Detailed Action
Status of Claims
This action is in reply to applicant response filed on April 13, 2026.
Claims 1-2, 4-5, 8-9, and 11 have been amended.
Claims 3, 6, and 7 have been cancelled.
Claim 12 has been added.
Claims 1-2, 4-5, and 8-12 are currently pending and have been examined.
The information disclosure statement (IDS) submitted on 02/9/2026 was filed. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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 1-2, 4-5, and 8-12 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim reproduced and parsed into limitations
Amended claim 1
An information processing apparatus comprising:
at least one processor or circuit programmed to
generate relationship information indicating a relationship between foot features of a plurality of users and features of specific footwear purchased by the plurality of users, by generating an approximation curve based on the foot features and the features of the specific footwear, the foot features including a plurality of foot dimensions;
estimate foot features of a target user from a purchase history of the target user, based on the relationship information;
generate a model based on a correlation between evaluation information on a plurality of sizes of the specific footwear evaluated by the plurality of users and foot sizes of the plurality of users, the model being based on a function that calculates scores indicating fitting degrees of feet of the plurality of users with respect to the plurality of sizes;
decide whether a certain size of the specific footwear is suitable for the estimated foot features of the target user based on whether a score output by the model … is equal to or higher than a predetermined threshold; and
provide a service according to the foot features of the target user based on a result of the deciding.
Amended claim 11
Method counterpart of claim 1 with substantially the same limitations.
Amended claim 12
Further defines the score semantics:
positive score increases when size is larger,
negative score increases when size is smaller,
zero indicates suitable.
Step 1: Statutory category determination.
Claim 1: Machine (information processing apparatus). Falls within a statutory category.
Claim 11: Process (method). Falls within a statutory category.
Step 2A, Prong 1: Identify judicial exception(s) with citations to PEG groupings; the claims “recite”
The amended claims still recite one or more judicial exceptions:
A. Mathematical concepts
The amendments now expressly recite mathematical subject matter.
concerning clauses:
“by generating an approximation curve”
“generate a model based on a correlation”
“the model being based on a function that calculates scores”
“based on whether a score output by the model … is equal to or higher than a predetermined threshold”
Claim 12: “a positive score increases … a negative score increases … and a zero score indicates …”
These clauses recite mathematical relationships, mathematical models, correlations, scoring functions, and thresholding, which fall squarely within the PEG grouping of mathematical concepts.
B. Mental processes
The claims also recite evaluative and inferential operations that can be performed conceptually or with pen-and-paper.
concerning clauses:
“estimate foot features of a target user from a purchase history”
“generate relationship information”
“generate a model based on a correlation”
“decide whether a certain size … is suitable”
“provide a service … based on a result of the deciding”
At a high level, this is still:
reviewing user purchase and evaluation data,
inferring foot characteristics,
comparing scores to a threshold,
deciding fit,
and recommending a size/service.
That is an abstract evaluative process.
C. Certain methods of organizing human activity
The claims remain directed to a retail sizing/recommendation workflow for footwear, i.e., commercial recommendation activity.
concerning clauses:
“features of specific footwear purchased by the plurality of users”
“estimate foot features … from a purchase history”
“decide whether a certain size … is suitable”
“provide a service”
This is still fundamentally directed to improving or automating consumer purchase recommendations and size selection in commerce, which fits the PEG grouping of certain methods of organizing human activity, particularly commercial interactions.
Step 2A, Prong 2: Analyze integration into a practical application; discuss any claimed technological improvement; address whether extra-solution activity or field-of-use limitations are present.
The amendments do not integrate the exception into a practical application
The amended claims still do not recite a practical application sufficient to render the claim eligible.
1. No improvement to computer functionality
The claim does not recite:
improved processor operation,
improved memory management,
improved network transmission,
improved database architecture,
improved model execution efficiency,
improved sensor operation,
or any other technological improvement to computer functioning.
The claim merely uses a generic processor to:
fit a curve,
generate a model,
score,
threshold,
and output a service result.
That is using a computer as a tool to perform abstract analysis.
