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 .
DETAILED ACTION
This action is in response to the claims filed 8/5/2026. Claims 1-20 are pending for examination.
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.
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.
Claims 1, 8 and 15 recite the abstract idea of “sending alerts based on expertise score and market indicator data”, which is grouped under “Certain Methods of Organizing Human Activity” such as “fundamental economic principles or practices (make decisions using market data). (MPEP 2016.04(a)). Specifically, claims 1, 8 and 15 recite “selecting among alerts for delivery to a … of a target user”, “using … determining, using a … topic model, that a number of content items relating to a particular topic that are shared by the target user to a … service exceeds a threshold number of content items”, “responsive to determining that the number of content items exceeds the threshold number of content items, setting a topic parameter to the particular topic”, “calculating an expertise score of the target user quantifying a level of experience the target user has in performing a task related to the particular topic, the expertise score calculated based upon a weighted sum of two or more of: an age of an account of the target user, a task completion volume of the target user, a number of interactions with past alerts, and an expertise given by the target user”, “determining an adjustment to a frequency parameter based upon the expertise score of the target user and market indicator data describing volatility of a market, a frequency increasing as the expertise score indicates that the target user is less experienced and decreasing as the expertise score indicates that the target user is more experienced; determining that a second item of content not shared by the target user matches the topic parameter”, “determining whether sending the second item of content to the target user would result in a frequency of sending content to the target user that is below the frequency parameter”, “responsive to determining that the second item of content matches the topic parameter” and “determining that sending the second item of content would result in the frequency of sending content that is below the frequency parameter, sending the second item of content as an alert to the target user as an … communication”. Claims 2-7, 9-14 and 16-20 are dependent on claims 1, 8 and 15 and include all the limitations of claims 1, 8 and 15. Therefore, claims 2-7, 9-14 and 16-20 recite the same abstract idea of “sending alerts based on expertise score and market indicator data”. The limitations recited in the depending claims (For example, the clustering, sharing, increasing steps and specific type of model and data used) are further details of the abstract idea and not significantly more. The concept described in claims 1-20 are not meaningfully different than those concepts found by the courts to be abstract ideas. As such, the description in claims 1-20 is an abstract idea. Accordingly, the claims recite an abstract idea.
This judicial exception is not integrated into a practical application because, when analyzed under prong two of step 2A (MPEP 2106.04II), the additional elements such as “device”, “one or more processors”, “network based”, “electronic”, “machine” and “memory” represent the use of a computer as a tool to perform an abstract idea and/or does no more than generally link the abstract idea to a particular technological environment or field of use. Additionally, the claims “machine-learned” limitations describe only the result but not how the learning is done other than to say it is by a “machine”. Merely apply generic machine learning techniques to new data environments without disclosing improvements to the machine learning models are ineligible. Further, as the additional elements do not provide a practical application, they do not improve computer functionality and do not improve another technology or technical field.
When analyzed under step 2B (MPEP 2106.04II), because the additional elements do no more than represent the use of a computer as a tool to perform an abstract idea and/or does no more than generally link the abstract idea to a particular field of use, they do not provide an improvement to computer functionality, or an improvement to another technology or technical field and, therefore, do not amount to significantly more than the judicial exception itself (MPEP 2106.05(I)(A)(f)&(h)).
Hence, claims 1-20 are not patent eligible.
Claims 1-20 have been searched and reviewed. No prior art (effective filing date 5/4/2016) has been found that discloses, either expressly or inherently, all of the limitations of the claimed invention. Even though ISMALON (US 2010/0049770 A1) discloses using one or more processors: determining, using a machine-learned topic model, that a number of content items relating to a particular topic that are shared by the target user to a network-based service; responsive to determining the number of content items, setting a topic parameter to the particular topic, calculating an expertise score of the target user quantifying a level of experience the target user has in performing a task related to the particular topic, the expertise score calculated based upon an expertise given by the target user, see at least paragraph 0074 (Latent Dirichlet allocation…Machine Learning), paragraph 0018 (content of a web page is analyzed to determine a list of one or more topics associated with that web page; keywords belonging to the list of one or more topics), paragraph 0134 (low level of user expertise in the topic), paragraph 0137 (higher association score generally correlates with a higher level of user expertise in the topic) and Gaglani et a. (US 2015/0325133 A1) discloses determining, using a machine-learned model, a particular topic that are shared to network-based service and determining an adjustment to a frequency parameter based upon expertise, determining that a second item of content matches the topic parameter, determining whether sending the second item of content to the target user would result in a frequency of sending content to the target user, responsive to determining that the second item of content matches the topic parameter and determining that sending the second item of content would result in the frequency of sending content, sending the second item of content as an alert to the target user as an electronic communication, see at least paragraph 0035 (the Knowledge Diffusion Platform uses machine learning algorithms that drive non-random recommendations), paragraph 0057 (Knowledge Diffusion Platform may provide information about…topic), paragraph 0032 (receives data over network… input data to Knowledge Diffusion Platform), paragraph 0054 (dynamically change factors such as the number of push notifications per day i.e. frequency…which specific topics they cover), paragraph 0046 (score maybe determined based on factors such as frequency that keywords associated with the knowledge) and claim1 of Gaglani. The rest of limitations recited in the independent claims 1, 8 and 15 considered as a whole, is not taught by the prior arts found in examiner’s search and STIC search. Therefore, no rejection under 102/103 is made.
