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 .
Priority
Application 18/953,799 was filed 11/20/2024, and claims priority to provisional application 63/624,463 filed 01/24/2024.
Status of Claims
This Final Office Action is in response to the amendments filed on 04/09/2026. Claims 26-36 are currently pending.
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 26-36 are rejected under 35 U.S.C. 101 because the claimed invention is directed to judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. Claims 26-36 are directed to a system, method, or product which are/is one of the statutory categories of invention. (Step 1: YES).
Claim 26, is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim recites a method and computing device for managing work records by managing a collaborative environment and displaying information. For Claim 26 the limitations of:
receiving, at an input […] of a compliance check system, a request to perform compliance testing of the regulated content items;
preprocessing, via […] compliance check system, the content items by modifying the content items before performing compliance testing of the content items;
identifying, from a plurality of compliance rulesets, a compliance ruleset satisfying a selection criteria; and
iteratively processing, via a compliance checker of the compliance check system, each content item of the plurality of content items, the processing comprising:
generating, via a prompt generator of the compliance checker, a tailored prompt for […] performing compliance testing of the content items, by identifying a configuration parameter from the compliance ruleset, the configuration parameter instructing […], on the operations to be performed on the compliance ruleset and the content items to reduce […] hallucinations;
processing, via […] the compliance check system, the tailored prompt by verifying compliance of the content item with the compliance ruleset;
receiving, via an output […] of the compliance check system, a compliance determination dataset indicating whether the content item satisfies one or more criteria within the compliance ruleset; and
outputting, via the output […] of the compliance check system, the compliance determination dataset from the compliance checker […] after performing post- processing operations on the compliance determination dataset, the post-processing operations comprising:
adding description to explain the compliance determination dataset;
comparing the compliance determination dataset with previously determined the compliance determination dataset;
providing suggestions on adjusting the content item to ensure that the content item satisfies one or more criteria within the compliance ruleset;
automatically correcting the compliance determination dataset; and
recursively evaluating the compliance of the content item by generating additional prompts based on an outcome of earlier prompts, as drafted, are processes that, under the broadest reasonable interpretation, covers certain methods of organizing human activity (i.e., commercial or legal interactions and/or managing personal behavior including following rules or instructions) but for recitation of generic computer components. The Examiner notes that certain “method[s] of organizing human activity” includes a person's interaction with a computer (see MPEP 2106.04(a)(2)(II)). If a claim limitation, under its broadest reasonable interpretation, covers managing personal behavior or interactions between people but for the recitation of generic computer components, then it falls within the “certain methods of organizing human activity” grouping of abstract ideas. Accordingly, Claim 26 recites an abstract idea. (Step 2A- Prong 1: YES. The claims recite an abstract idea).
This judicial exception is not integrated into a practical application. Claim 26 recites the additional elements of a processor, machine learning model, and user interface, that implements the identified abstract idea. These additional elements are not described by the applicant and are recited at a high-level of generality (i.e., one or more generic computers performing a generic computer functions) such that it amounts no more than mere instructions to apply the exception using a generic computer components. Accordingly, even in combination these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Claim 26 is directed to an abstract idea. (Step 2A-Prong 2: NO: the additional claimed elements are not integrated into a practical application).
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of a processor, machine learning model, and user interface, to perform the noted steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept (“significantly more”). Accordingly, even in combination, these additional elements do not provide significantly more. As such claim 26 is not patent eligible. (Step 2B: NO. The claims do not provide significantly more).
