DETAILED ACTION
This communication is a Non-Final Rejection Office Action in response to the 7/22/2026 submission filed in Application 18/603,014. Claims 1, 3, 7, 9, 13 are amended. Claims 2, 8, 14 are cancelled. Claims 1, 3-7, 9-13, 15-18 are now presented.
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 7/22/2026 has been entered.
Response to Arguments
Applicant’s arguments, filed 7/22/2026 have been fully considered but they are not persuasive.
Regarding the rejection under 101, the Applicant “Applicant submits that the amended claim recites a multi-priority, threshold- triggered top-up generation thereby applying the exception in a meaningful, constrained way. The Advisory Action characterized the claims as directed to "analyzing data to determine shelf replenishment". Applicant asserts that the amended claim limitations recite a specific, rule- governed control mechanism rather than open-ended data analysis. As amended, the top-up requests are generated at multiple time periods based on varied levels of projected quantity on the shelves in accordance with defined rules: when the projected quantity reaches X%, a top- up list is created with a first priority; when it reaches Y%, a second priority; and when it reaches Z%, a third priority. This is a concrete, threshold-triggered, priority-differentiated procedure that governs when and at what priority the computer generates each output, not an abstract instruction to "analyze data." As disclosed at paragraphs [0087]-[0089] and [0093], the system identifies checkpoints when on-hand capacity breaches defined percentages (e.g., y1 = 50% (Low), 70% (Medium), 90% (High) of maximum shelf capacity) and generates the corresponding refill quantity/effort for each priority. Applicant submits that these enumerated, checkpoint-indexed rules apply any alleged exception "in some other meaningful way beyond generally linking [it] to a particular technological environment" (MPEP § 2106.05(e)), analogous to the specific rule set held eligible in McRO, Inc. v. Bandai Namco Games America Inc., 837 F.3d 1299 (Fed. Cir. 2016). The claim is thereby integrated into a practical application.”
The Examiner respectfully disagrees. On page 23of McRo the court states “We therefore look to whether the claims in these patents focus on a specific means or method that improves the relevant technology or are instead directed to a result or effect that itself is the abstract idea and merely invoke generic processes and machinery. Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1336 (Fed. Cir. 2016) (“Enfish”); see also Rapid Litig. Mgmt. Ltd. v. CellzDirect, Inc., No. 2015-1570, 2016 WL 3606624, at *4(Fed. Cir. July 5, 2016).”
The examiner asserts that in the instant case that claims do not focus on a specific means or method that improves the relevant technology. The examiner asserts that analyzing data for identification and replenishments of items on shelves is not a technology or technical field, but rather a business practice.
Further, on page 22 of McRo the court determined “As the specification confirms, the claimed improvement here is allowing computers to produce “accurate and realistic lip synchronization and facial expressions in animated characters” that previously could only be produced by human animators.”In the instant case there is no such technical improvement disclosed in the Applicant’s specification.
Further, page 24 of McRo states “Claim 1 of the ’576 patent is focused on a specific asserted improvement in computer animation, i.e., the automatic use of rules of a particular type. We disagree with Defendants’ arguments that the claims simply use a computer as a tool to automate conventional activity. While the rules are embodied in computer software that is processed by general-purpose computers, Defendants provided no evidence that the process previously used by animators is the same as the process required by the claims. See Defs.’ Br. 10–15, 39–40. In support, Defendants point to the background section of the patents, but that information makes no suggestion that animators were previously employing the type of rules required by claim 1. Defendants concede an animator’s process was driven by subjective determinations rather than specific, limited mathematical rules.”
As such, in McRO the process used by human animators is a different process than the computer implemented rules recited claims. In the instant case, the rules recited in the claims are the same rules that a human user would use to perform the process. As such, the claims here are not similar to the claims at issue in McRO.
