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
Claims 1-13 and 15-21, as recited in a preliminary amendment filed on November 28, 2022, were previously pending and subject to a non-final office action filed on July 1, 2025 (the “July 1, 2025 Non-Final Office Action”). Following the July 1, 2025 Non-Final Office Action, Applicant: (i) amended claims 1-11, 13, 15, and 17; (ii) canceled claims 12, 16, and 18; and (iii) added new claims 22-24, in an amendment filed on September 5, 2025 (the “September 5, 2025 Amendment”), see Applicant’s amended claims (pp. 2-10 of the September 5, 2025 Amendment). As such, claims 1-11, 13, 15, 17, and 19-24, as recited in the September 5, 2025 Amendment, are currently pending and subject to the final office action below.
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Response to Applicant’s Remarks
Response to Applicant’s Remarks Concerning Rejections under 35 U.S.C. § 101
Applicant’s arguments, see Applicant’s Remarks, pp. 12-20, The Claims are Patent-Eligible Subject Matter Section, filed September 5, 2025, with respect to rejections of claim 1-13 and 15-21 under 35 U.S.C. § 101 have been fully considered, but they are not persuasive. Further, in light of the 2019 Revised Patent Subject Matter Eligibility Guidance (available at MPEP § 2106) (the “2019 Revised PEG”), the § 101 rejections of claims 1-11, 13, 15, 17, and 19-21 are maintained and the § 101 rejections of new claims 22-24 are added in this final office action.
Applicant generally argues that the claims are not directed to an abstract idea, because the claims recite computer elements and specific instructions for performance by a computer processor. Applicant’s Remarks, at p. 13. Specifically, Applicant provides support for this assertion by pointing to amendments which provide the “pre-trained prescription image analyzing model which is configured to output prescription information when a corresponding recognition result derived from a paper prescription is input to the pre-trained prescription image analyzing model”. Applicant’s Remarks, at p. 13. Examiner respectfully disagrees with this argument. While the claims recite a prescription image analyzing model and a machine learning model is not a feature that can be performed in the human mind, this is not the test for determining whether the claims recite an abstract mental process. Claims can recite a mental process even if they are claimed as being performed on a computer. MPEP § 2106.04(a)(2)(III)(C). The Supreme Court recognized this in Benson, determining that a mathematical algorithm for converting binary coded decimal to pure binary within a computer’s shift register was an abstract idea. Id.
Similarly, in the present case, the identified abstract idea is: (1) a method for pushing information, comprising: recognizing a prescription image to obtain a recognition result comprising position information indicating a position of a character in the prescription image; pushing information based on the prescription information; obtaining a training sample set, where the training sample includes a sample recognition result and sample prescription information; selecting pre-annotated feature text from the sample prescription image; determining a character string with a closest distance from the selected feature text; and determining that character string as prescription information corresponding to the selected feature text; and (2) a method for pushing information, comprising: identifying prescription information based on proximity of a character string to a character in a feature text according to text direction; recognizing a prescription image to obtain a recognition result comprising position information indicating a position of a character in the prescription image; pushing information based on the prescription information; verifying the prescription information; determining a time difference between a current time and the prescription issuing time, and determining whether the time difference is greater than the prescription validity duration; and pushing abnormal prompt information when the prescription is expired.. Under the broadest reasonable interpretation of the claims, (1) recognizing a prescription image (i.e., a type of observation, evaluation, judgment, and/or opinion where a person could mentally recognize an image as being an image of a prescription); (2) obtaining a training sample set (i.e., a type of observation, evaluation, judgment, and/or opinion where a person could mentally and/or manually collect sample set data); (3) selecting pre-annotated feature text (i.e., a type of observation, evaluation, judgment, and/or opinion to select feature text from the sample prescription image); (4) determining a character string with a closest distance from the selected feature text (i.e., a type of observation, evaluation, judgment, and/or opinion to determine a type of character string); (5) determining that character string as prescription information corresponds to the selected feature text (i.e., a type of observation, evaluation, judgment, and/or opinion to determine that the character string corresponds to the selected feature text); (6) pushing information based on the prescription information (i.e., a type of observation, evaluation, judgment, and/or opinion to where a person could manually write down the prescription information); (7) verifying the prescription information by determining whether a difference between a current time and the prescription issuing time is greater than the prescription validity duration (i.e., a type of observation, evaluation, judgment, and/or opinion where a person could verify prescription information); and (8) pushing an abnormal prompt information when the prescription is expired (i.e., a type of observation, evaluation, judgment, and/or opinion where a person could manually write down “abnormal prompt information” after the person determines that a prescription is expired), are human cognitive functions which are capable of being performed mentally and/or reasonably with the aid of a pen and paper. The fact that Applicant uses a machine learning model to output prescription information and identify prescription information based on the proximity of a character string to a character in a feature text merely amounts to performing the abstract mental process in a computer environment with a machine learning model. For example, a person naturally performs the aforementioned steps of identifying prescription information based on the proximity of a character string to a feature text (e.g., identifying the name of a medication by looking at the words that are the closest to the word “Medication”, “Medication Name”, or “Drug Name”) when reading a medication bottle.