Dependent Claims 27-36 are similarly rejected because they either further define/narrow the abstract idea of independent claim 26 as discussed above and/or do not further limit the claim to a practical application or provide an inventive concept such that the claims are subject matter eligible even when considered individually or as an ordered combination. Claim(s) 27 merely describe(s) the plurality of regulated content items being of mixed data type. Claim(s) 28 merely describe(s) the preprocessing comprising removing a portion of the content items that is irrelevant to compliance testing, converting a format of the content items, segmenting the content items into segments for ease of processing, or the like. Claim(s) 30 merely describe(s) the selection criteria comprising a content type of the content item. Claim(s) 31 merely describe(s) the selection criteria comprising a presentation medium of the content item, the presentation medium is at least one of a print media, a radio media, a website media, and a billboard media. Claim(s) 32 merely describe(s) wherein the selection criteria comprises, a user interaction with a compliance ruleset selection interface. Claim(s) 33 merely describe(s) wherein the selection criteria comprises, metadata associated with the mixed data type content item, metadata comprising at least one of a size of the content item, a source of the content item, a last modification time when the content item was modified and, a last access time when the content item was accessed. Claim(s) 34 merely describe(s) the content items being provided. Claim(s) 35 merely describe(s) the content items being identified. Claim(s) 36 merely describe(s) wherein the compliance check system searches, identifies, and retrieves the content items using various network information searching techniques.
Dependent Claim(s) 29 recites limitations that further define the abstract idea noted in independent claim 26. In addition, it recites the additional elements of a large language model. The large language model is recited at a high level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computing component. Even in combination, this additional element does not integrate the abstract idea into a practical application and does not amount to significantly more than the abstract idea itself.
Claims 29, 34-35 include the additional elements of a machine learning model and user interface. The machine learning model and user interface are analyzed in the same manner as the machine learning model and user interface in the independent claim and does not provide a practical application or significantly more for the same reasons above. Therefore claims 27-36, are considered patent ineligible for the reasons given above.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 26-36 are rejected under 35 U.S.C. 103 as being unpatentable over Nair (US 11681417 B2), in view of Lo (US 20250117627 A1).
Regarding Claim 26,
Nair discloses, (New) A computer implemented method of generating accurate compliance check results of a plurality of regulated content items comprising: (Nair, Abstract)
receiving, at an input processor of a compliance check system, a request to perform compliance testing of the regulated content items; “In some examples, the UI 400b of FIG. 4B includes an option 425 (e.g., in the form of a button) to start analyzing the codes 422 for accessibility guidelines violation" (Nair Col. 16 Lines 30-32, Col. 10 Lines 53-56).
preprocessing, via a machine learning model of the compliance check system, the content items by modifying the content items before performing compliance testing of the content items; "In another example, the suggestion module generates and recommends descriptive alternate texts using the generated keywords (and/or keywords extracted via the image processing, such as OCR). "(Nair Col. 9 Lines 20-23).
identifying, from a plurality of compliance rulesets, a compliance ruleset satisfying a selection criteria; and "Also discussed herein are techniques for creating an accessibility guidelines compliant template for drafting digital documents. For example, the system (such as a template building module of the system) builds or generates a template for drafting digital documents. The template can include space-holder components, such as empty textboxes and forms, and/or space for inserting images, that a user can later use to draft a digital document…. The system (such as the template building module) sets a range of attributes for one or more parameters of the template, such that the range conforms to one or more corresponding accessibility guidelines. The accessibility guidelines used can be any appropriate accessibility guidelines, such as WCAG accessibility guidelines (e.g., WCAG 2.1 accessibility guidelines, or any versions thereof). For example, a line spacing less than 1.0 can violate an accessibility guideline." (Nair Col. 9 Lines 37-59).
