DETAILED ACTION
This Non-Final Office Action is in response to the arguments, amendments, and Request for Continued Examination filed February 17, 2026.
Claims 1 and 5 have been amended.
Claim 10 is newly added.
Claims 1-10 are currently pending and have been 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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on February 17, 2026 has been entered.
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 1-10 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. Independent claims 1, 5, and 10 are directed towards, “wherein the one or more machine learning algorithms comprise: (i) a behavioral analysis and user needs module trained to analyze user behaviors based on cultural, religious, linguistic, and socioeconomic factors derived from user profile information and historical user data; (ii) a global compliance module trained on diverse legal frameworks from one or more governmental databases associated with the domicile address to ensure that a generated will complies with the one or more laws of the domicile address”. Claim 1 is additionally directed towards, “implementing a continuous improvement cycle wherein legal framework changes received from one or more network resources are fed back into the one or more machine learning algorithms to retrain the global compliance module”. Claim 5 is additionally directed towards, “implementing a continuous improvement cycle by: identifying a change in the one or more laws associated with the domicile address; retraining the one or more machine learning algorithms based on change data associated with the one or more laws received from a dynamic data feedback loop; and updating the will using the one or more machine learning algorithms that were retrained”. Claim 10 is additionally directed towards, “implementing a continuous improvement cycle wherein legal framework changes received from one or more network resources are fed back into the one or more machine learning algorithms to retrain the global compliance module”. The specific element in terms of the new matter consideration is training and retraining the machine learning model. The originally filed specification provides training in the specific instance of, “The Real-time Adaptation Module includes one or more machine learning algorithms that are trained and learn user behaviors to adapt in real-time to future user needs and changing legal requirements globally” and “The Global Compliance Module includes one or more machine learning algorithms that integrate diverse legal frameworks from local service providers in the estate planning business to ensure compliance with changing global requirements. The documents module 142 can implement a dynamic data feedback loop where changes and inputs from legal frameworks are incorporated into the platform for continuous improvement” [pg. 6]. The claims are providing specific elements and requirements towards training modules based on specific datasets and retraining and utilizing updated models based on specific triggers or data instances, such as the limitation regarding updated laws. The specification provides aspects of updates for certain broad aspects and a feedback loop, however, ML training, retraining, and utilizing trained/retrained models denotes more specific requirements that are not supported by the originally filed specification. Aspects of training and retraining would denote to one of ordinary skill in the art specific techniques, architecture, and elements that are otherwise not provided to the level to show that the specification has possession of such a specific requirement as currently claimed. As such, independent claims 1, 5, and 10 are rejected under 35 USC 112(a) for failing the written description requirement as considered with respect to new matter. Dependent claims 2-4 and 6-9 inherit the deficiency of the respective parent claim. Therefore, claims 1-10 are rejected under 35 USC 112(a).
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 1-10 rejected under 35 U.S.C. 101 because the claimed invention is directed towards an abstract idea.
In terms of Step 1, claims 1-10 are directed towards one of the four categories of statutory subject matter.
In terms of Step 2(a)(I), the independent claim 1 is directed towards, “receiving user profile information from a user, the user profile information comprising a domicile address;
verifying the identity of the user using a Dynamic Legacy Verification Algorithm (DLVA) comprising: [performing] biometric recognition using at least one of facial recognition, voice authentication, or fingerprint scanning; (ii) [analyzing] real-time GPS location data captured by a user device of the user and compares the real-time GPS location data to the domicile address; (iii) [extracting and verifying] a name and address from the at least one document against the user profile information; and (iv) [monitoring] interaction patterns of the user within the social media account and triggers one or more additional authentication steps upon detecting deviations from an established baseline interaction pattern;
creating a social media account based on the user profile information, wherein the creating of the social media account verifies the domicile address using the DLVA, [displaying a] graphical representations of at least one possession of the user, wherein each possession has a digitizable ownership indicium, and the news feed is viewable by the user and a plurality of second users selected by the user;
receiving identifying information for the at least one possession of the user to be added to the social media account, the identifying information comprising at least one of an image, a make, a model, a serial number, or financial account data associated with the at least one possession;
applying the user profile information, each digitizable ownership indicium, and the identifying information to analyze user behaviors based on cultural, religious, linguistic, and socioeconomic factors derived from user profile information and historical user data; ensure that a generated will complies with the one or more laws of the domicile address; adapt to evolving user needs and changing legal requirements associated with the domicile address;
automatically creating a will that covers each identified possession, wherein the will is automatically verified for compliance with one or more laws associated with the domicile address;
storing the will as a first entry and storing an acknowledgment of the at least one second user as a second entry; and
implementing a continuous improvement cycle wherein legal framework changes received from one or more network resources are fed back”. The claims are describing a social media account verification and legal will creation based on the social media account. The first set of claims provide aspects in terms of account creation and verification and then using the account to provide a legal will creation. These elements both fall within the certain method of organizing human activity subgroupings for relationships and interactions between people and commercial/legal interaction. As such, the claims are directed towards an abstract idea under the certain method of organizing human activity grouping.