Further, the machine learning model and the one or more processors are claimed at a high level of generality, because Applicant has not described the machine learning model with any specificity. Applicant merely claims the idea of a solution (i.e., identifying and outputting prescription information) without providing the details of how the solution to a problem is accomplished (i.e., the specific steps, algorithm, flowcharts, etc. for accomplishing the solution). Therefore, this claim limitation is recited at a high level of generality, and amounts to using a computer as a tool to perform an abstract mental process. For these reasons, this argument is not persuasive.
Next, Applicant generally argues that the claims should be eligible because they are analogous to the method recited in Example 39. See Applicant’s Remarks, at p. 15. Examiner respectfully disagrees. While Applicant’s claims similarly describe steps which are similar to Example 39, namely the steps for obtaining training data and using the training data to train a model, Applicant’s claims are different than the limitations described in Example 39. The method described in Example 39 did not recite any steps directed to an abstract mental process, because the steps of: collecting a set of digital images; applying one or more transformations to the digital images including mirroring, rotating, smoothing, or contrast reduction; creating a first training set comprising a modified set of digital facial images and a set of digital non-facial images; training the neural network in a first stage with the first training set; creating a second training set comprising the first training set and digital non-facial images that are incorrectly detected after the first stage of training; and training the neural network in a second stage with the second training set, are steps which cannot practically be performed in the human mind and/or with the aid of pen and paper.
The specification in Example 39 explicitly discloses that the claims are directed to training a neural network for facial detection in digital images, where prior methods have been unable to robustly detect human faces in images where there are shifts, distortions, and variations in scale and rotation of the face pattern in the image. Further, the specification in Example 39 discloses that the neural network applies mathematical transformations to the facial images, including rotating, shifting, mirroring, smoothing, or contrast reduction, which are steps that are not practically performed in the human mind. Therefore, the method in Example 39 is firmly rooted in the computer environment. Conversely, Applicant’s claims recite several features which are capable of being performed in the human mind, such as, recognizing a prescription image; determining a character string with a closest distance from the selected feature text ; determining that character string as prescription information corresponds to the selected feature text; and verifying the prescription information by determining whether a difference between a current time and the prescription issuing time is greater than the prescription validity duration, as analyzed previously above. Applicant’s claims merely use a machine learning model to identify and output prescription information from a paper prescription, which are mental steps. For these reasons, Applicant’s claims are not similar to the method described in Example 39 and this argument is not persuasive.
Lastly, Applicant argues that the claims provide a technological improvement in the field of computer technologies, because the claimed invention reduces the need for synchronization with hospital systems when starting from a paper prescription in the hands of the end user. See Applicant’s Remarks, at pp. 16-17. Examiner respectfully disagrees with this assertion. The consideration of whether the claim as a whole includes an improvement to a computer or to a technological field requires an evaluation of the specification and the claim to ensure that a technical explanation of the asserted improvement is present in the specification, and that the claim reflects the asserted improvement. See MPEP § 2106.04(d)(1).
When evaluating whether claims recite an improvement to the functioning of a computer or a technical field, the disclosure must provide sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement. MPEP § 2106.05(a). 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. Id. 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. An indication that the claimed invention provides an improvement can include a discussion in the specification that identifies a technical problem and explains the details of an unconventional technical solution expressed in the claim, or identifies technical improvements realized by the claim over the prior art.
For example, in the McRO, Inc. v. Bandai Namco Games Am. Inc. case, the Federal Circuit relied on the specification’s explanation of how the particular rules recited in the claim enabled the automation of specific animation tasks that previously could only be performed subjectively by humans, when determining that the claims were directed to improvements in computer animation instead of an abstract idea. Id. Conversely, the Federal Circuit has held claims which merely record, transmit, and archive data by use of conventional or generic technology in a nascent but well-known environment, without any assertion that the invention reflects an inventive solution to any problem may not be sufficient to show an improvement in computer-functionality. See MPEP § 2106.05(a) (citing the TLI Communications LLC v. AV Auto case). Further, gathering and analyzing information using conventional techniques, was also determined to be insufficient to show an improvement in computer-functionality. See MPEP § 2106.05(a) (also citing the TLI Communications case).