iteratively processing, via a compliance checker of the compliance check system, each content item of the plurality of content items, the processing comprising: compliance checker, a tailored prompt for, "In some embodiments, the developer can choose to download an accessibility report (e.g., by selecting the appropriate option displayed on top right corner of the UI 400d), which lists the violations identified by the system 102. FIG. 4D1 is an accessibility report 427 listing the violations discussed with respect to FIG. 4D. The accessibility report 427 is self-explanatory, in view of the discussion with respect to FIG. 4D. The words or phrases in the “description” column of FIG. 4D1 are mere examples, and different words or phrases can be used to describe the corresponding violation. As discussed, the three violations of three corresponding accessibility guidelines, as presented in FIG. 4D, are mere examples, and the violation determination module 106 of the system 102 can check for possible violations of many other accessibility guidelines. Table 1 below includes various example accessibility guidelines that are checked for possible violations by the violation determination module 106" (Nair Col. 18 Line 49- Col. 18 Line 67)
processing"The image analysis module is to identify that the first image lacks any alternate text; the suggestion module is to generate an alternate text, based at least in part on the one or more keywords, wherein the alternate text is to be read out, in lieu of the first or second image, by a text-to-speech synthesizer when the digital content is to be presented; and the correction module is to provide an option to associate the alternate text with the first image or the selected second image" (Nair Col. 32 Lines 6-13).
receiving, via an output processor of the compliance check system, a compliance determination dataset indicating whether the content item satisfies one or more criteria within the compliance ruleset; and "For example, an accessibility verification and correction system (also referred to herein simply as a “system”) checks for compliance of digital content to the accessibility guidelines, and detects possible violations. As will be discussed in further detail below, according to some such embodiments, the system assigns an accessibility score to the digital content, where the accessibility score is indicative of a severity and/or a number of any detected violations of accessibility guidelines" (Nair Col. 4 Lines 38-46, Figure 4E).
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outputting, via the output processor of the compliance check system, the compliance determination dataset from the compliance checker on a user interface " As will be discussed in further detail below, according to some such embodiments, the system assigns an accessibility score to the digital content, where the accessibility score is indicative of a severity and/or a number of any detected violations of accessibility guidelines. Additionally, the system allows the developer to the view the current version of the digital content, the accessibility score, the violation(s), and options to correct the violations in corresponding different areas of a same User Interface (UI). In some such examples, the system further allows the developer to view and select one or more correction options suitable for correcting the violations" (Col. 4 Line 42-53, Figure 4E).
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after performing post- processing operations on the compliance determination dataset, the post-processing operations comprising: adding descriptions to explain the compliance determination dataset; "The violation 438a is, for example, a critical violation regarding absence of discernable text associated with the button 426c. For example, the pseudo codes 423 of FIG. 4B indicate the following: “Discernable text for Button 426c: none.” Thus, here the example attribute “Discernable text for Button 426c” has a value of “none” or null value, which violates a corresponding one of the accessibility guidelines. The violation determination module 106 of the system 102 checks the codes 422, to identify that the button 426c does not have any associated discernable text" (Nair Col. 17 Lines 50-59).
providing suggestions on adjusting the content item to ensure that the content item satisfies one or more criteria within the compliance ruleset; "For example, referring to the UI 400e of FIG. 4E, assuming that the manual selection option 434b is selected, a first window 440a within the UI 400e is displayed to correct the violation 438a, and a second window 440b within the UI 400e is displayed to correct the violation 438b" (Nair Col. 23 Lines 30-34).
automatically correcting the compliance determination dataset; and " For example, an auto-selection option would allow the system 102 to automatically choose one or more system-recommended correction options to correct the different violations, and then the system 102 automatically alters the associated codes using the system-recommended correction options. In another example, a manual selection option allows the developer to manually select correction options to alter the codes, and then the system 102 automatically alters the code based on the developer-selected correction options, thereby correcting the different violations" (Nair Col. 28 Lines 3-13).