2. No improvement to another technology or technical field
Applicant may argue the amendments improve footwear fitting accuracy by using:
a plurality of foot dimensions,
an approximation curve,
correlation-based modeling,
threshold-based decisioning.
However, the claim still does not recite a technical mechanism improving a technological process. It does not:
improve a foot scanner,
improve image processing,
improve a measurement device,
control a manufacturing machine,
transform footwear,
or change operation of any physical fitting apparatus.
The claim remains directed to information analysis for recommendation.
3. No meaningful tie to a particular machine
The claim recites only:
“at least one processor or circuit programmed to”
That is a generic implementation. There is no particularized hardware architecture or machine integral to performing the claimed function in a non-generic way.
4. No transformation of an article
The claim does not transform:
footwear,
a foot scan into a physical object,
a shoe last,
or any physical article.
The output is still a service/recommendation.
5. Field-of-use limitation only
Limiting the abstract analysis to:
footwear,
purchase history,
foot dimensions, and
fitting scores
does not integrate the exception into a practical application. It merely narrows the context to a particular business field.
6. Extra-solution activity
The final limitation:
“provide a service according to the foot features … based on a result of the deciding”
is still post-solution activity, i.e., reporting or using the result of the abstract analysis in a recommendation context.
Net result under Prong 2
The amended claims still amount to:
collecting data,
mathematically modeling the data,
inferring a user characteristic,
scoring and thresholding,
and providing a recommendation/service.
That is not integration into a practical application under the 2019 PEG.
The claims do not integrate the abstract ideas into a practical application. The “at least one processor or circuit” is generic. The specification’s hardware description (CPU, RAM, HDD, NIC, display, etc.; see Spec. ¶¶ 20 & 176, FIG. 15) confirms use of conventional components. There is no recited improvement to computer functionality, no particular machine beyond a generic processor, no effecting of a transformation of an article, and no other meaningful limitation that meaningfully applies the abstract concepts.
The “service” is not a technological process; it is a result-oriented commercial output (size/fit recommendation or advisement). Steps such as “acquire evaluation information,” “generate relationship/correlation information,” “estimate foot features from purchase history,” “calculate scores,” “decide suitability,” and “provide service” are data collection/analysis and presentation activities—typical extra-solution activity limited to the field of retail footwear fitting. Field-of-use limitations (footwear commerce) and instructing to apply the abstract analyses within that context do not integrate the exception into a practical application (Oct. 2019 Update).
The claims merely apply the abstract ideas using generic computer components in a commercial context and therefore do not integrate the judicial exceptions into a practical application.
Step 2B: Assess whether additional elements are significantly more; discuss WURC with evidentiary considerations
Additional elements
The only meaningful additional implementation element remains:
“at least one processor or circuit programmed to”
The rest of the claim limitations are the abstract operations themselves.
No significantly more
The additional elements do not amount to significantly more than the judicial exception.
Why not
The processor/circuit is generic.
Curve generation, correlation, model generation, score calculation, and threshold comparison are conventional forms of data analysis.
The service output is a routine result presentation/recommendation function.
The specification, as previously discussed, describes conventional computing hardware (CPU, RAM, ROM, HDD, communication interface, etc.), which supports a WURC finding as to the computer implementation.
Berkheimer/WURC considerations
If asserting that the processor/circuit and associated implementation are well-understood, routine, and conventional, factual support can rely on:
the specification’s disclosure of generic hardware components,
the absence of any recited unconventional computing architecture,
the absence of any claimed non-conventional data structure,
the absence of any specific unconventional training or fitting procedure.
The newly added limitations do not change that conclusion because they merely further define the abstract mathematical processing.
Additional elements: “at least one processor or circuit,” storage sections, servers/terminals (in the spec), and routine computer functions (communication, acquisition, generation, decision, provision).
These are well-understood, routine, and conventional (WURC) computer components performing their ordinary functions of receiving, storing, processing, and outputting data. The specification affirmatively describes a conventional computing environment (CPU 1100, RAM 1200, ROM 1300, HDD 1400, NIC 1500, I/O 1600; Spec. ¶¶ 176–181, FIG. 15) and generic networked servers/clients (Spec. FIGs. 1, 3, 4, 7). There is no claimed unconventional architecture, no non-generic data structure, and no new hardware that changes computer operation.
