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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 2/11/2026 has been entered.
Claims 1, 3, 8, 10-12, and 14 have been amended. Claims 5, 6, 9, 16, 17, and 20 have been canceled. Claims 1-4, 8, 10-15, and 19 remain pending and have been examined.
Response to Arguments
A. Applicant's arguments with respect to the rejection of claims 1-6, 8-17, 19, and 20 under 35 USC 101 have been fully considered but are not persuasive.
Applicant argues starting on page 5 that claim 1, as amended, recites “using at least one processor to generate a digitally signed assertion and to authenticate the digitally signed assertion using a cryptographic hash and a digital signature,” and “[t]hese operations require execution by a computer processor to perform cryptographic computations that cannot be practically performed by a human or by organizing human activity alone,” and therefore that the claim cannot recite a method of organizing human activity. Examiner respectfully disagrees.
The elements reciting a processor the use of a cryptographic hash and digital signature to generate and authenticate a digitally signed assertion are not construed as falling with the scope of the abstract idea, and are instead interpreted as additional elements analyzed under Step 2A Prong 2 and Step 2B. As set out in the analysis below, these elements only amount to instructions to apply computing elements and techniques as tools to implement the abstract idea. The use of these computing elements does not itself preclude the claim from reciting elements falling with the scope of a method of organizing human activity under Step 2A Prong 1.
Applicant further argues that claim 1 “integrates any alleged exception into a practical application by implementing a privacy-preserving and cryptographically verifiable record pipeline,” and more specifically that it recites “a concrete technical workflow that (i) removes personal identifying information prior to persistence, (ii) cryptographically binds guidance outputs to the data with personal identifying information removed, and (iii) anchors the resulting signed artifact into an encrypted, temporally sequential listing implemented by transaction authentication nodes with controlled key custody and authenticated retrieval.” Examiner respectfully disagrees.
Examiner initially notes that the step of removing personally identifying information is construed as falling within the scope of the abstract idea rather than as an additional element.
With respect to claim 1 reciting “cryptographically binds guidance outputs to the data with personal identifying information removed,” it is not clear which limitation or limitations Applicant is referring to given that “cryptographically binds” is neither a term of art nor appears in the disclosure. Assuming that Applicant is referring to the step of “authenticate the digitally signed assertion using a cryptographic hash and a digital signature applied to the at least an unspecified datum and the cannabis guidance output,” the use of a hash function and digital signature are extensively utilized in the encryption and authentication of digital information, and do not involve “cryptographically binding” the unspecified datum and cannabis guidance output beyond the inclusion of both pieces of information in the file prior to encryption. Applicant’s disclosure reflects this, and only broadly discloses the digital signature and hash function. For example, paragraph 14 states that:
“A "digitally signed assertion," as used in this disclosure, is a collection of textual data signed using a secure proof as described in further detail below. A digital signature may include, as a non-limiting example, an encrypted mathematical representation of a file or other set of data such as without limitation a cryptographic hash and/or checksum, signed using, for instance, the private key of a public key cryptographic system; in such an example, signature may be verified by decrypting the encrypted mathematical representation using the corresponding public key and comparing the decrypted representation to a purported match that was not encrypted…” (emphasis added).
Paragraphs 17 and 18 further provide an overview of hashing functions and their general use in cryptography, and provide a list of over thirty different hash functions which could be applied. No disclosure of “authentication” of the digitally signed assertion is provided with respect to the unspecified datum and the cannabis guidance output beyond the application of these standard security architectures such as public/private key encryption.
The above holds true for Applicant’s further assertions regarding claim 1 requiring “cryptographic integrity binding by "authenticat[ing] the digitally signed assertion using a cryptographic hash and a digital signature applied to the ... unspecified datum and the cannabis guidance output," thereby enabling tamper detection, authenticity verification, and non-repudiation across remote devices.” Paragraph 18 provides the only description of “tamper-proofing,” and lists it as a characteristic of hash algorithms in general. Paragraph 14 describes verification via the digital signature, stating that “a mathematical representation to which the signature may be compared may be included with signature, for verification purposes; in other embodiments, the algorithm used to produce the mathematical representation is publicly available...”. Paragraph 56 provides the only description of “non-repudiation,” again disclosed simply as an effect of applying hashing functions, stating that “[a]s a non-limiting example one-way cryptographic function may include a hash chain, in which data is added during a successive hashing process to ensure non-repudiation.”
