a token (Lee at paras. 0082-83, 0086)1;
processing the data stream using a first machine learning model to extract a first substream including first events associated with the first user and conducted using the token and a second substream including second events associated with the second user and conducted using the token, by determining, using the first machine learning model, that the first events are associated with the first user based on the first events occurring within a first period of time and that the second events are associated with the second user based on the second events occurring within a second period of time (Lee at paras. 0086, 0091-92, 0094-96, 0114, 0123-124, 0131-32, 0147)2;
generating a first profile for the first user based on the first substream and a second profile for the second user based on the second substream (Lee at paras. 0095, 0125)3;
Lee does not expressly disclose processing the first profile using a second machine learning model to determine that the first profile is associated with a first cluster of profiles, processing the first substream using a third machine learning model to determine that the first substream includes events associated with the first cluster of profiles and is above a threshold related to the first cluster of profiles to trigger a notification related to the first cluster of profiles, and based on determining that the first substream includes events above the threshold, generating the notification to a first user device associated with the first user. Lee does disclose utilizing clustering to track shopping behavior. Lee at para. 0143.
Haas discloses a system and method for generating shareable user interfaces using purchase history data. Purchase history of a user is processed to generate user profiling data. The user profiling data is utilized as a basis for comparison with groups of related users (i.e., first cluster of profiles). If a first user’s purchase activity reaches a particular count or percentage of particular items as a group of related users (i.e., determine that the first substream includes events associated with the first cluster of profiles and is above a threshold …), other items associated with the group of related users (i.e., trigger a notification related to the first cluster of profiles) generated and sent to the user in response to a query (i.e., generating the notification to a first user device …). Haas at paras. 0028, 0059, 0061.
Lee and Haas are analogous art because they are directed to the same field of endeavor of tracking and processing user activity in e-commerce.
At the time before the effective filing date of the instant application, it would have been obvious to one of ordinary skill in the art to modify Lee by adding the features of processing the first profile using a second machine learning model to determine that the first profile is associated with a first cluster of profiles, processing the first substream using a third machine learning model to determine that the first substream includes events associated with the first cluster of profiles and is above a threshold related to the first cluster of profiles to trigger a notification related to the first cluster of profiles, and based on determining that the first substream includes events above the threshold, generating the notification to a first user device associated with the first user, as disclosed by Haas.
The motivation for doing so would have been to enable provision of advertisements, recommendations, and discounts based on the purchasing habits of a user. Haas at para. 0028.
In regards to claim 2, Lee discloses a method, comprising:
processing, using a first machine learning model, a data stream including events associated with a plurality of users assigned to a token to extract a first substream including first events associated with a first user by using the first machine learning model to determine that the first events are associated with the first user based on the first events occurring within a particular time of day (Lee at paras. 0086, 0091-92, 0094-96, 0114, 0123-124, 0131-32, 0147)4;
generating a first profile for the first user based on the first substream (Lee at paras. 0095, 0125)5;
Lee does not expressly disclose processing the first profile using a second machine learning model to determine that the first profile is associated with a first cluster of profiles, processing the first substream using a third machine learning model to determine that the first substream includes events associated with the first cluster of profiles and is above a threshold to trigger a notification, and generating the notification to a first user device associated with the first user. Lee does disclose utilizing clustering to track shopping behavior and training machine learning models to process the user activity. Lee at paras. 0131-32, 0143.
Haas discloses a system and method for generating shareable user interfaces using purchase history data. Purchase history of a user is processed to generate user profiling data. The user profiling data is utilized as a basis for comparison with groups of related users (i.e., first cluster of profiles). If a first user’s purchase activity reaches a particular count or percentage of particular items as a group of related users (i.e., determine that the first substream includes events associated with the first cluster of profiles and is above a threshold …), other items associated with the group of related users (i.e., trigger a notification related to the first cluster of profiles) generated and sent to the user in response to a query (i.e., generating the notification to a first user device …). Haas at paras. 0028, 0059, 0061. Haas further discloses utilizing machine learning algorithms to process purchase history. Haas at para. 0058.
Lee and Haas are analogous art because they are directed to the same field of endeavor of tracking and processing user activity in e-commerce.
At the time before the effective filing date of the instant application, it would have been obvious to one of ordinary skill in the art to modify Lee by adding the features of processing the first profile using a second machine learning model to determine that the first profile is associated with a first cluster of profiles, processing the first substream using a third machine learning model to determine that the first substream includes events associated with the first cluster of profiles and is above a threshold to trigger a notification, and generating the notification to a first user device associated with the first user, as disclosed by Haas.