Step 2(a)(II) considers the additional elements with respect to being transformative into a practical application. The additional elements of the independent claims are, “biometric login module; geo-authentication module; utility bill matching module that performs optical character recognition (OCR) and natural language processing on at least one document uploaded by the user; behavioral analysis module; the social media account comprises a graphical user interface generated by an interface module executing on a processing device, the graphical user interface comprising a news feed; one or more machine learning algorithms executing on the processing device, wherein the one or more machine learning algorithms comprise: behavioral analysis and user needs module trained; global compliance module trained on diverse legal frameworks from one or more governmental databases associated with the domicile address; real-time adaptation module trained; by the one or more machine learning algorithms; by the global compliance module; in a distributed cryptographic ledger; in the distributed cryptographic ledger, wherein the distributed cryptographic ledger employs consensus algorithms and cryptographic methods to provide a tamper-resistant record of the will and the acknowledgment; into the one or more machine learning algorithms to retrain the global compliance module”. The additional elements of the social media account and graphical interface are described in the originally filed specification figures 1-3 and pages 3-6 and 12-13. The originally filed specification merely describes the social media account and graphical interface as generic technology to implement the abstract idea. The social media and graphical interface are not describing a technical improvement, but rather using a general purpose computer to apply the commonplace business method (will creation and asset assignment). In terms of the machine learning, the originally filed specification describes the ML in pages 6-7. The ML is merely describing generic ML elements to train and provide compliance based on a feedback loop, but that is describing generic ML elements. There is no specific algorithm or technique provided beyond stating machine learning and artificial intelligence. The ML is not directed towards a technical improvement but rather generic technology to implement the abstract idea. Further additional elements include the modules that provide OCR techniques, biometric login, and behavior analysis. These additional elements are described in the originally filed specification pgs. 7-8. The modules and their respective aspects are merely describing generic technology to implement the abstract idea. The additional elements are not describing technical improvements but rather generic techniques. As such, the additional elements are not transformative into a practical application. Refer to MPEP 2106.05(f).
Step 2(b) considers the additional elements in terms of being significantly more than the identified abstract idea. The additional elements of claim 1 are, “biometric login module; geo-authentication module; utility bill matching module that performs optical character recognition (OCR) and natural language processing on at least one document uploaded by the user; behavioral analysis module; the social media account comprises a graphical user interface generated by an interface module executing on a processing device, the graphical user interface comprising a news feed; one or more machine learning algorithms executing on the processing device, wherein the one or more machine learning algorithms comprise: behavioral analysis and user needs module trained; global compliance module trained on diverse legal frameworks from one or more governmental databases associated with the domicile address; real-time adaptation module trained; by the one or more machine learning algorithms; by the global compliance module; in a distributed cryptographic ledger; in the distributed cryptographic ledger, wherein the distributed cryptographic ledger employs consensus algorithms and cryptographic methods to provide a tamper-resistant record of the will and the acknowledgment; into the one or more machine learning algorithms to retrain the global compliance module”. The additional elements of the social media account and graphical interface are described in the originally filed specification figures 1-3 and pages 3-6 and 12-13. The originally filed specification merely describes the social media account and graphical interface as generic technology to implement the abstract idea. The social media and graphical interface are not describing a technical improvement, but rather using a general purpose computer to apply the commonplace business method (will creation and asset assignment). In terms of the machine learning, the originally filed specification describes the ML in pages 6-7. The ML is merely describing generic ML elements to train and provide compliance based on a feedback loop, but that is describing generic ML elements. There is no specific algorithm or technique provided beyond stating machine learning and artificial intelligence. The ML is not directed towards a technical improvement but rather generic technology to implement the abstract idea. Further additional elements include the modules that provide OCR techniques, biometric login, and behavior analysis. These additional elements are described in the originally filed specification pgs. 7-8. The modules and their respective aspects are merely describing generic technology to implement the abstract idea. The additional elements are not describing technical improvements but rather generic techniques. As such, the additional elements are not significantly more than the identified abstract idea(s). Refer to MPEP 2106.05(f).