Regarding the rejection under 101, the Applicant further argues “Considered as a whole, the amended claims recite a self-updating, machine-learning-driven inventory-reconciliation architecture that selects and periodically retrains/re-deploys a metric- validated predictive model, re-forecasts in real time throughout the day, generates multi- priority threshold-triggered top-up requests, and enforces in-memory deletion and re- indexing of execution state between runs. Consistent with the 2019 Revised Patent Eligibility Guidance and MPEP §§ 2106.04(d)(1) and 2106.05(a), these additional elements reflect a specific improvement in the functioning of the computer itself and apply any alleged exception in a meaningful, constrained manner (MPEP § 2106.05(e)). The judicial exception, if any, is therefore integrated into a practical application, and claims 1-18 are not directed to an abstract idea (Step 2A, Prong Two: No). Withdrawal of the § 101 rejection is respectfully requested.”
The Examiner respectfully disagrees. In the instant case, the deleting data from memory when it is no loner needed is a mental process which is abstract. Further, the additional elements of the broadly recited machine learning attempt to cover any solution to the identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result, which does not integrate a judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words "apply it”. The claims do not state how the machine learning classifier generate forecasts a rate of sales for one or more items. As such, the broadly recited ML model does not integrate a judicial exception into a practical application or provide significantly more.
Regarding the rejection under 101, the Applicant further argues Even if each additional element were considered individually insufficient - which Applicant does not concede - the elements must be evaluated as an ordered combination (MPEP § 2106.05(d); BASCOM). Taken together, the claims recite a self-updating inventory- reconciliation architecture in which a metric-validated, periodically retrained ML model feeds a real-time intraday re-forecasting loop, which drives multi-priority, threshold-triggered top-up generation, which relies on checkpoint-indexed execution state that is deleted and re-indexed in memory before each run. This particular ordered arrangement is non-conventional and is not found in the routine practice of the field. As in BASCOM, the inventive concept lies in the non-generic, non-conventional ordered combination of the recited elements, which together transform any alleged abstract idea into a patent-eligible application. Applicant respectfully submits that, taking all claim elements individually and in combination, the claim as a whole amounts to significantly more than an abstract idea (Step 2B: Yes).
The Examiner respectfully disagrees. Page 15 of Bascom states “The inventive concept inquiry requires more than recognizing that each claim element, by itself, was known in the art. As is the case here, an inventive concept can be found in the non-conventional and non-generic arrangement of known, conventional pieces.”
Page 16 of Bascom states “Filtering content on the Internet was already a known concept, and the patent describes how its particular arrangement of elements is a technical improvement over prior art ways of filtering such content. As explained earlier, prior art filters were either susceptible to hacking or dependent on local hardware and software, or confined to an inflexible one size- fits-all scheme… Thus, construed in favor of the nonmovant— BASCOM—the claims are “more than a drafting effort designed to monopolize the [abstract idea].” Alice, 134 S. Ct. at 2357. Instead, the claims may be read to “improve an existing technological process.” Id. at 2358 (discussing the claims in Diehr, 450 U.S. 175).”
The instant claims differs in that the Bascom patent “described how its particular arrangement of additional elements is a technical improvement over prior art ways of filtering such content.” (see Bascom page 16). In the instant case there is no such explanation or a technical improvement of the arrangement of additional in the specification.
In the instant case the examiner has identified the Abstract idea recited in the Claims. Like Bascom, “the claims and their specific limitations do not readily lend themselves to a step-one finding that they are directed to a non-abstract idea”. As such, the examiner moved on to evaluate the additional elements of the claim. In the instant case, the additional elements of the broadly recited machine learning attempt to cover any solution to the identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result, which does not integrate a judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words "apply it”. The claims do not state how the machine learning classifier generate forecasts a rate of sales for one or more items. As such, the broadly recited ML model does not integrate a judicial exception into a practical application or provide significantly more.
Viewing the generic computer elements in combination with the data gathering and machine learning does not add anything further than looking at the limitations individually. When viewed either individually, or as an ordered combination, the additional limitations do not amount to a claim as a whole that is significantly more than the abstract idea.