Further, the Federal Circuit has held that “claims that do no more than apply established methods of machine learning to a new data environment are not patent eligible”. Recentive Analytics, Inc. v. Fox Corp., Fox Broadcasting Company, LLC, Fox Sports Productions, LLC, Case No. 23-2437, (Fed. Cir. 2025), pp. 10, 14. An abstract idea does not become non-abstract by limiting the invention to a particular field of use or technological environment. Id. Requirements that the machine learning model be “iteratively trained” or dynamically adjusted do not represent a technological improvement, because iterative training using selected training material and dynamic adjustments based on real-time changes are incident to the very nature of machine learning. Id., at p. 12. “[T]he way machine learning works is the inputs are defined, the model is trained, and then the algorithm is actually updated and improved over time based on the input.” Id. Therefore, using existing machine learning technology to perform a task previously undertaken by humans with greater speed and efficiency than could previously be achieved does not confer patent-eligibility. Id., at p. 15.
In the present case, Applicant’s claims do not describe an improvement to the functioning of a computer or any other technology or technical field. Similar to the TLI Communications and Recentive Analytics, Inc. cases, Applicant’s claims merely implement conventional techniques, such as collecting data (i.e., recognizing the recognition result comprising position information; obtaining a training sample set comprising a sample recognition result and sample prescription information; and extracting time-related information from the prescription information); analyzing the data (i.e., verifying the prescription information; selecting feature text; determining a character string as prescription information corresponding to the selected feature text; and determining a time difference between the current time and prescription issuing time, and determining whether the time difference is greater than the prescription validity duration); and displaying certain results about the collection and analysis (i.e., pushing information based on the prescription information and pushing the abnormal prompt information) with greater speed through the use of existing machine learning technology. Applicant’s claims merely describe a conventional process of collecting data, analyzing the data, and displaying certain results of the analysis with a computer and a machine learning model. Applicant’s claims and specification do not describe details for how the machine learning model improves the prescription recognition features. Therefore, this argument is not persuasive and Applicant’s claims do not recite an improvement to a technological field.
As such, Applicant’s claims are reasonably deemed to recite an abstract mental process without integrating the abstract idea into a practical application or providing significantly more than the abstract idea. Consequently, the rejections of claims 1-11, 13, 15, 17, and 19-21 under 35 U.S.C. § 101 are maintained in this office action. Likewise, the rejections of new claims 22-24 under § 101 are added herein. Please see the amended rejections under the Claim Rejections – 35 U.S.C. § 101 Section below, for further clarification and complete analysis.
Response to Applicant’s Remarks Concerning Rejections under 35 U.S.C. § 103
Applicant’s arguments, see Applicant’s Remarks, pp. 20-25, The Claims are Novel and The Claims are Non-Obvious Sections, filed September 5, 2025, with respect to (i) rejections of claims 1, 13, and 15 under § 102; and (ii) rejections of claim 2-4 and 16-18 under 35 U.S.C. § 103, have been fully considered, but they are moot in light of Applicant’s amendments to independent claims 1, 13, and 15. Furthermore, the prior art search attached to this office action failed to generate closer prior art results. Accordingly, independent claims 1, 13, and 15 are considered to be novel and non-obvious over the prior art and the prior art rejections of claims 1-4, 13, and 15-18 are withdrawn.
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 1-11, 13, 15, 17, and 19-24 are rejected under 35 U.S.C. §101 because the claimed invention is directed to an abstract idea without significantly more. See MPEP § 2106 (hereinafter referred to as the “2019 Revised PEG”).
Step 1 of the 2019 Revised PEG
Following Step 1 of the 2019 Revised PEG, claims 1-11 and 22 are directed to a computer-implemented method for pushing information, which is within one of the four statutory categories (i.e., a process). See MPEP § 2106.03. Claims 13, 17, 19-21, and 23 are directed to an apparatus for pushing information, which is also within one of the four statutory categories (i.e., a machine or apparatus). See id. Claims 15 and 24 are directed to a non-transitory computer readable medium, storing a computer program, which is also within one of the four statutory categories (i.e., a manufacture). See id.
