DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
This action is in response to the reply filed 2/10/2026.
Claims 4-5 were canceled and claims 21-22 were added 2/10/2026.
Claims 1, 6-7, 18-19 were amended 2/10/2026.
Claims 1-3, 6-22 are currently pending and have been examined.
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 1-3, 6-22 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 1-3, 6-22 are drawn to a method, computing system and a non-transitory computer-readable storage medium which are statutory categories of invention (Step 1: YES).
Independent claims 1, 19, and 20 recite: receiving a prompt, in performing one clinical task, wherein the one clinical task is (i) generating a report of a patient's medical records (ii) guiding a patient through a care plan, (iii) creating patient care guidelines based on a patient's health profile, (iii) identifying patients requiring follow-up at a hospital, (v) identifying changes in a standard of care for a disease setting, or (vi) evaluating unstructured data associated with a patient to identify a cohort of similar patients; identifying based on content of the prompt, a domain from a plurality of domains, wherein each domain in the plurality of domains is associated with a corresponding repository of data from among a plurality of repositories, selecting and the corresponding repository of data based on the identified domain; in response to receiving the prompt, generating, a natural- language response that is responsive to the prompt and is based on an analysis of the corresponding repository of data and providing the natural-language response that is distinct.
The recited limitations, as drafted, under their broadest reasonable interpretation, cover certain methods of organizing human activity between a provider and a patient, as reflected in the specification, which states that “For instance, in some embodiments, an agent module is configured to obtain one or more physician reports (e.g., obtain on a recurring and/or periodic, or non-period basis, such as after every scan is performed at hospital, etc.) and applies one or more models 228 in detect one or more patients that need further follow up (e.g., due to an anomaly detected after the scan, etc.). In some embodiments, this agent module also has the ability to generate a request (e.g., communication to the patient and/or medical provider) for the follow up order to direct the patient to the relevant physician and a reminder app to ensure the patients comes back for further care” (see: specification paragraph 425). If a claim limitation, under its broadest reasonable interpretation, covers managing personal behavior or relationships or interactions between people, then it falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. The present claims cover certain methods of organizing human activity because they address “In particular, it is no longer reasonable for an oncologist to be familiar with all new research in the field of cancer care. Similarly, it is extremely challenging for an oncologist to be able to manually analyze the medical records and outcomes of thousands or millions of cancer patients each time they want to make a specific treatment recommendation regarding a particular patient they are treating. As an initial matter, oncologists often do not even have access to health information from institutions other than their own…. Every patient has health information that includes hundreds or even thousands of data elements. When including sequencing information in the health information to be accessed and analyzed, such as from next-generation sequencing, the volume of health information that could be analyzed grows intensely.” (see: specification paragraph 4). Accordingly, the claims recite an abstract idea(s) (Step 2A Prong One: YES).”
The judicial exception is not integrated into a practical application. The claims are abstract but for the inclusion of the additional elements including “at a first computing system in communication with a machine-learning model configured to assist”, “machine-learning model”, “machine learning model of a plurality of machine-learning models”, “second computing system”, “computing system”, “control circuitry”, “memory”, “non-transitory computer-readable storage medium” are recited at a high level of generality (e.g., that the generating, analyzing and displaying of healthcare data is performed using generic computer components with instructions are executed to perform the claimed limitations). Such that they amount to no more than mere instructions to apply the exception using generic computer components. See: MPEP 2106.05(f).
Hence, the additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Accordingly, the claims are directed to an abstract idea (Step 2A Prong Two: NO).
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, using the additional elements to perform the abstract idea amounts to no more than mere instructions to apply the exception using generic components. Mere instructions to apply an exception using a generic component cannot provide an inventive concept. See MPEP 2106.05(f).