Nair discloses a compliance check system using machine learning techniques, but fails to disclose the checker using machine learning, the operations to be performed on the compliance ruleset and the content items to reduce machine learning model hallucinations, recursively evaluating the compliance of the content item by generating additional prompts based on an outcome of earlier prompts, and comparing the compliance determination dataset with previously determined the compliance determination dataset. Lo discloses information requiring compliance with guidelines, rules, regulations and/or other standards for accuracy and conformance employing one or more machine learning and/or heuristic search algorithms. Lo, further discloses,
on the operations to be performed on the compliance ruleset and the content items to reduce machine learning model hallucinations; "The following embodiments provide technical solutions and technical improvements that overcome technical problems, drawbacks and/or deficiencies in the technical fields involving data search scalability when searching across multiple private and/or public data sources, data source integration where each data source typical requires a customized and particular set of tools for interaction, LLM answers that often result in false information delivered as if it were true (commonly referred to as “hallucination”) and/or in violation of rules, standards and/or guidelines. As explained in more detail, below, technical solutions and technical improvements herein include aspects of improved integration of machine learning (ML)-based software agents with one or more LLMs such that the LLM(s) provide orchestration of the ML-based software agents enabling improved scalability of data sources, search and analytics, while the ML-based software agents provide parallel checks and verifications of each other and the LLM to reduce hallucination and non-compliant information" (Lo Col. 5 Line 63- Col. 6 Line 15). "The ruleset itself can be provided in natural language. Recursive capability continuously modifies the input text/intents until compliance is met, while logging each of the changes made to the text/intents. Large language models are susceptible to hallucination, this is a side effect of how many large language models are trained. In financial services applications where the provider of the chat service is a regulated entity, tolerance for hallucinations is low. In some embodiments, the compliance agent 116 may be an adversarial agent that tests the response 102 to ensure compliance. For example, in financial-related and/or health-related implementations, certain rules, regulations and/or guidelines must be followed to meet industry standards. Thus, the compliance agent 116 works separate to the text generation, retrieval, agents and does not know what the user's request is. Instead, the compliance agent 116 may first test whether the text meets a compliance ruleset, and secondly, progressively modify the text until it is compliant. In some embodiments, the compliance agent 116 may progressively modify the test by applying small variations that do not change the meaning of the response 102 until the response 102 meets the compliance ruleset" (Lo Col. 18 Lines 17-39).
recursively evaluating the compliance of the content item by generating additional prompts based on an outcome of earlier prompts. "In some embodiments, where the compliance verification is an indication of a failure or non-compliance, the response 102 may be input a response variation generator 306 of the compliance agent 116 to output a variation to the response 102. In some embodiments, the variation to the response 102 may be input into the model orchestration LLM 114 with the at least one compliance verification prompt to output at least one new compliance verification of the variation to the response 102. This process may be repeated until the response 102 is determined to have passed the compliance ruleset." (Lo Col. 21 Lines 8-18).
Examiner Note: The process is repeated since the previous prompts did not match compliance
for the machine learning model “In some aspects, the techniques described herein relate to a method, further including: inputting, by the at least one processor, at least one compliance rule into at least one compliance verification machine learning agent to output at least one compliance verification prompt based at least in part on a plurality of compliance verification parameters; wherein the at least one compliance verification prompt is configured to cause the model orchestration large language model to verify compliance of the at least one natural language response with the at least one compliance rule; and inputting, by the at least one processor, the at least one compliance verification prompt into the model orchestration large language model to output at least one compliance verification of the at least one natural language model based at least in part on the trained parameters of the model orchestration large language model and the at least one context attribute” (Lo Par. 0008).
comparing the compliance determination dataset with previously determined the compliance determination dataset; “In some embodiments, the training query may be provided to the classifier model to produce a predicted class. In some embodiments, an optimizer associated with the classifier model may then compare the predicted class with the known output of a training pair to determine an error of the predicted class” (Lo Col. 14 Lines 15-20).
It would have been obvious to one of ordinary skill in the art at the time of the claimed invention to have combined the compliance check system of Nair with using machine learning where the operations to be performed on the compliance ruleset and the content items to reduce machine learning model hallucinations, comparing the compliance dataset with previously determined dataset, and recursively evaluating the compliance of the content item by generating additional prompts based on an outcome of earlier prompts of Lo to provide improved data timeliness and accuracy, and reduced infrastructure costs (Lo Col. 6 Lines 62-63).