Dependent claims 2-4 are directed towards additional elements beyond those identified above. The claims are directed towards, “receiving at least one document as proof of the domicile address; performing optical character recognition on the at least one document to extract data; and comparing the data to the user profile information”, “receiving geographic location information captured by a user device of the user; and comparing the geographic location information to the user profile information”, and “storing the will as a first entry in a distributed cryptographic ledger; and storing an acknowledgment of the at least one second user as a second entry in the distributed cryptographic ledger”. The claims are further describing the abstract idea in terms of providing location elements for the enforcement and validation of the will and storing the will. The dependent claims are further describing additional elements beyond those identified above. The additional elements are OCR, geographic location, and cryptographic ledger. The additional elements are described in the originally filed specification pages 7-9. The OCR, geographic location, and cryptographic ledger are merely implementing the abstract idea using generic technology. The ledger is merely storing information and the location and OCR are providing location elements to provide address verification for the legal contract of the will creation. The additional elements are not transformative into a practical application or significantly more than the identified abstract idea. Refer to MPEP 2106.05(f).
Independent claim 5 is directed towards, “A method for social media based will creation, the method comprises: receiving user profile information from a user, the user profile information comprising a domicile address of the user and demographic information of the user the demographic information comprising at least one of cultural background, religious affiliation, linguistic preference, or socioeconomic information of the user; verifying the identity of the user using a Dynamic Legacy Verification Algorithm (DLVA) comprising: (i) that performs biometric recognition using at least one of facial recognition, voice authentication, or fingerprint scanning and implements dynamic biometric pattern analysis that adapts over time based on physiological changes of the user; (ii) that analyzes historical location data and real-time GPS information captured by a user device and applies machine learning to detect unusual location patterns, triggering additional authentication steps upon detecting location inconsistencies; (iii) that applies OCR and pattern recognition to verify the user's identity against at least one document uploaded by the user, employing natural language processing to match name and address details from the at least one document against the user profile information; and creating a social media account based on the user profile information, wherein the creating of the social media account verifies domicile address of the user using the DLVA, and the social media account the graphical user interface comprising a news feed that displays graphical representations of a plurality of assets of the user that are viewable by the user and a plurality of second users selected by the user, wherein each asset has a digitizable ownership indicium; receiving identifying information for the plurality of assets of the user to be added to the social media account, the identifying information comprising at least one of an image, a make, a model, a serial number, or financial account data associated with the plurality of assets; applying the user profile information, each digitizable ownership indicium, and the identifying information to one or more machine learning algorithms executing on the processing device, automatically creating, by the one or more machine learning algorithms, a will that covers the plurality of assets; assigning the plurality of assets to at least one beneficiary, wherein the one or more machine learning algorithms suggest the at least one beneficiary based on the demographic information and historical demographic information; updating the graphical representation in the news feed for the plurality of assets to reflect assigning the plurality of assets to the at least one beneficiary; automatically updating the will based on assigning the plurality of assets to the at least one beneficiary;”. The claims are describing a social media account verification and legal will creation based on the social media account. The first set of claims provide aspects in terms of account creation and verification and then using the account to provide a legal will creation. These elements both fall within the certain method of organizing human activity subgroupings for relationships and interactions between people and commercial/legal interaction. As such, the claims are directed towards an abstract idea under the certain method of organizing human activity grouping.
