DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Election/Restrictions
Claims 9-13 withdrawn from further consideration pursuant to 37 CFR 1.142(b) as being drawn to a nonelected invention, there being no allowable generic or linking claim. Election was made without traverse in the reply filed on 09/25/2025.
Response to Amendment
This action is in response to the amendments filed on 03/03/2026. Claims 1, 14, and 16-20 have been amended. Claims 1-8 and 14-20 are examined below.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-8 and 14-20 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Regarding claims 1 and 14, the limitation “receiving health information from a user, the health information including current health data associated with the user and anonymized user identification information, wherein the anonymized user identification information includes an anonymous user identifier derived from user-identifying information” is not supported by the specification. The Examiner points to paragraph [0025] which states, “As used herein, the term "anonymous" refers to the treatment of information without user-identifying information. For example, anonymous may refer to information in which identifying information has been removed and/or encrypted. User-identifying information may include name, address, social security number, any other user-identifying information, and combinations thereof. In some embodiments, health information may be anonymized. For example, health information may be anonymized using end-to-end encryption, such that the health management system has no access to user-identifying information. In some embodiments, user-identifying information may be anonymized, and a user may receive an anonymous user ID.” A user ID does not equate to anonymized user identification information. If anything, the specification describes the pseudonymization and/or encryption of user-identifying information.
Next, the Examiner points to paragraph [0055] which states, in part, “In some embodiments, the anonymization may the user's data may occur before requesting the health history from the secure health datastore 308. For example, the user identification information may be anonymized into an anonymous user ID, and the health history in the secure health datastore 308 may be stored under the same anonymous user ID. In this manner, the secure health datastore 308 may not include any user-identifying information.” User identifying information being anonymized into an anonymous user ID does not equate to the anonymous user ID being derived from user identifying information. Stating that the anonymous user ID is derived from user identifying information implies that the user identifying information plays some role in the resultant anonymous user ID. For example, an anonymous user ID of “JS78” may be considered as derived from user identifying information of “Name: John Smith”, “Born: 1978”. The Examiner further asserts that if the user identifier is derived from user-identifying information, that it is not truly “anonymous”, as it would still feature user identifying information (although shortened, or transformed).
Dependent claims are rejected as well since they inherit the limitations of the independent claims.
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-8 and 14-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Regarding claims 1 and 14, the limitation “based on the anonymized user identification information, accessing, from a secure health datastore, health history for the user, wherein the anonymized user identification information includes an anonymous user identifier derived from user-identifying information” is indefinite. It is unclear how health history for the user is accessed if the user identification information is truly anonymized. The Examiner points to paragraph [0025] which states, “As used herein, the term "anonymous" refers to the treatment of information without user-identifying information. For example, anonymous may refer to information in which identifying information has been removed and/or encrypted. User-identifying information may include name, address, social security number, any other user-identifying information, and combinations thereof. In some embodiments, health information may be anonymized. For example, health information may be anonymized using end-to-end encryption, such that the health management system has no access to user-identifying information. In some embodiments, user-identifying information may be anonymized, and a user may receive an anonymous user ID.” (emphasis added) An anonymous user ID may still serve as an identifier. Further, paragraph [0055] states, in part, “In some embodiments, the anonymization may the user's data may occur before requesting the health history from the secure health datastore 308. For example, the user identification information may be anonymized into an anonymous user ID, and the health history in the secure health datastore 308 may be stored under the same anonymous user ID. In this manner, the secure health datastore 308 may not include any user-identifying information.” It is unclear how the user identification information may be anonymized if an anonymous user ID is utilized. In other words, if health history associated with the user may be retrieved based the anonymous user ID, then the user identification information is not truly anonymized. As stated above, in the 112(a) rejection, the specification describes the pseudonymization and/or encryption of user-identifying information. As such, the Examiner shall interpret “anonymized” as “pseudonymized” or “encrypted”.
Dependent claims are rejected as well since they inherit the limitations of the independent claims.