Regarding Claim 27,
The combination of Nair and Lo disclose the method of claim 26, as shown above. Nair further discloses, (New) wherein the plurality of regulated content items are of mixed data type. "the digital content includes other types of emails and/or brochures, content of a webpage, including a landing page of a website, digital presentations, a rich text document, a spreadsheet document, a Portable Document Format (PDF) document, an audio file, a video, and/or any other appropriate type of digital content that a disabled person can access" (Nair Col. 5 Lines 25-31).
Regarding Claim 28,
The combination of Nair and Lo disclose the method of claim 26, as shown above. Nair further discloses, (New) wherein preprocessing comprises removing a portion of the content items that is irrelevant to compliance testing, converting a format of the content items, segmenting the content items into segments for ease of processing, or the like. "In some such examples, due to one or more of the above discussed factors, the system determines that it may be desirable to replace the original image with an alternative image, or to tag the image with a text label indicating the text written on the image, as extracted via an OCR process" (Nair Col. 8 Lines 51-55).
Examiner Note: The image is being converted to have text
Regarding Claim 29,
The combination of Nair and Lo disclose the method of claim 26, as shown above. Lo further discloses, (New) wherein the machine learning model is a large language model performing at least a portion of the preprocessing. "In some embodiments, a user may interact with an LLM orchestrated data search platform 110 via a graphical user interface (GUI) of a user computing device 140. In some embodiments, the LLM orchestrated data search platform 110 may leverage a model orchestration LLM 114 to orchestrate data retrieval and data processing of one or more data sources 120 and/or data record processing machine learning (ML) agents 130. In some embodiments, the user may query the LLM orchestrated data search platform 110 for information associated with one or more domains, such as financial instruments, medical information, patient data, scientific information, personal data, business data, among other information and/or data associated with one or more domains or any combination thereof. In some embodiments, to improve accuracy of the data as well as presentation in the GUI, a context engine 112 may inject context data into the model orchestration LLM 114 based on the identity of the user, the user's query, among other factors or any combination thereof. The model orchestration LLM 114 may then task the data sources 120 and/or agent(s) 130 to search, generate, transform or otherwise act on the query to obtain a response 102 to the user's query. A compliance agent 116 may verify the response 102 for compliance to one or more rules defining one or more standards, regulations, guidelines or other rules or any combination thereof. The verified response may then be returned to the user via the GUI of the user computing device 140 to provide the user with compliant, context-dependent information" (Lo Col. 7 Lines 34-61).
It would have been obvious to one of ordinary skill in the art at the time of the claimed invention to have combined the compliance check system of Nair and Lo with the machine learning model being a large language model performing at least a portion of the preprocessing of Lo for improved data search, data source integration, data analytics and user interfacing (Lo Col. 6 Lines 58-59).
Regarding Claim 30,
The combination of Nair and Lo disclose the method of claim 26, as shown above. Nair further discloses, (New) wherein the selection criteria comprises a content type of the content item. "At 364 of the method 360, the system 102 (such as the template building module 118 of the system 102, illustrated in FIGS. 1 and 2) builds or generates a template for drafting digital documents. According to an embodiment, the template includes space-holder components, such as empty textboxes and forms, and space for inserting images. In any such cases, a user can use the template to draft a digital document that meets a given set of accessibility guidelines. The template can be used, for example, for drafting marketing emails, marketing brochures, web pages, or any other digital content to be consumed by an audience that may include consumers that can benefit from such accessibility. The developer can generate many different templates, each having its unique layout. When a user is to draft a digital document, the user can select from one of these layouts, and use the corresponding template to draft an accessibility-compliant digital document" (Nair Par. 28 Lines 51-67).