Step 2(a)(II) considers the additional elements with respect to being transformative into a practical application. The additional elements of the independent claims are, “a biometric login module; a geo-authentication module; a utility bill matching module; a consent and acknowledgment protocol that records beneficiary acknowledgments in a blockchain-based distributed ledger to ensure the integrity of an acknowledgment trail; comprises a graphical user interface generated by an interface module executing on a processing device, wherein the one or more machine learning algorithms comprise: (i) a behavioral analysis and user needs module trained on training data comprising cultural, religious, linguistic, and socioeconomic factors derived from demographic information and historical demographic information to analyze user behaviors and predict future user legacy planning needs; (ii) a global compliance module trained on training data comprising diverse legal frameworks from one or more governmental databases and one or more laws associated with the domicile address to generate a will that is compliant with the laws applicable to the domicile address; and (iii) areal-time adaptation module trained to adapt in real-time to future user needs and changing legal requirements globally by incorporating updated legal framework data received from a network of local service providers;wherein the will is generated using training data comprising the demographic information, cultural, religious, linguistic, and socioeconomic factors, and the one or more laws associated with the domicile address; storing the will as a first entry in a distributed cryptographic ledger employing consensus algorithms and cryptographic methods and storing an acknowledgment of the at least one beneficiary as a second entry in the distributed cryptographic ledger; and implementing a continuous improvement cycle by: identifying a change in the one or more laws associated with the domicile address; retraining the one or more machine learning algorithms based on change data associated with the one or more laws received from a dynamic data feedback loop; and updating the will using the one or more machine learning algorithms that were retrained”. The additional elements of the social media account and graphical interface are described in the originally filed specification figures 1-3 and pages 3-6 and 12-13. The originally filed specification merely describes the social media account and graphical interface as generic technology to implement the abstract idea. The social media and graphical interface are not describing a technical improvement, but rather using a general purpose computer to apply the commonplace business method (will creation and asset assignment). In terms of the machine learning, the originally filed specification describes the ML in pages 6-7. The ML is merely describing generic ML elements to train and provide compliance based on a feedback loop, but that is describing generic ML elements. There is no specific algorithm or technique provided beyond stating machine learning and artificial intelligence. The ML is not directed towards a technical improvement but rather generic technology to implement the abstract idea. Further additional elements include the modules that provide OCR techniques, biometric login, and behavior analysis. These additional elements are described in the originally filed specification pgs. 7-8. The modules and their respective aspects are merely describing generic technology to implement the abstract idea. The additional elements are not describing technical improvements but rather generic techniques. As such, the additional elements are not transformative into a practical application. Refer to MPEP 2106.05(f).
