DETAILED ACTION
Status of the Application
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 January 1, 2026, has been entered.
In response, the Applicant amended claims 1, 6, 9-15, and 20. Claims 3, 4, 17, and 18 were cancelled. Claims 2, 5, 16, 19, and 21-26 were previously cancelled. Claims 1, 6-15, and 20 are pending and currently under consideration for patentability.
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 .
Response to Amendments and Arguments
v Applicant’s arguments, with respect to the rejection of claims 1, 6-15, and 20 under 35 U.S.C. 101 have been fully considered and are not persuasive. The rejections of claims 1, 6-15, and 20 under 35 U.S.C. 101 have been maintained accordingly.
Applicant specifically argues that
1) “Technical Solution to Multi-Terminal Computing Challenges…The present invention as amended is directed to solving a specific computing challenge that arises in distributed, multi-terminal environments that receive heterogeneous streams of medical information. In such settings, input data is not homogeneous but instead varies based on terminal type and user interaction…a medical device terminal may transmit sensor-derived clinical measurements, a medical device user terminal-typically operated by healthcare staff-may transmit operational metadata or procedural context, and a patient terminal may transmit self-reported inputs or behavioral indicators. Each of these inputs differs in both semantic content and structural format, making unified interpretation and processing by a single computational system inherently difficult. Traditional systems that treat all inputs uniformly fail to account for this diversity, resulting in inconsistent outputs and contextually irrelevant decisions. The present invention as amended overcomes this by introducing a novel identifier- oriented architecture in which input data is decomposed and reorganized based on multiple identifier attributes, including a medical device identifier, medical device user identifier, patient identifier, test item identifier, and test type identifier. This identifier-based decomposition is not merely a categorization step but a transformation process in which incoming data is filtered, normalized, and aggregated into structured summary information- referred to herein as "identifier-specific summary representations". By performing this transformation, the system standardizes semantically inconsistent inputs into a canonicalized form that is tailored to each identifier type.
For example, data associated with a particular device may be distilled into a format that preserves device operational history and context, while user-associated data may emphasize procedural sequences or interaction logs, and patient-associated data may retain personalized metrics or demographic traits. This standardization enables downstream computational stages-such as vector encoding and context-driven similarity matching-to be performed on a harmonized data substrate. Importantly, this identifier-specific structuring serves as the foundation for later context-aware operations such as state-based reweighting. Without having decomposed the data into distinct identifier channels, it would be computationally infeasible to apply dynamic weight adjustments or to prioritize specific identifiers based on terminal status transitions. The system's ability to selectively amplify or attenuate the contribution of each identifier relies entirely on this decomposition step, underscoring its non-generic and technical nature.
Furthermore, the decomposition not only improves data consistency but also facilitates scalability and modularity. In a multi-terminal environment where new input sources or identifiers may be added, the system remains stable by simply extending the decomposition schema without reengineering the entire pipeline. This modular capability is particularly important in healthcare settings, where device types and user behaviors evolve over time, and where the integrity of computational interpretation must be maintained across varying configurations. Thus, this identifier-based preprocessing is not a conventional data-handling step, but rather a machine-implemented logic that reorganizes and transforms semantically diverse inputs into machine-actionable structures. This structure enables complex, real-time decisions to be made in later stages and is a prerequisite for ensuring accurate, context-aligned outputs. It forms the computational backbone of the system's overall architecture and supports eligibility under the second prong of Step 2A of the Alice/Mayo framework. By addressing the specific technical problem of input inconsistency and identifier ambiguity in distributed computing environments, the present invention offers a concrete solution that goes well beyond abstract theorization or mental process automation.”
Examiner respectfully disagrees with Applicant’s first argument.
Applicant’s argument is replete with statements that are incommensurate with what is actually claimed. The instant claims do not recite steps where “input data is decomposed and reorganized based on multiple identifier attributes” or a “a transformation process in which incoming data is filtered, normalized, and aggregated into structured summary information- referred to herein as "identifier-specific summary representations" such that the system “standardizes semantically inconsistent inputs into a canonicalized form that is tailored to each identifier type”, or where “data associated with a particular device may be distilled into a format that preserves device operational history and context, while user-associated data may emphasize procedural sequences or interaction logs, and patient-associated data may retain personalized metrics or demographic traits” or where data is “decomposed…into distinct identifier channels”, and does not require making “real-time decisions”. As best as understood, the referred to "identifier-specific summary representations" corresponds to the “summary information” that is actually claimed (e.g., because Applicant later suggests that the claimed invention transforms the “the structured, identifier-specific summary information into numerical representations through a pre-trained encoder model, such as one incorporating an artificial neural network”). Nothing in the claims resembles decomposing and reorganizing, filtering, normalizing, or aggregating data into structured data, and/or standardizing semantically inconsistent inputs into a canonicalized form tailored to each identifier type. The claims merely include a step of receiving medical information that already includes tagged attributes/identifiers, and then a high-level step of “generating summary information extracted from the medical information on the basis of the one or more attributes” somehow “based on the test item identifier or test type identifier” and “based on the identifier of the medical device” and “based on the identifier of the medical device user” and “based on the identifier of the patient”. The large number of incommensurate statements associated with the alleged solution/improvement are not persuasive as they are divorced from what is actually being claimed.
Furthermore, there is no suggestion whatsoever in the original disclosure that there are any “specific computing challenge that arises in distributed, multi-terminal environments that receive heterogeneous streams of medical information”, or that “traditional systems…treat all inputs uniformly fail to account for this diversity, resulting in inconsistent outputs and contextually irrelevant decisions”, or that generating the summary information and/or summary vector “facilitates scalability and modularity” that enables the system to “remain(s) stable by simply extending the decomposition schema without reengineering the entire pipeline” or that this is “particularly important in healthcare settings, where device types and user behaviors evolve over time, and where the integrity of computational interpretation must be maintained across varying configurations” or that it “ensure(s) accurate, context-aligned outputs”. None of this would be apparent to a PHOSTIA from the original specification. Per MPEP 2106.05(a) “If it is asserted that the invention improves upon conventional functioning of a computer, or upon conventional technology or technological processes, a technical explanation as to how to implement the invention should be present in the specification.” Per MPEP 2106.04(d)(1) “The specification need not explicitly set forth the improvement, but it must describe the invention such that the improvement would be apparent to one of ordinary skill in the art. Conversely, if the specification explicitly sets forth an improvement but in a conclusory manner (i.e., a bare assertion of an improvement without the detail necessary to be apparent to a person of ordinary skill in the art), the examiner should not determine the claim improves technology.”
Finally, the present invention does not appear to be concerned with providing an inventive/novel “identifier-specific structuring” that “serves as the foundation for later context-aware operations such as state-based reweighting” or “decompos(ing) the data into distinct identifier channels”. Again, the specification merely states that the medical information the system receives (e.g., from different sources/devices) is tagged with different attributes that include different identifiers. There is nothing to suggest such data/metadata is anything other than what is conventionally included in such medical information. Furthermore, the claims are concerned with how this received data is processed to select an advertisement, not a particular tagging schema or way to generate various tagged data. The receiving of the data (and the structure of the data being received by association) is insignificant pre-solution activity in the claimed invention.
Applicant specifically argues that
2) “Computational Foundation for State-Aware Reweighting… The claimed invention as amended introduces a crucial preprocessing stage that systematically generates identifier-specific summary information, which serves as a prerequisite for dynamic reweighting operations downstream. The core insight here is that in order for the system to dynamically adjust the relative importance of different categories of medical input-such as data related to the device, the user, or the patient-it must first isolate and represent these categories in distinct, addressable forms. To this end, the claimed invention as amended performs structured decomposition of the input data, generating separate summaries for each of several identifier types, including medical device identifier, medical device user identifier, and patient identifier. These summaries are not mere metadata; rather, they are synthesized informational aggregates that capture the salient features associated with each identifier dimension.