Step 2A of the 2019 Revised PEG - Prong One
Following Prong One of Step 2A of the 2019 PEG, the claim limitations are to be analyzed to determine whether they “recite” a judicial exception or in other words whether a judicial exception is “set forth” or “described” in the claims. See MPEP §2106.04. An “abstract idea” judicial exception is subject matter that falls within at least one of the following groupings: (1) Mathematical Concepts; (2) Certain Methods of Organizing Human Activity, and (3) Mental Processes. See MPEP § 2106.04(a).
Claims 1-11, 13, 15, 17, and 19-24 are rejected under 35 U.S.C. § 101, because the claimed invention is directed to an abstract idea without significantly more. Representative independent claims 1, 13, and 15 include limitations that recite an abstract idea. Note that independent claim 13 is an apparatus, while claim 1 covers a similar method claim and claim 15 covers the matching non-transitory computer readable medium. Specifically, independent claim 1 recites the following limitations:
A computer-implemented method for pushing information, comprising:
obtaining, from a user mobile terminal, a prescription image presenting prescription information, the prescription image is derived from a paper prescription;
recognizing the prescription image to obtain a recognition result which comprises position information configured to indicate a position of a character in the prescription image;
inputting the recognition result into a pre-trained prescription image analyzing model to obtain the prescription information presented in the prescription image; and
pushing information to the user mobile terminal based on the prescription information,
the method further comprising training a prescription image analyzing model to obtain the pre-trained prescription image analyzing model, comprising:
obtaining a training sample set, wherein a training sample includes a sample recognition result and sample prescription information, the sample recognition result is recognized from a sample prescription image, and the sample prescription information is determined based on a position of each character of a corresponding sample recognition result in the sample prescription image,
wherein the sample recognition result is obtained by the following steps comprising:
selecting a pre-annotated feature text from the sample prescription image, the feature text representing a feature category which is to have corresponding prescription information;
determining a character string with a closest distance from the selected feature text based on a vector of a text direction, and
determining that character string as prescription information corresponding to the selected feature text; and
training the prescription image analyzing model by taking sample recognition results of training samples in the training sample set as an input, and by taking sample prescription information corresponding to inputted sample recognition results as an expected output.
Similarly, independent claim 13 recites the following limitations (and claim 15 similarly recites the following limitations):
An apparatus for pushing information, comprising:
one or more processors;
a storage apparatus, storing a pre-trained prescription image analyzing model which is configured to output prescription information when a corresponding recognition result derived from a paper prescription is input to the pre-trained prescription image analyzing model, wherein the pre-trained prescription image analyzing model is configured to identify prescription information based on proximity of a character string to a character in a feature text according to text direction, and also storing at least one instruction, wherein the at least one instruction, when executed by the processors, causes the processors to perform operations comprising:
obtaining from a user mobile terminal a prescription image presenting prescription information, the prescription image is derived from a paper prescription;
recognizing the prescription image to obtain a recognition result, which comprises position information configured to indicate a position of a character in the prescription image;
inputting the recognition result into the pre-trained prescription image analyzing model to obtain the prescription information presented in the prescription image; and
pushing information to the user mobile terminal based on the prescription information, comprising:
verifying the prescription information, and
pushing the information based on a verification result;
wherein the verifying the prescription information comprises:
extracting time-related information from the prescription information, wherein the time-related information comprises a prescription issuing time and a prescription validity duration;
determining a time difference between a current time and the prescription issuing time, and determining whether the time difference is greater than the prescription validity duration; and
pushing an abnormal prompt information when the verifying result indicates that the prescription information fails to pass the verification, comprising:
pushing the abnormal prompt information when determining that the time difference is greater than the prescription validity duration, wherein the abnormal prompt information is configured to indicate that a prescription indicated by the prescription image is expired.