With respect to Applicant’s assertion that claim 1 “requires insertion of the digitally signed assertion into a temporally sequential listing that is encrypted and implemented by transaction authentication nodes with decryption key custody at the nodes,” Applicant’s disclosure describes the temporally sequential listing as a blockchain and provides the Bitcoin blockchain as one particular example. Paragraph 56 states that “[t]emporally sequential listing 204 may include a block chain. In one embodiment, a block chain is temporally sequential listing 204 that records one or more new at least a digitally signed assertion 200 in a data item known as a sub-listing 208 or "block." An example of a block chain is the BITCOIN block chain…”.
“Decryption key custody at the nodes” is also simply a description of the use of the commonly-applied public/private key encryption method addressed above. The specific language in claim 1 recites the temporally sequential listing as “implemented by a plurality of transaction authentication nodes, wherein both the digitally signed assertion and the temporally sequential listing are encrypted and wherein a decryption key for the temporally sequential listing is stored by the plurality of transaction authentication nodes”. Paragraph 19 of the specification reflects this, stating that “temporally sequential listing 132 may, for instance be encrypted, and decryption keys may be distributed only to devices authorized to participate in authentication as described herein. In an embodiment, decryption key may be stored by transaction authentication nodes…”. In standard public key encryption, a private key is required to be held by any party seeking to decrypt the information. A blockchain, by definition, consists of nodes including nodes which store the ledger. “Authentication nodes,” at the level recited, are nodes able to authenticate the information by decrypting it using corresponding private keys.
This technique similarly applies to Applicant’s last assertion regarding claim 1 requiring “authenticated, gated retrieval by authenticating the request from the second remote device and retrieving/transmitting the signed assertion only upon authentication, which implements an access-control perimeter around sensitive records and outputs.” As provided in paragraph 41, “Authenticating a request may include confirming a user identifier such as by authenticating a cryptographic public/private key pair or checking a hash function that represents a user identifier. Authenticating a request may include confirming with a certificate authority that a requestor for a user datum 112 has permission and/or authority to request such information.” While Applicant uses language such as “authenticated, gated retrieval,” the actual functions being recited and disclosed encompass standard user-authentication using standard encryption and verification techniques.
The argued elements do not integrate the abstract idea into a practical application because each of they only amount to the use of standard security algorithms and data architectures, such as public key encryption, hashing, and blockchains, as tools to derive the known security features of each technique, and they are not recited or disclosed in a manner in which the combination yields more than the elements individually.
Applicant lastly argues starting on page 9 that claim 1 recites additional elements which amount to significantly more than the abstract idea under Step 2B. Examiner respectfully disagrees.
Applicant points to the arguments provided previously with respect to whether the claim integrates the abstract idea into a practical application, and again points to the elements of removing personally identifying information, authenticating the digitally signed assertion using a cryptographic hash and digital signature, insertion into a temporally sequential listing and encryption of the data, and authentication of a request for the information. Examiner reiterates the rationales provided with respect to each of these as set out above.
With respect to the assertion that the system "receives labeled training data and trains a machine-learning model by creating a convolutional neural network having defined layers and adjusting connections and weights between nodes, and generates a cannabis guidance output as a function of the user attribute datum, including a pre-existing medical condition, where the guidance output includes a dose-specific cannabis product recommendation,” these elements only amount to the general use of a trained machine learning algorithm in the form of a convolutional neural network as a tool to provide the cannabis product recommendation. Initially, each of the above steps such as receiving training data, creating the CNN, and applying it to a pre-exiting condition to generate the recommendation, is recited at a high level of generality. These elements are likewise only disclosed at a high level of generality.
As provided below, paragraphs 37 through 39 describes machine learning algorithms as encompassing any of a plurality of possible algorithms, including a convolutional neural network. Paragraphs 39 and 69 each provide:
“[a]s a further non-limiting example, a machine-learning model 148 may be generated by creating an artificial neural network, such as a convolutional neural network comprising an input layer of nodes, one or more intermediate layers, and an output layer of nodes. Connections between nodes may be created via the process of "training" the network, in which elements from a training dataset are applied to the input nodes, a suitable training algorithm (such as Levenberg-Marquardt, conjugate gradient, simulated annealing, or other algorithms) is then used to adjust the connections and weights between nodes in adjacent layers of the neural network to produce the desired values at the output nodes. This process is sometimes referred to as deep learning.” (Emphasis added)
Examiner notes that, as stated in the last sentence above, this is a generic description of the process of deep learning, and a generic description of the training process for a convolutional neural network. Adjusting connections and weights between nodes in adjacent layers is the fundamental mechanism by which any convolutional neural network is trained. Furthermore, an input layer of nodes, one or more intermediate layers of nodes, and an output layer of nodes is, similarly, a high-level description of the data structure of any convolutional neural network.
