Office Action Analysis — App 19646249 (public record)
Full Analysis

Office Action Response Analysis · Non-Final (CTNF)

App. No. 19/646,249

Art Unit
2128
Examiner
LEE, TSU-CHANG
Mailed
06/02/2026
Response period stated in the OA
“3 MONTHS FROM THE MAILING DATE OF THIS COMMUNICATION”
Rejections
§101 ×3
Claims
20 rejected
Generated
Aug 20, 2026

Because this is a §101-only action with no asserted prior art, the posture centers on integration (Prong 2) and the Berkheimer evidentiary gap (Step 2B), where the highest-value lever — feedback-driven model improvement — is also the one most exposed to a 'result vs. mechanism' comeback, so counsel may weigh whether the current claim language captures the model-modification mechanism or whether an amendment anchoring that mechanism would strengthen the integration theory. Several arguments (2, 4, 5) identify defects the examiner can cure by re-articulation rather than by withdrawal, suggesting counsel consider these as pressure points that pair well with an interview given this examiner's documented interview-to-allowance correlation and low average number of actions to allowance. The choice between pressing the arguments as written and amending to bring the fine-tuning/model-improvement structure into the claims is a strategic judgment for counsel, informed by how much of the asserted technological improvement is presently reflected in the claim scope.

Examiner Tsu-Chang Lee (AU 2128): allowance rate 83% (n=281); avg 1.72 OAs to allowance; interviews held in 46% of cases, and when an interview was held allowance followed 85% of the time (correlation, not causation); RCE filed in 36% of cases. Based on n=288 applications; USPTO public data, 2016-01-01..2022-12-31. Correlational — it informs, it never decides.

Generated on a published USPTO office action — no confidential disclosure involved. First-pass analysis for attorney review — not a drafted response.

1.

Indicated Allowable Subject Matter & Examiner Interview

Examiner interview (MPEP 713) — a consideration. The strongest candidate arguments below are close calls (see the likely examiner responses in the Argument Bank), so an examiner interview to test the arguments and probe what would put the case in condition for allowance may be worth weighing before filing a written response.

2.

Per-Claim Strategy

An at-a-glance recommendation per rejected claim, composed deterministically from the analysis below. A triage summary for counsel to weigh, not a decision.

ClaimRejectionsRecommended pathBasisFallback amendmentConfidence
Claims 1–20§101 (eligibility)ArgueEligibility rebuttal (#1)high
3.

Argument Bank

Candidate arguments for counsel, ranked strongest-first — brainstorming inputs for counsel to evaluate, not a drafted response.

1

Feedback-driven AI-model fine-tuning is a practical application / technological improvement (Step 2A Prong 2)

Eligibility rebuttalClaim 1Claim 2Claim 3Claim 4Claim 5Claim 6Claim 7Claim 8Claim 9Claim 10Claim 11Claim 12Claim 13Claim 14Claim 15Claim 16Claim 17Claim 18Claim 19Claim 20

provide feedback to the AI model indicating the one or more portions to fine-tune the AI model (claim 1); automatically providing feedback to the AI model indicating one or more portions of the first representation that contributed to the approval or the rejection of the output (claim 7); provide feedback to the AI model indicating one or more reasons for flagging the output of the AI model as the anomaly (claim 14)

For counsel to weigh: each independent claim closes a loop that uses the anomaly-detection result to fine-tune or improve the AI model itself — a candidate technological improvement under MPEP § 2106.05(a) that integrates any recited exception into a practical application at Step 2A Prong 2 (Enfish/McRO line). The office action dismisses the feedback step for claims 7-13 as mere 'filed of use and technological environment' under MPEP 2106.05(h) (OA1 rejection 2(c)) and does not separately address, at Prong 2, that the feedback is directed to modifying the operation of the AI model rather than to a field of use. Counsel may argue that improving how a generative AI model performs by feeding back the specific portions of a representation that caused a rejection is an improvement to the functioning of the computer/model, not an abstract idea implemented on a generic computer. This lever targets the Prong 2 analysis directly, where a finding of integration ends the inquiry in the applicant's favor.

  • Claim 1: 'generate a detailed report outlining one or more reasons for blocking the transmission ... indicating one or more portions of the first representation that contributed to the match accuracy failing to satisfy the anomaly detection threshold; and provide feedback to the AI model indicating the one or more portions to fine-tune the AI model'
  • Claim 14: 'provide feedback to the AI model indicating one or more reasons for flagging the output of the AI model as the anomaly'
  • Office action, claims 7-13 Prong 2: characterizes 'automatically providing feedback to the AI model indicating one or more portions of the first representation that contributed to the approval or the rejection of the output' as 'filed of use and technological environment (see MPEP 2106.05(h))'
MPEP § 2106.05(a) — improvements to the functioning of a computer or to another technology; § 2106.04(d) — integration into a practical application (Step 2A Prong 2)

Risk The examiner will likely respond that 'fine-tune the AI model' is claimed at a high level of generality with no disclosed technical mechanism, and reassert it as linking the exception to a technological environment (2106.05(h)). Prosecution-history caution: framing the invention as an 'improvement to AI model functioning' characterizes the claims around the feedback loop and may narrow scope in the file wrapper as to what the claims cover; confirm the specification actually describes the fine-tuning mechanism before committing counsel to that characterization.