Further, the claimed additional elements, identified above, are not sufficient to amount to significantly more than the judicial exception because they are generic components that are configured to perform well-understood, routine, and conventional activities previously known to the industry. See MPEP 2106.05(d). Said additional elements are recited at a high level of generality and provide conventional functions that do not add meaningful limits to practicing the abstract idea. The originally filed specification supports this conclusion at Figure 1, Figure 2A, Figure 24 and
Paragraph 54, where “The present disclosure describes, among other things, a platform for generating, deploying, and using task-specific orchestrations (e.g., task-specific agents) that include taskspecific machine-learning models (e.g., language models, transformer models, and other types of models) for specific tasks and/or within specific domains. The platform may include a plurality of individual task-specific orchestrations that may operation independently or in combination to return accurate and relevant information (e.g., identifying target cohorts, clinical tiial information, and/or members of target populations). In some embodiments, each task-specific orchestration (or agent) may include one or more machine-learning models, such as a language model trained and/or fine-tuned on a particular domain. The platform may also include one or more composite orchestrations (e.g., composite agents) that give instructions to, and combine results from, a plurality of task-specific orchestrations configured for different tasks.”
Paragraph 240, where “Figure 24 is a flow diagram illustrating a method 2400 of performing a clinical task in accordance with some embodiments. The method 2400 is performed at a computing system (e.g., a client device, server system, and/or service platform) having one or more processors (e.g., the CPUs 202 and/or 302) and memory (e.g., the memory 218 and/or 310). In some embodiments, the memory stores one or more programs configured for execution by the one or more processors. At least some of the operations shown in Figure 24 correspond to instructions stored in a computer memory or a computer-readable storage medium. In some embodiments, the computing system is the platform 100, the client device(s) 102, and/or the server system 106.”
Paragraph 242, where “The computing system receives (2402) a prompt. In some embodiments, the computing system is in communication with a machine-learning model (e.g., a model 228) that was trained to assist in performing one clinical task, such as by storing the model 228 at the first computing system or via a communication network 104.”
Paragraph 247, where “In some embodiments, each respective repository of data from among a plurality of repositories is associated with a corresponding domain in the plurality of domains. In some embodiments, the machine-learning model is selected by a conditional logic from among multiple available machine-learning models based on content of the prompt. In some embodiments, a first node includes a corresponding logic 6112 that evaluates an intent inferred from the prompt and identifies a corresponding domain associated with the intent. In some embodiments, generating the report of the patient's medical records includes de-identifying personally identifiably information from the patient's medical records in accordance with one or more rules defined by task-specific machine-learning model.”
Paragraph 252, where “The computing system provides (2406) the natural language response to a second computing system that is distinct from the computing system. For example, the computing system causes the natural language response to be displayed at a remote display”
Paragraph 493, where “In another aspect, some embodiments include a computing system ( e.g., the platform 100, the client device 102, or the server system 106) including control circuitry (e.g., the CPUs 302) and memory (e.g., the memory 310) coupled to the control circuitry, the memory storing one or more sets of instructions configured to be executed by the control circuitry, the one or more sets of instructions including instructions for performing one or more of the methods described herein (e.g., the methods 1300, 2100, 2200, 2300, 2400, 2500, 2600, 2700, 2800, Al-A30, Bl-B6, Cl-C20, Dl-D20, El-E13, Fl-Fl l, GI, Hl-H20, Il-I2, JI, Kl-KIO, LlL17, Ml-M7, Nl-N16, and 01-012 above).”
Paragraph 494, where “In yet another aspect, some embodiments include a non-transitory computer-readable storage medium st01ing one or more sets of instructions for execution by control circuitry of a computing system, the one or more sets of instructions including instructions for performing one or more of the methods described herein (e.g., the methods 1300, 2100, 2200, 2300, 2400, 2500, 2600, 2700, 2800, Al-A30, Bl-B6, Cl-C20, Dl-D20, El-E13, Fl-Fl l, GI, Hl-H20, Il-I2, JI, Kl-KIO, Ll-L17, Ml-M7, Nl-N16, and 01-012 above).”
Paragraph 495, where “Various types of models and algorithms may be used with the agents and components disclosed herein. In some embodiments, a model is a supervised machine learning algorithm. Nonlimiting examples of supervised learning algorithms include, but are not limited to, logistic regression, neural networks, support vector machines, Naive Bayes algorithms, nearest neighbor algorithms, random forest algorithms, decision tree algorithms, boosted trees algorithms, multinomial logistic regression algorithms, linear models, linear regression, GradientBoosting, mixture models, hidden Markov models, Gaussian NB algorithms, linear discriminant analysis, or any combinations thereof. In some embodiments, a model is a multinomial classifier algorithm. In some embodiments, a model is a 2-stage stochastic gradient descent (SOD) model. In some embodiments, a model is a deep neural network (e.g., a deepand-wide sample-level classifier).”
