DETAILED ACTION
This Office action is in reply to correspondence filed 5 January 2026 in regard to application no. 18/655,195. Claims 1-20 are pending and are considered below.
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 .
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 an abstract idea without significantly more. The claims lie within statutory categories of invention, as each is directed either to a system (machine) or method (process). The claim(s) recite(s) gathering data (a user interaction and a user review message), authenticating a user's identity in no particular way, generating a link, determining a sentiment score associated with the review, and sending a follow-up message based on the score.
As the user interaction is in response to a call to action and the follow-up message is related to it, the claim recites advertising behavior and/or commercial interaction, each of which is among the "certain methods of organizing human activity" deemed abstract.
Further, in the absence of computers, these are steps that can be performed mentally and using a pen and paper. A merchant can see that a customer has interacted with a call to action if the customer brings the merchant's coupon into her store. She can verify the customer's identity by mentally recognizing him, can receive a review e.g. verbally, can mentally determine the customer's sentiment based on what he has said, can associate a number with it mentally, and can give the customer an additional incentive or other information based on what she has learned. None of this presents any practical difficulty, and none requires any technology beyond a pen and paper.
This judicial exception is not integrated into a practical application because aside from what was set forth above, the only limitations recite generic computers and nondescript use of AI, which does not go beyond generally linking the abstract idea to the technological environment of networked, Al-enabled computers. See MPEP § 2106.05(h).
As the claims only manipulate data pertaining to a user's interactions and sentiments and messages provided to the user, they do not improve the "functioning of a computer" or of "any other technology or technical field". See MPEP § 2106.05(a). They do not apply the abstract idea "with, or by use of a particular machine", MPEP § 2106.05(b), as the below-cited Guidance is clear that a generic computer is not the particular machine envisioned.
They do not effect a "transformation or reduction of a particular article to a different state or thing", MPEP § 2106.05(c). First, such data, being intangible, are not a particular article at all. Second, the claimed manipulation is neither transformative nor reductive; as the courts have pointed out, in the end, data are still data.
They do not apply the abstract idea "in some other meaningful way beyond generally linking [it] to a particular technological environment", MPEP § 2106.05(e), as the lack of technical and algorithmic detail in the claims is so as not to go beyond such a general linkage.
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception the additional claim limitations considered individually and as an ordered combination, are insufficient to elevate an otherwise-ineligible claim to patent eligibility.
Claim 1, which has the most, includes a processor and memory storing instructions; claim 11 includes training an algorithm in no particular manner. The specification is explicit, 1 149, that no specific kind of computer is required but that "one or more general-purpose computers associated with one or more networks" will suffice, which encompasses a generic computer.
It only performs generic computer functions of nondescriptly manipulating information and sharing information with persons and/or other devices. Generic computers performing generic computer functions, without an inventive concept, do not amount to significantly more than the abstract idea.
The type of information being manipulated does not impose meaningful limitations or render the idea less abstract. In light of Recentive1, simply using known machine learning techniques in a new data environment is not enough to elevate an otherwise-ineligible claim to eligibility. The claim limitations when considered in ordered combination - a generic computer performing a chronological sequence while making nondescript use of generic AI techniques - do nothing more than when they are analyzed individually.
The other independent claim is simply a different embodiment but is likewise directed to a generic computer performing, essentially, the same process. The dependent claims further do not amount to significantly more than the abstract idea: claims 2, 6, 7, 9, 10, 12, 16, 17, 19 and 20 are simply further descriptive of the type of information being manipulated. Claims 3 and 13 simply describe data input to, and the purpose of, machine learning; claims 4, 5, 8, 14, 15, and 18 simply recite further, abstract manipulation of data.
For further guidance please see MPEP § 2106.03 – 2106.07(c) (formerly referred to as the “2019 Revised Patent Subject Matter Eligibility Guidance”, 84 Fed. Reg. 50, 55 (7 January 2019)).
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1-4, 6, 7, 11-14, 16 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Balasubramanian et al. (U.S. Publication No. 2016/0173428) in view of Gross (U.S. Publication No. 2016/0092959).
In-line citations are to Balasubramanian.
