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 January 24, 2025 for the application filed January 24, 2025 which claims priority to a foreign application filed on July 25, 2022. Claims 1-13 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.
Claim 10 is rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
The claim does not fall within at least one of the four categories of patent eligible subject matter because the control unit does not have a physical or tangible form, such as information (often referred to as "data per se") or a computer program per se (often referred to as "software per se") when claimed as a product without any structural recitations. See MPEP §2106.03.
Claims 1-11 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 1-9 are directed towards a non-transient computer-readable medium (i.e. a manufacture), which is a statutory category. Claim 11 is directed towards a method (i.e. a process), which is a statutory category. Assuming claim 10 is amended such that it is also directed to a statutory categor7, 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).
Eligibility Step 2A, Prong One:
Under step 2A, prong one of the 2019 Revised Patent Subject Matter Eligibility Guidance, independent claims 1, 10 and 11 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 1 is identified as:
A non-transient computer-readable medium comprising a computer program that causes a computer to execute processing comprising:
acquiring pain related information regarding pain of a patient;
inputting the acquired pain related information into a learning model that outputs an index indicating an effect of a pain treatment, and acquiring from the learning model the index indicating an effect of a pain treatment regarding the patient;
generating pain treatment assistance information for the patient based on the acquired index; and
outputting the generated pain treatment assistance information.
The identified limitations of the abstract idea of fall within the subject matter grouping of certain methods of organizing human activity related and the sub grouping of managing personal behavior or relationships or interactions between people, (including social activities, teaching, and following rules or instructions). The claims recite the human activity of using patient pain data to determine an effect of a pain treatment and generate and provide treatment assistance information which is an activity performed by humans, such as doctors.
The identified limitations of the abstract idea of fall within the subject matter grouping of mental processes. 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 inputting acquiring an index indicating an effect of a pain treatment based on input information and determining treatment assistance information based on the index can be performed mentally using observations, evaluation, judgements and opinions.
Accordingly, claims 1, 10 and 11 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 XXX are identified in italics as:
A non-transient computer-readable medium comprising a computer program that causes a computer to execute processing comprising:
acquiring pain related information regarding pain of a patient;
inputting the acquired pain related information into a learning model that outputs an index indicating an effect of a pain treatment, and acquiring from the learning model the index indicating an effect of a pain treatment regarding the patient;
generating pain treatment assistance information for the patient based on the acquired index; and
outputting the generated pain treatment assistance information.
The additional limitations of “A non-transient computer-readable medium comprising a computer program that causes a computer to execute processing comprising:” are determined to be mere instructions to apply an abstract idea under MPEP §2106.05(f). The additional limitations of “into a learning model that” and “from the learning model are also determined to be mere instructions to apply an abstract idea under MPEP §2106.05(f). The learning model is used to generally apply the abstract idea without placing any limits on how the model functions. Rather, these limitations only recite the outcome of “acquiring an index” and do not include any details about how the acquiring of the index” 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, 10 and 11 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 are determined to be mere instructions to apply an abstract idea under MPEP §2106.05(f), which is do 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. Regarding claim 2-9, the additionally information acquired and using to determine the index and treatment information is determined to be encompassed by the abstract idea. Similarly the limitations pertaining to what information is outputted is determined to be encompassed by the abstract idea. Furthermore, the outputting to a terminal device of claims 7-9 are determined to be mere instructions to apply an abstract idea under MPEP §2106.05(f).
Therefore, whether taken individually or as an ordered combination, 1-11 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 1-8 and 10-13 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Lin et al. (U.S. Pub. No. 2019/0362843.