Paragraph 496, where “In some embodiments, a model is, or includes, a neural network (e.g., a convolutional neural network and/or a residual neural network). Neural network algorithms, also known as artificial neural networks (ANNs), include convolutional and/or residual neural network algorithms (deep learning algorithms). Neural networks can be machine learning algorithms that may be trained to map an input data set to an output data set, where the neural network comprises an interconnected group of network nodes organized into multiple layers of network nodes. For example, the neural network architecture may comprise at least an input layer, one or more hidden layers, and an output layer. The neural network may comprise any total number of layers, and any number of hidden layers, where the hidden layers function as trainable feature extractors that allow mapping of a set of input data to an output value or set of output values. As used herein, a deep learning algorithm can be a neural network comprising a plurality of hidden layers, e.g., two or more hidden layers.”
Viewing the limitations as an ordered combination, the claims simply instruct the additional elements to implement the concept described above in the identification of abstract idea with route, conventional activity specified at a high level of generality in a particular technological environment.
Hence, the claims as a whole, considering the additional elements individually and as an ordered combination, do not amount to significantly more than the abstract idea (Step 2B: NO).
Dependent claims 2-3, 6-22 when analyzed as a whole, considering the additional elements individually and/or as an ordered combination, are held to be patent ineligible under 35 U.S.C. 101 because the additional recited limitations fail to establish that the claims are directed to an abstract idea without significantly more. Claim 2-3, 6-22 further recite using the generic machine learning model on the generic computing devices to generate data to be output to a provider to treat a patient and calculating similarities between prompts and domains that do not provide significantly more to the abstract idea and are part of the abstract idea as shown in the parent claims above.
Claims 6 and 7 further recite “multiple available machine-learning models” and “task-specific machine-learning model” which are recited at a high level of generality (e.g., that the reporting and identifying of patient data are performed using generic machine learning models implemented on generic computer components with instructions are executed to perform the claimed limitations) as shown in the specification paragraphs 495-496. Such that they amount to no more than mere instructions to apply the exception using generic computer components. See: MPEP 2106.05(f).
These claims fail to remedy the deficiencies of their parent claims above, and therefore rejected for at least the same rationale as applied to their parent claims above, and incorporated herein.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1-3, 6-21 is/are rejected under 35 U.S.C. 103 as being unpatentable over Pang (US 2022/0293272 A1) in view of Khan (US 20210313049 A1).
CLAIM 1-
Pang teaches the limitations of:
Receiving a prompt, at a first computing system in communication with a machine-learning model configured to assist in performing one clinical task, wherein the one clinical task is: (i) generating a report of a patient's medical records (Pang teaches that the computing system uses a machine learning model that is trained with data to collect patient history to create a collection of patient’s history (i.e., patient medical records) that can be outputted as a report to assist clinical decision making(para [0074-0076, 0085, 0092]))
(ii) guiding a patient through a care plan (Pang teaches that the machine learning model is trained based on recommendations of drug prescriptions to determine a care plan (para [0108, 0109, 0087]), Figure 8)
(iii) creating patient care guidelines based on a patient’s health profile (Pang teaches evaluating contraindications (i.e., guidelines) based on the patient data and risk measurements of the therapy (para [0109, 0073]))
(v) identifying changes in a standard of care for a disease setting (Pang teaches that there is a patient summary that includes risk, symptoms and adherence of the care plan for the patient (i.e., changes are monitored) (para [0134, 0088, 0104] Figures 7-8))
Or (vi) evaluating unstructured data associated with a patient to identify a cohort of similar patients (Pang teaches that the computing system uses a machine learning model that is trained with data to collect patient history to create a collection of patient’s history (i.e., patient medical records) that can be outputted as a report to assist clinical decision making and can include a multitude of patient medical records that may have similarities (para [0074-0076, 0085, 0092, 0123]))