Unlike the claims in Bascom, the instant claims do not include additional elements that result in the “non-conventional and non-generic arrangement of known, conventional pieces.” As such, unlike the claims in Bascom, the instant claims remain ineligible under 35 U.S.C. 101.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1, 3-7, 9-13, 15-18 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
When considering subject matter eligibility under 35 U.S.C. 101, in step 1 it must be determined whether the claim is directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter. If the claim does fall within one of the statutory categories, in step 2A prong 1 it must then be determined whether the claim is recite a judicial exception (i.e., law of nature, natural phenomenon, and abstract idea). If the claim recites a judicial exception, under step 2A prong 2 it must additionally be determined whether the recites additional elements that integrate the judicial exception into a practical application. If a claim does not integrate the Abstract idea into a practical application, under step 2B it must then be determined if the claim provides an inventive concept.
In the Instant case, Claims 1, 3-5 are directed toward a method for creating, via the one or more hardware processors, a pick-up list based on the one or more top-up requests and one or more configurable parameters. Claim 7, 9-12 is directed toward a system to create a pick-up list based on the one or more top-up requests and one or more configurable parameters. Claims 13, 15-18 are directed toward a computer program product for creating a pick-up list based on the one or more top-up requests and one or more configurable parameters. As such, each of the Claims is directed to one of the four statutory categories of invention.
MPEP 2106.04 II. A. explains that in step 2A prong 1 Examiners are to determine whether a claim recites a judicial exception. MPEP 2106.04(a) explains that:
To facilitate examination, the Office has set forth an approach to identifying abstract ideas that distills the relevant case law into enumerated groupings of abstract ideas. The enumerated groupings are firmly rooted in Supreme Court precedent as well as Federal Circuit decisions interpreting that precedent, as is explained in MPEP § 2106.04(a)(2). This approach represents a shift from the former case-comparison approach that required examiners to rely on individual judicial cases when determining whether a claim recites an abstract idea. By grouping the abstract ideas, the examiners’ focus has been shifted from relying on individual cases to generally applying the wide body of case law spanning all technologies and claim types.
The enumerated groupings of abstract ideas are defined as:
1) Mathematical concepts – mathematical relationships, mathematical formulas or equations, mathematical calculations (see MPEP § 2106.04(a)(2), subsection I);
2) Certain methods of organizing human activity – fundamental economic principles or practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations); managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions) (see MPEP § 2106.04(a)(2), subsection II); and
3) Mental processes – concepts performed in the human mind (including an observation, evaluation, judgment, opinion) (see MPEP § 2106.04(a)(2), subsection III).
As per step 2A prong 1 of the eligibility analysis, claim 1 is directed to the abstract idea of analyzing data to create a pick-up list based on the one or more top-up requests and one or more configurable parameters; tag one or more skill specific users to the pick-up list based on the one or more configurable parameters; and calculating an updated quantity of items on the one or more shelves of the store based on an execution of the pick-up list by the one or more tagged skill specific user which falls into the abstract idea categories of certain methods of organizing human activity and mental processes.
The elements of Claim 1 that represent the Abstract idea include:
A method, comprising:
generating, a forecast of rate of sales for one or more items from the plurality of items for a pre-defined time interval during real- time or near real-time events of transactions at stores, purchase,
wherein the at least one machine learning (ML) model amongst the one or more ML models is selected based on a level of training on the historical sales data and validation of associated performance therebetween with performance metrics including a true positive rate, a recall, a false positive rate, a precision, an accuracy, a F-measure, a Mean Absolute Error (MAE),
periodically training model with a new dataset pertaining to sales, replenishment and re-deployed to predict the rate of sales;
identifying, one or more item bucket sizes at each of the one or more SKUs and the pre-defined time interval based on a historical sales data, wherein one or more items in each of the one or more item bucket sizes are identified as being sold during a specific time duration based on an item threshold for a given day;