Regarding claim 1, Lin discloses a non-transient computer-readable medium comprising a computer program that causes a computer to execute processing comprising (Paragraph [0095]):
acquiring pain related information regarding pain of a patient (Paragraph [0025], the data collection component 108 can measure a quantity of interest using one or more sensors or can obtain the quantity of interest via feedback from a user, for example, via a user device (e.g., a mobile phone, a hand-held device, a laptop, or the like). Paragraph [0058], the data collection component 205 can take as input 206 parameters including, but not limited to: (i) data regarding the user (including user preferences and psycho-physical variables like user's sleep, overall health condition, and the like) (ii) environmental conditions; (iii) past decisions; (iv) one or more treatments determined by the system 200). Paragraph [0073], a data collection component can determine at least one parameter associated with a pain perception of a user. In some aspects, the data collection component can include one or more sensors to gather relevant information, such as a user temperature, an environmental temperature, one or more facial features of the user, a gait or limp associated with the user's movements, sounds (e.g., groans, screams, and the like) from the user, and the like. Also see paragraph [0082].);
inputting the acquired pain related information into a learning model that outputs an index indicating an effect of a pain treatment, and acquiring from the learning model the index indicating an effect of a pain treatment regarding the patient (Paragraph [0006], cause the processor to determine, by the processor, a relationship between a pain perception of a subject and at least one parameter using artificial intelligence. Paragraph [0025], the system 100 can generate a non-linear dynamical system or model of an arbitrary mathematical order that can relate the treatment to a quantity of interest, for example, a measure of the pain or a variable that correlates with pain (for example, a quality of sleep, a quality of life, an overall satisfaction, and the like associated with user. Paragraph [0059], the user may be asked to provide a numerical score to a pain felt before and after the application of the treatment. The user may be further asked to include a numerical uncertainty associated with the numerical score. For example, the user may indicate that the pain is 7 out of 10 on a scale of 1 to 10, where 1 represents the least pain and 10 represents the most pain. Paragraph [0070], the feedback interface 320 can include an interface which allows the user to score the treatment. Paragraph [0075], a computing component can determine a relationship between the pain perception and the at least one parameter using artificial intelligence (AI). Paragraph [0094], At block 442, a relationship can be determined, by a processor, between a pain perception of a subject and at least one parameter using artificial intelligence. At block 444, a treatment can be determined, by the processor, to reduce pain perception for the subject based on the relationship, the treatment including an implantable device that provides medication delivery to the subject.);
generating pain treatment assistance information for the patient based on the acquired index (Paragraph [0028], computer program products that facilitate the generation of a prescription by a medical professional, using artificial intelligence (AI) technology. Paragraph [0036], the treatment component 114 can determine, based on the dynamic model, what an optimal treatment for the user entails, specific for the user's conditions. Paragraph [0078], the computing component can determine a treatment for the user based on the relationship. In one or more example embodiments, the computing component can determine, based on the dynamic model, what an optimal treatment for the user entails, specific for the user's conditions. Paragraph [0084], the system can determine a given treatment for the given user based on the data and output of the machine learning algorithms.); and
outputting the generated pain treatment assistance information (Paragraph [0028], computer program products that facilitate the generation of a prescription by a medical professional, using artificial intelligence (AI) technology. Paragraph [0038], The system 100 can further notify, via the communication component 112, a doctor or medical practitioner of a severity of a pain. In some embodiments, the system 100 can contact a hospital or an emergency system in case the system determines, based on sensor input at the data collection component 108 or based on user feedback that indicates pain exceeding a predetermined threshold, or that the user is not responsive to further treatment. Paragraph [0063], a recommendation for a treatment can be sent 218 b to the user, for example, at a user interface displayed on a user device (e.g., mobile device, laptop, and the like. Also see paragraphs [0069]-[0070], [0080]-[0081] and [0094].).
Regarding claim 2, Lin further discloses wherein: the pain related information includes at least one of an evaluation index used to evaluate pain, vital data, medication information, and a physical condition (Paragraphs [0051], [0058]-[0059], [0082]-[0083], and [0086]).
Regarding claim 3, Lin further discloses wherein the computer program causes the computer to execute processing comprising:
acquiring medical information regarding medical care of the patient (Paragraph [0058], he data collection component 205 can take as input 206 parameters including, but not limited to: (i) data regarding the user (including user preferences and psycho-physical variables like user's sleep, overall health condition, and the like) (ii) environmental conditions; (iii) past decisions; (iv) one or more treatments determined by the system 200. Paragraph [0031], The medical data (e.g., one or more brain images and/or medical history) can be sent to the system 100.); and
inputting the acquired medical information into the learning model that outputs the index indicating the effect of the pain treatment, and acquiring from the learning model the index indicating the effect of the pain treatment regarding the patient (Paragraph [0006], cause the processor to determine, by the processor, a relationship between a pain perception of a subject and at least one parameter using artificial intelligence. Paragraph [0025], the system 100 can generate a non-linear dynamical system or model of an arbitrary mathematical order that can relate the treatment to a quantity of interest, for example, a measure of the pain or a variable that correlates with pain (for example, a quality of sleep, a quality of life, an overall satisfaction, and the like associated with user. Paragraph [0059], the user may be asked to provide a numerical score to a pain felt before and after the application of the treatment. The user may be further asked to include a numerical uncertainty associated with the numerical score. For example, the user may indicate that the pain is 7 out of 10 on a scale of 1 to 10, where 1 represents the least pain and 10 represents the most pain. Paragraph [0070], the feedback interface 320 can include an interface which allows the user to score the treatment. Paragraph [0075], a computing component can determine a relationship between the pain perception and the at least one parameter using artificial intelligence (AI). Paragraph [0094], At block 442, a relationship can be determined, by a processor, between a pain perception of a subject and at least one parameter using artificial intelligence. At block 444, a treatment can be determined, by the processor, to reduce pain perception for the subject based on the relationship, the treatment including an implantable device that provides medication delivery to the subject.).