in response to receiving the prompt, generating, at the first computing system, a natural- language response that is responsive to the prompt and is based on an analysis by the machine-learning model of the corresponding repository of data; and (Pang teaches that based on the traing of the machine learning model based on semantic text provided (i.e., natural language) that is relevant to the inputted data and outputted as a recommendation in human interpreted text (i.e., natural language) (para [0082, 0094, 0110], Figure 8)
Pang teaches using a network of computing systems (i.e., paragraph 76), but does not explicitly teach, however Khan teaches:
providing the natural-language response to a second computing system that is distinct from the first computing system (Khan teaches that the system’s communication modalities ensure communication between multiple devices such as between users regarding a patient and their care and can include the text data regarding a patient from a machine learning model (para [0077-0078, 0063-0064, 0067], Figure 8))
identifying, based on content of the prompt, a domain from a plurality of domains, wherein each domain in the plurality of domains is associated with a corresponding repository of data from among a plurality of repositories and a corresponding machine-learning model of a plurality of machine-learning models; (Khan teaches that the artificial intelligence engine sorts through the collected data through semantic and structural normalization with rules that filter out clinical and genetic groupings (a plurality of domains associated with a repository of data) and multiple models to identify the content of the alerts (content of the prompt) and determines which data to present to the user from the data and that the data is sorted using multiple machine learning engines (para [0107, 0111-0112, 0074]))
selecting the machine-learning model and the corresponding repository of data based on the identified domain (Khan teaches that the rules queries can be selected for the machine learning algorithms in order to perform filtering of specific data results including clinical, genetic (i.e., domains) (para [0116-0118], Figure 12))
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to modify the healthcare system of Pang to integrate the application of multiple computing systems communicating healthcare data of Khan with the motivation of improving user usability of patient workflow data for a healthcare provider (see: Khan, abstract).
CLAIM 2-
Pang in view of Khan teach the limitations of claim 1. Regarding claim 2, Pang further teaches:
wherein the one clinical task is generating a summary report of a patient's medical records, and the machine-learning model is trained using medical records of patients other than the patient (Pang teaches that the computing system uses a machine learning model that is trained with data to collect patient history to create a collection of patient’s history (i.e., patient medical records) that can be outputted as a report to assist clinical decision making and can include a multitude of patient medical records (para [0074-0076, 0085, 0092, 0123]))
CLAIM 3-
Pang in view of Khan teach the limitations of claim 1. Regarding claim 3, Pang further teaches:
wherein the one clinical task is guiding a patient through a first care plan and the machine-learning model is trained using a second care plan different from the first care plan (Pang teaches that the machine learning model is trained based on recommendations of drug prescriptions on past data (i.e., a past care plan, second care plan) which can output a different care plan (i.e., present care plan, first care plan) (para [0108, 0109, 0087]))
CLAIM 6-
Pang in view of Khan teach the limitations of claim 1. Regarding claim 6, Khan further teaches:
wherein the machine-learning model is selected by a conditional logic from among the plurality of machine-learning models based on the identified domain (Khan teaches that the rules queries including statistical rules (i.e., logic) can be selected for the machine learning algorithms in order to perform filtering of specific data results including clinical, genetic (i.e., domains) (para [0116-0118], Figure 12))
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to modify the healthcare system of Pang to integrate the application of multiple computing systems communicating healthcare data of Khan with the motivation of improving user usability of patient workflow data for a healthcare provider (see: Khan, abstract).
