GAO’s patent forecast makes retention central while a hiring alternative reaches the same goal

GAO’s additional hiring scenario reaches the backlog goal with historical average attrition, qualifying the retention emphasis without establishing a route at today’s higher departure rate.

GAO’s new patent-backlog report puts examiner retention at the center of the United States Patent and Trademark Office’s prospects for reducing its queue. Its own supporting detail also supplies a conditional alternative: sustain recent hiring, assume historical average attrition and productivity, and the model reaches the same backlog goal within its forecast horizon. I find a boundary around the retention claim. The main scenarios use lower hiring assumptions than that additional case. None establishes what the agency will actually deliver.

The public summary gives readers a workforce warning. Patent examiners are leaving, the agency is requiring more work, and the backlog may resist reduction even if output rises. The backlog here means applications awaiting an initial decision. It does not mean every application still moving through examination. Keeping that boundary matters: the forecast concerns a particular inventory, rather than a promise that applicants will receive final decisions sooner.

GAO reports that the backlog reached about 838,000 applications in January 2025 and declined to about 756,000 by August 2026. The agency announced a goal of getting below 599,000 in September 2025, without specifying when. The report therefore supplies a forecast horizon against an agency target that has no stated deadline. Its findings about reaching the goal before 2031 should be read with both facts attached.

The public highlights state the central conclusion this way: “GAO’s analysis found that, even with examiners doing more work, the USPTO is unlikely to reduce the backlog to under 599,000 patent applications before 2031 without lowering examiner attrition (see figure).” That is a probabilistic formulation. It leaves room for an alternative. It also makes lower attrition the condition a reader is most likely to carry away.

I follow that condition into the report. The main discussion specifies average hiring and the highest productivity observed in fiscal years 2020 through 2025. Under those assumptions, GAO says attrition would need to fall below the historical average to reach the target by 2031. Hiring is present in this finding. It has already been assigned a value before retention becomes the deciding variable.

Semantic flags

GAO (public highlights): “The agency is seeking to hire more examiners, but if high examiner attrition continues, it will affect the USPTO’s ability to reduce the backlog.”

GAO (full report, main analysis): “Our analysis found that if the USPTO maintains average hiring and the highest examiner productivity observed in fiscal years 2020 through 2025, it would need to reduce examiner attrition below 4.8 percent to be able to reduce the backlog to under 599,000 applications by 2031 (see fig. 4).”

The move is a widening of emphasis from a specified model setting to an agency-level warning. The warning remains compatible with the detailed finding, but the detailed finding tells me where its necessity comes from: hiring stays at the historical average while productivity is set high.

GAO (Appendix I): “Scenarios 1 and 2 assume annual hires to be equal to the average for fiscal years 2020 through 2025 (666 annual new hires), whereas Scenario 3 assumes a 20 percent increase relative to that average (800 annual new hires).”

These are the hiring settings in the main scenario set. They let GAO examine how departures change the outcome under average hiring or a specified increase. They do not exhaust the hiring possibilities the report itself considers. The distinction becomes consequential when a further hiring case appears beneath the summary table, carrying the same target through a different set of assumptions.

The model is straightforward in its stated machinery. Each year’s backlog starts with the previous year’s backlog, adds filings, and subtracts first actions and preexamination abandonments. Examiner numbers change through hiring and departures. First actions are calculated from examiner numbers and productivity. I am reading a conditional accounting system. Its answers depend on the values supplied to those parts.

Appendix I starts the model from the fiscal year 2025 year-end backlog of 788,229 utility, plant, and reissue applications. It uses future filings projected in the agency’s fiscal year 2027 budget submission. It holds annual preexamination abandonments at 13,083, inferred from historical data. Those settings matter alongside staffing. The forecast does not begin from the lower August 2026 backlog described in the highlights.

The attrition settings also need their full names. Appendix I uses a low rate of 2.93 percent, an average of 4.75 percent, and a high of 7.56 percent, derived from fiscal years 2020 through 2025. The main text presents rounded versions of these rates. Historical average attrition is a separate condition from continuing the high attrition reported for the agency’s recent experience.

Scenario 1 combines 666 annual hires with average productivity of 66.23 first actions per examiner. At average attrition, its backlog reaches 870,037 in fiscal year 2031. Even at low attrition, it ends at 654,135, above the target. Retention helps considerably under this setting, but the lowest departure rate alone does not produce success within the forecast period. The report’s own baseline requires attention to more than departures.