Regarding claim 4, Lin further discloses wherein: the medical information includes at least one of diagnosis information, examination information, treatment information, and prescription information (Paragraph [0058], one or more treatments determined by the system 200. Paragraph [0031], The medical data (e.g., one or more brain images and/or medical history) can be sent to the system 100.).
Regarding claim 5, Lin further discloses wherein the computer program causes the computer to execute processing comprising:
acquiring literature information regarding a pain treatment; and
inputting the acquired literature information into the learning model that outputs the index indicating the effect of the pain treatment, and acquiring from the learning model the index indicating the effect of the pain treatment regarding the patient (Paragraph [0051], the cloud computing environment can store information related to users (for example, user identity information, medical records, insurance information, data related to treatments, and the like), information related to the environment (environmental temperatures, humidity, weather, news events, and the like), overhead data (headers, data structures and files, and the like). In one or more example embodiments, the cloud computing environment that can pool data related to many users over a given geographical region (for example, the United States) to obtain data and/or determine statistics related to given treatments and/or pain perception models and systems. Such data and statistics can be used to provide enhanced treatment options to users.).
Regarding claim 6, Lin further discloses wherein: the pain treatment assistance information includes at least one of the index indicating the effect of the pain treatment used to assist a doctor who examines the patient, recommended prescription information, and recommended treatment information (Paragraph [0028], The treatment unit 114 can further make a recommendation of the prescription based on the dynamic model of pain perception generated for the user. Paragraph [0029] an update to the status of a medicine prescription can comprise: changing one or more of the medicine prescription's features (e.g., a change to the frequency of distribution by enabling refills of the medicine prescription), and/or confirming one or more of the medicine prescription's features. Paragraph [0038], the system 100 can further notify, via the communication component 112, a doctor or medical practitioner of a severity of a pain. Paragraph [0059], a numerical score to a pain felt before and after the application of the treatment. Paragraph [0091], the user can choose a treatment or a combination of treatments from the treatments recommended. The user can for example, choose a treatment that includes obtaining a prescription medication from an authorized medical professional or taking a prescription medication at a predetermined dosage and predetermined time as recommended by the system.).
Regarding claim 7, Lin further discloses wherein the computer program causes the computer to execute processing comprising: outputting the pain treatment assistance information to a doctor terminal device (Paragraph [0028], the system 100 can notify, via the communication component 112, a medical professional that a pain threshold has been exceeded and to therefore prescribe a stronger medication. The treatment unit 114 can further make a recommendation of the prescription based on the dynamic model of pain perception generated for the user. Paragraph [0029]an update to the status of a medicine prescription can comprise: changing one or more of the medicine prescription's features (e.g., a change to the frequency of distribution by enabling refills of the medicine prescription), and/or confirming one or more of the medicine prescription's features. Paragraph [0038], the system 100 can further notify, via the communication component 112, a doctor or medical practitioner of a severity of a pain. Paragraph [0059], a numerical score to a pain felt before and after the application of the treatment. Paragraph [0091], the user can choose a treatment or a combination of treatments from the treatments recommended. The user can for example, choose a treatment that includes obtaining a prescription medication from an authorized medical professional or taking a prescription medication at a predetermined dosage and predetermined time as recommended by the system.).
Regarding claim 8, Lin further discloses wherein the computer program causes the computer to execute processing comprising: outputting a temporal change in the index indicating the effect of the pain treatment to a doctor terminal device (Paragraph [0086], the system can monitor the user to determine the effectiveness of the treatment. a reduction in the frequency or intensity of moans, groans, and/or screams from the user can be captured by the sensor and can correlate with a reduction of the and intensity of pain for the user. Paragraph [0087], the treatment can be modified in real-time, based on the data collected in previous steps. For example, the treatment can be adapted based on a feedback loop.).
Regarding claims 10-11: all limitations as recited have been analyzed and rejected with respect to claim 1. Claims 10-11 do not teach or define any new limitations beyond claim 1; therefore claims 10-11 are rejected under the same rationale.