This preprocessing step forms the computational foundation for the state-aware reweighting mechanism described in later stages of the claimed invention. Without decomposing the incoming data into distinct identifier-based summary structures, it would not be computationally feasible to modulate the influence of each identifier type based on terminal context. For example, increasing the contribution of patient-related data when a terminal transitions to a use state is only possible if patient-specific summary information has already been isolated and independently encoded. Similarly, suppressing the influence of user-related or device-related data when it becomes contextually irrelevant requires that such data be modularly separable within the system architecture.
In this sense, identifier-specific decomposition is more than a preparatory stage; it is a technical enabler that allows for fine-grained and context-responsive control of input relevance. It transforms an unstructured, intermingled dataset into a format that allows selective amplification or attenuation of information streams according to external computing signals- namely, terminal state changes. Such computational structuring also addresses critical stability and interpretability challenges in multi-terminal systems, where overlapping data sources may compete or conflict in significance depending on operational context. By defining a clear computational boundary for each identifier category, the claimed invention as amended introduces a modular information model that facilitates real-time adaptability in the matching process. This architecture enables the invention to transcend traditional rule-based filtering or static logic flows, instead allowing machine operations to prioritize or deprioritize information categories dynamically. This identifier-specific structuring thus represents not only a technical improvement over prior data processing methods but also an essential step in satisfying the functional requirements of a distributed, multi-input, context-sensitive computing system. It is precisely this modularity and architectural foresight that distinguishes the invention from mental processes or abstract formulations and supports its eligibility under Step 2A of the Alice/Mayo framework.”
Examiner respectfully disagrees with Applicant’s second argument.
Applicant’s argument is again replete with statements that are incommensurate with what is actually claimed. The high-level requirement of “generating summary information extracted from the medical information on the basis of the one or more attributes” (e.g., in any way “based on the test item identifier or test type identifier” and “based on the identifier of the medical device” and “based on the identifier of the medical device user” and “based on the identifier of the patient”) does not amount to a systematic preprocessing stage or “structured decomposition” where different categories of medical inputs are isolated and/or represented into distinct, addressable forms. The claims do not require that the summary information be “synthesized informational aggregates that capture the salient features associated with each identifier dimension”. The large number of incommensurate statements associated with the alleged solution/improvement are not persuasive as they are divorced from what is actually being claimed.
Furthermore, there is no suggestion whatsoever in the original disclosure that “it would not be computationally feasible to modulate the influence of each identifier type based on terminal context” without “decomposing the incoming data into distinct identifier-based summary structures” or that “increasing the contribution of patient-related data when a terminal transitions to a use state is only possible if patient-specific summary information has already been isolated and independently encoded”. There is no suggestion that the claimed invention addresses “critical stability and interpretability challenges in multi-terminal systems, where overlapping data sources may compete or conflict in significance depending on operational context”, or problems with “traditional rule-based filtering or static logic flows”, or addresses “functional requirements of a distributed, multi-input, context-sensitive computing system”. These alleged problem not be apparent to a PHOSTIA in light of the original specification. Per MPEP 2106.05(a) “If it is asserted that the invention improves upon conventional functioning of a computer, or upon conventional technology or technological processes, a technical explanation as to how to implement the invention should be present in the specification.” Per MPEP 2106.04(d)(1) “The specification need not explicitly set forth the improvement, but it must describe the invention such that the improvement would be apparent to one of ordinary skill in the art. Conversely, if the specification explicitly sets forth an improvement but in a conclusory manner (i.e., a bare assertion of an improvement without the detail necessary to be apparent to a person of ordinary skill in the art), the examiner should not determine the claim improves technology.”
Furthermore, the claimed solution is broadly “generating summary information” (e.g., based on the identifiers in the received data) and “generating a summary vector corresponding to the summary information” (e.g., using an encoder having an ANN), and then weighting certain values in the summary vector. Not only is this framework quite broad, representing information (e.g., content information, profile information, etc.) in vector form to facilitate calculation of similarity as part of recommender systems is and was very common. There is no novelty found in the concept of having data tagged with “identifiers”, generating summary information from this information, generating a summary vector with the information, and determining similarity (e.g., cosine distance) between this vector and candidate product vector in order to determine a product to be advertised. The novelty is found elsewhere, including at least in part on the specific requirement for the summary information to be based on a test item identifier (or test type identifier), and a medical device identifier, and a medical device user identifier, and a patient identifier. In other words, it is the type of data included in the analysis rather than the use of a summary vector encoding values associated with different “identifiers” where the focus of Applicant’s invention lies. Examiner notes that targeting based on some information while not targeting based on user tracking or behavior prediction is not a technical improvement. If anything, it is an improvement to an abstract idea itself.
Applicant specifically argues that
3) “Transformation via Machine-Native Vectorization…The claimed invention as amended further advances its technical architecture by transforming the structured, identifier-specific summary information into numerical representations through a pre-trained encoder model, such as one incorporating an artificial neural network. This vectorization process constitutes a fundamental computational transformation, converting semantically rich, multi-dimensional medical information into a set of quantifiable summary vectors that are embedded within a shared numerical space. The result is a standardized data format that enables the system to perform similarity scoring and matching operations across inputs that originate from different terminal types and vary significantly in their structure, timing, and semantics.
Unlike traditional rule-based systems that might attempt to compare input records through static keys or attribute lookups, the claimed invention as amended applies machine learning-based encoding to generate continuous, mathematically manipulable vectors. These vectors capture latent relationships and statistical patterns learned during prior training, allowing the system to generalize across diverse inputs and align them meaningfully within the same computational framework. For example, summary vectors for a diagnostic signal from a medical device terminal, a usage log from a healthcare provider, and a biometric input from a patient terminal can all be embedded in the same multi-dimensional space and subsequently compared to candidate advertisement vectors using similarity functions such as cosine similarity or Euclidean distance.
Importantly, this process is neither trivial nor merely algorithmic in nature-it requires significant computational resources, structured training, and an architectural commitment to machine-native data representation. It cannot be emulated through mental steps, paper-based processes, or simple spreadsheet operations. Rather, it represents an engineering effort to abstract and normalize multi-source medical data into a machine-interpretable vector space, enabling real-time computation of relevance scores in a scalable and technically sound manner. This vector-based representation also enhances the system's robustness by abstracting away data source idiosyncrasies. Whether data originates from a high-frequency sensor reading or a manual patient input, once encoded as a summary vector, it becomes interoperable with other vectors within the system, enabling consistent and objective evaluation. Moreover, the encoder architecture ensures that the system can continuously evolve through retraining or fine-tuning, further improving the semantic fidelity and predictive strength of the generated vectors over time.
Through this vectorization mechanism, the claimed invention as amended not only supports the subsequent reweighting and matching steps but also fundamentally transforms how distributed medical data can be processed within computational environments. It allows heterogeneous data to be treated homogeneously, not by ignoring their differences, but by embedding their core informational content into a common analytic substrate. This computational transformation step is central to demonstrating that the claimed invention as amended materially improves the functioning of the computer system and does not merely rely on generic computer implementation. It is a concrete, machine-specific solution to the real- world challenge of data standardization and matching in distributed, identifier-rich healthcare settings.”
Examiner respectfully disagrees with Applicant’s third argument.
