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 the Claims
The office action is in response to the claims filed on September 12, 2025 for the application filed September 12, 2025 which claims priority to a foreign application filed on March 16, 2023. Claims 1-12 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 1-12 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.
Eligibility Step 1:
Under step 1 of the 2019 Revised Patent Subject Matter Eligibility Guidance, claims 1-4 are directed towards a method (i.e. a process), which is a statutory category. Claims 5-8 are directed towards an apparatus (i.e. a machine), which is a statutory category. Claims 9-12 are directed towards a non-transitory, computer-readable recording medium (i.e. a manufacture), which is a statutory category. Since the claims are directed toward statutory categories, it must be determined if the claims are directed towards a judicial exception (i.e. a law of nature, a natural phenomenon, or an abstract idea). In the instant application, the claims are directed towards an abstract idea.
Eligibility Step 2A, Prong One:
Under step 2A, prong one of the 2019 Revised Patent Subject Matter Eligibility Guidance, independent claims 1, 5 and 9 are determined to be directed to an judicial exception because an abstract idea is recited in the claims which fall within the subject matter groupings of abstract ideas. The abstract idea (identified in bold) recited in the representative claim 5 is identified as:
An information processing apparatus comprising a processor configured to:
calculate, in a case where a conditional branch item included in a workflow is acquired, an indicator value in a case where any one of a plurality of candidate items is used by using a machine learning model, by referring to a storage that stores the plurality of candidate items satisfying a condition of a branch destination corresponding to the conditional branch item and the machine learning model that calculates the indicator value of each of the plurality of candidate items;
select an item of which the calculated indicator value satisfies a predetermined condition from among the plurality of candidate items; and
generate a workflow in which the selected item is arranged at the branch destination of the conditional branch item.
The identified limitations fall within the subject matter grouping of mental processes. The recited limitations of calculating an indicator value, selecting an item for which the calculated indicator value satisfied a predetermined condition and generating a workflow are all data processing steps which can be performed in the human mind using observations, evaluations, judgments and opinions. If a claim recites a limitation that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper, the limitation falls within the mental processes grouping, and the claim recites an abstract idea.
Accordingly, claims 1, 5 and 9 recite an abstract idea under step 2A, prong one.
Eligibility Step 2A, Prong Two:
Under step 2A, prong two of the 2019 Revised Patent Subject Matter Eligibility Guidance, it must be determined whether the identified abstract ideas are integrated into a practical application. After evaluation, there is no indication that any additional elements or combination of elements integrate the abstract idea into a practical application, such as through: an additional element that reflects an improvement to the functioning of a computer, or an improvements to any other technology or technical field; an additional element that applies or uses a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition; an additional element that implements the judicial exception with, or uses the judicial exception in connection with, a particular machine or manufacture that is integral to the claim; an additional element that effects a transformation or reduction of a particular article to a different state or thing; or an additional element that applies or uses the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception. As shown below, the additional elements, other than the abstract idea per se, when considered both individually and as an ordered combination, amount to no more than a recitation of: generally linking the abstract idea to a particular technological environment or field of use; insignificant extra-solution activity to the judicial exception; and/or adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea as evidenced below.
The additional elements recited in representative claim 5 are identified in italics as:
An information processing apparatus comprising a processor configured to:
calculate, in a case where a conditional branch item included in a workflow is acquired, an indicator value in a case where any one of a plurality of candidate items is used by using a machine learning model, by referring to a storage that stores the plurality of candidate items satisfying a condition of a branch destination corresponding to the conditional branch item and the machine learning model that calculates the indicator value of each of the plurality of candidate items;
select an item of which the calculated indicator value satisfies a predetermined condition from among the plurality of candidate items; and
generate a workflow in which the selected item is arranged at the branch destination of the conditional branch item.