Likely examiner response survives — moderate

For an improvement-to-technology theory to control at Step 2A Prong 2 under MPEP § 2106.05(a), the claimed improvement must be reflected in the claim language and be an improvement to the functioning of the computer or model itself — not merely a recited desired result. The examiner can respond that the hooks recite only 'provide feedback to the AI model indicating the one or more portions to fine-tune the AI model' (claim 1) / 'automatically providing feedback ... indicating one or more portions ... that contributed to the approval or the rejection' (claim 7) — i.e., outputting information — without reciting HOW the model is retrained or its parameters altered. On that reading the examiner can maintain the MPEP 2106.05(h) field-of-use / 2106.05(g) post-solution characterization (per OA1 rejection 2(c)), arguing the model is invoked as a generic tool that receives data and the loop is nominally recited (no fine-tuning algorithm claimed), so the exception is not integrated into a practical application.

How to adjust Strongest if counsel can point to specific specification passages describing HOW the feedback modifies model operation (a concrete retraining/parameter-adjustment mechanism) and tie the improvement to what the claim actually recites, framing it in the Enfish/McRO 'improvement to the model' register rather than 'better result.' If the claim recites only 'provide feedback' as an output with no operative fine-tuning step, consider amending to bring the model-modification mechanism into the claim so the Prong-2 improvement is anchored in claim scope — arguing pure integration on the current wording carries category-of-use risk.

2

Berkheimer evidentiary gap — unsupported 'well-understood, routine, conventional' finding at Step 2B

Eligibility rebuttalClaim 1Claim 2Claim 3Claim 4Claim 5Claim 6Claim 7Claim 8Claim 9Claim 10Claim 11Claim 12Claim 13Claim 14Claim 15Claim 16Claim 17Claim 18Claim 19Claim 20

input, into an AI model, the user prompt to cause the AI model to generate an output based on the user prompt; retrieve, based on the user prompt, the data over the time period (claim 1); block transmission of the report to the user device (claim 1)

For counsel to weigh: at Step 2B the office action concludes the additional elements are well-understood, routine, and conventional by citing only MPEP 2106.05(d)(II) categories ('receiving or transmitting data over a network,' 'electronic record keeping,' 'storing and retrieving information in memory') (OA1 rejections 1-2, Step 2B). Under Berkheimer and MPEP § 2106.05(d), a conventionality finding must be supported by one of the four evidentiary showings (a court holding, a citation to a publication, an official-notice-type statement, or an applicant admission). Counsel may press that the office action supplies no such support for treating the specific ordered combination — generating dual representations, comparing them for match accuracy against an anomaly-detection threshold, and gating transmission — as conventional, and that the generic categories cited do not reach that combination. This is an evidentiary defect in the rejection that the examiner must cure to sustain Step 2B.

  • Office action, claim 1 Step 2B: 'The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), "receiving or transmitting data over a network", "electronic record keeping," and "storing and retrieving information in memory")'
  • Office action provides no § 1.132 declaration, publication citation, or applicant admission supporting the WURC characterization of the claimed comparison-and-gating combination
MPEP § 2106.05(d) — Berkheimer; a WURC finding requires factual support (§ 1.132 declaration, publication, official notice, or admission)

Risk The examiner will likely respond that the elements are recited at a high level of generality and re-cite the 2106.05(d)(II) enumerated categories, and may add a supporting citation on the next action to cure the gap. Note this is an evidentiary/procedural lever, not a merits win — even if pressed successfully it shifts the burden rather than establishing eligibility.

Likely examiner response survives — moderate

The examiner can respond that the elements flagged (receiving/transmitting data over a network, electronic recordkeeping, storing/retrieving information in memory) are exactly the categories MPEP § 2106.05(d)(II) enumerates as court-recognized well-understood, routine, conventional functions, and that citing those enumerated examples is itself the Berkheimer support MPEP § 2106.05(d) contemplates. The examiner can further invoke the settled rule that the abstract idea itself (the dual-representation generation, match-accuracy comparison, and threshold gating) cannot supply the inventive concept, so any novelty in that ordered arrangement is analyzed at Step 2A, not as 'significantly more' at 2B. Practically, this is a curable defect: the examiner can add a citation or an official-notice statement and re-issue, rather than withdraw the rejection.