There is no suggestion in the original disclosure that the claimed invention addresses any sort of technical problem associated with “inputs that originate from different terminal types and vary significantly in their structure, timing, and semantics” or problems that occur when “traditional rule-based systems that might attempt to compare input records through static keys or attribute lookups”, that the claimed invention is directed to enabling “real-time computation of relevance scores in a scalable and technically sound manner”, that vector-based representation “enhances the system's robustness by abstracting away data source idiosyncrasies”, that “once encoded as a summary vector, it becomes interoperable with other vectors within the system, enabling consistent and objective evaluation”, that “the encoder architecture ensures that the system can continuously evolve through retraining or fine-tuning, further improving the semantic fidelity and predictive strength of the generated vectors over time”, or that the claimed invention is directed to “the real- world challenge of data standardization and matching in distributed, identifier-rich healthcare settings”. These alleged problems/improvements would not be apparent to a PHOSTIA in light of the original specification. Per MPEP 2106.05(a) “If it is asserted that the invention improves upon conventional functioning of a computer, or upon conventional technology or technological processes, a technical explanation as to how to implement the invention should be present in the specification.” Per MPEP 2106.04(d)(1) “The specification need not explicitly set forth the improvement, but it must describe the invention such that the improvement would be apparent to one of ordinary skill in the art. Conversely, if the specification explicitly sets forth an improvement but in a conclusory manner (i.e., a bare assertion of an improvement without the detail necessary to be apparent to a person of ordinary skill in the art), the examiner should not determine the claim improves technology.”
Furthermore, the claimed invention does not involve an “encoder architecture” that “ensures that the system can continuously evolve through retraining or fine-tuning, further improving the semantic fidelity and predictive strength of the generated vectors over time”. The claimed invention merely requires a high level requirement of “generating a summary vector….using a pre-trained model including an encoder having an artificial neural network”. Not only is this model utilized at an “apply it” level in the claims, the model/encoder/ANN is pre-trained. The training of this model is not part of the claimed invention, nor does Applicant’s specification discuss the training of this model. In other words, training and/or retraining of this model is not part of Applicant’s claimed invention. This model may be received from some other entity, and is merely being applied to the “summary information” in order to generate a summary vector. Using a pre-trained model to generate the summary vector is part of the abstract idea encompassed by the claims. The requirement for this mode to include “an encoder having an artificial neural network” provides nothing more than mere instructions to implement an abstract idea on a generic computer. See MPEP 2106.05(f) and the July 2024 Subject Matter Eligibility Examples and corresponding analysis. MPEP 2106.05(f) provides the following considerations for determining whether a claim simply recites a judicial exception with the words “apply it” (or an equivalent), such as mere instructions to implement an abstract idea on a computer: (1) whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished; (2) whether the claim invokes computers or other machinery merely as a tool to perform an existing process; and (3) the particularity or generality of the application of the judicial exception. The encoder having an artificial neural network is used to generally apply the abstract idea without placing any limits on how the encoder having an artificial neural network functions. Rather, these limitations only recite the outcome of calculating a numerical vector representing the summary information, and do not include any details about how the calculating is accomplished. See MPEP 2106.05(f) and the July 2024 Subject Matter Eligibility Examples and corresponding analysis. This requirement also merely indicates a field of use or technological environment in which the judicial exception is performed. Although the additional element “by an encoder having an artificial neural network” limits the identified judicial exceptions to calculating the numerical vector “by an encoder having an artificial neural network”, this type of limitation merely confines the use of the abstract idea to a particular technological environment (artificial neural networks) and thus fails to add an inventive concept to the claims. See MPEP 2106.05(h) and the July 2024 Subject Matter Eligibility Examples and corresponding analysis. This/these limitation(s) do/does not impose any meaningful limits on practicing the abstract idea, and therefore do/does not integrate the abstract idea into a practical application (see MPEP 2106.05(g)).
Applicant specifically argues that
4) “State-Aware Reweighting Mechanism...The claimed invention as amended introduces a state-aware reweighting mechanism that significantly enhances the system's ability to reflect real-time operational context within its computational matching process. In particular, this mechanism allows the system to dynamically adjust the contribution of each identifier-specific summary vector-namely, those derived from medical device identifiers, device user identifiers, and patient identifiers-based on the detected state of the terminal-to-advertise. Rather than assigning fixed weights, the system continuously monitors the terminal's operational state, identifying transitions between standby and use conditions and responding by recalibrating the relative weight of each identifier vector in real time. This capability addresses a fundamental computational challenge inherent in distributed, multi-terminal environments-namely, the contextual misalignment that can occur when different identifiers become more or less relevant depending on user engagement.
For example, when a terminal is in standby mode, patient information is often unavailable or incomplete, and thus the system increases the weight applied to vectors associated with the medical device and device user. These components tend to reflect static or system-level signals that are more stable and reliable under idle conditions. In contrast, when the terminal transitions into use mode-typically triggered by events such as a patient login, data input, or diagnostic test activation-the system dynamically increases the contribution of the patient identifier vector to reflect the now available, highly relevant context. This reweighting process is performed entirely through machine-implemented logic, operating at the system level in response to terminal signals rather than human input. These state transitions are captured in real time, and the corresponding vector weight adjustments are calculated dynamically before similarity scoring takes place.
As a result, the system is able to adapt its matching behavior to reflect the real-world engagement status of the terminal, improving both the relevance and the accuracy of the advertisement targeting process. Moreover, this capability is only achievable because the system maintains separate summary vectors for each identifier type. Without this separation, it would be computationally impossible to apply selective amplification or attenuation based on terminal status. The system's architectural commitment to identifier-specific decomposition, vectorization, and dynamic weighting thus forms a coherent pipeline that allows for fine- grained, context-sensitive control over the output. It avoids the pitfalls of static matching logic, which often leads to errors or inconsistencies in multi-user, multi-state environments. In sum, the state-aware reweighting mechanism represents a specific and non-conventional improvement in the art. It cannot be reduced to abstract mental processes or implemented through traditional database logic. Instead, it is a computational innovation that allows a machine to dynamically interpret and respond to its operating conditions, recalibrating its internal data processing mechanisms to produce accurate, stable, and contextually appropriate outputs. This approach not only enhances the practical utility of the claimed invention but also satisfies the criteria for subject matter eligibility under prevailing legal standards, as it materially improves the operation of the computer system in the context of distributed healthcare applications.”
Examiner respectfully disagrees with Applicant’s fourth argument.
First, “state-aware reweighting” is only included in independent claims 1 and 15 (and dependent claims 6-8, 10, and 20 by virtue of their dependency on one of these claims).
Second, the assertion that the claimed invention “continuously monitors the terminal's operational state, identifying transitions between standby and use conditions and responding by recalibrating the relative weight of each identifier vector in real time” is incommensurate with what is actually claimed. The claims merely require state knowledge, or knowledge of state transition. There is no continuous monitoring being claimed. Regardless, this is simply data analysis that is part of the abstract idea.