However, the Examiner submits that the foregoing underlined limitations constitute a process that, under its broadest reasonable interpretation, falls within the “Mental Processes” grouping of abstract ideas. See 2019 Revised PEG. The Mental Processes category covers concepts which are capable of being performed in the human mind or encompasses a human performing the step(s) mentally with the aid of a pen and paper (including an observation, evaluation, judgment, or opinion) (i.e., (1) a method for pushing information, comprising: recognizing a prescription image to obtain a recognition result comprising position information indicating a position of a character in the prescription image; pushing information based on the prescription information; obtaining a training sample set, where the training sample includes a sample recognition result and sample prescription information; selecting pre-annotated feature text from the sample prescription image; determining a character string with a closest distance from the selected feature text; and determining that character string as prescription information corresponding to the selected feature text; and (2) a method for pushing information, comprising: identifying prescription information based on proximity of a character string to a character in a feature text according to text direction; recognizing a prescription image to obtain a recognition result comprising position information indicating a position of a character in the prescription image; pushing information based on the prescription information; verifying the prescription information via extracting time-related information including prescription issuing time and a prescription validity duration from the prescription information, determining a time difference between a current time and the prescription issuing time, determining whether the time difference is greater than the prescription validity duration, and pushing abnormal prompt information when the prescription is expired). See MPEP § 2106.04(a)(2)(III). That is, other than reciting some computer components and functions (the foregoing limitations in claims 1 and 13 which are not underlined), the context of claims 1, 13, and 15 encompasses concepts that are capable of being performed in the human mind or encompasses a human performing the step(s) mentally with the aid of a pen and paper (including an observation, evaluation, judgment, and/or opinion) (i.e., (1) a method for pushing information, comprising: recognizing a prescription image to obtain a recognition result comprising position information indicating a position of a character in the prescription image; pushing information based on the prescription information; obtaining a training sample set, where the training sample includes a sample recognition result and sample prescription information; selecting pre-annotated feature text from the sample prescription image; determining a character string with a closest distance from the selected feature text; and determining that character string as prescription information corresponding to the selected feature text; and (2) a method for pushing information, comprising: identifying prescription information based on proximity of a character string to a character in a feature text according to text direction; recognizing a prescription image to obtain a recognition result comprising position information indicating a position of a character in the prescription image; pushing information based on the prescription information; and verifying the prescription information via extracting time-related information including prescription issuing time and a prescription validity duration from the prescription information, determining a time difference between a current time and the prescription issuing time, determining whether the time difference is greater than the prescription validity duration, and pushing abnormal prompt information when the prescription is expired).
The aforementioned claim limitations described in claims 1, 13, and 15 are analogous to claim limitations directed toward concepts which are capable of being performed in the human mind or encompasses a human performing the step(s) mentally with the aid of a pen and paper, because they merely recite limitations which encompass a person mentally and/or manually: (1) recognizing a prescription image (i.e., a type of observation, evaluation, judgment, and/or opinion where a person could mentally recognize an image as being an image of a prescription); (2) obtaining a training sample set (i.e., a type of observation, evaluation, judgment, and/or opinion where a person could mentally and/or manually collect sample set data); (3) selecting pre-annotated feature text (i.e., a type of observation, evaluation, judgment, and/or opinion to select feature text from the sample prescription image); (4) determining a character string with a closest distance from the selected feature text (i.e., a type of observation, evaluation, judgment, and/or opinion to determine a type of character string); (5) determining that character string as prescription information corresponds to the selected feature text (i.e., a type of observation, evaluation, judgment, and/or opinion to determine that the character string corresponds to the selected feature text); (6) pushing information based on the prescription information (i.e., a type of observation, evaluation, judgment, and/or opinion to where a person could manually write down the prescription information); (7) verifying the prescription information by extracting time-related information including prescription issuing time and a prescription validity duration from the prescription information and determining whether a difference between a current time and the prescription issuing time is greater than the prescription validity duration (i.e., a type of observation, evaluation, judgment, and/or opinion where a person could visually review a paper prescription to "extract" the issuing time and validity duration and then mentally compare the current time to the issuing time to verify the prescription); and (8) pushing an abnormal prompt information when the prescription is expired (i.e., a type of observation, evaluation, judgment, and/or opinion where a person could manually write down “abnormal prompt information” after the person determines that a prescription is expired).
Further, Applicant’s claims are similar to claims which have been held to recite an abstract mental process. For example, the Federal Circuit held the a claim directed to “collecting information, analyzing it, and displaying certain results of the collection and analysis”, where the data analysis steps are recited at a high level of generality amounted to steps that could practically be performed in the human mind. See MPEP § 2106.04(a)(2)(III)(A) (citing Electric Power Group v. Alstom, S.A.). Similarly, Applicant’s claims recite steps for collecting information (i.e., recognizing the recognition result comprising position information; obtaining a training sample set comprising a sample recognition result and sample prescription information; and extracting time-related information from the prescription information); analyzing the data (i.e., verifying the prescription information; selecting feature text; determining a character string as prescription information corresponding to the selected feature text; and determining a time difference between the current time and prescription issuing time, and determining whether the time difference is greater than the prescription validity duration); and displaying certain results about the collection and analysis (i.e., pushing information based on the prescription information and pushing the abnormal prompt information), at a high level of generality. Therefore, the aforementioned underlined claim limitations may reasonably be interpreted as mental/manual observations, evaluations, judgments, and/or opinions made by a person, such as a healthcare professional. If a claim limitation, under its broadest reasonable interpretation, covers concepts which are capable of being performed in the human mind or encompasses a human performing the step(s) mentally with the aid of a pen and paper, then it falls within the “Mental Processes” grouping of abstract ideas. See 2019 Revised PEG. Accordingly, claims 1, 13, and 15 recite an abstract idea that falls within the Mental Processes category.