How to adjust The ordered-combination angle is the durable part — press that the § 2106.05(d)(II) categories reach the individual generic functions but not the specific arrangement (generate dual representations → compare against an anomaly-detection threshold → gate transmission), for which the record shows no evidentiary support. Recognize this lever tends to force a better-supported rejection, not allowance; treat it as pressure to prompt an interview/amendment posture rather than a stand-alone win, given the examiner's high interview-to-allowance correlation.

3

Additional elements dismissed piecemeal rather than evaluated as an ordered combination (Prong 2 / Step 2B)

Eligibility rebuttalClaim 1Claim 2Claim 7Claim 14

based on the match accuracy failing to satisfy the anomaly detection threshold, block transmission of the report to the user device (claim 1); based on the comparison, flag the output of the AI model as an anomaly (claim 14)

For counsel to weigh: the office action addresses the additional elements one-by-one — labeling the AI-model step as generic-computer implementation (2106.05(f)), the receive/input/retrieve steps as pre-solution data gathering (2106.05(g)), and the block-transmission step as field of use (2106.05(h)) — but the record does not show an evaluation of the elements as an ORDERED COMBINATION as MPEP § 2106.05(I) requires. Counsel may argue that the combination of validating an AI output against retrieved source data, gating transmission on an anomaly-detection threshold, and looping the failure reasons back to the model is a specific, non-generic arrangement that must be assessed together at both Prong 2 and Step 2B. Characterizing anomaly-based blocking of an outbound report as mere 'field of use' arguably understates its role as an operative control on the system's output. This attacks the analytical completeness of the rejection.

  • Office action, claim 1: separately labels elements under '2106.05(f)', '2106.05(g)', and '2106.05(h)' and concludes 'the additional elements individually or in combination do not integrate the judicial exception into a practical application' without a combination-specific analysis
  • Claim 1: 'based on the match accuracy failing to satisfy the anomaly detection threshold, block transmission of the report to the user device'
  • Claim 14: 'based on the comparison, flag the output of the AI model as an anomaly'
MPEP § 2106.05(I) — additional elements must be considered both individually and as an ordered combination; § 2106.05(g)/(h) — insignificant extra-solution activity vs. field of use

Risk The examiner will likely respond that the boilerplate phrase 'individually or in combination' satisfies the ordered-combination requirement and reassert each element's category label. This is a completeness/procedural lever; standing alone it may not establish eligibility.

Likely examiner response survives — moderate

The examiner can respond that MPEP § 2106.05 requires the elements be considered both individually and as a whole, and can assert that the office action did consider the combination and found it adds nothing beyond the sum of generic parts — i.e., that validating an output against retrieved data and gating transmission on a threshold is a conventional arrangement of the individually-identified generic elements. The examiner can also recharacterize 'block transmission of the report' as controlling a field of use (output delivery) rather than an operative technical control, reinforcing the 2106.05(h) label. Because this argument attacks analytical completeness rather than a substantive error, the examiner can cure it by adding an explicit ordered-combination sentence.

How to adjust This argument gains force only when yoked to Arguments 1 and 3 — the ordered combination matters if the individual pieces (AI generation, anomaly-threshold gating, feedback loop) are themselves shown to be non-generic or non-mental. Frame it as the completeness frame that carries the substantive points, not as a stand-alone procedural objection, and be ready for the examiner to simply supply the missing combination analysis.

4

AI-model generation and machine tokenization are not practically performed in the human mind (Step 2A Prong 1)

Eligibility rebuttalClaim 1Claim 3Claim 4Claim 5Claim 6Claim 7Claim 9Claim 10Claim 11Claim 12Claim 13Claim 14Claim 16Claim 17Claim 18Claim 19Claim 20

input, into an AI model, the user prompt to cause the AI model to generate an output based on the user prompt (claim 1); apply a text filter to the output ... tokenize the plurality of text-based analytics to extract a first plurality of keywords (claim 3)

For counsel to weigh: the office action characterizes the generating and comparing steps as mental processes performable by a human 'using paper / pen / calculator' (OA1 rejection 1, Step 2A Prong 1). Counsel may argue that limitations requiring an AI model to generate the output, and requiring tokenization of text-based analytics to extract pluralities of keywords for category-based similarity comparison against thresholds (claims 3-6, 12-13, 19-20), are not steps that can practically be performed in the human mind, which under MPEP § 2106.04(a)(2)(III) removes them from the mental-processes grouping. The record ties the representations to the machine-generated AI output and to programmatic tokenization/keyword extraction, not to observation or judgment a person performs unaided. Whether the recited operations fall outside the mental-processes grouping is a Prong 1 question for the examiner to reassess.