CLAIM 7-
Pang in view of Khan teach the limitations of claim 1. Regarding claim 7, Pang further teaches:
wherein generating the report of the patient's medical records comprises deidentifying personally identifiably information from the patient's medical records in accordance with one or more rules (Pang teaches that the system anonymizes the patient data in the machine learning system’s inference outputs quantified through cross-entropy loss (i.e., defined rules) and the machine learning system is healthcare data task specific in its anonymization and tokenization process (para [0090, 0095, 0080], Figure 3))
CLAIM 8-
Pang in view of Khan teach the limitations of claim 1. Regarding claim 8, Pang further teaches:
wherein generating the report of the patient's medical records comprises determining demographic information associated with the patient (Pang teaches that demographic data is utilized and determined for diagnosis in the machine learning system when generating reports (para [0096-97, 00134]))
CLAIM 9-
Pang in view of Khan teach the limitations of claim 1. Regarding claim 9, Pang further teaches:
wherein generating the report of the patient's medical records comprises determining a past medical condition of the patient (Pang teaches that the computing system uses a machine learning model that is trained with data to collect patient history to create a collection of patient’s history (i.e., patient medical records) that can be outputted as a report to assist clinical decision making(para [0074-0076, 0085, 0092]))
CLAIM 10-
Pang in view of Khan teach the limitations of claim 1. Regarding claim 10, Pang further teaches:
wherein generating the report of the patient's medical records comprises determining one or more care plans for the patient (Pang teaches that multiple recommendations and treatment protocols can be output (para [0035, 0116]))
CLAIM 11-
Pang in view of Khan teach the limitations of claim 1. Regarding claim 11, Pang further teaches:
wherein generating the report of the patient's medical records comprises determining one or more therapies administered to the patient (Pang teaches that multiple recommendations and treatment protocols can be output including prescriptions to be issued to the patient and whether the patient has any contraindications of existing medications (para [0035, 0116, 0087, 0109]))
CLAIM 12-
Pang in view of Khan teach the limitations of claim 1. Regarding claim 12, Pang further teaches:
wherein generating the report of the patient's medical records comprises determining a summary of specific care instructions for the patient (Pang teaches that there is a patient summary that includes risk, symptoms and adherence of the care plan for the patient (para [0134, 0088] Figures 7-8))
CLAIM 13-
Pang in view of Khan teach the limitations of claim 1. Regarding claim 12, Pang further teaches:
wherein guiding the patient through the care plan comprises evaluating one or more clinical publications associated with a different care plan (Pang teaches that the machine learning model includes feature extraction of data from different clinical information EHRs of past care plans (i.e., different care plan) when outputting their recommendation (para [0019, 0077, 0096]))
CLAIM 14-
Pang in view of Khan teach the limitations of claim 1. Regarding claim 14, Pang further teaches:
wherein guiding the patient through the care plan comprises conducting an assessment of the patient (Pang teaches that there is a patient summary that includes risk, symptoms and adherence of the care plan for the patient (i.e., adherence is the assessing of the patient following the care plan) that the clinician can consult with the patient (para [0134, 0088, 0086] Figures 7-8))
CLAIM 15-
Pang in view of Khan teach the limitations of claim 14. Regarding claim 15, Pang further teaches:
wherein the assessment comprises one or more prompts configured to elicit information from the patient (Pang teaches that the patient assessment includes prompts for further questions to analyze the previous question answers (para [0083, Figure 6))
CLAIM 16-
Pang in view of Khan teach the limitations of claim 14. Regarding claim 16, Pang further teaches:
wherein the assessment comprises a biometric assessment of the patient (Pang teaches an assessment of patient lifestyle risk factors (i.e., health parameters are the biometric assessment as taught by the specification paragraph 422) (para [0073]))
CLAIM 17-
Pang in view of Khan teach the limitations of claim 1. Regarding claim 17, Pang further teaches:
wherein creating the patient care guidelines based on the patient's health profile comprises determining one or more discordances between a first therapy (Pang teaches evaluating contraindications (i.e., guidelines) based on the patient data and risk measurements of the therapy (para [0109, 0073]))
and one or more biometrics or health parameters associated with the patient's medical records (Pang teaches an assessment of patient lifestyle risk factors (i.e., health parameters are the biometric assessment as taught by the specification paragraph 422) which elicits further questions when making a therapy recommendation (para [0073]))
CLAIM 18-
Pang in view of Khan teach the limitations of claim 1. Regarding claim 17, Pang further teaches:
based on a determination the prompt requires information from at least two machine-learning models: routing information between a first machine-learning model and a second machine-learning model, each of the first machine-learning model and the second machine-learning model configured to perform a different task (Pang teaches that the system uses three different machine learning models each pre-trained to learn representations of different data types and that the machine learning models work together in the system to analyze different data (i.e., different tasks) (para [0082, 0102]))
generating a natural language response based on information from each of the first machine-learning model and the second machine-learning model (Pang teaches that can summarize the text (i.e., natural language response) based on the trained base models (para [0082]))
CLAIM 19-
Claim 19 is significantly similar to claim 1 and is rejected upon the same prior art as claim 1.