Step 2(b) considers the additional elements in terms of being significantly more than the identified abstract idea. The additional elements of claim 1 are, “a biometric login module; a geo-authentication module; a utility bill matching module; a consent and acknowledgment protocol that records beneficiary acknowledgments in a blockchain-based distributed ledger to ensure the integrity of an acknowledgment trail; comprises a graphical user interface generated by an interface module executing on a processing device, wherein the one or more machine learning algorithms comprise: (i) a behavioral analysis and user needs module trained on training data comprising cultural, religious, linguistic, and socioeconomic factors derived from demographic information and historical demographic information to analyze user behaviors and predict future user legacy planning needs; (ii) a global compliance module trained on training data comprising diverse legal frameworks from one or more governmental databases and one or more laws associated with the domicile address to generate a will that is compliant with the laws applicable to the domicile address; and (iii) areal-time adaptation module trained to adapt in real-time to future user needs and changing legal requirements globally by incorporating updated legal framework data received from a network of local service providers;wherein the will is generated using training data comprising the demographic information, cultural, religious, linguistic, and socioeconomic factors, and the one or more laws associated with the domicile address; storing the will as a first entry in a distributed cryptographic ledger employing consensus algorithms and cryptographic methods and storing an acknowledgment of the at least one beneficiary as a second entry in the distributed cryptographic ledger; and implementing a continuous improvement cycle by: identifying a change in the one or more laws associated with the domicile address; retraining the one or more machine learning algorithms based on change data associated with the one or more laws received from a dynamic data feedback loop; and updating the will using the one or more machine learning algorithms that were retrained”. The additional elements of the social media account and graphical interface are described in the originally filed specification figures 1-3 and pages 3-6 and 12-13. The originally filed specification merely describes the social media account and graphical interface as generic technology to implement the abstract idea. The social media and graphical interface are not describing a technical improvement, but rather using a general purpose computer to apply the commonplace business method (will creation and asset assignment). In terms of the machine learning, the originally filed specification describes the ML in pages 6-7. The ML is merely describing generic ML elements to train and provide compliance based on a feedback loop, but that is describing generic ML elements. There is no specific algorithm or technique provided beyond stating machine learning and artificial intelligence. The ML is not directed towards a technical improvement but rather generic technology to implement the abstract idea. Further additional elements include the modules that provide OCR techniques, biometric login, and behavior analysis. These additional elements are described in the originally filed specification pgs. 7-8. The modules and their respective aspects are merely describing generic technology to implement the abstract idea. The additional elements are not describing technical improvements but rather generic techniques. As such, the additional elements are not significantly more than the identified abstract idea(s). Refer to MPEP 2106.05(f).
Dependent claims 6-9 are directed towards additional elements beyond those identified above. The claims are directed towards, “wherein the one or more machine learning algorithms suggest one or more beneficiaries to be associated with the will based on the demographic information and historical demographic information”, “assigning the plurality of assets to at least one of the one or more beneficiaries; updating the graphical representation for the plurality of assets to reflect assigning the plurality of assets to the at least one of the one or more beneficiaries; automatically updating the will based on assigning the plurality of assets to the at least one of the one or more beneficiaries”, “receiving, via the graphical representations, an acknowledgment of the at least one of the one or more beneficiaries; storing the will as a first entry in a distributed cryptographic ledger; and storing an acknowledgment of the at least one of the one or more beneficiaries as a second entry in the distributed cryptographic ledger”, and “identifying a change in the one or more laws associated with the domicile address; retraining the one or more machine learning algorithms based on change data associated with the one or more laws; and updating the will using one or more machine learning algorithms that were retrained”. The claims are further describing the abstract idea in terms of providing suggested beneficiaries, assignment of assets, storing the will, and updating the will. The dependent claims are further describing additional elements beyond those identified above. The additional elements are machine learning algorithms and cryptographic ledger. The additional elements are described in the originally filed specification pages 6-9. The machine learning (retrain and generic ML model) and cryptographic ledger are merely implementing the abstract idea using generic technology. The ledger is merely storing information and the ML model, including the retrain aspects, are describing generic technology to implement the abstract idea. The ML models and training elements are not describing the specific technique or providing an improved model, but rather using generic ML analysis and training to implement the abstract idea(s). The additional elements are not transformative into a practical application or significantly more than the identified abstract idea. Refer to MPEP 2106.05(f).
