Kamal v. Femtosense Inc

I. Executive Summary and Procedural Information

• Parties & Counsel:
  • Plaintiff: Andrew Magdy Kamal (Michigan)
  • Defendant: Femtosense, Inc. (California) and Sam Fok (California)
  • Plaintiff’s Counsel: Pro Se
• Case Identification: 1:24-cv-00989, D. Del., 09/30/2025
• Venue Allegations: The complaint does not allege a specific basis for venue in the District of Delaware, but notes that the matter was removed from the Delaware Chancery Court and remanded to the federal district court.
• Core Dispute: Plaintiff alleges that Defendants' artificial intelligence and data compression technology infringes a patent related to a two-phase method for compressing data by categorizing values into relevant "complexities" and irrelevant "memoryless" data.
• Technical Context: The technology at issue resides in the field of data compression for large-scale data analytics, where preserving mathematically complex but significant data points is critical for applications like AI and scientific research.
• Key Procedural History: The complaint alleges the case was removed from the Delaware Chancery Court. Plaintiff also alleges providing Defendants with pre-suit notice of infringement via a certified letter on April 15, 2024, a fact which may be relevant to the claim of willful infringement.

Case Timeline

Date	Event
2018-08-09	’315 Patent Priority Date
2021-03-30	’315 Patent Issue Date
2024-04-15	Plaintiff allegedly sent notice of infringement
2025-09-30	Amended Complaint Filing Date

II. Technology and Patent(s)-in-Suit Analysis

• Patent Identification: U.S. Patent No. 10,965,315, "Data Compression Method," issued March 30, 2021 (’315 Patent).

The Invention Explained

• Problem Addressed: The patent addresses a problem in data analytics where software performing "data scrubbing" often omits or discards "unexpected data," such as irrational or complex numbers. This is particularly problematic for scientific datasets (e.g., particle accelerator or cancer genomics data) where such values are not errors but represent significant information (’315 Patent, col. 1:15-32).
• The Patented Solution: The invention is a two-phase data compression method. In a first "training" phase, data values are compared against predefined criteria and sorted into two categories: a "first category" of more complex, relevant values that are added to a compressed dataset, and a "second category" of "memoryless" data that are excluded. As the second-category data are excluded, a "statistical distribution" of these values is created. In a subsequent second phase, the system uses this statistical distribution, rather than the initial rigid criteria, to categorize new data values more efficiently, for instance by using Bayes' theorem to determine the probability that a value is memoryless (’315 Patent, Abstract; col. 5:15-33).
• Technical Importance: This approach claims to preserve the integrity of datasets by retaining mathematically complex but vital data points that conventional tools might discard, while still achieving data compression (’315 Patent, col. 10:55-61).

Key Claims at a Glance

• The complaint asserts independent claims 1 (a method) and 25 (a computing device) (’315 Patent, col. 11:59-col. 12:17, col. 14:48-col. 14:67; Compl. ¶17).
• Independent Claim 1 (Method) requires:
  • obtaining a data set and criteria for determining how to categorize values;
  • determining whether values correspond to a first or second category;
  • adding first-category values to a compressed data set;
  • excluding second-category values from the compressed data set and updating a statistical distribution based on them;
  • performing this determination in a "first phase" based on comparison to the criteria; and
  • performing this determination in a "second phase" based on the created statistical distribution.
• Independent Claim 25 (Computing Device) requires:
  • memory and a processing circuit configured to perform the steps parallel to those in method claim 1.
• The complaint reserves the right to assert dependent claims 2-17 and 20-24 (Compl. ¶31).

III. The Accused Instrumentality

Product Identification

The complaint identifies "Femtosense's technology," which it describes as a "data compression method" used in electronics for "efficient, scalable, and affordable AI" (Compl. ¶¶16, 17).

Functionality and Market Context

• The complaint alleges the accused method obtains a dataset and categorizes it into a "first category (e.g., Tuple)" and a "second category (e.g., IOTARGET)" based on complexity using Bayesian techniques (Compl. ¶18).
• It further alleges that values from the first category are added to a compressed dataset via a "quantization operation," while values from the second category are excluded and used to update a statistical distribution (Compl. ¶19).
• The accused process is alleged to use a two-phase operation, with a first phase based on criteria and a second phase involving "dequantization" based on the statistical distribution (Compl. ¶20).
• No probative visual evidence provided in complaint.

IV. Analysis of Infringement Allegations

The complaint references a claim chart analysis in "EXHIBIT B" but does not attach it to the pleading (Compl. ¶22). In the absence of a claim chart, the infringement theory is based on the narrative allegations in the complaint.