Third, there is no suggestion in the original disclosure that the claimed invention “addresses a fundamental computational challenge inherent in distributed, multi-terminal environments-namely, the contextual misalignment that can occur when different identifiers become more or less relevant depending on user engagement”, or that “when a terminal is in standby mode, patient information is often unavailable or incomplete” or that “this capability is only achievable because the system maintains separate summary vectors for each identifier type. Without this separation, it would be computationally impossible to apply selective amplification or attenuation based on terminal status”, or that the claimed process “avoids the pitfalls of static matching logic, which often leads to errors or inconsistencies in multi-user, multi-state environments”. These alleged problems/improvements would not be apparent to a PHOSTIA in light of the original specification. Per MPEP 2106.05(a) “If it is asserted that the invention improves upon conventional functioning of a computer, or upon conventional technology or technological processes, a technical explanation as to how to implement the invention should be present in the specification.” Per MPEP 2106.04(d)(1) “The specification need not explicitly set forth the improvement, but it must describe the invention such that the improvement would be apparent to one of ordinary skill in the art. Conversely, if the specification explicitly sets forth an improvement but in a conclusory manner (i.e., a bare assertion of an improvement without the detail necessary to be apparent to a person of ordinary skill in the art), the examiner should not determine the claim improves technology.”
Fourth, any advantages associated with performing the steps “entirely through machine-implemented logic, operating at the system level in response to terminal signals rather than human input” is merely an inherent advantage of implementing the claimed data analysis using general-purpose computers. Such advantages/improvements are not enough to infer patent eligibility.
Fifth, whereas the majority of Applicant’s arguments refer to alleged problems and solutions that are not discussed in the original disclosure, Applicant’s final argument does highlight the “improvement” actually discussed in the original specification. Applicant suggests that through the claimed data analysis steps, “the system is able to adapt its matching behavior to reflect the real-world engagement status of the terminal, improving both the relevance and the accuracy of the advertisement targeting process”. Applicant’s published disclosure similarly suggests that it is advantageous for advertisers/businesses to implement the claimed process for determining a product to advertise to a user because doing so can increase the level of personalization/customization of the advertisement provided to a user, can ensure the advertisement is considerate of the user’s needs regardless of the level of knowledge regarding their advertisement consumption propensity, and can increase the likelihood of the user engaging with the ad or purchasing an advertised product (see, for example, paragraphs [0002]-[0005] & [0033]-[0037] of Applicant’s published disclosure). These are non-technical subjective business advantages/improvements. At most, the ordered combination of claim elements is directed to a non-technical improvement to an abstract idea itself (e.g., an improved data-analyzing process for determining a product to be advertised to a user).
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.
v Claim(s) 1, 6-15, and 20 is/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.
Step 1:
Claim(s) 1 and 6-13is/are drawn to methods (i.e., a process), claim(s) 14 is/are drawn to non-transitory computer readable media (i.e., a machine/manufacture), and claim(s) 15 and 20 is/are drawn to apparatus (i.e., a machine/manufacture). As such, claims 1, 6-15, and 20 is/are drawn to one of the statutory categories of invention (Step 1: YES).
Step 2A - Prong One:
In prong one of step 2A, the claim(s) is/are analyzed to evaluate whether it/they recite(s) a judicial exception.
Claim 1 (representative of independent claim(s) 9, 11, 12, 13, 14, and 15) recites/describes the following steps;
receiving medical information including one or more attributes from one or more terminals, wherein the one or more terminals include a medical device terminal, a medical device user terminal, and a patient terminal, and the medical device terminal, the medical device user terminal, and the patient terminal are configured to process different input information
generating summary information extracted from the medical information on the basis of the one or more attributes;
generating a summary vector corresponding to the summary information using a pre- trained model
wherein the medical information is stored…and is tagged with the one or more attributes, the one or more attributes include at least a part of one or more test item identifiers performed on a patient, one or more test type identifiers performed on the patient, identifier of a medical device, identifier of a medical device user, and identifier of the patient
wherein the generating of the summary information comprises: generating test result summary information based on the test item identifier or test type identifier, respectively; generating test result summary information based on the identifier of the medical device generating test result summary information based on the identifier of medical device user: and generating test result summary information based on the identifier of the patient
calculating a degree of matching between the summary vector and one or more candidate products by using a preset function;
determining a product-to-be-advertised to be displayed…from among the candidate products, on the basis of the degree of matching and
displaying the product-to-be-advertised as a medical advertisement
wherein the calculating of the degree of matching further comprises setting weights on the summary vector
setting the weight of the summary vector for the identifier of the medical device and/or the summary vector for the identifier of the medical device user higher than the weight of the summary vector for the identifier of the patient when the terminal-to-advertise is in a standby state and setting the weight of the summary vector for the identifier of the patient higher than the weight of the summary vector for the identifier of the medical device and the summary vector for the identifier of the medical device user when the transition of the terminal-to-advertise from a standby state to a use state occurs within a predetermined period of time from the time of setting the weights
These steps, under its broadest reasonable interpretation, describe or set-forth a process for determining a product to advertise to a user based on received medical information. More specifically, these steps describe a process for determining a product to advertise based on medical information including attributes and identifiers, by at least generating summary information extracted from the medical information on the basis of the one or more attributes (and generating test result summary information based on the test item identifier or test type identifier, respectively; generating test result summary information based on the identifier of the medical device generating test result summary information based on the identifier of medical device user: and generating test result summary information based on the identifier of the patient), generating a summary vector corresponding to the summary information using a pre- trained model; calculating a degree of matching between the summary vector and one or more candidate products by using a preset function (including setting weights of the summary vector for the identifier of the medical device and/or the summary vector for the identifier of the medical device user higher than the weight of the summary vector for the identifier of the patient when the terminal-to-advertise is in a standby state and setting the weight of the summary vector for the identifier of the patient higher than the weight of the summary vector for the identifier of the medical device and the summary vector for the identifier of the medical device user when the transition of the terminal-to-advertise from a standby state to a use state occurs within a predetermined period of time from the time of setting the weights); and determining a product-to-be-advertised to be displayed from among the candidate products, on the basis of the degree of matching. This amounts to a commercial or legal interactions (including specifically, an advertising, marketing or sales activity or behavior). These limitations therefore fall within the “certain methods of organizing human activity” subject matter grouping of abstract ideas.
Additionally and/or alternatively, the above-recited steps of receiving medical information including attributes and identifiers, generating the summary information extracted from the medical information on the basis of the one or more attributes; generating a summary vector corresponding to the summary information using a pre-trained model; calculating a degree of matching between the summary vector and one or more candidate products by using a preset function (including setting of weights in the summary vector), and determining a product-to-be-advertised to be displayed…from among the candidate products, on the basis of the degree of matching, under their broadest reasonable interpretation, encompass a human manually (e.g., in their mind, or using paper and pen) performing each of these steps (e.g., because they each encompass one or more concepts performed in the human mind, such as one or more observations, evaluations, judgments, opinions), but for the recitation of generic computer components. If one or more claim limitations, under their broadest reasonable interpretation, covers performance of the limitation(s) in the mind but for the recitation of generic computer components, then it falls within the “mental processes” subject matter grouping of abstract ideas.
As such, the Examiner concludes that claim 1 recites an abstract idea (Step 2A – Prong One: YES).
Independent claim(s) 9, 11, 12, 13, 14, and 15 recite/describe nearly identical steps (and therefore also recite limitations that fall within this subject matter grouping of abstract ideas), and this/these claim(s) is/are therefore determined to recite an abstract idea under the same analysis.
Each of the depending claims likewise recite/describe these steps (by incorporation - and therefore also recite limitations that fall within this subject matter grouping of abstract ideas), and this/these claim(s) is/are therefore determined to recite an abstract idea under the same analysis. Any element(s) recited in a dependent claim that are not specifically identified/addressed by the Examiner under step 2A (prong two) or step 2B of this analysis shall be understood to be an additional part of the abstract idea recited by that particular claim. The same reasoning is similarly applicable to the limitations in the remaining dependent claims, and their respective limitations are not reproduced here for the sake of brevity.