  • Office action, claim 1 Prong 1(a): '"generate", "provide feedback ... indicating" ... encompasses generating data representation model, comparing data, and generating report for anomaly detection, which is based on observation, evaluation, judgement, and/or opinion, that could be performed by human using paper / pen / calculator'
  • Claim 3: 'apply a text filter to the output to generate a modified output ... tokenize the plurality of text-based analytics to extract a first plurality of keywords within the plurality of text-based analytics'
  • Claim 1: 'input, into an AI model, the user prompt to cause the AI model to generate an output based on the user prompt'
MPEP § 2106.04(a)(2)(III) — mental processes; limitations that cannot practically be performed in the human mind are not within the grouping

Risk The examiner will likely reply that the AI-model and tokenization steps were treated as additional elements (not the abstract idea itself) and that the representation/comparison logic remains a mental process at a high level of generality. Prosecution-history caution: arguing the operations are inherently machine-scale may be read as conceding the underlying comparison concept is otherwise abstract; frame carefully.

Likely examiner response survives — moderate

The examiner can concede AI-model generation is not literally done in the head yet respond that the identified judicial exception is the comparing/anomaly-detection/keyword-similarity evaluation — a mental process of observation and judgment — and that the AI model and tokenization are recited at a high level of generality as tools that merely apply the exception (MPEP § 2106.05(f)), which does not remove the claim from the mental-processes grouping. The examiner can characterize tokenizing text and extracting keywords for category-based comparison as a parsing/matching step a person can perform with pen and paper, per the OA1 Prong-1 framing, so the recitation of a generic AI model does not defeat the mental-process finding.

How to adjust Separate the two sub-arguments: the AI-model-generates-the-output point is stronger (generation of a generative-model output is not practically performable in the mind), while the tokenization/keyword point is weaker because parsing/matching is more readily cast as a mental step. Anchor the argument in specific claim language and specification detail showing the operations are machine-bound (e.g., what the AI model does that a person cannot), and pair with Argument 1 so the Prong-1 win narrows the exception even if the comparison step remains characterized as mental.

5

Claim 14 mischaracterized as a 'process' and the ¶9 analysis is truncated / incomplete

Claim constructionClaim 14Claim 15Claim 16Claim 17Claim 18Claim 19Claim 20

A system comprising: a storage device; and one or more processors communicatively coupled to the storage device storing instructions thereon (claim 14)

For counsel to weigh: at Step 1 for claims 14-20 the office action states 'The claim recites a process' (OA1 rejection 3, Step 1), but claim 14 recites 'A system comprising: a storage device; and one or more processors,' i.e., a machine, not a process. The office action's Step 2A Prong 1 analysis for claims 14-20 also appears cut off mid-sentence (OA1 rejection 3 notes the text is truncated), so the full articulated basis for the rejection of claims 14-20 is not on the present record. Counsel may argue the examiner has not set out a complete prima facie eligibility rejection for these claims and has misidentified the statutory category, which bears on the correct additional-element/Prong-2 analysis for a machine claim. This is a defect in the rejection's articulation that the examiner should correct or complete before the rejection can be sustained against claims 14-20.

  • Claim 14: 'A system comprising: a storage device; and one or more processors communicatively coupled to the storage device storing instructions thereon'
  • Office action, claims 14-20 Step 1: 'The claim recites a process, which falls into one of the statutory categories.'
  • OA1 rejection 3: 'The available text of the office action for claims 14-20 is cut off mid-analysis at Step 2A Prong 1'
MPEP § 2106.03 — statutory categories (machine vs. process); § 2106 — a complete prima facie eligibility rejection must be articulated

Risk The examiner will likely treat the 'process' label as a harmless typographical error and supply a corrected, complete analysis in the next action that parallels claims 1-6/7-13, so this may only prompt a cleaned-up rejection rather than withdrawal. Verify against the full mailed office action whether the ¶9 text is genuinely incomplete or merely truncated in the provided excerpt before relying on the incompleteness point.

Likely examiner response fragile — the comeback likely defeats it

The examiner can treat the 'process' label for claim 14 as a harmless clerical error — claim 14 is a machine (storage device plus processors), but a machine claim can still be directed to a judicial exception under Alice, so the category label does not change the eligibility outcome; the examiner will simply correct 'process' to 'machine' and reach the same Step 2A/2B result. The truncated ¶9 analysis is a record/formatting matter the examiner cures by issuing a complete or corrected action, and the parallel structure of claims 14-20 to claims 1-13 lets the examiner map the same reasoning without substantive change.

How to adjust This is a procedural/articulation point, not a patentability lever — it forces a complete prima facie case for claims 14-20 (legitimate, since an incomplete rejection is not sustainable on the present record) but does not advance the merits and is trivially curable. Use it to require a full articulation and to preserve the record, but do not lead with it; the substantive Prong-2/Prong-1 arguments for 14-20 should track the machine-claim analysis once the category is corrected. Consider raising it in an interview rather than spending written-response weight on it.