With regard to Claim 1:
Balasubramanian teaches: A system for link-initiated secure voting and review, comprising:
a first trained machine learning algorithm [0049; "the system may identify a language model that has been modified based on information learned about a user or based on a user's use of a device"] configured to analyze the content of a user review to determine a sentiment [0077; the "system performs settlement analysis" related to usage of content] and
a computing device comprising a processor, a memory, and a first plurality of programming instructions stored in the memory and operable on the processor, [0023; a computer includes a "processor" and "non-transitory memory" storing "instructions"] wherein the first plurality of programming instructions, when operating on the processor, cause the computing device to:
receive a user interaction from a mobile device substantially corresponding to a call-to-action and a mobile device metadata… [0060; the "system can anticipate a topic based on a received user selection" which may relate to "a particular product or service"; it may also include the "region or geographic location of the user" which may, 0068, be obtained from "GPS coordinates" of the user’s device which may be, 0063, a mobile device such as a “smart phone” which may also provide its “model or brand”]
generate a redirect comprising a deep link and a payload, [0073; "supplemental content" may be accompanied by a "deep link"] wherein the redirect is configured to auto-populate a message on a messaging application on the mobile device;
receive a user review message from the mobile device; [0002; "a user may wish to send reviews of a restaurant to his friend" as additional "supplemental content"]
use the user review and the mobile device metadata as inputs into the first trained machine learning algorithm to determine a sentiment score associated with the user review; [0077; the "sentiment analysis" is related to the "usage of supplemental content" and "provides feedback"; that the input came from the mobile device reads on its metadata being among the input] and
send a follow-up message to the mobile device based on the sentiment… [0077; it provides feedback to a creator; the difference between sending it to a creator or to the user's mobile device is simply a substitution of one known part for another with predictable results]
Balasubramanian does not explicitly teach that a sentiment is represented by a score, to authenticate an identity of a user of the mobile device using a verification rule associated with the call-to-action, or to take a step responsive to user authentication, but it is known in the art. Gross teaches a tagging system using mobile interfaces. [abstract] It creates "scores" based on "sentiment". [0527] A "mobile user" is asked to perform a verification step. [0548]
Verification in some embodiments includes a user being "authenticated" by supplying a "password" to an "automated verification system", [0278] as a result of which additional steps are taken. [0279] The user may be sent a "targeted advertisement". [0024] Viewers may provide reviews of target images. [0140] Gross and Balasubramanian are analogous art as each is directed to electronic means for providing mobile advertising.
It would have been obvious to one of ordinary skill in the art just prior to the filing of the claimed invention to combine the teaching of Gross with that of Balasubramanian in order to improve user ease and engagement, as taught by Gross; [abstract] further, it is simply a substitution of known parts for others with predictable results, simply measuring sentiment using a score as in Gross rather than by the means of Balasubramanian and performing user authentication as in Gross rather than presuming a user's identity as in Balasubramanian; the substitutions produce no new and unexpected result.
In this and the subsequent claims, that a "redirect is configured to auto-populate a message on a messaging application on the mobile device" consists entirely of nonfunctional printed matter, disclosing at most the content of output which bears no functional relation to the claimed substrate and is therefore considered but given no patentable weight. Referring to an algorithm as a "machine learning algorithm", "trained machine learning algorithm", etc., without more, is considered mere labeling and given no patentable weight.
With regard to Claim 2:
The system of claim 1, wherein the call-to-action is associated with a voting event or user review process. [Gross, 0140 as cited above in regard to claim 1]
This claim is not patentably distinct from claim 1 as it consists entirely of nonfunctional printed matter, disclosing at most the content of output which bears no functional relation to the substrate and so is considered but given no patentable weight. The reference is provided for the purpose of compact prosecution.
With regard to Claim 3:
The system of claim 1, further comprising a second trained machine learning algorithm configured to analyze the content of a user review to determine a positivity score; and wherein the computing device is further configured to use the user review and the mobile device metadata as inputs into the second trained machine learning algorithm to determine a positivity score associated with the user review. [Gross as cited above; a sentiment score reads on a positivity score; the change of training from one set of data to another would have been obvious to one then of ordinary skill in the art as a substitution of one known part for another with predictable results]
With regard to Claim 4:
The system of claim 3, further comprising a third trained machine learning algorithm configured to generate the follow-up message; and wherein the computing device is further configured to: use the user review, the mobile device metadata, the sentiment score, and the positivity score as inputs into the third trained machine learning algorithm to generate the follow-up message. [Balasubramanian as cited above in regard to claim 1; using this data in place of that from the earlier claim is simply a substitution of known parts with predictable results]
With regard to Claim 6:
The system of claim 1, wherein the payload comprises a ready-to-send user review.