Step 2A - Prong Two:
In prong two of step 2A, an evaluation is made whether a claim recites any additional element, or combination of additional elements, that integrate the exception into a practical application of that exception. An “addition element” is an element that is recited in the claim in addition to (beyond) the judicial exception (i.e., an element/limitation that sets forth an abstract idea is not an additional element). The phrase “integration into a practical application” is defined as requiring an additional element or a combination of additional elements in the claim to apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that it is more than a drafting effort designed to monopolize the exception.
The claim(s) recite the additional elements/limitations of
“performed by a computing device comprising one or more processors; and a memory storing one or more programs executed by the one or more processors…in the memory” (independent claims 1, 9, 11, 12 and 13)
“a non-transitory computer readable recording medium for executing” (independent claim 14)
“an apparatus…comprising one or more processors, wherein the one or more processors is configured to” (independent claim 15)
“a pre-trained model including an encoder having an artificial neural network” (independent claims 1, 9, 11, 12, 13, 14, and 15)
“on a terminal-to-advertise…displaying the product-to-be-advertised as a medical advertisement on the terminal-to-advertise, wherein the terminal-to-advertise is a terminal of the medical device, a terminal of the medical device user or a terminal of the patient” (independent claims 1, 9, 11, 12, 13, 14, and 15)
“wherein the instructions which, when executed by the one or more processors, further cause the one or more processors to” (dependent claim 20)
The requirement to execute the claimed steps/functions “performed by a computing device comprising one or more processors; and a memory storing one or more programs executed by the one or more processors…in the memory” (independent claims 1, 9, 11, 12 and 13) and/or “a non-transitory computer readable recording medium for executing” (independent claim 14) and/or “an apparatus…comprising one or more processors, wherein the one or more processors is configured to” (independent claim 15) and/or “wherein the instructions which, when executed by the one or more processors, further cause the one or more processors to” (dependent claim 20) is equivalent to adding the words “apply it” on a generic computer and/or mere instructions to implement the abstract idea on a generic computer. Applicant’s own disclosure explains that these elements may be embodied as a general-purpose computer (e.g., paragraph [0044] of the published disclosure which states “the embodiment described herein may have aspects of entirely hardware, partly hardware and partly software, or entirely software. The term "unit", "module", "device", "server'' or "system" used herein refers to computer related entity such as hardware, software or a combination thereof. For example, the unit, module, device, server or system may refer to hardware that makes up a platform in part or in whole and/or software such as an application for operating the hardware”, and paragraph [0087] of the published disclosure which states “The embodiments of the present disclosure may include a program for running the methods described herein on a computer and a computer-readable recording medium including the program. The computer-readable recording medium may include program instructions, local data files, local data structures, and the like, alone or in a combination thereof. The medium…may be known to and used by a person having ordinary skill in the field of computer software. Examples of the program instructions may include machine language code made by a compiler and a high-level language code executable by a computer using an interpreter or the like”). This/these limitation(s) do/does not impose any meaningful limits on practicing the abstract idea, and therefore do/does not integrate the abstract idea into a practical application (see MPEP 2106.05(f)).
The recitation of “a pre-trained model including an encoder having an artificial neural network” (independent claims 1, 9, 11, 12, 13, 14, and 15) provides nothing more than mere instructions to implement an abstract idea on a generic computer. See MPEP 2106.05(f) and the July 2024 Subject Matter Eligibility Examples and corresponding analysis. MPEP 2106.05(f) provides the following considerations for determining whether a claim simply recites a judicial exception with the words “apply it” (or an equivalent), such as mere instructions to implement an abstract idea on a computer: (1) whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished; (2) whether the claim invokes computers or other machinery merely as a tool to perform an existing process; and (3) the particularity or generality of the application of the judicial exception. The encoder having an artificial neural network is used to generally apply the abstract idea without placing any limits on how the encoder having an artificial neural network functions. Rather, these limitations only recite the outcome of calculating a numerical vector representing the summary information, and do not include any details about how the calculating is accomplished. See MPEP 2106.05(f) and the July 2024 Subject Matter Eligibility Examples and corresponding analysis.
The recited additional element(s) of “on a terminal-to-advertise…displaying the product-to-be-advertised as a medical advertisement on the terminal-to- advertise, wherein the terminal-to-advertise is a terminal of the medical device, a terminal of the medical device user or a terminal of the patient” (independent claims 1, 9, 11, 12, 13, 14, and 15) serves merely to generally link the use of the judicial exception to a particular technological environment or field of use. Specifically, it/they serve(s) to limit the application of the abstract idea to computing environments, such as distributed computing environments and/or the internet, where information is represented digitally, exchanged between computers (i.e., “terminals”) over a network, and presented using graphical user interfaces. This reasoning was demonstrated in Intellectual Ventures I LLC v. Capital One Bank (Fed. Cir. 2015), where the court determined "an abstract idea does not become nonabstract by limiting the invention to a particular field of use or technological environment, such as the Internet [or] a computer"). This/these limitation(s) do/does not impose any meaningful limits on practicing the abstract idea, and therefore do/does not integrate the abstract idea into a practical application (see MPEP 2106.05(g)).
The recitation of “a pre-trained model including an encoder having an artificial neural network” (independent claims 1, 9, 11, 12, 13, 14, and 15) also merely indicates a field of use or technological environment in which the judicial exception is performed. Although the additional element “by an encoder having an artificial neural network” limits the identified judicial exceptions to calculating the numerical vector “by an encoder having an artificial neural network”, this type of limitation merely confines the use of the abstract idea to a particular technological environment (artificial neural networks) and thus fails to add an inventive concept to the claims. See MPEP 2106.05(h) and the July 2024 Subject Matter Eligibility Examples and corresponding analysis. This/these limitation(s) do/does not impose any meaningful limits on practicing the abstract idea, and therefore do/does not integrate the abstract idea into a practical application (see MPEP 2106.05(g)).
The recited element(s) of “displaying the product-to-be-advertised as a medical advertisement on the terminal-to- advertise, wherein the terminal-to-advertise is a terminal of the medical device, a terminal of the medical device user or a terminal of the patient” (independent claims 1, 9, 11, 12, 13, 14, and 15), even if treated as an “additional” element for the purposes of this analysis, would simply append insignificant extra-solution activity to the judicial exception, (e.g., mere post-solution activity, such as data output, in conjunction with an abstract idea). The term “extra-solution activity” is understood as activities incidental to the primary process or product that are merely a nominal or tangential addition to the claim. The recited additional element(s) do are deemed “extra-solution” because such data output steps have long been held to be insignificant post-solution activity. This/these limitation(s) do/does not impose any meaningful limits on practicing the abstract idea, and therefore do/does not integrate the abstract idea into a practical application (see MPEP 2106.05(h) and (g)).
The recited element(s) of “receiving medical information including one or more attributes from one or more terminals, wherein the one or more terminals include a medical device terminal, a medical device user terminal, and a patient terminal, and the medical device terminal, the medical device user terminal, and the patient terminal are configured to process different input information…wherein the medical information…is tagged with the one or more attributes, the one or more attributes include at least a part of one or more test item identifiers performed on a patient, one or more test type identifiers performed on the patient, identifier of a medical device, identifier of a medical device user, and identifier of the patient” (independent claims 1, 9, 11, 12, 13, 14, and 15), even if treated as an “additional” element for the purposes of this analysis, would simply append insignificant extra-solution activity to the judicial exception, (e.g., mere pre-solution activity, such as data gathering, in conjunction with an abstract idea). The term “extra-solution activity” is understood as activities incidental to the primary process or product that are merely a nominal or tangential addition to the claim. The recited additional element(s) do are deemed “extra-solution” because all implementations of the abstract idea require such data to be gathered/received, and because such data gathering steps have long been held to be insignificant pre-solution activity. This/these limitation(s) do/does not impose any meaningful limits on practicing the abstract idea, and therefore do/does not integrate the abstract idea into a practical application (see MPEP 2106.05(h) and (g)).