creating, a sales profiler for each of the one or more items based on the one or more item bucket sizes being identified using the historical sales data;
splitting the forecast of rate of sales for the one or more items for the pre-defined time interval into at least one of (i) the one or more item bucket sizes, and (ii) a pre-determined time period based on the sales profiler;
generating, one or more top-up requests for the one or more SKUs based on a pre-defined threshold using the forecast of rate of sales for the one or more items being split, wherein the one or more top-up requests comprise a set of items requiring replenishment on one or more shelves in the store;
wherein the top-up requests are generated at multiple time periods based on varied levels of projected quantity on the shelves in accordance with one of the rules including (a) when the projected quantity on a shelf becomes X%, create a top-up list with a first priority, (b) when the projected quantity on the shelf becomes Y%, create the top-up list with a second priority, when the projected quantity on the shelf becomes Z%, create the top-up list with a third priority;
creating, a pick-up list based on the one or more top-up requests and one or more configurable parameters comprises;
obtaining a beginning quantity at start of day;
generating a rate of sale for every item bucket size from the ML model;
(c) identifying one or more checkpoints that is an item bucket end time by when on hand capacity breaches a predefined percentage of a maximum shelf capacity corresponds to low, medium, high of the maximum shelf capacity and generate a refill quantity at beginning of day for the SKU;
(d) continuing execution for a next run by obtaining an input from the pick- up list by ascertaining one of low, medium, high, from the generated refill quantity is in the pick-up list and set that point as a next checkpoint; and
(e) repeating steps (c) and (d) until end of day is reached or store inventory is out of stock for the SKU;
re-predicting, via the one or more hardware processors, the forecast of rate of sales for the one or more items multiple times throughout the day based on actual intraday sales using one or more intraday models;
repeating the step of generating the one or more top-up requests, creating the pick- up list in accordance with the re-predicted forecast of rate of sales;
performing constraint checks including (a)when SKI does not breach a predefined percentage of maximum capacity for an entire day, then generation of the top-up requests is failed, (b) when the store inventory reaches zero for the given SKU, a next inventory arrival is checked and waits till that time before generating further top-ups requests;
tagging, one or more skill specific users to the pick-up list based on the one or more configurable parameters and consider availability of skill specific users, shift indices assigned to the skill specific user, a shift type generated in a shift roster, a break time between shifts;
calculating, an updated quantity of items on the one or more shelves of the store based on an execution of the pick-up list by the one or more tagged skill specific users; and
determining availability of the skill specific user prior to generating the pick-up list, when the skill specific user is not available, the generated pick-up list is unassigned, and while re-running a next pick-up list, the generated pick-up list is deleted from a memory or a database, and the next pick-up list considers SKUs with the updated quantity of items.
and passing a refill record quantity as an index, after a first run, as an input for generating the one or more top-up requests being selected using batching logic to reset the store inventory available and recheck point facilitates deleting the generated pick-up list and the re-indexing are performed within the memory or the database prior to each subsequent run.
MPEP 2106.04(a)(2) II. states:
The phrase "methods of organizing human activity" is used to describe concepts relating to:
fundamental economic principles or practices (including hedging, insurance, mitigating risk);
commercial or legal interactions (including agreements in the form of contracts, legal obligations, advertising, marketing or sales activities or behaviors, and business relations); and
managing personal behavior or relationships or interactions between people, (including social activities, teaching, and following rules or instructions).
The Supreme Court has identified a number of concepts falling within the "certain methods of organizing human activity" grouping as abstract ideas. In particular, in Alice, the Court concluded that the use of a third party to mediate settlement risk is a ‘‘fundamental economic practice’’ and thus an abstract idea. 573 U.S. at 219–20, 110 USPQ2d at 1982. In addition, the Court in Alice described the concept of risk hedging identified as an abstract idea in Bilski as ‘‘a method of organizing human activity’’. Id. Previously, in Bilski, the Court concluded that hedging is a ‘‘fundamental economic practice’’ and therefore an abstract idea. 561 U.S. at 611–612, 95 USPQ2d at 1010.