This claim is not patentably distinct from claim 1 as it consists entirely of nonfunctional printed matter which bears no functional relation to the substrate and so is considered but given no patentable weight.
With regard to Claim 7:
The system of claim 1, wherein the payload comprises a ready-to-send vote.
This claim is not patentably distinct from claim 1 as it consists entirely of nonfunctional printed matter which bears no functional relation to the substrate and so is considered but given no patentable weight.
With regard to Claim 11:
Balasubramanian teaches: A method for link-initiated secure voting and review, comprising the steps of:
training a first machine learning algorithm configured to analyze the content of a user review [0049; "the system may identify a language model that has been modified based on information learned about a user or based on a user's use of a device"; 0077; data which may be used includes feedback] to determine a sentiment… [0077; the "system performs settlement analysis" related to usage of content]
receiving a user interaction from a mobile device substantially corresponding to a call-to-action and a mobile device metadata... [0060; the "system can anticipate a topic based on a received user selection" which may relate to "a particular product or service"; it may also include the "region or geographic location of the user" which may, 0068, be obtained from "GPS coordinates" of the user's device which may be, 0063, a mobile device such as a "smart phone" which may also provide its "model or brand"]
generating a redirect comprising a deep link and a payload, [0073; "supplemental content" may be accompanied by a "deep link"] wherein the redirect is configured to auto-populate a message on a messaging application on the mobile device;
receiving a user review message from the mobile device; [0002; "a user may wish to send reviews of a restaurant to his friend" as additional "supplemental content"]
using the user review and the mobile device metadata as inputs into the first trained machine learning algorithm to determine a sentiment score associated with the user review; [0077; the "sentiment analysis" is related to the "usage of supplemental content" and "provides feedback"; that the input came from the mobile device reads on its metadata being among the input] and
sending a follow-up message to the mobile device based on the sentiment... [0077; it provides feedback to a creator; the difference between sending it to a creator or to the user's mobile device is simply a substitution of one known part for another with predictable results]
Balasubramanian does not explicitly teach that a sentiment is represented by a score, to authenticate an identity of a user of the mobile device using a verification rule associated with the call-to-action, or to take a step responsive to user authentication, but it is known in the art. Gross teaches a tagging system using mobile interfaces. [abstract] It creates "scores" based on "sentiment". [0527] A "mobile user" is asked to perform a verification step. [0548]
Verification in some embodiments includes a user being "authenticated" by supplying a "password" to an "automated verification system", [0278] as a result of which additional steps are taken. [0279] The user may be sent a "targeted advertisement". [0024] Viewers may provide reviews of target images. [0140] Gross and Balasubramanian are analogous art as each is directed to electronic means for providing mobile advertising.
It would have been obvious to one of ordinary skill in the art just prior to the filing of the claimed invention to combine the teaching of Gross with that of Balasubramanian in order to improve user ease and engagement, as taught by Gross; [abstract] further, it is simply a substitution of known parts for others with predictable results, simply measuring sentiment using a score as in Gross rather than by the means of Balasubramanian and performing user authentication as in Gross rather than presuming a user's identity as in Balasubramanian; the substitutions produce no new and unexpected result.
With regard to Claim 12:
The method of claim 11, wherein the call-to-action is associated with a voting event or user review process. [Gross, 0140 as cited above in regard to claim 11]
This claim is not patentably distinct from claim 11 as it consists entirely of nonfunctional printed matter, disclosing at most the content of output which bears no functional relation to the substrate and so is considered but given no patentable weight. The reference is provided for the purpose of compact prosecution.