The correlation generation, approximation curve, and score calculation are presented at a high level without reciting a particular non-conventional algorithmic technique or a specific improvement to computer functioning. The claimed mathematical modeling (claim 3) is itself the abstract idea (PEG: mathematical concepts) and cannot supply the inventive concept.
Under Berkheimer, absent evidence of non-conventionality, routine generic computer implementation does not constitute “significantly more.” The record, including the specification’s admissions of generic components, supports a finding that the additional elements are WURC.
Dependent claims 2, 4, 5, 8, 9, 10, and 12, as presented, are ineligible under 35 U.S.C. § 101. They recite abstract ideas (commercial recommendations/organizing human activity; mental processes; mathematical concepts) and do not integrate those ideas into a practical application. The additional elements are generic computing components and WURC.
correlation information” and “scores … indicating fitting degrees,” i.e., calculating and using mathematical correlations and computed scores. Collecting, analyzing, correlating, modeling, scoring, estimating, deciding, and providing information/recommendations based on user purchase histories and evaluations are within the abstract idea categories recognized by the PEG (see also Electric Power Group v. Alstom; SAP v. InvestPic).
These claims also remain ineligible because they merely add:
more granularity to the model,
more correlations,
more evaluation rules,
a particular evaluator type,
or score semantics,
without reciting a technological improvement or non-generic technical implementation.
Therefore, the limitations on the invention, when viewed individually and in ordered combination are directed to in-eligible subject matter.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim 1-2, 4-5, 8-9, and 11 are rejected under 35 U.S.C. 103 as being unpatentable over Mazmanyan (US 2011/0295711) in view of Masuko (US 2014/0108202).
Regarding Claims 1 and 11: Mazmanyan teaches an information processing apparatus (Mazmanyan teaches a computerized apparel/footwear fit recommendation system using user/item data and fit prediction ¶¶ 33, 44, 54-75; Figs. 1, 2, 4, 5A/B, 6) comprising:
at least one processor or circuit programmed to:
generate relationship information that indicates a relationship between foot features of a plurality of users and features of specific footwear purchased by the plurality of users (Mazmanyan teaches a dataset/relationships between user profiles/body or foot-related features and apparel/footwear features for fit prediction. ¶¶ 33, 42, 44, 54-61, 66-75);
generating an approximation curve (Masuko teaches obtaining an approximate expression / approximate curve from combinations of body size information and purchased item size, weighted by evaluations. See ¶¶ 187-194, 195-197; Fig. 14A; claim 37) based on the foot features and the features of the specific footwear (Masuko teaches approximate expression based on body size info and item size of specified item. ¶¶ 0187-0194), the foot features including a plurality of foot dimensions (Masuko teaches a plurality of body-size dimensions, e.g., height and weight; which may include additional dimensions. ¶¶ 0090-0091, 0196);
estimate foot features of a target user from a purchase history of the target user, based on the relationship information (Mazmanyan teaches the system predicts fit indicators for a user based on their purchase/fit history and the reference dataset, using collaborative filtering and similarity measures, ¶¶ 0033, 0044, 0054-0075; Figs. 2, 4, 5A/B, 6; further Masuko teaches using prior purchased item sizes as body-size-related information and using purchase history to infer user size info even when direct body-size registration is unavailable. See ¶¶ 24-25, 199-205, 235-241);
generate a model based on a correlation between evaluation information on a plurality of sizes of the specific footwear evaluated by the plurality of users and foot sizes of the plurality of users, the model being based on a function that calculates scores indicating fitting degrees of feet of the plurality of users with respect to the plurality of sizes (Mazmanyan teaches a collaborative filtering uses similarities between users and their fit indicators, including user profile/body data ¶¶ 61-75, 83, 84, 89; while Masuko explicitly teaches the scoring framework based on evaluations; total scores per size; approximate expression weighted by scores. ¶¶ 83-88, 93, 138-139, 188-194);
decide whether a certain size of the specific footwear is suitable for the estimated foot features of the target user based on whether a score output by the model for the estimated foot features is equal to or higher than a predetermined threshold (Mazmanyan teaches calculating predicted fit indicators (scores), compares to thresholds to recommend sizes ¶¶ 61-75, 89; recommendations are based on predicted fit indicators and comparison to suitability thresholds ¶¶ 54-61, 89 ; while Masuko explicitly teaches the scoring framework such as score generation, final size selection based on highest score, weight coefficients, some threshold values for excluding opposite-gender/children purchases Figs.12 & 13, ¶¶ 83-88, 93, 95-96, and 246)
provide a service according to the foot features of the target user based on a result of the deciding (Mazmanyan teaches a system that provides size recommendations and/or fit assessments to the user based on the estimated fit, ¶¶ 0033, 0054, 0096, 0105; Figs. 2, 6; while Masuko explicitly teaches Outputs recommended size information to the user. ¶¶ 74-76, 123-125, 158-160, claim 21).