The motivation for doing so would have been to enable provision of advertisements, recommendations, and discounts based on the purchasing habits of a user. Haas at para. 0028.
In regards to claim 3, Lee in view of Haas discloses the method of claim 2, wherein processing the data stream using the first machine learning model to extract the first substream comprises validating the first events. Lee at paras. 0099-0100, 0102, 0144.6
In regards to claim 5, Lee in view of Haas discloses the method of claim 2, wherein processing the data stream using the first machine learning model to extract the first substream including the first events associated with the first user and comprises determining a time stamp when the first events occurred. Lee at para. 0146.7
In regards to claim 6, Lee in view of Haas discloses the method of claim 2, wherein the threshold to trigger the notification is based on information for the first user extracted from the first substream with an event tracker. Haas at para. 0028.8
In regards to claim 7, Lee in view of Haas discloses the method of claim 6, wherein the threshold comprises a total number of events conducted by the first user with the token and associated with a first event type. Haas at para. 0028.9
In regards to claim 8, Lee in view of Haas discloses the method of claim 2, wherein generating the notification to the first user device associated with the first user comprises determining a user device from a plurality of user devices associated with the token. Lee at para. 0102.10
In regards to claim 11, Lee in view of Haas discloses the method of claim 2, wherein processing the data stream using the first machine learning model to extract the first substream including the events associated with the first user comprises:
transmitting a request to a first database for the data stream, wherein the first database includes event data from one or more event trackers (Lee at paras. 0086, 0094, 0176-78)11;
receiving the data stream from the first database (Lee at para. 0086) 12;
identifying the events associated with the first user within the data stream (Lee at paras. 0086, 0091-92, 0094-96, 0131-32)13;
generating the first substream including the events associated with the first user (Lee at paras. 0114-16, 0119)14; and
transmitting the first substream including the events associated with the first user to a second database, wherein the second database includes event data associated with the token. Lee at paras. 0114, 0119.15
In regards to claim 13, Lee in view of Haas discloses the method of claim 2, wherein processing the first substream using the third machine learning model to determine that the first substream includes the first events associated with the first cluster of profiles and is above the threshold to trigger the notification comprises:
determining, for the first cluster of profiles, the threshold to trigger the notification (Haas at paras. 0028, 0059)16;
processing the first substream to identify events, of the first events, associated with the first profile (Haas at paras. 0028, 0059); and
determining, for the first profile, whether the events are above the threshold. Haas at paras. 0028, 0059.17
In regards to claim 14, Lee in view of Haas discloses the method of claim 2, wherein processing the data stream using the first machine learning model comprises identifying a first flag for validating events associated with the first user. Lee at paras. 0099-0100, 0102, 0144.18
Claim(s) 15, 18, and 19 is/are essentially the same as claim 2, 14, and 11, respectively, in the form of a non-transitory computer readable storage medium. Lee at para. 0184. Therefore, it/they is/are rejected for the same reasons.
Claims 4 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Lee et al. (US Patent Pub 2022/0012773) (Lee) in view of Haas et al. (US Patent Pub 2019/0340700) (Haas), further in view of Rabkin (US Patent Pub 2014/0172544).
In regards to claim 4, Lee in view of Haas discloses the method of claim 2, further comprising:
updating the first profile for the first user based on the new events to generate an updated first profile (Lee at paras. 0123-4, 0162); and
processing the updated first profile using the second machine learning model to determine that the updated first profile is associated with a new cluster of profiles. Haas at para. 0059.19
Lee in view of Haas does not expressly disclose receiving a negative feedback response from the first user with respect to the notification and subsequent to receiving the negative feedback response, detecting, in the first substream, new events associated with the first user.
Rabkin discloses a system and method of analyzing and processing user activities in order to provide advertisements (i.e., notification) and further analyzing how a user interacts with the notification. Rabkin at para. 0021-22. In regards to how a user interactions with an advertisement, the method includes determining when a user provides a negative response (i.e., negative feedback response … with respect to the notification). In response to receiving the negative feedback, the characteristics of the user are analyzed (i.e., detecting … new events associated with the first user) to determine a cluster of other users (i.e., new cluster of profiles) with which to associate with the user. Rabkin at para. 0055.
Lee, Haas, and Rabkin are analogous art because they are directed to the same field of endeavor of tracking user activity and characteristics to provide better user experiences.
At the time before the effective filing date of the instant application, it would have been obvious to one of ordinary skill in the art to modify Lee in view of Haas by adding the features of receiving a negative feedback response from the first user with respect to the notification and subsequent to receiving the negative feedback response, detecting, in the first substream, new events associated with the first user, as disclosed by Rabkin. As explained above, Lee in view of Haas discloses updating user profiles with new user activity information and using that new information to find new groups to associate with the user. As modified by Rabkin, the combination results in the ability to perform these features in response to receiving negative feedback from a user in response to a notification.