4.

Element-by-Element Claim Chart

Claim 1
Status glyphClaim elementStatusDisclosure / notesLocation
GLOBAL RECORD NOTE — receive, from a user device, a user prompt indicating a request for a report summarizing data over a time periodNot taughtGLOBAL CAVEAT (analysis, applies to every element in every chart below): this office action contains ONLY a §101 subject-matter-eligibility rejection and expressly states there is no art rejection; NO prior-art references are cited or asserted. This claim-chart tool is built for §102/§103 prior-art mapping, so the prior-art teaching status enum does not fit. The 'status' field is therefore INAPPLICABLE here — do not read 'not_taught' as a prior-art disclosure gap or a §102/§103 argument; it merely reflects that no reference is asserted. What follows maps each limitation to how the examiner characterized it under the §101 Alice/Mayo framework, for counsel to weigh. As to THIS limitation: the examiner treated it as an additional element amounting to extra-solution pre-solution data gathering under MPEP 2106.05(g), and at Step 2B as well-understood/routine/conventional 'receiving or transmitting data over a network' / 'storing and retrieving information in memory' under MPEP 2106.05(d)(II).Office action, Detailed Action ¶4 ('There is no art rejection for claims 1-20.'); Office action ¶7, Step 2A Prong 2 (b) and Step 2B (b)
generate a first representation by inputting the user prompt into an AI model to cause the AI model to generate an output based on the user prompt, and generating the first representation based on the outputNot taughtStatus inapplicable (§101 only — see global note). Examiner split this limitation: the 'generate a first representation'/'generate ... based on the output' portion was placed in the mental-processes abstract-idea grouping (¶7 Prong 1(a), 'generating data representation model ... could be performed by human using paper/pen/calculator'), while 'inputting into an AI model' was treated as a generic-computer implementation under MPEP 2106.05(f) and, in part, as pre-solution data gathering under 2106.05(g). CONTESTABLE POINT for counsel: whether characterizing generation of a representation from an AI model's output as a purely mental process is supportable, and whether the AI model's role is more than a generic tool — an inquiry the examiner did not develop on the available text.Office action ¶7, Step 2A Prong 1 (a); Step 2A Prong 2 (a) and (b)
generate a second representation by retrieving, based on the user prompt, the data over the time period and generating the second representation based on the data over the time periodNot taughtStatus inapplicable (§101 only). Examiner split: 'retrieve ... the data over the time period' treated as extra-solution pre-solution data gathering (2106.05(g)) and as WURC storing/retrieving information in memory (2106.05(d)(II)); 'generate the second representation' placed in the mental-processes grouping (Prong 1(a)).Office action ¶7, Step 2A Prong 1 (a); Step 2A Prong 2 (b); Step 2B (b)
perform a comparison of the first representation and the second representation to determine a match accuracyNot taughtStatus inapplicable (§101 only). Placed by examiner squarely in the mental-processes/math-concepts abstract-idea grouping (grouping (b)).Office action ¶7, Step 2A Prong 1 (b) ('comparing data to detect anomaly ... could be performed by human using paper/pen/calculator')
determine whether the match accuracy satisfies an anomaly detection thresholdNot taughtStatus inapplicable (§101 only). Examiner grouped as mental process (grouping (b)).Office action ¶7, Step 2A Prong 1 (b)
based on the match accuracy failing to satisfy the anomaly detection threshold, block transmission of the report to the user deviceNot taughtStatus inapplicable (§101 only). Examiner dismissed this as merely limiting to a field of use / technological environment under MPEP 2106.05(h). CONTESTABLE POINT for counsel to weigh: whether automatically blocking transmission of an AI-generated report based on an anomaly comparison is a field-of-use recitation or an affirmative operational step that may bear on the Prong-2 'improvement/practical application' inquiry (MPEP 2106.05(a)/(e)) — the examiner's analysis on the available text characterizes it only as field of use.Office action ¶7, Step 2A Prong 2 (c) and Step 2B (c) (MPEP 2106.05(h))
generate a detailed report outlining one or more reasons for blocking the transmission, the reasons indicating one or more portions of the first representation that contributed to the match accuracy failing to satisfy the anomaly detection thresholdNot taughtStatus inapplicable (§101 only). Examiner grouped as mental process (generating report for anomaly detection, grouping (a)).Office action ¶7, Step 2A Prong 1 (a)