Furthermore, although the claims recite a specific sequence of computer-implemented functions, and although the specification suggests certain functions may be advantageous for various reasons (e.g., business reasons), the Examiner has determined that the ordered combination of claim elements (i.e., the claims as a whole) are not directed to an improvement to computer functionality/capabilities, an improvement to a computer-related technology or technological environment, and do not amount to a technology-based solution to a technology-based problem. For example, Applicant’s published disclosure suggests that it is advantageous for advertisers/businesses to implement the claimed process for determining a product to advertise to a user because doing so can increase the level of personalization/customization of the advertisement provided to a user, can ensure the advertisement is considerate of the user’s needs regardless of the level of knowledge regarding their advertisement consumption propensity, and can increase the likelihood of the user engaging with the ad or purchasing an advertised product (see, for example, paragraphs [0002]-[0005] & [0033]-[0037] of Applicant’s published disclosure). These are non-technical subjective business advantages/improvements. At most, the ordered combination of claim elements is directed to a non-technical improvement to an abstract idea itself (e.g., an improved data-analyzing process for determining a product to be advertised to a user).
Dependent claims 6-8 fail to include any additional elements. In other words, each of the limitations/elements recited in respective dependent claims 6-8 is/are further part of the abstract idea as identified by the Examiner for each respective dependent claim (i.e. they are part of the abstract idea recited in each respective claim). For example, claim 6 recites “wherein in the generating of the summary vector, the summary information for each identifier is numerically distributed in a space of one or more dimensions using a pre-trained model, and a summary vector for each identifier is generated based on the distributed numerical values”. This is an abstract limitation which further sets forth the abstract idea encompassed by claim 6 (e.g., further defines the summary information). This limitation is not an “additional element”, and therefore it is not subject to further analysis under Step 2A- Prong Two or Step 2B. The same logic applies to each of the other dependent claims, whose limitations are not being repeated here for the sake of brevity and clarity.
The Examiner has therefore determined that the additional elements, or combination of additional elements, do not integrate the abstract idea into a practical application. Accordingly, the claim(s) is/are directed to an abstract idea (Step 2A – Prong two: NO).
Step 2B:
In step 2B, the claims are analyzed to determine whether any additional element, or combination of additional elements, is/are sufficient to ensure that the claims amount to significantly more than the judicial exception. This analysis is also termed a search for an "inventive concept." An "inventive concept" is furnished by an element or combination of elements that is recited in the claim in addition to (beyond) the judicial exception, and is sufficient to ensure that the claim as a whole amounts to significantly more than the judicial exception itself. Alice Corp., 134 S. Ct. at 2355, 110 USPQ2d at 1981 (citing Mayo, 566 U.S. at 72-73, 101 USPQ2d at 1966)
As discussed above in “Step 2A – Prong 2”, the requirement to execute the claimed steps/functions “performed by a computing device comprising one or more processors; and a memory storing one or more programs executed by the one or more processors…in the memory” (independent claims 1, 9, 11, 12 and 13) and/or “a non-transitory computer readable recording medium for executing” (independent claim 14) and/or “an apparatus…comprising one or more processors, wherein the one or more processors is configured to” (independent claim 15) and/or “wherein the instructions which, when executed by the one or more processors, further cause the one or more processors to” (dependent claim 20) is equivalent to adding the words “apply it” on a generic computer and/or mere instructions to implement the abstract idea on a generic computer. These limitations therefore do not qualify as “significantly more” (see MPEP 2106.05(f)).
As discussed above in “Step 2A – Prong 2”, the recitation of “a pre-trained model including an encoder having an artificial neural network” (independent claims 1, 9, 11, 12, 13, 14, and 15) is equivalent to adding the words “apply it” on a generic computer and/or mere instructions to implement the abstract idea on a generic computer. These limitations therefore do not qualify as “significantly more” (see MPEP 2106.05(f)).
As discussed above in “Step 2A – Prong 2”, the recited additional element(s) of “on a terminal-to-advertise…displaying the product-to-be-advertised as a medical advertisement on the terminal-to- advertise, wherein the terminal-to-advertise is a terminal of the medical device, a terminal of the medical device user or a terminal of the patient” (independent claims 1, 9, 11, 12, 13, 14, and 15) serves merely to generally link the use of the judicial exception to a particular technological environment or field of use. These limitations therefore do not qualify as “significantly more” (see MPEP 2106.05(g)).
As discussed above in “Step 2A – Prong 2”, the recitation of “a pre-trained model including an encoder having an artificial neural network” (independent claims 1, 9, 11, 12, 13, 14, and 15) also serves merely to generally link the use of the judicial exception to a particular technological environment or field of use. These limitations therefore do not qualify as “significantly more” (see MPEP 2106.05(g)).
As discussed above in “Step 2A – Prong 2”, the recited element(s) of “displaying the product-to-be-advertised as a medical advertisement on the terminal-to- advertise, wherein the terminal-to-advertise is a terminal of the medical device, a terminal of the medical device user or a terminal of the patient” (independent claims 1, 9, 11, 12, 13, 14, and 15) and “receiving medical information including one or more attributes from one or more terminals, wherein the one or more terminals include a medical device terminal, a medical device user terminal, and a patient terminal, and the medical device terminal, the medical device user terminal, and the patient terminal are configured to process different input information…wherein the medical information…is tagged with the one or more attributes, the one or more attributes include at least a part of one or more test item identifiers performed on a patient, one or more test type identifiers performed on the patient, identifier of a medical device, identifier of a medical device user, and identifier of the patient” (independent claims 1, 9, 11, 12, 13, 14, and 15), even if treated as “additional” elements for the purposes of this analysis, would simply append insignificant extra-solution activity to the judicial exception, (e.g., mere pre-solution activity, such as data gathering, in conjunction with an abstract idea; mere post-solution activity in conjunction with an abstract idea). These additional element(s), taken individually or in combination, additionally amount to well-understood, routine and conventional activities previously known to the industry, specified at a high level of generality, appended to the judicial exception. These additional elements, taken individually or in combination, are well-understood, routine and conventional to those in the field of marketing/advertising and/or medical product recommendation. These limitations therefore do not qualify as “significantly more”. (see MPEP 2106.05(d)). This conclusion is based on a factual determination. The determination that receiving data/messages over a network is well-understood, routine, and conventional is supported by Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362; TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014), and MPEP 2106.05(d)(II), which note the well-understood, routine, conventional nature of receiving data/messages over a network. Furthermore, Examiner takes Official Notice that these steps were well-understood, routine, and conventional at the effective filing date of the claimed invention. Furthermore, the lack of technical detail/description in Applicant’s own specification provides implicit evidence that these steps were well-understood, routine, and conventional.
Viewing the additional limitations in combination also shows that they fail to ensure the claims amount to significantly more than the abstract idea. When considered as an ordered combination, the additional components of the claims add nothing that is not already present when considered separately, and thus simply append the abstract idea with words equivalent to “apply it” on a generic computer and/or mere instructions to implement the abstract idea on a generic computer, generally link the abstract idea to a particular technological environment or field of use, append the abstract idea with insignificant extra solution activity associated with the implementation of the judicial exception, (e.g., mere data gathering, post-solution activity), and appended with well-understood, routine and conventional activities previously known to the industry.