Independent claim 10 is directed towards, “A method for a social networking-based management of an inventory of assets, the method comprises: receiving user profile information from a user, the user profile information comprising a domicile address; verifying the identity of the user using a Dynamic Legacy Verification Algorithm (DLVA) comprising: (i) that performs biometric recognition using at least one of facial recognition, voice authentication, or fingerprint scanning; (ii) that analyzes real-time GPS location data captured by a user device of the user and compares the real-time GPS location data to the domicile address; (iii) that performs optical character recognition (OCR) and natural language processing on at least one document uploaded by the user to extract and verify a name and address from the at least one document against the user profile information; and (iv) that monitors interaction patterns of the user within the social network account and triggers one or more additional authentication steps upon detecting deviations from an established baseline interaction pattern; creating a social network account based on the user profile information, wherein the creating of the social network account verifies the domicile address using the DLVA, the graphical user interface comprising a news feed that displays graphical representations of at least one possession of the user, wherein each possession has a digitizable ownership indicium, and the news feed is viewable by the user and a plurality of second users selected by the user; receiving identifying information for the at least one possession of the user to be added to the social network account, the identifying information comprising at least one of an image, a make, a model, a serial number, or financial account data associated with the at least one possession; applying the user profile information, each digitizable ownership indicium, and the identifying information to one or more machine learning algorithms executing on the processing device, automatically creating, by the one or more machine learning algorithms, an inventory of assets that covers each identified possession, wherein the inventory of assets is automatically verified for compliance with one or more laws associated with the domicile address by the global compliance module; storing the inventory of assets as a first entry and storing an acknowledgment of the at least one second user as a second entry ledger, provide a tamper- resistant record of the inventory of assets and the acknowledgment”. The claims are describing a social media account verification and legal will creation based on the social media account. The first set of claims provide aspects in terms of account creation and verification and then using the account to provide a legal will creation. These elements both fall within the certain method of organizing human activity subgroupings for relationships and interactions between people and commercial/legal interaction. As such, the claims are directed towards an abstract idea under the certain method of organizing human activity grouping.
Step 2(a)(II) considers the additional elements with respect to being transformative into a practical application. The additional elements of the independent claims are, “a biometric login module; a geo-authentication module; a utility bill matching module; a consent and acknowledgment protocol that records beneficiary acknowledgments in a blockchain-based distributed ledger to ensure the integrity of an acknowledgment trail; comprises a graphical user interface generated by an interface module executing on a processing device, wherein the one or more machine learning algorithms comprise: (i) a behavioral analysis and user needs module trained on training data comprising cultural, religious, linguistic, and socioeconomic factors derived from demographic information and historical demographic information to analyze user behaviors and predict future user legacy planning needs; (ii) a global compliance module trained on training data comprising diverse legal frameworks from one or more governmental databases and one or more laws associated with the domicile address to generate a will that is compliant with the laws applicable to the domicile address; and (iii) areal-time adaptation module trained to adapt in real-time to future user needs and changing legal requirements globally by incorporating updated legal framework data received from a network of local service providers;account and graphical interface as generic technology to implement the abstract idea. The social media and graphical interface are not describing a technical improvement, but rather using a general purpose computer to apply the commonplace business method (will creation and asset assignment). In terms of the machine learning, the originally filed specification describes the ML in pages 6-7. The ML is merely describing generic ML elements to train and provide compliance based on a feedback loop, but that is describing generic ML elements. There is no specific algorithm or technique provided beyond stating machine learning and artificial intelligence. The ML is not directed towards a technical improvement but rather generic technology to implement the abstract idea. Further additional elements include the modules that provide OCR techniques, biometric login, and behavior analysis. These additional elements are described in the originally filed specification pgs. 7-8. The modules and their respective aspects are merely describing generic technology to implement the abstract idea. The additional elements are not describing technical improvements but rather generic techniques. As such, the additional elements are not transformative into a practical application. Refer to MPEP 2106.05(f).