Likewise, the recitation of tokenizing the expert guidance input, generating vectors from the input within a vector space, and performing a vector similarity calculation to produce the user condition only recites, at a high level, a standard architecture of a neural network. As set out in further detail below, paragraph 27 describes a number of general techniques which may be applied as part of the language processing model, while paragraph 28 describes the role of vector spaces and vector similarities. With respect to the use of vector similarity, paragraph 28 states that “associating language elements to one another as described above may include computing a degree of vector similarity between a vector representing each language element and a vector representing another language element; vector similarity may be measured according to any norm for proximity and/or similarity of two vectors, including without limitation cosine similarity…”. However, the above elements are only disclosed at a high level in terms of how they function in the context of generating a model. With respect to its application to outputting a user condition, paragraphs 24 and 25 only provide that the language processing module may be used to map an expert datum to a user condition and may be configured to extract one or more words from an expert datum 136 and retrieve a user condition. While these elements recite steps performed as part of applying a neural network, they still only amount to steps which would be performed as part of the general use of a neural network to analyze input from a user and generate a suggested user condition.
Each of the argued elements only amounts to the recitation of general computing elements such as public key cryptography, hashing protocols, neural network algorithms, and blockchain architectures as tools to implement functions such as encrypting and authenticating data, determining a recommended cannabis product, and identifying a user condition. These elements, as recited in their ordered combination, do not amount to more than each element individually.
The rejection under 35 USC 101 is maintained.
B. Applicant’s arguments with respect to the rejection of claims 1-4, 8, 10-15, and 19 under 35 USC 103 have been fully considered. The corresponding rejection has been withdrawn on the basis that the closest prior art of record does not teach or render obvious all of the limitations in respective claims 1 and 12 in their entirety and as an ordered combination.
Claim Objections
The previous objection to claim 1 is withdrawn based on the amendments filed 2/11/2026.
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 1-4, 8, 10-15, and 19 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. Claims 1-4, 8, and 10-11 are drawn to a system, while claims 12-15, and 19 are drawn to a method, each of which is within the four statutory categories.
Step 2A(1)
Claim 1 recites, in part, performing the steps of
receive at least a user datum from a first source, wherein the at least a user datum includes at least a first user cannabis article, wherein the at least a user datum comprises data related to a user's purchase of a flavor of cannabis products;
receive a user attribute datum;
receive a request from a second user for a user datum record, wherein the user datum record comprises insurance coverage request for a cannabis article;
authenticate the request from the second user;
receive at least an expert datum from the second user, wherein the at least an expert datum includes a user identifier;
locate a temporally sequential listing relating to the user as a function of the user identifier;
generate an expert guidance datum;
generate a signed assertion containing the expert guidance datum, wherein generating the signed assertion containing the expert guidance datum comprises:
extracting one or more words from the at least an expert datum;
parsing the at least an expert datum into a plurality of tokens;
generating one or more associations between the plurality of tokens and one or more user condition categories;
outputting a user condition; and
generating the signed assertion as a function of the user condition;
generate a cannabis guidance output as a function of the user attribute datum, wherein the user attribute datum comprises a pre-existing medical condition of the user and the cannabis guidance output comprises a recommendation for a cannabis product to be consumed by the user at a particular dose;
create at least an unspecified datum as a function of the at least a user datum by removing personal identifying information (PII) from the user datum prior to storage of the at least a user datum;
authenticate the signed assertion;
insert the signed assertion in the temporally sequential listing;
retrieve the signed assertion containing the at least an unspecified datum and the cannabis guidance output upon authentication;
store the signed assertion containing the expert guidance datum linked to the user; and
transmit the signed assertion containing the at least an unspecified datum to the second user.