Dependent claims 6-8 fail to include any additional elements. In other words, each of the limitations/elements recited in respective dependent claims 6-8 is/are further part of the abstract idea as identified by the Examiner for each respective dependent claim (i.e. they are part of the abstract idea identified by the Examiner to which each respective claim is directed).
The Examiner has therefore determined that no additional element, or combination of additional claims elements is/are sufficient to ensure the claim(s) amount to significantly more than the abstract idea identified above (Step 2B: NO).
Claim Rejections - 35 USC § 112
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, 6-15, and 20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, or for pre-AIA the applicant regards as the invention.
v Claims 1, 9, and 11-15 each recite “receiving medical information including one or more attributes from one or more terminals, wherein the one or more terminals include a medical device terminal, a medical device user terminal, and a patient terminal, and the medical device terminal, the medical device user terminal, and the patient terminal are configured to process different input information…generating summary information extracted from the medical information on the basis of the one or more attributes…wherein the medical information is…tagged with the one or more attributes, the one or more attributes include at least a part of one or more test item identifiers performed on a patient, one or more test type identifiers performed on the patient, identifier of a medical device, identifier of a medical device user, and identifier of the patient…wherein the generating of the summary information comprises: generating test result summary information based on the test item identifier or test type identifier, respectively; generating test result summary information based on the identifier of the medical device; generating test result summary information based on the identifier of medical device user; and generating test result summary information based on the identifier of the patient”. Claims 1 and 15 also recite “setting the weight of the summary vector for the identifier of the medical device and/or the summary vector for the identifier of the medical device user higher than the weight of the summary vector for the identifier of the patient when the terminal-to-advertise is in a standby state and setting the weight of the summary vector for the identifier of the patient higher than the weight of the summary vector for the identifier of the medical device and the summary vector for the identifier of the medical device user when the transition of the terminal-to-advertise from a standby state to a use state occurs within a predetermined period of time from the time of setting the weights”. One of ordinary skill in the art would not be reasonably apprised of the scope of the invention. Specifically, the claims appear to contradict themselves by suggesting the received medical information may include only one attribute and may be received from only one terminal (see “receiving medical information including one or more attributes from one or more terminals, wherein the one or more terminals include a medical device terminal, a medical device user terminal, and a patient terminal, and the medical device terminal, the medical device user terminal, and the patient terminal are configured to process different input information…generating summary information extracted from the medical information on the basis of the one or more attributes… wherein the medical information is…tagged with the one or more attributes”), and may be tagged with only one of the recited identifiers (see “the one or more attributes include at least a part of one or more test item identifiers performed on a patient, one or more test type identifiers performed on the patient, identifier of a medical device, identifier of a medical device user, and identifier of the patient”), yet the generating of the summary information is required to comprise generating different test result summaries based on a plurality of different identifiers (see “generating test result summary information based on the test item identifier or test type identifier, respectively; generating test result summary information based on the identifier of the medical device; generating test result summary information based on the identifier of medical device user; and generating test result summary information based on the identifier of the patient”. It is unclear how one could generate each of these summary information based on each of these identifiers while also suggesting the system may only receive medical information that includes only one attribute and one identifier. The system must receive a plurality medical information that comprises all of these identifiers. This apparent contradiction also results in there being insufficient antecedent basis for the subsequent recitations of “generating test result summary information based on the test item identifier or test type identifier, respectively; generating test result summary information based on the identifier of the medical device; generating test result summary information based on the identifier of medical device user; and generating test result summary information based on the identifier of the patient” (e.g., because the claim language previously suggests each of these identifiers may not exist and/or may not have been received). Therefore, the claim is indefinite for failing to particularly and distinctly claim the subject matter which the application regards as the invention.
For the purpose of examination, the phrase “receiving medical information including one or more attributes from one or more terminals, wherein the one or more terminals include a medical device terminal, a medical device user terminal, and a patient terminal, and the medical device terminal, the medical device user terminal, and the patient terminal are configured to process different input information…generating summary information extracted from the medical information on the basis of the one or more attributes…wherein the medical information is…tagged with the one or more attributes, the one or more attributes include at least a part of one or more test item identifiers performed on a patient, one or more test type identifiers performed on the patient, identifier of a medical device, identifier of a medical device user, and identifier of the patient…wherein the generating of the summary information comprises: generating test result summary information based on the test item identifier or test type identifier, respectively; generating test result summary information based on the identifier of the medical device; generating test result summary information based on the identifier of medical device user; and generating test result summary information based on the identifier of the patient” will be interpreted as being “receiving medical information including a plurality of attributes from a plurality of terminals, wherein the plurality of terminals include a medical device terminal, a medical device user terminal, and a patient terminal, and the medical device terminal, the medical device user terminal, and the patient terminal are configured to process different input information…generating summary information extracted from the medical information on the basis of the plurality of attributes…wherein the medical information is…tagged with the plurality of attributes, the plurality of attributes include at least one or more identifiers of a medical device, one or more identifiers of a medical device user, and an identifier of the patient…wherein the generating of the summary information comprises: generating test result summary information based on the one or more test item identifiers or one or more test type identifiers, respectively; generating test result summary information based on the one or more identifiers of the medical device; generating test result summary information based on the one or more identifiers of the medical device user; and generating test result summary information based on the identifier of the patient.”
Each of the dependent claims are similarly rejected by virtue of their dependency on one of these claims.
Indication of Novel and Non-Obvious Subject Matter
Independent claims 1, 9, 11, 12, 13, 14, and 15 recite novel and non-obvious subject matter. Each of the dependent claims recite novel and non-obvious subject matter by virtue of their dependency on one of these claims.
The following is an examiner’s statement of reasons for indication of novel and non-obvious subject matter:
The closest prior art of record is Rose et al. (U.S. PG Pub No. 2017/0287044, October 5, 2017 - hereinafter "Rose”); Shrivastava et al. (U.S. PG Pub No. 2021/0365965, November 25, 2021 - hereinafter "Shrivastava”); Serbinis et al. (U.S. PG Pub No. 2024/0028654 January 25, 2024 - hereinafter "Serbinis”); Seo (U.S. PG Pub No. 2021/0183483 June 17, 2021 - hereinafter "Seo”); Rao et al. (U.S. PG Pub No. 2022/0335489 October 20, 2022 - hereinafter "Rao”); Zhang (U.S. PG Pub No. 2022/0223245 July 14, 2022 - hereinafter "Zhang”); Onoro Rubio (U.S. PG Pub No. 2019/0205964 July 4, 2019 - hereinafter "Onoro Rubio”);
Rose discloses receiving medical information including one or more attributes from one or more terminals; generating summary information extracted from the medical information on the basis of the one or more attributes; generating a summary vector corresponding to the summary information using a pre- trained model; calculating a degree of matching between the summary vector and one or more candidate products by calculating dot product between the summary vector and product vectors; and determining a product-to-be-advertised to be displayed on a terminal-to-advertise, from among the candidate products, on the basis of the degree of matching.
Shrivastava discloses a recommender system using captured medical information and generating summary information as a numerical vector and further discloses wherein the summary information is a numerical vector calculated by an encoder having an artificial neural network.
Serbinis discloses receiving medical information including one or more attributes from one or more terminals; generating summary information extracted from the medical information on the basis of the one or more attributes; generating a summary vector corresponding to the summary information using a pre- trained model; calculating a degree of matching between the summary vector and one or more candidate products by calculating cosine similarity between the summary vector and product vectors; and determining a product-to-be-advertised to be displayed on a terminal-to-advertise, from among the candidate products, on the basis of the degree of matching.