Regarding claims 2 and 15, the limitation “prior to generating the contextual health query, anonymizing the health information and the health history included in the contextual health query” is indefinite. Claim 1 states, “receiving health information from a user, the health information including current health data associated with the user and anonymized user identification information” Thus, it appears as if the health information is already anonymized. However, it is unclear if the anonymization occurs prior to receiving the health information, or if an additional form of anonymization occurs after the health information is received.
Regarding claim 3 and 16, the limitation “de-anonymizing the health analysis” is indefinite. The health analysis was never anonymized; thus it is unclear as to what de-anonymizing entails.
Claim 8 recites the limitation "the health information in the secure health datastore". There is insufficient antecedent basis for this limitation in the claim.
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 1-8 and 14-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Claim 14 recites (additional elements crossed out):
A system, comprising:
receive health information from a user, the health information including current health data associated with the user and anonymized user identification information wherein the anonymized user identification information includes an anonymous user identifier derived from user-identifying information;
based on the anonymized user identification information, access, from a secure health datastore, health history for the user;
based on the health information and the health history, generate a contextual health query
provide the contextual health query as an input
receive,
The above limitations as drafted, is a process that, under its broadest reasonable interpretation covers managing personal behavior or relationships or interactions between people, and mental processes. That is, other than reciting the steps as being performed by a “processor”, “memory”, and a “foundational model system” nothing in the claim precludes the steps as being described as managing personal behavior or relationships or interactions between people, and mental processes. For example, but for the recited computing language, the limitations describe a system for receiving symptoms associated with an “anonymous” person, retrieving historical health data for the person, generating a query based on the symptoms and health data, providing the query as input, and receiving a health analysis based on the query. The limitations describe the management of personal behavior, as well as actions that can be performed mentally or with pen and paper. If a claim limitation, under its broadest reasonable interpretation, describes managing personal behavior or relationships or interactions between people, then it falls within the “Certain Methods of Organizing Human Activities” grouping of abstract ideas. Further, if a claim limitation, under its broadest reasonable interpretation, describes steps that may be performed mentally or with pen and paper, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
The judicial exception is not integrated into a practical application. In particular, the claims recite the additional elements of a “processor”, “memory”, and a “foundational model system” to perform the steps. These additional elements are recited at a high level of generality (see at least Paras. [0083]-[0084]) such that it amounts to no more than mere instructions to apply the exception using generic computing components. Further, in regards to the “foundational model system”, the functionality intended to be performed by it appears to be based on very rudimentary constraints (e.g., health information and health history). Without some prohibition in the claims regarding scalability, computation load, etc., this “foundational model system” could reasonably be considered an additional abstract idea in the “mental process” category, but for which is simply automated (i.e., “apply it”).
Accordingly, the additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. More specifically, the additional elements fail to include (1) improvements to the functioning of a computer or to any other technology or technical field (see MPEP 2106.05(a)), (2) applying or using a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition (see Vanda memo), (3) applying the judicial exception with, or by use of, a particular machine (see MPEP 2106.05(b)), (4) effecting a transformation or reduction of a particular article to a different state or thing (see MPEP 2106.05(c)), or (5) applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception (see MPEP 2106.05(e) and Vanda memo).
Rather, the limitations merely add the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)) or generally link the use of the judicial exception to a particular technological environment or field of use (see MPEP 2106.05(h)), particularly as it relates to the recited “processor”, “memory”, and a “foundational model system” elements. This is not sufficient to amount to significantly more than the judicial exception. The claims are therefore still directed to an abstract idea.
Claim 1 features limitations similar to those of claim 14, and is therefore also found to be directed to an abstract idea without significantly more.
Claims 2-8 are dependent on claim 1 and include all the limitations of claim 1. Claims 15-20 are dependent on claim 14, and include all the limitations of claim 14. Therefore, they are also directed to the same abstract idea. Claims 4 and 17 feature “a plurality of foundational models”, however, as stated above regarding the foundational model system, the functionality intended to be performed by the models appears to be based on very rudimentary constraints (e.g., health information and health history). Without some prohibition in the claims regarding scalability, computation load, etc., this “plurality of foundational models” could reasonably be considered an additional abstract idea in the “mental process” category, but for which is simply automated (i.e., “apply it”). The remaining dependent claims have not been found to integrate the judicial exception into a practical application, or provide significantly more than the abstract idea since they merely further narrow the abstract idea. Therefore, the dependent claims are found to be directed to an abstract idea without significantly more.