provide feedback to the AI model indicating the one or more portions to fine-tune the AI modelNot taughtStatus inapplicable (§101 only). Examiner grouped 'provide feedback ... indicating' in the mental-processes bucket. CONTESTABLE POINT for counsel: the 'to fine-tune the AI model' aspect frames the feedback as modifying/improving the model itself; whether that is a mental process or a potential technological-improvement consideration (MPEP 2106.05(a)) appears not to have been separately addressed on the available text.Office action ¶7, Step 2A Prong 1 (a) ('provide feedback ... indicating')
Claim 2
Status glyphClaim elementStatusDisclosure / notesLocation
based on the match accuracy satisfying the anomaly detection threshold, approve the output of the AI model for inclusion in the report requested by the user promptNot taughtStatus inapplicable (§101 only — see claim 1 global note). Examiner treated this added limitation as extra-solution post-solution data output under MPEP 2106.05(g) and found it neither integrates the exception nor adds significantly more.Office action ¶7, claim 2 discussion (MPEP 2106.05(g))
Claim 3
Status glyphClaim elementStatusDisclosure / notesLocation
apply a text filter to the output to generate a modified output comprising the plurality of text-based analytics and excluding the plurality of visual analytics; tokenize the plurality of text-based analytics to extract a first plurality of keywordsNot taughtStatus inapplicable (§101 only). Examiner characterized the text-filter/tokenize/keyword-extraction detail as further detail of the same mental-processes abstract idea, with only generic computer elements added. CONTESTABLE POINT for counsel: whether applying a text filter to separate text-based from visual analytics and tokenizing to extract keywords is realistically performable in the human mind, or reflects a specific computer-implemented technique — an argument counsel may weigh under Prong 2. Distinct limitation; parallels dependent claims 9 and 16 (omitted below as duplicative).Office action ¶7, claim 3 discussion
Claim 6
Status glyphClaim elementStatusDisclosure / notesLocation
determine a first category of keywords requiring a first similarity between subsets of the first and second keyword pluralities to satisfy a first similarity threshold, and a second category requiring a second similarity between different subsets to satisfy a second similarity thresholdNot taughtStatus inapplicable (§101 only). Examiner grouped the category-based, multi-threshold similarity scheme as further detail of the same mental process. CONTESTABLE POINT for counsel: category-specific similarity thresholds compared across keyword subsets may support a specificity/practical-application argument for counsel to evaluate. Distinct limitation; parallels claims 12 and 19.Office action ¶7, claim 6 discussion
perform a first comparison between the first similarity and the first similarity threshold; perform a second comparison between the second similarity and the second similarity threshold; and determine the match accuracy based on the first and second comparisonsNot taughtStatus inapplicable (§101 only). Examiner grouped the dual-comparison/match-accuracy determination as mental-process detail. Parallels claims 13 and 20.Office action ¶7, claim 6 discussion
Claim 7
Status glyphClaim elementStatusDisclosure / notesLocation
receiving a request indicating a request for data over a time periodNot taughtStatus inapplicable (§101 only — see claim 1 global note). Independent method claim. Examiner treated as extra-solution pre-solution data gathering and WURC data receipt.Office action ¶8, Step 2A Prong 2 (b); Step 2B (b) (MPEP 2106.05(g), 2106.05(d)(II))
generating a first representation by inputting the request into an AI model to generate an output based on the request, and generating the first representation based on the outputNot taughtStatus inapplicable (§101 only). Examiner split: representation generation into mental-processes grouping; 'AI model' as generic-computer implementation under 2106.05(f). Same contestable AI-model-as-tool point noted for claim 1.Office action ¶8, Step 2A Prong 1 (a); Prong 2 (a); Step 2B (a)
generating a second representation by retrieving the data over the time period and generating the second representation based on the dataNot taughtStatus inapplicable (§101 only). Mental-process generation plus 2106.05(g) retrieval, per examiner.Office action ¶8, Step 2A Prong 1 (a); Prong 2 (b)
performing a comparison of the first representation and the second representation; based on the comparison, determining an approval or a rejection of the output of the AI modelNot taughtStatus inapplicable (§101 only). Placed in mental-processes grouping (comparing data to detect anomaly), per examiner.Office action ¶8, Step 2A Prong 1 (b)