Furthermore, Examiner notes that dependent claims 2-11, 17, and 19-24 further define the at least one abstract idea (and thus fail to make the abstract idea any less abstract) as set forth below. Examiner notes that: (1) dependent claims 5, 10, and 19 include limitations that are deemed to be additional elements, and require further analysis under Prong Two of Step 2A; and (2) dependent claims 2-4, 6-9, 11, 17, and 20-24 do not provide any limitations that are deemed to be additional elements which require further analysis under Prong Two of Step 2A. For example, claims 2-4 and 17 merely recites a mental step for verifying the prescription information and additional data that is pushed based on a verification result (i.e., this step is deemed to be reasonable performed manually using a pen and paper, because they merely represent additional information that can be written on a piece of paper).
Next, claims 6, 7, 20, and 21 merely recite steps for selecting a target drugstore based off of different criteria (i.e., these steps are deemed to be reasonably performed mentally or manually using a pen and paper, because they modify the data that is used for observations, evaluations, judgments, and/or opinions for selecting a target drugstore). Similarly, claims 8, 9, and 11 merely recite mental steps for determining whether the prescription information includes certain data and manual steps for pushing certain information (e.g., writing down an abnormal prompt) if the prescription information does not include the necessary certain data (i.e., these steps are deemed to be reasonably performed mentally or manually using a pen and paper, because they modify the data that is used for the observations, evaluations, judgments, and/or opinions). Lastly, claims 22-24 merely recite additional mental steps for: (1) obtaining prescription information comprising a name of physician and/or pharmacist; (2) determining whether the name of the physician and/or pharmacist is present in a pre-stored respective name list; and (3) producing a result that indicates the prescription information failed to pass verification in response to determining that the name physician and/or pharmacist is not present in the pre-stored respective name list (i.e., these steps are deemed to be reasonably performed mentally or manually using a pen and paper, because they amount to additional observations, evaluations, judgments, and/or opinions that a person is capable of performing in their mind and/or with the aid of pen and paper). As such, dependent claims 2-4, 6-9, 11, 17, and 20-24 do not provide any limitations that are deemed to be additional elements which require further analysis under Prong Two of Step 2A.
Step 2A of the 2019 Revised PEG - Prong Two
Regarding Prong Two of Step 2A of the 2019 Revised PEG, it must be determined whether the claim as a whole integrates the abstract idea into a practical application. As noted in the 2019 Revised PEG, it must be determined whether any additional elements in the claims are indicative of integrating the abstract idea into a practical application in a manner that imposes a meaningful limit on the judicial exception. The courts have indicated that additional elements merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application.” See MPEP § 2106.05 (f), (g), and (h).