It would have been obvious to a POSITA (person of ordinary skill in the art) to modify Mazmanyan’s fit recommendation system with Masuko’s approximate-expression and score-weighting techniques because both references are directed to estimating a fitting size for wearable items using purchase history and user evaluations without requiring an actual try-on. Masuko teaches that approximate expressions/curves based on user size information and purchased item sizes, weighted by evaluations, improve estimation accuracy. A POSITA would have been motivated to incorporate such techniques into Mazmanyan’s recommendation framework to improve fit prediction accuracy, particularly where direct target-user size data is incomplete or unavailable, with predictable results.
A POSITA would have been motivated to modify Mazmanyan’s fit recommendation system with Masuko’s approximate-expression / approximate-curve and evaluation-based scoring techniques because both references address the same problem of estimating a fitting size for wearable items using purchase history and evaluation data without requiring actual try-on. Masuko expressly teaches that using approximate expressions based on user size information and purchased size data, weighted by evaluations, improves estimation accuracy. Incorporating those teachings into Mazmanyan’s recommendation engine would have predictably improved fit prediction for target users, especially where direct measurement data is unavailable or incomplete.
Regarding Claim 2: The information processing apparatus according to claim 1, wherein the at least one processor or circuit is further programmed to: generate the relationship information for each of the specific footwear, and estimate the foot features of the target user based on the specific footwear purchased by the target user and a size of the specific footwear purchased by the target user (Mazmanyan teaches item-specific / apparel-specific records and recommendations using item data and size. ¶¶ 33, 44, 54-61.; while Masuko expressly teaches a specified item for sale, acquisition of purchased size for that specified item, and estimation based on the specified item and its size. See ¶¶ 7-9, 74-82, claim 21; also product code / item-specific handling at ¶¶ 109-112, 128).
Regarding Claim 4: The information processing apparatus according to claim 1, wherein the at least one processor or circuit is further programmed to: identify an allowable range of sizes of specific footwear that is allowable for a specific user based on evaluation information on a plurality of sizes of the specific footwear evaluated by the specific user; generate first correlation information that indicates a first correlation between the allowable range and a foot size of the specific user; and provide a service according to the foot features of the target user based on the first correlation information (Mazmanyan teaches a system that identifies suitable sizes based on predicted fit indicators and user preferences; can compare predicted fit to thresholds ¶¶ 54-61, 89, 96; while Masuko expressly teaches a multi-size evaluation context: same item sold in plural sizes; return/not-return among plural sizes; size finally purchased vs not purchased; size “between” two estimated sizes; possible recommendation of more than one size. ¶¶ 76, 83-88, 159-163, 172-173, 180, 191; Approximate expression mapping user size info to item size, weighted by evaluations. ¶¶ 0187-0194, 0196; Masuko further teaches an approximate expression mapping user size info to item size, weighted by evaluations. ¶¶ 0187-0194, 0196).