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) 1-3 and 14-16 is/are rejected under 35 U.S.C. 103 as being unpatentable over “Dr A.I. with a little heart emoji” by Bruce Sterling, available January 11, 2017, hereinafter referred to as Sterling1, in view of “Medical record search engines, using pseudonymized patient identity: An alternative to centralised medical records” by Catherine Quantin, published October 2, 2010, hereinafter referred to as Quantin2
Regarding claim 1, Sterling partially discloses a method comprising:
receiving health information from a user, the health information including current health data associated with the user and anonymized user identification information wherein the anonymized user identification information includes an anonymous user identifier derived from user-identifying information;
based on the anonymized user identification information, accessing, from a secure health datastore, health history for the user;
based on the health information and the health history, generating a contextual health query for a foundation model system;
providing the contextual health query as an input to the foundation model system; and
receiving, from the foundation model system, a health analysis for the user based on the contextual health query.
(See “Just like a sympathetic human physician, Dr. A.I.• converses with the user to understand their current complaints or concerns and uses the user's health profile to compute the probable causes of their symptoms. First. using HealthTap's Health Operating System (HOPES™). Dr. A.I.• analyzes the user's current symptoms in the context of relevant data from the personal health record they created. on HealthTap, including age, gender, prior medical conditions, medications etc. Next based on the user's symptoms, Dr. AI.• uses advanced deep learning algorithms and HealthTap's vast repository of doctor knowledge and data to apply doctor-sourced clinical expertise and guide patients to the right level of doctor-recommended care. Dr. A.I.• can immediately route a patient towards a variety of solutions that doctors previously suggested to people like them in similar situations.” However, while heavily implied (see language regarding , Sterling does not disclose receiving…anonymized user identification information, or accessing health history for the user based on the anonymized user identification. See Quantin, Page 3 “When a medical practitioner wants to request a patient’s information, distributed in some other HS, he has to send a request to two MRSEs and authenticate both himself and the patient. Exchanges between the MP and the MRSEs are protected by using an asymmetric encryption algorithm (like the RSA encryption). In this communication, the public keys (PMRE1 and PMRE2) of the MREs are used by the MP.”, and “Locally, each HS decrypts messages issued by MRSEs. Then, they also decrypt the pseudonymous patient identifier (H(PI)) with the session key K. Each hospital ‘hi’ can then search for medical records corresponding to this pseudonym (comparing it with hashed identities of the patients hospitalised in hi).” It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify Sterling to utilize the teachings of Quantin since it may preserve the privacy of the patient.
Regarding claim 2, in light of the 112 rejection, Sterling and Quantin disclose the method of claim 1, further comprising, prior to generating the contextual health query, anonymizing the health information and the health history included in the contextual health query. As indicated above, Sterling utilizes a model to a user’s symptoms and health profile. However, Sterling is silent in regards to anonymized data. Quantin teaches the retrieval of encrypted patient medical records from health structures (see at least pages 3-4). It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify Sterling to utilize the teachings of Quantin since it may preserve the privacy of the patient.
Regarding claim 3, in light of the 112 rejection above, Sterling discloses The method of claim 2, further comprising:
de-anonymizing the health analysis; and
providing the health analysis to the user.
See “Next based on the user's symptoms, Dr. AI.• uses advanced deep learning algorithms and HealthTap's vast repository of doctor knowledge and data to apply doctor-sourced clinical expertise and guide patients to the right level of doctor-recommended care. Dr. A.I.• can immediately route a patient towards a variety of solutions that doctors previously suggested to people like them in similar situations.”
Claim 14 features limitations similar to those of claim 1, and is therefore rejected using the same rationale.
Claim 15 features limitations similar to those of claim 2, and is therefore rejected using the same rationale.
Claim 16 features limitations similar to those of claim 3, and is therefore rejected using the same rationale.