These elements amount to a form of managing personal behavior or relationships or interactions between people, and therefore fall within the scope of a method of organizing human activity. Fundamentally the process is that of managing access, retrieval, editing, and storing of information related to a user’s cannabis treatments, including by receiving information about a user’s cannabis activity along with providing a healthcare provider with access to stored user cannabis records, receiving information from the healthcare provider about the user (see paragraph 21 of Applicant’s specification regarding “expert datum”), identifying particular products and potential user medical conditions based on the user’s medical information, and storing the information following deidentification. This constitutes a form of managing the behavior of the healthcare provider interacting with the user record and making medical decisions for the patient, as well as the individual providing information about cannabis purchases.
With respect to the parsing of the expert datum into a plurality of tokens and generating associations between the tokens and user condition categories, Examiner notes paragraph 25 of the specification as originally filed, which states that “[t]extual data may be parsed into tokens, which may include a simple word (sequence of letters separated by whitespace) or more generally a sequence of characters as described previously. The term "token," as used herein, refers to any smaller, individual groupings of text from a larger source of text; tokens may be broken up by word, pair of words, sentence, or other delimitation. These tokens may in turn be parsed in various ways. Textual data may be parsed into words or sequences of words, which may be considered words as well.” Given the definition of a token as portions of text such as words or groups of words and the breadth of the parsing and generating elements, an individual would be able to parse a sequence of text into components and determine associations between those components and categories of potential user conditions.
Independent claim 12 recites similar limitations and also recites an abstract idea under the same analysis.
Step 2A(2)
This judicial exception is not integrated into a practical application because the additional elements within the claims only amount to:
A. Instructions to Implement the Judicial Exception. MPEP 2106.05(f)
Claims 1 and 12 recite additional elements of
a) a memory recited as containing instructions configuring the processor,
b) a processor communicatively coupled to the memory and recited as performing the subsequent data processing functions,
c) a first remote device recited as providing the user datum,
d) training a machine learning model trained as a function of training data comprising a plurality of user attribute labels correlated to a plurality of cannabis article labels, wherein generating the machine-learning model comprises: creating a convolutional neural network comprising an input layer of nodes, one or more intermediate layers of nodes, and an output layer of nodes; adjusting one or more connections and one or more weights between nodes in adjacent layers of the convolutional neural network, and being recited as used to generate the recommendation for a cannabis product;
e) a second remote device recited as requesting the user datum record, providing the at least an expert datum, and receiving the signed assertion containing the unspecified datum,
f) the signed assertion being a “digitally” signed assertion;
g) a language processing model recited as used to generate the one or more associations between the plurality of tokens and user condition categories;
h) generating a vector space comprising at least a vector, wherein the at least a vector comprises the one or more unique extracted words of the at least an expert datum, computing a degree of vector similarity between the at least a vector comprising the one or more unique extracted word and the at least a vector from the vector space comprising a user condition category, and outputting, by a classification algorithm, the user condition as a function of the degree of vector similarity;
i) performing the authentication using a cryptographic hash and a digital signature applied to the at least an unspecified datum and the cannabis guidance output;
j) the temporally sequential listing being implemented by a plurality of transaction authentication nodes, wherein both the digitally signed assertion and the temporally sequential listing are encrypted and wherein a decryption key for the temporally sequential listing is stored by the plurality of transaction authentication nodes;
k) a repository engine recited as communicated with to retrieve the digitally signed assertion.
Paragraphs 85, 86, 88, and 89 of the specification of originally filed describe a computing device operable to perform any of the disclosed functions as including a processor executing instructions stored in a memory device, with examples provided of a computer workstation, server computer, smartphone, or tablet. The memory and at least a processor are accordingly given their broadest reasonable interpretation as encompassing generic computing elements.
Paragraph 11 further describes remote devices as including any of mobile computing devices, laptops, desktops, and other such devices. The first and second remote devices are accordingly given their broadest reasonable interpretation as encompassing generic computing elements.