Regarding Claim 5: The information processing apparatus according to claim 4, wherein the at least one processor or circuit is further programmed to: generate second correlation information that indicates a second correlation between evaluations by the plurality of users who have purchased the specific footwear, as the specific user, and foot sizes of the plurality of users, and identify the allowable range of the sizes of the specific footwear estimated to be allowable for each of the plurality of users based on the second correlation information (Mazmanyan teaches a collaborative filtering / user similarity / fit indicators based on other users and their evaluations ¶¶ 61-75, 83, 84; while Masuko expressly teaches multiple users; evaluation-derived scores; body-size information of users; approximate expression/relationship between body-size information and purchased item size; see ¶¶ 79-96, 187-194, 195-197).
Regarding Claim 8: The information processing apparatus according to claim 4, wherein the at least one processor or circuit is further programmed to: identify the allowable range based on the evaluation information that the specific user has evaluated by selecting from among evaluations based on a predetermined evaluation criterion (Mazmanyan teaches users provide fit assessments on a scale (e.g., -5 to +5) when trying on apparel ¶¶ 35-37, 44, 83; while Masuko expressly teaches predefined evaluation categories and scoring criteria: returned / not returned, review recommendation levels, finally purchased / not finally purchased; see ¶¶ 83-88, 146-157; Fig. 3A).
Regarding Claim 9: The information processing apparatus according to claim 4, wherein the at least one processor or circuit is further programmed to: identify the allowable range based on the evaluation information on the plurality of sizes specified and evaluated in a predetermined order (Mazmanyan teaches system can predict fit for a range of sizes, may use central size and evaluate upward / downward ¶¶ 87, 88; while Masuko expressly teaches multiple-size purchase/evaluation scenarios, but does not clearly teach that sizes are evaluated in a predetermined order ¶¶ 76, 83, 86, 152-156).
Claims 10 and 12 are rejected under 35 U.S.C. 103 as being unpatentable over Mazmanyan in view of Masuko, and further in view of Bright (US 2011/0099122).
Mazmanyan in view of Masuko fail to teach, but Bright does teach the use of expert/salesperson fit evaluations for new products to supplement user fit data (Item data and expert evaluations may be used to supplement data, including fit testing by experts. ¶¶ 25, 35, 48, 49) and a positive score increases as the certain size is evaluated as larger relative to a foot size of a respective user of the plurality of users, a negative score increases as the certain size is evaluated as smaller relative to the foot size, and a zero score indicates the certain size is evaluated as suitable for the foot size (a fit estimate could be provided in a range of -1 to 1, wherein a negative number indicates too short, tight, small, etc., and positive numbers indicate too large, loose, long, etc., and 0 indicates a perfect fit ¶ 52).
Bright teaches the additional limitation of claim 12. Specifically, Bright discloses that a fit estimate may be expressed on a scale from -1 to 1, where a negative number indicates that the item is too short, tight, or small, a positive number indicates that the item is too large, loose, or long, and 0 indicates a perfect fit. See Bright ¶ 52. This corresponds to the claimed limitation that a positive score increases as the size is evaluated as larger relative to the user, a negative score increases as the size is evaluated as smaller relative to the user, and a zero score indicates suitability. It would have been obvious to a person of ordinary skill in the art to implement the fit-scoring model of Mazmanyan and Masuko using Bright’s known signed fit-score convention because Bright provides a recognized and intuitive way to encode both degree and direction of fit, thereby predictably improving interpretability and usefulness of the resulting fit determination.
It would have been obvious to combine the known advantages of machine learning models (as in Bright) with Mazmanyan’s collaborative filtering system to automate and improve fit predictions, especially as both references address similar technical problems and use overlapping types of data.
A POSITA would have found it obvious to combine the collaborative filtering and user feedback system of Mazmanyan with the machine learning model generation and expert data input of Bright, because both address the same problem (personalized fit prediction) and Bright explicitly teaches the use of advanced modeling and expert data to improve prediction accuracy. The combination would predictably yield a system with improved flexibility and accuracy in fit recommendations, especially in cases of sparse user history or new products.
Response To Arguments/Remarks
Applicant's arguments filed 4/13/2026 have been fully considered.