The complaint alleges that Femtosense's technology directly maps onto the elements of the asserted claims. The core of the infringement theory is that Femtosense’s process of categorizing data into "Tuple" and "IOTARGET" corresponds to the patent’s "first category" and "second category" (Compl. ¶18). The complaint alleges that Femtosense's use of a "quantization operation" to build a compressed dataset meets the claim limitation of "adding values" (Compl. ¶19). Finally, the complaint alleges that the accused two-phase process, which uses criteria in a first phase and a statistical distribution in a second phase, directly corresponds to the two-phase limitation central to the asserted independent claims (Compl. ¶20). The complaint further alleges that the use of Bayesian learning, quadtree structures, and Riemann Zeta verification by the accused technology infringes various dependent claims (Compl. ¶21).

• Identified Points of Contention:
  • Scope Questions: A primary question will be whether Femtosense's proprietary terms "Tuple" and "IOTARGET" correspond in scope and function to the patent's "first category" (complexities) and "second category" (memoryless data). Further, discovery will be needed to determine if Femtosense's "quantization" and "dequantization" operations are structurally and functionally the same as the patent's claimed steps of "adding...to a compressed dataset" and "determining...based on the statistical distribution."
  • Technical Questions: What evidence does the complaint provide that the accused two-phase system operates in the manner claimed? The allegation that the second phase is "based on the statistical distribution" (Compl. ¶20) will require technical evidence demonstrating a functional dependency equivalent to the probabilistic method described in the patent, such as the use of Bayes' theorem for classification (’315 Patent, col. 8:1-17).

V. Key Claim Terms for Construction

• The Term: "first category" / "second category"

  • Context and Importance: The entire inventive concept rests on this binary classification of data. The definition of these terms will determine whether the patent is limited to specific mathematical data types or covers a broader range of data sorting methods.
  • Intrinsic Evidence for a Broader Interpretation: The claim language itself does not explicitly limit the categories, referring generally to "individual values from the data set" (’315 Patent, col. 11:61-64). This could support an interpretation covering any system that sorts data into two groups for compression.
  • Intrinsic Evidence for a Narrower Interpretation: The specification consistently defines the "first category" as "complexities" (e.g., irrational numbers, complex numbers, mixed hashes) and the "second category" as "memoryless data" (e.g., integers, zeros) (’315 Patent, col. 5:10-14, col. 6:1-18). This language may be used to argue that the claim scope is limited to methods that process these specific types of data.

• The Term: "statistical distribution"

  • Context and Importance: This is the engine of the more efficient "second phase" of the claimed method. Its definition is critical to distinguishing the invention from prior art and determining infringement. Practitioners may focus on this term because the functionality of the second phase depends entirely on its nature and use.
  • Intrinsic Evidence for a Broader Interpretation: The term itself could be interpreted broadly to mean any organized collection of data about the excluded "second category" values.
  • Intrinsic Evidence for a Narrower Interpretation: The specification describes the distribution as being used to determine a "probability that a particular value...corresponds to the second category" using Bayes' theorem (’315 Patent, col. 2:58-col. 3:2, col. 8:1-17). This suggests the term may be construed to require a data structure suitable for and used in such a probabilistic analysis, not merely a simple list.

VI. Other Allegations

• Indirect Infringement: The complaint includes headings and conclusory statements alleging induced and contributory infringement (Compl. p.1; ¶¶32-33). However, it does not provide specific facts alleging how Defendants would cause a third party (e.g., an end-user) to infringe the patent.
• Willful Infringement: The complaint alleges that Defendants had actual notice of the patent and their alleged infringement as of April 15, 2024, through a certified cease and desist letter, and continued their infringing activities despite this notice (Compl. ¶¶23-24, 36). These allegations form the basis for the willfulness claim.

VII. Analyst’s Conclusion: Key Questions for the Case

• A core issue will be one of definitional scope: can the terms "first category" and "second category," which the patent specification links to specific mathematical concepts like "complexities" and "memoryless data," be construed broadly enough to read on the accused technology's alleged "Tuple" and "IOTARGET" data classifications?
• A key evidentiary question will be one of operational equivalence: does the accused two-phase system, particularly its alleged use of "dequantization" in its second phase, perform the same function in substantially the same way to achieve the same result as the patent’s claimed method of using a "statistical distribution" for probabilistic classification?
• A central challenge for the plaintiff will be one of substantiation: can the high-level technical allegations in the pro se complaint—such as the use of Bayesian techniques and specific data structures—be supported with concrete evidence from the accused Femtosense technology during the discovery phase of litigation?