With regard to Claim 13:
The method of claim 11, further comprising the steps of: training a second trained machine learning algorithm configured to analyze the content of a user review to determine a positivity score; and using the user review and the mobile device metadata as inputs into the second trained machine learning algorithm to determine a positivity score associated with the user review. [Gross as cited above; a sentiment score reads on a positivity score; the change of training from one set of data to another would have been obvious to one then of ordinary skill in the art as a substitution of one known part for another with predictable results]
With regard to Claim 14:
The method of claim 13, further comprising the steps of: training a third trained machine learning algorithm configured to generate the follow-up message; and using the user review, the mobile device metadata, the sentiment score, and the positivity score as inputs into the third trained machine learning algorithm to generate the follow-up message. [Balasubramanian as cited above in regard to claim 1; using this data in place of that from the earlier claim is simply a substitution of known parts with predictable results; the training discloses only an intended purpose which is manner-of-use language, considered but given no patentable weight]
With regard to Claim 16:
The method of claim 11, wherein the payload comprises a ready-to-send user review.
This claim is not patentably distinct from claim 11 as it consists entirely of nonfunctional printed matter which bears no functional relation to the substrate and so is considered but given no patentable weight.
With regard to Claim 17:
The method of claim 11, wherein the payload comprises a ready-to-send vote.
This claim is not patentably distinct from claim 11 as it consists entirely of nonfunctional printed matter which bears no functional relation to the substrate and so is considered but given no patentable weight.
Claim(s) 8-10 and 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over Balasubramanian et al. in view of Gross further in view of Bhogal (U.S. Publication No. 2018/0292092).
Claims 8 and 18 are similar so are analyzed together.
With regard to Claim 8:
The system of claim 3, further comprising a fourth trained machine learning algorithm configured to classify a quality of the user review.
With regard to Claim 18:
The method of claim 13, further comprising the step of training a fourth trained machine learning algorithm configured to classify a quality of the user review.
Balasubramanian and Gross teach the system of claim 3 and method of claim 13, but do not explicitly teach classifying a quality of a review, but it is known in the art. Bhogal teaches a food preparation method which uses subjected user feedback [abstract] and a "machine learning model" using "features extracted from user comments to classify the verbal feedback" using a "numerical feedback value". [0129] The feedback value may be related to a quality parameter [id.] and the data may be used "to improve the quality of user feedback". [0034] Bhogal and Balasubramanian are analogous as both are directed to electronic means for using learning and conducting a dialogue with a user.
It would have been obvious to one of ordinary skill in the art just prior to the filing of the claimed invention to combine the teaching of Bhogal with that of Balasubramanian and Gross in order to improve the quality of feedback, as taught by Bhogal; further, it is simply a combination of known parts with predictable results, simply performing Bhogal's step after those of Balasubramanian. Each part works independently of the other, and each works in combination identically to how it works when not combined, with no new and unexpected result inherent or disclosed.
These claims are not patentably distinct from claims 3 and 13 as they disclose merely a purpose of training, which is manner-of-use language and therefore is considered but given no patentable weight. The reference is provided for the purpose of compact prosecution.
With regard to Claim 9:
The system of claim 8, wherein the user review and mobile device metadata, the sentiment score, and the positivity score are used as inputs into the fourth trained machine learning algorithm to classify the quality of the user review.
Given the above combination, this would have been obvious to one then of ordinary skill in the art, as using this data in place of that from the earlier claim for Bhogal's purpose in place of that of Balasubramanian is simply a substitution of known parts with predictable results.
With regard to Claim 10:
The system of claim 9, wherein the follow-up message is further based on the quality of the user review.
Given the above combination, this would have been obvious to one then of ordinary skill in the art, as using this data in place of that from the earlier claim for Bhogal's purpose in place of that of Balasubramanian is simply a substitution of known parts with predictable results.
With regard to Claim 19:
The method of claim 18, wherein the user review and mobile device metadata, the sentiment score, and the positivity score are used as inputs into the fourth trained machine learning algorithm to classify the quality of the user review.
Given the above combination, this would have been obvious to one then of ordinary skill in the art, as using this data in place of that from the earlier claim for Bhogal's purpose in place of that of Balasubramanian is simply a substitution of known parts with predictable results.
With regard to Claim 20:
The method of claim 19, wherein the follow-up message is further based on the quality of the user review.