Regarding 101:
Summary of Applicant’s arguments
Applicant contends that amended independent claims 1 and 11 are now patent-eligible under Step 2A, Prong 2 because the amendments allegedly recite a practical application of any abstract idea. In particular, Applicant argues:
Technical problem identified in the specificationApplicant asserts the claims address a specific technical problem: when direct physical foot measurement data for a target user is unavailable, a computerized fit prediction system cannot determine whether a given footwear size is suitable. Applicant cites paragraph [0123].
Specific ordered combination of stepsApplicant argues the claims now recite a particular sequence of operations, including:
generating relationship information by generating an approximation curve based on a plurality of foot dimensions,
estimating target-user foot features from purchase history using that relationship information,
generating a separate model based on correlations between evaluation information and user foot sizes,
deciding suitability using a score output by the model and a predetermined threshold, and
providing a service based on that decision.
Not a mental processApplicant argues this ordered combination is not practically performable in the human mind because it involves approximation curves, multiple users, multiple foot dimensions, score generation, and threshold-based decisions.
Not directed to commercial recommendations or organizing human activityApplicant argues the claims are not directed to the commercial act of recommending a shoe size, but instead to a specific information processing technique for estimating foot features and evaluating suitability when direct measurement data is unavailable. Applicant characterizes the recommendation/service as merely downstream of the technical processing.
Applicant therefore requests withdrawal of the § 101 rejection.
Applicant’s arguments have been fully considered but are not persuasive. The rejection under 35 U.S.C. § 101 is therefore maintained. (The examiner has provided an updated 101 rejection, see above).
Examiner response:
Step 2A, Prong 1: The amended claims still recite a judicial exception
The amendments do not remove the abstract nature of the claims. Rather, they make the abstract analytical processing more explicit.
Mathematical concepts
The amended claims expressly recite mathematical concepts, including:
“generating an approximation curve”
“generate a model based on a correlation”
“the model being based on a function that calculates scores”
“whether a score output by the model … is equal to or higher than a predetermined threshold”
These limitations recite mathematical relationships, mathematical modeling, score calculation, and threshold comparison, all of which fall within the mathematical concepts grouping identified in the 2019 PEG.
Mental processes
The claims also continue to recite abstract evaluative and inferential steps, such as:
estimating foot features from purchase history,
correlating user evaluation data with foot sizes,
determining whether a certain size is suitable, and
providing a service based on the determination.
These are forms of observation, evaluation, and judgment. Even if the volume of data makes manual performance impractical, the claimed operations remain abstract analytical steps. In any event, the claims independently recite mathematical concepts, which are abstract ideas regardless of whether they are practically performed mentally.
Certain methods of organizing human activity
The claims remain directed to determining suitable footwear sizes for users based on user purchase/evaluation history and then providing a service based on that determination. That is still a commercial recommendation or fit-assistance activity in the footwear retail context, i.e., a form of commercial interaction.
Accordingly, the amended claims still recite one or more judicial exceptions.
Step 2A, Prong 2: The amended claims do not integrate the exception into a practical application. Applicant argues that the claims solve a “technical problem” of unavailable foot measurement data. This argument is not persuasive.
1. The claimed problem is not a technological problem in computer technology. The problem identified by Applicant is, at bottom, an information insufficiency problem: how to infer a user’s fit or foot features when direct measurements are unavailable. That is a problem in data analysis and recommendation, not a technological problem in computer operation or another technology.
The claims do not recite:
an improvement to a foot scanner,
an improvement to image acquisition or image processing,
an improvement to a measurement device,
an improvement to database structure,
an improvement to processor efficiency,
an improvement to memory usage,
an improvement to network performance,
or any other technological improvement to computer functionality or another technical field.
2. The claims use a generic processor as a tool to perform the abstract idea
The claims are still implemented only by “at least one processor or circuit programmed to” perform the recited operations. This is generic computer implementation. No particular machine architecture, specialized hardware arrangement, or non-generic technical mechanism is recited.