The motivation for doing so would have been to provide a better user experience for the user by associating the user with groups/users with which the user has greater affinity. Rabkin at para. 0055.
Claim 16 is essentially the same as claim 4 in the form of a non-transitory computer readable storage medium. Therefore, it is rejected for the same reasons.
Claims 9, 10, and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Lee et al. (US Patent Pub 2022/0012773) (Lee) in view of Haas et al. (US Patent Pub 2019/0340700) (Haas), further in view of Del Vecchio et al. (US Patent Pub 2015/0193868) (Del Vecchio).
In regards to claim 9, Lee in view of Haas discloses the method of claim 8, but does not expressly disclose wherein determining the first user device comprises determining the first user device within a threshold distance to the first events.
Del Vecchio discloses a system and method for providing notifications for accounts. The method includes identifying devices of a plurality of devices to receive a notification. The identifying includes determining whether the geographic location associated with a user event is within a threshold distance of a geographic location associated with the user (i.e., determining the first user device within a threshold distance to the first events). Del Vecchio at paras. 0121-2, 0133, 0216.
Lee, Haas, and Del Vecchio are analogous art because they are directed to the same field of endeavor of monitoring/tracking user activity and events.
At the time before the effective filing date of the instant application, it would have been obvious to one of ordinary skill in the art to modify Lee in view of Haas by adding the features of wherein determining the first user device comprises determining the first user device within a threshold distance to the first events, as disclosed by Del Vecchio.
The motivation for doing so would have been to ensure notifications are sent to eligible user devices. Del Vecchio at para. 0173.
In regards to claim 10, Lee in view of Haas and Del Vecchio discloses the method of claim 9, wherein determining the first user device within the threshold distance to the first events, further comprises:
determining the first user device is within the threshold distance (Del Vecchio at paras. 0121-2, 0133, 0216); and
determining the first user device is associated with events within the first substream. (Del Vecchio at paras. 0121-2, 0133, 0216)20
Claim 17 is essentially the same as claim 10 in the form of a non-transitory computer readable storage medium. Therefore, it is rejected for the same reasons.
Claims 12 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Lee et al. (US Patent Pub 2022/0012773) (Lee) in view of Haas et al. (US Patent Pub 2019/0340700) (Haas), further in view of Ju et al. (US Patent Pub 2015/0370798) (Ju).
In regards to claim 12, Lee in view of Haas discloses the method of claim 2, but does not expressly disclose wherein processing the first profile using the second machine learning model to determine that the first profile is associated with the first cluster of profiles comprises:
processing the events associated with the first user, using a classification function, to determine an event type that was most frequent; and
associating the first profile with the first cluster of profiles associated with the event type that was most frequent.
Ju discloses a system and method for analyzing user activity and interactions in order to associate the user with a particular group. Ju discloses determining the frequency of a user’s interactions with items associated with a particular group, such as interacting with the same topics as users in the particular group. Upon identifying the group with which the user has the most affinity based on the most frequent interactions (i.e., most frequent event type), the particular group is provided to the user as a recommendation (i.e., associating the first profile with the first cluster of profiles with event type that was most frequent). Ju at paras. 0004, 0081.
Lee, Haas, and Ju are analogous art because they are directed to the same field of endeavor of analyzing user activity to identify associations.
At the time before the effective filing date of the instant application, it would have been obvious to one of ordinary skill in the art to modify Lee in view of Haas by adding the features of processing the events associated with the first user, using a classification function, to determine an event type that was most frequent and associating the first profile with the first cluster of profiles associated with the event type that was most frequent, as disclosed by Ju.
The motivation for doing so would have been to improve associations between the user and user groups based on the user’s activity. Ju at para. 0081.
Claim 20 is essentially the same as claim 12 in the form of a non-transitory computer readable storage medium. Therefore, it is rejected for the same reasons.
Response to Amendment
Drawings
Applicant’s amendment to the drawings to address missing reference characters is acknowledged. Consequently, objection to the drawings is withdrawn.
Objection to claims 3, 5, 10, and 13 for Minor Informalities
Applicant’s amendment to claims 3, 5, 10, and 13 to address the minor informalities is acknowledged. Consequently, the objection to claims 3 and 5 is withdrawn. The objection to claims 10 and 13 are maintained for the reasons set forth above.
Rejection of Claims 8-10 and 17 under 35 U.S.C 112(b)
Applicant’s amendment to claims 8-10 and 17 is acknowledged. The rejection to claims 8-10 and 17 under 35 U.S.C. 112(b) is withdrawn.