Claim(s) 4-7 and 17-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over “Dr A.I. with a little heart emoji” by Bruce Sterling, available January 11, 2017, hereinafter referred to as Sterling, in view of “Medical record search engines, using pseudonymized patient identity: An alternative to centralised medical records” by Catherine Quantin, published October 2, 2010, hereinafter referred to as Quantin, and in further view of “Ensemble Learning for Disease Predication: A Review” by Palak Mahajan, published June 20, 2023, hereinafter referred to as Mahajan3
Regarding claim 4, Sterling and Quantin do not explicitly disclose The method of claim 1, wherein the foundation model system includes a plurality of foundation models and wherein receiving the health analysis includes receiving a plurality of health analyses from the plurality of foundation models in the foundation model system. (See at least Mahajan, Page 1 – “Ensemble learning is a machine learning technique that combines multiple classifiers to improve performance by making more accurate predictions than a single classifier. Although numerous studies have employed ensemble approaches for disease prediction, there is a lack of thorough assessment of commonly used ensemble approaches against highly researched diseases.”, and Figure 1. It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the system of Sterling and Quantin to utilize the teachings of Mahajan since it may boost the accuracy of the model of Sterling.
Regarding claim 5, Sterling and Quantin do not explicitly disclose the method of claim 4, further comprising generating a personalized health analysis based on the plurality of health analyses. (See at least Mahajan, Page 2 – “Disease diagnosis refers to the process of determining which disease best reflects a person’s symptoms. The most challenging issue is diagnosis because certain symptoms and indications are vague, and disease identification is vital in treating any sickness [6]. Machine learning is a field that can help anticipate disease diagnosis based on prior training data [7]. Many scientists have created various machine learning algorithms to effectively identify a wide range of conditions.”, and “Given the increasing relevance and efficiency of the ensemble approach for predictive disease modelling, the field of study appears to be expanding.” It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the system of Sterling and Quantin to utilize the teachings of Mahajan since at least Sterling and Mahajan are in the same field of endeavor (i.e., generation of medical diagnosis using models), and all of the claimed elements were known in the prior art and one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions and the combination would have yielded predictable results to one of ordinary skill in the art at the time of the invention.
Regarding claim 6, Sterling and Quantin do not explicitly disclose The method of claim 5, wherein generating the personalized health analysis includes combining at least two health analyses of the plurality of health analyses. (See at least Mahajan, Page 2 – “Ensemble learning is a machine learning approach that combines predictions from multiple models to increase predictive performance [7]. Ensemble techniques integrate various machine learning algorithms to make more accurate predictions than a single classifier. The use of ensemble models is intended to reduce the generalisation error. This technique reduces model prediction error when the base models are diverse and independent. As outlined in Figure 1, The approach relies on the collective output of individuals for generating forecasting. Although several base models exist, the ensemble model works and behaves as a single model [7].”, and Figure 1. It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the system of Sterling and Quantin to utilize the teachings of Mahajan since it may boost the accuracy of the model of Sterling.
Regarding claim 7, Sterling and Quantin do not explicitly disclose The method of claim 5, wherein generating the personalized health analysis includes ranking the plurality of health analyses. (See Mahajan Pages 5-6, “The Voting Classifier ensemble approach is a strategy that aggregates predictions from numerous independent models (base estimators) to make a final prediction [19]. It uses the “wisdom of the crowd” notion to create more accurate predictions by taking into account the aggregate judgement of numerous models rather than depending on a single model. In the Voting Classifier, there are two types of voting: hard voting, in which each model makes a prediction, and soft voting, in which each model forecasts the probability or confidence ratings for each class or label. The final prediction is made by summing the expected probabilities across all models and choosing the class with the highest average probability [20]. Weighted voting allows multiple models to have different influences on the final forecast, which can be assigned manually or learned automatically based on the performance of the individual models. Because of this diversity, different models can affect the final prediction differently [19].” It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the system of Sterling and Quantin to utilize the teachings of Mahajan since it may boost the accuracy of the model of Sterling.
Claim 17 features limitations similar to those of claim 4, and is therefore rejected using the same rationale.
Claim 18 features limitations similar to those of claim 5, and is therefore rejected using the same rationale.