Paragraphs 37 through 39 describes machine learning algorithms as encompassing any of a plurality of possible algorithms, including a convolutional neural network. Paragraphs 39 and 69 each provide:
“[a]s a further non-limiting example, a machine-learning model 148 may be generated by creating an artificial neural network, such as a convolutional neural network comprising an input layer of nodes, one or more intermediate layers, and an output layer of nodes. Connections between nodes may be created via the process of "training" the network, in which elements from a training dataset are applied to the input nodes, a suitable training algorithm (such as Levenberg-Marquardt, conjugate gradient, simulated annealing, or other algorithms) is then used to adjust the connections and weights between nodes in adjacent layers of the neural network to produce the desired values at the output nodes. This process is sometimes referred to as deep learning.” (Emphasis added)
Examiner notes that, as stated in the last sentence above, this is a generic description of the process of deep learning, and a generic description of the training process for a convolutional neural network. Adjusting connections and weights between nodes in adjacent layers is the fundamental mechanism by which any convolutional neural network is trained. Furthermore, an input layer of nodes, one or more intermediate layers of nodes, and an output layer of nodes is, similarly, a high-level description of the data structure of any convolutional neural network.
Paragraph 14 describes digitally signed assertions as textual data signed using a secure proof, and further provides an example of an encrypted mathematical expression of a file. The digitally signed assertion is therefore construed as encompassing encrypted or secured files. Paragraphs 14, 17, and 18 further describe the cryptographic hash as stored in the digitally signed assertion and generated using any of a plurality of known hashing algorithms, as well as verifying the digitally signed assertion using the digital signature, key pairs, and hash. The digital signature and cryptographic hash are therefore construed as encompassing generic signature techniques such as private/public key pairs and any hashing algorithm.
Paragraphs 27 and 28 describe the use of a language processing model and classification algorithm. Paragraph 27 describes a number of general techniques which may be applied as part of the language processing model, while paragraph 28 describes the role of vector spaces and vector similarities. Paragraph 28 describes a vector space as a collection of vectors, where each extracted word or language element may be represented by a vector in the vector space. With respect to the use of vector similarity, paragraph 28 states that “associating language elements to one another as described above may include computing a degree of vector similarity between a vector representing each language element and a vector representing another language element; vector similarity may be measured according to any norm for proximity and/or similarity of two vectors, including without limitation cosine similarity…”. However, the above elements are only disclosed at a high level in terms of how they function in the context of generating a model. The representation of textual information as vectors within a vector space and the use of vector similarity calculations, such as cosine similarity, to identify relationships between elements are fundamental elements of machine learning models such as neural networks. With respect to its application to outputting a user condition, paragraphs 24 and 25 only provide that the language processing module may be used to map an expert datum to a user condition and may be configured to extract one or more words from an expert datum 136 and retrieve a user condition. The elements reciting generating a vector space comprising at least a vector, wherein the at least a vector comprises the one or more unique extracted words of the at least an expert datum, computing a degree of vector similarity between the at least a vector comprising the one or more unique extracted word and the at least a vector from the vector space comprising a user condition category, and outputting, by a classification algorithm, the user condition as a function of the degree of vector similarity are therefore construed as encompassing the use of a general neural network or other machine learning architecture.
Paragraphs 14, 19, and 20 disclose transaction authentication nodes, including storage of a decryption key thereon, as well as encryption of the digitally signed assertion and temporally sequential listing. Paragraph 19 states that “[t]emporally sequential listing 132 may, for instance be encrypted, and decryption keys may be distributed only to devices authorized to participate in authentication.” Paragraph 20 further states that “temporally sequential listing 132 may be implemented by a plurality of transaction authentication nodes,” and that “a plurality of transaction authentication nodes implementing temporally sequential listing 132 may allow for multiple asset transfers to occur simultaneously.” No further disclosure is provided of the “authentication nodes” themselves or how they implement the temporally sequential listing beyond allowing multiple transfers, verifications, or updates. The authentication nodes are therefore construed as encompassing generic computing elements. The encryption of the digitally signed assertion temporally sequential listing and storage of decryption keys is likewise construed as encompassing generic forms of encryption and associated keys.
Paragraph 12 similarly states that “repository engine 120 may be implemented as any hardware and/or software module.” The repository engine is therefore construed as encompassing generic computing elements.
Each of the above elements therefore only amounts to mere instructions to implement the abstract idea using computing elements as tools. For example, the processor, memory, and remote device are each only recited at a high level of generality as implementing respective data processing, storage, and transmission functions. The machine learning algorithm, convolutional neural network, and vector space and similarity are likewise recited only at a high level of generality as comprising elements universal to neural networks and as used to generate the recommendation for a cannabis product and the user condition. Likewise, the use of “authentication nodes” is only recited at a high level of generality as “implementing” the temporally sequential listing” and storing a “decryption key,” each of which is only broadly disclosed in the specification. These elements are therefore not sufficient to integrate the abstract idea into a practical application.