In the instant case, the limitations of
generating, a forecast of rate of sales for one or more items from the plurality of items for a pre-defined time interval during real- time or near real-time events of transactions at stores, purchase,
wherein the at least one machine learning (ML) model amongst the one or more ML models is selected based on a level of training on the historical sales data and validation of associated performance therebetween with performance metrics including a true positive rate, a recall, a false positive rate, a precision, an accuracy, a F-measure, a Mean Absolute Error (MAE),
periodically training model with a new dataset pertaining to sales, replenishment and re-deployed to predict the rate of sales;
identifying, one or more item bucket sizes at each of the one or more SKUs and the pre-defined time interval based on a historical sales data, wherein one or more items in each of the one or more item bucket sizes are identified as being sold during a specific time duration based on an item threshold for a given day;
creating, a sales profiler for each of the one or more items based on the one or more item bucket sizes being identified using the historical sales data;
splitting the forecast of rate of sales for the one or more items for the pre-defined time interval into at least one of (i) the one or more item bucket sizes, and (ii) a pre-determined time period based on the sales profiler;
generating, one or more top-up requests for the one or more SKUs based on a pre-defined threshold using the forecast of rate of sales for the one or more items being split, wherein the one or more top-up requests comprise a set of items requiring replenishment on one or more shelves in the store;
wherein the top-up requests are generated at multiple time periods based on varied levels of projected quantity on the shelves in accordance with one of the rules including (a) when the projected quantity on a shelf becomes X%, create a top-up list with a first priority, (b) when the projected quantity on the shelf becomes Y%, create the top-up list with a second priority, when the projected quantity on the shelf becomes Z%, create the top-up list with a third priority;
creating, a pick-up list based on the one or more top-up requests and one or more configurable parameters comprises;
obtaining a beginning quantity at start of day;
generating a rate of sale for every item bucket size from the ML model;
(c) identifying one or more checkpoints that is an item bucket end time by when on hand capacity breaches a predefined percentage of a maximum shelf capacity corresponds to low, medium, high of the maximum shelf capacity and generate a refill quantity at beginning of day for the SKU;
(d) continuing execution for a next run by obtaining an input from the pick- up list by ascertaining one of low, medium, high, from the generated refill quantity is in the pick-up list and set that point as a next checkpoint; and
(e) repeating steps (c) and (d) until end of day is reached or store inventory is out of stock for the SKU;
re-predicting, via the one or more hardware processors, the forecast of rate of sales for the one or more items multiple times throughout the day based on actual intraday sales using one or more intraday models;
repeating the step of generating the one or more top-up requests, creating the pick- up list in accordance with the re-predicted forecast of rate of sales;
performing constraint checks including (a)when SKI does not breach a predefined percentage of maximum capacity for an entire day, then generation of the top-up requests is failed, (b) when the store inventory reaches zero for the given SKU, a next inventory arrival is checked and waits till that time before generating further top-ups requests;
tagging, one or more skill specific users to the pick-up list based on the one or more configurable parameters and consider availability of skill specific users, shift indices assigned to the skill specific user, a shift type generated in a shift roster, a break time between shifts;
calculating, an updated quantity of items on the one or more shelves of the store based on an execution of the pick-up list by the one or more tagged skill specific users; and
determining availability of the skill specific user prior to generating the pick-up list, when the skill specific user is not available, the generated pick-up list is unassigned, and while re-running a next pick-up list, the generated pick-up list is deleted from a memory or a database, and the next pick-up list considers SKUs with the updated quantity of items.
and passing a refill record quantity as an index, after a first run, as an input for generating the one or more top-up requests being selected using batching logic to reset the store inventory available and recheck point facilitates deleting the generated pick-up list and the re-indexing are performed within the memory or the database prior to each subsequent run.
are directed to fundamental principles of analyzing data to forecast required replenishment of shelves and tagging workers with tasks.
MPEP 2106.04(a)(2) states:
The courts consider a mental process (thinking) that "can be performed in the human mind, or by a human using a pen and paper" to be an abstract idea. CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1372, 99 USPQ2d 1690, 1695 (Fed. Cir. 2011). As the Federal Circuit explained, "methods which can be performed mentally, or which are the equivalent of human mental work, are unpatentable abstract ideas the ‘basic tools of scientific and technological work’ that are open to all.’" 654 F.3d at 1371, 99 USPQ2d at 1694 (citing Gottschalk v. Benson, 409 U.S. 63, 175 USPQ 673 (1972)). See also Mayo Collaborative Servs. v. Prometheus Labs. Inc., 566 U.S. 66, 71, 101 USPQ2d 1961, 1965 (2012) ("‘[M]ental processes[] and abstract intellectual concepts are not patentable, as they are the basic tools of scientific and technological work’" (quoting Benson, 409 U.S. at 67, 175 USPQ at 675)); Parker v. Flook, 437 U.S. 584, 589, 198 USPQ 193, 197 (1978) (same).