The additional limitations of “An information processing apparatus comprising a processor configured to” and the corresponding computer and recording medium of claims 1 and 9 are determined to be mere instructions to apply an abstract idea under MPEP §2106.05(f). The processor,, computer and recording medium are recited at a high level of generality and merely used to implement the abstract idea. The use of the machine learning model is also recited at a high level of generality and only recited the desired outcome of “calculate… an indicator value” without any details as to how the calculating is accomplished. Therefore, these additional elements amount to no more than a recitation of the words "apply it" (or an equivalent) or no more than mere instructions to implement an abstract idea or other exception on a computer or no more than merely using a computer as a tool to perform an abstract idea.
Accordingly, claims 1, 5 and 9 do not recite additional elements which integrate the abstract idea into a practical application.
Eligibility Step 2B:
Under step 2B of the 2019 Revised Patent Subject Matter Eligibility Guidance, it must be determined whether provide an inventive concept by determining if the claims include additional elements or a combination of elements that are sufficient to amount to significantly more than the judicial exception. After evaluation, there is no indication that an additional element or combination of elements 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, the additional limitations amount to mere instructions to apply an abstract idea under MPEP §2106.05(f), which does not amount to significantly more than the abstract idea.
Furthermore, looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements amounts to an inventive concept.
Dependent Claims:
The dependent claims merely present additional abstract information in tandem with further details regarding the elements from the independent claims and are, therefore, directed to an abstract idea for similar reasons as given above. None of these limitations are deemed to integrate the claims into a practical application or to amount to significantly more than the abstract idea as detailed below.
Regarding claims 2, 6 and 10, extracting information from previous workflow data is a mental process and using of the data for training the machine learning model is mere instructions to apply an abstract idea under MPEP §2106.05(f).
Regarding claims 3-4, 7-8 and 11-12, merely defining the indicator value does not change the analysis under step 2A, prong one, such that these claims are also directed to the abstract idea
Therefore, whether taken individually or as an ordered combination, 1-12 are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-12 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Schmidt et al. (U.S. Pub. No. 2012/0232930).
Regarding claim 1, Schmidt discloses a computer-implemented workflow generation method in which a computer executes processing of (Paragraph [0007]):
calculating, in a case where a conditional branch item included in a workflow is acquired, an indicator value in a case where any one of a plurality of candidate items is used by using a machine learning model, by referring to a storage that stores the plurality of candidate items satisfying a condition of a branch destination corresponding to the conditional branch item and the machine learning model that calculates the indicator value of each of the plurality of candidate items (Paragraph [0056], Associated with each decision point is a classifier 48 labeled with a capital C. The classifier at each decision point is also called “CDS Logic.” Each classifier 48 classifies a potential next step or steps using information in the patient's electronic health record and in the clinical database “MedBase” 22. In a first embodiment of FIG. 10, the classifier generates a success value applicable to the entire path of clinical actions applied to another patient based on the electronic health record of that patient. Paragraph [0034], FIG. 1 shows a semantic network with nodes linked from a starting point to multiple possible end points. Each of the nodes corresponds to a clinical action that leads towards one or more clinical end points. For example, each of the actions that lead towards the “End Point State 1” results in a different cost because the additional diagnostic steps reduce the risk for the patient. Paragraph [0070], For every confidence level proposed in CDS system 20, the classifier is trained through data mining or image analysis and is later applied to each given case so as to calculate all the different confidence levels.);
selecting an item of which the calculated indicator value satisfies a predetermined condition from among the plurality of candidate items (Paragraph [0056], The CDS system 20 then indicates which combination of clinical actions generates the greatest success value. In the first embodiment for example, FIG. 10 indicates that applying a therapy of Taxan and Trastuzumab (Herceptin) on the current patient “Marie Schulz” has the greatest success value (0.85).); and
generating a workflow in which the selected item is arranged at the branch destination of the conditional branch item (Paragraph [0056], At the center of the third version of the GUI is a visual representation of the network 45 of clinical actions and decision points. A dashed line labeled “You are here” 46 indicates the current point in time. The network 45 indicates that as of the current point in time, Marie Schulz's physician has three options on how to proceed after her surgery 47. Associated with each decision point is a classifier 48 labeled with a capital C. The classifier at each decision point is also called “CDS Logic.” Each classifier 48 classifies a potential next step or steps using information in the patient's electronic health record and in the clinical database “MedBase” 22. In a first embodiment of FIG. 10, the classifier generates a success value applicable to the entire path of clinical actions applied to another patient based on the electronic health record of that patient. The CDS system 20 then indicates which combination of clinical actions generates the greatest success value. In the first embodiment for example, FIG. 10 indicates that applying a therapy of Taxan and Trastuzumab (Herceptin) on the current patient “Marie Schulz” has the greatest success value (0.85).).