3. The newly added limitations are still abstract analytical steps.
The added “approximation curve,” “correlation,” “function,” “scores,” and “threshold” do not integrate the exception into a practical application. Instead, they further define the abstract analysis itself. That is, Applicant has narrowed the claims by specifying how the abstract idea is mathematically carried out, but has not tied the idea to a technological application that improves computer technology or another technology.
4. No transformation or meaningful physical application is recited
The claims do not transform any physical article. They do not control a manufacturing process, alter footwear, calibrate or operate a measurement device, or generate machine-control signals for physical equipment. The output remains a fit/suitability determination and a resulting service.
5. “Providing a service” remains post-solution activity
The final step of “provid[ing] a service … based on a result of the deciding” is merely the use or presentation of the result of the abstract analysis. This is extra-solution activity and does not meaningfully integrate the exception into a practical application.
Accordingly, the claims do not satisfy Step 2A, Prong 2.
Response to Applicant’s specific points:
Applicant’s point: the claims solve a specific technical problem
Response: Not persuasive. The alleged problem is not a technological problem with computers or measurement hardware, but rather a problem of inferring user characteristics from available consumer data. That is an abstract analytical problem.
Applicant’s point: the claims recite a specific ordered combination of steps
Response: Not persuasive. An ordered combination of abstract mathematical and evaluative steps remains abstract where the combination is still directed to analyzing data, generating a model, comparing a score to a threshold, and producing a recommendation/service. The ordered nature of the steps does not, by itself, create a practical application.
Applicant’s point: the claims cannot practically be performed in the human mind
Response: Not persuasive. First, several limitations remain evaluative and inferential in nature. Second, regardless of the mental-process characterization, the claims expressly recite mathematical concepts such as approximation curves, correlation-based models, scoring functions, and threshold comparison, which are abstract ideas under the PEG.
Applicant’s point: the claims are not directed to commercial recommendations
Response: Not persuasive. The claims are ultimately directed to determining whether a footwear size is suitable for a target user and then providing a service based on that determination. That remains a commercial fit/recommendation context. Framing the recommendation as “downstream” does not remove the abstract nature of the underlying analysis or the commercial context of its application.
Step 2B: The claims still do not amount to significantly more
The additional claim elements do not amount to significantly more than the abstract idea itself.
The recited processor or circuit is generic.
The recited approximation curve, correlation, scoring function, and threshold are part of the abstract analytical concept, not an inventive technological implementation.
The specification describes conventional computing components, supporting a finding that the computer implementation is well-understood, routine, and conventional.
Thus, the claims do not recite an inventive concept sufficient to transform the judicial exception into patent-eligible subject matter.
Applicant’s amendments do not overcome the § 101 rejection. The amended claims continue to recite abstract ideas, including mathematical concepts and mental processes, as evidenced by the newly added limitations reciting generation of an approximation curve, generation of a model based on a correlation, use of a function to calculate scores, and threshold-based suitability determination. These limitations merely elaborate the abstract analytical technique and do not integrate the judicial exception into a practical application. The claims remain directed to analyzing purchase/evaluation data to infer user foot characteristics and provide a footwear-related service recommendation using a generic processor or circuit. Accordingly, the claims remain ineligible under 35 U.S.C. § 101.
Applicant’s amendments and arguments are not persuasive. The amended claims still recite abstract ideas, including mathematical concepts, mental processes, and commercial recommendation activity, and do not integrate those ideas into a practical application. Nor do the claims add significantly more than generic computer implementation.
Accordingly, the rejection of claims 1, 2, 4, 5, 8-12 under 35 U.S.C. § 101 is maintained.
Regarding 103: Applicant’s arguments, have been fully considered and are persuasive. Therefore, the previous 102/103 rejection(s) has/have been withdrawn. However, upon further consideration, a new ground(s) of rejection is made over Mazmanyan (US 2011/0295711) in view of Masuko (US 2014/0108202).
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to FAHD A OBEID whose telephone number is (571)270-3324. The examiner can normally be reached Monday-Friday 8:30am-5:00pm.
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.
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.
/FAHD A OBEID/Supervisory Patent Examiner, Art Unit 3627
Prosecution Insights