B. Insignificant Extra-Solution Activity. MPEP 2106.05(g)
Claims 1 and 12 further recite the additional element of receiving the training data. This limitation amounts to insignificant extra-solution activity in the form of mere data gathering, especially given that it is recited at a high level of generality. The function of receiving the training data for subsequent use in training the machine learning model is not itself sufficient to integrate the abstract idea into a practical application.
The above claims, as a whole, are therefore directed to an abstract idea.
Step 2B
The present claims do not include additional elements that are sufficient to amount to more than the abstract idea because the additional elements or combination of elements amount to no more than a recitation of:
A. Instructions to Implement the Judicial Exception. MPEP 2106.05(f)
As explained above, claims 1 and 12 only recite the additional elements described above as tools for performing the steps of the abstract idea, and mere instructions to perform the abstract idea using a computer is not sufficient to amount to significantly more than the abstract idea. MPEP 2106.05(f)
B. Insignificant Extra-Solution Activity. MPEP 2106.05(g)
Claims 1 and 12 further recite the additional element of receiving the training data. As set out above, this limitation amounts to insignificant extra-solution activity in the form of mere data gathering, especially given that it is recited at a high level of generality. The function of receiving the training data for subsequent use in training the machine learning model is not itself sufficient to integrate the abstract idea into a practical application or to amount to significantly more than the abstract idea.
C. Well-Understood, Routine and Conventional Activities. MPEP 2106.05(d)
In addition to amounting to insignificant extra-solution activity, the function of receiving the training data as recited in claims 1 and 12 amounts to well-understood routine and conventional activity. This limitation is recited at a high level of generality and as insignificant extra-solution activity, and constitutes a form of receiving or transmitting data over a network and/or retrieving information from memory. See MPEP 2106.05(d)(II)
Thus, taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually.
Depending Claims
Claims 2 and 13 recite wherein the at least a user datum further includes a health history datum. These limitations fall within the scope of the abstract idea as set out above.
Claims 3 and 14 recite wherein creating the at least an unspecified datum further comprises: removing personal identifying data pertaining to the user from the at least a user datum; generating the signed assertion containing a unique identifier for the user; inserting the signed assertion containing the unique identifier for the user in the temporally sequential listing; and storing the personal identifying data. These limitations fall within the scope of the abstract idea as set out above.
Claims 3 and 14 further recite additional element of the signed assertion being a “digitally” signed assertion.
As cited above, paragraph 14 describes digitally signed assertions as textual data signed using a secure proof, and further provides an example of an encrypted mathematical expression of a file. The digitally signed assertion is therefore construed as encompassing encrypted or secured files.
The above element therefore only amounts to mere instructions to implement the abstract idea using computing elements as tools. Specifically, the signed assertion being “digitally” signed is only recited at a high level of generality and encompasses using computer software to secure the text data. This element is therefore not sufficient to integrate the abstract idea into a practical application or to amount to significantly more than the abstract idea.
Claims 4 and 15 recite wherein generating the signed assertion further comprises: creating a secure timestamp when the at least a user datum is received; and generating the signed assertion containing the secure timestamp. These limitations fall within the scope of the abstract idea as set out above.
Claims 4 and 15 further recite additional elements of a) the signed assertion being a “digitally” signed assertion, and b) the first remote device performing the function of providing the user datum.
As cited above, paragraph 14 describes digitally signed assertions as textual data signed using a secure proof, and further provides an example of an encrypted mathematical expression of a file. The digitally signed assertion is therefore construed as encompassing encrypted or secured files.
Paragraph 11 further describes a remote device as being devices such as mobile computing devices, laptops, desktops, and other such devices. The first remote device is accordingly given its broadest reasonable interpretation as encompassing generic computing elements.
The above elements therefore only amount to mere instructions to implement the abstract idea using computing elements as tools. Specifically, the signed assertion being “digitally” signed is only recited at a high level of generality and encompasses using computer software to secure the text data, while the first remote device is likewise only recited at a high level of generality as providing the user datum. These elements are therefore not sufficient to integrate the abstract idea into a practical application or to amount to significantly more than the abstract idea.