Regarding claim 2, Schmidt further discloses wherein the computer executes processing of extracting, in a case where another workflow in which some of the plurality of candidate items are arranged is acquired, some candidate items from the acquired another workflow, and training the machine learning model by using the indicator values regarding the some candidate items when the another workflow is executed (Paragraph [0038], To obtain probabilities used in choosing clinical actions for the current patient, all similar paths from the clinical database 22 are aggregated using the similarity values as weighting factors. Using the aggregated path as an input to the algorithm for finding the optimal path provides the physician with a suggestion for the next diagnostic and therapeutic steps. Paragraph [0039], The value of the clinical database 22 increases with the number of patient histories it contains. Each patient history (if recorded correctly) contributes to the available network of actions and decision points. The success or failure of each diagnosis and therapy in the past enables the system 20 to repeat (or prevent) such routes. Paragraph [0061], The clinical database 22 stores data on patients whose clinical outcome and treatment success is known. Here as well, for each patient in the clinical database 22 there is a reference to the underlying data sources (e.g. PACS, HIS), information about clinical decision points and clinical actions, the actual clinical outcomes such as disease free and overall survival times, and the total health care costs actually incurred. The CDS application 21 performs various services on the data in database 22 and database 23, such as image analysis, data mining, text mining, and a combination of these functions. Paragraph [0070], . For every confidence level proposed in CDS system 20, the classifier is trained through data mining or image analysis and is later applied to each given case so as to calculate all the different confidence levels.).
Regarding claim 3, Schmidt further discloses wherein the indicator value includes a value representing at least one of an effect, a cost, and a resource when each of the plurality of candidate items is used (Paragraph [0059], Each suggested treatment or therapy option is displayed together with a confidence level that indicates the probability that the option will successfully contribute to a quality measure of the patient's health care plan. A quality measure is determined based on multiple factors, such as the probability of success of the treatment or therapy option, the quality of life of the patient, the survival time, the health care costs and the patient's available health care budget.).
Regarding claim 4, Schmidt further discloses wherein the indicator value includes a value representing at least one of an effect, a cost, and a resource when each of the plurality of candidate items is used (Paragraph [0059], Each suggested treatment or therapy option is displayed together with a confidence level that indicates the probability that the option will successfully contribute to a quality measure of the patient's health care plan. A quality measure is determined based on multiple factors, such as the probability of success of the treatment or therapy option, the quality of life of the patient, the survival time, the health care costs and the patient's available health care budget.).
Regarding claims 5-12: all limitations as recited have been analyzed and rejected with respect to claims 1-4. Claims 5-8 pertain to an information processing apparatus, corresponding to the computer-implemented workflow generation method of claims 1-4. Claims 9-12 pertain to a non-transitory computer-readable recording medium, corresponding to the computer-implemented workflow generation method of claims 1-4. Claims 5-12 do not teach or define any new limitations beyond claims 1-4; therefore claims 5-12 are rejected under the same rationale.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Javer et al. (EP 4266318) discusses using machine learning to determine selection criteria for clinical trials.
Wirth et al. (U.S. Pub. No. 2022/0189617) discusses determining impactability of certain areas and allocation healthcare resource and costs to the areas.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Devin C. Hein whose telephone number is (303)297-4305. The examiner can normally be reached 9:00 AM - 5:00 PM M-F MDT.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jason B. Dunham can be reached at (571) 272-8109. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/DEVIN C HEIN/Examiner, Art Unit 3686
Prosecution Insights