Regarding Claim 31,
The combination of Nair and Lo disclose the method of claim 30, as shown above. Nair further discloses, (New) wherein the selection criteria comprises a presentation medium of the content item, the presentation medium is at least one of a print media, a radio media, a website media, and a billboard media. "At 364 of the method 360, the system 102 (such as the template building module 118 of the system 102, illustrated in FIGS. 1 and 2) builds or generates a template for drafting digital documents. According to an embodiment, the template includes space-holder components, such as empty textboxes and forms, and space for inserting images. In any such cases, a user can use the template to draft a digital document that meets a given set of accessibility guidelines. The template can be used, for example, for drafting marketing emails, marketing brochures, web pages, or any other digital content to be consumed by an audience that may include consumers that can benefit from such accessibility. The developer can generate many different templates, each having its unique layout. When a user is to draft a digital document, the user can select from one of these layouts, and use the corresponding template to draft an accessibility-compliant digital document" (Nair Col. 28 Lines 51-67).
Regarding Claim 32,
The combination of Nair and Lo disclose the method of claim 30, as shown above. Nair further discloses, (New) wherein the selection criteria comprises, a user interaction with a compliance ruleset selection interface "In some such examples, when the developer selects an option to analyze the codes, another UI is presented by the system. The digital content is rendered and presented in accordance with the codes within a first area of this UI. … For example, the developer can select another version (e.g., newer or older version than version 2.1) of the WCAG accessibility guidelines, or can select other appropriate accessibility guidelines, and/or can create accessibility guidelines" (Nair Col. 5 Lines 55-67).
Regarding Claim 33,
The combination of Nair and Lo disclose the method of claim 30, as shown above. Nair further discloses, (New) wherein the selection criteria comprises, metadata associated with the mixed data type content item, metadata comprising at least one of a size of the content item, a source of the content item, a last modification time when the content item was modified and, a last access time when the content item was accessed. "In another example, the template can set a minimum font size for texts within the digital documents, a slightly larger minimum font size for headings, and so on, in accordance with corresponding accessibility guidelines. Thus, any digital documents drafted using the template must adhere to such minimum font sizes, thereby ensuring that the digital documents comply with the corresponding accessibility guidelines associated with font size" (Nair Col. 29 Lines 16-23).
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Regarding Claim 34,
The combination of Nair and Lo disclose the method of claim 26, as shown above. Nair further discloses, (New) wherein the content items are provided via the user interface. "The digital content presented in accordance with the code is displayed on a first area of a User Interface (UI), data indicative of the violation is displayed on a second area of the UI, and an option to correct the violation is displayed on a third area of the UI. In response to receiving an input indicative of a selection of the option to correct the violation, one or more correction options to correct the violation are provided" (Nair Col. 1 Lines 59-66).
Regarding Claim 35,
The combination of Nair and Lo disclose the method of claim 34, as shown above. Nair further discloses, (New) wherein the content items are identified, uploaded, or generated via the user interface. "In some embodiments, the system provides a User Interface (UI). The UI can be launched within a web browser, or via an application installed on a computing device. The accessibility verification and correction system can access the codes in one of many different manners. In some examples, the UI provides an option to upload the codes for the digital content from a file that is stored locally within a computing device, or stored remotely such as in the cloud or in the remote digital content database" (Nair Col. 5 Lines 32-40).
Regarding Claim 36,
The combination of Nair and Lo disclose the method of claim 26, as shown above. Nair further discloses, (New) wherein the compliance check system searches, identifies, and retrieves the content items using various network information searching techniques. "The system (such as a suggestion module of the system) recommends, based on the one or more key words, one or more images to replace the original image and/or alternate text for the image. For example, the suggestion module searches one or more image databases to find at least one recommended image, which can be used as an alternative to the original image. In another example, the suggestion module generates and recommends descriptive alternate texts using the generated keywords (and/or keywords extracted via the image processing, such as OCR). For example, an example alternate text can be: “This is an image of a cheese pizza, with one slice partially removed.” In some embodiments, an accessibility score associated with the recommended image and/or the recommended alternate texts are higher than those generated for the original image and/or the original alternate text. The developer can choose to select the recommended image and/or the recommended alternate text, or choose to use the original image and/or the original alternate text, as discussed in further detail herein in turn. In some instances, if the developer selects the recommended image and/or the recommended alternate text, the system alters the associated codes, to reflect the new image and/or the new alternate text" (Nair Col. 9 Lines 14-36).