Seo discloses receiving medical information including one or more attributes from one or more terminals; generating summary information extracted from the medical information on the basis of the one or more attributes; generating summary vectors corresponding to the summary information using a pre-trained model; calculating a degree of matching between the summary vectors and one or more candidate products by calculating cosine similarity between the summary vectors and product vectors; and determining a product-to-be-advertised to be displayed on a terminal-to-advertise, from among the candidate products, on the basis of the degree of matching.
Rao discloses a recommender system for recommending products to users based on user and product vectors/embeddings, and calculating degrees of matching between these vectors/embeddings using a preset function. Rao further discloses wherein the preset function includes an identity function, a step function, a Rectified Linear Unit function (ReLU), a sigmoid function, a K-means clustering algorithm, and/or a Support Vector Machine (SVM).
Zhang discloses receiving medical information including one or more attributes from one or more terminals; generating summary information extracted from the medical information on the basis of the one or more attributes; generating summary vectors corresponding to the summary information using a pre-trained model; calculating a degree of matching between the summary vectors and one or more candidate drugs by calculating cosine similarity between the summary vectors and product vectors; and determining a drug to be recommended to the user from among candidate druges, on the basis of the degree of matching.
Onoro Rubio discloses receiving medical information including one or more attributes from one or more terminals; generating summary information extracted from the medical information on the basis of the one or more attributes; generating summary vectors corresponding to the summary information using a pre-trained model; calculating a degree of matching between the summary vectors and one or more candidate products by calculating cosine similarity between the summary vectors and product vectors; and determining a product-to-be-advertised to be displayed on a terminal-to-advertise, from among the candidate products, on the basis of the degree of matching.
With respect to Independent claims 1, 14, and 15 (as well as dependent claims 6-8 and 10 by virtue of their dependency upon claim 1, and dependent claim 20 by virtue of its dependency upon claim 15), the closest prior art of record taken either individually or in combination with other prior art of record fails to teach or suggest “setting the weight of the summary vector for the medical device identifier and/or the summary vector for the medical device user identifier higher than the weight of the summary vector for the patient identifier when the terminal-to-advertise is in a standby state and setting the weight of the summary vector for the patient identifier higher than the weight of the summary vector for the medical device identifier and the summary vector for the medical device user identifier when the transition of the terminal-to-advertise from a standby state to a use state occurs within a predetermined period of time from the time of setting the weights” and/or “wherein the generating of the summary information comprises: generating test result summary information based on the test item identifier or test type identifier, respectively; generating test result summary information based on the identifier of the medical device; generating test result summary information based on the identifier of medical device user; and generating test result summary information based on the identifier of the patient” (i.e., wherein the summary information is generated based on respective summary information based on each of these identifiers), in combination with each of the other limitations required by the claims. While each of these individual features may be known per se, there is no teaching or suggestion absent applicants’ own disclosure to combine these features other than with impermissible hindsight.
With respect to Independent claims 9 and 14, although the closest prior art discloses using a generation time of each medical information as a factor in determining products/treatments to recommend for a user (and sometimes weighting certain information based on this generation time), and although the prior art discloses wherein calculating a degree of matching between the summary vector and one or more candidate product vectors, the closest prior art of record taken either individually or in combination with other prior art of record fails to teach or suggest “wherein the calculating of the degree of matching comprises: setting a first advertising weight for the summary vector based on a generation time of each medical information; and calculating the degree of matching by dot producting the first advertising weight with the summary vector”. Dot producting the first advertising weight with the summary vector is distinct from dot producting a weighted summary vector with a candidate product vector, or dot producting a summary vector with a weighted candidate product vector, as the claim language explicitly requires dot producting the first advertising weight with the summary vector. The closest prior art of record taken either individually or in combination with other prior art of record fails to teach or suggest this feature in combination with each of the other limitations required by claim 9. The closest prior art also does not teach or suggest “wherein the generating of the summary information comprises: generating test result summary information based on the test item identifier or test type identifier, respectively; generating test result summary information based on the identifier of the medical device; generating test result summary information based on the identifier of medical device user; and generating test result summary information based on the identifier of the patient” (i.e., wherein the summary information is generated based on respective summary information based on each of these identifiers), in combination with each of the other limitations required by the claim. While each of these individual features may be known per se, there is no teaching or suggestion absent applicants’ own disclosure to combine these features other than with impermissible hindsight.
With respect to Independent claims 11 and 14, although the closest prior art discloses using a generation time of each medical information as a factor in determining products/treatments to recommend for a user (and sometimes weighting certain information based on this generation time), and although the prior art discloses wherein calculating a degree of matching between the summary vector and one or more candidate product vectors, the closest prior art of record taken either individually or in combination with other prior art of record fails to teach or suggest “wherein the calculating of the degree of matching comprises: setting a third advertising weight for a test item based on whether a matching ratio between a value of the test item included in a certain medical information and a value of the test item included in the rest of the medical information exceeds a predetermined threshold; and calculating the degree of matching by dot producting the third advertising weight with the summary vector”. Dot producting the third advertising weight with the summary vector is distinct from dot producting a weighted summary vector with a candidate product vector, or dot producting a summary vector with a weighted candidate product vector, as the claim language explicitly requires dotproducting the third advertising weight with the summary vector. The closest prior art of record taken either individually or in combination with other prior art of record fails to teach or suggest this feature in combination with each of the other limitations required by claim 11. The closest prior art also does not teach or suggest “wherein the generating of the summary information comprises: generating test result summary information based on the test item identifier or test type identifier, respectively; generating test result summary information based on the identifier of the medical device; generating test result summary information based on the identifier of medical device user; and generating test result summary information based on the identifier of the patient” (i.e., wherein the summary information is generated based on respective summary information based on each of these identifiers), in combination with each of the other limitations required by the claims. While each of these individual features may be known per se, there is no teaching or suggestion absent applicants’ own disclosure to combine these features other than with impermissible hindsight.
Independent claims 12, 13, and 14 require “wherein the generating of the summary information comprises: generating test result summary information based on the test item identifier or test type identifier, respectively; generating test result summary information based on the identifier of the medical device; generating test result summary information based on the identifier of medical device user; and generating test result summary information based on the identifier of the patient” (i.e., wherein the summary information is generated based on respective summary information based on each of these identifiers), in combination with each of the other limitations required by the claims. Although the prior art discloses “receiving medical information including one or more attributes from one or more terminals”, further discloses “generating summary information extracted from the medical information on the basis of the one or more attributes”, further discloses “generating a summary vector corresponding to the summary information using a pre- trained model”, further discloses “wherein the attributes include at least a part of one or more test item identifiers performed on a patient, one or more test type identifiers performed on a patient, medical device identifiers, medical device user identifiers, and patient identifiers”, the closest prior art of record taken either individually or in combination with other prior art of record fails to teach or suggest performing these steps while also generating of the summary information by generating test result summary information based on the test item identifier or test type identifier; generating test result summary information based on the identifier of medical device; generating test result summary information based on the identifier of a medical device user; and generating test result summary information based on the identifier of the patient. While each of these individual features may be known per se, there is no teaching or suggestion absent applicants’ own disclosure to combine these features other than with impermissible hindsight.
Conclusion
No claim is allowed
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JAMES M DETWEILER whose telephone number is (571)272-4704. The examiner can normally be reached on Monday-Friday from 8 AM to 5 PM ET.
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/JAMES M DETWEILER/Primary Examiner, Art Unit 3621