Response to Arguments
Rejection of claims 1-20 under 35 U.S.C. 101
Applicant’s arguments in regards to the rejections to claims 1-20 under 35 U.S.C. 101, have been fully considered and they are not persuasive. Applicant alleges under step 2A-1, the claims do not recite a mental process because the claims involve machine learning model operations that cannot be practically performed in the human mind. Remarks at 13-14. Applicant further argues the claims are analogous to example 39 of the USPTO guidelines. Remarks at 14. The Examiner respectfully disagrees.
Examiner is required to give claim limitations their broadest reasonable interpretation in light of the specification. However, limitations from the specification are not read into the claims. MPEP 2111.
In regards to Applicant’s first argument, as set forth in the rejection above, the recitations of the first, second, and third machine learning models are at a high level and merely apply the mental steps to a computer for execution. There is nothing in the limitations that makes the steps performed by the machine learning models impractical or impossible for a person to perform in the mind with the aid of pen and paper or with the aid of a computer as a tool to perform the abstract idea.
For example, using the first machine learning model to determine first events that are associated with a first user based on the first events occurring within a first period of time, is merely the mental steps of observation, evaluation, and judgment. A person can review a data stream, such as a purchase history or watch history, review when purchase or watch events occur and evaluate and judge whether the time those events occurred should be associated with a particular user. The same is true for the other operations performed by the second and third machine learning models. The claims do not recite specific steps that would render the limitations impossible or impractical for a person to perform, as explained above.
In response to Applicant’s second argument, as noted by Applicant, example 39 is directed to training of a neural network and the claim recites specific features that are applicable to that training. Contrary to example 39, the instant claims are not directed to training of the recited models with particular and specific steps and the instant claims also recite limitations that are practically performed by a person, as explained above.
For at least these reasons, claims 1-20 are directed to an abstract idea without significantly more. Consequently, the rejection to claims 1-20 under 35 U.S.C. 101 is maintained.
Rejection of claims 1-3, 5-8, 11, 13-15, 18, and 19 under 35 U.S.C. 103
Applicant’s arguments in regards to the rejections to claims 1-3, 5-8, 11, 13-15, 18, and 19 under 35 U.S.C. 103, have been fully considered but they are not persuasive. Applicant alleges Examiner did not identify a portion of Haas that discloses a third machine learning model that is distinct from the first and second machine learning models recited in the claims. Remarks at 15. The Examiner respectfully disagrees.
Examiner is required to give claim limitations their broadest reasonable interpretation in light of the specification. However, limitations from the specification are not read into the claims. MPEP 2111.
In response to Applicant’s allegation that Examiner has not established a prima facie case of obviousness because a portion of Haas was not cited for disclosing the third machine learning model, the rejection cites to portions of Haas that disclose the features of the second machine learning model and the third machine learning model. For example, the feature of determining that a first profile is associated with a first cluster of profiles and determining that the first substream includes events associated with the first cluster of profiles and is above a threshold to trigger a notification related to the first cluster of profiles, as set forth in the rejection above. Haas also discloses the use of machine learning algorithms to process the purchase history (i.e., first substream) of a user in order to perform the aforementioned features. Haas at paras. 0032-33, 0037, 0042, 0058. This is also set forth in the rejection of claim 2 above. Furthermore, Lee also discloses the use of machine learning models for analysis of the data to generate metrics associated with a particular user. Lee at paras. 0130-132. Therefore, based on the combination of Lee in view of Haas, the result is a system and method that provides the claimed features of claim 1 and similarly claims 2 and 15.
In response to Applicant’s argument that Haas does not disclose a third machine learning model that is distinct from the first and second machine learning models, such features are not recited in the claim. Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993). The broadest reasonable interpretation of the claimed limitations includes interpreting that the first, second, and third machine learning models are the same model.
Applicant does not present additional arguments with regards to the remaining limitations. Therefore, Examiner asserts the cited prior art discloses all the limitations of claim 1, and similarly claims 2 and 15, for the reasons explained above. In regards to the remaining claims, Applicant refers to the arguments presented in regards to claim 1, which are addressed above. Consequently, the rejection to claims 1-3, 5-8, 11, 13-15, 18, and 19 under 35 U.S.C. 103 is maintained.
Rejection of claims 4 and 16 under 35 U.S.C. 103
Applicant does not present arguments in regards to the rejections to claims 4 and 16 under 35 U.S.C. 103. Consequently, the rejection to claims 4 and 16 under 35 U.S.C. 103 is maintained for at least the reasons explained above.