Response to Arguments
Applicant's arguments filed 04/09/2026 with respect to 35 U.S.C. § 101, have been fully considered but they are not persuasive. Applicant argues that operations (1)-(6) and (a)-(e) are not steps that can practically be performed mentally and therefore does not recite organizing human activity. The Examiner Respectfully disagrees. MPEP 2106.04(a)(2)(II) states that a claimed invention is directed to certain methods of organizing human activity if the identified claim elements contain limitations that encompass fundamental economic principles or practices, commercial or legal interactions, or managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions). The Examiner submits that the identified claim elements represent a series of rules or instructions that a person or persons, with or without the aid of a computer, would follow to receive a request for compliance checking by preprocessing data, identifying a criteria, generating a prompt from a ruleset, verifying compliance, outputting the determination, and post processing the determination by adding descriptions and suggestions to the content. Because the claim elements fall under a series of rules or instructions that a person or persons would follow to ensure compliance checks of regulated content, the claimed invention is directed to an abstract idea.
Applicant further argues that Claim 26 is not directed to generic computer functions but are directed to a specific implantation including specific components. The additional elements of an input/output processor, machine learning model, and user interface are recited at a high level of generality and do not amount to a practical application that integrates the abstract idea into a specific technical improvement in computer functionality or another technology. The claimed features do not reflect an improvement to machine learning technology, but rather use that technology as a tool to perform the abstract analysis. Therefore the claims do not recite significantly more. At best, the problems(s) described in the as-filed disclosure are business problems. Further, there is no technical architecture that would amount to a practical application/significantly more, only high-level instructions of verifying compliance.
Applicant further argues that the courts have held computer-implemented processes to be significantly more than an abstract idea, where generic computer components are able in combination to perform functions that are not merely generic. The Examiner has fully considered this argument, but respectfully disagrees. The additional elements taken alone, and in combination are no more than mere instructions to perform the abstract idea on a generic computer, as it is merely instruction the computer to perform basic computing tasks to carry out the abstract idea. Nothing in the claims, even when considered as a whole, provides significantly more than the abstract idea, including the alleged combination of steps.
Applicant argues that the improvements are analogous to those of DDR Holdings, LLC. v. Hotels.com. The Examiner respectfully disagrees, and the applicant has not provided a convincing argument as to how this court cause is relevant to the present discussion. According to MPEP 2106.05(d), “The claims in DDR Holdings were directed to systems and methods of generating a composite webpage that combines certain visual elements of a host website with the content of a third- party merchant. 773 F.3d at 1248, 113 USPQ2d at 1099. The court found that the claim had additional elements that amounted to significantly more than the abstract idea, because they modified conventional Internet hyperlink protocol to dynamically produce a dual-source hybrid webpage, which differed from the conventional operation of Internet hyperlink protocol that transported the user away from the host's webpage to the third party's webpage when the hyperlink was activated. 773 F.3d at 1258-59, 113 USPQ2d at 1106-07." At the time of the invention in DDR holdings, which was decided in December 2012, but was filed in 1989, producing a dual-source hybrid webpage was an unconventional technical solution to a technology-based system, at the time of filing. The examiner does not find this relevant to the disclosed method filed in 2024, since the alleged improvement is merely reducing hallucinations for content compliance determination without specifically limiting its use. Therefore, the argument is not persuasive, and the rejection under 35 U.S.C. 101 stands.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Emily M Kraisinger whose telephone number is (703)756-4583. The examiner can normally be reached M-F 7:30 AM -4:30 PM.
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, Jessica Lemieux can be reached at 571-270-3445. 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.
/E.M.K./Examiner, Art Unit 3626
/JESSICA LEMIEUX/Supervisory Patent Examiner, Art Unit 3626
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