Accordingly, the "mental processes" abstract idea grouping is defined as concepts performed in the human mind, and examples of mental processes include observations, evaluations, judgments, and opinions
In the instant case, the generating, identifying, creating, generating, splitting, tagging, calculating and deleting steps cover performance of the limitations in the mind but for the recitation of generic computer components. Further, a human can delete unneeded information from a memory or a database. Further, the amendments that are directed to selection a ML model based on the level of training can also be performed mentally as a human can select the ideal ML model. That is, other than reciting “a processor” nothing in the claim element precludes the steps from being performed in the human mind.
Under step 2A prong 2 the examiner must then determine if the recited abstract idea is integrated into a practical application. MPEP 2106.04 states:
Limitations the courts have found indicative that an additional element (or combination of elements) may have integrated the exception into a practical application include:
• An improvement in the functioning of a computer, or an improvement to other technology or technical field, as discussed in MPEP §§ 2106.04(d)(1) and 2106.05(a);
• Applying or using a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, as discussed in MPEP § 2106.04(d)(2);
• Implementing a judicial exception with, or using a judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim, as discussed in MPEP § 2106.05(b);
• Effecting a transformation or reduction of a particular article to a different state or thing, as discussed in MPEP § 2106.05(c); and
• Applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception, as discussed in MPEP § 2106.05(e)
The courts have also identified limitations that did not integrate a judicial exception into a practical application:
• Merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f);
• Adding insignificant extra-solution activity to the judicial exception, as discussed in MPEP § 2106.05(g); and
• Generally linking the use of a judicial exception to a particular technological environment or field of use, as discussed in MPEP § 2106.05(h).
In the instant case, this judicial exception is not integrated into a practical application. In particular, Claim 1 recites the additional elements of:
A processor implemented method, comprising:
receiving, via one or more hardware processors, input data pertaining to sales of a plurality of items specific to (i) a store, (ii) one or more stock keeping units (SKUs), and (iii) one or more influencing periods;
using hardware processors to perform the abstract idea;
using at least one machine learning (ML) model amongst one or more ML models via the one or more hardware processors
periodically training the selected at least one ML
However, the computer elements (the processors) are recited at a high level of generality and given the broadest reasonable interpretation are simply generic computers performing generic computer functions. Generic computers performing generic computer functions, alone, do not amount to significantly more than the abstract idea and mere instructions to implement an abstract idea on a computer.
Further, the receiving data is recited broadly. Under the broadest reasonable interpretation, the limitations amounts to data gathering which the MPEP says is insignificant extra solution activity (see MPEP 2106.05(g). Further, the output is also recited broadly and amounts to insignificant post solution activity.
Further, the use of a machine learning is indicative of adding the words “apply it” (or an equivalent) with the judicial exception. MPEP 2106.05(f) states:
When determining whether a claim simply recites a judicial exception with the words "apply it" (or an equivalent), such as mere instructions to implement an abstract idea on a computer, examiners may consider the following:
(1) Whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished. The recitation of claim limitations that attempt to cover any solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result, does not integrate a judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words "apply it". See Electric Power Group, LLC v. Alstom, S.A., 830 F.3d 1350, 1356, 119 USPQ2d 1739, 1743-44 (Fed. Cir. 2016); Intellectual Ventures I v. Symantec, 838 F.3d 1307, 1327, 120 USPQ2d 1353, 1366 (Fed. Cir. 2016); Internet Patents Corp. v. Active Network, Inc., 790 F.3d 1343, 1348, 115 USPQ2d 1414, 1417 (Fed. Cir. 2015). In contrast, claiming a particular solution to a problem or a particular way to achieve a desired outcome may integrate the judicial exception into a practical application or provide significantly more. See Electric Power, 830 F.3d at 1356, 119 USPQ2d at 1743.