Claim 19 features limitations similar to those of claim 6, and is therefore rejected using the same rationale.
Claim 20 features limitations similar to those of claim 7, and is therefore rejected using the same rationale.
Claim(s) 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over “Dr A.I. with a little heart emoji” by Bruce Sterling, available January 11, 2017, hereinafter referred to as Sterling, in view of “Medical record search engines, using pseudonymized patient identity: An alternative to centralised medical records” by Catherine Quantin, published October 2, 2010, hereinafter referred to as Quantin, and in further view of Bates (US 2019/0214134)
Regarding claim 8, Sterling and Quantin do not explicitly disclose The method of claim 1, further comprising updating the health history in the secure health datastore with at least one of the contextual health query or the health information in the secure health datastore. (See Bates, Para. [0044] – “In embodiments, the API 310 may further couples to an internal Electronic Health Record (EHR) database, which may be integrated within the patient management application 390. Input from the patient through the API 310, such as newly reported symptoms, updated drug history, etc., may be used to update the patient's internal EHR.”. It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify Sterling and Quantin to utilize the teachings of Bates since it would allow for the most up-to-date information to be utilized in future use.
Response to Arguments
Applicant's arguments regarding claims rejected under 35 U.S.C. 112 have been fully considered but they are not persuasive. Applicant argues with substance:
Applicant argues that the amended language of “…wherein the anonymized user identification information includes an anonymous user identifier derived from user-identifying information” remedies the issues brought forth in the 112(a) and (b) rejections. This is not persuasive. The Examiner notes that the amended language brings up further 112(a) issues as indicated in the rejection above. Further, the amended language still fails to indicate how health history for the user is accessed if the user identification information is truly anonymized as indicated in the 112(b) rejection.
Applicant's arguments regarding claims rejected under 35 U.S.C. 101 have been fully considered but they are not persuasive. Applicant argues with substance:
Applicant argues that “submitting the query using anonymized health data solves the technical problem of utilizing foundation models to answer health questions”. This is not persuasive. The claims feature no improvement to foundation models. Querying a model with “Patient JS has pain in left toe and has a history of diabetes” vs “Patient John Smith has pain in left toe and has a history of diabetes” results in no operational difference in the foundation model providing a health analysis. The 101 rejection is maintained.
Applicant's arguments regarding claims rejected under 35 U.S.C. 103 have been fully considered but they are not persuasive. Applicant argues with substance:
Applicant argues that Sterling is silent regarding “asking the question using “anonymized user identification information include[ing] an anonymous user identifier derived from user-identifying information” (Claims 1 and 14). Applicant then argues that secondary reference Quantin fails to teach this since “Quantin’s “MRSEs” appear to be related to collection of medical records by a doctor, and not “generating a contextual health query for a foundation model system””. This is not persuasive. Quantin’s purpose was not to disclose “generating a contextual health query for a foundation model system”. Quantin was included to teach the use of anonymized user identification, which was disclosed at least in the previously cited language “Locally, each HS decrypts messages issued by MRSEs. Then, they also decrypt the pseudonymous patient identifier (H(PI)) with the session key K. Each hospital ‘hi’ can then search for medical records corresponding to this pseudonym (comparing it with hashed identities of the patients hospitalised in hi).” (Page 3). Therefore, the 103 rejection is maintained.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
“What is pseudonymization?” by Cloudflare4 discusses the differences between anonymization and pseudonymization
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any 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 KYLE G ROBINSON whose telephone number is (571)272-9261. The examiner can normally be reached Monday - Thursday, 7:00 - 4:30 EST; Friday 7:00-11:00 EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Kambiz Abdi can be reached at 571-272-6702. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/KYLE G ROBINSON/Examiner, Art Unit 3685
/KAMBIZ ABDI/Supervisory Patent Examiner, Art Unit 3685
1 Available at https://www.wired.com/beyond-the-beyond/2017/01/dr-little-heart-emoji/
2 Available at https://www.sciencedirect.com/science/article/pii/S1386505610001747
3 Available at https://www.mdpi.com/2227-9032/11/12/1808
4 Available at https://www.cloudflare.com/learning/privacy/what-is-pseudonymization/
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