automatically providing feedback to the AI model indicating one or more portions of the first representation that contributed to the approval or the rejection of the outputNot taughtStatus inapplicable (§101 only). Examiner dismissed as field of use / technological environment (2106.05(h)). CONTESTABLE POINT for counsel: 'automatically providing feedback to the AI model' frames a self-improving/model-updating loop that counsel may argue bears on the technological-improvement inquiry (MPEP 2106.05(a)); the available text treats it only as field of use.Office action ¶8, Step 2A Prong 2 (c); Step 2B (c) (MPEP 2106.05(h))
Claim 8
Status glyphClaim elementStatusDisclosure / notesLocation
determining a match accuracy between the first and second representations; determining whether the match accuracy satisfies an anomaly detection threshold; accepting or rejecting the output based on whether the match accuracy satisfies the anomaly detection thresholdNot taughtStatus inapplicable (§101 only). Examiner characterized the match-accuracy/threshold determination as further mental-process detail. Note internal-consistency point for counsel: claim 8 recites 'accepting or rejecting' while parent claim 7 recites 'approval or ... rejection' — an antecedent-basis observation counsel may separately weigh (not raised by examiner on the available text). Distinct limitation; parallels claim 15.Office action ¶8, claim 8 discussion
Claim 14
Status glyphClaim elementStatusDisclosure / notesLocation
a storage device and one or more processors communicatively coupled to the storage device storing instructionsNot taughtStatus inapplicable (§101 only — see claim 1 global note). ATTRIBUTION/RECORD NOTE for counsel: the examiner's Step 1 characterizes claim 14 as reciting 'a process,' but claim 14 as filed recites 'A system' with a storage device and one or more processors (claims document, claim 14). This apparent mischaracterization of the statutory category, plus the fact that the available office-action text for claims 14-20 is cut off mid-analysis at Step 2A Prong 1, are record/procedural points for counsel to weigh. These generic hardware elements were (by parallel to ¶7/¶8) treated as generic-computer implementation under MPEP 2106.05(f).Office action ¶9, Step 1; claims document, claim 14
receive a request for data over a time periodNot taughtStatus inapplicable (§101 only). By parallel to claims 1 and 7, examiner would treat as pre-solution data gathering (2106.05(g)); note the ¶9 analysis is truncated on the available text, so the specific treatment of this and later claim-14 limitations is not fully set out in the record.Office action ¶9 (analysis stated to parallel ¶7/¶8; text cut off)
generate a first representation by inputting the request into an AI model to generate an output, and generating the first representation based on the output; generate a second representation from the retrieved dataNot taughtStatus inapplicable (§101 only). Examiner (on the truncated ¶9 text) placed representation generation in the mental-processes grouping and the AI model as generic tool.Office action ¶9, Step 2A Prong 1 (a) (partial); claims document, claim 14
perform a comparison of the first and second representations; based on the comparison, flag the output of the AI model as an anomalyNot taughtStatus inapplicable (§101 only). By parallel, mental-processes grouping. Note the ¶9 record text does not expressly reach this limitation before cutting off — a completeness point for counsel.Office action ¶9 (parallel to ¶7 Prong 1(b); text cut off)
provide feedback to the AI model indicating one or more reasons for flagging the output of the AI model as the anomalyNot taughtStatus inapplicable (§101 only). By parallel to claim 7, likely treated as field of use (2106.05(h)); same model-improvement contestable point as claims 1 and 7 for counsel to weigh. Record note: ¶9 analysis is truncated.Office action ¶9 (parallel to ¶7 Prong 2(c); text cut off)
Claim 19
Status glyphClaim elementStatusDisclosure / notesLocation
determine a first category of keywords with a first similarity threshold across subsets of the first and second keyword pluralities, and a second category of keywords with a second similarity threshold across different subsetsNot taughtStatus inapplicable (§101 only — see claim 1 global note). System-claim counterpart to claims 6 and 12 (category-based multi-threshold similarity). CONTESTABLE POINT for counsel: same specificity/practical-application argument noted for claim 6. OMISSION NOTE: due to the 8-claim cap, the following claims were not separately charted because their distinct limitations duplicate charted claims — claim 4 (tokenize data ≈ claim 10, 17), claim 5 (compare keywords ≈ claim 11, 18), claim 9 (text filter/tokenize ≈ claim 3), claim 10, claim 11, claim 12 (category ≈ claim 6, 19), claim 13 and claim 20 (dual comparison/match accuracy ≈ claim 6), claim 15 (match accuracy/threshold ≈ claim 8), claim 16 (tokenize output ≈ claim 3/17), claim 17, claim 18. Counsel should apply the same §101 characterization mapping to those un-charted dependents.Office action ¶9 (parallel to ¶7/¶8 dependent-claim treatment; text cut off)