Claims 8 and 19 recite receiving a request for a plurality of cannabis article features; filtering a plurality of signed assertions to locate signed assertions that contain the cannabis article features; retrieving located signed assertions that contain the cannabis article features; and transmitting the located signed assertions wherein the located signed assertions contain an unspecified datum. These limitations fall within the scope of the abstract idea as set out above.
Claims 8 and 19 further recite additional elements of a) the processor performing the subsequent data processing functions including filtering the plurality of digitally signed assertions, retrieving located digitally signed assertions, and transmitting the located digitally signed assertions, b) the second remote device performing the function of providing the request for a plurality of cannabis article features, and c) the signed assertions being a “digitally” signed assertions.
As cited above, paragraphs 85, 86, 88, and 89 of the specification of originally filed describe a computing device operable to perform any of the disclosed functions as including a processor executing instructions stored in a memory device, with examples provided of a computer workstation, server computer, smartphone, or tablet. The memory and at least a processor are accordingly given their broadest reasonable interpretation as encompassing generic computing elements.
Paragraph 14 describes digitally signed assertions as textual data signed using a secure proof, and further provides an example of an encrypted mathematical expression of a file. The digitally signed assertion is therefore construed as encompassing encrypted or secured files.
Paragraph 11 further describes a remote device as being devices such as mobile computing devices, laptops, desktops, and other such devices. The second remote device is accordingly given its broadest reasonable interpretation as encompassing generic computing elements.
The above elements therefore only amount to mere instructions to implement the abstract idea using computing elements as tools. Specifically, the signed assertion being “digitally” signed is only recited at a high level of generality and encompasses using computer software to secure the text data, while the processor and second remote device are likewise only recited at a high level of generality as performing data analysis functions and providing the request. These elements are therefore not sufficient to integrate the abstract idea into a practical application or to amount to significantly more than the abstract idea.
Claim 10 recites displaying a cannabis tracking consent agreement; receiving a user response containing a positive cannabis tracking consent agreement; prompting a user informed advisor identification; receiving an informed advisor identification; and transmitting the user datum containing the at least a first user cannabis article and the data related to the user’s purchase of a flavor of cannabis products and the informed advisor identification. These limitations fall within the scope of the abstract idea as set out above.
Claim 10 further recites additional elements of a) a dispensary computing remote device performing the subsequent data processing and display functions including generating the user datum, displaying the cannabis tracking consent agreement, and transmitting the user datum and informed advisor identification, b) a processor performing the function of receiving the user datum and informed advisor identification.
Paragraph 44 states that “dispensary computing remote device 172 may include any device suitable for use as remote device 116 as described above,” while paragraph 11 describes a remote device as being devices such as mobile computing devices, laptops, desktops, and other such devices. The dispensary computing remote device is accordingly given its broadest reasonable interpretation as encompassing generic computing elements.
Paragraphs 85, 86, 88, and 89 of the specification of originally filed describe a computing device operable to perform any of the disclosed functions as including a processor executing instructions stored in a memory device, with examples provided of a computer workstation, server computer, smartphone, or tablet. The memory and at least a processor are accordingly given their broadest reasonable interpretation as encompassing generic computing elements.
The above elements therefore only amount to mere instructions to implement the abstract idea using computing elements as tools. Specifically, the dispensary computing remote device is only broadly recited as performing data processing functions such as generating the user datum and receiving the user response, while the processor is likewise only broadly recited as receiving the user datum and informed advisor identification. These elements are therefore not sufficient to integrate the abstract idea into a practical application or to amount to significantly more than the abstract idea.
Claim 11 recites receiving the signed assertion containing a first user datum; generating a second user datum wherein the second user datum contains a response to the first user datum; linking the first user datum and the second user datum; and transmitting the linked first user datum and the second user datum. These limitations fall within the scope of the abstract idea as set out above.
Claim 11 further recites additional elements of a) a professional computing remote device performing the subsequent data processing functions including receiving the digitally signed assertion, generating the second user datum, and transmitting the linked first user datum and second user datum, b) a processor performing the functions of providing the digitally signed assertion and receiving the linked first user datum and second user datum, c) the signed assertions being “digitally” signed assertions.
Paragraph 45 states that “professional computing remote device 176 may include any device suitable for use as remote device 116 as described above,” while paragraph 11 describes a remote device as being devices such as mobile computing devices, laptops, desktops, and other such devices. The professional computing remote device is accordingly given its broadest reasonable interpretation as encompassing generic computing elements.