Given the above combination, this would have been obvious to one then of ordinary skill in the art, as using this data in place of that from the earlier claim for Bhogal's purpose in place of that of Balasubramanian is simply a substitution of known parts with predictable results.
Response to Arguments
Applicant's arguments filed 5 January 2026 have been fully considered but they are not persuasive. First, the Examiner must respectfully disagree with the applicant’s characterization of the phrase “call-to-action”. The applicant nowhere defines the term, and in advertising it is generally used simply to mean “a piece of content intended to induce a viewer, reader, or listener to perform a specific act”, (from Oxford Languages) which in general encompasses nearly any type of advertisement. It certainly is not limited to the applicant’s examples, and even if it was, e.g. a phone number is merely data given to a person who might use it to make a phone call; it is simply a string of a bit more than half a dozen numbers, and is in no way technical.
Any type of authentication must necessarily rely upon some type of rule or criterion, so the phrase “using a verification rule” is not specific, and that it is associated with the call to action requires nothing more than being stored on the same computer. That a user can perform steps only once authenticated is ubiquitous in modern computing, and as neither the mobile device nor its messaging application is within the scope of any claim, the purpose of a message directed to causing these unclaimed devices to perform steps is nonfunctional printed matter with respect to the claimed embodiments.
The Examiner explained previously, and has repeated above, that the use of machine learning requires more than using known ML techniques where the only possible point of novelty is in the type of data being used or generated. What the “metadata provides” is simply data within the abstraction.
Sending a follow-up message is insignificant, extra-solution activity. The elements taken in sequence describe a generic computer performing a chronological sequence of abstract steps while making nondescript use of known ML techniques, which is not patent eligible.
That the data are electronic and are sent to an external device which does something with them do not alter the essentially mental characterization of the claims. The essential functions of the claims could be, as the Examiner explained, performed without computers; the fact that computers are used does not, by itself, alter the analysis.
The preemption argument has been considered, and dismissed, by the Federal Circuit, in Ariosa2, where the court reasoned that “[w]hile preemption may signal patent ineligible subject matter, the absence of complete preemption does not demonstrate patent eligibility… [w]here a patent’s claims are deemed only to disclose patent ineligible subject matter under the Mayo framework, as they are in this case, preemption concerns are fully addressed and made moot”.
The claims are not patent eligible and the rejection is maintained.
In regard to § 103, again, the use of verification rules “associated with the call to action” is a very broad link and is satisfied merely by the rules and the call to action being present within the same computer. That the redirect is configured to cause an external, unclaimed device to perform steps consists of nonfunctional printed matter with respect to the claimed substrate.
The process of retrieving “verification rules based on which call to action was interacted with, enabling different security levels for different use cases” appears to be an unclaimed limitation, so it is not relevant whether the prior art cited teaches it. In regard to the use of mobile device data or metadata, using it for one purpose rather than another is an obvious modification; the references determine a sentiment and customization of content delivery. Also, there is no bright, clear line between metadata and data, and metadata is not a narrow term – any information that describes or indicates other information reasonably reads on metadata.
The Examiner must respectfully disagree that the claim requires “fundamental architectural changes” compared to the cited references, and the applicant gives no hint as to why this ought to be thought so. Further, the applicant makes a conclusory statement that Balasubramanian “teaches away from using sentiment analysis for user follow-up”, but cites to no portion of Balasubramanian that gives any hint of such teaching away, and the Examiner finds none. Simply performing an operation in a different manner, if that is indeed the case, falls far short of a “teaching away”.
Conclusion
As mentioned in the previous Office action, claims 5 and 15 are not rejected under 35 U.S.C. § 102 or 103 herein; an explanation of this was previously set forth.
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 SCOTT C ANDERSON whose telephone number is (571)270-7442. The examiner can normally be reached M-F 9:00 to 5:30.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Bennett Sigmond can be reached at (303) 297-4411. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/SCOTT C ANDERSON/Primary Examiner, Art Unit 3694
1 Recentive Analytics, Inc. v. Fox Corp. et al., 692 F.Supp.3d 438 (Fed. Cir. 2025)
2 Ariosa Diagnostics, Inc. v. Sequenom, Inc., 788 F.3d 1371 (Fed. Cir. 2016)