Elements not shown by the cited art (1)

  • Claim 1-20 — “N/A — no limitation can be recorded as a prima-facie-failure (missing-element) candidate here”: This is a prima-facie-failure list for §102/§103 rejections, which turn on a limitation being absent from the asserted prior art. The office action contains ONLY a §101 subject-matter-eligibility rejection and expressly states 'There is no art rejection for claims 1-20' (Detailed Action ¶4); NO references are cited, asserted, or grounded (the reference-grounding and OA2 blocks are empty). With no asserted prior art, there is no reference against which to find any limitation missing, so no missing-element/prima-facie-failure finding is available on this record. The contestable §101 points for counsel are captured in the chart notes above (e.g., AI-model-as-more-than-generic-tool under MPEP 2106.05(f); block-transmission and automatic-feedback/model-fine-tuning as potential technological-improvement/practical-application considerations under MPEP 2106.05(a)/(e)/(h); the examiner's apparent mischaracterization of claim 14 'system' as a 'process' and the truncated ¶9 analysis for claims 14-20).
5.

Rejection Map

§101Eligibility — claims 1, 2, 3, 4, 5, 6

Step 1: Article of manufacture — statutory category. Step 2A Prong 1: Examiner identifies two groupings of abstract idea (mental processes/math concepts): (a) generating first and second representations, generating a detailed report, and providing feedback to the AI model — characterized as generating data representation model, comparing data, and generating report for anomaly detection performable by human with paper/pen/calculator; (b) performing comparison of representations to determine match accuracy and determining whether match accuracy satisfies anomaly detection threshold — characterized as comparing data to detect anomaly performable mentally. Step 2A Prong 2: Not integrated into practical application: (a) non-transitory computer-readable storage medium and system executing instructions are mere instructions to implement abstract idea on generic computer (MPEP 2106.05(f)); (b) receiving user prompt, inputting into AI model, and retrieving data over time period are extra-solution pre-solution data gathering (MPEP 2106.05(g)); (c) blocking transmission of report to user device is field of use / technological environment (MPEP 2106.05(h)). Step 2B: No significantly more — same additional elements dismissed: generic computing (2106.05(f)); pre-solution data gathering found well-understood, routine, conventional citing MPEP 2106.05(d)(II) (receiving/transmitting data over network, electronic recordkeeping, storing/retrieving information in memory); field of use (2106.05(h)). Dependent claims 2-6 add only further details of abstract mental process or extra-solution activity (claim 2: approving output as post-solution data output under 2106.05(g); claims 3-6: further details on tokenizing, keyword extraction, category-based similarity comparisons — all characterized as mental processes).

§101Eligibility — claims 7, 8, 9, 10, 11, 12, 13

Step 1: Process — statutory category. Step 2A Prong 1: Examiner identifies two groupings of abstract idea (mental processes/math concepts): (a) generating output, generating first representation based on output, generating second representation based on data over time period — characterized as generating data representation model performable mentally; (b) performing comparison of first and second representations, determining approval or rejection of AI model output — characterized as comparing data to detect anomaly performable mentally. Step 2A Prong 2: Not integrated into practical application: (a) AI model is mere instructions to implement abstract idea on generic computing device (MPEP 2106.05(f)); (b) receiving request, inputting into AI model, retrieving data are extra-solution pre-solution data gathering (MPEP 2106.05(g)); (c) automatically providing feedback to AI model indicating portions contributing to approval/rejection is field of use and technological environment (MPEP 2106.05(h)). Step 2B: No significantly more — same additional elements dismissed: generic computing (2106.05(f)); pre-solution data gathering well-understood, routine, conventional per MPEP 2106.05(d)(II); field of use (2106.05(h)). Dependent claims 8-13 add only further details of abstract mental process (match accuracy/threshold determination, text filtering, tokenizing, keyword extraction, category-based similarity comparisons).

§101Eligibility — claims 14, 15, 16, 17, 18, 19, 20

Step 1: Examiner states the claim recites a process (note: claim 14 actually recites 'A system' with storage device and processors). Step 2A Prong 1: Examiner identifies abstract idea (mental processes/math concepts) in generating first representation (inputting into AI model, generating output), and generating second representation. The available text of the office action for claims 14-20 is cut off mid-analysis at Step 2A Prong 1, but the office action summary clearly states claims 1-20 are rejected under 101. The examiner's analysis for claims 14-20 parallels the analysis for claims 1-6 and 7-13, characterizing the generating, comparing, flagging, and feedback steps as mental processes performable by humans with paper/pen/calculator.

6.

Record & Grounding

Data Egress Log

Note

Your uploads stay in-boundary. External retrieval: none — no claim text, no client material left the environment.

Documents processed
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  • 35436a07-c3b6-4050-b49a-0d9dde365176.pdfclaims
Processed in-boundary — never transmitted externally.

Obviousness Framework

Field of endeavor
Computer-implemented systems and methods for validating AI-model-generated outputs (e.g., reports summarizing data over a time period) by generating representations of the model output and of the underlying source data, comparing them to detect anomalies, and providing feedback to fine-tune the AI model. (analysis) This framing is grounded in the pending claims (claims 1, 7, 14) and the office action's own characterization.
PHOSITA
For argument purposes only (a proposed construction for counsel to adopt or adjust, NOT a factual finding): a person with a bachelor's degree in computer science, software engineering, or a related field plus a few years of experience in machine learning / applied AI, including familiarity with large language or generative AI models, natural-language processing techniques such as tokenization and keyword extraction, similarity/threshold-based comparison methods, anomaly detection, and model fine-tuning via feedback. The office action itself does not articulate any PHOSITA construction, so this must be confirmed against the record before it is relied upon.A construction for argument — not asserted as fact.

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