Rejection of claims 9, 10, and 17 under 35 U.S.C. 103
Applicant does not present arguments in regards to the rejections to claims 9, 10, and 17 under 35 U.S.C. 103. Consequently, the rejection to claims 9, 10, and 17 under 35 U.S.C. 103 is maintained for at least the reasons explained above.
Rejection of claims 12 and 20 under 35 U.S.C. 103
Applicant does not present arguments in regards to the rejections to claims 12 and 20 under 35 U.S.C. 103. Consequently, the rejection to claims 12 and 20 under 35 U.S.C. 103 is maintained for at least the reasons explained above.
Additional Prior Art
Additional relevant prior art are listed on the attached PTO-892 form. Some examples are:
Embiricos et al. (US Patent Pub 2018/0189343) discloses a system and method for providing notifications based on monitored events.
Giard et al. (US Patent Pub 2013/0054433) discloses a system and method for identifying users based on user behavior and interactions.
Roosenraad et al. (US Patent Pub 2016/0241579) discloses a system and method for analyzing data and online activity to correlate the data with subscribers.
Forman et al. (US Patent Pub 2013/0091088) discloses a system and method for making recommendations to a user generating events based on a subset of historical event data.
Conclusion
THIS ACTION IS MADE FINAL. 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 mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Examiner Michael Le whose telephone number is 571-272-7970 and fax number is 571-273-7970. The examiner can normally be reached Mon-Fri 9:30 AM – 6 PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Tony Mahmoudi can be reached on 571-272-4078. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MICHAEL LE/Examiner, Art Unit 2163
/TONY MAHMOUDI/Supervisory Patent Examiner, Art Unit 2163
1 User activity (i.e., data stream) of a master account (i.e., assigned token) includes data from multiple users (i.e., first and second user).
2 User activity (i.e., data stream) of the master account (i.e., using the token) is analyzed/processed to associate particular user activity data with new or existing sub-account (i.e., first user, second user). This processing can be done by a machine learning model. User activity is analyzed and compared with other collected user metrics during the same given time period/interval in order to determine a sub-user account (i.e., determining that the first/second events are associated with the first/second user based on the first/second events occurring within a first/second period of time …).
3 The identified, respective, user activity data can be identified as belonging to a new sub-account, which is generated (i.e., generating a first profile … second profile).
4 User activity (i.e., data stream) of the master account (i.e., using the token) is analyzed/processed to associate particular user activity data with new or existing sub-account (i.e., first user, second user). This processing can be done by a machine learning model. User activity is analyzed and compared with other collected user metrics during the same given time period/interval in order to determine a sub-user account (i.e., to determine that the first events are associated with the first user based on the first events occurring within a particular time of day).
5 The identified, respective, user activity data can be identified as belonging to a new sub-account, which is generated (i.e., generating a first profile … second profile).
6 Lee describes various methods and techniques to ensure that extracting of user activity data for a particular sub-account (i.e., first user) is correct. This includes considering multiple streams of information and analysis techniques (i.e., validating).
7 User activity includes timestamps.
8 Quantity or percentage of purchase characteristics is used as a threshold.
9 Quantity of purchases of a particular item (i.e., total number of events … associated with a first event type) associated with the user.
10 A particular device is identified from the plurality of customer devices that interact with the system.
11 The user activity engine performs cookie requests for user activity associated with the master account.
12 User activity (i.e., data stream) is received from storage at a data facility (i.e., first database), which stores user activity from user devices (i.e., event rackers).
13 User activity (i.e., data stream) of the master account (i.e., using the token) is analyzed/processed to associate particular user activity data with new or existing sub-account (i.e., first user, second user). This processing can be done by a machine learning model.
14 User action metrics (i.e., substream) are generated and associated with a particular sub-account (i.e., first user).
15 User action metrics are stored to be used for future comparisons (i.e., transmitted .. to a second database). They are associated with the subaccount, which is associated with the master account (i.e., associated with the token).
16 A threshold count or percentage is determined for a particular group of related users (i.e., first cluster of profiles).
17 Purchase history of a user is processed and analyzed to determine if events meet a threshold to be associated with a group of related users.
18 Lee describes various methods and techniques to ensure that extracting of user activity data for a particular sub-account (i.e., first user) is correct. This includes considering multiple streams of information and analysis techniques (i.e., validating). Here, a “first flag” can be any particular value/pattern shown in the collected data.
19 As new actions are processed and new thresholds are satisfied, the user is associated with appropriate groups (i.e., processing the updated first profile … to determine that the updated first profile is associated with a new cluster of profiles).
20 Based on geographical proximity, the device is associated with user event/activity (i.e., associated with events within the first substream).
Prosecution Insights