Regarding claim 12, Lin discloses learning model generation method comprising:
acquiring first training data including pain related information regarding pain of a plurality of patients and an index indicating an effect of a pain treatment regarding the plurality of patients (Paragraph [0051], In some embodiments, the cloud computing environment can store information related to users (for example, user identity information, medical records, insurance information, data related to treatments, and the like), information related to the environment (environmental temperatures, humidity, weather, news events, and the like), overhead data (headers, data structures and files, and the like). In one or more example embodiments, the cloud computing environment that can pool data related to many users over a given geographical region (for example, the United States) to obtain data and/or determine statistics related to given treatments and/or pain perception models and systems. Such data and statistics can be used to provide enhanced treatment options to users. Paragraph [0070], The feedback interface 320 can take user feedback and transmit to one or more internal components, for example, a control component (like control component 215 shown and described in connection with FIG. 2), to compute a dynamical system or a model or an updated dynamical system or model. Paragraph [0071], train a classifier program (e.g., such as a support vector machine) based on the one or more extracted features to determine a condition and/or treatment associated with the user. Also see paragraph [0059].); and
generating a learning model that receives pain related information of a patient and outputs the index indicating an effect of a pain treatment regarding the patient based on the acquired first training data (Paragraph [0053], Components disclosed herein can employ various classification (explicitly trained (e.g., via training data) as well as implicitly trained (e.g., via observing behavior, preferences, historical information, receiving extrinsic information, etc.)) schemes and/or systems (e.g., support vector machines, neural networks, expert systems, Bayesian belief networks, fuzzy logic, data fusion engines, etc.) in connection with performing automatic and/or determined action in connection with the claimed subject matter. Thus, classification schemes and/or systems can be used to automatically learn and perform functions, actions, and/or determinations. Paragraph [0070], The feedback interface 320 can take user feedback and transmit to one or more internal components, for example, a control component (like control component 215 shown and described in connection with FIG. 2), to compute a dynamical system or a model or an updated dynamical system or model. Paragraph [0071], train a classifier program (e.g., such as a support vector machine) based on the one or more extracted features to determine a condition and/or treatment associated with the user. Paragraph [0059], In some embodiments, the model and/or the system can be continuously updated based on one or more machine learning processes. Also see paragraphs [0052] and [0054].).
Regarding claim 13, Lin further discloses:
acquiring second training data further including medical information of the plurality of patients, literature information regarding the pain treatment, and the index indicating the effect of the pain treatment regarding the plurality of patients (Paragraphs [0051] In some embodiments, the cloud computing environment can store information related to users (for example, user identity information, medical records, insurance information, data related to treatments, and the like), information related to the environment (environmental temperatures, humidity, weather, news events, and the like), overhead data (headers, data structures and files, and the like). In one or more example embodiments, the cloud computing environment that can pool data related to many users over a given geographical region (for example, the United States) to obtain data and/or determine statistics related to given treatments and/or pain perception models and systems. Such data and statistics can be used to provide enhanced treatment options to users. Also see paragraphs [0052]-[0053].); and
generating the learning model so as to output the index indicating the effect of the pain treatment based on the acquired second training data (Paragraph [0053], Components disclosed herein can employ various classification (explicitly trained (e.g., via training data) as well as implicitly trained (e.g., via observing behavior, preferences, historical information, receiving extrinsic information, etc.)) schemes and/or systems (e.g., support vector machines, neural networks, expert systems, Bayesian belief networks, fuzzy logic, data fusion engines, etc.) in connection with performing automatic and/or determined action in connection with the claimed subject matter. Thus, classification schemes and/or systems can be used to automatically learn and perform functions, actions, and/or determinations. Paragraph [0070], The feedback interface 320 can take user feedback and transmit to one or more internal components, for example, a control component (like control component 215 shown and described in connection with FIG. 2), to compute a dynamical system or a model or an updated dynamical system or model. Paragraph [0071], train a classifier program (e.g., such as a support vector machine) based on the one or more extracted features to determine a condition and/or treatment associated with the user. Paragraph [0059], In some embodiments, the model and/or the system can be continuously updated based on one or more machine learning processes. Also see paragraphs [0052] and [0054].).
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.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over Lin et al. (U.S. Pub. No. 2019/0362843) in view of Goetzke et al. (U.S. Pub. No. 2002/0128866).
Regarding claim 9, Lin does not appear to explicitly disclose, but Goetzke teaches that it was old and well known in the art of chronic pain patient care at the time of the filing wherein the computer program causes the computer to execute processing comprising: outputting introduction information of a doctor having experience in a recommended treatment method to a doctor terminal device (Paragraphs [0009], [0028]-[0033] and [0055] discuss using patient profilers to provide primary care physicians with treatment information and specialist referral information.) to assist providers in practice management (Paragraph [0082]).
Therefore, it would have been obvious to one of ordinary skill in the art of chronic pain patient care at the time of the filing to modify Lin to include outputting introduction information of a doctor having experience in a recommended treatment method to a doctor terminal device, as taught by Goetzke, in order to assist providers in practice management.
Conclusion
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.
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/DEVIN C HEIN/Examiner, Art Unit 3686