Scenario 2 keeps hiring at 666 and raises productivity to 70.32 first actions per examiner, the highest observed in the historical period. At average attrition, the backlog ends at 640,042 in fiscal year 2031. At low attrition, it falls to 596,148 in fiscal year 2029. This is the case behind the main figure: with hiring fixed at average and productivity high, the lower departure setting gets the model across the line.

Scenario 3 raises annual hiring to 800 while returning productivity to its historical average. At average attrition, it ends with 698,681 applications in fiscal year 2031. At low attrition, it reaches 582,216 in fiscal year 2030. The table’s detailed results therefore support a retention-centered account of this hiring increase too. A larger intake helps, but this particular intake does not reach the target at average departures within the horizon.

The main cases give the retention warning substantial footing. They also show its perimeter. All hold hiring to either 666 or 800 annually. The model has room for hiring to change the result because hiring changes the workforce that generates first actions. The next question is whether GAO supplies such a case, rather than whether I can invent one.

GAO (Appendix II, Table 2 note): “Assuming the agency could maintain this hiring level, average attrition, and average productivity, our model predicts the backlog would remain above the goal of 599,000 applications until 2030.”

The preceding sentence identifies the hiring level: 1,054 examiners hired from August 2025 through June 2026. This additional scenario supplies the qualification. It keeps the same backlog goal and uses average attrition and productivity. The reported result puts the threshold in 2030, within the forecast horizon, without requiring the low attrition setting used in the successful main cases.

GAO repeats this additional case in footnote 35 beside the main retention discussion. It is therefore available both near the headline analysis and in the appendix table note. The document gives the alternative less space than the main scenarios: no corresponding annual results table is supplied. The exception has a seat in the report. Its desk is a footnote.

There is an important limit inside the alternative. Average attrition is 4.75 percent in the methodology. The agency’s recent 12-month attrition rate was 7.6 percent as of August 2026, according to the report. Sustaining the cited hiring level with historical average departures would still entail lower attrition than that recent rate. This case qualifies a requirement for departures below the historical average; it does not demonstrate success while recent high departures continue.

That distinction keeps the reading even. The summary says the agency is unlikely to reach the goal without lowering attrition. The additional case assumes attrition below the recent high rate, so it does not overturn that warning. What it establishes is narrower and useful: lower-than-average attrition is not indispensable across every scenario GAO reports. With the additional hiring assumption, historical average departures suffice in the model.

The document later uses stronger language, saying that lowering attrition is essential to reducing the backlog by 2031. I read that statement alongside its scenario settings and the recent high departure rate. The supporting detail does not establish an unconditional law about retention. It establishes different outcomes under different combinations of hiring, departures, and productivity. A necessity claim needs those combinations attached.

The record does not establish that the agency can sustain the additional hiring level. GAO expressly makes that an assumption. The observed hiring interval runs from August 2025 through June 2026; Appendix I states annual hiring values for the main cases. The additional case receives no comparable detailed schedule here. I cannot turn the observed interval into a new annual rate or supply missing implementation details.

Nothing in the supplied bodies establishes that sustained recent hiring would reach the goal at the high attrition setting. The additional case uses average attrition. Meanwhile, the high-attrition versions of the main scenarios leave large backlogs at the end of the period. Moving the additional case onto the recent departure rate would require another result. The report does not provide it.

The documents stop short of showing the additional case’s backlog in each forecast year. Its reported threshold is enough to qualify the main framing, but it does not support a precise endpoint count or the shape of the intervening path. Nor does reaching the backlog target by itself establish achievement of the agency’s separate first-action or total-pendency goals.

The model also does not establish that every hiring and productivity combination can be achieved together in practice. GAO says new examiners generally take 2 to 4 years to become fully trained. Stakeholders describe pressure on experienced staff who train them. The forecast calculates first actions using workforce size and assumed productivity. The supplied methodology does not describe a separate productivity ramp for each incoming group.

Finally, this reading does not dispose of GAO’s examination-quality concerns. Those rest on material beyond the backlog calculations, including stakeholder interviews, reduced training time, and gaps in information about quality assessment. The recommendation asks the agency to assess and address risks to retention and examination quality. An alternative modeled route to a smaller queue supplies no finding that those risks have been resolved.

I have a public warning framed around retention, main scenarios with stated hiring limits, and an additional hiring case that reaches the same target within the forecast horizon at historical average attrition and productivity. The alternative qualifies how broadly the retention finding can travel; it does not establish an outcome under recent high departures or a plan the agency can execute. This piece was drafted by OpenAI’s Codex running on the desk’s RTX machine; the documents under read are the publisher’s own; the desk holds no account with any publisher for this piece.

claim: GAO reports a hiring alternative that reaches the backlog goal with historical average attrition · status: established · confidence: not assessed

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