CLAIM 20-
Claim 20 is significantly similar to claim 1 and is rejected upon the same prior art as claim 1.
CLAIM 22-
Pang in view of Khan teach the limitations of claim 1. Regarding claim 22, Khan further teaches:
Wherein the plurality of domains comprises at least two of: a fifth domain associated with treatments and a sixth domain associated with clinical guidelines (Khan teaches that the groupings of data could be service line of a patient (i.e., treatment) and patient records (i.e., clinical guidelines as recited in paragraph 57 of specification) (para [0116-0118]))
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to modify the healthcare system of Pang to integrate the application of multiple computing systems communicating healthcare data of Khan with the motivation of improving user usability of patient workflow data for a healthcare provider (see: Khan, abstract).
Claim(s) 21 is/are rejected under 35 U.S.C. 103 as being unpatentable over Pang (US 2022/0293272 A1) in view of Khan (US 20210313049 A1) and further in view of Shapiro (WO 2022/072346 A1).
CLAIM 21-
Pang in view of Khan teach the limitations of claim 1. Regarding claim 21, Pang in view of Khan does not explicitly teach, however Shapiro teaches:
wherein identifying the domain from the plurality of domains comprises determining a cosine similarity between the content of the prompt and each domain in the plurality of domains (Shapiro teaches that the mapping of data using the machine learning models include using a similarity matrix that uses a cosine calculation to determine the similarity between the disease data (i.e., domains) through multiple mappings of ngrams of a topic (i.e., prompt) (para [0055-0056, 0157], Figure 12))
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to modify the healthcare system using selections of rules to filter out specific data of Pang in view of Khan to integrate the application of calculating cosine similarities between groupings of data of Shapiro with the motivation of improving patient treatment data by using complex data sorting (see: Shapiro, paragraphs 1-3).
Response to Arguments
The arguments filed 2/10/2026 have been fully considered.
Regarding the arguments pertaining to the Claim Objection, these arguments are persuasive as the claim amendments overcome the objection and it has been withdrawn.
Regarding the arguments pertaining to the 101 rejection, these arguments are not persuasive. Applicant argues that the claimed invention recites a specific technical architecture. Examiner respectfully disagrees. Using multiple generic machine learning models as outlined in the rejection above does not provide a practical application to overcome the abstract idea. Further, Des Jardins provided a specific technological improvement to provide an improved machine learning model. The current claimed invention does not improve the machine learning model itself, but rather moves the data through multiple generic machine learning models through a filtering process. No technological improvement on the machine learning model itself is present in the claimed invention and it is supported in the specification that the machine learning algorithms act as generic neural networks without significantly more (paragraph 54, 496).
Applicant further argues that the claimed invention recites a specific technical configuration. Examiner respectfully disagrees. The description of information flowing through the system does not provide a technological improvement that solves a technical problem, but is merely using generic machine learning models to input/output data. The functions argued are representative of the abstract idea. The claims here are not directed to a specific improvement to computer functionality that amount to a practical application. Rather, they are directed to the use of conventional or generic technology in a well-known environment, without any claim that the invention reflects an inventive solution to a technical problem presented by combining the two. In the present case, the claims fail to recite any elements that individually or as an ordered combination transform the identified abstract idea(s) in the rejection into a patent-eligible application of that idea.
Further, not every claim that recites concrete, tangible components escapes the reach of the abstract-idea inquiry. (See, e.g., Alice, 134). It is well-settled that mere recitation of concrete, tangible components that are generic is insufficient to confer patent eligibility to an otherwise abstract idea. In order to amount to an inventive concept, the components must involve more than performance of “’well-understood, routine, conventional activities’ previously known to the industry.” (Alice, 134 S. Ct. at 2359 (quoting Mayo, 132 S.Ct. at 1294)). The originally filed specification was investigated and found to support this conclusion.
Regarding the arguments pertaining to the 103 rejection, these arguments are moot, as Khan is used to teach the newly amended independent claims.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/K.A.S./Examiner, Art Unit 3686
/JASON B DUNHAM/Supervisory Patent Examiner, Art Unit 3686
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