Step 2(b) considers the additional elements in terms of being significantly more than the identified abstract idea. The additional elements of claim 1 are, “biometric login module; geo-authentication module; utility bill matching module that performs optical character recognition (OCR) and natural language processing on at least one document uploaded by the user; behavioral analysis module; the social media account comprises a graphical user interface generated by an interface module executing on a processing device, the graphical user interface comprising a news feed; one or more machine learning algorithms executing on the processing device, wherein the one or more machine learning algorithms comprise: behavioral analysis and user needs module trained; global compliance module trained on diverse legal frameworks from one or more governmental databases associated with the domicile address; real-time adaptation module trained; by the one or more machine learning algorithms; by the global compliance module; in a distributed cryptographic ledger; in the distributed cryptographic ledger, wherein the distributed cryptographic ledger employs consensus algorithms and cryptographic methods to provide a tamper-resistant record of the will and the acknowledgment; into the one or more machine learning algorithms to retrain the global compliance module; implementing a continuous improvement cycle wherein legal framework changes received from one or more network resources are fed back into the one or more machine learning algorithms to retrain the global compliance module”. The additional elements of the social media account and graphical interface are described in the originally filed specification figures 1-3 and pages 3-6 and 12-13. The originally filed specification merely describes the social media account and graphical interface as generic technology to implement the abstract idea. The social media and graphical interface are not describing a technical improvement, but rather using a general purpose computer to apply the commonplace business method (will creation and asset assignment). In terms of the machine learning, the originally filed specification describes the ML in pages 6-7. The ML is merely describing generic ML elements to train and provide compliance based on a feedback loop, but that is describing generic ML elements. There is no specific algorithm or technique provided beyond stating machine learning and artificial intelligence. The ML is not directed towards a technical improvement but rather generic technology to implement the abstract idea. Further additional elements include the modules that provide OCR techniques, biometric login, and behavior analysis. These additional elements are described in the originally filed specification pgs. 7-8. The modules and their respective aspects are merely describing generic technology to implement the abstract idea. The additional elements are not describing technical improvements but rather generic techniques. As such, the additional elements are not significantly more than the identified abstract idea(s). Refer to MPEP 2106.05(f).
The claimed invention is describing an abstract idea without additional elements that are significantly more or transformative into a practical application. Therefore, claims 1-10 are rejected under 35 USC 101 for being directed towards non-eligible subject matter.
Response to Arguments
In response to the arguments filed February 17, 2026 on pages 10-16 regarding the 35 USC 101 rejection, specifically that the claimed invention is directed towards eligible subject matter.
Examiner respectfully disagrees.
The arguments allege that the claim limitations are providing a technical improvements to a computer-based authentication system. This is through the module elements that provide biometric, gps location, behavior analysis, and OCR of a utility bill. In terms of the above consideration, the claim elements of the modules are directed towards generic technology. The modules themselves are merely providing generic technology techniques to implement the authentication. The OCR, gps location, behavior analysis, and biometrics are not providing a technical improvement to the modules themselves but rather implementing the abstract idea using generic technology. Further, in terms of the algorithm, the claims are really providing a black box with the additional elements. There is no specific interaction, model, or algorithm provided by the use of the technology beyond generically using the elements. This further includes the consideration of the machine learning. The arguments allege that the machine learning is directed towards a specific model based on the training. Examiner first notes that the training steps for two of the three claimed elements (global compliance and behavior analysis) are rejected under 35 USC 112(a) for being directed towards new matter. The specification does not provide specific written description support for training or otherwise providing the specific requirements within the model modules beyond the real-time adaption. Further, the retraining and utilizing the retraining is not described with appropriate description as considered in the 35 USC 112(a). While the specification provides aspects of feedback loops and updates, the specific claim limitation requires training and retraining that one of ordinary skill in the art would not recognize that the claimed invention has possession of based on the considered passages. As such, the machine learning is generic technology to implement the abstract idea. Merely providing the inputs and expected output are not providing technical improvements.
The arguments further continue in terms of the distributed ledger/cryptographic aspects. The claims describe and provide a cryptographic ledger to store information, however, the specification and claims are merely describing generic blockchain/ledger technology to implement the abstract idea. There is no specific technical improvement to the ledger beyond using it for the intended purpose. As such, the cryptographic/ledger elements are merely generic technology that are not transformative into a practical application nor is it significantly more than the identified abstract idea. As such, based on the consideration above, the claim limitations are directed towards an abstract idea and the additional elements are not significantly more or transformative into a practical application. Refer to MPEP 2106.05(f). Therefore, claims 1, 5, and 10 are maintaining the 35 USC 101 rejection, as considered above in light of the amended and newly added claims above.
Lacking any further arguments, claims 1-10 are maintaining the 35 USC 101 rejection, as considered above in light of the amended and newly added claims above.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ANDREW CHASE LAKHANI whose telephone number is (571)272-5687. The examiner can normally be reached M-F 730am - 5pm (EST).
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/ANDREW CHASE LAKHANI/Primary Examiner, Art Unit 3629