Paragraphs 85, 86, 88, and 89 of the specification of originally filed describe a computing device operable to perform any of the disclosed functions as including a processor executing instructions stored in a memory device, with examples provided of a computer workstation, server computer, smartphone, or tablet. The memory and at least a processor are accordingly given their broadest reasonable interpretation as encompassing generic computing elements.
Paragraph 14 describes digitally signed assertions as textual data signed using a secure proof, and further provides an example of an encrypted mathematical expression of a file. The digitally signed assertion is therefore construed as encompassing encrypted or secured files.
The above elements therefore only amount to mere instructions to implement the abstract idea using computing elements as tools. Specifically, the professional computing remote device is only broadly recited as performing data processing functions such as receiving the digitally signed assertion and generating the second user datum, while the processor is likewise only broadly recited as receiving the user datum and informed advisor identification and the signed assertion being “digitally” signed is only recited at a high level of generality and encompasses using computer software to secure the text data. These elements are therefore not sufficient to integrate the abstract idea into a practical application or to amount to significantly more than the abstract idea.
Claims 1-4, 8, 10-15, and 19 are therefore rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Claim Interpretation
The previous construction of claim 12 under 35 USC 112(f) is no longer applied based on the amendments filed 2/11/2026.
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are:
a repository engine as recited in claim 1.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
However, Examiner is unable to locate corresponding portions of the disclosure which clearly link the above elements to corresponding structure, material, or acts for performing all of the associated functions. For example, paragraphs 10, 12, 25, and 41 each merely state that respective ones of the repository engine, repository module, transmission module, and language processing module “may be implemented as any hardware and/or software module.”
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 112
The previous rejection of claims 12-17, 19, and 20 under 35 USC 112(b) is withdrawn based on the amendments filed 8/26/2025.
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 1-4, 8, and 10-11 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.
As explained above, claim 1 recites a “repository engine” which invokes 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function.
The disclosure states that each of the repository engine, repository module, transmission module, and language processing module “may be implemented as any hardware and/or software module” (see e.g. paragraphs 10, 12, 25, and 41). This disclosure of each of the above elements as being “any hardware and/or software module” fails to clearly link structure, material, or acts to the functions performed by each of these elements.
Therefore, the claim is indefinite and is rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph.
Applicant may:
(a) Amend the claim so that the claim limitation will no longer be interpreted as a limitation under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph;
(b) Amend the written description of the specification such that it expressly recites what structure, material, or acts perform the entire claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(c) Amend the written description of the specification such that it clearly links the structure, material, or acts disclosed therein to the function recited in the claim, without introducing any new matter (35 U.S.C. 132(a)).
If applicant is of the opinion that the written description of the specification already implicitly or inherently discloses the corresponding structure, material, or acts and clearly links them to the function so that one of ordinary skill in the art would recognize what structure, material, or acts perform the claimed function, applicant should clarify the record by either:
(a) Amending the written description of the specification such that it expressly recites the corresponding structure, material, or acts for performing the claimed function and clearly links or associates the structure, material, or acts to the claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(b) Stating on the record what the corresponding structure, material, or acts, which are implicitly or inherently set forth in the written description of the specification, perform the claimed function. For more information, see 37 CFR 1.75(d) and MPEP §§ 608.01(o) and 2181.
Claims 2-4, 8, and 10-11 inherit the deficiencies of claim 1 through dependency and are likewise rejected.
Claims 1 and 12 recite the limitation " the vector space comprising a user condition category" in lines 70-72 and lines 61-62 respectively. There is insufficient antecedent basis for this limitation in the claims. While each claim previously recites a vector space, neither previously recites a vector space comprising a user condition category.
Claims 2-4, 8, 10-11, 13-15, and 19 inherit the deficiencies of claims 1 and 12 through dependency and are likewise rejected.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Kinzer (US Patent Application Publication 2016/0171164);
Saribekyan (US Patent Application Publication 2017/0017773);
Curbera et al (US Patent Application Publication 2018/0082024).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to WILLIAM G LULTSCHIK whose telephone number is (571)272-3780. The examiner can normally be reached 9am - 5pm.
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/Gregory Lultschik/Examiner, Art Unit 3682