Related But Not Relied Upon
Relevant prior art cited but not applied: Sreenivasan US 2022/0237700 A1, directed to setting alert preferences and utilizing predictive analytics techniques including machine learning
Response to Arguments
Applicant's arguments filed 8/5/2026 have been fully considered but they are not persuasive.
Applicant argues that the claims are statutory under 35 U.S.C. 101 because 1) the Office Action presents no rejection under 102 or 103 2) the claims are not directed to fundamental economic practice because the claims recite no commercial or financial transaction, no hedging, no lending and no economic decision-making 3) similar to McRO, the claims improves operation of content-delivery and notification system and reduces unnecessary transmissions 4) Similar to DDR Holdings, the claims are necessarily rooted in computer technology in order to cover a problem specifically arising in the realm of computer networks 5) the Office Action offers no evidence for treating “dynamically adjusting…” as generic or conventional, it has no established that the claims lack inventive concept 6) the model is specified as Latent Dirichelt Allocation model in depending claims. The Examiner disagrees. In response to applicant’s argument that there is no 102/103 rejection, it is noted that patent eligibility under 35 U.S.C. 101 is a separate inquiry from novelty and non-obviousness under 35 U.S.C. 102 and 103. Accordingly, the absence of a rejection under 102 or 103 does not, by itself, establish that the claims are patent eligible under 101. In response applicant’s argument that the claims are not directed to abstract idea, it is noted that the claims recite the abstract idea of “sending alerts based on expertise score and market indicator data”, which is grouped under “Certain Methods of Organizing Human Activity” such as “fundamental economic principles or practices (make decisions using market data). (MPEP 2016.04(a)). In response to applicant’s argument that the claims are similar to McRO, it is noted that McRO were directed to a specific asserted improvement in computer animation in which the claimed rules were used to produce the improved technological result. In contract, the presently claimed limitations use computer processing to determine characteristics of information and user, and based upon that determination, decide whether and how frequently information should be communicated to the user. Although the claimed process may improve the relevance or frequency of alerts from the perspective of the user, it does not establish an improvement to computer functionality or another technology. Applicant’s reliance on DDR Holdings is also unpersuasive. In DDR Holdings, the claims address a problem specifically arising in computer networks and provide a technological solution that modified conventional Internet operation. Here, the claimed problem is determining which information should be provided and whether particular information satisfies those criteria. This is not a technological problem arising from computer networks. The claims do not alter the functioning of a computer network, nor do it provide a technical solution rooted in computer technology comparable to the claims in DDR Holdings. Thus, unlike the technological solution in DDR Holdings, the claimed use of processors, a network-based service and electronic communications principally provide a technological environment in which the information-selection process is performed. In response to applicant’s argument the Office has not provided evidence establishing that “dynamically adjusting” is generic or conventional, it is noted that that the rejection did not rely on a finding that the claimed adjustment constitutes insignificant activity. Accordingly, the evidence establishing that the claimed adjustment is well-understood routine and conventional is not required to support the sept 2A Prong Two determination. With respect to Step 2B, the additional elements, considered individually and in combination, fail to amount to significantly more than the identified judicial exception for the reasons set forth in the rejection. In response to applicant’s argument that certain dependent claims further specify that the machine-learned topic model comprises a Latent Dirichelt Allocation model, it is noted that merely specifying a particular computational model does not necessarily establish an improvement to computer functionality or another technological field. The relevant inquiry concerns how the model is used in the claim. Applicant has not persuasively established that the claimed use of the Latent Dirichlet Allocation model improves the operation of the model or another technological component, as opposed to using the model as a tool for determining information subsequently employed in the claimed selection process. Therefore, applicant’s arguments are not persuasive.
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 nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CHIA-YI LIU whose telephone number is (571)270-1573. The examiner can normally be reached Mon-Thurs 9-8 pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, RYAN DONLON can be reached at (571) 270-3602. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/CHIA-YI LIU/Primary Examiner, Art Unit 3692
Prosecution Insights