By way of example, in Intellectual Ventures I v. Capital One Fin. Corp., 850 F.3d 1332, 121 USPQ2d 1940 (Fed. Cir. 2017), the steps in the claims described "the creation of a dynamic document based upon ‘management record types’ and ‘primary record types.’" 850 F.3d at 1339-40; 121 USPQ2d at 1945-46. The claims were found to be directed to the abstract idea of "collecting, displaying, and manipulating data." 850 F.3d at 1340; 121 USPQ2d at 1946. In addition to the abstract idea, the claims also recited the additional element of modifying the underlying XML document in response to modifications made in the dynamic document. 850 F.3d at 1342; 121 USPQ2d at 1947-48. Although the claims purported to modify the underlying XML document in response to modifications made in the dynamic document, nothing in the claims indicated what specific steps were undertaken other than merely using the abstract idea in the context of XML documents. The court thus held the claims ineligible, because the additional limitations provided only a result-oriented solution and lacked details as to how the computer performed the modifications, which was equivalent to the words "apply it". 850 F.3d at 1341-42; 121 USPQ2d at 1947-48 (citing Electric Power Group., 830 F.3d at 1356, 1356, USPQ2d at 1743-44 (cautioning against claims "so result focused, so functional, as to effectively cover any solution to an identified problem")).
In the instant case, the additional elements of the broadly recited machine learning attempt to cover any solution to the identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result, which does not integrate a judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words "apply it”. The claims do not state how the machine learning classifier generate forecasts a rate of sales for one or more items. As such, the broadly recited ML model does not integrate a judicial exception into a practical application or provide significantly more.
Viewing the generic computer elements in combination with the data gathering and machine learning does not add anything further than looking at the limitations individually. When viewed either individually, or as an ordered combination, the additional limitations do not amount to a claim as a whole that is significantly more than the abstract idea.
In step 2B, the examiner must determine whether the claim adds a specific limitation other than what is well-understood, routine, conventional activity in the field - see MPEP 2106.05(d).
As discussed with respect to Step 2A Prong Two, the additional elements of the information classification unit; the information collection unit amount to no more than mere instructions to apply the exception using a generic computer component. The same analysis applies here in 2B, i.e., mere instructions to apply an exception on a generic computer cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Further, nothing in the claim indicates that the retrieval of information is anything other than conventional. See MPEP 2106.05(d) that states “Receiving or transmitting data over a network, e.g., using the Internet to gather data is conventional when claimed in a merely generic manner (see Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network).
Also see MPEP 2106.05(d) that states storing and retrieving information in memory is conventional when claimed in a merely generic manner (see Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93).
Further, similar to the analysis with respect to step 2A prong 2 recitation of claim limitations that attempt to cover any solution to an identified problem with no restriction on how the result is accomplished cannot provide an inventive concept under step 2B of the eligibility analysis.
Viewing the generic computer elements in combination with the data gathering and machine learning does not add anything further than looking at the limitations individually. When viewed either individually, or as an ordered combination, the additional limitations do not amount to an inventive concept.
Further Claims 3-6 further limit the abstract idea of an analysis that can be performed mentally or certain methods of human activity that were already rejected in claim 1, but fail to remedy the deficiencies of the parent claim as they do not impose any limitations that amount to significantly more than the abstract idea itself.
Accordingly, the Examiner concludes that there are no meaningful limitations in claims 3-6 that transform the judicial exception into a patent eligible application such that the claim amounts to significantly more than the judicial exception itself.
The analysis above applies to all statutory categories of invention. As such, the presentment of claim 1 otherwise styled as a system or computer program product, for example, would be subject to the same analysis. Therefore, Claims 7, 9, 11-13, 15-18 are rejected for the same rational that applied to claims 1-6.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DEIRDRE D HATCHER whose telephone number is (571)270-5321. The examiner can normally be reached Monday-Friday 8-4:30.
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, Brian Epstein can be reached at 571-270-5389. 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.
/DEIRDRE D HATCHER/Primary Examiner, Art Unit 3625
Prosecution Insights