In the present case, for independent claim 1, the additional limitations beyond the above-noted at least one abstract idea are as follows (where the bolded portions are the “additional limitations” while the underlined portions continue to represent the at least one “abstract idea”):
A computer-implemented (the Examiner submits that this additional element amounts to adding the words “apply it” (or an equivalent), or mere instructions to implement the abstract idea on a computer, see MPEP § 2106.05(f)) method for pushing information, comprising:
obtaining, from a user mobile terminal (the Examiner submits that this additional element amounts to adding the words “apply it” (or an equivalent), or mere instructions to implement the abstract idea on a computer, see MPEP § 2106.05(f)), a prescription image presenting prescription information, the prescription image is derived from a paper prescription (the Examiner submits that this additional element amounts to adding insignificant extra-solution activity as noted below, see MPEP § 2106.05(g); and the Examiner further submits that such steps are not unconventional as they merely consist of receiving data over a network, as evidenced by the Intellectual Ventures v. Symantec case, as noted below in the Step 2B Analysis Section, see MPEP § 2106.05(d));
recognizing the prescription image to obtain a recognition result which comprises position information configured to indicate a position of a character in the prescription image;
inputting the recognition result into a pre-trained prescription image analyzing model to obtain the prescription information presented in the prescription image (the Examiner submits that this additional element amounts to adding the words “apply it” (or an equivalent), or mere instructions to implement the abstract idea on a computer, see MPEP § 2106.05(f); and the Examiner further submits that this additional element amounts to generally linking the abstract idea to a particular field of use or technological environment as noted below, see MPEP § 2106.05(h)); and
pushing information to the user mobile terminal (the Examiner submits that this additional element amounts to adding the words “apply it” (or an equivalent), or mere instructions to implement the abstract idea on a computer, see MPEP § 2106.05(f)) based on the prescription information,
the method further comprising training a prescription image analyzing model to obtain the pre-trained prescription image analyzing model (the Examiner submits that this additional element amounts to adding the words “apply it” (or an equivalent), or mere instructions to implement the abstract idea on a computer, see MPEP § 2106.05(f); and the Examiner further submits that this additional element amounts to generally linking the abstract idea to a particular field of use or technological environment as noted below, see MPEP § 2106.05(h)), comprising:
obtaining a training sample set, wherein a training sample includes a sample recognition result and sample prescription information, the sample recognition result is recognized from a sample prescription image, and the sample prescription information is determined based on a position of each character of a corresponding sample recognition result in the sample prescription image,
wherein the sample recognition result is obtained by the following steps comprising:
selecting a pre-annotated feature text from the sample prescription image, the feature text representing a feature category which is to have corresponding prescription information;
determining a character string with a closest distance from the selected feature text based on a vector of a text direction, and
determining that character string as prescription information corresponding to the selected feature text; and
training the prescription image analyzing model by taking sample recognition results of training samples in the training sample set as an input, and by taking sample prescription information corresponding to inputted sample recognition results as an expected output (the Examiner submits that this additional element amounts to adding the words “apply it” (or an equivalent), or mere instructions to implement the abstract idea on a computer, see MPEP § 2106.05(f); and the Examiner further submits that this additional element amounts to generally linking the abstract idea to a particular field of use or technological environment as noted below, see MPEP § 2106.05(h)).
Similarly, for independent claims 13 and 15, the additional limitations beyond the above-noted at least one abstract idea are as follows (where the bolded portions are the “additional limitations” while the underlined portions continue to represent the at least one “abstract idea”):
An apparatus (the Examiner submits that this additional element amounts to adding the words “apply it” (or an equivalent), or mere instructions to implement the abstract idea on a computer, see MPEP § 2106.05(f)) for pushing information, comprising:
one or more processors (the Examiner submits that this additional element amounts to adding the words “apply it” (or an equivalent), or mere instructions to implement the abstract idea on a computer, see MPEP § 2106.05(f));
a storage apparatus (the Examiner submits that this additional element amounts to adding the words “apply it” (or an equivalent), or mere instructions to implement the abstract idea on a computer, see MPEP § 2106.05(f)), storing a pre-trained prescription image analyzing model which is configured to output prescription information when a corresponding recognition result derived from a paper prescription is input to the pre-trained prescription image analyzing model, wherein the pre-trained prescription image analyzing model is configured to (the Examiner submits that this additional element amounts to adding the words “apply it” (or an equivalent), or mere instructions to implement the abstract idea on a computer, see MPEP § 2106.05(f); and the Examiner further submits that this additional element amounts to generally linking the abstract idea to a particular field of use or technological environment as noted below, see MPEP § 2106.05(h)) identify prescription information based on proximity of a character string to a character in a feature text according to text direction, and also storing at least one instruction, wherein the at least one instruction, when executed by the processors, causes the processors to perform operations (the Examiner submits that this additional element amounts to adding the words “apply it” (or an equivalent), or mere instructions to implement the abstract idea on a computer, see MPEP § 2106.05(f)) comprising:
obtaining from a user mobile terminal (the Examiner submits that this additional element amounts to adding the words “apply it” (or an equivalent), or mere instructions to implement the abstract idea on a computer, see MPEP § 2106.05(f)) a prescription image presenting prescription information, the prescription image is derived from a paper prescription (the Examiner submits that this additional element amounts to adding insignificant extra-solution activity as noted below, see MPEP § 2106.05(g); and the Examiner further submits that such steps are not unconventional as they merely consist of receiving data over a network, as evidenced by the Intellectual Ventures v. Symantec case, as noted below in the Step 2B Analysis Section, see MPEP § 2106.05(d));
recognizing the prescription image to obtain a recognition result, which comprises position information configured to indicate a position of a character in the prescription image;
inputting the recognition result into the pre-trained prescription image analyzing model to obtain the prescription information presented in the prescription image (the Examiner submits that this additional element amounts to adding the words “apply it” (or an equivalent), or mere instructions to implement the abstract idea on a computer, see MPEP § 2106.05(f); and the Examiner further submits that this additional element amounts to generally linking the abstract idea to a particular field of use or technological environment as noted below, see MPEP § 2106.05(h)); and
pushing information to the user mobile terminal (the Examiner submits that this additional element amounts to adding the words “apply it” (or an equivalent), or mere instructions to implement the abstract idea on a computer, see MPEP § 2106.05(f)) based on the prescription information, comprising:
verifying the prescription information, and
pushing the information based on a verification result;
wherein the verifying the prescription information comprises:
extracting time-related information from the prescription information, wherein the time-related information comprises a prescription issuing time and a prescription validity duration;
determining a time difference between a current time and the prescription issuing time, and determining whether the time difference is greater than the prescription validity duration; and
pushing an abnormal prompt information when the verifying result indicates that the prescription information fails to pass the verification, comprising:
pushing the abnormal prompt information when determining that the time difference is greater than the prescription validity duration, wherein the abnormal prompt information is configured to indicate that a prescription indicated by the prescription image is expired; and
a non-transitory computer readable medium, storing a computer program thereon, wherein the computer program, when executed by a processor, causes the processor to perform operations comprising (as described in claim 15) (the Examiner submits that this additional element amounts to adding the words “apply it” (or an equivalent), or mere instructions to implement the abstract idea on a computer, see MPEP § 2106.05(f)). However, the recitation of these generic computer components and functions in claims 1, 13, and 15 are recited at a high-level of generality (i.e., using generic computer devices to perform the abstract idea of: (1) a method for pushing information, comprising: recognizing a prescription image to obtain a recognition result comprising position information indicating a position of a character in the prescription image; pushing information based on the prescription information; obtaining a training sample set, where the training sample includes a sample recognition result and sample prescription information; selecting pre-annotated feature text from the sample prescription image; determining a character string with a closest distance from the selected feature text; and determining that character string as prescription information corresponding to the selected feature text; and (2) a method for pushing information, comprising: identifying prescription information based on proximity of a character string to a character in a feature text according to text direction; recognizing a prescription image to obtain a recognition result comprising position information indicating a position of a character in the prescription image; pushing information based on the prescription information; verifying the prescription information via extracting time-related information including a prescription issuing time and a prescription validity duration from the prescription information, determining a time difference between a current time and the prescription issuing time, determining whether the time difference is greater than the prescription validity duration, and pushing abnormal prompt information when the prescription is expired), such that it amounts to no more than: (1) adding the words “apply it” (or is the equivalent of) with the judicial exception; mere instructions to implement an abstract idea on a computer; or merely uses a computer as a tool to perform an abstract idea; (2) adding insignificant extra-solution activity to the judicial exception; and (3) generally linking the use of a judicial exception to a particular technological environment or field of use. See MPEP §§ 2106.05(f)-(h). For the following reasons, the Examiner submits that the above identified additional limitations do not integrate the above-noted at least one abstract idea into a practical application.
- Claims that do no more than apply established methods of machine learning to a new data environment are not patent eligible. Recentive Analytics, Inc. v. Fox Corp., Fox Broadcasting Company, LLC, Fox Sports Productions, LLC, Case No. 23-2437, (Fed. Cir. 2025), pp. 10, 14. An abstract idea does not become non-abstract by limiting the invention to a particular field of use or technological environment. Id.
- The following is an example of court decisions that demonstrate merely applying instructions by reciting the computer structure as a tool to implement the claimed limitations (e.g., see MPEP § 2106.05(f)):
- Reciting only the idea of a solution or outcome without reciting details of how a solution to a problem is accomplished, e.g., see Intellectual Ventures I v. Symantec – similarly, the steps directed to: “inputting the recognition result into a pre-trained prescription image analyzing model to obtain the prescription information presented in the prescription image”; and “training the prescription image analyzing model by taking sample recognition results of training samples in the training sample set as an input, and by taking sample prescription information corresponding to inputted sample recognition results as an expected output”, merely recite the idea of a solution or outcome (i.e., performing these mental steps using a pre-trained model) without reciting the necessary details to show what the model does with the data.
- A commonplace bu